Merge Workers' Compensation Analytics with Automation to Gain Efficiencies

by Karen Wolfe

“Industry 4.0[1] allows manufacturers to capture and analyze data from nearly every point in their operations. When these capabilities are coupled with data analytics and automation tools, manufacturers can wield insights to improve processes, discover efficiencies and implement cost-saving measures like predictive maintenance.”[2] Workers’ Compensation payers can mirror this process to achieve the similar results.

The key factors to achieve these results are to 1) capture data, 2) make data the operational glue, 3) analyze the data, 4) Implement automated tools to transmit knowledge, 5) institute accountability and 6) report efficiencies and cost savings. These functions implemented by Workers’ Compensation claims and medical case managers lead to efficiencies and measured cost-savings.

Data capture
Industry captures and analyzes data from every point in their operations. Claims management payers similarly capture data from multiple sources. Among the data points are bill review, claims systems, utilization review, case management, and pharmacy benefit management. However, in order to analyze the data it must be merged to view the entire claim. Fragmented data points will always omit important information.

Data glue
Raw data is known to be nearly useless for analysis. Moreover, fragmented data or data sets are similarly insufficient. Data is the operational glue that when merged at the claim level provides a comprehensive view of a claim, risk factors as they emerge, and progress of the claim. When the data is captured, merged, and monitored at the claim level, it can be analyzed to gain actionable insights for optimal claim management efficiency.

Analytics
Pre-defined analyses of the integrated data is executed at all points in the claim process by monitoring the data continually. Risks, conditions, and events are identified in the concurrent data that merit attention. They are tagged and transmitted to the appropriate professional in the organization.

Automated tools
A powerful form of automation in claim medical management are electronic alerts generated throughout the claim process. Systems are designed to concurrently monitor the integrated claim data and automatically generate alerts of situations, conditions, events, or other information of concern to the appropriate persons immediately. Early and timely intervention is known to improve outcomes and save money. The alerts provide claim and case management professionals with actionable insights.

Decision support
Actionable insights are made even more powerful when combined with knowledge support. Relevant information accompanying an alert prompts appropriate and timely action, thereby creating efficiency in the medical management process.

Accountability
In manufacturing as well as in claims management, automated alerts are ineffective if the targeted professional ignores them. Therefore, the automated system must be accompanied by an accountability system that documents alerts sent, to whom, for what claim, and the reason for the alert. This does two things: It creates a tool for management to follow up on automated alerts and it also provides the necessary information to allocate costs appropriately.

Report efficiencies and savings
Analysis of automated alert activity creates even more efficiencies. Compare claim outcomes with similar past claims to measure savings gained through intense data monitoring, analysis, and early intervention. Document the efficiencies and cost savings achieved by information-supported timely intervention. Additionally, compare the process and outcome efficiency and savings with other payer organizations through medical management benchmarking by a third party.

Benchmark outcome performance
Payer clients and constituents such as senior management want and deserve proof of claim and medical management quality. Historically, savings have been reported in terms of medical network discounts and reduction from billed to paid amounts from bill review. Both are problematic. However, benchmarking claim outcomes against independent third party payer data offers reliable proof of quality performance. Moreover, it offers outcome information with enough granularity to guide improvement in performance going forward.

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[1] Industry 4.0, refers to the fourth industrial revolution, the cyber-physical transformation of manufacturing.www.TechTarget.com
[2] Tiernan, K. Discover New Efficiencies with Data Analytics and Automation. https://www.linkedin.com/pulse/discover-new-efficiencies-data-analytics-automation-kirstie/?trk=eml-email_feed_ecosystem_digest_01-recommended_articles-10-Unknown&midToken=AQEsLNGx7t4Zlg&fromEmail=fromEmail&ut=3qM8IQ5R7khUA1

 Karen Wolfe is the founder and President of MedMetrics®, LLC, an independent 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. MedMetrics also provides medical management outcome benchmarking services.  karenwolfe@medmetrics.org

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

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

 

Make Your Data a Work-in-Process Power Tool

By Karen Wolfe

Heard recently, “Our organization has lots of analytics, but we really don’t really know what to do with them.” This is a common dilemma. Analytics (data analysis) are abundant, they are presented in annual reports and published in colorful graphics. Too often the effort ends there. Nice information, but how does it change operational flow, claim outcomes and profitability?

Obviously, the basic ingredient for analytics is data. After that comes skill, ingenuity, and creativity. Business intelligence and knowledge are severely limited without data. Fortunately, the last thirty years have been primarily devoted to data gathering.

Data evolution
Over the past thirty years or more, all industries have evolved through several phases in data collection and management. Main frame and mini-computers produced data and with the inception of the PC in the 80’s, data gathering became the business of everyone. DOS systems were clumsy and there were significant restrictions to screen size and data volume.

Recall the Y2K debacle caused by limiting  the year to two characters instead of four. The two digit year was made necessary in early computing because of restricted capacity.

Happily for the data gathering effort, progress in technology has been rapid. Advancement was enhanced first by local and wide area networks, then by the Internet along with ever more powerful hardware and lower costs. Data gathering has been overwhelmingly successful.

Big data
Now we have truckloads of data, often referred to as Big Data. In fact, a new industry has developed around understanding and managing huge data volumes. Once Big Data is corralled, analytic possibilities are endless. 

The Workers’ Compensation industry has also collected enormous volumes of data. Now, much is being done in the industry to actualize the analytics to produce knowledge that support reductions in costs and improved outcomes.

Imbed analytic intelligence
The best way to apply analytics in Workers’ Compensation is to create ways to translate and deliver intelligence to the operational front lines, to those who make critical decisions daily. Knowledge derived from analytics cannot change processes or outcomes unless it is imbedded into the work of adjusters, medical case managers, and other key personnel. These professionals make decisions that affect the course of claims and they need electronic knowledge tools to assist them.

Consulting graphics for guidance is cumbersome, interpretation is uneven or unreliable, and the results cannot be verified. Therefore, intelligence must be made easily accessible and easy to interpret and apply. Front line decision-makers need online tools designed to support decisions and direct actions.

Electronic monitoring
To effectively imbed analytic intelligence into operations, all claims data must be continuously monitored electronically. Data in claims must be monitored continuously so the system can identify claims that contain conditions cautioned by the analytics. The interpreted information is then linked to operations.

By electronically monitoring all claims for high risk events and conditions informed by analytics, high risk and migrating claims cannot slip through the cracks.

Personnel can be alerted of all claims with risky conditions identified through analytics. Additionally, the analytic delivery system should automatically document itself.

Self-documenting
The system that is developed to deliver analytic knowledge to operations should automatically self-document. That is, it should keep its own audit trail to record to whom the intelligence alert was sent, when, and why.

Without self-documentation, the analytic delivery system lacks authenticity. Those who receive the information cannot be held accountable for whether or how they acted on it. When the system automatically self-documents, those who have received the information can be commended for, or held accountable for their part. Management is able to review current status at any time.

Self-verifying
A system that is self-documenting can also self-verify, meaning results of delivering analytics to operations can be measured. Claim conditions and costs can be measured. Moreover, further analyses can be executed to measure what analytic intelligence is most useful, in what form, and importantly, what action responses generate best results.

The analytics-informed knowledge delivery system monitors all claims data, identifies claims that contain risk elements, and creates knowledge tools for front-line workers. The data becomes a work-in-process information and decision-support tool while analytics are linked directly to outcomes and savings are objectively measured.

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 that link analytics to operations, thereby making them actionable and measureable. karenwolfe@medmetrics.org

 

How to Effectively Imbed Nurse Case Management in WC Claims

by Karen Wolfe

Nurse case management (NCM) has a powerful impact on Workers’ Compensation claim cost and outcome. Positive results of nurse involvement have long been anecdotally accepted, however widespread evidence of nurse impact has not emerged and objective proof of value is still missing. Several factors account for this.

Inconsistent referrals

For one thing, NCM’s are usually considered an adjunct to the claims process, called upon in sticky situations. Too often referrals to nurses is a last resort rather than an integral and standardized part of claim management.[1] When claims adjusters have the sole responsibility to refer to NCM’s, it can be subjective, uneven, and therefore unmeasurable.

Besides receiving referrals for sundry issues at different points in the course of the claim, nurses have not clearly articulated their case management interventions. Claims adjusters sometimes misunderstand the nurses’ approach. However, consistent referrals and standardized procedures can bring about major change.


Consistent referrals
Referrals to NCM should be made based on specific medical conditions in claims such as comorbidity like diabetes or problematic injuries like low back strains that tend to morph into complexity and high cost. Specific risky situations found in in claims data should automatically trigger NCM notification.


A recent article published in Business Insurance, “Nurses a linchpin in reducing workers’ comp costs”, points out how Liberty Mutual has developed a tool that notifies claims adjusters of cases that would most benefit from a nurse’s involvement.[2] Decision burdens for claims adjusters are eliminated. Referrals to NCM are automatic based on specific high risk situations found in the claim. Inconsistency disappears and several benefits evolve from this approach.


Process standardization
An operational process can be dissected and categorized, thereby gaining better understanding of its components and relative importance. Review the data to determine which medical conditions in claims result in longer disability, lower rates of return to work, and, of course, higher costs. Select the conditions in claims that should activate an NCM referral.


An example is a mental health diagnosis appearing in the data well into the claim process. A mental health diagnosis appearing during the claim for a physical injury such as a low back strain is a strong indicator of trouble. The injured worker is not progressing toward recovery. However, the only way to know this diagnosis has occurred in a claim is to electronically monitor claims on a continuous basis.


Data monitoring
To identify problematic medical situations in claims and intervene early enough to impact outcome, the data should be monitored continually. Clearly, this is an electronic, not a human function. When the data in a claim matches a select indicator, an automatic notice is sent to the appropriate person.


Standardized procedures
Catching high risk conditions in claims is just the first step. NCM procedures must be established to guide responses to each situation triggered. Standardized procedures should describe what the NCM should evaluate and advise possible interventions. Such processes not only explain the NCM contribution, they assist in documentation and are the basis for defining value.


Measuring value
NCM has been under-appreciated in the industry because measuring apples to apples cost benefit has been impractical. When claims adjusters decide about referring to NCM’s and individual nurses create their own methodology, variables are endless and little is measurable.


In contrast to the subjective approach, specific conditions in claims found through continuous data monitoring can automatically trigger a referral to the NCM. In response, the nurse is guided by the standard procedures of the organization. When referrals are based on specific conditions in claims and response procedures are delineated, outcomes can be analyzed and objectively scored.


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 link analytics to operations, thereby making them actionable. MedMetrics analyzes data continuously and sends alerts as appropriate. MedMetrics also analyzes and scores medical provider performance. karenwolfe@medmetrics.org

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[1] K. Wolfe. Early Intervention Drives Better Outcomes, But is Not Really Pursued http://medmetrics.blogspot.com/2014/10/early-intervention-drives-better.html

[2] B. Kenealy. Business Insurance. http://www.businessinsurance.com/article/20150610/NEWS08/150619977?tags=|92|329|304