Without high-quality patient data, it is difficult and sometimes impossible for clinicians to treat individuals safely and effectively at the point of care. Similarly, a lack of rapid access to quality data can pose a health risk to entire populations.
Specifically, successful population health initiatives require data analytics that help both identify populations in need of care and measure the care delivered. This ensures that the right care is delivered to the right patients.
For example, accurate and comprehensive analytics help providers identify social determinants of health that affect patients. SDOH data can be used by clinicians to optimize preventive care rather than waiting for patients to get sick.
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 population health preventive measures can improve outcomes and reduce healthcare spending, data-related barriers to implementing population health initiatives and how hospitals and health systems can overcome them, and what healthcare providers need from analytics to ensure population health initiatives are successful.
Q. Why is high-quality patient data so essential for public health?
A. Public health, by definition, requires comprehensive data on individual patients and, collectively, on large groups of patients. These data must be structured, accurate, retrievable and updated in real time. This is necessary for decisions that affect a single patient, as well as for the planning and evaluation of broader initiatives and research.
But it’s almost as if the American healthcare system was designed to prevent this kind of data collection. Most patients see multiple doctors, often in different healthcare systems, and their data is not centralized or easily accessible.
Federal regulations on how data can be collected and shared and patient control over their information can be another hurdle. And hospitals often have different technologies and standards they use to collect, structure, store and transmit clinical information.
Providers, planners, and researchers need to be confident that the data they’re using is the best it can be. Otherwise, it’s like trying to run a high-performance engine on dirty fuel. You just won’t get the results you need. Providers who want to get into population health management need to continually monitor, optimize, and improve their data processes, because there’s no single technology that’s the perfect answer.
Q. How can preventive public health measures improve outcomes and reduce health care expenditures?
A. At this point, most people in the industry understand that prevention is more personal, easier, and more affordable than addressing acute or chronic conditions. More broadly, caring for individuals with later, preventable conditions can overwhelm an organization’s ability to meet the needs of individuals with unpreventable conditions.
While everyone agrees that prevention is the best public health strategy, implementing such a strategy can be challenging. It requires thoughtful people who can ask the right behavioral questions, such as, “When would education or outreach be most effective for a patient who is showing early symptoms of this chronic condition?”
In addition to asking the right questions, it also takes people who are committed to follow through to ensure the successful implementation of the new approach. Then it is crucial to continuously measure and assess the effectiveness of the approach.
Too often, it’s hard to get data-driven answers to behavioral questions because organizations lack the staff and process infrastructure to effectively implement discovery. And often, when we spend our resources and energy on the initial phase of gaining initial insights, we end up with insufficient capacity to capitalize on those insights by implementing change and measuring results.
As an industry, we need to invest more time, resources and focus in a thoughtful approach to healthcare, rather than a reactive one. By empowering brilliant and thoughtful clinicians to ask simple and fast questions using their organisation’s wealth of data, we can make real strides to drive change that can improve outcomes and reduce spending across the population.
Q. What are data-related barriers to implementing population health initiatives and how can hospitals and health systems overcome them?
A. Data quality is paramount. When we start working with clients, they are often shocked by how bad their data is. They are either too close to it to see the problems or they have become accustomed to working around the shortcomings. We do a lot of quality testing and discovery at the beginning of an engagement and it is often an eye-opener for clients to see how messy the data is.
The customers with the best data quality are those with data governance structures in place. For example, small teams that watch for emerging quality issues, such as a new employee entering data incorrectly or a broken interface. They spot these issues and fix them before the entire system is corrupted.
Of course, system interoperability and the fragmented nature of the U.S. healthcare system are also major hurdles. Hospitals must focus on ensuring that all of their internal systems are interoperable with each other and with vendors and partners.
Q. What do hospitals and health systems need in analytics to ensure population health initiatives are successful?
A. Hospitals and health systems need three things from their analytics platform to make their population health initiatives successful. These factors alone do not guarantee success, but the absence of any one of these factors makes it difficult, if not impossible, to make significant improvements in population health.
The first is data quality and data quality maintenance. I keep coming back to this because it’s so fundamental and so many organizations miss it. Missing or bad data makes it impossible to do accurate analysis. And data quality isn’t a one-time-and-done thing; it’s an ongoing process that requires dedicated resources.
The second imperative is an analytics platform that provides analysts and clinicians with fast, easy access to data. Ideally, these queries can be performed without the help of database experts. If it takes users too much time or effort to get answers, they will be discouraged from using the platform, to the detriment of public health initiatives.
The third is open, frequent, and high-quality communication between clinicians and IT. This should be based on trust, shared goals, and a mutual understanding of what the population health initiatives need to achieve.
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