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Workers Compensation Issues

Escaping from the Land of Big Data

Joe Galusha | July 28, 2017

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The increased modes and methods of capturing big data have placed the insurance industry, not to mention society, in an unprecedented position when it comes to having the ability to sort, process, and interpret trends. 

Whether interpreting your own shopping tendencies or benchmarking data from your insurer or third-party administrator (TPA), we are challenged daily on how to make the data we are immersed in actionable. Specifically, leveraging data more effectively in workers compensation programs continues to be a challenge for companies of all sizes. Organizations are moving away from what has been a myopic focus on outcome benchmarks, and instead, due to better information available from business partners and risk management information systems, workers compensation managers often use process or claim element benchmarking in conjunction with traditional claim benchmarking.

Outcome Benchmarking

Outcome benchmarking, such as average claim costs, has long been the objective and sole destination when looking for benchmarking information to compare an organization's results and defend or direct changes within a program. Most TPAs and insurers provide a wealth of information broken down into multiple levels of Standard Industrial Classification (SIC) codes. However, as most are aware that have relied on this type of benchmarking, it is typical to encounter a number of challenges when attempting to use the data. For example, claim awards and amounts are significantly affected by matters such as jurisdictional issues to the age or maturity of the claim and litigious culture of the organizations that make up the pool of data.

Additionally, there is a concern that, once an organization expands beyond pure manufacturing, most other organizations do not fit squarely into any SIC from an exposure perspective. These concerns leave an underwhelming feeling of confidence in acting on the resulting comparison. The three R's are key to keep in mind—right data, right time, and right comparison.

Challenges of Benchmarking

While the three R's point out a few challenges to using average costs of claims, this measure can be extremely effective during internal benchmarking. Measuring too fine or creating too many categories of data is another challenge. For example, combining injury types and diagnosis into buckets that can help directionally understand cost drivers is helpful. Additionally, injuries related to ergonomic conditions could be diagnosed as tendonitis, carpal tunnel, epicondylitis, strains, sprains, or other ailments. The information becomes more directional once it is grouped or mapped into a category called ergonomic injuries. One should consider handling the multiple categories of slips, trips, and falls in the same manner.

Process or Claim Element Benchmarking

As analytic capabilities have increased, including the ability to collect and sort claim processing elements more accurately, studying the attributes of claims offers a more compelling representation of controllable causal and claim process elements that drive claim costs. Examples of actionable variables range from traditional elements, such as the number of temporary total disability days per claim to medical only or indemnity conversion. These variables are less affected by the three R's and can be of more significance in directing focus, especially when investigating opportunities for cost savings from a particular claim management process or strategy.

The following are a few areas for consideration when implementing claim element benchmarking as well as a few examples of appropriate metrics that include medical cost containment, disability management, litigation management, and claim process.

Key Metrics

The next step in claim element benchmarking is to identify what to measure, or the key metrics. This is the essence of a successful approach to ring fencing a claim management program and may include several metrics for each of the categories.

Medical Cost Containment

Key metric—Medical network utilization is monitoring the percentage of claimant treatment inside the established medical network. This is an effective way to watch several aspects of a program, helping answer important questions, such as the following.

  • Is the provider's network matching up to your needs?
  • Is there effective early communication with claimants and physicians?
  • Are your managers and claim adjusters directing care when appropriate?
In Aon's Casualty Laser Benchmarking Data, a study of claim element outcomes for more than 400 companies and $8 billion in workers compensation claim values across 16 industries, network utilization varies somewhat by industry, with the highest averaging penetration being construction at more than 79.3 percent and the lowest being transportation at 57.7 percent.

PPO Penetration

Disability Management

Key metric—Average temporary total disability (TTD) days per indemnity claim is likely the most effective measurement of the severity of injuries and the effectiveness of an organization's return-to-work program. The results can lead to important insights, particularly when comparing department, location, or division results against one another. Who—among internal and external partners—is supporting the return-to-work program will quickly be uncovered. As one would suspect, the target is industry specific and has a fairly large range from 117 average TTD days per indemnity claim in mining to just over 60 in education. Average TTD Days per TTD Claim

Disability Management

Key metric—Medical only to indemnity conversion rates are often the result of the limited opportunity in the life of a workers compensation claim to mitigate the cost of that claim as well as the extent of an injury that is not always apparent at the time of claim reporting. Therefore, claims routinely convert from medical only (minor in nature) to indemnity. Converted claims are often an organization's most expensive claims, primarily because effective causal connection investigation and medical management practices are not applied at the onset of the claims management process. When conversion rates are reduced, so are costs.

The use of data mining enables the identification traits of past converted claims applied to future claims, which can also identify problematic claims. Flagging those claims at inception and applying more aggressive cost containment strategies typically applied to indemnity claims have shown to significantly reduce claim conversion rates and ultimate costs by building claim strategy based on quantitative analysis of a client's unique claims profile. 

Medical only to indemnity conversion rates also vary by industry with the highest being in education, at more than 53 percent, to the lowest being in construction, at 20.1 percent.

Med Only to Indemnity Conversion

Litigation Management

Key metric—Ratio of paid legal expense shows that it is important to monitor the litigation costs as a percentage of the claim expense category. Historically, too many organizations chose their litigators based on the blended rate, choosing firms with the lowest rate. Numerous studies of litigation costs across various firms indicate that some firms with higher rates were actually more efficient and also more successful. In addition to monitoring litigation trends as a percentage of total claims that organizations confront, it is important to also monitor the ratio of paid legal expense.

Ratio of Paid Legal Expense

Claim Process

Key metric—Claim closure rate is an excellent metric to monitor how effectively claims are being managed to closure. While there are several ways to make the calculation, use the most recent 12 months (the loss date within the most recent 12 months from the valuation date) and take a percentage of the closed claims to the total claims within that same 12-month period. This resulting rate is not affected by initiatives to close older claims, but it reflects the effectiveness of the current claims management process. In examining the closure rates of hundreds of organizations, it has been found that low closure percentages are more commonly caused by poor internal claim management and settlement authority issues than the effectiveness of the third-party administrator or insurance company.

Claim Closure Rate

Over the last few decades, data has become more available and more precise. Looking ahead, the challenge will be gaining an understanding of how to make access to data more directional in nature and thus actionable. The data and analytics industry has outgrown the annual stewardship process, and companies should seek to leverage information more effectively. Committing to key process measures and working with business partners and providers from a common dashboard offer the opportunity for joint targets with clear, concise measures. Organizations that escape from the land of big data through the use of key actionable metrics will begin to gain valuable insights by spending less time gathering data and more time analyzing its meaning.


Carol Ungaretti, managing consultant, Aon Global Risk Consulting

Kevin Combes, director, Aon Global Risk Consulting 

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