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The MedMetrics blog provides comments and insights regarding the world of Workers’ Compensation, principally, issues that are medically-related. The blog offers viewpoints regarding issues affecting the industry written by persons who have long experience in the industry. Our intent is to offer additional fabric, perspective, and hopefully, inspiration to our readers.

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Wednesday, July 12, 2017

How to Avoid Insuretech Disruption

By Karen Wolfe

In insurance, those who hold the data, hold potential power. Those who analyze the data and apply the knowledge control their destiny.

“The abundance of data – and the technology used to capture it - is driving profound disruption in the relationship structure of the insurance industry. As the traditional gatherers and guards of massive amounts of data, insurers face threats from new, tech-savvy competitors who can adapt to changes more quickly.

There are very powerful trends coming together to cause serious industry disruption. That can be a big threat, but if insurers start responding now and embracing the change, it could also be a big opportunity.”[i]

What is Insuretech?
Google defines Insurtech as referring to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model. Insurtech is a portmanteau of “insurance” and “technology” that was inspired by the term Fintech (Financial Technology).

The problem
Longstanding reluctance to change is preventing many organizations in the Workers’ Comp industry from embracing new technology, especially technology that streamlines processes and worker performance. However, this approach is no longer sustainable. Simply stated, those who cling only to the old-time culture and ways will be disrupted. Think Amazon or Uber.

“Big Data and analytics are forcing insurers to adjust their processes when it comes to collecting and using data. With the expansion of the Internet of Things, sensor technology, machine learning and artificial intelligence, there is more information available than ever before.”[ii]

Data, the asset
Organizations that continue to ignore the facts will wonder why they are no longer competitive. Others who are open to new approaches using new technology will experience positive results. It is a matter of attitude and willingness to try newer methods.

Nevertheless, insuretech need not be invasive or costly. To make a positive impact on processes and outcomes, an organization must first take the position of data-centeredness, believing data is its valued asset.

Step ahead of disruption
Having accepted the position that data is the organization’s valuable asset, accepting and incorporating new technology requires focusing on only three basic initiatives: data quality, data analysis, and intelligent application of the intelligence gained through analytics.

Data quality
If data is an asset, then its quality must be valued and protected. Using poor or erroneous data never ends well. Information gained from poor quality data will not improve an organization’s processes or outcomes, will lead to poor decisions, and detrimental actions. Therefore, resources must be applied to guaranteeing quality data input. Moreover, considerable resources may be needed to improve historic data.

Over the last twenty-five years organizations have been focused on collecting data, but little attention has been paid to insuring the data is accurate and complete. Now that must change. A data-centered organization will also guarantee its data is pristine.

Analytics
The second initiative needed to avoid insuretech disruption is to analyze the organization’s data. Collect and analyze all data over the previous five years. Methods such as predictive analytics can be applied to gain greater understanding of the organization, how well it operates, and what are the cost drivers both operationally and at the transaction level. This is simply a matter if analyzing historic data and monitoring concurrent data to reveal trends, threats, and possibilities. Know thyself.

Intelligent knowledge application
Having quality data and analyzing it leads to the next critical step of designing intelligent solutions to problems identified during the analysis phase. Apply the knowledge gained to specific areas of need by creating “apps” that solve problems and improve processes in the organization.

Alert the right person when conditions or events pose a risk to the organization or work product identified in the analysis phase. Deliver key intelligence to specific individuals or groups at the exact time they need it for decision support. Facilitate timely communication within the organization. Knowledge assistance provided at the right time to the right persons saves time, creates accuracy, efficiency, and increases profitability.

Stepping into the world of insurtech and avoiding disruption is largely a matter of perspective and attitude. It requires a view that data is an asset and when properly managed, lets the organization define its destiny.


[i] Will You Survive the Great InsureTech Disruption? James Dodge, Senior Consultant, Advanced Analytics & Data Solutions, Milliman. Risk and Insurance. 6-27-17
[ii] Ibid.
 
MedMetrics, a WC insuretech company, leverages predictive analytics to provide WC claims reps knowledge assistance by projecting probable ultimate medical losses, automatically integrating claims and medical management to contain predictive medical threats, and offering additional intelligent tools such as provider performance analysis and diagnostic severity scoring. MedMetrics clients are easily and affordably more accurate, efficient, and profitable.
 

Thursday, May 25, 2017

Seek Counsel from the Data for WC Medical Management

by Karen Wolfe

“Given the avalanche of information that has become available to ­businesses over the past several years, data-driven decision-making (DDDM), the practice of basing business decisions on data analysis rather than intuition, has become a critical tool to help organizations reduce risk, avoid costly mistakes and take advantage of opportunities.”[1] In Workers’ Comp, there are scores of reasons to seek council from the data.
 
 
Predictive Analytics
Nevertheless, raw data must be analyzed to be useful. Predictive analytics looks for trends and patterns in historic data. Such analysis can predict probable ultimate medical costs in new claims when similar conditions occur. Predictive analytics uncovers cost drivers that might include organizational traits, timeliness of action, or specific serious injuries. Moreover, analysis of past performance by medical doctors offers decision support for selecting best providers going forward. These are only a few examples.

 
Data-driven decision-making in medical management is a powerful tool, providing knowledge and guidance for those who make direction-changing decisions in real time. Yet, all data-driven decision-making efforts are dependent on the quality and limitations of the data used.[2] Those relying on information provided by analytics must be confident the data is accurate and complete.

 

Data reliability

If data quality is inconsistent, incomplete, or has errors, conclusions derived from it cannot be trusted by those who would rely on it for decision-making. For instance, medical provider performance analysis relies heavily on provider data accuracy. Missing data items such as NPI (National Provider Identifier from CMS, Centers for Medicare and Medicaid Services) prevents accurate individual or entity identification. Individual providers cannot be accurately distinguished. Still other data quality issues are concerning.

 
Misspelling addresses creates duplicate entities in the data, thereby skewing analysis. Some of the supporting data is attributed to a provider name and address spelled one way and the rest of the data is attributed to another that is spelled differently, but is actually the same person. Separate files for the same provider prevent fair analysis of performance because the data for both is incomplete.
 
Similarly, data omissions lead to difficulties in interpreting the data. Missing key data elements such as medical provider specialty can make the data ineffectual for evaluating performance.
 
Errors are often caused by manual data entry. Years ago, typists competed for jobs based on how fast they could type a paragraph without errors. Today’s data entry personnel should be evaluated on accuracy, as well. Make accuracy a performance measurement.
 
Data quality
Often data is transmitted to an organization from outside entities. Hard copy forms are translated to digital formats using optical character recognition, OCR. The organization wanting to analyze the data for use in decision-making did not create the data, but it should not fall victim to it. The organization should select critical data elements and proceed to correct them in the data. The resources required are easily justified by the end game: applying analytics to create accurate decision support.
 
Fix the flaws
Data-driven decision-making in WC medical management is powerful. Frontline professionals and other stakeholders are made accurate and efficient. That means costly mistakes and re-dos are avoided. Moreover, interventions and actions are timely, all resulting in significant cost savings for the organization. If the data producing knowledge and decision support is not accurate and complete, fix it!

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical loss management and technical services company. MedMetrics offers intelligent medical management systems that link analytics to operations, thereby making insights actionable and the results measureable. karenwolfe@medmetrics.org

[1] Heires, K. Flaws in the Data. Risk Management. April 3, 2017.
[2] Ibid.


Friday, April 14, 2017

How to Monetize Intelligent WC Medical Management

by Karen Wolfe     

”If data is the currency of the new digital economy, then organizations that know how to monetize data will generate the highest returns. They will make better decisions that lower costs, grow customer loyalty, and increase revenues.”[1]
 
Over the past twenty-five years, the Workers’ Comp industry has collected vast amounts of data. Moreover, organizations within the industry have easy access to their most valuable asset: their data. Their challenge now is to monetize the data and profit from it.
 
Experts say medical costs now amount to 60% of claim costs in Workers’ Comp. If true, organizations should be charging ahead to find ways to optimize medical loss management and monetize their data for profit.
 
Data integration
The first step toward monetizing medical data is to integrate data from disparate data silos. All bill review, claims system, pharmacy (PBM) and other relevant data should be integrated at the claim level to gain a full picture of individual claims. Once integrated, predictive analytics methodologies are applied to covert the data to usable information.
 
Past is prologue
What happened in the past is a good indicator of what will occur in the future when similar conditions appear. Organizational culture, protocol, and individual preferences are consistent influencers. Consequently, data gathered from other organizations may offer inaccurate results.
 
Analyze historic data using predictive analytics to discover conditions that are cost drivers or cost accelerators. Uncover trends. What conditions or combinations initiate or perpetuate high cost situations? Where are the gaps in timing in operational flow? What actions encourage positive or negative claim resolution? Finally, the information must be made actionable.
 
Inform stakeholders
Portray the predictive analytics-informed information for claim stakeholders in timely, informative notifications when risk situations occur. An example of this is a diagnosis of a comorbidity such as diabetes appearing in the data long after the date of loss. Predictive analytics has determined the comorbidity of diabetes adds complexity and cost to claims, therefore an alert is generated and key Information is conveyed to appropriate persons.
 
Probable ultimate medical costs
Based on predictive analytics, the probable ultimate medical costs for the claim are portrayed for the claims rep along with other key information regarding the claim in question. The claims rep adjusts medical reserves accordingly and moves on. Time is saved and accuracy is optimized.
 
At the same time, the predictive analytics-informed system automatically notifies the nurse case manager based on the organization’s referral protocol. The claims rep is informed of the referral but is not required to take action.
 
Similar claim information is presented to the nurse case manager for quick review, thereby integrating and coordinating claims and nurse case management initiatives.
 
Monetize medical management
Data is made intelligent and can be monetized through predictive analytics combined with a timely information delivery system. Searching for decision-support information takes time and is inefficient. Manually entering data is time-consuming, annoying, and often inaccurate. On the other hand, intelligent information delivered appropriately is monetized as claim stakeholders make informed decisions quickly, effortlessly, and accurately without need for data gathering and data entry.
 
Projected probable ultimate claim cost with comprehensive supportive information displayed for claims reps does not require data search or data entry. Even less-experienced adjusters are accurate and efficient. Accuracy and efficiency is optimized, productivity is increased, and profitability follows. Moreover, early intervention through timely alerts allows for action before further damage is incurred.
 
Medical loss management is also monetized by the ability to objectively measure claim cost savings. Having projected the ultimate medical costs for a claim, quantifiable cost savings are available at claim closure due to coordinated medical management initiatives. Monetization is realized through client satisfaction, customer loyalty, and client retention. Moreover, the story is proof of value serving the organization’s strategic competitive advantage.
 
Intelligent medical management
Organizations that monetize their data have greater returns, including return on investment. The intelligent medical management system is monetized internally and externally, thereby paying for itself. Such statements are familiar as sales platitudes, but with intelligent medical management, positive results are objectively measured. Savings are greater than the cost.
 


[1] Eckerson, W. How to Monetize Data: Strategies for Creating Data-Driven Applications. Eckerson-How-to-monetize-data.pdf Zoomdata. March, 2016.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical loss management and technical services company. MedMetrics offers intelligent medical management systems that link analytics to operations, thereby making insights actionable and the results measureable. karenwolfe@medmetrics.org 

Thursday, March 30, 2017

Intelligent WC Medical Management, a Process for Efficiency and Measured Results

by Karen Wolfe

Technology in Workers’ Comp is hardly new, but new ways to infuse technology and predictive analytics into the claims and medical management processes can significantly improve accuracy, efficiency, outcomes, and, importantly, profitability. Well-designed technology that streamlines operational flow, provides key knowledge to the right stakeholders at the right time, promotes efficiency, and generates measureable savings is formidable. The system is intelligent and includes these key components:
1.     Predictive analytics
2.     Data monitoring
3.     Knowledge for decision support

Predictive analytics
Predictive analytics is the foundation for creating an intelligent medical management process. Analysis of historic data to understand the risks and cost drivers is the basis for an intelligent medical management system. For the risks identified, the organization sets its standards and priorities for which stakeholders are automatically alerted to those specific conditions in claims as they occur.

The stakeholders are usually claims reps and nurse case managers but others inside or outside the organization can be alerted, such as upper management or clients, depending upon the situation and the organization’s goals. Upper management establishes specific action procedures for specified conditions or situations, thereby creating consistent procedures that can be measured against outcomes.

Data monitoring
Incoming data must be updated and monitored continuously. Random or interval monitoring leaves gaps in important claim knowledge that is overlooked until the next monitoring session. The damage may have escalated by then. With continuous data monitoring, everything is reviewed continually so nothing is missed. When the data in a claim matches the conditions outlined by the predictive modeling, an alert is sent to the stakeholder so action or intervention is initiated.

Some say the stakeholders will not comply with such a structured program because they resist being directed. To solve that problem, accountability procedures in the form of audit trails in the system act as overseer. At any point, management can view what alerts have been sent, to whom they were sent, for what claim, and for what reason, thereby observing participation and supporting accountability.

Knowledge for decision support
The alerts sent offer collected knowledge about the claim needing attention so the stakeholder is not forced to search for information before deciding upon an action. The reason the alert was triggered, detailed claim history including medical costs paid to date is displayed for alert recipients. Importantly, the projected costs for a claim with similar characteristics are portrayed, making reserving adjustments easy and accurate.

The projected ultimate medical costs for the identified claim is portrayed for the claims rep based on the analytics, thereby providing decision support for adjusting reserves. Data entry into the system is never needed, therefore, accuracy and efficiency is optimized.

At the same time, a nurse case manager is automatically notified of the situation if indicated by the organization’s rules in the system and is informed with the same claim detail. Now the case manager and claims rep are collaborating to mitigate the costs for this claim. They know the projected ultimate medical cost for the claim and the projected duration of the claim so they have a common and concrete target to challenge. Moreover, improvements on the projections offer objective and defensible cost savings analysis.

Predictive analytics combined with properly designed technology to create an intelligent medical management process establishes a distinct advantage. Knowledge made available at the appropriate time for the right people leads to efficiency and accuracy. Early, intelligent intervention drives better results.  Stakeholders coordinate efforts to mitigate the claim, working toward a shared goal. Finally, knowledge provided for decision-support positions for measureable, objective, reportable savings at claim closure.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical management and technical services company. MedMetrics offers online apps that link analytics to operations, thereby making insights actionable and the results measureable. karenwolfe@medmetrics.org

 

Tuesday, February 21, 2017

Proof of Value for Workers' Comp Medical Management

by Karen Wolfe

Everyone knows the bulk of Workers’ Comp costs now are medical. Claims reps and nurse case managers handle injured workers and their medical costs with utmost care. Anecdotally, their work saves time and money. The problem is that concrete evidence of their value has been elusive—until now.


Magic?
How can costs avoided and time saved be measured? It’s like pulling invisible rabbits from a magician’s hat. It should be awe-inspiring, but what really happened? Quantifying what did not happen is usually impossible. However, quantifying and measuring savings is completely feasible when a different approach using predictive analytics is used.

The real magic
The Workers’ Comp industry does not readily embrace change or innovation. That is changing as pressure increases to become more efficient to sustain profitability as resources shrink. The best approach to meeting this challenge is incorporating advanced technical strategies such as predictive analytics that are designed to support and streamline the business process and make workers smarter. The collateral benefit is being able to objectively measure and report savings.

Solution design
The solution is to extensively analyze the organization’s historic data using predictive analytics and deliver the insights in the form of actionable information to all the stakeholders including claims reps, medical managers, and other decision-makers. Just a few steps are needed including data analysis, data monitoring, informing and integrating the efforts of stakeholders, and then measuring the savings. The first and most critical initiative is analyzing historic data using predictive analytics methodologies.

Predictive analytics
Analyzing historic data utilizes predictive analytics methodologies. Deeply analyze the organization’s data to identify cost drivers, actions taken, and outcomes. Organizations differ in their client bases so the kind of injuries they experience varies.

Organizations are also unique because they develop distinctive internal and cultural processes regarding claims handling and medical management. Therefore, an organization’s data is the most meaningful data in understanding future costs. Because of the differentiating factors, using others’ data, regardless of how large the database, can mislead.

Monitor the data
Situations and conditions found in the past are likely to recur. Once the risks are identified in historic data, they can be searched programmatically in current data through continuous data monitoring. When problematic situations occur in the data, appropriate responses and interventions are mobilized immediately. The insights are delivered to medical management stakeholders, including claims reps, medical case managers, senior management, and others as appropriate. The knowledge delivered is structured to assist them in decision support and coordinating efforts.

Deliver insights
Risk information in claims is delivered concurrently to stakeholders so they can make early and sound decisions, then initiate appropriate action. Importantly, all medical management participants receive similar information so initiatives are coordinated and integrated, thereby implementing strong, multi-disciplinary approaches.

Reserving
When risk conditions in claims are identified in this manner it means reserves in that claim need attention as well.  When events and conditions in claims change indicating a need for more intense medical management, reserving should also be addressed. Based on predictive analytics, the probable ultimate medical costs are projected and portrayed for claims reps, thereby providing key knowledge to support appropriate action.

Coordination impact
Data monitoring identifies claims with risk conditions concurrently and informs the stakeholders immediately. Intervention efforts are coordinated between claims reps, medical case managers, and others, providing broad-based, integrated initiatives leading to improved results. Savings are gained through proactive, coordinated intervention by professionals who are offered key information for decision support making them accurate, efficient, and effective.

Measure savings
When claims are closed, objective savings are measured by comparing projected performance based on predictive analytics with what was accomplished through proactive, integrated initiatives across all medical management participants. The calculations are quantifiable and objective.

Outsource
The simplest and most rewarding approach is to outsource this process to a knowledgeable medical analytics company. Internal processes need not change, but professionals and business processes are made more accurate and efficient—a win for the organization, its employees, and its clients.

Technology is far less expensive than people. When it is designed to assist professional workers by making them more accurate and efficient, the return on investment is profound.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical management and technical services company. MedMetrics offers online apps that link analytics to operations, thereby making insights actionable and the results measureable. karenwolfe@medmetrics.org

 

Tuesday, February 7, 2017

Will Watson Replace Workers' Comp Professionals?

by Karen Wolfe

A recent post in LinkedIn noted the Japanese firm, Fukokui Mutual Life Insurance has replaced more than thirty office workers with artificial intelligence.[1] The Artificial Intelligence (AI) replacement in this case is the famed IBM Watson. Watson, or one of its doubles, is in fact impacting nearly all industries in multiple ways. Eliminating workers is the paramount goal. But could Watson replace workers in Workers’ Comp?

AI
AI has been around for decades but now with advanced technology, it has fully caught on and its applications are widely varied. AI is what drives driverless vehicles and operates machinery sans human involvement. Short of that and more practically, AI machine technology is used to enhance worker productivity, accuracy, and efficiency. Importantly, AI should never reach Workers’ Comp if more pragmatic, technology-based strategies are implemented now.

Wake-up call
Replacing Workers’ Comp professionals with Watson or its double is not feasible at this point or hopefully, ever. The possibility of replacement by the likes of Watson should not panic anyone in the Workers’ Comp industry, at least not now. Yet, it is a wake-up call to the industry.

Watson in WC
Imagine injured workers navigating the Workers’ Comp system without claims adjusters and medical case managers. Picture Watson managing claims. It could make payments without difficulty, and even review the bills effectively. Watson could also determine which claims are the most challenging and refer them to medical case management.

Stop there!

Watson as case manager
Envisioning Watson as medical case manager is a real stretch. Human interaction is central to medical case management effectiveness. Likewise, Watson delivering claim management services without dialogue with the claimant would be spotty and unpleasant at best. Accuracy and efficiency under Watson management could be nearly perfect, but claim adjusting relies heavily on human interaction. Injured workers managed by Watson would feel victimized in a heartless system. The only recourse would be to litigate. Watson might have trouble with that.

While replacing professionals with technology like Watson is going too far, it should prompt Workers’ Comp payers to actively engage current technology to improve processes and outcomes—just to keep up. Clearly, the momentum in every industry is more technology in order to gain efficiency and Workers’ Comp cannot afford to lag behind. To stay in the game, technology designed to assist workers with task-relevant knowledge and decision support that makes them more accurate, more efficient, and, yes, smarter is crucial.

Dodging Watson
Watson will replace health insurance industry administrative workers fairly easily. Essentially, bills are paid if they match the benefit plan and the treating doctor is in the PPO. However, the Workers’ Comp industry is very different from general health and much more complex. The question is how can the Workers’ comp industry optimize efficiency and productivity without discarding its professionals and alienating injured workers? The answer is to apply currently available predictive analytics technology to make WC professionals smarter, more accurate, and highly efficient. Of course, that also spells profitability for the organization.

Knowledge assistance
Apply predictive analytics to understand historic data and the cost drivers inherent in it. Monitor the data continuously to identify risk conditions as they occur. Create apps that inform claims reps of conditions and events in claims that need attention in real time so action is early and proactive.
 
Assist claims reps by providing information for decision support such as the probable ultimate medical reserve amount for a claim. Time and effort is saved, while accuracy and efficiency is gained. Rather than laboring with decisions such as adjusting reserves, a timely and accurate projection is presented, thereby optimizing efficiency.
 
Similarly, relevant information should be available for medical case managers so they can avoid searching for claim information and status. Timely alerts and shared information promote collaboration and integration of efforts between claims and case management decision-makers in the organization. Watson is thwarted.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical management and technical services company. MedMetrics offers online apps that link analytics to operations, thereby making insights actionable and the results measureable. karenwolfe@medmetrics.org

 

 

 

 

Friday, January 20, 2017

Making the WC Medical Management Auto Pilot Smart

by Karen Wolfe

Medical management in Workers’ Comp has continued essentially unchanged for decades. The rationale for medical case management services continues to be valid, yet the processes guiding it have remained unchanged. As Ron Skrocki suggests, we need to get case management off auto pilot.[1]

Auto pilot challenge
The auto pilot primarily refers to doing things the same way they have always been done. Improvements that might be achieved are unknown because the processes have not been analyzed, challenged, and redesigned. How case management is mobilized, why, and when needs review. Moreover, upgrading the medical management process with current technology is imperative.

For instance, claims adjusters decide when and for what reason medical case management is needed. How structured are these referrals? Are they proactive, consistent, and timely? How do they reflect the organization’s business standards?  Without business process standards, the old auto pilot remains in control. But what if the auto pilot were made smart? What if processes were made consistently and accurately actionable?

Making the auto pilot smart
The first step is to educate the medical management process using predictive analytics to assess the organization’s medical loss history. Every organization is unique in its mix of medical losses, how costly they are, and the internal processes used to mitigate them. Predictive analytics should be implemented to examine the organization’s historic data, uncover its medical loss cost drivers, and past processes.

Business process standards
Use the new knowledge to establish organizational process standards by carving out the medical loss diagnoses, comorbidities, and conditions that are of highest risk and for which the organization wants immediate action. Establish in the business process standards the persons or positions in the organization that will be notified for each kind of condition. Who will be notified and held accountable for addressing each issue?

Automate
Now that senior management knows what conditions in claims are of highest risk and in need of immediate attention, create a system that continually monitors the data, tagging those risk conditions as they occur. The condition might be obvious, especially at claim opening or it might slip into the data at any point. The subtle ones are easily missed when relying on auto pilot methods.

Alert stakeholders
Part of automation is automatically alerting the appropriate persons when a risk condition occurs in a claim. When establishing business process standards, pre-identify all stakeholders who should be alerted for each risk condition. Multiple stakeholders can be notified, even beyond the claims rep and the nurse case manager. No guessing. No relying on memory to engage case management. Moreover, claims reps can also be notified of the need to address reserving at the same time.

Alert for reserving
The predictive analytics-informed structured process has an even broader positive impact on the organization. When the identified medical loss risk conditions occur in claims and are tagged by the system, reserves probably need attention, as well. The claims rep is alerted and the system provides comprehensive knowledge assistance for claims professionals to know how to quickly and accurately adjust reserves.

Probable ultimate medical reserve amount
The system projects and displays the probable ultimate medical reserve amount for that claim based on the predictive analytics. Claim detail and projections portrayed eliminate the need to search for additional information. Claim documentation and projections displayed make it simple to complete the reserve adjustment. The organization’s business process impact of medical loss management is made broader in scope, thereby more powerful, accurate, and effective.

Coordinated power play
When the medical case manager is automatically notified of risk conditions along with the claims rep, coordinated initiatives can be mobilized. All appropriate resources are brought to bear early and intentionally resulting in improved outcomes. Moreover, the process delivers objective savings.

$ Calculate savings
The structured medical management process generates calculated objective savings at claim closure based on the projected ultimate medical reserve amount and real-time integrated, proactive medical management initiatives. Calculating savings is accurate and easy, yet concrete and defensible.

Turn off the auto pilot
The traditional auto pilot medical management process is undefined and inconsistently implemented. Measureable savings are elusive. The Workers’ Comp industry must turn off its medical management auto pilot and step up to this accurate and effective structured business process approach.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, predictive analytics-informed medical management and technical services company. MedMetrics offers online apps and alerts that link analytics to operations, thereby making them actionable and measureable. We don’t do medical management. We make your medical management initiatives more powerful. karenwolfe@medmetrics.org 


[1] Skrocki, R. Getting Case Management Off Auto Pilot. WC Magazine. The CLM. 2015