Seven Ways to Improve Health Equity with Data
As anyone who works in healthcare knows, there is no scarcity of data. In practice it’s more like a disjointed tsunami: all sorts of different data formats colliding across clinical and claims information.
Now add the wave of data called social determinants of health (SDOH), and the picture becomes even more complicated. SDOH indicate all the conditions around an individual that shape their personal experience of health. This includes education, neighborhood, social support networks, access to health care, and socioeconomic status. As we have gathered and analyzed more of this data, SDOH have risen to prominence as a primary impact for maintaining health, even more so than what happens within the healthcare system.
In our current American healthcare system, the challenge and the opportunity in harmonizing all this data to create greater health equity lies in incentives. Typical healthcare organizations don’t focus on factors like SDOH because this hasn’t been their primary business. This is not anyone’s fault; it’s simply the way our current system does or does not incentivize certain behaviors.
Taking action on health equity through data
Based on my experience in supporting healthcare organizations to develop strategy insights and drive interventions using data, as well as my background career experience as a primary care physician and a data quality analyst in large hospitals, I can recommend several approaches that data leaders in healthcare should consider to increase health equity within their populations.
- Prioritize accountable care models that flip fee-for-service incentives. The best way to create alignment about SDOH is to declare it an organizational priority, from the top leadership on down. In Minnesota, where I work, SDOH are included as part of ACO requirements by the state for participating health systems. However, participation in these contracts is voluntary, and organizations can pull out of ACOs if they think the targets for incentives are out of reach. Healthcare risk models still exert a gravity that dampens innovative thinking here.
- Look beyond typical data sources. Healthcare data that goes beyond clinical information is often invaluable. If a patient is screened for social needs while in the hospital, for example, a data platform could refer them to community-based organizations that could help them with food or housing resources. Data also can be used for discharge planning, so you can get ahead of issues before they become problems. Collecting and deploying data in these non-traditional areas helps to ensure a better continuum of care.
- Focus on better data infrastructure and training. Healthcare lags other industries in terms of applying tools and technologies to take action for its customers, the patients. I’ve worked in organizations that received data for patients through ACO contracts, but the infrastructure to even ingest that data properly wasn’t in place. Moreover, the right skill sets are often lacking to analyze data, share insights with leadership, or help to drive the right investment in resources and infrastructure to meet the needs of their patients. Healthcare providers must move with the trends in terms of tools and technologies here.
- Use data visualizations to transform productivity. The typical workflow in the healthcare system is to go in search of an answer to a question by pulling a report from an electronic medical record and then putting it into an Excel file. If you want to answer another question, you must repeat the same process. With visualizations, you’re able to quickly answer multiple questions, slice and dice your data in different ways within a very short timeframe, and open up thinking about potential interventions that can have a positive impact on patients or entire patient populations. I’ve had several “aha” moments when sharing data in visualizations with healthcare colleagues. And the mere fact that people know what is being measured in a system also helps them to think about what they’re doing and how they could do it better.
- Incorporate taxonomies that can support and drive new insights. Some of these include county health rankings and other census data as well as the CDC’s Vulnerability Index. Using this kind of data, providers can begin to understand the unique needs of communities where their patients reside and take action by connecting patients to the services that they need. This allows them to connect patients more successfully to services within their physical proximity.
- Deploy data to train on the value of proactive health. Health is all about an individual’s state of physical, mental, and social wellbeing, not just the absence of disease. Many healthcare systems don’t yet think about health as the sum total of mental and social wellbeing. In part, that’s a function of training. As a practicing clinician in Nigeria, the focus was on helping patients stay healthy by using preventive measures so they could optimize their health and avoid illnesses. A combination of this cultural shift and baking ACO incentives into the whole payment structure is required. Data holds the key to moving beyond a fee-for-service world.
- Put data to work in creating more equitable clinical trials. Nearly a quarter of US hospitals conduct clinical trials research. Here, too, there is significant opportunity to be more intentional in research, especially from a health equity standpoint. This includes how you get people involved in research and how you can translate the results of those trials across different populations. Trials that don’t represent a diversity of populations lead to results that are not generalizable to everyone, which doesn’t create a strong foundation for personalized care.
Bridging concept to reality
One of the things I’ve come to understand is the significant limitation that lack of data infrastructure or visualization tools puts on achieving health equity. Without an ability to ingest and analyze data, providers are hobbled in meeting their incentive goals and population health management remains an abstract concept.
Yet just as we must start evolving our current cultural thinking from a reactive approach of treating disease to a more proactive one of preventing diseases, we also must prioritize, fund, and implement data initiatives that can start to rewrite the operating system of how healthcare is delivered. In both cases, the key to improving health equity starts with the data.