Category: Health Plan/Provider Transparency

Insurers and Healthcare Providers Find Common Ground in Population Health Data

Earlier this month, I spoke with Henry Powderly, Editor-in-Chief of Healthcare Finance News to discuss the major shift in the healthcare industry with population health management. Henry and I focused on the growing payer and provider collaboration and how MedeAnalytics is adapting to meet the needs of our customers. Here are a few key takeaways from our conversation:

“While payer-provider collaboration has long been important, new trends in managing population health data is strengthening that relationship as never before, and in many ways it's changing how providers think.

A growing number of payer clients are leveraging the exact same data, but they're asking questions in a different way, adding that the company has seen more insurers sign on as clients recently.

On the financial side, providers are looking for ways to make their fee-for-service business more efficient, increasing revenue. Meanwhile, insurers can use the data to spot highest utilizers of care to measure that against their premiums and negotiated payment rates with healthcare providers.

But what's really interesting is that it's the providers who are starting to think like payers as the industry move towards value-based care and population health.”

Within our client base we are seeing them target specific data points to mitigate cost and risks, specifically: 

“Many payer clients want to track pediatric prescriptions of asthma medicine albuterol in a certain state, looking to find out which children hadn't filled their prescriptions in the past three months.

The way the payer thinks about it is: If those children don't fulfill that $20 script then we'll apply predictive analytics to determine how many of those kids are going to end up in the emergency room. Then there can be intervention and the payer saves on a $1,500 ED visit.

That's the kind of thinking payers have long held, since unnecessary hospitalizations meant they had to shell out cash for medical care that could have been avoided. But that's not how providers traditionally thought under fee-for-service.

Not to sound harsh, but in the past it wasn't part of the business model for the provider to care about whether that kid fulfilled that script because when they showed up in the ER the provider was going to get paid for it. But today in value-based reimbursement, they're going to get paid a single amount of money to manage that child's asthma care. It's now in the provider's best interest to understand who filled that script.”

To learn more about my insights on population health and payer/provider collaboration, read the full Healthcare Finance News article, “Insurers, healthcare providers find common ground in population health data, MedeAnalytics CEO says.”

Boost Data Transparency with APCDs

All-payer claims databases (APCDs) would benefit from mapping morbidity rates to the data resulting in pricing transparency, as well as pricing, efficiency and performance assessments that can be compared among providers. Mapping morbidity rates using publicly available data would create a clear idea of healthcare effectiveness and enrich the data, enabling patterns of care and value to emerge and in result, improve the future of healthcare. Here's a preview of Virginia Long and David Mould's piece on iHealthBeat that went live today:

All-payer claims databases (APCDs) that are in place in several states and about to be implemented in many more, are intended to make provider and hospital costs transparent.  The readily available data within the APCDs includes claims data for medical, mental health, pharmacy, and dental procedures across providers.  Access to this data enables analysis, analysis that can be done to make important comparisons such as price for services or performance measures at the level of physician or hospital—across a state.  Thus, the APCDs can provide powerful and illuminating information, from tracking state healthcare spending to guiding patient/consumer choices.

Augmenting APCD data

Although APCDs can provide useful information on cost and value of healthcare within a state, augmenting the claims data with publically available morbidity data would provide users with a more holistic dataset where cost information is directly tied to clinical outcomes.  Mapping disease morbidity information to APCDs would allow effectiveness of treatment to be assessed, alongside cost and quality measures.  Treatment costs relative to disease burden within distinct locations can be used to pinpoint areas with health disparities, highlighting potential problems or incongruities in access to or quality of care. 

Health and Morbidity Data

Publically available health data is available through many sources.  A few of the major health related data sources are the Center for Disease Control and Prevention (CDC), the Community Health Status Indicators (CHSI), and the County Health Rankings.  The CDC is not just concerned with communicable diseases; they are at the forefront of the fight against chronic diseases that adversely affect health in America.  CDC data on disease are readily available and data maps for social determinants of health have the potential to bring together highly relevant and sometimes overlooked data. The CHSI provides information on health indicators such as tobacco use, diet and activity, alcohol and drug use, to create community profiles.  Measures of health, causes of death, population vulnerability, and access to care, demographics, and risk factors are among the reports available on a county by county basis.  The County Health Rankings & Roadmap program collects data about factors that influence health that includes high school graduation rates, obesity, unemployment, access to healthful foods, air and water quality. County Health Rankings provides information about health outcomes such as length of life and quality of life, as measured by poor health, poor mental health, and low birth weight.

Integration of public datasets with the APCDs

The addition of health factors and morbidity data would create a rich data set. Advanced analysis of the APCDs in conjunction with health and morbidity measurements will highlight interesting, important, or overlooked relationships among claims data, health outcomes, and factors influencing health.  Through exploring and understanding these relationships, all stakeholders in the healthcare system will have a better understanding of how cost, quality, and effectiveness of care intersect.  Literal mapping of these data, by using location based datasets, would enable users of all analytic levels to consume the data visually.

To read more about Virginia Long and David Mould’s thoughts on APCDs, visit the full iHealthBeat post, “Link All-Payer Claims Databases With Other Data To Boost Transparency.”