Predictive Analytics--Winning Through Better Information

by Karen Wolfe

“Predictive analytics, is a way to predict the future using data from the past and helps businesses answer questions about the probabilities of certain events occurring. From recommending additional purchases based on the items that customers place in online shopping carts to pinpointing hospital patients who have a greater risk of readmission, the use of predictive analytics tools and techniques is enabling organizations to tap their collections of data to predict future business outcomes—if the process is managed properly” (emphasis added).[1] A well-managed process means planning, implementing, measuring, and repeating.

“However, many companies are doing analytics to do analytics, and they aren't pursuing analytics that are measurable, purposeful, accountable and understandable.”[2] The idea is to utilize this technology-driven process for analyzing data in order to present actionable information to those responsible for decision-making and to streamline operational processes. That’s why it is so essential to Workers’ Comp employers, payers, and medical management organizations. It is how they can win.

Winning
Stated simply, winning for employers and payers in Workers’ Comp means curbing medical costs while improving outcomes for injured workers. Multiple medial cost management initiatives have been implemented over the years in the industry including bill review, nurse case management, utilization review, and peer review to name a few. Yet, managing medical costs while improving outcomes has been elusive. While injury frequency has decreased, severity, both in terms of physical injury and cost has increased.

Plan
Enabling an organization to tap its collection of data to predict future business outcomes and managing the process more efficiently requires detailed planning. First, decide what knowledge is needed, how it will be used, and where the supporting data can be found. Integrate the data at the claim level to create a complete view of the claim. That means integrating the data contained in bill review, claims system, and pharmacy at a minimum.

Implement
Analyze the collected and integrated data. Identify cost drivers, risk conditions, trends, and outliers. Monitor the data to alert the appropriate persons when those conditions occur going forward. Critical conditions can occur or become known in the data at any point in the claim, not just at the beginning or at planned intervals. Early intervention through better and more timely information will impact cost, duration, and outcome of the claim.

Implementation should emphasize simplicity for the recipients. They should not be required to search for additional information to move forward or enter data in order to reach conclusions. Complete information should be presented to the appropriate persons, including detailed projected costs based on predictive analytics. Knowing the detail of predicted costs serves to guide appropriate initiatives.  Moreover, results of mitigation efforts can be measured.

Measure
When untoward conditions occur during the course of the claim, probable ultimate costs for the claim can be projected based on predictive analytics of the historic data. Past is prologue, meaning the costs that occurred in the past in similar situations are likely to repeat. Predicted costs are a powerful knowledge assistant for claims reps in resetting reserves and medical managers targeting the most advantageous approaches to intervention. On case closure, success in mitigating medical costs can be objectively measured compared to projected costs.

Repeat
A well-managed process always means returning to the beginning to re-evaluate the plan and implementation strategies. Are the results as expected? What additional information is needed or possible? What do the recipients want or need? The process continually improves though execution, learning from the information gained, and how it is best utilized. That’s a win.

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 measurable. karenwolfe@medmetrics.org

 

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[1] Voorhees, C. Search Business Analytics. Techtarget.com

 

[2] King, E. The Modeling Agency. Techtarget.com

The Myth of Predictive Analytics--Really??

by Karen Wolfe

Mark Walls recently posted an article entitled, “The Myth of Predictive Analytics”[1] where he says he has yet to see cost savings from predictive analytics. Such a statement will surely generate a firestorm of comments. This is one of them—and it specifically addresses predictive analytics-informed medical cost management in Workers’ Comp.

The power of predictive analytics to mitigate medical costs is entirely dependent on the operational design of the delivery system. Predictive analytics is the information vehicle that creates knowledge for claims adjusters and others in the medical management process. System design determines how the information gained from predictive analytics is operationalized. How the information is implemented makes all the difference.

Walls goes on to say, “However, the potential for cost savings doesn’t come from the flag, but what you do in response to it. You need to take action and do something differently than you would have done without the flag.”[2] To a point, that’s true. However, the timing and method of delivery and format of the “flag” is critical. The conditions of information delivery that drive cost savings are timeliness, accuracy and efficiency, ease of use, and structured protocols. All are functions of delivery system design.

Timeliness for early intervention
Information must be delivered in the form of alerts as concurrently as possible. The claims rep should receive the information very close to the time of the risk occurrence. To achieve that, the data must be monitored continually with alerts sent immediately. Factors unknown early in the claim can occur at any time throughout the claim. Timely notification activates early intervention, before further damage is done and before the situation becomes more complex. That saves time, money, and leads to better outcomes.

Accuracy and Efficiency
An alert is useful only if it contains all the information the claims rep needs to make an informed decision, to adjust reserves, and to initiate measures that will prevent further medical loss. Predictive analytics is used to calculate and portray projected costs based on history, differentiated costs, and expected time lines. The alert also displays a medical summary of the claim. All the information is portrayed for the claims rep and requires no data look-up and no data entry.

Accuracy and efficiency are cost savers because they are time savers. Even less-experienced claim reps can take accurate steps when all necessary information is provided.

Easy
The information generated by predictive analytics notifies and informs the claims adjuster at the appropriate time without additional effort on the part of the adjuster. The system automatically portrays all pertinent information without need for searching or data entry. At that point, the adjuster can take appropriate and informed action.

Structured protocols
Medical management in Workers’ Comp is traditionally designed and delivered by individuals in the organization in one-off situations. That means processes are inconsistent. Good system design that draws from predictive analytics infuses structure and measurability into the process. Those situations in claims that should be referred to a nurse case manager are tagged in the system by senior management in advance, so they are referred automatically. The claims adjuster is relieved of the problem of when to refer.

The system is designed to make referrals automatically, thereby making them timely and consistent. Pre-determining what kinds of conditions will be referred and to whom, is how the organization sets up standardized medical management protocols. Such consistent, intelligent process management generates measurable results.

Measure results
Cost savings are objectively and accurately measured in a predictive analytics-supported system. On case closure, actual medical costs for the claim are compared to predicted costs based on documented history. Because of predictive analytics and continuous data monitoring, interventions are executed early, making them more effective. Appropriate referrals are made automatically according to protocol rather than intuition. The medical management team collaborates to improve on projected costs.

Documented process
Medical management alert activity on the claim has been documented by the system throughout the course of the claim. Therefore, costs can be appropriately allocated to the claim, the client, or policy-holder including activity detail, thereby creating transparency and trust among constituents. The organization enjoys increased profitability and strategic competitive advantage.

Walls also states, “In the end, good old-fashioned claims handling skills are still the best way to achieve superior outcomes on claims.”[3] However, when the claims handler is supported by a well-designed, predictive analytics-informed intelligent assistant, claims handling the old way is simply obsolete.

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 measurable. karenwolfe@medmetrics.org

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[1] Walls, M. The Myth of Predictive Analytics. Leaders Speak. WorkCompWire. 9-19-2017. http://www.workcompwire.com/2017/09/mark-walls-the-myth-of-predictive-analytics/

[2] Ibid.

[3] Ibid.

T

A Practical Approach to the WC Data Quality Imperative

by Karen Wolfe

Big data drawn from bad data makes big, bad data

 

The abundance of data and the technology used to capture and understand it is causing powerful shifts in the insurance industry. Insuretech is the term used to describe the use of technology innovations designed to promote accuracy and efficiency while reducing costs from the current insurance industry model. Now nimble insuretech organizations are disrupting the insurance market by challenging the status quo.

However, organizations can step ahead of the oncoming disruption by following the common-sense steps noted in the article, “How to Avoid Insuretech Disruption”.[1] The first important component of this effort is accurate data. Data should be viewed as a significant organizational asset that is respected, valued, and protected at its collection points and throughout its useful life.

 

No longer will incomplete and erroneous data be tolerated because little can be done to actualize the benefits of technology and prevent disruption without addressing the issue of data integrity. Organizations can confront their data issues by addressing the essential elements of collection, correction, and accountability.

 

Collection

Examine the data. Data is frequently (usually) incomplete or erroneous. Begin by evaluating the data collected by the organization, identifying its various sources. Usually, an organization has multiple data sources.

 

Bill review is a good example. Consider the fact that the bill review company collects the data from medical provider bills, often by means of digital scanning, called OCR (optical character recognition) that converts the hard copy of the bill to digital data. While OCR is considered fairly accurate, it makes sense to go upstream to evaluate the data input source.

 

Medical providers generate the bills and now most are able to produce and deliver digital bills. That eliminates the need for scanning, however, for the provider to produce bills, manual data entry is required. This is where essential information is omitted. Often the treating location on the bill is listed as a PO Box, while the specialty of the treating physician or other professionals are omitted, as well as individual NPI numbers (National Provider Identification).

Correction
Inaccurate and incomplete data from providers ranges from deliberate fraud to simple negligence. Data integrity is the basis for accurate provider performance evaluation and selection, essential to quality care and accurate claims adjudication. Source networks should require accuracy from their provider members or correct the data at their point of data progression. Payers, the purchasers of networks and bill review services, can use their leverage to encourage data correction at these points, as well.

Keep in mind manually entered data, is the least reliable method of data collection. Misspelled and abbreviated names and addresses create duplicate entities in the data, thereby skewing downstream analysis. Because the data entry source is not arms-length for payers, they will need to correct the data themselves.

Claim data entry is an internal manual data entry process for payers. Years ago, typists were tested and scored for how fast they could type a paragraph without errors. Accurate typing performance determined hiring or dismissal. Today’s data entry personnel should be similarly evaluated for accuracy, regardless of their level in the organization. Make accuracy a performance measurement for everyone.

Manually correcting the data is a challenging prospect, but the benefits to downstream analysis are enormous. Programmatic systems are available to merge duplicate records, for instance, and they are accurate to a point. Also, systems can be created to highlight data needing manual attention to facilitate data corrections.

Accountability
Inform and train employees regarding the corporate value of data integrity. Design systems that highlight errors and omissions for work in progress. They can also be used as accountability tools. A simple audit trail will identify the error perpetrators going forward. Moreover, objective feedback is the best way to improvement.

For the last thirty years, the big push in the insurance industry has been collecting data while little emphasis was placed on accuracy. Now the focus has shifted. Predictive analytics relies on historic data to inform so be sure the data is as robust as possible.

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 predicted 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.

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[1] Wolfe, K. How to Avoid Insuretech Disruption. http://medmetrics.blogspot.com/2017/07/how-to-avoid-insuretech-disruption.html

 

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.

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[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.

 

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

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

[2] Ibid.

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.

 

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[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 

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

 

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