All for One and One for All: 5 Ways Data and Technology Will Shift Our Population Health Focus
The nation’s top health and data policy decision makers are gathered in Washington, DC this week for Health Datapalooza IV – a conference devoted to sharing the latest and greatest ways data can drive innovation and improvement in health care.
This year features a track dedicated to ”community,” recognizing that the data collected and used locally must be actionable for individuals and communities before it works at the population level. If a new breakthrough in data can’t work at this level, it won’t work – period. Execution is the key to making new ideas in using health data stick and scale. This is what gets us so excited about the potential of data and technology to shift the public consciousness toward “all for one and one for all” thinking.
Here are five ways data and technology will combine to improve health at the personal and public levels:
1. Data Reveals New Insights and Connection
Patient records, outcomes data and analytics allow community health care workers to identify patterns in data that reveal important insights and connections. In a fascinating TEDMED talk, director of the Institute for Health Metrics and Evaluation (IHME) Dr. Christopher Murray shows how a new health data tool reveals startling patterns in global health data. By drilling down through more than a billion results, you can learn things like 80 percent of premature deaths in China are now from heart disease, cancer and mental disorders. Murray suggests that these offer lessons China can teach the developing world and lessons China can learn from countries like the US that are starting to make improvements in heart disease.
In colorful pie charts, Murray shows us the top risk factors connected to the most prevalent diseases in the US: (in priority order) diet, smoking, obesity, high blood pressure, high blood sugar, level of physical activity and alcohol.
2. Data Joins Population Health and Clinical Practice Sectors
Murray raises a central question: how do health systems move from giving care to individuals to transforming health for entire populations? The answer is that we do both, but we do them smarter by coordinating around lessons and insights uncovered in health data. Some of the nation’s most intractable population health challenges are most effective when made personally relevant and actionable: high-risk pregnancies, depression, addiction, obesity and the wave of conditions that come with it – diabetes, heart disease and more.
Many say that about 80 percent of population health can be attributed to personal biological factors like genetics and social determinants of health, such as education, housing, safety and sanitation. Health care (the clinical practice) accounts for only about 20 percent of the influences on population health. With data sets signaling patients’ needs, health care providers don’t have to wait for patients to either become conscientious about their health or so sick they need costly interventions.
- Target areas with environmental factors like air quality and food deserts that predict vulnerability to specific conditions
- Raise awareness among people with risk factors about how to reduce their vulnerability
- Tailor guidance to avert health problems before they start
- Give timely, personalized information to patients who have specific conditions
- Reach out to people due for check ups and tests with condition-specific information
3. Data Allows Greater Risks
Being able to see more clearly and even (to some degree) see around corners helps marshal the kind of courage needed to tackle big problems with big money. Consider the Laura and John Arnold Foundation’s “Manhattan Project” to fight obesity. The $26 million nutrition study is expected to dramatically improve knowledge of what works (and doesn’t) in fighting obesity – an effort that currently relies on research that is either inconclusive or flawed. According to the Wall Street Journal story:
The Arnolds want to see if they can use their money to solve some of the country’s biggest problems through data analysis and science, with an unsentimental focus on results and an aversion to feel-good projects—the success of which can’t be quantified.
4. Data Deploy Care in Underserved Areas
Businessweek recently profiled the findings of an 18-month e-health pilot project in one of the poorest parts of Rio de Janeiro. Using a mix of mobile diagnostic equipment, health care teams assigned to mostly elderly patients with a host of chronic conditions and living on steep winding streets were able to make diagnoses earlier and respond to patients’ needs faster.
The project saved the state-funded public health care system hundreds of thousands of dollars while improving access to health care in an underserved urban community. A study calculated annual cost savings for the city broken down by medical condition. For example, savings ranged from $4,000 (for those at risk of heart failure) to $200,000 (kidney dysfunction) per 100 elderly patients who participated in the e-health trial. The cost savings due to avoided hospitalizations of patients with cardiovascular diseases was roughly $136,000 per 1,000 patients.
5. Data Shifts Focus from Individual to Collective Good
A new University of California at San Francisco website MeForYou.org encourages people to share their personal health data to help a new multi-industry, collaborative precision medicine initiative. Precision medicine integrates genomic knowledge and other molecular research with input from patients’ health records, along with social and environmental data. The results are expected to improve diagnosing and customize treatment based on personal genetics, and include a feedback loop to monitor effectiveness.
The initiative represents a paradigm shift from thinking only about one’s self to asking “what can I do” to help others?
This approach is underway in breast cancer’s I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2) — a clinical trial for women with newly diagnosed advanced breast cancer to test whether adding experimental drugs to standard chemotherapy is better than standard chemotherapy alone before having surgery. The trial will use the information from each participant who completes the study treatment to help decide treatment for future women who join the trial. The “pay it forward” approach helps identify more quickly which investigational drugs will be most beneficial for women with certain tumor characteristics.
Cancer patients have an obvious motivation to contribute and use data, but research shows that most people are not as eager consumers of health information as they are information on restaurants, movies and clothes. The pay off data and analytics could provide for personalized prevention and health care is profound, but there is still quite a distance from potential to reality.
Health Datapalooza goers, what say you? Who is stepping forward with ideas and money to move from promising idea to reality: providers? Health plans? Disease organizations? Pharma? Government? What’s the best way to make bigger, faster changes in using health data?
*Also check out:
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Not your grandmother’s patient engagement – The Public Health Graph
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Mark Tobias (@PanthTech) is president of Pantheon, which combines technology expertise and a deep knowledge of health care, education, and social impact markets to provide online technology solutions for nonprofits, associations, and government.