Analytics-Informed Early Intervention Drives Best Outcomes

by Karen Wolfe

Early medical management intervention in Workers’ Compensation drives better outcomes. When a problem is discovered early, finding a positive solution is quicker, easier, and more effective. Moreover, predictive analytics-informed early intervention is even more powerful.

Past is prologue
Even though early medical management intervention is known to work, it is not always pursued aggressively. To be most effective, knowing what to look for in open and active claim data is key. 

The past is a preface to the future. Analyzing historic data to identify the kinds of conditions that have been costly to the organization reveals what to look for going forward. Some conditions are generally known. Examples are comorbidities that accompany injuries and certain problematic or severe injury or illness types. Yet, other less obvious, but troublesome conditions and preemptive situations must be teased out of the data using predictive analytics methodologies.

Predictive analytics
Predictive analytics is a methodology used to identify historic claim conditions that are likely to be troublesome in future claims. Conditions in claims that have led to high costs and poor outcomes in the past are isolated. Once the conditions are pinpointed a system is designed to continually monitor the data going forward and to notify adjusters and medical case managers when those conditions occur.

Continuous data monitoring
A practical method for uncovering problematic claims is to electronically monitor the data to reveal dicey conditions as they occur. Technology is used to find the claims that bear high risk conditions whenever they occur throughout the course of the claim. All claims are monitored continuously, so nothing is missed. Even subtle claim migration is exposed.

Intervention
Data monitoring for conditions discovered through predictive analytics is powerful. Yet, the next step is also essential. Organizations that implement an analytic process for identifying risky claims early stand to overlook the entire benefit unless they also structure procedures for intervention. Importantly, the appropriate persons must be notified immediately and they must carry out the organization’s recommended procedures.

Claims adjusters are often alerted first. However, when medical case managers are alerted as well, the two can execute intervention procedures collaboratively. Claims adjusters may neglect to refer to medical case management as early as they could. But when the automatic referral to medical case management is made simultaneously by the system, that issue is eliminated.

Collateral opportunity
When claims adjusters receive an alert of adverse conditions developing in a claim, they know reserves should be adjusted. Predictive analytics can also inform adjusters of the probable ultimate medical reserve amount for that claim based on history, thereby making reserving easy, timely and accurate.

Measured success
A bonus advantage of an early intervention process informed by predictive analytics is the ability to objectively measure success. At claim closure, costs and other outcomes can be measured and compared with those for similar claims in the past. The savings effects of early problem identification and intervention by claims reps and medical case managers are posted for constituents as objectively measured savings.

Conclusion
Simply stated, early intervention is more effective than later intervention. Damage control is far more achievable. The problems have not yet morphed into catastrophic or irreversible states. Predictive analytics-informed systems and data monitoring can be established to automate tagging problem conditions in claims as they occur. Those who will intervene early are alerted. Finally, reporting objective measurements of success is the payoff.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, 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 stronger. karenwolfe@medmetrics.org

 

Finding Actionable Provider Ratings in Workers’ Comp

by Karen Wolfe

With the tongue-in-cheek title, “When Yelp reviews are better than hospital rating systems” Jason Beans of Rising Medical Solutions discloses the inconsistencies of standard hospital reviews.[1] He cites a Health Affairs study[2] that points out traditional rating systems, those that have been relied upon in the healthcare industry for years, rarely come up with the same results for the same hospital.


Hospital rating systems score hospital performance in an effort to determine quality and safety in hospitals. The Health Affairs study concludes discrepancies among rating systems is likely explained by the fact that each system uses its own rating methods, defines quality differently, and stresses different measures of performance. Apparently, no standards for quality and safety in hospitals are available.


This begs the question of how scoring systems for rating other medical providers differ from those of hospital scoring systems. More specifically, what about those used to score provider performance in Workers’ Compensation? Lest the conclusion be that all provider performance scoring systems lack credibility, it might be instructive to at least loosely compare hospital rating systems with physician scoring in Workers’ Compensation.


Not similar 
They are very different. The conditions, methodology, and approaches are significantly different. The hospital rating systems cited by Health Affairs evaluate general health in acute care settings. To measure cost, they measure an episode of care on a per diem (per day) basis for individual hospital stays, adjusted by diagnosis and procedures. Often subjective reports are used, as well.


On the other hand, measures of quality performance in Workers compensation are unique to the industry and the number of measurable variables are numerous. How a medical provider acknowledges and influences distinctive industry factors along with success of the medical treatment procedures are indicators of quality performance.

Episode of care

One major difference is the episode of care in Workers’ Compensation is not per diem, but is defined by the scope of the claim. An episode of care (claim) is from the date of injury to claim closure and includes all treatment, medical providers, vendors, events, and outcomes that occur during that time. It may or may not include hospitalization, but when it does, those costs and events are included with total claim. In other words, the episode of care is highly definable in Workers’ Compensation. It is broad and comprehensive.

Quality indicators 
A number of non-medical indicators found in the data reflect unique conditions in Workers’ Compensation that are influenced by treating providers. Measures of quality include return to work and indemnity costs, neither of which is medical treatment precisely, but is strongly influenced by the treating provider and affects the cost of the claim. Consequently, these factors must be included in evaluating performance.


Frequency and duration of treatment, as well as duration of the claim are indicators of provider performance. Providers can contain or increase costs described by these factors. Functional outcome described in the data as disability ratings at the conclusion of the claim are also measures of treatment success.


Clinical factors and treatment processes are important quality indicators, of course, and must be included in the evaluation and scoring. Abuse of Schedule II drugs are, for example, a major cost driver in Workers’ Compensation. Dispensing medications is another.


Source data
Each data-rich claim contains the information necessary to evaluate medical provider performance for Workers’ Compensation. Importantly, the data must be integrated from the silos of bill review, claims system, and PBM (pharmacy) in order to achieve a comprehensive picture of the claim and medical providers’ involvement.

Workers’ Compensation can be more complex than general health because it is a legal system rather than a defined benefit. Every claim has administrative aspects as well as medical assessment and treatment. It’s all in the data.

Objective data 
Scores of medical provider quality indicators can be found in Workers’ Compensation data. The beauty is data describes what actually took place during the course of the claim, not what should have happened or an opinion about it. It is concrete and objective. Yelp can’t help.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. MedMetrics also analyzes and scores medical provider performance. karenwolfe@medmetrics.org

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[1] Beans, J. When Yelp Reviews Are Better Than Hospital Rating Systems. May 12, 2015. http://www.riskandinsurance.com/when-yelp-reviews-are-better-than-hospital-rating-systems/

[2] National Hospital Rating Systems Share Few Common Scores and May Generate Confusion Instead of Clarity. Health Affairs. May, 2015. http://content.healthaffairs.org/content/34/3/423.abstract

How to Make Data a Robust Medical Managment Tool

by Karen Wolfe

“Data makes all the difference.” This is according to a White Paper published by LexisNexis®. Entitled, “More Data, Earlier: The Value of Incorporating Data and Analytics in Claims Handling” states that carriers can reduce severity payments by up to 25 percent.[1]

This is true for P&C carriers, but especially true for Workers’ Compensation payers where medical costs have steadily increased for decades. In Workers’ Compensation medical services are not limited by plan design. The costs for medical now amount to more than 60% of claim cost and they continue to climb. Nevertheless, data managed correctly, can make all the difference and save real dollars.

Big data

Big Data is currently in vogue. Everyone is talking about Big Data as though it will deliver a panacea of some sort. The notion is that organizing and analyzing copious amounts of data will produce new and improved insights, thereby gaining desired results. That may be true however, this outcomes is a function of complete, consistent, and accurate data. Unfortunately, data purity is rare, regardless of the size of the data set.

Bad data

The gains promised by Big Data are dependent upon data quality. Whether Big Data is comprised of large data sets or made up of many small data sets, quality may be the elusive factor. In order to achieve positive results using any data set, it must be complete and accurate. Duplicate records must be cleansed and merged for starters. More importantly, bad data input processes must be altered upstream where data is created. Standards for data quality must be set and enforced.

One reason data is of such poor quality is that little value has been placed on its veracity. That is changing as the vision for improved outcomes based on analytics is increasingly clear. Nevertheless, data input from the outset should be set to rigorous standards with accountability checks along the way. Automated imaging systems must be regularly calibrated to insure accuracy while individuals who input data along with their managers must be held responsible for the quality of the data.

Voluminous data

In Worker’ Compensation as with all insurance lines, comprehensive data is a fete accompli. Data has been collected digitally for decades, driven by claims payment requirements. In Workers’ Compensation, the claim is set up in the payer’s system and continually fed by incoming data. Mandatory reports of injury are submitted by employers and treating physicians. Bills from medical providers and others are streamed through bill review systems, then to claims systems throughout the course of the claim. Events such as litigation, court dates, and bills paid are documented in the claims system. Pharmacy is managed by the PBM (Pharmacy Benefit Management), thereby setting up an additional unique database related to the claim. Most payers also collect medical utilization review and medical case management data. The question is not the amount of data, but its quality and what can done with it. How is it applied?

Disparate data

Unfortunately, in Workers’ Compensation much of the data remains in separate silos. The focus has been on collecting the data. Now the question is, how to make data an operational tool that achieves the kind of positive savings results reported in the LexisNexis study. A different approach is needed.

Integrated data

Making data a useful work-in-progress tool is a matter of first integrating the data across multiple data sets relating to claims. This is sometimes a tedious process, but invaluable. The request and funding must come from the business units where anything related to data is not usually a priority. Business managers must begin to value the process of collecting good data and converting it to actionable information.

Analyzed data

Once the data is collected and integrated, analyzing it to gain the business knowledge is the task. Business managers can learn to articulate for IT what they want and need for decision support and other initiatives. IT has a role in assisting business managers in understanding how to ask more effectively for what they need. Cost drivers and trends can be uncovered in the analyzed data. Raw data is not a usable claims management tool, but analyzed and logically portrayed information can be powerful.

Current data

The power of data is best exploited when it is analyzed and made available to the business units as concurrently as possible. Intervention is far more effective when it is mobilized early.[2] Damage control is best achieved before it is irretrievable. Moreover, the analyzed data must be linked to operations, thereby making it actionable.

Knowledge derived from analytics is useless

until it is acted upon.

Linked data

Regardless of how impressive the analyzed data, it is useless unless acted upon. To actualize the data for useful application, it must be analyzed and re-presented to the business units in ways that can be easily accessed, understood, and applied. Through analytics, the data is transformed to knowledge: knowledge about conditions in claims, events, costs, and performance of vendors. Knowledge should not only be current, but should reach the operational front lines and portrayed in ways that promote action.

Actionable knowledge

Actionable knowledge is derived from analysis of the data that is presented to the business units in a functional form. To achieve measureable cost savings, continuously monitor the data, integrate, and analyze it, then re-present it to the business units in the form of easily interpreted knowledge and action tools. Individuals can be prompted by the system to take specific initiatives based on the knowledge, thereby creating a structured and powerfully enhanced approach to medical management with measurably positive results.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

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[1] A.Hassib. T.Fannin. More Data, Earlier: The Value of Incorporating Data and Analytics in Claims Handling LexisNexis® Risk Solutions. June, 2014

[2] K. Wolfe. Early Intervention Drives Better Outcomes, But is Not Really Pursued. http://medmetrics.blogspot.com/2014_10_01_archive.htmlenh

A Healthy Disrespect for the Impossible

by Karen Wolfe

When people are extraordinarily successful, examining their characteristics, values, and attitudes can be instructive. The rest of us can learn from them and possibly adopt some of them to advance our own goals. Larry Page, co-founder of Google is an example of one who has achieved exceptional heights. Peering into his thought process can be enlightening.
 

Page says, “Have a healthy disrespect for the Impossible”. While the statement may not be original with him, it does reflect his world view.

Disrespect the impossible

To conceive and develop the Google concept and then the massive company, its young founders had to have a very healthy disrespect for the impossible. Others besmirched the idea of collecting all the information in the world and then making it available to everyone in the world. Not only was it a bold idea, it was thought by most to be ridiculous and impossible. But Larry Page and Sergey Brin had a very healthy disrespect for the impossible. They made it happen.

The concept of disrespecting the impossible could be entertained by those of us in the Workers’ Compensation industry. True, few of us are likely to reach the pinnacle level of Larry and Sergey, but we can borrow some of their bold thinking to get past the assumptions and barriers that keep us from achieving more.

Everyone agrees Workers’ Compensation as an industry needs a healthy nudge to try new things. The industry is known for its resistance to change. Refer to the article, “Are You an Industry Disruptor? It can be a good thing.”[1] Maybe the way to change the industry, to be an industry disruptor, is to begin with an attitude of disrespecting the impossible.

The impossible in WC

Many people in general, including those in the Workers’ Compensation industry focus on why something cannot be done. Reasons for this notion are many, but probably cultural tradition plays a role. Creativity and inventiveness are not expected or appreciated. Too often, the best way to keep a job in corporations is to keep your head down and avoid being noticed. Spearheading a new idea is risky.

Stonewalling new ideas or doing things differently or adopting new technology in an organization thwarts creative thought and certainly diverts progress. I was once told we have a very good product, but to incorporate it would mean doing things differently in the organization. So the answer is automatically no!

Check assumptions

We all know the old saying about the word ass-u-me. It actually packs some truth. To avoid the trap, check assumptions for veracity. Incorrect assumptions can be highly self-limiting.

Problem-solve

Begin the process of problem-solving with new thinking—disrespect the impossible. What could be done if the perceived barriers did not exist? What could be accomplished if new methods were invented and implemented?

Tenacity

Probably the most important ingredient for achievement in any context is tenacity. It’s easy to quit when the barriers seem daunting. Tenacity combined with a disrespect for the impossible might be the unbeatable formula.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

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[1] http://medmetrics.blogspot.com/2014/12/are-you-industry-disruptor-it-can-be.html
 

Early Intervention Drives Better Outcomes, But is Not Really Pursued

by Karen Wolfe

Early intervention drives better outcomes. It’s a statement that garners little resistance. Everyone agrees when a problem is found early, the solution is easier. Thwarting a problem early in its course produces better results. Yet few systems are in place to guarantee early intervention into problematic Workers’ Compensation claims.

Early intervention is a fine idea, but not always pursued. Work flows and systems in the industry are not typically designed to target the goal. To be effective, early intervention requires identifying the claims needing attention as soon as possible in their course, then mobilizing action procedures.

Predictive modeling

One effort to identify claims early that is widely acclaimed in the industry is predictive modeling. Elaborate analysis of the data using advanced mathematical methods is used to identify new claims that will likely become problematic. Regardless of the sophistication of the math, finding those claims remains a guessing game. Not every future problematic claim will be tagged. Likewise, claims that do not meet the modeling criteria, yet migrate anyway, will not be recognized. Predictive modeling can be helpful but it is imperfect and costly.

Containerizing human responses

Containerizing human conditions and responses is possible to a point. However, predicting human behavior is imperfect at best. Moreover, a second step is necessary: consistently intervening in the identified problematic claims. Once the troublesome claims are detected, certain action steps must follow.

Data monitoring

A more practical methodology for identifying costly claims is to monitor the data on a concurrent basis to uncover dicey conditions in them.

Rather than predicting which claims will be risky, the conditions that portend risk and cost in claims are isolated and then identified in claims as they occur.

Technology is used to find the claims that bear those conditions whenever they occur throughout the course of the claim. All claims are monitored, so none are missed. No guesswork is involved.

Step two

The data monitoring approach is powerful, but as with predictive modeling, that is true only when the next step is taken. Organizations that undertake a process for identifying risky claims early stand to lose the entire benefit unless they also structure procedures for intervention. The appropriate persons must be notified about problematic claims immediately and those persons must act to carry out the recommended procedures.

Whether the alert recipients are claims adjusters, medical case managers, or someone else, they must follow intervention procedures for best results. Using the data monitoring approach, each condition that is sought in the data should be associated with a prescribed intervention procedure.

Standardized interventions

Follow up procedures should be specific so that analyses can be made of their effects. For instance, when the condition identified in a claim is the third prescription for a Schedule II drug, the system might be set to alert the medical director. A standard method of intervention or approach for intervening with the treating doctor is followed.

Another example of a problem is extended medical treatment past a designated point for low back strain. The alert is sent to the claims adjuster whose procedure is to engage a medical case manager to investigate. The investigation procedure is standardized.

Examples of how this works are unlimited, but the important thing is that intervention procedures are analyzed in advance and clearly stated so the actions are consistent across the organization and going forward, regardless of who actually carries out the intervention. Obviously, details of actions may be variable, but the general course should be followed.

Measuring results
The plight of medical case management has long been that results could not be measured. Consequently, medical case management as a strategy has been undervalued.

Categorize interventions

Moreover, medical case management could not be measured because interventions are delivered by individuals, even though usually professional nurses, each is different. However, intervention tactics can be categorized so outcomes can be calculated. The benefit of using a standardized approach is that outcomes can be compared and measured by the cause condition and intervention employed.

Automated referral

Claims adjusters sometimes fail to refer to medical case management for a variety of reasons. When early intervention procedures are standardized, the referral is automatically made by the system, thereby eliminating the burden of referral for claims adjusters. Efficiency is good.

Summary

Simply stated, early intervention is more effective than late intervention. The problems have not yet morphed into catastrophe and are usually easier to solve. Moreover, systems and procedures should be established to automate problem claim identification and follow up procedures. Best results can be pinpointed and continued. Lesser results can be modified. The upward spiral of quality improvement is continuous.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org