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.
Q. Why is high-quality patient data so essential to population health?
A. Population health by its definition requires extensive data on individual patients and, collectively, on large groups of patients. This data must be structured, accurate, retrievable and updated in real time. This is necessary for decisions affecting a single patient, as well as for the planning and evaluation of broader initiatives and research.
But it’s almost as if the U.S. healthcare system were designed to prevent this sort of data collection. Most patients see a number of physicians, often in different health 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 obstacle. And, hospitals often have differing technology and standards they are using to collect, structure, store and transmit clinical information.
Providers, planners and researchers must be able to trust that the data they are using is the best it can possibly be. Otherwise, it’s like trying to operate a high-performance engine on contaminated fuel. You simply don’t get the results you need. Providers who want to engage in population health management need to continually monitor, optimize and improve their data processes as no single technology is the perfect answer.
Q. How can preventive population health measures improve outcomes and reduce healthcare spending?
A. At this point, most people in the industry understand prevention is more personal, simple and affordable than addressing acute or chronic conditions. More broadly, caring for individuals with later-stage, preventable conditions can overburden an organization’s ability to serve the needs of those with unpreventable conditions.
While everyone agrees prevention is the superior population 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 exhibiting early-stage symptoms of this chronic condition?”
Beyond just asking the right questions, it also takes individuals who are devoted to follow through to ensure the successful implementation of the new approach. Then it’s critical to continuously measure and assess the effectiveness of the approach.
Too often it is hard to get data-driven answers to behavioral questions because organizations have insufficient staff and lack the right process infrastructure to effectively implement discoveries. And often, when we do spend our resources and energy on the initial phase of getting to initial insights, we end up having insufficient capacity left to take advantage of those insights by implementing changes and measuring outcomes.
As an industry, we need to invest more time, resources and focus on a thoughtful approach to healthcare, instead of a reactive one. By giving brilliant and thoughtful clinicians the ability to easily and quickly ask questions using their organization’s wealth of data, we can take real steps to implement changes that can improve outcomes and reduce spending across the population.
Q. What are data-related barriers to implementation of population health initiatives and how can hospitals and health systems overcome them?
A. Data quality is paramount. When we start to work with clients, they often are shocked by how poor their data is. They’re too close to it to see the problems or they’ve become used to working around its shortcomings. We do a lot of quality testing and discovery at the beginning of an engagement and it’s usually an eye-opener for clients to see just how messy the data is.
The clients with the best data quality are those that have data governance structures in place. For example, small teams that watch for emerging quality problems, like a new employee entering data incorrectly or a broken interface. They spot these problems and fix them before the whole system is corrupted.
Of course, system interoperability and the fragmented nature of American healthcare also are huge obstacles. Hospitals should focus on making sure all their internal systems are interoperable with each other and with vendors and partners.
Q. What do hospitals and health systems need from analytics to ensure population health initiatives succeed?
A. Hospitals and health systems need three things from their analytics platform to help their population health initiatives succeed. In and of themselves, these factors don’t guarantee success, but the absence of any one of them makes it difficult, if not impossible, to significantly improve population health.
The first is data quality and data quality maintenance. I keep returning to this because it is so fundamental and so many organizations lack it. Missing or faulty data make it impossible to perform accurate analytics. And data quality isn’t an enter-it-once-and-it’s-done proposition; it’s an ongoing process that requires dedicated resources.
The second necessity is an analytics platform that allows analysts and clinicians fast and easy access to data. Ideally these queries can be done without the help of database experts. If it takes users too much time or effort to get answers, they will be discouraged from engaging with the platform, to the detriment of population health initiatives.
The third is open, regular and high-quality communication between clinicians and the IT department. This must be based on trust, shared goals and a mutual understanding of what the population health initiatives are designed to achieve.
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