The demand for data-driven insights from
Nevertheless, supply of this data has been low: even the most modest of price transparency measures, such as requiring hospitals to post chargemasters or a handful of service prices by carrier, have been met with widespread resistance. Outcomes and quality data are also scarce to come by. This data drought punishes employees most, who ultimately bare the cost of low value healthcare through anemic wage increases and unaffordable deductibles when their health plans cannot manage the supply chain to avoid overpayments.
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Unfortunately, the data accessibility problem masks a more complicated one: when data is finally accessed, it is misused. The sophisticated, but misguided, approach to health plan data use is to have it fed to an independent analytics vendor or consultant who applies dozens of clinical and pricing models and provides back visualizations to guide the health plan. Unfortunately, these analytics — while well intentioned — generally just show a benefits leader expenses by category, region, provider, demographic and segment. Some reports will also show risk-based cohorts and condition groups. The hundreds of data visualizations churned out by the industry almost never suggest a concrete action, or if they do, they fail to quantify the value of such an action.
Examples like “Number of patients with mismanaged hypertension,” “Patients at high risk of diabetes,” or “Top ten spend categories” all leave the employer asking “So what? How do we manage the hypertension or reduce the diabetes risk? How would it improve the lives of these patients? Does it cost more to manage these conditions, or does it save money, and how much of either?” The knowledge gained from
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This data pollution is even more damaging than it seems: when employers repeatedly fail to derive value from healthcare reporting, their confidence in and enthusiasm for data use begins to wane. Some become cynical towards any use of analytics to make decisions and quit searching for better insights. The quality of healthcare choices and benefits plan designs may suffer, which has real world, even life and death, implications for plan members.
Correcting data accessibility requires employers to include strong contract requirements around data ownership and sharing, including turnaround times on data feed requests. Employers should insist that the service agreement NDA terms cover the employer for all data uses. Lastly, and most importantly, they should assert their rights as an ERISA fiduciary and plan administrator, which clearly justify the employer (and agents) in getting and using its data to carry out its health plan duties.
Resolving the data use problem requires employers to be ruthless in demanding quantified answers to the “so what?” questions on every report and analysis proffered by a carrier or consultant, namely:
- What specific question is this information answering? (Eliminate reports that do not answer a question, answer a useless question, or answer a question whose answer is already known)
- What is the specific action plan this data recommends? (Eliminate reports on solutionless problems, any suggesting vague approaches, or those requiring mass behavior change)
- What is the value in dollars and health outcomes that will result from the action plan? (Exclude reports that cannot concretely quantify — or demonstrate precisely how to quantify — the value and cost in dollars of the suggested action)
Like an intervention cleaning crew on an episode of Hoarders, employers must mercilessly discard analytics that cannot meet these three requirements. Valueless information is not harmless — it is toxic to a benefits team seeking to isolate and prioritize highly strategic choices. By obtaining ample data and then focusing only on actionable, quantifiable insights, employers can retake control of their health plans and deliver the health value their employees enterprises deserve.
