By Jeff Klein

The health care industry is experiencing rapid and evolutionary changes that will continue into the next three to five years. A significant part of the change involves the growing use of data analytics. Although a late adopter, the health care industry is now seeing the positive impact data analytics has had on retail and financial services. Payer organizations also have gone a long way using predictive analytics for population health management and reducing costs. Several changes are creating a paradigm shift in health care and driving the use of data analytics, including:

• The way providers are reimbursed under the value-based care model has shifted the responsibility for care to providers.
• Providers need to deal with external pressures in areas such as health care exchanges, where millions of previously uninsured people have joined the ranks of the insured. The exchanges also are changing how employers offer insurance.



• MACRA (Medicare Access and CHIP Reauthorization Act of 2015) and MIPS (Meritbased Incentive Payment System) compliance are driving accountability and reimbursement for quality care from payer to provider.
• Payer and provider organizations are converging into combined risk-bearing organizations to manage insured populations and care in one process.

For providers, these changes heighten the need for actionable data insights accessible in real time. Such access is not the norm today, as the majority of health care data exists in isolation. Hospitals, doctors’ offices and insurance companies all maintain their data differently. However, the technology already exists to bring the solution to providers in the near future.

Aggregating patient information across a higher spectrum of data — from the public records to commercial data — helps derive statistically relevant insights and provides a holistic view of the patient, allowing providers to see the patient move through time longitudinally. A focus on an episode of care as opposed to each incident of care, and throughout the entire patient journey, drives improved outcomes.

When socioeconomic factors (such as financial indicators, education and crime information, etc.) are added to the clinical data, providers have a much fuller view of patients as people and are positioned for improved decision-making about whether and how they can intervene. As health care systems and hospitals further incorporate data analytics into the DNA of their organizations, they need to plan not only for their short-term needs, but also have a long-term vision of how they will use analytics to improve outcomes. Aligning with an analytics partner will help organizations to be positioned for success in the following critical areas:

• Real-time access to clinical analytics will drive provider behavior through a more prescribed set of best practices in clinical care. This is based on a patient’s condition and driven by analytics and insights, with the goal toward producing a higher quality outcome for the patient. From a provider perspective, there is more to patient care than population health management or electronic health record (EHR) data. It is also about the ability to see how high-stress life events — as when a patient moves, changes jobs, marries or divorces — may impact his or her health. This kind of new information is essential to high-quality clinical care, rounding up the patient data set. Yet, it is not available in most clinical decisioning today.

• The patient will inevitably demand more transparency on provider quality and procedure costs. High-deductible plans are driving cost awareness among health care consumers (in addition to the CMS-issued mandate to increase transparency). One area consumers tend to focus on is unexpected health care costs. During an emergency room visit, a patient may be examined by an out-of-network provider, driving up the cost of the visit. This is where providers can be proactive using real-time data analytics.

Regulations require providers to declare the networks they belong to. Once there is a common universal provider data set, it can be used by all downstream hospitals and facilities to inform the patient about the network status of a provider in question.

• Providers will take full advantage of the benefits offered by the socioeconomic attributes that are clinically validated and correlated to health outcomes. If a physician prescribing a high blood pressure medication can identify an individual as a non-adherence risk, the provider can have a sidebar conversation with the patient to incent him or her to adhere to the treatment protocol. This extra five minutes may make all the difference in the patient’s health.

The better providers know their patients, the better care they can deliver. The use of socioeconomic data to help assess risk is emerging quickly and will likely be adopted broadly in the next few years. This is why it is essential that organizations lay a solid foundation in data analytics and continually build upon it for the best possible outcomes in years to come.

About the author: Jeff Klein is the senior vice president of health care at LexisNexis.