No single technology is the perfect answer to pop health

Provider organizations that wish to engage in population health management must continually monitor, optimize and improve their data processes, says one analytics leader.

Without high-quality patient data, it's difficult and sometimes impossible for clinicians to safely and effectively treat individuals at the point of care. Similarly, a lack of quick access to quality data can represent a health risk to entire populations.

Specifically, successful population health initiatives require data analytics that both help identify populations in need of care and measure the care provided. This ensures the right care is delivered to the right patients.

Accurate and comprehensive analytics, for example, help providers identify social determinants of health that affect patients. SDOH data can be used by clinicians to optimize preventive care instead of waiting for patients to become ill.

Brandi Meyers is vice president of revenue operations at MDClone, a healthcare data analytics company. We interviewed her to discuss why high-quality patient data is so essential to population health, how preventive population health measures can improve outcomes and reduce healthcare spending, data-related barriers to implementation of population health initiatives and how hospitals and health systems can overcome them, and what provider organizations need from analytics to ensure population health initiatives succeed.

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