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Today, most analytics teams are designed for retrospective analysis, not prospective. While the shift to prospective analysis is underway, the need for retrospective analysis continues. As we move forward, we need to focus our information stewardship responsibility to insure we all serve demands that we be worthy of the public’s trust.

This plea is for a Prospective Analytic Focus on Social Determinants of Health (SDOH).

Based on Research at the University of Wisconsin’s Population Health Institute, the four primary social determinants that drive our health outcomes are:

  • 40% Socio-economic factors (education, employment, income, social and family support, community safety.
  • 30% Health behaviors (tobacco use, diet & exercise, alcohol and drug use, sexual activity)
  • 20% Clinical Care (access to, and quality of)
  • 10% Physical environment (air and water quality, housing, transit)

The previous blog asked how to create insights that improve health outcomes and reduce costs from this information? UW’s Institute for Research on Poverty has shed light on how existing health policies are incapable of responding to health needs of today’s US families. A Majority of U.S. children will not spend their entire childhood living with both biological parents. Additionally, most children born to unmarried parents will live in complex family situations and experience family fluidity and parental multi-partnered fertility. Even with considering differences in resources at birth, father absence and family complexity and fluidity, there are numerous sub-optimal outcomes that can be directly tied to these populations, including greater mental and physical health problems as these children grow into adulthood.

So what is driving this?

US health policies are designed for relatively static family structures,

So, what can “data to insights” do in this situation? Leverage Big Data Analytics(BDA) to create those insights. Initial BDA has already found levers. One controversial response (among others) is preventing family complexity in the first place. There is promising research that indicates a sizeable potential for reducing family complexity by making long-acting reversible contraceptives widely available to women seeking family planning services. Promising research which needs additional studies and confirmations.

We know that consumers are open to organizations using their data for forces of good. ONC and CMS are putting increasingly larger amounts of this data under consumer ‘ownership’. We can improve outcomes and drive down healthcare costs if we apply insights from the Data for Health Advisory Committee:

“Data moves at the speed of trust. People want their data used for important and helpful reasons, while simultaneously being protected from invasion of personal privacy and breaches of their personal information.”

So what are the steps?

Big Data: People recognize the potential to reveal meaningful insights about the livability and health of their community and to support analytics that can inform health practices. ONC and CMS have empowered the owners (the people themselves) to support the enrichment of both Payer and Provider data sources, as well as share the information for research.

Longitudinal Data: People want knowledge based upon tracking health data over time to see patterns, trends, and predict potential. Get it and Use it where it’s needed. Personal Ownership of a complete health history created by the interoperability capability of a person gathering their personal history can be shared and insights gained from those records that are shared.

Infrastructure Competition: Stop Jousting. Payer and Provider data is ultimately the Person’s data. Lack of sharing and alignment inhibits communities to grow healthier. The Person, whether in your context they are Member or Patient, can help you see the big picture. Work together to ease the sharing using interoperability and/or blockchain trust.

Data Science: This is the sort of challenge that data science was made for. Move beyond simple models, leverage Machine Learning and Advanced AI. Partner when needed. The goal is the key; Prospective first, Prescriptive next.

We as an industry have choices to make on how information can change everyone’s lives for the better. Let’s choose wisely.