By Karen Wolfe
Costs continue to rise
Regardless of the myriad of interventions directed at containing claim costs in Workers’ Compensation, costs continue to increase. Now that the medical portion of claim costs amounts to sixty percent or more, the fact must be acknowledge that traditional managed care initiatives are inadequate. Moreover, new medical costs seem to be appearing from unfamiliar places, leaving no apparent recourse. Costs are finding new avenues of expression in the form of drug costs, complex medical procedures, and exponential costs due to comorbidities.
What now?
It seems everything that can be done, has been done. Managed care programs including provider networks, bill review, utilization review, peer review, and medical case management are conceptually well-founded. Still, outcomes are disappointing. What more can be done?
Starting over is absolutely not an option. Disbanding current managed care programs and creating new ones is completely impractical. Sunk costs of existing programs are huge, and building new ones is not feasible or affordable. Besides, managed care programs in Workers’ Compensation are well-founded conceptually, and based on solid principals. They just need to function more effectively.
Managed care is tired
Managed care programs in Workers’ Compensation are tired. Like much of our country’s infrastructure, they have not been revitalized over the years of their existence. They operate today just like they did twenty years ago. Specifically, most managed care programs have not taken advantage of the exponential advances in technology during their tenure.
Same tenure—different results
Think about it. As recently as twenty years ago (1991 seems like yesterday) Microsoft’s Disk Operating System (DOS) was the predominant operating system for personal computers. PC’s were large, expensive, and scarce in companies. Local area networks were just emerging and required hard-wiring to connect, servers, PC’s and printers. The Internet was not yet available for general use. Significantly, this was also the time of Workers’ Comp managed care ascendency. Yet, it would be some time before managed care programs were even computerized.
Computerization in managed care is relatively recent and the uptake has been laboriously slow. At the same time, evolution in technology has been explosive. Reluctant technology upgrades in managed care have been dedicated to hardware and operating software at a pace consistent with Microsoft operating system advances. Little has been done in managed care to exploit technology to actually benefit outcomes.
In contrast, PC’s (350 million were sold in 2010!), cell phones, and smart phones have proliferated. It is estimated 4.6 billion cell phones are in use worldwide. They are enabled with text messaging, web browsers and cameras, as well as by wireless connectivity in place of landlines to reach remote communities, as well as by new social networks that enable collaboration on more and more devices. As recently as 2005, Facebook was a start-up phenomenon, Twitter was still a sound, the cloud was something in the sky, and 3G was a parking space.1 The flood of technology and its applications has serious and exciting implications for Workers’ Comp managed care.
Differentiate through technology
Underscoring the point, Joel Cawley, the vice president for strategy at IBM is quoted as saying, “Two things will differentiate companies, countries, and individuals from one another. One is analytics. Once everyone is connected, prosperity will depend on how well you or your company can analyze and apply all the data pouring through these networks to optimize your ability to provide better…(services).”2
The Workers’ Compensation industry must of necessity step up to the challenge because continuing to do business as usual is ever more unconscionable in light of claim cost escalation and deteriorating outcomes. Workers’ Comp organizations, whether they are insurers, third party payers, self-insured employers, or service providers to the industry, must leverage technology to improve their services and control costs. To do otherwise is derelict.
Move to the intersection of technology and managed care
Analyze the data to gain insight into best practices and procedures and who is providing them. Leverage the data to find best in class doctors and other providers. Enable current data to inform adjusters and medical case managers of claims containing potentially calamitous conditions. Let technology notify appropriate persons of approaching key benchmarks and other pivotal conditions. Most importantly, act on the findings of analytics.
Drive the results of analytics to operations to mobilize appropriate action to intervene in time to prevent further damage. Make analytics and technology work-in-progress tools that lead people to informed decisions and to taking action early enough to contain costs. Most importantly, embrace technology to ramp-up, revitalize, and recharge managed care programs. Use analytics backed by technology to take charge of outcomes. Move to the intersection of technology and managed care.
Learn how MedMetrics will move you to the intersection of technology and managed care, thereby gaining more control of costs and outcomes.
1 Friedman, T., Mandelbaum, M. That Used to Be Us: How America Fell Behind in the World It Invented and How We Can Come Back. Farrar, Straus and Giroux. 2011.
2 Ibid.
Tuesday, December 13, 2011
Sunday, November 27, 2011
How to Convert Your Data to a Valuable Corporate Asset by Karen Wolfe
Data is the organizations’ most valuable asset
At the beginning of the personal computing era back in the 1980’s and early 1990’s a frequent motivation for computerizing was that data is the organization’s most valuable asset. The idea was a persuasive argument for investing big dollars in computerization. Nevertheless, though the value of data as an asset could be envisioned back then, it was actually far from it.
Rather than an asset, computers, software, and data were burdensome and expensive. Continuous capital investments were needed to improve the hardware as technology rapidly advanced. Software, limited by hardware memory and capacity resulted in incomplete data. Workers struggled to adapt to data entry discipline, an entirely new way to work. Moreover, data was often corrupted or lost by systems that were primitive by today’s standards. Managing networks was arduous. Data backup and storage required ever more hardware and new systems demanded data migration, along with the IT personnel to manage the all the processes. It was an all-encompassing and pricey undertaking with little benefit realized.
Now what?
Despite the challenges, organizations overcame the obstacles and committed to computerization, still chasing the data value proposition. Over the years, organizations have amassed boatloads of data, begging the question: Now what? Sitting in storage, the data certainly is not an asset! Instead, it must be gathered, integrated and analyzed to gain intelligence about how to proceed.
Analytics
Walmart modeled the next step many years ago by implementing analytics. They began analyzing their data to derive intelligence about their own organization, the effectiveness of their processes, and their customers’ buying patterns. Walmart leveraged analytics to improve processes, optimize operations, trim costs and improve profitability. Following their lead, most other organizations in every industry have adopted initiatives for analyzing their data.
Workers’ Comp boards the bandwagon
The Workers’ Compensation industry is no exception. Of late, many organizations in the industry are executing analytics. Departments have been created with dedicated experts who analyze the data to derive intelligence. Nevertheless, people are now asking the question, “What should we do with the analytics?” They realize analytics alone cannot elevate the copious data to the level of an organizational asset.
Analytics alone do not an asset make
Having amassed so much data, people correctly think it should be made useful. Yet, analytics too often reside in attractive graphic reports made available to the top echelon, the board, shareholders and managers. However, to be useful, to be an asset that can change processes and outcomes based in the intelligence gained, analytics must made available to the people who do the organization’s work. Line personnel must have access to easy, actionable tools that cause them to act on the intelligence gained through analytics. Only those who do the organization’s work can change processes and create value. But they cannot achieve change without help. Analytics must be operationalized.
Operationalize the intelligence
Again using Walmart as an example, one piece of information gained from their analyzed data is exactly what people purchase in different geographic regions when a weather disaster is forecast. That information told them what to do in response, what action to take. Walmart systems were reengineered to automatically shift distribution channels in response to weather advisories, thereby insuring the right commodities arrive in time in the stores affected by the weather. The quantifiable benefits are that customers are satisfied by finding the products they need and Walmart profits are boosted because they have enough goods to sell.
Analytics tell Walmart what is needed and what processes must be changed to respond to a new set of conditions. But nothing will happen until their systems are changed to apply that information. That is linking analytics to operations to actualize the knowledge gained through analytics.
In the case of Workers’ Comp., opportunities to apply analytics to modify processes in real time are abundant. For example, analyze the data to find the best medical doctors for treating low back strain in specific geographic areas. Claimants can be directed to those doctors who have a record of excellence. Or, analyze provider prescription practices to identify those prescribing opioids and other potentially addictive drugs. But most importantly, do not stop there! Automatically alert line personnel so they can initialize appropriate action.
The critical final stage—simplicity
Whatever method is used to link analytics to operations must be elegantly simple for the user. If Walmart required store managers to follow a written procedure to rush-order products based on local forecasts, chances are good the results would be unsatisfactory. If the process adds to the workload or requires looking up directions, little will be gained. To be effective, systems should do the work and automatically direct or redirect actions.
For data to become a valuable corporate asset, it must be gathered from all the appropriate sources, integrated, and analyzed, with the results automatically linked to operations, thereby mobilizing appropriate and timely action.
Learn how MedMetrics will transform your data to valuable corporate assets for you. Finally, the promise is fulfilled.
At the beginning of the personal computing era back in the 1980’s and early 1990’s a frequent motivation for computerizing was that data is the organization’s most valuable asset. The idea was a persuasive argument for investing big dollars in computerization. Nevertheless, though the value of data as an asset could be envisioned back then, it was actually far from it.
Rather than an asset, computers, software, and data were burdensome and expensive. Continuous capital investments were needed to improve the hardware as technology rapidly advanced. Software, limited by hardware memory and capacity resulted in incomplete data. Workers struggled to adapt to data entry discipline, an entirely new way to work. Moreover, data was often corrupted or lost by systems that were primitive by today’s standards. Managing networks was arduous. Data backup and storage required ever more hardware and new systems demanded data migration, along with the IT personnel to manage the all the processes. It was an all-encompassing and pricey undertaking with little benefit realized.
Now what?
Despite the challenges, organizations overcame the obstacles and committed to computerization, still chasing the data value proposition. Over the years, organizations have amassed boatloads of data, begging the question: Now what? Sitting in storage, the data certainly is not an asset! Instead, it must be gathered, integrated and analyzed to gain intelligence about how to proceed.
Analytics
Walmart modeled the next step many years ago by implementing analytics. They began analyzing their data to derive intelligence about their own organization, the effectiveness of their processes, and their customers’ buying patterns. Walmart leveraged analytics to improve processes, optimize operations, trim costs and improve profitability. Following their lead, most other organizations in every industry have adopted initiatives for analyzing their data.
Workers’ Comp boards the bandwagon
The Workers’ Compensation industry is no exception. Of late, many organizations in the industry are executing analytics. Departments have been created with dedicated experts who analyze the data to derive intelligence. Nevertheless, people are now asking the question, “What should we do with the analytics?” They realize analytics alone cannot elevate the copious data to the level of an organizational asset.
Analytics alone do not an asset make
Having amassed so much data, people correctly think it should be made useful. Yet, analytics too often reside in attractive graphic reports made available to the top echelon, the board, shareholders and managers. However, to be useful, to be an asset that can change processes and outcomes based in the intelligence gained, analytics must made available to the people who do the organization’s work. Line personnel must have access to easy, actionable tools that cause them to act on the intelligence gained through analytics. Only those who do the organization’s work can change processes and create value. But they cannot achieve change without help. Analytics must be operationalized.
Operationalize the intelligence
Again using Walmart as an example, one piece of information gained from their analyzed data is exactly what people purchase in different geographic regions when a weather disaster is forecast. That information told them what to do in response, what action to take. Walmart systems were reengineered to automatically shift distribution channels in response to weather advisories, thereby insuring the right commodities arrive in time in the stores affected by the weather. The quantifiable benefits are that customers are satisfied by finding the products they need and Walmart profits are boosted because they have enough goods to sell.
Analytics tell Walmart what is needed and what processes must be changed to respond to a new set of conditions. But nothing will happen until their systems are changed to apply that information. That is linking analytics to operations to actualize the knowledge gained through analytics.
In the case of Workers’ Comp., opportunities to apply analytics to modify processes in real time are abundant. For example, analyze the data to find the best medical doctors for treating low back strain in specific geographic areas. Claimants can be directed to those doctors who have a record of excellence. Or, analyze provider prescription practices to identify those prescribing opioids and other potentially addictive drugs. But most importantly, do not stop there! Automatically alert line personnel so they can initialize appropriate action.
The critical final stage—simplicity
Whatever method is used to link analytics to operations must be elegantly simple for the user. If Walmart required store managers to follow a written procedure to rush-order products based on local forecasts, chances are good the results would be unsatisfactory. If the process adds to the workload or requires looking up directions, little will be gained. To be effective, systems should do the work and automatically direct or redirect actions.
For data to become a valuable corporate asset, it must be gathered from all the appropriate sources, integrated, and analyzed, with the results automatically linked to operations, thereby mobilizing appropriate and timely action.
Learn how MedMetrics will transform your data to valuable corporate assets for you. Finally, the promise is fulfilled.
Sunday, November 6, 2011
How to Find “Best in Class” Doctors
It’s a safe bet that claims will not have a happy ending if the treating physician has a history of being associated with poor claim outcomes. In fact, physicians rated poorly in analytic studies based on past performance are 100% predictive of high costs and inferior outcomes in future claims where they are involved. The question is, how can those providers be identified?
Applying analytics
Evaluating physician and other provider performance is a matter of scrutinizing the data. The data offers a clear picture of actual provider performance. Whether the cause of poor performance is misunderstanding Workers’ Compensation or deliberate fraud, the claim results will be dismal. Nevertheless, in order to analyze provider performance, one must know where to look for the data, what to look for, and how to apply the knowledge gained from analysis to achieve improved results.
Where to find the data
Billing data tells the story of diagnoses, treatments and the billed amounts. However, billing data is never broad enough in scope to evaluate providers because it tells only a part of the story. Claims level data tells another part of the story. It describes the actual paid amounts, the amount of indemnity paid, whether legal was involved, and the final disability rating, the ultimate outcome indicator. But there is more.
Investigating PBM (Pharmacy Benefit Management) data has become imperative in recent years. Overuse and abuse of prescribed narcotic pain relievers is now a major concern in Workers’ Compensation medical management. Prescribing excessive opioids is unconscionable, but the guilty are often not identified and avoided as they should be.
Provider performance should be weighted by outcome combined with costs and other factors. Unless the initial injury was catastrophic, return to work following a workplace injury is often a function of medical management. Analyzing multiple data indicators from disparate data sources can describe individual physician performance.
Integrating the data for analysis
Any one Workers’ Compensation data source by itself is inadequate for the purpose of evaluating providers. Only the broad scope of data concerning a claim can provide a clear picture of the claim and provider culpability in outcome. Therefore, collecting the data from its various sources and integrating current and historical data are the first two crucial steps in provider performance analytics. The next steps are identifying, evaluating, and monitoring the data elements that are indicators of performance both from the medical and Workers’ Compensation viewpoints.
Industry research tells what to look for
Exposing substandard providers is a matter of integrating and analyzing the data to understand the course of the claim and the providers who contributed to poor claim results. Selecting the data items to monitor can be guided in the first instance by industry research. Organizations such as NCCI (National Council on Compensation Insurance), CWCI (California Workers’ Compensation Institute), WCRI (Workers’ Compensation Research Institute) continually publish their research based on data they collect from members. These organizations offer research regarding medical issues causing cost escalation in the industry, and usually make results available from their individual websites.
Academia and other organizations produce and publish research, as well. The best way to access other research is to use Google to find research studies regarding specific issues and interest areas. For instance, if the concern is low back pain management, simply use Google to find research and scholarly articles on the topic as it relates to Workers’ Compensation. Google is an extraordinary resource in that regard.
When the indicators of performance are identified, they can be applied to analyze providers. Providers tagged with a preponderance of negative indicators will not fall into the best in class category. On the other hand, those whose results are exemplary will rise to the top—best in class.
Link analytics to operations
Analytics results of any variety that remain in graphic form, in a fancy brochure, or pinned to a wall are useless in the effort of containing costs. The findings must be functionally applied to operations to make them actionable. Information regarding best (and worst) in class doctors identified through the methods discussed here must be made available to network managers in a usable form. Moreover, the information should be specific, current, dynamic, easily accessible, and contain objective supportive detail. The work of analytics is not complete until its results are operationalized, thereby linking analytics to implementation.
Learn more about MedMetrics analytics or contact karenwolfe@medmetrics.org.
Applying analytics
Evaluating physician and other provider performance is a matter of scrutinizing the data. The data offers a clear picture of actual provider performance. Whether the cause of poor performance is misunderstanding Workers’ Compensation or deliberate fraud, the claim results will be dismal. Nevertheless, in order to analyze provider performance, one must know where to look for the data, what to look for, and how to apply the knowledge gained from analysis to achieve improved results.
Where to find the data
Billing data tells the story of diagnoses, treatments and the billed amounts. However, billing data is never broad enough in scope to evaluate providers because it tells only a part of the story. Claims level data tells another part of the story. It describes the actual paid amounts, the amount of indemnity paid, whether legal was involved, and the final disability rating, the ultimate outcome indicator. But there is more.
Investigating PBM (Pharmacy Benefit Management) data has become imperative in recent years. Overuse and abuse of prescribed narcotic pain relievers is now a major concern in Workers’ Compensation medical management. Prescribing excessive opioids is unconscionable, but the guilty are often not identified and avoided as they should be.
Provider performance should be weighted by outcome combined with costs and other factors. Unless the initial injury was catastrophic, return to work following a workplace injury is often a function of medical management. Analyzing multiple data indicators from disparate data sources can describe individual physician performance.
Integrating the data for analysis
Any one Workers’ Compensation data source by itself is inadequate for the purpose of evaluating providers. Only the broad scope of data concerning a claim can provide a clear picture of the claim and provider culpability in outcome. Therefore, collecting the data from its various sources and integrating current and historical data are the first two crucial steps in provider performance analytics. The next steps are identifying, evaluating, and monitoring the data elements that are indicators of performance both from the medical and Workers’ Compensation viewpoints.
Industry research tells what to look for
Exposing substandard providers is a matter of integrating and analyzing the data to understand the course of the claim and the providers who contributed to poor claim results. Selecting the data items to monitor can be guided in the first instance by industry research. Organizations such as NCCI (National Council on Compensation Insurance), CWCI (California Workers’ Compensation Institute), WCRI (Workers’ Compensation Research Institute) continually publish their research based on data they collect from members. These organizations offer research regarding medical issues causing cost escalation in the industry, and usually make results available from their individual websites.
Academia and other organizations produce and publish research, as well. The best way to access other research is to use Google to find research studies regarding specific issues and interest areas. For instance, if the concern is low back pain management, simply use Google to find research and scholarly articles on the topic as it relates to Workers’ Compensation. Google is an extraordinary resource in that regard.
When the indicators of performance are identified, they can be applied to analyze providers. Providers tagged with a preponderance of negative indicators will not fall into the best in class category. On the other hand, those whose results are exemplary will rise to the top—best in class.
Link analytics to operations
Analytics results of any variety that remain in graphic form, in a fancy brochure, or pinned to a wall are useless in the effort of containing costs. The findings must be functionally applied to operations to make them actionable. Information regarding best (and worst) in class doctors identified through the methods discussed here must be made available to network managers in a usable form. Moreover, the information should be specific, current, dynamic, easily accessible, and contain objective supportive detail. The work of analytics is not complete until its results are operationalized, thereby linking analytics to implementation.
Learn more about MedMetrics analytics or contact karenwolfe@medmetrics.org.
Wednesday, October 19, 2011
How to Stop Opioid Use in Workers’ Compensation, a White Paper
Rather than trying to rescue drowning victims, we should find out who is pushing them in the water upstream—and stop them!
It’s no secret opioid use in Workers’ Compensation has reached the critical level, having escalated over the past ten years. The issue is serious, not only because of the cost in dollars, but it also has a human toll. Productivity in the workplace is jeopardized, the risk for new injuries is exacerbated, and claimants’ lives are devastated by addiction. Much has been written and important studies have been conducted on the topic.
Recent studies illuminate the problem
A central location that links to recent studies and articles on the topic along with serious discussion is found on Linkedin, the Work Comp Analysis Group. The studies by NCCI and CWCI are convincing. The only reasonable conclusion is that the problem is real, it is serious and it is growing. Specifically, the increase in drugs as a percentage of claim costs is disturbing. Moreover, the studies also show overutilization is the cost driver, not increases in drug costs. This article is offered by way of contributing a tool to the solution side of the problem.
The solution side of the issue
To address the solution side of the issue, it seems only logical that efforts are directed to the upstream source, those who prescribe the drugs. That narrows the scope considerably since only specially-licensed MD’s can prescribe DEA (Drug Enforcement Administration) controlled drugs. Moreover, only those drugs that have been prescribed and billed through the Workers’ Comp system are causing huge increases in claim costs. Consequently, the spotlight of prevention should focus on the prescribing doctors.
Of course, illicit drugs and drug trafficking exist everywhere. While these drugs may contribute to reduction in employee productivity and risk of new injuries, illicit drugs will not impact pharmacy costs in Workers’ Compensation claims. Only prescribed drugs can do that.
Using analytics to nab the perpetrators
As a Workers’ Compensation analytics company, MedMetrics analyzes and quantifies physician performance based on the data. Networks, insurers, TPA’s, and self-insured employers are increasingly using this information to create outcome-based, quality medical provider networks. MedMetrics includes prescribing behavior along with multiple other performance indicators analyzed for individual providers.
Not as easy as it would seem
Identifying physicians who overprescribe Schedule II drugs should be easy. According to the studies they comprise only a very small percentage of treating physicians. However, analyzing physician performance in Workers’ Comp requires collecting data from multiple sources. Billing data is needed for diagnostic and treatment information, though billing for drugs is typically not found there. Yet, some is, particularly when physician dispense the drugs themselves.
Additionally, claims data is needed to evaluate outcomes of the treatment such as lost time, actual paid amounts, and disability ratings. Yet another data set is needed, that of prescribed drugs found in Pharmacy Benefit Management (PBM) data.
Adding to the complexity of what would otherwise seem simple is the proliferation of drugs in this category. Many of the drugs are opioids, meaning they are artificial versions of the real thing—morphine. As new iterations of these drugs emerge, so do new drug names and NDC’s (National Drug Code) that is supposed to identify them. The DEA (Drug Enforcement Agency) classifies the drugs with still another set of codes.
Overall provider performance analysis
Once collected from the various sources, the data must be integrated, validated and analyzed. Comprehensive data analysis that is very simply described here provides a complete picture of provider performance in context with conditions in the entire claim. When provider performance is evaluated using all the key factors, a fair determination can be made about providers’ practices.
Link analytics to action
Those charged with carving out quality networks can make use of this information about individual physician performance, including prescribing behavior on an ongoing and current basis. Moreover, they also have in hand the objective and tangible rationale for removing poorly performing physicians from their networks.
MedMetrics takes this process a step further. User organizations can elect to be notified when a low-ranking physician, including those who have been identified as over-prescribers of Schedule II drugs submit a bill. This “head-up“ approach allows organizations to proactively intervene, thereby linking analytics to action.
Thursday, October 6, 2011
Steve Jobs—Remembering and Connecting the Dots
“You can only connect the dots in your life by looking back—not forward.” Steve Jobs
This quote is from Steve’s famous Stanford commencement address in 2005. Of course, his message to new college graduates was if you look back you can see how far you have come and also gain some insight into how you arrived here.
It’s worth reflecting on his idea, especially as we mourn Steve’s loss. Look back and connect the dots, those events and decisions made along the way in life and also in organizations. Consider also paths not taken. Then consider how each has affected, inspired, and lead to the present.
Steve Jobs was an extraordinary innovator, and he was also pragmatic. He designed and developed useful, easy to use products while incorporating artistry. His work exemplified powerful, yet elegant simplicity. Remarkably, Steve Jobs’ values can be translated into many lives and processes, even Workers’ Compensation analytics.
Looking back to connect the dots is a very powerful way to understand the present. Descriptive analytics are quantitative analyses of historic data. Descriptive analytics inform and suggest conclusions by connecting the dots in the data to evaluate processes and participants' actions. For instance, evaluate provider performance, frequency and duration of medical services, direct medical and indemnity costs, and disability status (among other factors) to describe the treatment pathways that led to the current status of a claim. Data elements (the dots) are analyzed to re-portray and inform users regarding outcome. Analytics also offer decision support regarding the effectiveness of past decisions and the results of the participants’ actions throughout the process.
Steve Jobs advised against looking forward in this particular speech. Yet predictive analytics can employ the same values and use the same techniques he proposed, thereby offering predictive knowledge about what is likely to happen next. Based on historic “dots”, future results can be predicted with defined levels of mathematic probability. Therefore, when the same combination of “dots” occurs again, predictive analytics can suggest the likely result.
Connecting the dots using analytics should always produce easy to use tools so that the user will gain understanding of what has occurred, what it produced, and where it is likely to lead. Moreover, analytics and predictive analytics should be elegantly simple to use.
Steve Jobs set a very high bar in the digital and communications world. We can best memorialize him by designing and developing powerful analytic tools that are elegantly simple and always with the user in mind.
Learn more about MedMetrics analytics tools.
This quote is from Steve’s famous Stanford commencement address in 2005. Of course, his message to new college graduates was if you look back you can see how far you have come and also gain some insight into how you arrived here.
It’s worth reflecting on his idea, especially as we mourn Steve’s loss. Look back and connect the dots, those events and decisions made along the way in life and also in organizations. Consider also paths not taken. Then consider how each has affected, inspired, and lead to the present.
Steve Jobs was an extraordinary innovator, and he was also pragmatic. He designed and developed useful, easy to use products while incorporating artistry. His work exemplified powerful, yet elegant simplicity. Remarkably, Steve Jobs’ values can be translated into many lives and processes, even Workers’ Compensation analytics.
Looking back to connect the dots is a very powerful way to understand the present. Descriptive analytics are quantitative analyses of historic data. Descriptive analytics inform and suggest conclusions by connecting the dots in the data to evaluate processes and participants' actions. For instance, evaluate provider performance, frequency and duration of medical services, direct medical and indemnity costs, and disability status (among other factors) to describe the treatment pathways that led to the current status of a claim. Data elements (the dots) are analyzed to re-portray and inform users regarding outcome. Analytics also offer decision support regarding the effectiveness of past decisions and the results of the participants’ actions throughout the process.
Steve Jobs advised against looking forward in this particular speech. Yet predictive analytics can employ the same values and use the same techniques he proposed, thereby offering predictive knowledge about what is likely to happen next. Based on historic “dots”, future results can be predicted with defined levels of mathematic probability. Therefore, when the same combination of “dots” occurs again, predictive analytics can suggest the likely result.
Connecting the dots using analytics should always produce easy to use tools so that the user will gain understanding of what has occurred, what it produced, and where it is likely to lead. Moreover, analytics and predictive analytics should be elegantly simple to use.
Steve Jobs set a very high bar in the digital and communications world. We can best memorialize him by designing and developing powerful analytic tools that are elegantly simple and always with the user in mind.
Learn more about MedMetrics analytics tools.
Thursday, September 22, 2011
Injury Severity—Scoring Injury Seriousness
Components of claim cost
Factors that drive claim costs in Workers’ Compensation are many. Among them are the type of injury, the claimant’s job, age and other health factors, as well as psycho-social factors. Psycho-social factors may be the most elusive in terms of predicting claim cost because they tend to be subjective, intangible and not well documented in the data. However, another potentially powerful predictive cost factor is injury severity.
What is severity?
The concept of severity in Workers’ Compensation begs definition because it refers to claim cost. The term severity is defined in the dictionary as seriousness, gravity, significance, magnitude, acuteness, badness, or awfulness.
Factors that drive claim costs in Workers’ Compensation are many. Among them are the type of injury, the claimant’s job, age and other health factors, as well as psycho-social factors. Psycho-social factors may be the most elusive in terms of predicting claim cost because they tend to be subjective, intangible and not well documented in the data. However, another potentially powerful predictive cost factor is injury severity.
What is severity?
The concept of severity in Workers’ Compensation begs definition because it refers to claim cost. The term severity is defined in the dictionary as seriousness, gravity, significance, magnitude, acuteness, badness, or awfulness.
When the term severity is used, most people assume the discussion is the dollar cost of the claim. However, this discussion of injury severity, while it leads to total dollar cost of the claim, is about one very significant component of claim cost—injury severity. How serious is the physical injury?
Injury severity
Until now the best way to determine the severity of an injury was to ask the doctor. One of the three-point contacts initiated at the outset of an injury is the treating doctor. The interviewer wants to get a sense of how serious the injury is during that exchange. However, any response from physicians will be a subjective comment, making it useless for predictive purposes. Obtaining a standard measure of injury seriousness from responses of treating physicians is impossible.
Medical diagnoses
Physicians describe injuries and illnesses in terms of ICD-9 (International Classification of Diseases, 9th Revision [1]). In fact, ICD-9’s are the only acceptable norm for describing medical conditions in medical records and billing procedures. The problem is most adjusters in Workers’ Comp cannot interpret them. Even medical case managers cannot decode ICD-9’s without looking them up individually. Consequently these very powerful information nuggets have typically been ignored in medical management and for predicting cost. To complicate matters, physicians ascribe multiple IDC-9’s to a claim.
ICD-9
By way of describing the injury, physicians assign ICD-9’s initially and add to them throughout the course of the claim. ICD-9’s are required on the bill to describe the injury or illness and justify billing for medical procedures and services delivered. Nevertheless, the ICD-9’s in a claim often multiply and migrate over the course of the claim. New providers who become involved in treating the claimant add new ICD-9’s. Sometimes ICD-9’s are added because comorbidity is documented. Also, ICD-9’s may also be added to insure the bill will successfully navigate bill review. The reasons for adding ICD-9’s to the claim are many, which is reason enough to pay better attention to them.
The primary diagnosis?
People often ask, “How do I determine which ICD-9 is the primary ICD-9?” One answer is to note the date the ICD-9 is added. Those entered toward the beginning of the claim might be most revealing—but not necessarily. The diagnostic category might be more revealing. Do all the diagnoses relate to musculoskeletal conditions of the low back? In practice, doctors may select from eighty to one hundred different diagnoses to define low back injuries.
One diagnosis does not a story tell
In point of fact, identifying a primary diagnoses may not be important at all. Any one diagnosis must be taken in context with the others assigned to the claim. Several ICD-9’s might be used to describe strain of multiple related body regions. This still begs the question, how serious is the injury, but it also raises the question of what other information is living in the medical diagnoses?
Of course, one extremely severe diagnoses, such as a severed cervical spinal cord, should automatically kick the claim into the high risk class. On the other hand, while a low back strain by itself may seem benign, when it is combined with the fact that the claimant is over 65 years old or diabetic, or both, it also portends high risk. That same mild low back strain should be followed closely when the physician has also identified the claimant as obese. In other words, all diagnoses in the claim must be assessed in context with the other diagnoses present. Together they tell the whole story and can have an exponential effect on cost and outcome.
The MedMetrics solution
MedMetrics has solved this problem by assigning a severity (seriousness) score to individual diagnoses—the Injury Severity Score. Each diagnosis found in a bill is scored individually for severity. When a diagnosis is scored extremely severe, the organization is notified immediately. For multiple diagnoses, the accumulated diagnostic scores are totaled. If the composite score reaches a certain level, the organization is notified. Comorbidity and age are factored into the combined scores.
MedMetrics electronically monitors an organization’s bills, scores the diagnoses, and alerts its client of those diagnoses that are extremely serious. Additionally, the organization is notified when the accumulated diagnostic scores exceed a certain level. Claims adjusters and medical case managers can step ahead to manage the claim proactively.
Injury Severity Scores are a powerful, concurrent medical intelligence and management tool for claims adjusters and medical case managers. Moreover, Injury Severity Scores can be added to the available intelligence for setting and adjusting reserves. Finally, MedMetrics Injury Severity Score is a measure of bodily injury, an applicable intelligence tool for all personal injury claims!
Learn about other medical analytics tools that recharge managed care in Workers’ Compensation.
1 ICD-10 will be implemented in October, 2012.
Wednesday, September 14, 2011
How to Build an Outcome-based Network
Medical networks under scrutiny
Medical networks in Workers’ Compensation have come under scrutiny of late. Their effectiveness as a centerpiece of Workers’ Comp managed care is being questioned. For most networks, the most obvious problem is that their business model has not changed in twenty-five years while medical costs have continued to rise.
Medical networks in Workers’ Comp, whether they are PPO (Preferred Provider Organization), MCO (Managed Care Organization), HCO (Health Care Organization), MPN (Medical Provider Network) or the latest, EPO (Employer Provider Organization), are under the microscope. Employers and payers now realize that contracting with every provider and applying arbitrary discounts on units of medical services tend to inflate the frequency, duration, and cost of medical care. Rather than saving money for medical services, this practice may actually add to the cost.
Do discount networks work?
In reality, whether discounting units of medical service adds to, or curbs medical costs under the network discount method is unknown because networks have not provided information in that regard. Proof of actual performance does not exist. “Savings” reports supplied by the networks simply tally the discounts with no attention paid to total claim cost or outcome.
Instead, the strategy of discount networks is to contract with as many providers as possible, then measure success based on network utilization, penetration, and total discounts. More network utilization produces more discounts and reported “savings”. Moreover, the discount network strategy relies on the presumption of medical excellence and perfect moral integrity among providers, along with knowledge of the unique characteristics of Workers’ Comp.
Everyone knows the huge networks contain bad apples, usually more than a few. So employers and payers now want to open the curtain to see the moving parts being engineered by the wizard. They want proof of performance.
What employers and payers want
Most employers want the best physicians treating their injured employees at the best possible price. They want quick, convenient access to excellent medical treatment and the earliest possible safe return to work for injured employees. What’s more, they want the most efficient and cost-effective Workers’ Comp claim process. Importantly, they also want evidence of quality care.
Proof of performance through analytics
The missing ingredient for most traditional, discount-based medical networks is documented performance in terms of outcome. The only way to gain such knowledge is through data analysis (analytics). How do the doctors perform in the context of Workers’ comp and what are their outcomes, both in cost and in human terms?
Measuring quality
A physician was once overheard saying, “You can’t really measure medical quality.” That is not true. Quality can be measured in terms of medical performance using multiple criteria, all analytically calculable. What is the mean frequency and duration of medical care for treating certain injuries by an individual physician compared to others of the same specialty treating the same injuries? Other quality factors are equally measureable, such as return to work or sustained return to work. Actually, another way to define quality is best outcome of the claim and for the claimant.
Measuring outcome
In many ways, outcome and quality are the same things in Workers’ Comp. Frequency and duration of medical treatment are easily inflated by providers in networks where discounts are applied to units of service. If providers must discount services, the best way to recover those lost fees is to expand and extend services. Direct medical costs are key to measuring performance, but outcome (quality) is also definable in terms of lost time, return to work, and disability rating at claim closure, along with many other factors. All are influenced by the treating providers and all are measureable.
The new outcome-based networks
Medical provider networks are evolving to the new outcome-based model where providers are contracted based on actual performance derived from the analyzed data. Outcome-based networks offer transparency rather discounts. Some have also created new revenue structures that reward positive outcomes. These new networks carve out the best in class providers evidenced by the data. They provide a new and different business model and an objective basis for selecting network doctors.
An evolving industry
The industry is evolving to new outcome-based networks guided by the analytics of provider performance. Frankly, the Workers’ Comp industry has lagged behind other industries in leveraging their data to enlighten decisions. The new outcome-based networks change that. When the best in class make up a network, logic says outcomes improve.
Read more regarding Workers’ Comp medical provider networks and the analytics of medical provider and network performance.
Medical networks in Workers’ Compensation have come under scrutiny of late. Their effectiveness as a centerpiece of Workers’ Comp managed care is being questioned. For most networks, the most obvious problem is that their business model has not changed in twenty-five years while medical costs have continued to rise.
Medical networks in Workers’ Comp, whether they are PPO (Preferred Provider Organization), MCO (Managed Care Organization), HCO (Health Care Organization), MPN (Medical Provider Network) or the latest, EPO (Employer Provider Organization), are under the microscope. Employers and payers now realize that contracting with every provider and applying arbitrary discounts on units of medical services tend to inflate the frequency, duration, and cost of medical care. Rather than saving money for medical services, this practice may actually add to the cost.
Do discount networks work?
In reality, whether discounting units of medical service adds to, or curbs medical costs under the network discount method is unknown because networks have not provided information in that regard. Proof of actual performance does not exist. “Savings” reports supplied by the networks simply tally the discounts with no attention paid to total claim cost or outcome.
Instead, the strategy of discount networks is to contract with as many providers as possible, then measure success based on network utilization, penetration, and total discounts. More network utilization produces more discounts and reported “savings”. Moreover, the discount network strategy relies on the presumption of medical excellence and perfect moral integrity among providers, along with knowledge of the unique characteristics of Workers’ Comp.
Everyone knows the huge networks contain bad apples, usually more than a few. So employers and payers now want to open the curtain to see the moving parts being engineered by the wizard. They want proof of performance.
What employers and payers want
Most employers want the best physicians treating their injured employees at the best possible price. They want quick, convenient access to excellent medical treatment and the earliest possible safe return to work for injured employees. What’s more, they want the most efficient and cost-effective Workers’ Comp claim process. Importantly, they also want evidence of quality care.
Proof of performance through analytics
The missing ingredient for most traditional, discount-based medical networks is documented performance in terms of outcome. The only way to gain such knowledge is through data analysis (analytics). How do the doctors perform in the context of Workers’ comp and what are their outcomes, both in cost and in human terms?
Measuring quality
A physician was once overheard saying, “You can’t really measure medical quality.” That is not true. Quality can be measured in terms of medical performance using multiple criteria, all analytically calculable. What is the mean frequency and duration of medical care for treating certain injuries by an individual physician compared to others of the same specialty treating the same injuries? Other quality factors are equally measureable, such as return to work or sustained return to work. Actually, another way to define quality is best outcome of the claim and for the claimant.
Measuring outcome
In many ways, outcome and quality are the same things in Workers’ Comp. Frequency and duration of medical treatment are easily inflated by providers in networks where discounts are applied to units of service. If providers must discount services, the best way to recover those lost fees is to expand and extend services. Direct medical costs are key to measuring performance, but outcome (quality) is also definable in terms of lost time, return to work, and disability rating at claim closure, along with many other factors. All are influenced by the treating providers and all are measureable.
The new outcome-based networks
Medical provider networks are evolving to the new outcome-based model where providers are contracted based on actual performance derived from the analyzed data. Outcome-based networks offer transparency rather discounts. Some have also created new revenue structures that reward positive outcomes. These new networks carve out the best in class providers evidenced by the data. They provide a new and different business model and an objective basis for selecting network doctors.
An evolving industry
The industry is evolving to new outcome-based networks guided by the analytics of provider performance. Frankly, the Workers’ Comp industry has lagged behind other industries in leveraging their data to enlighten decisions. The new outcome-based networks change that. When the best in class make up a network, logic says outcomes improve.
Read more regarding Workers’ Comp medical provider networks and the analytics of medical provider and network performance.
Tuesday, August 30, 2011
What Does a Fish Know?
“What does the fish know about the water in which it swims?” Albert Einstein.
An existential question
Leave it to Einstein to ask the existential question regarding what we know about the environment in which we live. Of course, he was referring to the universe, but his question might just as well be applied to the Workers’ Comp world. Those of us who have long-labored in Workers’ Comp assume we have full knowledge of it. But do we? We swim in such a huge vessel of information, can we actually know much of it?
The industry is swimming in data
The Workers’ Comp industry has successfully amassed vast amounts of data. Computers become available to manage claims in the late 1970’s and early 1980’s when digitalized data was first collected. However at that time, few companies could afford to computerize. Computers were bulky, costly and the software available to run them was scant. Not until the late 1980’s were simple DOS-based claims management systems available and more affordable.
Since that time, in just twenty-five years, the industry has collected immense amounts of claims-related data. Yet, we know little of it!
What don't' we know?
Mark Twain put it this way, “What gets us into trouble is not what we don’t know. It’s what we know for sure that just ain’t so”.
We assume we know the industry in which we work. Moreover, we feel sure of the operations and actions of our own organization, along with their effects. The truth is, we actually know little of the Workers’ Comp world in which we live, but we could know much more through analytics.
Analytics is a solution
Analytics can be a forbidding notion, but it simply means data analysis, analysis of the data to gain knowledge, find meaning and direction. Data, without analytics, is useless because in its natural form, data is fragmented bits of information. Relationships are unknown. Conclusions cannot be reached. Decisions are not supported. Predictions cannot be found. Raw data keeps us profoundly unaware of the world in which we live.
When analytics are applied, the data are summarized, calculated, calibrated, and re-presented so that relationships become apparent. Conclusions and decisions are quicker, easier, and more defensible. Predictions can be made based on past experience. As a result of analytics, processes can be made more standardized and cost efficient. Computer-aided management is made real.
More specific to Workers’ Comp medical analytics, data subjected to analytics can provide even more awareness and efficiency. The spotlight can be trained on providers, treatment pathways, and outcomes. Such analyses lead to derivative knowledge about the treatments and processes that lead to successful outcomes.
Reasons for resistance
The question then becomes, if analytics is so powerful, what has kept the Workers’ Comp industry from embracing it? Among the reasons the Workers’ Comp industry has been slow to embrace analytics, a couple can be noted here.
People have long discussed the data silos in Workers’ Comp. Data silos include claims systems, bill review systems, UR systems, provider network systems, and medical case management systems, along with digitalized FROI (First Report of Injury), and OSHA (Occupational Safety and Health Administration) reports. Each lives within its own business domain that does not communicate with the others. Yet, each contains critical claim information. Essential to analytics is integrating data, however to date, appetite for data integration is tepid.
Another reason for slow adoption of analytics, particularly relating to the medical portion of claims is those most knowledgeable are not computer savvy or involved in IT. Medical professionals do not understand what is missing and the possibilities for enlightenment. They cannot imagine possibilities related to using data as a work-in-process tool. They have no experience to rely on. So they do not ask and they are not sought for input.
Nevertheless, the barriers to analytics in Workers’ Comp are easy to overcome. The industry needs to step up to analytics, particularly medical analytics, the most unenlightened portion of claims management. There is little reason to continue swimming in water we know little of.
Please visit MedMetrics for more information.
Monday, August 8, 2011
You Might be Helping Doctors Defraud the System
Medical fraud in Workers’ Comp comes in many forms. Determined abuse of the system, perpetrated by the most callous of providers is the most destructive form. Fraudulent doctors and other providers know the system and how to manipulate it to their financial benefit.
Fraudulent providers use tactics such as increasing the frequency and duration of medical services, billing at the highest levels regardless of state fee schedules, and billing repeatedly to generate duplicate payments. Even more subversive are those who add multiple diagnoses so their exaggerated billing to avoid exposure by bill review systems. Such perpetrators also shrewdly submit bills using slightly altered names and addresses so their maneuverings are not easily noticed by electronic systems.
Modifying names and addresses is an easy and effective way to obfuscate data. Computer systems are literal, meaning they accept the data as it is. Consequently, adding a comma, reversing first and last names as they appear in one field, and adding or omitting a suite number, are all common ways to cause multiple records. Each iteration of the information is treated as unique by the computer system so that each becomes a separate record representing the same person or entity. While providers are perpetrators of these data deceptions, payers often contribute to the problem.
Data quality is a people problem
Data quality in provider records is critical to evaluating provider performance. How can individual provider performance be evaluated when multiple records representing the same person are present in the data? How can individual providers be identified when several hide under the same TaxID number? How can we differentiate the good and the bad from the ugly?
Accurate data entry is critical to data quality, yet little attention is paid to the process. A policy requiring names and addresses be pulled from a drop-down list of provider records would prevent creating multiple entries caused by misspellings and similar errors. This is basic software design. For those unable to create a hard-coded list from which the data entry person can select, a copy and paste policy should be established. Manually typing the information for each bill guarantees error, record duplication, and confusion. Process management is needed to resolve the data entry problem.
Developing software interpretive rules to correct and combine multiple records is fraught with uncertainties. For instance, a software rule could be written to interpret name reversals by looking for a comma indicating the last name is first. However, the comma is often not present, so even more confusion is created. Commas and periods, present or not, in names and address are a common issue of data quality and very difficult to correct programmatically. It’s a people problem.
Unique identifier
Still, the best way to resolve the problem, whether it results from provider billing practices or data entry at the payer level, is to require unique provider identifiers such as NPI or state license numbers. NPI (National Provider Identifier) is a system required by CMS (Centers for Medicare and Medicaid Services). Individual providers must have an NPI number to be reimbursed by Medicare. Workers’ Compensation payers should require the number, as well.
Most medical providers currently have NPI numbers because they want to be reimbursed by CMS for non-Workers’ Comp services. NPI numbers in the bill would eliminate the disguise offered by deliberate or unintended data duplication and confusion.
Fighting medical fraud
Fighting medical fraud is frustrating and elusive. But it isn’t only providers who contribute to the problem. Clean and complete provider records where the data are entered exactly the same way for every bill received from a provider will go a long way to correcting the problem.
Evaluating provider performance and rating providers analytically depends on correct individual identification. Multiple records in the data for the same provider generated by sloppy data entry practices simply perpetuate and exaggerate the problem.
Learn more about Provider Performance management.
Fraudulent providers use tactics such as increasing the frequency and duration of medical services, billing at the highest levels regardless of state fee schedules, and billing repeatedly to generate duplicate payments. Even more subversive are those who add multiple diagnoses so their exaggerated billing to avoid exposure by bill review systems. Such perpetrators also shrewdly submit bills using slightly altered names and addresses so their maneuverings are not easily noticed by electronic systems.
Modifying names and addresses is an easy and effective way to obfuscate data. Computer systems are literal, meaning they accept the data as it is. Consequently, adding a comma, reversing first and last names as they appear in one field, and adding or omitting a suite number, are all common ways to cause multiple records. Each iteration of the information is treated as unique by the computer system so that each becomes a separate record representing the same person or entity. While providers are perpetrators of these data deceptions, payers often contribute to the problem.
Data quality is a people problem
Data quality in provider records is critical to evaluating provider performance. How can individual provider performance be evaluated when multiple records representing the same person are present in the data? How can individual providers be identified when several hide under the same TaxID number? How can we differentiate the good and the bad from the ugly?
Accurate data entry is critical to data quality, yet little attention is paid to the process. A policy requiring names and addresses be pulled from a drop-down list of provider records would prevent creating multiple entries caused by misspellings and similar errors. This is basic software design. For those unable to create a hard-coded list from which the data entry person can select, a copy and paste policy should be established. Manually typing the information for each bill guarantees error, record duplication, and confusion. Process management is needed to resolve the data entry problem.
Developing software interpretive rules to correct and combine multiple records is fraught with uncertainties. For instance, a software rule could be written to interpret name reversals by looking for a comma indicating the last name is first. However, the comma is often not present, so even more confusion is created. Commas and periods, present or not, in names and address are a common issue of data quality and very difficult to correct programmatically. It’s a people problem.
Unique identifier
Still, the best way to resolve the problem, whether it results from provider billing practices or data entry at the payer level, is to require unique provider identifiers such as NPI or state license numbers. NPI (National Provider Identifier) is a system required by CMS (Centers for Medicare and Medicaid Services). Individual providers must have an NPI number to be reimbursed by Medicare. Workers’ Compensation payers should require the number, as well.
Most medical providers currently have NPI numbers because they want to be reimbursed by CMS for non-Workers’ Comp services. NPI numbers in the bill would eliminate the disguise offered by deliberate or unintended data duplication and confusion.
Fighting medical fraud
Fighting medical fraud is frustrating and elusive. But it isn’t only providers who contribute to the problem. Clean and complete provider records where the data are entered exactly the same way for every bill received from a provider will go a long way to correcting the problem.
Evaluating provider performance and rating providers analytically depends on correct individual identification. Multiple records in the data for the same provider generated by sloppy data entry practices simply perpetuate and exaggerate the problem.
Learn more about Provider Performance management.
Tuesday, July 26, 2011
Want to know how serious an injury is?
Wouldn’t it be great to know how medically serious an injury is without calling the doctor? Automatically receiving an injury severity score is an invaluable decision support tool for many reasons, but especially for setting reserves and applying resources such as medical case management. Until now, such knowledge has been elusive because it necessarily involves talking directly with the doctor. Injury severity is a medical matter, but medical data analysis is more readily accessible.
Injury severity drives cost
The severity of physical injury is central to claim cost and complexity. The medical seriousness of the injury drives not only the cost of a claim, but indemnity costs, claim duration, and often legal involvement. Measuring and predicting these costs is difficult, but a necessary business requirement. The process relies on solid information.
Obviously, the more serious the injury, the more medical services will be required. Medical services are costly. Regardless of other factors, injury severity is the core component of claim cost. Key decisions rest on how serious the injury is, but measuring severity has been elusive until now.
Predicting costs
Predictive modeling is a valuable tool in addressing the end question of claim cost. It provides insight into future costs based on historic data regarding similar cases. Analyzing historical data can suggest the future when similar circumstances occur in a claim. Nevertheless, another, easier and less expensive way to gain future cost insight is through injury severity scoring.
Scoring Injury Severity
MedMetrics assigns an injury severity score to diagnoses found in medical bills. Bills, usually via bill review data, are monitored electronically throughout the claim, beginning at the onset. When the score reaches a predetermined level, MedMetrics informs its client. The score represents the seriousness of an injury taking into consideration all the claimant’s diagnoses plus an age factor.
Medical diagnoses are the way doctors describe medical conditions. The treating doctor uses ICD-9 codes, a standardized system to describe claimants’ injuries and other conditions. While many factors can contribute to claim complexity, risk, and cost, a highly significant indicator of claim risk is the severity of the injury.
Injury Severity Predictive Score is MedMetrics proprietary methodology for assigning scores to diagnoses portrayed in billing or bill review data. The data are continually updated and electronically monitored to track additions and changes in the diagnostic picture. Total diagnostic scores for a claim are calculated for the accumulated diagnoses and MedMetrics client organization is notified electronically when the score exceeds a specified level.
Elements of injury severity
Research has demonstrated what many professionals have long known. Comorbidity adds to claim complexity and cost. Comorbidity means the claimant has other medical conditions in addition to the workplace injury defined in a claim. For instance, the claimant might also be diabetic or have a cardiac condition. These additional medical conditions can have an exponentially negative effect on recovery. When described by a diagnosis in the data, comorbidity is also considered in MedMetrics severity analysis.
Research has also shown that age impacts claim complexity and cost. MedMetrics also weighs the claimant’s age in its injury severity score algorithm.
MedMetrics analysis of hundreds of thousands of claims reveals another important and probably well-known fact: diagnoses in claims tend to accrue and migrate over the course of a claim. Consequently, it is important to score injury severity at claim outset and also continuously throughout the course of a claim. Claims that begin as Medical Only often insidiously creep into much more menacing levels without notice. MedMetrics addresses this by continuously monitoring the data, scoring and reporting findings to its clients.
Timely knowledge saves money
The medical portion of Workers’ Compensation claims now accounts for 60% of claim costs, therefore, medical analytics is now an even more critical component of claim management. MedMetrics develops medical analytic tools designed to recharge existing managed care initiatives which have fallen short in controlling medical costs.
MedMetrics clients benefit from its Injury Severity Predictive Score by setting reserves more accurately at the outset of a claim, and also by adjusting reserves in a timely manner over the course of the claim. Moreover, proactive medical management initiatives are launched earlier to step ahead of further financial and claimant damage, thereby improving outcomes and saving dollars.
Calling the doctor to determine injury severity is an unreliable and frustrating approach. MedMetrics Injury Severity Predictive Score provides immediate, consistent intelligence based on medical analytics.
Knowledge is the best cost management tool!
Read More MedMetrics Injury Severity Predictive Score
1 ICD is the abbreviation widely used for the International Statistical Classification of Diseases and Related Health Problems. ICD-9 refers to the ICD version currently in use. The ICD-10 version will be in required use in October, 2013
Injury severity drives cost
The severity of physical injury is central to claim cost and complexity. The medical seriousness of the injury drives not only the cost of a claim, but indemnity costs, claim duration, and often legal involvement. Measuring and predicting these costs is difficult, but a necessary business requirement. The process relies on solid information.
Obviously, the more serious the injury, the more medical services will be required. Medical services are costly. Regardless of other factors, injury severity is the core component of claim cost. Key decisions rest on how serious the injury is, but measuring severity has been elusive until now.
Predicting costs
Predictive modeling is a valuable tool in addressing the end question of claim cost. It provides insight into future costs based on historic data regarding similar cases. Analyzing historical data can suggest the future when similar circumstances occur in a claim. Nevertheless, another, easier and less expensive way to gain future cost insight is through injury severity scoring.
Scoring Injury Severity
MedMetrics assigns an injury severity score to diagnoses found in medical bills. Bills, usually via bill review data, are monitored electronically throughout the claim, beginning at the onset. When the score reaches a predetermined level, MedMetrics informs its client. The score represents the seriousness of an injury taking into consideration all the claimant’s diagnoses plus an age factor.
Medical diagnoses are the way doctors describe medical conditions. The treating doctor uses ICD-9 codes, a standardized system to describe claimants’ injuries and other conditions. While many factors can contribute to claim complexity, risk, and cost, a highly significant indicator of claim risk is the severity of the injury.
Injury Severity Predictive Score is MedMetrics proprietary methodology for assigning scores to diagnoses portrayed in billing or bill review data. The data are continually updated and electronically monitored to track additions and changes in the diagnostic picture. Total diagnostic scores for a claim are calculated for the accumulated diagnoses and MedMetrics client organization is notified electronically when the score exceeds a specified level.
Elements of injury severity
Research has demonstrated what many professionals have long known. Comorbidity adds to claim complexity and cost. Comorbidity means the claimant has other medical conditions in addition to the workplace injury defined in a claim. For instance, the claimant might also be diabetic or have a cardiac condition. These additional medical conditions can have an exponentially negative effect on recovery. When described by a diagnosis in the data, comorbidity is also considered in MedMetrics severity analysis.
Research has also shown that age impacts claim complexity and cost. MedMetrics also weighs the claimant’s age in its injury severity score algorithm.
MedMetrics analysis of hundreds of thousands of claims reveals another important and probably well-known fact: diagnoses in claims tend to accrue and migrate over the course of a claim. Consequently, it is important to score injury severity at claim outset and also continuously throughout the course of a claim. Claims that begin as Medical Only often insidiously creep into much more menacing levels without notice. MedMetrics addresses this by continuously monitoring the data, scoring and reporting findings to its clients.
Timely knowledge saves money
The medical portion of Workers’ Compensation claims now accounts for 60% of claim costs, therefore, medical analytics is now an even more critical component of claim management. MedMetrics develops medical analytic tools designed to recharge existing managed care initiatives which have fallen short in controlling medical costs.
MedMetrics clients benefit from its Injury Severity Predictive Score by setting reserves more accurately at the outset of a claim, and also by adjusting reserves in a timely manner over the course of the claim. Moreover, proactive medical management initiatives are launched earlier to step ahead of further financial and claimant damage, thereby improving outcomes and saving dollars.
Calling the doctor to determine injury severity is an unreliable and frustrating approach. MedMetrics Injury Severity Predictive Score provides immediate, consistent intelligence based on medical analytics.
Knowledge is the best cost management tool!
Read More MedMetrics Injury Severity Predictive Score
1 ICD is the abbreviation widely used for the International Statistical Classification of Diseases and Related Health Problems. ICD-9 refers to the ICD version currently in use. The ICD-10 version will be in required use in October, 2013
Wednesday, June 15, 2011
Provider Networks: Failure, Folly, and Overhaul
A critical topic in Workers’ Comp managed care is the state of medical provider networks. People are awakening to the fact that provider networks, continuing to operate as designed in the eighties, are not working. Actually, they are working, but not for the purpose of controlling costs. Quite the opposite is true, in fact. Medical costs are spiraling while claim outcomes are mostly unknown, a fact that seems to go unnoticed.
Traditional networks
Traditional medical provider networks in Workers’ Comp were designed to emulate group health insurance networks. In group health the benefit plan strictly controls access and fees. In group health, providers not in the network and medical services not within the authorized range are simply not reimbursed. Moreover, while discounts in the group health arena may be tied to the contractual relationship with providers, they are not used to gain competitive advantage for providers as they are in Workers’ Comp.
In Workers’ Comp, traditional networks contract with providers who, in exchange for business directed to them, offer discounts on units of medical services. Every unit of service is discounted and reported to network subscribers as units of savings. While the savings reports appear to be a positive result, they are actually a major problem that most people realize, but few acknowledge openly.
Opportunity for provider manipulation
Medical providers understood from the beginning they could simply increase frequency and duration of medical services to offset the discounts and bolster profit margins. Under the system of reporting discounts as savings, increases in frequency and duration are reported as even more savings!
Medical providers come in many flavors. Most are excellent treating providers seeking the best outcome for their patient, the claimant. They are also willing to work with employers to control Workers’ Comp costs by supporting modified work programs, for example. Yet, a number of treating providers are unaware, inept and some are downright fraudulent. Unfortunately, the latter group, while small in number, is costing the Workers Comp industry millions of dollars annually and little is being done to change the system.
Until networks are restructured so that incentives reward different behavior, they will continue to do business as usual. Doing nothing is profitable and there is little motivation or provocation to change.
Network overhaul
Nevertheless, the groundswell from employers and other purchasers of networks is building and some forward-thinking provider network organizations are leading the way. A few networks are being overhauled by changing incentives and focusing on claim outcomes. Creating outcome-based networks requires selecting providers that have proven records as evidenced in the data.
Best practice providers are identified in the Workers’ Comp world by their patients’ early return to work and return to full duty. Indemnity costs are limited. Frequency and duration of medical treatment are reasonable and direct medical costs are comparable to other treating providers treating the same injuries. Several additional factors come into play in evaluating provider performance, such as whether there is legal involvement in the claim and duration of the claim from date of injury to closure.
Injury severity adjustment
Beyond these and other measures of performance, injury severity must be computed and applied to provider performance analytics to level the playing field among providers. The performance of those who treat more complex cases should be compared with others treating similar cases. Adjustments for fairness in evaluating providers is important to the credibility and reception of the process.
Our broader opinions on this topic have been written and posted in the following articles. A four part series on this topic is available below and you are invited to review these and other articles posted under Blogs
Part I
Rating Medical Providers
Part II
How to Rate Medical Providers in Workers' Compensation
Part III
Transforming Provider Networks into Quality Networks
Part IV
Monitoring Provider Performance for Predictive Profiling
Traditional networks
Traditional medical provider networks in Workers’ Comp were designed to emulate group health insurance networks. In group health the benefit plan strictly controls access and fees. In group health, providers not in the network and medical services not within the authorized range are simply not reimbursed. Moreover, while discounts in the group health arena may be tied to the contractual relationship with providers, they are not used to gain competitive advantage for providers as they are in Workers’ Comp.
In Workers’ Comp, traditional networks contract with providers who, in exchange for business directed to them, offer discounts on units of medical services. Every unit of service is discounted and reported to network subscribers as units of savings. While the savings reports appear to be a positive result, they are actually a major problem that most people realize, but few acknowledge openly.
Opportunity for provider manipulation
Medical providers understood from the beginning they could simply increase frequency and duration of medical services to offset the discounts and bolster profit margins. Under the system of reporting discounts as savings, increases in frequency and duration are reported as even more savings!
Medical providers come in many flavors. Most are excellent treating providers seeking the best outcome for their patient, the claimant. They are also willing to work with employers to control Workers’ Comp costs by supporting modified work programs, for example. Yet, a number of treating providers are unaware, inept and some are downright fraudulent. Unfortunately, the latter group, while small in number, is costing the Workers Comp industry millions of dollars annually and little is being done to change the system.
Until networks are restructured so that incentives reward different behavior, they will continue to do business as usual. Doing nothing is profitable and there is little motivation or provocation to change.
Network overhaul
Nevertheless, the groundswell from employers and other purchasers of networks is building and some forward-thinking provider network organizations are leading the way. A few networks are being overhauled by changing incentives and focusing on claim outcomes. Creating outcome-based networks requires selecting providers that have proven records as evidenced in the data.
Best practice providers are identified in the Workers’ Comp world by their patients’ early return to work and return to full duty. Indemnity costs are limited. Frequency and duration of medical treatment are reasonable and direct medical costs are comparable to other treating providers treating the same injuries. Several additional factors come into play in evaluating provider performance, such as whether there is legal involvement in the claim and duration of the claim from date of injury to closure.
Injury severity adjustment
Beyond these and other measures of performance, injury severity must be computed and applied to provider performance analytics to level the playing field among providers. The performance of those who treat more complex cases should be compared with others treating similar cases. Adjustments for fairness in evaluating providers is important to the credibility and reception of the process.
Our broader opinions on this topic have been written and posted in the following articles. A four part series on this topic is available below and you are invited to review these and other articles posted under Blogs
Part I
Rating Medical Providers
Part II
How to Rate Medical Providers in Workers' Compensation
Part III
Transforming Provider Networks into Quality Networks
Part IV
Monitoring Provider Performance for Predictive Profiling
Sunday, May 15, 2011
Aristotle was a “know-it-all”…
In his recently published memoir, Paul Allen, co-founder of Microsoft, reflected, “Aristotle, the Greet scientist and philosopher, was literally a know-it-all. He mastered the knowledge of his day on every topic that mattered, from history and political science to medicine and physics. Even more impressively, he could explain what he knew to his students. But in today’s world, where scientific knowledge may be doubling by the year, it is impossible for any one person to absorb more than a small fraction of it.”1
Incoming information overload
Stated more broadly, just reviewing the general knowledge available today is overwhelming, if not impossible. How many newspapers can you read, news casts can you view, newsletters, Internet pod-casts or streaming webcasts, and email can you absorb daily?
Taking a step further, for those of us working in Workers’ Comp industry, the problem can be even more daunting. Claim information comes in continuously from every angle. Claims adjusters, medical case managers, UR professionals, managers, and supervisors, those who are supposed to manage all this information are inundated.
Of course, it’s not possible to adequately manage the information, at least without help. Collecting, organizing, analyzing, and acting on the flood of incoming claim information is unachievable for anyone, even Aristotle. Yet most continue to manage by going it alone, assuming they can “catch” the important stuff and keep their heads above water. Managed Care (medical cost containment) results demonstrate how wrong that idea is.
The results of information overconfidence are evident in unbridled medical costs. Medical cost control is elusive because no mere human can touch all the information, let alone understand and act on it logically in reasonable time. Sadly, the Workers’ Comp industry has not widely embraced the computerized tools that can address the end question. In fact, the industry has lagged well behind other industries in implementing the tools that will leverage success. but they could--quickly, easily, and affordably.
Computer-aided medical cost management
Computers do two things extremely well that address the issue of effective medical information management appropriately and proactively. Computers organize and analyze information—exactly what is needed to manage the deluge of claim information. Organized, easily understood, concurrent, actionable information will inspire cost control success. This is not news, of course, but computerized information resources are not typically applied in the Workers’ Comp industry.
Linking analytics to operations
To illustrate, consider how well-designed analytics can answer these questions in real time within the operational process:
Who are the best orthopedic physicians in a specific geo-zip region?
Which doctors have the poorest Workers’ Comp outcomes for specific injury types?
Which doctors exploit expensive, high risk treatments?
Which claimants have co-morbidities that portend high risk, high cost and poor outcomes?
Notify me when a claim involves very severe injuries.
Each of these scenarios relies on analytics pushed to operations. Moreover, each represents an opportunity to manage claim information efficiently and cost-effectively—while in progress. Why not let computer-aided medical management expand the knowledge, abilities, and effectiveness of front line workers?
Applying technology to the problem of claim information overload will not turn everyone into an Aristotle, but it will powerfully impact claim costs, outcomes and organizational profitability.
MedMetrics is a Workers’ Comp medical cost analytics provider.
1 Allen, Paul. Idea Man: A Memoir by the Cofounder of Microsoft. Penguin Publishing. 2011.
Incoming information overload
Stated more broadly, just reviewing the general knowledge available today is overwhelming, if not impossible. How many newspapers can you read, news casts can you view, newsletters, Internet pod-casts or streaming webcasts, and email can you absorb daily?
Taking a step further, for those of us working in Workers’ Comp industry, the problem can be even more daunting. Claim information comes in continuously from every angle. Claims adjusters, medical case managers, UR professionals, managers, and supervisors, those who are supposed to manage all this information are inundated.
Of course, it’s not possible to adequately manage the information, at least without help. Collecting, organizing, analyzing, and acting on the flood of incoming claim information is unachievable for anyone, even Aristotle. Yet most continue to manage by going it alone, assuming they can “catch” the important stuff and keep their heads above water. Managed Care (medical cost containment) results demonstrate how wrong that idea is.
The results of information overconfidence are evident in unbridled medical costs. Medical cost control is elusive because no mere human can touch all the information, let alone understand and act on it logically in reasonable time. Sadly, the Workers’ Comp industry has not widely embraced the computerized tools that can address the end question. In fact, the industry has lagged well behind other industries in implementing the tools that will leverage success. but they could--quickly, easily, and affordably.
Computer-aided medical cost management
Computers do two things extremely well that address the issue of effective medical information management appropriately and proactively. Computers organize and analyze information—exactly what is needed to manage the deluge of claim information. Organized, easily understood, concurrent, actionable information will inspire cost control success. This is not news, of course, but computerized information resources are not typically applied in the Workers’ Comp industry.
Linking analytics to operations
To illustrate, consider how well-designed analytics can answer these questions in real time within the operational process:
Who are the best orthopedic physicians in a specific geo-zip region?
Which doctors have the poorest Workers’ Comp outcomes for specific injury types?
Which doctors exploit expensive, high risk treatments?
Which claimants have co-morbidities that portend high risk, high cost and poor outcomes?
Notify me when a claim involves very severe injuries.
Each of these scenarios relies on analytics pushed to operations. Moreover, each represents an opportunity to manage claim information efficiently and cost-effectively—while in progress. Why not let computer-aided medical management expand the knowledge, abilities, and effectiveness of front line workers?
Applying technology to the problem of claim information overload will not turn everyone into an Aristotle, but it will powerfully impact claim costs, outcomes and organizational profitability.
MedMetrics is a Workers’ Comp medical cost analytics provider.
1 Allen, Paul. Idea Man: A Memoir by the Cofounder of Microsoft. Penguin Publishing. 2011.
Sunday, April 17, 2011
Why Analytics by Themselves Do Not Change Processes
Decision-making
Research suggests 40% of major business decisions are based not on facts, but on the manager’s gut.1 Hopefully, most of those gut-based decisions are not life endangering. Yet, many of them directly impact organizational viability.
In Workers’ Compensation, critical decisions are made daily by front-line workers. Claims adjusters and medical case managers make course-swerving decisions every day. On what basis do they make these decisions? What kind of decision support is available to them? How timely are the decisions they make? How are outcomes traced back to the decisions made? Are any of the decision based on analytics?
Only analytics fused into operations can respond to these questions. Only analytics that are linked to operations can consistently provide good decision support and positively affect outcomes.
Distributed knowledge
“If you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done and value gets created.”2
Both garden variety and highly sophisticated analytics are common now in many organizations. A few apply the analytics to their operational process effectively, thereby making significant impacts on outcomes and profitability. Unfortunately, those exceptions are not often found in the Workers’ Comp industry. Applied analytics in Workers’ Comp, and particularly those relating to the medical aspect of claims, is rare.
In Workers’ Comp, analytics are most often sequestered on the executive floor. Analytic results are displayed at board meetings and are lavishly portrayed at marketing shindigs. They are represented in colorful graphics while decision-makers ponder them. Nevertheless, just executing and reviewing analytics has little impact on outcome. Analytics must be linked to operations seamlessly to the end that processes are changed and changes are documented.
Dashboards—an attempt that falls short
Dashboards have become a fashionable way to display analytics, but they don’t necessarily link the analytics to operations. They are designed to present conditions in the organization across a broad swath of indicators in one view.
An example is a hospital where a dashboard displays vital operational statistics including admissions and discharges for the period, average lengths of stay, acuity rates, and mortality rates. Dashboards are interesting and informative of activity and organizational performance, but what will or should be done operationally to influence the indicators going forward is not always clear. Results may vary depending upon the leader present that day.
Basically, dashboards are for viewing, and unless the organization has designed response procedures and accountable persons, the impact is negligible. Dashboards have no direct relationship with operations and usually there is no mechanism for tracking action responses to the information.
Changing processes in this top-down manner is difficult, time-consuming and often inaccurate and costly. Corporate communications, regardless of how sophisticated, do not effectively translate analytic knowledge into actions on the front line.
Actionable analytics
For analytics to be actionable they must be linked to, and fused into operations electronically. The best way to do this is to continuously monitor current and historic data, execute the analytics in real time, and initiate the desired actions among workers by means of an electronic message. This approach hurdles the communication log jam found in most organizations with an immediate, specific directive. It requires a computer system designed to monitor and analyze all transactions and to automatically send the action message, thereby communicating the results of current analytics to the appropriate persons.
Computer-aided analytics
Design and build intelligent systems designed to monitor the data continuously and identify data combinations that portend risk. Set up rules-based profiles defining data combinations that can be captured by the computer. IT will build the system or outsource to a vendor that specializes in this service. The latter is usually a much less expensive and quicker option. Outsource to a turn-key solution provider.
Infused accountability
When the computer finds a claim containing the data elements in a profile, the appropriate person is automatically notified electronically. The system should also keep an audit trail noting all claims tagged, the reason it was tagges, and to whom the alert was sent. The end-to-end process will infuse analytics into the process, render the process more efficient, and keep everyone accountable.
It’s true, analytics by themselves will not change processes. But analytics linked to operations with built-in tracking systems will.
MedMetrics is an Internet-based Workers’ Compensation analytics company that provides the services described here. You are invited to read other articles dealing with issues of Workers' Comp medical cost management. Click MedMetrics Blogs.
1. Davenport, T. Harris, J., and Morison, R. Analytics at Work, Smarter Decisions, Better Results. Harvard Business School Publishing Corporation. 2010.
2. Ibid.
Research suggests 40% of major business decisions are based not on facts, but on the manager’s gut.1 Hopefully, most of those gut-based decisions are not life endangering. Yet, many of them directly impact organizational viability.
In Workers’ Compensation, critical decisions are made daily by front-line workers. Claims adjusters and medical case managers make course-swerving decisions every day. On what basis do they make these decisions? What kind of decision support is available to them? How timely are the decisions they make? How are outcomes traced back to the decisions made? Are any of the decision based on analytics?
Only analytics fused into operations can respond to these questions. Only analytics that are linked to operations can consistently provide good decision support and positively affect outcomes.
Distributed knowledge
“If you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done and value gets created.”2
Both garden variety and highly sophisticated analytics are common now in many organizations. A few apply the analytics to their operational process effectively, thereby making significant impacts on outcomes and profitability. Unfortunately, those exceptions are not often found in the Workers’ Comp industry. Applied analytics in Workers’ Comp, and particularly those relating to the medical aspect of claims, is rare.
In Workers’ Comp, analytics are most often sequestered on the executive floor. Analytic results are displayed at board meetings and are lavishly portrayed at marketing shindigs. They are represented in colorful graphics while decision-makers ponder them. Nevertheless, just executing and reviewing analytics has little impact on outcome. Analytics must be linked to operations seamlessly to the end that processes are changed and changes are documented.
Dashboards—an attempt that falls short
Dashboards have become a fashionable way to display analytics, but they don’t necessarily link the analytics to operations. They are designed to present conditions in the organization across a broad swath of indicators in one view.
An example is a hospital where a dashboard displays vital operational statistics including admissions and discharges for the period, average lengths of stay, acuity rates, and mortality rates. Dashboards are interesting and informative of activity and organizational performance, but what will or should be done operationally to influence the indicators going forward is not always clear. Results may vary depending upon the leader present that day.
Basically, dashboards are for viewing, and unless the organization has designed response procedures and accountable persons, the impact is negligible. Dashboards have no direct relationship with operations and usually there is no mechanism for tracking action responses to the information.
Changing processes in this top-down manner is difficult, time-consuming and often inaccurate and costly. Corporate communications, regardless of how sophisticated, do not effectively translate analytic knowledge into actions on the front line.
Actionable analytics
For analytics to be actionable they must be linked to, and fused into operations electronically. The best way to do this is to continuously monitor current and historic data, execute the analytics in real time, and initiate the desired actions among workers by means of an electronic message. This approach hurdles the communication log jam found in most organizations with an immediate, specific directive. It requires a computer system designed to monitor and analyze all transactions and to automatically send the action message, thereby communicating the results of current analytics to the appropriate persons.
Computer-aided analytics
Design and build intelligent systems designed to monitor the data continuously and identify data combinations that portend risk. Set up rules-based profiles defining data combinations that can be captured by the computer. IT will build the system or outsource to a vendor that specializes in this service. The latter is usually a much less expensive and quicker option. Outsource to a turn-key solution provider.
Infused accountability
When the computer finds a claim containing the data elements in a profile, the appropriate person is automatically notified electronically. The system should also keep an audit trail noting all claims tagged, the reason it was tagges, and to whom the alert was sent. The end-to-end process will infuse analytics into the process, render the process more efficient, and keep everyone accountable.
It’s true, analytics by themselves will not change processes. But analytics linked to operations with built-in tracking systems will.
MedMetrics is an Internet-based Workers’ Compensation analytics company that provides the services described here. You are invited to read other articles dealing with issues of Workers' Comp medical cost management. Click MedMetrics Blogs.
1. Davenport, T. Harris, J., and Morison, R. Analytics at Work, Smarter Decisions, Better Results. Harvard Business School Publishing Corporation. 2010.
2. Ibid.
Sunday, April 3, 2011
How to Leap-frog to the Next Generation of Managed Care
Workers’ Comp medical costs continue to scale, yet managed care programs persist in doing business as usual. Such industry-wide paralysis is hard to explain, especially because realistic answers are available. Managed care initiatives can be powerfully recharged through analytics because the core problems inherent to Workers’ Comp managed care can be addressed using technology. Foremost among the deficits is fragmented data.
Like the weather, data silos are one of those issues everyone talks about but little effort is applied to change. The prevailing attitude is “it is what it is”. Managed care programs (bill review, provider discount networks, medical case management, utilization review, and PBM) individually produce their unique data, but do not consistently share it. The data are so fractured that a unified and current data platform is rarely available for current constructive analysis, new insights, and decision support that could change claim outcomes.
Efforts to integrate data have focused on pulling it all into claims systems, a slow and complex endeavor. But there is a quicker, easier and far less costly way to correct the problem, one that supports an organization’s IT rather than depleting it. The new, more effective strategy is contracting with a specialized Workers’ Comp managed care SaaS partner.
Get a “SaaSy” partner
A cloud-based, web-enabled SaaS (Software as a Service) partner makes it happen—now. Cloud-based means it is Internet-based, with SaaS being the software and other technical tools and services made available from the web. Data are imported from disparate sources for an organization by the SaaS partner, then integrated and analyzed on a concurrent basis. The Internet-based SaaS partner grants all authorized persons in an organization access to secure, analyzed, and re-portrayed data online. Information is integrated across managed care programs and analyses are based on historic and current data from all the relevant sources. And yet, some still resist.
It really does not need to be invented here
Some are still mired in the notion that all initiatives must be created in-house to be of value. This idea could be simply territorial and protective, but it’s time for the old mantra to vanish. IT departments are too overburdened and underfunded to achieve the level of performance available from an specialized SaaS provider. Smart IT leadership will quickly see the value of an expert SaaS managed care partner to relieve them of the burden of retooling managed care.
Create a unified and concurrent data platform
Data is the prerequisite for everything analytical.1 Moreover, data integrated across the enterprise, analyzed, and results made accessible to all the business units involved is critical to improving managed care. Businesses in other industries have proven repeatedly that analytics change outcomes and now those in Workers’ Comp can similarly realize the benefits.
Once the data transfer to the SaaS partner is in place, it can be set to an automatic scheduler for updates. Rather than reviewing graphic presentations of the previous month, quarter or year, the most current data available is analyzed and the information made available to those who need it. Critical information can be accessed on demand and alerts sent to the right person as events in a claim develop. It is the integrated, analyzed data that generates knowledge and results.
Link analytics to operations
The process described here links analytics to operations, making the insights immediately available and actionable. It accelerates current decision support knowledge, making it available to claims adjusters, UR, medical case managers, medical directors, and supervisors. Linking analytics to operations permits and sustains proactive medical cost management.
For instance, current and historic integrated claim data is used to analyze and rate provider performance based on the broad scope of data. Likewise, the SaaS partner develops rules-based electronic monitoring technology to continually search for data combinations in claims that portend high risk or cost. The process of integrating and analyzing data in near-real time by a knowledgeable SaaS partner produces computer-aided, intelligence-guided managed care. The efficiencies and knowledge created through such applied analytics enable communication and timely action, thereby making medical cost management initiatives effective.
Economies of scale reap affordability
A knowledgeable Managed care SaaS partner spreads the cost of development and technical management over multiple clients, thereby significantly reducing the cost for individual organizations. The challenges of system design, development and programming, support and maintenance are all managed by the SaaS partner. And yet, for most organizations the annual cost of partnering with a SaaS provider is far less than hiring one analyst.
Therefore, the question is not why, but when. Who can justify continuing business as usual? “Those who get caught in the past and resist change will be forced deeper into commoditization. Those who can create value through leadership, relationships and creativity will transform the industry and strengthen relationships with their existing and new clients.”2
Note: MedMetrics is an Internet-based SaaS provider experienced in managed care and cost management solutions. You are invited to read other MedMetrics Blogs.
1. Davenport, T. Harris, J., and Morison, R. Analytics at Work, Smarter Decisions, Better Results. Harvard Business School Publishing Corporation. 2010.
2. Friedman, T. The World is Flat. A Brief History of the Twenty-First Century. 2005.
Like the weather, data silos are one of those issues everyone talks about but little effort is applied to change. The prevailing attitude is “it is what it is”. Managed care programs (bill review, provider discount networks, medical case management, utilization review, and PBM) individually produce their unique data, but do not consistently share it. The data are so fractured that a unified and current data platform is rarely available for current constructive analysis, new insights, and decision support that could change claim outcomes.
Efforts to integrate data have focused on pulling it all into claims systems, a slow and complex endeavor. But there is a quicker, easier and far less costly way to correct the problem, one that supports an organization’s IT rather than depleting it. The new, more effective strategy is contracting with a specialized Workers’ Comp managed care SaaS partner.
Get a “SaaSy” partner
A cloud-based, web-enabled SaaS (Software as a Service) partner makes it happen—now. Cloud-based means it is Internet-based, with SaaS being the software and other technical tools and services made available from the web. Data are imported from disparate sources for an organization by the SaaS partner, then integrated and analyzed on a concurrent basis. The Internet-based SaaS partner grants all authorized persons in an organization access to secure, analyzed, and re-portrayed data online. Information is integrated across managed care programs and analyses are based on historic and current data from all the relevant sources. And yet, some still resist.
It really does not need to be invented here
Some are still mired in the notion that all initiatives must be created in-house to be of value. This idea could be simply territorial and protective, but it’s time for the old mantra to vanish. IT departments are too overburdened and underfunded to achieve the level of performance available from an specialized SaaS provider. Smart IT leadership will quickly see the value of an expert SaaS managed care partner to relieve them of the burden of retooling managed care.
Create a unified and concurrent data platform
Data is the prerequisite for everything analytical.1 Moreover, data integrated across the enterprise, analyzed, and results made accessible to all the business units involved is critical to improving managed care. Businesses in other industries have proven repeatedly that analytics change outcomes and now those in Workers’ Comp can similarly realize the benefits.
Once the data transfer to the SaaS partner is in place, it can be set to an automatic scheduler for updates. Rather than reviewing graphic presentations of the previous month, quarter or year, the most current data available is analyzed and the information made available to those who need it. Critical information can be accessed on demand and alerts sent to the right person as events in a claim develop. It is the integrated, analyzed data that generates knowledge and results.
Link analytics to operations
The process described here links analytics to operations, making the insights immediately available and actionable. It accelerates current decision support knowledge, making it available to claims adjusters, UR, medical case managers, medical directors, and supervisors. Linking analytics to operations permits and sustains proactive medical cost management.
For instance, current and historic integrated claim data is used to analyze and rate provider performance based on the broad scope of data. Likewise, the SaaS partner develops rules-based electronic monitoring technology to continually search for data combinations in claims that portend high risk or cost. The process of integrating and analyzing data in near-real time by a knowledgeable SaaS partner produces computer-aided, intelligence-guided managed care. The efficiencies and knowledge created through such applied analytics enable communication and timely action, thereby making medical cost management initiatives effective.
Economies of scale reap affordability
A knowledgeable Managed care SaaS partner spreads the cost of development and technical management over multiple clients, thereby significantly reducing the cost for individual organizations. The challenges of system design, development and programming, support and maintenance are all managed by the SaaS partner. And yet, for most organizations the annual cost of partnering with a SaaS provider is far less than hiring one analyst.
Therefore, the question is not why, but when. Who can justify continuing business as usual? “Those who get caught in the past and resist change will be forced deeper into commoditization. Those who can create value through leadership, relationships and creativity will transform the industry and strengthen relationships with their existing and new clients.”2
Note: MedMetrics is an Internet-based SaaS provider experienced in managed care and cost management solutions. You are invited to read other MedMetrics Blogs.
1. Davenport, T. Harris, J., and Morison, R. Analytics at Work, Smarter Decisions, Better Results. Harvard Business School Publishing Corporation. 2010.
2. Friedman, T. The World is Flat. A Brief History of the Twenty-First Century. 2005.
Thursday, March 24, 2011
How to Get Really Good Analytics
Many in Workers’ Compensation are now turning to analytics, searching for the last best solution to controlling costs. Analytics will go a long way to meet that expectation if applied correctly. However, the components of good analytics may be elusive for many. The only way to produce really good analytics is to build them using really good data.
Good data will produce useful analytics
To say the prerequisite for analytics is good data is a bit simplistic because many factors are involved. Yet, good analytics does require good data before anything else. The volume of data is not as important as quality and content of the data. Stated even more strongly, only extraordinarily pristine and comprehensive data can result in uniquely useful analytics. The opposite is also true. Bad data can never result in good analytics. So, let’s discuss what makes for good data.
Our recent article, “WC Analytics Can't Live on Bill Review Data Alone” suggests that while bill review data is good, it is not enough. It’s scope is too narrow. The article focuses on why good analytics cannot be built on bill review data alone, as some propose. But that is not the only problem. There are many more conditions, omissions, and misapplications of data in the industry that limit the usefulness of analysis. Moreover, most are straightforward, simple issues. A few of them are the following.
System design effects data quality
One source of bad data is poor system design. Omitted data fields and awkward or illogical flow confuse users or force them to enter misleading data. Unfortunately, many users are clever at “beating the system”, thereby creating unusable data. But an even bigger problem is data systems that simply do not contain important data fields. One critical data field that is often missing is physician specialty.
Systems frequently omit physician specialty
To be fair, physician specialty has not been an important data element until recently. Now that medical costs amount to 60% of claim costs, deeper analysis of cost drivers is of considerable importance. Medical doctors exert a pronounced effect on the outcome of claims and their performance should be evaluated and rated. However, it doesn’t seem fair or logical to compare the performance of an emergency department physician with that of a neurologist. Comparing a psychiatrist with an orthopedic surgeon is just as unreasonable. Yet, when the data lumps all medical doctors into the same category, more precise analysis is not possible. Adding the one data element of physician specialty, analysis can rationally target costs in claims.
Physician NPI numbers are critical
Another critical data element frequently omitted from data sets is a physician unique identifier. Reliance on tax ID alone is no longer acceptable. In the past, the tax ID was the only data element of interest, in order to accurately pay the bill. But for good provider performance analytics, accurately identifying the individual provider is vital. Provider performance cannot be analyzed unless individuals can be differentiated in the data.
It’s true; some treating providers seek ways to obfuscate their individual identity by using multiple tax ID numbers or conceal their identify behind organizational or facility tax ID numbers. A few are even using multiple NPI numbers, and not all of them registered.
NPI (National Provider Identifier) is a national registry of individual medical providers with unique numbers for individuals. Medical providers, but especially medical doctors, should register and obtain an NPI umber. In fact, to be reimbursed for group health medical services, an NPI number is required. Unfortunately, NPI’s are not required for Workers’ Compensation reimbursement.
Taken a step further, claim systems should insist upon and utilize the NPI number to identify individual providers as a condition of payment, similar to group health. The practice would prevent fraudulent gaming of the system on that level. Requiring and implementing the NPI number is a simple step that would achieve powerful analytic results because individuals would be recognized accurately.
Good data is a management imperativeUltimately good data is a management function. Systems will not improve until organizational leadership mandates it. Business management must share this responsibility with IT, not simply delegate the responsibility to those who may not grasp the business impact of poor or missing data.
Additionally, business leadership should aggressively establish data entry accuracy accountabilities. Data entry persons must be held accountable for data entry errors that effect the system and the organization. For instance, multiple records in the data for the same person or entity significantly diminish data quality and the ability to product good analytics.
Too often, the data entry person creates a new record for a vendor, medical provider, or other entity rather than search out the appropriate record already in the system. Creating a new record automatically creates a unique record and results in duplicate records for the same person or entity, each with different identifying numbers. The system regards each record as different and unique, thereby rendering the system inaccurate. Basing analytics on such data results in misinformation in the organization. Insisting upon accuracy in this regard is a management imperative.
These are but a few of the data issues facing developers of analytics in Workers’ Compensation. Suffice it to say, poor quality data directly impacts the accuracy of information, and, therefore, the quality of products, services and outcomes of the organization. Conversely, those who insist on good and accurate data will enjoy the benefits of good analytics.
Learn more about Workers' Comp Analytics: MedMetrics Blogs
Good data will produce useful analytics
To say the prerequisite for analytics is good data is a bit simplistic because many factors are involved. Yet, good analytics does require good data before anything else. The volume of data is not as important as quality and content of the data. Stated even more strongly, only extraordinarily pristine and comprehensive data can result in uniquely useful analytics. The opposite is also true. Bad data can never result in good analytics. So, let’s discuss what makes for good data.
Our recent article, “WC Analytics Can't Live on Bill Review Data Alone” suggests that while bill review data is good, it is not enough. It’s scope is too narrow. The article focuses on why good analytics cannot be built on bill review data alone, as some propose. But that is not the only problem. There are many more conditions, omissions, and misapplications of data in the industry that limit the usefulness of analysis. Moreover, most are straightforward, simple issues. A few of them are the following.
System design effects data quality
One source of bad data is poor system design. Omitted data fields and awkward or illogical flow confuse users or force them to enter misleading data. Unfortunately, many users are clever at “beating the system”, thereby creating unusable data. But an even bigger problem is data systems that simply do not contain important data fields. One critical data field that is often missing is physician specialty.
Systems frequently omit physician specialty
To be fair, physician specialty has not been an important data element until recently. Now that medical costs amount to 60% of claim costs, deeper analysis of cost drivers is of considerable importance. Medical doctors exert a pronounced effect on the outcome of claims and their performance should be evaluated and rated. However, it doesn’t seem fair or logical to compare the performance of an emergency department physician with that of a neurologist. Comparing a psychiatrist with an orthopedic surgeon is just as unreasonable. Yet, when the data lumps all medical doctors into the same category, more precise analysis is not possible. Adding the one data element of physician specialty, analysis can rationally target costs in claims.
Physician NPI numbers are critical
Another critical data element frequently omitted from data sets is a physician unique identifier. Reliance on tax ID alone is no longer acceptable. In the past, the tax ID was the only data element of interest, in order to accurately pay the bill. But for good provider performance analytics, accurately identifying the individual provider is vital. Provider performance cannot be analyzed unless individuals can be differentiated in the data.
It’s true; some treating providers seek ways to obfuscate their individual identity by using multiple tax ID numbers or conceal their identify behind organizational or facility tax ID numbers. A few are even using multiple NPI numbers, and not all of them registered.
NPI (National Provider Identifier) is a national registry of individual medical providers with unique numbers for individuals. Medical providers, but especially medical doctors, should register and obtain an NPI umber. In fact, to be reimbursed for group health medical services, an NPI number is required. Unfortunately, NPI’s are not required for Workers’ Compensation reimbursement.
Taken a step further, claim systems should insist upon and utilize the NPI number to identify individual providers as a condition of payment, similar to group health. The practice would prevent fraudulent gaming of the system on that level. Requiring and implementing the NPI number is a simple step that would achieve powerful analytic results because individuals would be recognized accurately.
Good data is a management imperativeUltimately good data is a management function. Systems will not improve until organizational leadership mandates it. Business management must share this responsibility with IT, not simply delegate the responsibility to those who may not grasp the business impact of poor or missing data.
Additionally, business leadership should aggressively establish data entry accuracy accountabilities. Data entry persons must be held accountable for data entry errors that effect the system and the organization. For instance, multiple records in the data for the same person or entity significantly diminish data quality and the ability to product good analytics.
Too often, the data entry person creates a new record for a vendor, medical provider, or other entity rather than search out the appropriate record already in the system. Creating a new record automatically creates a unique record and results in duplicate records for the same person or entity, each with different identifying numbers. The system regards each record as different and unique, thereby rendering the system inaccurate. Basing analytics on such data results in misinformation in the organization. Insisting upon accuracy in this regard is a management imperative.
These are but a few of the data issues facing developers of analytics in Workers’ Compensation. Suffice it to say, poor quality data directly impacts the accuracy of information, and, therefore, the quality of products, services and outcomes of the organization. Conversely, those who insist on good and accurate data will enjoy the benefits of good analytics.
Learn more about Workers' Comp Analytics: MedMetrics Blogs
Sunday, February 27, 2011
WC Analytics Can't Live on Bill Review Data Alone
Analytics are all the rage in Workers’ Comp—finally. It’s been a long time in coming to this industry, but people are making serious moves to embrace and implement the concept. Analytics is the only untried option remaining that has the potential to control costs and create efficiencies in the industry. Intelligently crafted and applied in operations, analytics will recharge managed care programs, making them more efficient and effective, thereby measurably controlling medical costs.
Analytics (a fancy term for data analysis) fall into two basic categories: Descriptive Analytics and Predictive Analytics. Descriptive analytics are “the what”—what happened in the past described through reports, queries, and data drill downs to gain deep understanding of claim processes and participants. Descriptive analytics identify critical business issues, trends, and cost drivers. The approach is essential to understanding relationships, the business process, and provides the platform for asking the right business questions. Moreover, descriptive analytics are crucial to decision support and are the foundation for determining the right focus going forward.
Predictive Analytics, on the other hand, are used for forecasting, advanced reporting, and optimizing algorithms. Advanced mathematical and actuarial analyses are used to predict the future based on the past. If X is true, what is the probability Y will occur? Or when Y occurs, what are the factors that could have predicted it?
Using predictive analytics to more accurately estimate the cost of a claim, thereby setting accurate reserves, is one example of how predictive analytics takes an organization to a higher level of effectiveness. Intelligent use of predictive analytics can yield greater measureable cost savings and competitive advantage for an organization.
Both analytics approaches are important to optimizing the effects of claim and medical management, along with cost control. Still, there are challenging hurdles that must be overcome to effectively implement analytics in the Workers’ Comp industry. The challenges relate to how people perceive sufficient application of the process.
Crimes against analytics in Workers’ Comp relate to how the data is selected and applied. The Workers’ Comp industry has truckloads of data—quantity of data is not the problem. The trouble for analytics in Workers’ Comp is data collection, integration and, too often, narrow understanding of what type of data should be tapped for analytics.
A well-known fact is that Workers’ Comp data lives in separate silos. The fact that relevant and important data resides in disparate locations and sometimes in different companies is not a small problem, but it is very manageable problem technologically. Data from different sources can be readily transferred and integrated. Of greater concern is the resistance to gathering and integrating all the relevant data in order to perform adequate analytics, analytics that will enlighten operations. For instance, many think bill review data is enough.
One reason people rely on bill review data is that it is the most plentiful and accessible. Nearly all medical bills in Workers’ Comp are run through bill review systems. Conveniently, different bill review systems contain the same range of data, that which is generated from standardized medical billing formats. The formats are those required by CMS (Centers for Medicare and Medicaid Services) such as HCFA-1500 (Health Care Finance Authority) and the UB-04 (Uniform Billing-2004). Medical billers use those standard billing formats for Medicare and Medicaid and typically use the same formats for Workers’ Comp billing. In some places, the formats are required. Standardized formatted data are run through bill review systems, making the data not only prolific, but relatively uniform.
Bill review data is detailed and specific. ICD-9 diagnostic codes and other standardized charge codes, such as CPT codes (Current Procedural Terminology) are available. The treatment process, treatment providers, and recommended payment can be derived from bill review data. However, actual paid costs, non-medical costs, and treatment effectiveness measured in terms of actual outcomes cannot.
Analysis of the medical treatment process and provider performance in terms of claim outcome or the actual claim cost cannot be derived from bill review data. Claims level data is needed to shed light on provider performance in terms of return to work, indemnity costs, litigation, as well as the duration and outcome of a claim. At a minimum, claims level data should be combined with bill review data for meaningful analytics. Moreover, predictive analytics is without foundation when applied to bill review data only.
It’s true, one can use descriptive statistics of bill review data to capture and understand trends in injury types, treatment processes and billed costs, but little beyond that. Bill review data alone will not reveal comprehensive actual claim costs or illuminate treatment effectiveness, provider performance, total cost of the claim or outcomes because it represents only a portion of claim information. First class analytics, that which produces predictive, actionable information cannot be limited to bill review data. Those who say bill review data is enough are mislead.
View additional articles by Karen Wolfe under Blogs at www.medmetrics.org
Analytics (a fancy term for data analysis) fall into two basic categories: Descriptive Analytics and Predictive Analytics. Descriptive analytics are “the what”—what happened in the past described through reports, queries, and data drill downs to gain deep understanding of claim processes and participants. Descriptive analytics identify critical business issues, trends, and cost drivers. The approach is essential to understanding relationships, the business process, and provides the platform for asking the right business questions. Moreover, descriptive analytics are crucial to decision support and are the foundation for determining the right focus going forward.
Predictive Analytics, on the other hand, are used for forecasting, advanced reporting, and optimizing algorithms. Advanced mathematical and actuarial analyses are used to predict the future based on the past. If X is true, what is the probability Y will occur? Or when Y occurs, what are the factors that could have predicted it?
Using predictive analytics to more accurately estimate the cost of a claim, thereby setting accurate reserves, is one example of how predictive analytics takes an organization to a higher level of effectiveness. Intelligent use of predictive analytics can yield greater measureable cost savings and competitive advantage for an organization.
Both analytics approaches are important to optimizing the effects of claim and medical management, along with cost control. Still, there are challenging hurdles that must be overcome to effectively implement analytics in the Workers’ Comp industry. The challenges relate to how people perceive sufficient application of the process.
Crimes against analytics in Workers’ Comp relate to how the data is selected and applied. The Workers’ Comp industry has truckloads of data—quantity of data is not the problem. The trouble for analytics in Workers’ Comp is data collection, integration and, too often, narrow understanding of what type of data should be tapped for analytics.
A well-known fact is that Workers’ Comp data lives in separate silos. The fact that relevant and important data resides in disparate locations and sometimes in different companies is not a small problem, but it is very manageable problem technologically. Data from different sources can be readily transferred and integrated. Of greater concern is the resistance to gathering and integrating all the relevant data in order to perform adequate analytics, analytics that will enlighten operations. For instance, many think bill review data is enough.
One reason people rely on bill review data is that it is the most plentiful and accessible. Nearly all medical bills in Workers’ Comp are run through bill review systems. Conveniently, different bill review systems contain the same range of data, that which is generated from standardized medical billing formats. The formats are those required by CMS (Centers for Medicare and Medicaid Services) such as HCFA-1500 (Health Care Finance Authority) and the UB-04 (Uniform Billing-2004). Medical billers use those standard billing formats for Medicare and Medicaid and typically use the same formats for Workers’ Comp billing. In some places, the formats are required. Standardized formatted data are run through bill review systems, making the data not only prolific, but relatively uniform.
Bill review data is detailed and specific. ICD-9 diagnostic codes and other standardized charge codes, such as CPT codes (Current Procedural Terminology) are available. The treatment process, treatment providers, and recommended payment can be derived from bill review data. However, actual paid costs, non-medical costs, and treatment effectiveness measured in terms of actual outcomes cannot.
Analysis of the medical treatment process and provider performance in terms of claim outcome or the actual claim cost cannot be derived from bill review data. Claims level data is needed to shed light on provider performance in terms of return to work, indemnity costs, litigation, as well as the duration and outcome of a claim. At a minimum, claims level data should be combined with bill review data for meaningful analytics. Moreover, predictive analytics is without foundation when applied to bill review data only.
It’s true, one can use descriptive statistics of bill review data to capture and understand trends in injury types, treatment processes and billed costs, but little beyond that. Bill review data alone will not reveal comprehensive actual claim costs or illuminate treatment effectiveness, provider performance, total cost of the claim or outcomes because it represents only a portion of claim information. First class analytics, that which produces predictive, actionable information cannot be limited to bill review data. Those who say bill review data is enough are mislead.
View additional articles by Karen Wolfe under Blogs at www.medmetrics.org
Sunday, January 23, 2011
Finally Control Workers’ Comp Costs—Analytics in the Cloud!
It’s all been tried—medical case management, utilization review, discounted provider networks (aka MCO’s, MPN’s), bill review, peer review, fee schedules, and medical practice guidelines. Yet, medical costs in Workers’ Compensation continue to soar. After twenty-five years or so, each of these managed care programs has morphed into substantial businesses, both as internal corporate initiatives and as independent companies. Still, medical costs continue to spiral upward. But now there is finally something new and powerful.
The one management initiative still largely untapped in the Workers’ Comp industry is technology-supported data analysis (analytics). While aggressively applied in other industries with quantifiably positive results, the use of analytics as a working tool has been skirted in Workers’ Compensation. Reasons for this are many, but foremost among them is probably fear—dread of complexity and cost. Be assured, when implemented correctly, technology-supported analytics is easy and affordable.
Analytics Implementation—the Key to Cost Containment
Just the term, analytics can be off-putting. However, as with much of the technical jargon, its meaning is simple—data analysis. Nonetheless, it is the implementation of analytics that matters. To effectively impact Workers’ Comp claim costs, analytics should be comprehensive, concurrent, and implemented as a work-in-process tool.
Unified and Current Data Platform
Begin with a commitment to a unified and current data analysis platform. In Workers’ Comp, the data are profuse, but rarely in one place. Claim-related data can be found in First Reports of Injury, provider first and subsequent reports, raw billing data, bill review data, OSHA logs, medical case management systems, utilization review systems, provider network systems, and, of course, claims adjudication systems. Just the number of silos containing useful data describes the problem. Combing data sets can be a daunting task for some, yet crucial to impacting cost. Minimally, billing and claims level data sets should be combined for analysis.
Integrating historic and current data across databases is important so the analytics are comprehensive and informative. Analyzing bill review data alone, for instance, is silly because billing has little meaning without outcome information, which is found only in claims level data. The medical treatment process can be understood by analyzing bill review data, but effectiveness of treatment is known only if viewed in context with indemnity payments, return to work, and disability rating information, all found in the claims system.
Moreover, access to integrated and interpreted data must be concurrent to influence cost. Reviewing reports from last year or even last quarter is pointless in terms of controlling the cost of current claims. Claim cost control can only occur during the course of a claim, not after the fact. Too often, analysis is focused only on closed claims.
To be fair, analysis of closed claims enlightens analysts regarding what to watch for going forward. That is the basis for predictive modeling. But if ongoing claims are not continually analyzed, even predictive analytics cannot perform.
Stated again, to effectively impact claim costs, analytics must be comprehensive, concurrent and implemented as a work-in-process tool. How is all that possible without complete overhaul of the Workers’ Comp claim management process? Meet the cloud.
The Internet Cloud
Online banking, online shopping and storing photos online, are all examples of cloud computing, now widely accepted Internet activities. Cloud computing is a general term for anything that involves delivering hosted services over the Internet. Hosted services means users work from their various computer devices anywhere while the infrastructure, data analysis, software, and data storage are handled by an Internet-based company, the host. The host has all the heavy equipment and does all the work.
The cloud symbolism is inspired by the cloud graphic, long used to depict the Internet on flowcharts. Once again, the technical jargon is demystified.
SaaS
Saas (Software as a Service) is one form of cloud computing, the one that finally impacts Workers’ Comp costs. It is the delivery mechanism for technology-supported analytics—analytics that are comprehensive, concurrent and implemented as a work-in-process tool. The process is simple. Data from the disparate sources of an organization are transmitted to the SaaS company online via secure file transfer protocol (SFTP). For Workers’ Comp, the data are de-identified, meaning claimant name, address and SSI are not included.
Regarding data, read Peter Rousmaniere’s recent article, “Make Claims Data Free”.
The SaaS provider imports and integrates its customer’s data, analyzes and presents the results online in the form of software tools that let customers easily analyze their live, current data. The data are updated frequently (usually daily) so that customer business units have access to the most current information possible for objective decision support, opportunity for early intervention, and cost control.
Benefits of SaaS-based Workers’ Comp Analytics
Using SaaS technology for Workers’ Comp analytics has multiple advantages, even beyond fully integrated, currently analyzed live data. Fully hosted SaaS providers are Internet technology veterans that not only handle the technical work, but also make available subject matter expertise. Knowledgeable Workers’ Comp managed care experts contribute to the SaaS model, designing the software to meet the customer’s information needs and also performing high level predictive modeling. A fully hosted SaaS provider offers a comprehensive solution at a fraction of the cost of building in-house.
Because the service is Internet-based, users access the information from anywhere, at any time, using any device that has Internet access. Users are not burdened with capital investment costs, nor are they bothered by installing updates to the software, or conducting backups and disaster recovery. The SaaS host does it all.
SaaS-based Workers’ Comp analytics is new to the industry—and is the only new and innovative option available for controlling Workers’ Comp costs. The approach leverages the power of the Internet and is unique, powerful, and effective. Happily, it is also affordable and available now.
Disclosure statement: MedMetrics is a fully hosted, SaaS-based Workers’ Compensation analytics provider.
View additional articles by Karen Wolfe under Blogs at www.medmetrics.org
The one management initiative still largely untapped in the Workers’ Comp industry is technology-supported data analysis (analytics). While aggressively applied in other industries with quantifiably positive results, the use of analytics as a working tool has been skirted in Workers’ Compensation. Reasons for this are many, but foremost among them is probably fear—dread of complexity and cost. Be assured, when implemented correctly, technology-supported analytics is easy and affordable.
Analytics Implementation—the Key to Cost Containment
Just the term, analytics can be off-putting. However, as with much of the technical jargon, its meaning is simple—data analysis. Nonetheless, it is the implementation of analytics that matters. To effectively impact Workers’ Comp claim costs, analytics should be comprehensive, concurrent, and implemented as a work-in-process tool.
Unified and Current Data Platform
Begin with a commitment to a unified and current data analysis platform. In Workers’ Comp, the data are profuse, but rarely in one place. Claim-related data can be found in First Reports of Injury, provider first and subsequent reports, raw billing data, bill review data, OSHA logs, medical case management systems, utilization review systems, provider network systems, and, of course, claims adjudication systems. Just the number of silos containing useful data describes the problem. Combing data sets can be a daunting task for some, yet crucial to impacting cost. Minimally, billing and claims level data sets should be combined for analysis.
Integrating historic and current data across databases is important so the analytics are comprehensive and informative. Analyzing bill review data alone, for instance, is silly because billing has little meaning without outcome information, which is found only in claims level data. The medical treatment process can be understood by analyzing bill review data, but effectiveness of treatment is known only if viewed in context with indemnity payments, return to work, and disability rating information, all found in the claims system.
Moreover, access to integrated and interpreted data must be concurrent to influence cost. Reviewing reports from last year or even last quarter is pointless in terms of controlling the cost of current claims. Claim cost control can only occur during the course of a claim, not after the fact. Too often, analysis is focused only on closed claims.
To be fair, analysis of closed claims enlightens analysts regarding what to watch for going forward. That is the basis for predictive modeling. But if ongoing claims are not continually analyzed, even predictive analytics cannot perform.
Stated again, to effectively impact claim costs, analytics must be comprehensive, concurrent and implemented as a work-in-process tool. How is all that possible without complete overhaul of the Workers’ Comp claim management process? Meet the cloud.
The Internet Cloud
Online banking, online shopping and storing photos online, are all examples of cloud computing, now widely accepted Internet activities. Cloud computing is a general term for anything that involves delivering hosted services over the Internet. Hosted services means users work from their various computer devices anywhere while the infrastructure, data analysis, software, and data storage are handled by an Internet-based company, the host. The host has all the heavy equipment and does all the work.
The cloud symbolism is inspired by the cloud graphic, long used to depict the Internet on flowcharts. Once again, the technical jargon is demystified.
SaaS
Saas (Software as a Service) is one form of cloud computing, the one that finally impacts Workers’ Comp costs. It is the delivery mechanism for technology-supported analytics—analytics that are comprehensive, concurrent and implemented as a work-in-process tool. The process is simple. Data from the disparate sources of an organization are transmitted to the SaaS company online via secure file transfer protocol (SFTP). For Workers’ Comp, the data are de-identified, meaning claimant name, address and SSI are not included.
Regarding data, read Peter Rousmaniere’s recent article, “Make Claims Data Free”.
The SaaS provider imports and integrates its customer’s data, analyzes and presents the results online in the form of software tools that let customers easily analyze their live, current data. The data are updated frequently (usually daily) so that customer business units have access to the most current information possible for objective decision support, opportunity for early intervention, and cost control.
Benefits of SaaS-based Workers’ Comp Analytics
Using SaaS technology for Workers’ Comp analytics has multiple advantages, even beyond fully integrated, currently analyzed live data. Fully hosted SaaS providers are Internet technology veterans that not only handle the technical work, but also make available subject matter expertise. Knowledgeable Workers’ Comp managed care experts contribute to the SaaS model, designing the software to meet the customer’s information needs and also performing high level predictive modeling. A fully hosted SaaS provider offers a comprehensive solution at a fraction of the cost of building in-house.
Because the service is Internet-based, users access the information from anywhere, at any time, using any device that has Internet access. Users are not burdened with capital investment costs, nor are they bothered by installing updates to the software, or conducting backups and disaster recovery. The SaaS host does it all.
SaaS-based Workers’ Comp analytics is new to the industry—and is the only new and innovative option available for controlling Workers’ Comp costs. The approach leverages the power of the Internet and is unique, powerful, and effective. Happily, it is also affordable and available now.
Disclosure statement: MedMetrics is a fully hosted, SaaS-based Workers’ Compensation analytics provider.
View additional articles by Karen Wolfe under Blogs at www.medmetrics.org
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