This piece is cross-posted on the HIMSS website.
Recently at the mHealth Summit I led a session on strategies for addressing health disparities using digital tools. While I don’t believe technology is a silver bullet, it can support clinicians and consumers alike in targeting treatments and changing behaviors.
I based my talk on case studies of successful initiatives, some of which use approaches you may be able to adapt; others–such as government programs–provide resources that are publicly available. Following are the 5 strategies I identified, which I’ll be exploring in a series of posts on the HIMSS Blog:
- Measure What You Want to Manage
- Navigate the Digital Divide (it’s more complex than you may realize!)
- Design for Your Population’s Needs
- Speak Your Population’s Language
- Build Community Relationships
Defining the Problem
Though health disparities can impact any group, those most likely to suffer a disproportionate number of health conditions and deaths are usually defined by race, ethnicity, disability, gender, sexual orientation, geography, and income. In the case of race, according to FamiliesUSA:
- Latino’s are 65% more likely to be diabetic than non-Hispanic Whites
- African Americans are 40% more likely to die from stroke
- American Indians & Alaska Natives are 15% more likely to have heart disease
- Asian Americans and Pacific Islanders are 80% more likely to die from liver cancer
Statistically, African Americans face the most health disparities. For example, African American men live 5 years less on average than White men. And, though African Americans make up only 13 percent of the US population, they represent almost half of all new HIV cases.
Measure What You Want to Manage
One of the greatest promises of health IT is measuring and managing the health of populations through aggregate data. One example of an organization that is leveraging population health to serve the underserved is Unity Health Care, a federallyqualified provider serving low income homeless, and uninsured residents of Washington DC, which presented on this topic at the HIMSS Population Health Summit.
On the policy front, the final Stage 3 Meaningful Use requirements include the capture in electronic health records of more granular data on race, ethnicity, sexual orientation, gender identity, and preferred language. They also require data on social, psychological and behavioral factors impacting patients such as financial resource strain, educational attainment, stress, depression, physical activity, alcohol use, social connection and isolation, and intimate partner violence. The ability to sort Clinical Quality Measures by these fields is a huge step forward in better understanding the extent of health disparities and the factors that correlate or contribute to them.
Are you leveraging health IT to address disparities? Are you specifically using it to measure and manage a particular problem or population? Please share your experience! And stay tuned for the next installment in this series, Leveraging Digital Strategies to Address Health Disparities: Navigate the Digital Divide.
Join the discussion of disparities on the HIMSS LinkedIn Group.