How to Avoid Insuretech Disruption

By Karen Wolfe

In insurance, those who hold the data, hold potential power. Those who analyze the data and apply the knowledge control their destiny.

“The abundance of data – and the technology used to capture it - is driving profound disruption in the relationship structure of the insurance industry. As the traditional gatherers and guards of massive amounts of data, insurers face threats from new, tech-savvy competitors who can adapt to changes more quickly.

There are very powerful trends coming together to cause serious industry disruption. That can be a big threat, but if insurers start responding now and embracing the change, it could also be a big opportunity.”[i]

What is Insuretech?
Google defines Insurtech as referring to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model. Insurtech is a portmanteau of “insurance” and “technology” that was inspired by the term Fintech (Financial Technology).

The problem
Longstanding reluctance to change is preventing many organizations in the Workers’ Comp industry from embracing new technology, especially technology that streamlines processes and worker performance. However, this approach is no longer sustainable. Simply stated, those who cling only to the old-time culture and ways will be disrupted. Think Amazon or Uber.

“Big Data and analytics are forcing insurers to adjust their processes when it comes to collecting and using data. With the expansion of the Internet of Things, sensor technology, machine learning and artificial intelligence, there is more information available than ever before.”[ii]

Data, the asset
Organizations that continue to ignore the facts will wonder why they are no longer competitive. Others who are open to new approaches using new technology will experience positive results. It is a matter of attitude and willingness to try newer methods.

Nevertheless, insuretech need not be invasive or costly. To make a positive impact on processes and outcomes, an organization must first take the position of data-centeredness, believing data is its valued asset.

Step ahead of disruption
Having accepted the position that data is the organization’s valuable asset, accepting and incorporating new technology requires focusing on only three basic initiatives: data quality, data analysis, and intelligent application of the intelligence gained through analytics.

Data quality
If data is an asset, then its quality must be valued and protected. Using poor or erroneous data never ends well. Information gained from poor quality data will not improve an organization’s processes or outcomes, will lead to poor decisions, and detrimental actions. Therefore, resources must be applied to guaranteeing quality data input. Moreover, considerable resources may be needed to improve historic data.

Over the last twenty-five years organizations have been focused on collecting data, but little attention has been paid to insuring the data is accurate and complete. Now that must change. A data-centered organization will also guarantee its data is pristine.

Analytics
The second initiative needed to avoid insuretech disruption is to analyze the organization’s data. Collect and analyze all data over the previous five years. Methods such as predictive analytics can be applied to gain greater understanding of the organization, how well it operates, and what are the cost drivers both operationally and at the transaction level. This is simply a matter if analyzing historic data and monitoring concurrent data to reveal trends, threats, and possibilities. Know thyself.

Intelligent knowledge application
Having quality data and analyzing it leads to the next critical step of designing intelligent solutions to problems identified during the analysis phase. Apply the knowledge gained to specific areas of need by creating “apps” that solve problems and improve processes in the organization.

Alert the right person when conditions or events pose a risk to the organization or work product identified in the analysis phase. Deliver key intelligence to specific individuals or groups at the exact time they need it for decision support. Facilitate timely communication within the organization. Knowledge assistance provided at the right time to the right persons saves time, creates accuracy, efficiency, and increases profitability.

Stepping into the world of insurtech and avoiding disruption is largely a matter of perspective and attitude. It requires a view that data is an asset and when properly managed, lets the organization define its destiny.

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[i] Will You Survive the Great InsureTech Disruption? James Dodge, Senior Consultant, Advanced Analytics & Data Solutions, Milliman. Risk and Insurance. 6-27-17

[ii] Ibid.

 

MedMetrics, a WC insuretech company, leverages predictive analytics to provide WC claims reps knowledge assistance by projecting probable ultimate medical losses, automatically integrating claims and medical management to contain predictive medical threats, and offering additional intelligent tools such as provider performance analysis and diagnostic severity scoring. MedMetrics clients are easily and affordably more accurate, efficient, and profitable.

 

How to Make Frontline Workers Smarter

by Karen Wolfe

Concern has been expressed in the Workers’ Comp industry about its aging workforce. Workers, especially claims reps with years of experience are retiring in greater numbers leaving a semi-vacuum in their place. This leads to a pressing need for training and providing tools that will help workers make smart decisions well before they earn seniority. Analytics is one of the tools that can make workers smarter.

Analytics
Most people say they want analytics. Yet, many are not sure what analytics is or what benefits will be gained. Some anticipate operational disruption and fear the cost, neither of which is necessarily well-founded. Whether it is because of lack of knowledge or fear of change, many still hold back. To learn more about how analytics and how it can impact medical management in Workers’ Comp, please read, “Making the Most of Analytics to Improve Medical Outcomes”[1] 

Why analytics?
The reason for implementing analytics of any variety is to gain knowledge about the organization by analyzing its data. The knowledge gained should be actionable knowledge, meaning it supports intelligent action and decisions while enlightening the way forward.

Learning more about the business provides information for decision support which can be applied in an organization from long term planning to transactional decision-making by frontline workers. Transactional decision support at the operational level is where knowledge gained from analytics makes workers smarter.

Transactional decision support
Analytics-informed transactional decision support is linking knowledge gained through analytics with operations. Analytics will have no impact on the organization, its clients, or its workers unless the information is driven to, and acted upon at the operational level. Moreover, the information must be presented to workers in a fashion that guides them to appropriate action.

The way workers receive information determines how the knowledge gained through analytics is acted upon. In other words, the system designed to deliver appropriate knowledge to the right workers at the right time is crucial. Information designed to generate action can be delivered at the right time in the form of electronic alerts. But alerts must contain all the information needed to take appropriate action, the correct action.

Smart information delivery
For example, alerts transmitted to claims reps prompting them to adjust medical reserves in a claim must contain all the information necessary for adjusting reserves accurately. Claim background information as well as the conditions found through claim data monitoring that generated the alert should be portrayed for them. Importantly, to enlighten claims reps further, the alert should display the probable ultimate medical reserve amount for the claim based on predictive analytics.

Similarly, information should be delivered to others in the organization who would benefit from the knowledge in managing the claim. The conditions that initiated the alert to claims reps regarding the need for medical loss reserve adjustment are often appropriate for nurse case management involvement as well. The system designed for information delivery can automatically notify nurse case managers along with claim reps, thereby coordinating initiatives leading to early claim resolution.

Consistent response
Analytics-informed transactional decision support transmitted to the operational level accrues additional benefits. Not only are frontline workers smarter, but responses are more timely and consistent leading to credible measures of savings.

Measures of savings
Continuing the example of using predictive analytics to alert for medical loss reserving, even more value can be gained. Objective medical savings can be calculated at claim closure based on reserve projections and real-time proactive claims handling by claims reps and coordinated medical intervention by nurses. Early intervention by informed workers will lead to measurable savings that can be communicated to clients and other constituents.

Smarter workers
Frontline workers can be made smarter and more efficient through technology. Driving information gained from predictive analytics to the transaction level makes workers more accurate and efficient. Even minimally experienced workers given the right information at the right time will make accurate and timely decisions. Moreover, experienced workers will elevate their accuracy and efficiency, thereby saving time and money for the organization while improving claim outcomes.

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. karenwolfe@medmetrics.org

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[1] Making the Most of Analytics to Improve Medical Outcomes. WellComp Managed Care Services.

https://www.wellcomp.com/images/emailmkt/MakingtheMostAnalytics.pdf

 

 

 

7 Reasons You Really, Really Want WC Medical Analytics

by Karen Wolfe
In a recent post, Joe Paduda stated, “The workers’ comp, and, for that matter, the entire property and casualty insurance industry, is chronically systems-poor.  While other industries view IT as a strategic asset, continually investing billions in IT, WC/P&C considers IT an expense category to be mined for pennies to add to earnings per share.”[1]

As one who has worked on the vendor IT side of the Workers’ Comp industry for decades, I know for a fact Joe is exactly right. Nevertheless, both inside and outside change is impacting the industry—compliance requirements and how IT is perceived, financed, and implemented.

Moreover, customers are demanding more usable information. So as a way of advancing the perception part of medical analytics, seven reasons are offered here to whet the appetite for analytics-informed medical management

Analytics-informed medical management means collecting, integrating, and analyzing all relevant current and historic data to gain insights that will improve performance and outcomes. The following are a few very good reasons to invest in analytics.

1.    Look forward, not backward
An unfortunate, but persistent perception of business intelligence and data analysis is that reports are for looking at the past—how many claims, the trend in slips and falls, or how much money was spent last quarter. Interesting, but not actionable.

Much greater advantage can be gained from analyzing the data to understand what is happening now in order to improve procedures going forward. Identify cost drivers and develop prompt, appropriate, and consistent actions to redirect the organization.

2.    Find meaning in your data
The industry has been diligently collecting data for years, yet little attention has been paid to what the data might reveal. Analytics looks at the data to derive meaning, suggest direction, and empower informed decision-making.

Corporate leaders are often victims of their own denial, assuming all important information is known and current processes are the best they can be. Rarely is that actually the case and analytics can be eye-opening.

3.    Deliver intelligence to those who need it
Analytics can be used to evaluate medical provider performance, for instance, and deliver the information in real time to those who are directing care. Since poorly performing providers are guaranteed to add cost and complexity to claims, analytics used in this manner will directly impact efficiency, cost, and outcome.

Similarly, information about untoward events and conditions in claims delivered to operations concurrently will add efficiency and improved outcomes.

4.    Standardize procedures
A rule-based approach can be used to monitor data and create alerts of high risk conditions and events that occur in claims. Doing so inserts credibility, consistency, and comprehensiveness into the medical management process. Moreover, when standard procedures and actions are established to respond to specific alerts, the entire process can be measured for organizational improvement, cost-savings, and outcome success.

5.    Data as a work-in-process tool
Data can be a working tool. Analytics can be structured to concurrently tag data items that portend risk and cost in claims, then alert the person who can take action. Front line workers will gain decision support information in time to intervene effectively and usually avoid irreversible damage.

6.    Discover unexpected opportunities
When analytics are employed, newly discovered information or conditions understood differently can reveal opportunities. Interventions, priorities, and procedures might be restructured, streamlined, or enhanced. New products or delivery methods may also be realized and developed.

7.    Ensure the organization’s competitive advantage
Having implemented standard and consistent methodologies, improved outcomes are demonstrated objectively for clients and prospects. Proof of value generates confidence in operations and outcomes, a much easier sell.

The forgoing seven reasons to invest in medical analytics are not by any means all-inclusive, nor are they exhaustive in their portrayal. Much more can be said and gained by implementing medical analytics. The data ingredients are available and waiting.

The general tenor in the industry is to continue business as usual, but doing so has not produced desired results. Nor will it. As Paduda points out, IT in the WC/P&C industry is exceedingly underappreciated and underfunded.

Therefore, a  little creativity may be required to obtain what is needed. Outsourcing to a company that uniquely provides Workers’ Compensation medical analytics is one approach. Fees can be sized to the organization, thereby making it affordable. Being analytics-poor is no longer an option.

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Paduda, J. Who’s running your company. 05/20/2016 http://www.joepaduda.com/2016/05/whos-running-company/#sthash.LJIERvyq.dpufv

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, analytics-Informed medical management and technical services company. MedMetrics analyzes and scores medical provider performance and offers other online apps that link analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

Analytics-powered WC Medical Management

by Karen Wolfe

“I firmly believe based on extensive qualitative and quantitative research and the many interviews and discussions I’ve conducted with insurance industry leaders—that analytics represent the industry’s best path to success and survival in a rapidly transforming world.”[1] 

This is not a controversial statement. Most people now acknowledge the importance of analytics. The question is what is the best approach to analytics in order to achieve optimum results? It’s not enough to prepare analytic graphs and pin them to the wall or publish them in reports for the C-Suite. The information might be eloquent, but nothing will happen or change until it is pushed into operations where action can be taken. Analytics should solve problems. 

Solve problems 
In Workers’ Compensation, a major problem is our inability to contain continually increasing medical costs. Cost containment initiatives implemented to date have helped, but the job is far from complete. Happily, a new methodology is now available—analytics-powered medical management. 

Analytics defined 
Analytics is basically a fancy term for data analysis and there are many forms. A good place to begin is with descriptive analytics, a preliminary stage of data processing that creates a summary of historical data and integrates data from different sources to yield useful information. It also prepares the data for further analysis. 

Developing knowledge 
The data can be re-organized to make it easy to identify patterns and relationships, not otherwise obvious. The data can be queried and reported for more insight. It can also be re-packaged for better understanding or to initiate an action. To an individual in an organization, analytics can make the information derived from the data easy to locate, access, understand, and act upon. 

Real time intelligence 
When the data is monitored continuously, business units gain the advantage of near real time intelligence. Concurrent knowledge of conditions and events in a claim offers the opportunity for early intervention. Early intervention means the damage can be curtailed, thereby reducing claim costs and complexity. Outcomes are improved. 

Actionable information
Information is most powerful when it is current. What occurred two months ago may not be accurate or even relevant now. Conditions about events may have changed substantially so time is wasted manually updating the information before taking action.

One of the most important benefits of analytics is the ability to push timely information to the people who need it and can act on it most effectively. In the case of Workers’ Compensation, those persons are most likely claims adjusters and medical case managers who are in the trenches with claims. When these business units are alerted to pre-defined conditions in claims, their responses are more authentic and better outcomes result. 

Structured notification 
Specific information in claims pushed to the appropriate person in near real time is powerful. That person might be a claims adjustor, medical case manager, supervisor, or medical director who will act on the information based on procedures developed by the organization. The organization determines what situations in claims should be addressed, those potentially the most costly or disruptive based on historic analysis. Moreover, the organization determines what actions should be taken, and by whom. 

Structured response 
Structured notification and responses in the form of standardized procedures lead to consistency. As with any organizational procedure, allowances are made for professional authority, nevertheless, cost savings gained through consistent processes can be reliably measured. 

Spon
Measureable results 
Measures of medical management savings in Workers’ Compensation have been elusive. Without structured problem identification and response, apples to apples comparisons and analyses are impossible. A major benefit of analytics-powered medical management is the ability to dependably measure the benefits derived through data monitoring, analysis, notification, and response.

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

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[1] Applebaum, S. Analytics and Survival in the Data Age. LinkedIn Pulse. news@linkedin.com. February 18, 2016.

The Best Data Might Be Your Own

by Karen Wolfe

Workers’ Compensation claims and medical managers are continually challenged by upper management to analyze their cost drivers. Moreover, upper management wants comparisons of the organization’s results to that of their peers.

The request is appropriate. Costs of doing business directly impact competitive performance of the organization. Understanding cost drivers is key to making adjustments to improve performance. Still, it’s not that simple.
 

Meaningful and relevant data
Executing the analysis is the lesser of the two demands. More challenging is finding industry or peer data that is similar enough to create an apples to apples study. In a recent article, Nick Parillo states “Regardless of the data source, whether it be peer-related or insurance industry-related, risk managers must be focused on aligning the data to their respective company and its operations.”[1] Parillo emphasizes the data should be meaningful and relevant to the organization.
 
Aligning the data to the situation can be challenging. Industry or peer data may not be situation-specific enough or granular enough to elicit accurate and illuminating information. State regulations vary, as do business products and practices, along with a multitude of other conditions that make truly accurate comparisons difficult.

Data variability

Variability in the data available for benchmarking can be especially disconcerting when considering medical cost drivers that now account for the majority of claim costs. Differences in state fee schedules and legislation such as required UR and the use of evidence-based guidelines can produce questionable comparative results. Additionally, whether the contributed data is from self-insured and/or self-administrated entities can skew the results.

Other variables that make comparing industry or peer data less valid are unionization, physical distribution of employees, employee age and gender, as well as industry type and local resources available. Potential differences are unlimited.

Cultural variables

External sources such as local cultural and professional mores, particularly among treating medical providers can play a significant role in disqualifying data for comparison. For instance, my company’s analysis of client data has uncovered consistent differences in medical practice patterns in one large state defined by geography. In one geographic sector, referrals to orthopedists with subsequent surgery and higher costs are far more frequent than in another sector of the state for the same type of injury.

Parillo continues, “Given the uncertainty and limitations on the kinds of peer group data a risk manager would need to perform a truly “apples to apples” comparison, the most “relevant and meaningful” data may be that which a risk manager already possesses: His own.”[2]

Internal data

Analyzing internal data can be highly productive. First, the conditions of meaningful and relevant are guaranteed, for obvious reasons. The differential across one state was found in one organization’s internal data which insures data variability is not a factor.

Analyses can be designed that dissect the data at hand. Follow up to the above example might include looking for other geographic variables in costs, in injury types, and in medical practice patterns. Compare physician performance for specific injury types in the same jurisdiction and then look for differences within. To gain this kind of specificity and relevance, drill down for other indicators.

Moving costs
Evaluate how costs move. Look at costs at intervals along the course of claims for specific injury types. In this case, utilizing ICD-9’s is more informative than the NCCI injury descriptors. One client found that injury claims which contained a mental health ICD-9 imbedded during the course of the claim, showed an upsurge in costs beginning the second year. Now further analysis can begin to discern earlier indicators of this outcome. In other words, dive further into the data to find leading indicators.

Imbedded indicators
Industry data is not likely to contain the detail necessary to evoke subtle mental health information during the course of the claim. Most analysis ignores the subtlety and sequence of diagnoses assigned. Few would uncover the mental health ICD-9 because few bother with ICD-9’s at all.

Drilling down, analyze claims that fall into this category for prescriptions, legal involvement, and other factors that might divulge prophetic signs. It is an investigative trail that relies on finite internal data analysis.

Undervalued data

Too often people disrespect their own data, thinking it is too poor in quality, therefore of little value. It’s true, much of the data collected over the years is of poorer quality, but it still has value. Begin by cleaning or enhancing the data and removing duplicates. Going forward, management emphasis should be on collecting accurate data.

Benchmarking data sourced from the industry may be useful, but should not necessarily be considered the most accurate or productive approach. Internal data analysis may be the best opportunity for cost driver discovery.

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] Parillo, N. The Caveats of Qualitative Benchmarking. Risk and Insurance. March 3, 2015. http://www.riskandinsurance.com/the-caveats-of-qualitative-benchmarking/

 

[2] Ibid.