Data can be your biggest asset in workers' comp, but you must reexamine all the processes you use to collect, analyze and report it.
Your data could be your most valuable asset, and participants in the workers’ compensation industry have loads available because they have been collecting and storing data for decades. Yet few analyze data to improve processes and outcomes or to take action in a timely way.
Analytics (data analysis) is crucial to all businesses today to gain insights into product and service quality and business profitability, and to measure value contributed. But processes need to be examined regarding how data is collected, analyzed and reported. Begin by examining these seven ways data can hurt or help.
1. Data silos
Data silos are common in workers’ compensation. Individual data sets are used within organizations and by their vendors to document claim activity. Without interoperability (the ability of a system to work with other systems without special effort on the part of the user) or data integration, the silos naturally fragment the data, making it difficult to gain full understanding of the claim and its multiple issues. A comprehensive view of a claim includes all its associated data.
2. Unstructured data
Unstructured documentation, in the form of notes, leaves valuable information on the table. Notes sections of systems contain important information that cannot be readily integrated into the business intelligence. The cure is to incorporate data elements such as drop-down lists to describe events, facts and actions taken. Such data elements provide claim knowledge and can be monitored and measured.
3. Errors and omissions
Manual data entry is tedious work and often results in skipped data fields and erroneous content. When users are unsure of what should be entered into a data field, they might make up the input or simply skip the task. Management has a responsibility to hold data entry people accountable for what they add to the system. It matters.
Errors and omissions can also occur when data is extracted by an OCR methodology. Optical character recognition is the recognition of printed or written text characters by a computer. Interpretation should be reviewed regularly for accuracy and to be sure the entire scope of content is being retrieved and added to the data set. Changing business needs may result in new data requirements.
4. Human factors
Other human factors also affect data quality. One is intimidation by IT (information technology). Usually this is not intended, but remember that people in IT are not claims adjusters or case managers. The things of interest and concern to them can be completely different, and they use different language to describe those things.
People in business units often have difficulty describing to IT what they need or want. When IT says a request will be difficult or time-consuming, the best response is to persist.
There needs to be timely appropriate reporting of critical information found in current data. The data can often reveal important facts that can be reported automatically and acted upon quickly to minimize damage. Systems should be used to continually monitor the data and report, thereby gaining workflow efficiencies. Time is of the essence.
6. Data fraud
Fraud finds its way into workers’ compensation in many ways, even into its data. The most common data fraud is found in billing—overbilling, misrepresenting diagnoses to justify procedures and duplicate billing are a few of the methods. Bill review companies endeavor to uncover these hoaxes.
Another, less obvious means of fraud is through confusion. A provider may use multiple tax IDs or NPIs (national provider numbers) to obscure the fact that a whole set of bills are coming from the same individual or group. The system will consider the multiple identities as different and not capture the culprit. Providers can achieve the same result by using different names and addresses on bills. Analysis of provider performance is made difficult or impossible when the provider cannot be accurately identified.
7. Data as a work-in-process tool
Data can be used as a work-in-process tool for decision support, workflow analysis, quality measurement and cost assessment, among other initiatives. Timely, actionable information can be applied to work flow and to services to optimize quality performance and cost control.
Accurate and efficient claims data management is critical to quality, outcome and cost management. When data accuracy and integrity is overlooked as an important management responsibility, it will hurt the organization.