"If it can't be measured, it can't be managed" is rapidly becoming the operating philosophy of the health care industry, aided in large part by more extensive use of electronic health care records, mobile applications and the growth of population health management. Using health care analytics - sometimes called business intelligence - researchers and consultants are mining clinical and claims data to discover gold standards and establish best practices for prevention, treatment and self-care and, ideally, help reduce waste, abuse and fraud.

The goals are simple but have remained elusive: Identify what works, what should be charged and how the cost should be shared, so maximum value can be derived from the $2.7 trillion being spent on health care. Potential beneficiaries include all of the five P stakeholders: providers, patients, payers (insurers), policymakers (legislators and regulators), and, of course, purchasers (employers), whose health plans cover nearly six out of 10 employees and their dependents.

Population health management - historically confined to public health practitioners - seeks to provide positive health outcomes for a group of individuals, including the distribution of such outcomes within the group, as well as reducing health inequities among high risk/vulnerable populations. As such, population health management goes beyond the individual-level focus of mainstream medicine and public health by addressing factors such as environment, social structure and resource distribution. PHM has proved especially effective for wellness and disease management programs.

An essential element in population health management is the role social determinants play in achieving and sustaining good health. Social determinants of health are the circumstances in which people are born, grow up, live, work and age, as well as the systems put in place to deal with illness.

Health care analytics comes in two basic forms. One is real-time analytics, which examine clinical information at the point of care and support health providers as they make prescriptive decisions during a patient encounter. These real-time systems typically use two or more items of patient data to generate case-specific advice. The other form is batch analytics that retrospectively evaluate population data sets, i.e., records of patients in a large medical system or claims data from an insured population.

Batch health care analytics, in turn, support predictive modeling, which is used on multiple clinical conditions. This process can identify undiagnosed conditions for patients within an insurer's patient population or suggest interventions to prevent conditions from developing. Such modeling uses historical data to anticipate future events with greater accuracy. By contrast, explanatory modeling documents how and why certain empirical phenomena occur.

Within the context of health care, predictive and explanatory modeling use patient health information to drive medical decision-making. Data are derived from a variety of sources, including point-of-care encounters, medical claims, pharmacy claims, lab values, health risk assessments, genetic markers and biometrics. These data are combined with medical guidelines and patient profiles to reveal contraindicated care, gaps in care and opportunities for cost savings.

According to LiveHealthier founder and CEO Mary Moslander, understanding how health care analytics support PHM is fundamental to delivering a workplace wellness program that provides measurable results. "Empathizing with employees and leveraging their behaviors, barriers to change, fears and motivations, are as important as predictive modeling and biometric outcomes," she says.

And all of this translates to a healthier bottom line. According to the consulting firm Aon Hewitt, population health management can help employers save as much as $700 per employee per year when they focus on any three of these eight major health care behaviors, which contribute to 80% of the cases of chronic illness: poor diet, physical inactivity, smoking, lack of health screening, poor standard of care, insufficient sleep, excess alcohol intake and poor stress management.

Experts predict that more employers will embrace a more quantified, analytic approach to health care because of its potential to contribute to value-based purchasing of health services and to increase accountability and transparency. Helen Darling, CEO of the National Business Group on Health, says that "health care analytics have become as essential to effective management as financial data are to business."

Leading the type of health plans that are embracing population health management and health care analytics are accountable care organizations and medical homes, encouraged by the Patient Protection and Affordable Care Action. A hallmark of ACOs is that providers agree to accept a flat payment for a given treatment plan for a disease or condition in exchange for sharing in the savings they may produce.

The analyses associated with the medical home and ACO "exemplify how the population's health needs have changed and how the marketplace must respond to this change," says Marci Nielsen, PhD, MPH, the CEO of the Patient-Centered Primary Care Collaborative.

"The success and impact of the medical home and medical neighborhood are critically dependent on gathering and analyzing health data from patient populations that will prevent leading causes of illness, manage and treat illness more effectively, and reduce costs to the system," she says.

Jan Peter Ozga is president of Medical Business Exchange, a Vienna, Va.-based consulting firm which connects progressive purchasers with innovative providers.

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