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

 

A New Year's Resolution for WC Medical Management

by Karen Wolfe

2015—a whole new year. Will it be back to the old routine or to a new resolve for Workers’ Compensation medical management?

Same old…

Managed care programs in Workers’ Compensation have been in place for over twenty-five years. Among them are provider discount networks, bill review, medical case management, utilization review, and peer review. They all continue to operate pretty much as they always have, yet medical costs continue to increase.

Lackluster results

With adherence to traditional managed care methods, the medical portion of claim costs continues to increase. Not all can be attributed to inflation or the medical system in the US. Logic says change is needed in medical management methods. We need to implement new and different initiatives to actually make a difference.

Keep the baby

This is not to say the old methods can or should be discarded. To discard them would be to eviscerate the medical management system, definitely a risky and costly move. Instead, traditional programs should be enhanced and supercharged using new methods. They need to be improved, accompanied by honest evaluation of their contributed value.

Supercharge medical management

Happily, new options are available for tackling the old issues of claim complexity and cost. Appropriately applied technology methods can put managed care programs ahead of trouble and make it easier to resolve problems at less cost.

While technology has advanced exponentially in the past twenty-five years, few of its advantages have made their way to Workers’ Compensation. However, with minimal effort and cost, the gains can be amazingly substantial. Some enhancements to current processes include the following.

Value-driven provider networks

Poorly performing medical providers are guaranteed to increase costs and generate substandard outcomes, a fact proven repeatedly through industry research. Yet, little attention is paid to selecting quality medical providers. Nevertheless, providers can be vetted by analyzing the data. The evidence of performance is there.

Providers associated with poor outcomes, high cost, and even fraud can be identified by scrutinizing the data. The next step is obvious—when they are found, don’t use them. Avoid directing injured workers to the costly providers even when a discount is promised.

In states where directing care is not possible, the list of suggested providers should not include the bad ones. Additionally, the best practice list should be updated continually to insure continued performance.

Save 20-30%!

By choosing best-in-class medical providers based on the analytics means 20-30% or more savings in overall claim costs measured in reduced medical costs, indemnity costs, and overall claim costs. The numbers are impossible to ignore.

Electronic data monitoring

Integrating and monitoring claims data can also produce equally powerful results. Integrating the data means combining claims data from the multiple silos that typically house it. Monitoring means electronically monitoring the data using a specialized rules-based system and alerting the appropriate person when certain conditions occur.

Electronic data monitoring carries the advantage of early intervention into troublesome or threatening claims. It also adds structure to existing adjustor and medical case management processes.

Structure delivers efficiency and efficiency generates cost savings both administratively and in medical cost control. Moreover, structure allows definitive and accountable analysis of savings.

Proof of value

Service organizations, particularly medical case managers, have long had the challenge of measuring and reporting their value. Too often individuals, not the organization, determine when to respond and what to do.

However, when the response is consistent for similar conditions, measures of value are possible. Continuous data monitoring allows a tally of the number of times the same or similar conditions occur in claims and calculates savings benefits of their documented responses. That leads to choosing the most effective responses to specific conditions, thereby continuously improving the process.

A New Year’s Resolution

Rather than returning this January to the old routines, resolve to update and upgrade medical management using the new and more powerful applications available.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and 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

 

How to Measue What Might Have Been

By Karen Wolfe

The most difficult cost savings measurement is calculating the savings gained from what did not happen. Call it cost-avoidance measurement. Some say it cannot be done because if it has not happened, it certainly cannot be measured.

However, another point of view says that if placed in the context of assumptions based on analysis of the data, assessing savings for avoidance is doable.

Did the risk ever exist?

The first challenge in this tricky process is proving that a costly situation would have actually occurred given the conditions surrounding it. When it did occur, did the interventions mobilized reduce potential costs?

Do-it-yourself

A sample scenario is examining the data and finding that when a certain combination of data elements occurs, the result is consistently a 20% increase in indemnity costs. One can make the reasonable assumption that whenever that data combination appears in the claim, indemnity payments will increase unless intervening action is taken. That is predictive modeling 101.

Hours of IT time can be spent analyzing the data to tease out conditions that consistently result in cost increases. This do-it-yourself approach is not for everyone, maybe not for any one.

Rocket science

An alternative is the “rocket science” (mathematically sophisticated) approach of predictive modeling.  Formal predictive modeling procedures can be applied to the data to discover costly situations that should be avoided. This is a good approach, but it requires another step: concurrently monitoring the data to identify the risky conditions and taking appropriate action to avoid or minimize them. Nevertheless, the cost savings of avoidance can be claimed using this method.

Short cut

While either of these approaches has the potential to result in appropriately measuring and claiming cost savings, another method abbreviates the process. The short-cut is leveraging industry research and it is an easier, less costly, and more practical for most organizations.

Workers’ Comp industry research studies offer a format for measuring cost avoidance. During the course of research, large data bases are analyzed, usually applying sophisticated mathematical methodologies that identify risks and costs given certain conditions.

Leveraging research

An example of applicable research is the study conducted by Dr. Ed Bernacki and his team at Johns Hopkins University published in 2010.[1] Using five years of data containing closed claims supplied by Louisiana Workers’ Compensation Corporation, the research team first carved out claims where the reserves had increased from $15,000 to $50,000. The idea was to find the claims that had migrated in a negative way and then look for consistent conditions among them. In this case, the research team was seeking characteristics of poorly performing doctors.

Cost-intensive physicians

Amazingly, it was found that in the migratory claims 72% of the costs could be attributed to 3.8% of the physicians. The research team named physicians in this group cost-intensive physicians and identified consistent traits and behaviors associated with them. For instance, the physicians in this group were consistently associated with higher medical costs, longer treatment duration, longer claim duration, and higher indemnity costs.

Moreover, the cost-intensive physicians tended to treat disorders that have variability of treatment options, that is, no clearly defined treatment pathway. The disorders in that group included carpal tunnel, joint pain, intervertebral disk disorders, and psychological disorders. Additionally, certain physician specialties were associated with the characteristics of this group.

The study is rich in detail that can be translated to identify cost-intensive physicians in other databases who are the risky condition in claims. Using the research as a guide, isolate data elements that epitomize the characteristics of cost-intensive physicians.

Once identified in the data, cost-intensive physicians can be avoided. Find the cost-intensive physicians using the criteria demonstrated in the study, then avoid them. Determining the amount of savings is a question of establishing organizational policy based on the study.

Measuring savings of avoidance

Each time a cost-intensive physician is avoided, the savings can be calculated. Referring back to the study, preventing reserve migration is the framework of savings. If in the study the reserves migrated from $15,000 to $50,000, the claim savings assumed when a cost-intensive physician is avoided can be set conservatively or aggressively within that range.

Savings policy

The amount of savings declared is a question of determining the organization’s savings policy statement. An organization should establish standards for how it proclaims savings in claims. The savings policy statement, based on the research might read, “Based in the indicators found in industry research, avoiding a cost-intensive physician saves approximately $15,000”.

Monitoring and documenting

Obviously, none of this is useful information unless it is embedded in an operational process of analyzing the data, identifying cost-intensive physicians, and documenting avoidance. Traditionally, organizations have relied on their medical provider networks, but it turns out most network administrators do not evaluate provider performance on any basis. So it’s back to do-it-yourself or get help.

Nevertheless, it is possible to measure cost saving of what might have been. When placed in the context of assumptions based on research, assessing savings for avoidance is valid. To learn more, you are invited to visit MedMetrics or contact karenwolfe@medmetrics.org.

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[1] Bernacki, et.al. “Impact of Cost Intensive Physicians on Workers’ Compensation” JOEM. Vol. 52. No. 1. January, 2010.