The Impact of Data on Value-Based Contract Success 

Like cliques in high school, most industries have their own lingo. Clunky phrasing is reduced to acronyms, innovative technology becomes a do-or-die marketing claim, and alliteration makes the mundane nearly seductive. Consider “data-driven,” or even better, “data-driven decision-making.” It’s all alight with Ds and hyphens. 

With smartphones in our pockets, tablets taking the place of order takers and laptops extending the workplace to the airport lounge, the hotel bar and the family room during movie night, who’s making decisions with anything but data?  

So, does it still need to be said? 

In healthcare, it does. 

That’s not because we have a dearth of data. On the contrary. Data may be coming out the ears of payors and providers, but too often, it’s not finding its way to the other — at least not with any speed or accuracy. 

Data to determine root cause  

In value-based care arrangements, especially those in accountable care organizations, Medicare Advantage and Medicaid, the populations are often sicker than the general public. Or maybe their health status is undetermined because their utilization is far from ideal: too few annual wellness checks, missed follow-ups after hospital stays, reliance on the emergency department (ED) for manageable conditions. 

In a contract designed to improve access to lower cost care, enhance care quality, improve health outcomes, reduce cost of care or all of the above, data analytics is the quickest path to success. But without insight from both clinical records and claims, neither the payor nor provider has much opportunity to figure out why the population’s sicker, why people are no-shows for clinic appointments, whether they’re taking the medication that’s been prescribed. 

A mechanical positioning of data points, aggregated and standardized for this purpose, can yield insights to the root cause of avoidable ED visits or unnecessary inpatient care. Blending quantitative and utilization data — claims, lab results, pharmacy records, diagnostic codes, etc. — with qualitative data, including clinical notes, patient surveys, behavioral health screenings, geographics and demographics, can suggest outreach and other action. Sometimes a rude cab driver or a delay in getting an appointment with a specialist erodes trust for people already living on the margins, with poor access to quality care, transportation barriers, low healthcare literacy or fear of medical procedures. 

As strange as it may sound, the sterile analysis of disparate data can enable better human connections and surely greater patient-centric care. The data that have always been parts of complex stories expose what’s hard to acknowledge in clinical encounters and sometimes what’s nearly impossible to see without advanced analytics. 

Data to determine the needs behind the numbers  

So, a member is readmitted to the hospital six weeks after discharge and an order to follow up with a primary care physician (PCP). There was no follow-up, no prescription for maintenance medication, no recommendation of lifestyle changes. The member didn’t follow through because 18 months before, the PCP was running behind for appointments and saw the member two hours after the scheduled time. Multiple buses and more than an hour of travel each way made the experience too frustrating for the member to repeat. 

Only the confluence of data and flag on high-cost, avoidable utilization directed the payor and provider to ask questions.  

In some cases, specific data sets are critical to optimal care for every person in a population, every patient a provider sees. 

In behavioral health practices, for example, pharmacy data is necessary to inform care and to meet value-based contract measures such as Antidepressant Medication Management and Use of First-Line Psychosocial Care for Children and Adolescents on Antipsychotics.  

For the Cervical Cancer Screening HEDIS measure common in Medicaid contracts, lab results alone mean little without clinical data to accompany them. In summary, the type of data matters. Without relevant data, there can be no data-driven decision-making. The source of the data, more than just the quantity of inputs, confirms performance and paves the way for healing and wellness. 

Motivation to share data 

There’s some traditional friction between payors and providers when it comes to data sharing, and sometimes it seems like a chicken-and-egg argument. Providers don’t see claims data with regular frequency or at least with perceived urgency, so they’re stubborn about prioritizing the sharing of encounter data or complete HCC coding with payors. But payors ruffle at the wealth of data that providers seem to have easy access to. They don’t want to acknowledge the power they think providers have lest they encourage it. 

But payors are willing to share data when it will benefit them or their members. In value-based contracts, payor data often defines provider incentives and/or penalties.  

CoreTechs® Analytics solutions from Gray Matter Analytics enable each client to provision a data view for its partner on the other side of the contract. Payors and providers invite one another to see each other’s data in near real time. This single source of truth facilitates payor-provider collaboration to identify problem points, set priorities and take action to arrive at their common goal: not only optimizing contract performance but growing together as responsible caretakers for population health. 

CoreTechs® is an analytics as a service solution set with near real-time data and insights. It ingests administrative claims, clinical, lab, depression screening, immunization records, health information exchange (HIE) and hospital admission, discharge and transfer (ADT) data as frequently as a client delivers it (HIE and ADT data can be sourced and ingested in real time by HL7 transfer), standardizes it all in a robust and comprehensive database and generates insights on cost and the quality of care.  

For a smooth transition to or management of value-based contracts, CoreTechs® optimizes data for payors and providers to accept risk without losing money. Check out the impact of different data types on common HEDIS measures in our “Data Source Impact on Measure Performance” tool.