It is perhaps the most important public health challenge today: how to influence the thousands of personal health decisions people make every day in ways that add up to big improvements in public health.

Like it or not, when it comes to health we humans respond to the personal over the professional. Personal fitness and other types of mobile health apps (such as Fitbit, RxMindMe, and Glucose Buddy) that help people take medications on time, eat better, and move more are growing in popularity and improving health. But, collectively, they could add up to so much more.

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 connections.

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, Murray delivers insights such as this: 80 percent of premature deaths in China in the last decade were due to heart disease, cancer, or mental disorders. In the United States, Murray shows that the top risk factors connected to the most prevalent diseases in the United States are (in priority order): diet, smoking, obesity, high blood pressure, high blood sugar, level of physical activity, and alcohol. He suggests that these data offer lessons for China and other nations about the origins of disease and how to avert them.

2. Data joins public and personal.

Murray also raises an important 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. We can effectively address some of the nation’s most intractable population health challenges—depression, addiction, obesity and the wave of conditions that come with it, diabetes, heart disease, and more—when we make them personally relevant to individuals, as well as actionable.

Predictive analytics allow the use of electronic health records and other data sources to consistently and efficiently:

  • Target areas with environmental factors such as low air quality and food deserts that predict vulnerability to specific conditions
  • Raise awareness among people with risk factors about how to avert health problems before they start
  • Reach out to people due for check-ups and tests with condition-specific information

3. Data allows greater risks.

The ability to see correlations and trend lines of progress and decline more clearly helps marshal the kind of courage we need to tackle big problems with big money. Consider the Laura and John Arnold Foundation’s Manhattan Project to fight obesity. The Manhattan Project is a $26 million nutrition study expected to dramatically improve knowledge of what kinds of initiatives work and don’t work to reduce obesity—an effort that currently relies on research that is either inconclusive or flawed.

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 who have a host of chronic conditions and live on steep, winding streets that frequently make travel to health facilities difficult—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.

5. We can “donate” data for personal and public good.

A new University of California at San Francisco website,, encourages people to share their personal health data to help improve diagnosis and customization of treatments based on personal genetics; the site also includes a feedback loop to monitor effectiveness.

Another example is the I-SPY 2 TRIAL for women who have newly diagnosed, advanced breast cancer; the program tests whether adding experimental drugs to a standard treatment regimen improves outcomes. The trial uses information from participants to help decide treatment for future women who join the trial. The “pay it forward” approach helps doctors more quickly identify which investigational drugs will be most beneficial for women with certain tumor characteristics.

These five trends are encouraging, but we need to continue to set more data free—all while protecting privacy and improving interoperability. Earlier this month, the US Department of Health and Human Services (HHS) announced that it will make more data available for researchers and technology developers. In addition, the Robert Wood Johnson Foundation launched Health Data Exploration, an initiative aimed at convincing companies that make tracking devices, connected health devices, and fitness apps to make the data their devices collect available for research purposes.

We’re living in exciting, disruptive times that could lead us to more affordable, effective care, while getting more people than ever engaged in improving their own health and the health of their neighbors.