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The MedMetrics blog provides comments and insights regarding the world of Workers’ Compensation, principally, issues that are medically-related. The blog offers viewpoints regarding issues affecting the industry written by persons who have long experience in the industry. Our intent is to offer additional fabric, perspective, and hopefully, inspiration to our readers.

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Monday, February 5, 2018

How to Make Analytics the Disrupter in WC

by Karen Wolfe

That the Workers’ Comp industry is uniquely slow in adopting analytics and applying the resulting intelligence to the operational process is a generally known fact. The reasons vary, including natural resistance to new ways of working, fear of additional work load, and cost-avoidance. The value of analytics may be misunderstood and is definitely under-valued. Notwithstanding amazing success with analytics by other industries, the Workers’ Comp industry remains disinclined to embrace it. Maybe the approach should be changed.

To their credit, many payer organizations have created a position identifying analytics as a role in the organization. Titles include Director of Analytics, Consultative Analytics, Claims Analytics, VP Consultative Analytics, and Analytics Manager. Yet none of the titles suggest positions with the power of decision-making or authority.

What will finally move industry leaders to value analytics as a legitimate and effective business initiative? Stated differently, how can analytics be made a disrupter in a Workers’ Comp organization, thereby pushing though the resistance barrier to create superior performance?

Analytics as a disrupter
One approach to making analytics a disrupter is to make it a profit center where analytics leadership is responsible and accountable for demonstrated savings and profitability in partnership with specific business units in the organization. Now the focus is on how the organization’s analytics-informed intelligence drives operational excellence and profitability! Senior management attention and support is quickly engaged. Analytics positioned as a profit center significantly transforms its visibility and perceived value to the organization.

Of course, several factors must also come into play to achieve this level of analytic empowerment. First, the analytics leadership is tasked with executing the analytics. It is also responsible for connecting the resulting intelligence to operations where appropriate actions are taken, thereby mobilizing superior performance. This is best accomplished by means of establishing partnerships between analytics and specific operational business units.

Analytics partnership
Analytics value is actualized at the business unit level where daily decisions are made and action is taken affecting the operational process, clients, the service product, and the organization. Analytic leadership partners with select business units where intelligence is transferred to action. That means systems are designed for easy access, easy use, and decision support at the operational level. Initiatives are smart, digital, and engaging for all participants.

Connecting analytic intelligence to action is dependent on creative system design. The design for each business unit is unique, depending on the unit’s activity, requirements, and goals. First, predictive analytics methods are used to analyze historic data related to the unit and the organization thereby acquiring the intelligence that will be transferred in the form of decision support and guiding action.

A major benefit of this approach is structuring and standardizing superior decision support and guidance for specific conditions and situations that occur. Organizational protocol is established and enforced while front-line professionals gain a personal knowledge assistant.

As with any business initiative, measuring its effect on the organization is crucial. Moreover, the analytics-business unit partnership must be made accountable through performance measurement. Measure the value gained by repositioning analytics leadership to insure accountability is accurately allocated. The bottom line goal of positioning analytics as a disrupter is streamlining operational flow and increasing profitability which can be measured in multiple ways.

Measuring overall profitability related to the initiative is the first imperative. Moreover, profitability can be further apportioned in terms of increased revenue, productivity, accuracy, efficiency, timeliness, quality, return on investment, improved claim outcomes, and strategic competitive advantage. The value is sustained by continually repeating the plan and measuring outcomes, always remembering the best outcomes are optimized by connecting analytic intelligence to action.

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

Tuesday, January 9, 2018

Avoid Analytic Death by Bad Data

by Karen Wolfe

“The promise of technology is not yet at pace with the reality of the data, plagued by continued issues of cleanliness, connectivity, and availability.”[1] Regardless of the analytic methods in use, they will not generate value unless the data is clean, complete, and integrated. Unfortunately, current data is none of those things.

Analyzing faulty data is costly and misleading, moreover it sabotages all analytic initiatives and outcomes going forward. True value can never be achieved from analytics executed on bad data. Regardless of how elaborate or elegant the analytic initiative, its outcome can never be trusted unless the data quality issue is addressed first.

Unfortunately, accurate and complete data sets are either non-existent or rare today in the Workers’ Comp world. Whether the data source is manually key-entered or transmitted to the organization in digitalized form, it is replete with errors, duplicates, and omissions.

Data quality precedes analytics
Before engaging in any form of analytics designed to develop business insights and efficiency solutions, Workers’ Comp organizations must begin by addressing data quality. It is the first step in any analytic endeavor and will lead to far greater satisfaction with the results. Every effort should be made to correct faulty data and maintain its excellence by means of advanced technology wherever possible.

Consider the grocery industry. All pricing is computerized, side-stepping human data entry. The dollar amounts for items are entered automatically and adjusted frequently for changes in wholesale prices or sales promotions. Obviously, accuracy is critical so personnel at the cash register level never enter the dollar amounts. Instead, they enter a code for the item. The system automatically verifies the item for the code and presents the correct price.

At the supermarket corporate level systems have been established that read bar codes or QR codes on individual items as they arrive from vendors and automatically enter the wholesale price. Markups to retail are automatically calculated by percentage for that vendor and item. Changes in pricing are made by entering a percentage adjustment selected from a list. The opportunity for human error is virtually eliminated. The question is how can that level of automated accuracy be transferred to the Workers’ Comp industry?

Productivity tools
While the degree of technical automation enjoyed by supermarkets is not yet practical in the Workers’ Comp claims process, some initiatives can be adapted and applied to insure better data. The computer system designed to avoid manual data entry is the first step. Wherever possible, provide a pop-up list from which to choose data items rather than manually entering data in the system. Develop simple pick lists so the data are always entered the same way and spelled correctly.

The lists include standard formats for abbreviations. “Suite” is never “Ste”, thereby avoiding duplicates for the same record. Go even deeper to create pick lists for adjuster and nursing notes that standardize initiative documentation and establish measurable outcomes.

The ability to add, change, or delete items on the list is limited to the supervisory level where accuracy is maintained and accountability is monitored. This approach is immediately suited to, and urgently needed to keep medical provider demographic records valid and serviceable for medical provider performance analytics.

Create data quality teams
Provide incentives or rewards for those who find errors and omissions in the data. Set up competitive teams to uncover bad data. Because data is often transmitted to an organization digitally, errors and omissions may already be present. Use interns or trainees to scrutinize the data rather than accepting data "as is".

Create a team that monitors data quality, infuses changes as appropriate, and rewards personnel for accurate performance. Not so long ago in days of typewriters, typist performance was measured for speed and accuracy with their jobs depending upon good performance. What happened to those standards of excellence?

Priority shift
Elevate the importance of good data in the organization. Create an automated audit log of user performance and include worker’s data accuracy as an element in their performance evaluation. Workers will not value data quality if the organization does not make it a priority.

Cost of data quality
Prioritizing and maintaining data quality is not an insignificant shift in most organizations’ value systems and additional costs will be generated. However, in order to realize trusted analytics-informed insights, efficiency, and to remain competitive, data quality is prerequisite. Analytics-informed value is generated by good data. Bad data is just costly.

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

[1] Bieda, L. Big Idea: Competing With Data & Analytics Blog. September 27, 2017.