Earlier this month, I was lucky enough to be invited to present at the Disability Management Employers Coalition (DMEC) annual conference. I shared the platform with two industry experts, Carol Harnett and Ed Quick.
The topic was quality of data, and we coalesced our different perspectives to a common conclusion…. Claims data is poor and we have to improve the data we use in claims management!
Whether this has been caused by under-investment in claims automation by insurers, or an ‘old school’ attitude that suggests that claim management is a highly skilled art form and must not be messed with, or just the old process does not seem to be broken so why bother to spend any time on it!
Carol is President of the Council for Disability Awareness (CDA), and needs better data to measure the industry’s performance. Ed runs an HR research group for a famous tech company that needs to remain nameless, and has run projects proving the worth of collecting innovative data. As for The Claim Lab, we regularly bump our heads on the ceiling of bad data when building analytical models.
Carol presented her perspective on poor data, with the CDA’s experience of receiving claim data submissions from carriers for research work. The data is generally of a poor quality, with badly coded ICD codes, no secondary diagnosis codes, missing data elements, etc.
Nothing new in this, but what is really worrying is that these data feeds are the same as those provided for the illustrious suppliers of industry benchmarking data, raising a serious question about the quality of their analysis and predictions. This was certainly an ‘Emperor-has-no-clothes’ moment!
Ed provided a fascinating overview of a call center project that he ran, studying absence rates in a call center. Firstly, setting up a process to collect more informed information from the workforce, and then monitoring the effect on absence of making changes in the work environment to improve the workplace conditions and the motivation of the team members. Call centers are notorious for bad absence and claims experience, but by making a few changes, Ed was able to make significant improvements….
I presented some of the findings from The Claim Lab’s work on the importance of collecting and analyzing psychosocial data relating to a claim, and the “ah-ha” moments our clients get from this revealing information…
So why are insurance carriers not taking a more active lead in this area?
Taking steps to improve the quality of their data is not hard. It’s not about having fancy new technology in the claims department, but about training claim managers on the importance of entering data correctly. This is not difficult and not expensive to do!
Why isn’t the collection of this information moved earlier in the process, before the claim, before the absence event, and into the workplace… Why aren’t carriers collecting data about the motivation and mental health of their client’s workforce and helping to prevent time out of work?
The ROI for this is not difficult… reducing absence has a dramatic effect on staffing and reducing claim incidence reduces insurance costs…. Is there a conflict of interest here that we are missing?
Please let us know your thoughts!
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