by Karen
Wolfe
Your data
could be your most valuable asset. Participants in the Workers’ Compensation
industry have been collecting and storing data for decades. Big Data (meaning a
lot of data) is available, as are vast numbers of smaller data sets, 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 meaningful insights into product and service quality, business profitability, and to measure value contributed. But data processes need to be examined regarding how data is collected, analyzed, and reported to determine and gain its current and potential value. Attention to data and its processes is crucial to insuring data is an asset, not a limitation. 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
leave valuable information on the table. Notes sections of systems contain important
information which cannot be readily tapped and integrated into the business
intelligence. The cure is to incorporate data elements such as drop-down lists
containing data elements 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.
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
Besides
manual data entry, other human factors effect data quality. One is intimidation
by IT (Information Technology). Usually this is not intended by IT, but they
are often perceived that way. Remember 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 them.
People in the business units often have difficulty describing to IT what they need or want. When IT says the request will be difficult or time-consuming, the best response is to persist. It’s their job and they will usually protect it by exclaiming its complexity.
5. Timeliness
Timeliness
regarding data, refers to timely reporting of critical information found in the
data. This does not refer to analysis of historic data. Rather, it means
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 seems
to find 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 when the provider seeks anonymity through confusion
by using multiple tax ID’s or NPI’s (National Provider Number) for the same
individual or group. The fraudulent provider is able to obfuscate the data,
thereby disqualifying analysis. The system will consider the multiple
identities as different and not capture the culprit.
The same
result is achieved by the provider 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.
