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How to Monetize Medical Management

Over the past 25 years, the workers’ comp industry has collected vast amounts of data, and organizations within the industry have easy access to this valuable asset. Their challenge now is to 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.

See also: Intelligent WC Medical Management  

The first step toward monetizing medical data is to integrate it 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 can be applied to convert the data to usable information.

You need to analyze historic data using predictive analytics to discover conditions that are cost drivers or cost accelerators. 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? And the information must be made actionable.

Predictive analytics has determined the comorbidity of diabetes adds complexity and cost to claims, so an alert can be generated, and key Information conveyed to appropriate persons.

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.

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.

See also: Proof of Value for 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.

Intelligent WC Medical Management

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 measurable 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 on 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.

See also: 25 Axioms Of Medical Care In The Workers Compensation System  

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.

See also: Even More Tips For Building A Workers Compensation Medical Provider “A” Team  

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 measurable, objective, reportable savings at claim closure.

Proof of Value for Medical Management

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. Anecdotes show that their work saves time and money. The problem is that concrete evidence of their value has been elusive—until now.

How can costs avoided and time saved be measured? The measurements are like rabbits pulled from a magician’s hat. What really happened?

Quantifying what did not happen is usually impossible. However, quantifying and measuring savings is completely feasible through a different approach, using predictive analytics.

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.

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 measuring the savings.

The first and most critical initiative is analyzing an organization’s historic data using predictive analytics methodologies — because each organization has unique internal and culture processes regarding claims handling and medical management, using others’ data, regardless of how large the database, can mislead.

See also: 2017 Issues to Watch in Workers’ Comp

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.

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.

When risk conditions in claims are identified in this manner, 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.

Data monitoring identifies claims with risk conditions concurrently and informs the stakeholders immediately. Intervention efforts are coordinated among 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.

See also: On-Demand Workers: the Implications

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

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.

Will Watson Replace WC Professionals?

The Japanese firm Fukokui Mutual Life Insurance has replaced more than 30 office workers with artificial intelligence (AI), in this case the famed IBM Watson. Watson, or one of its doubles, is in fact affecting nearly all industries in multiple ways. Eliminating workers is a major goal. But could Watson replace workers in workers’ comp?

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. More practically, AI enhances worker productivity, accuracy and efficiency. But AI should never reach workers’ comp if more pragmatic, technology-based strategies are implemented now.

Replacing workers’ comp professionals with Watson is not feasible at this point or, I hope, ever. Yet, it is a wake-up call to the industry.

See also: 10 Questions That Reveal AI’s Limits  

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!

Envisioning Watson as medical case manager is a real stretch. Human interaction is central to effective medical case management. 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 engage current technology to improve processes and outcomes—just to keep up. Clearly, the momentum in every industry is more technology to gain efficiency, and workers’ comp cannot afford to lag. 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.

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.

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 taken early.

Assist claims reps by providing information for decision support such as the probable ultimate medical reserve amount for a claim. Time and effort are saved, while accuracy and efficiency are gained. Rather than labor with decisions such as adjusting reserves, you can present a timely and accurate projection, optimizing efficiency.

See also: Next Big Thing: Robotic Process Automation  

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.

How to Power Down WC Medical Costs

It just makes sense. When an injured worker has an underlying medical condition, recovery is compromised in one way or another. The case will be more complex, and it is likely to have a longer duration, higher severity scores and cost more. A recent article published by Denise Johnson in Claims Journal describes how identifying comorbidities early can help control workers’ comp claim costs.

Johnson identifies common comorbidities to watch for, including obesity, diabetes, hypertension and depression. There are many more, too. For instance, a pregnant injured worker will require careful medical management. Pregnancy should be considered a comorbidity and followed closely. Other examples include HIV, hepatitis C, cardiac disease and chronic pulmonary disease. The important thing is to identify the comorbid conditions in claims so they are monitored carefully and referred to nurse case management early.

See also: 25 Axioms Of Medical Care In The Workers Compensation System  

Comorbid diagnoses can be found in the data—usually. Treating doctors can include the comorbid diagnosis in the list of diagnoses on the bill, but sometimes they do not. They might consider a general health problem irrelevant to a workers’ comp claim, while it might be critical.

Reviewing diagnoses in a claim by the date they were added can be revealing. A diagnosis of diabetes or obesity can appear weeks after the injury date and well into the treatment process. Moreover, when in the course of treatment a diagnosis appears can be enlightening and deserves attention.

Some comorbid diagnoses appear late in the data because they are newly discovered or the treating doctor becomes aware of them later. An example is discovering a diagnosis for a mental disorder in the data long after the actual injury.

A mental disorder diagnosis might result from delayed or unsuccessful recovery as the patient acts out in frustration. Or the late diagnosis might imply previously unrecognized psycho-social factors. Nevertheless, the data should be monitored continually to tag any diagnosis that creeps into the claim picture at any point.

When comorbid or any apparently unrelated diagnoses appear later in a claim, it could be a pre-emptive signal of poor response to treatment or even impending litigation. Monitoring the data continually to uncover new diagnoses is essential to avoid missing subtle issues.

Data can be made smarter by the form and mechanism in which it is presented to those managing the claim. The manner in which diagnostic data is portrayed for claims reps and medical managers can be not only informative, but actionable. An example is portraying all diagnoses by the date they were added to the claim in bills. Such views can disclose subtleties about what is occurring in the treatment process and inform those managing a claim of ensuing problems.

See also: Even More Tips For Building A Workers Compensation Medical Provider “A” Team  

Identifying comorbidities and other troublesome conditions in claims using predictive analytics and continuous data monitoring leads to early intervention and best results. For additional perspectives on this topic, please see, “Analytics-Informed Early Intervention Drives Best Outcomes.”