Without high-quality patient data, it is difficult and sometimes impossible for clinicians to safely and effectively treat patients at the point of care. Likewise, lack of rapid access to quality data can pose health risks to entire populations.
Specifically, successful population health initiatives require data analytics that help identify populations in need of care and measure the care provided. This ensures 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 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 essential to public health, how preventative public health measures can improve outcomes and reduce healthcare spending, data-related barriers to implementing public health initiatives and how hospitals and health systems can overcome them, and what provider organizations need from analytics to ensure public health initiatives are successful.
Q. Why is high-quality patient data important to public health?
ONE. By definition, population health requires extensive data about individual patients and, more generally, large patient groups. This data must be structured, accurate, retrievable and updated in real time. This is necessary for decisions affecting individual patients, as well as for planning and evaluating broader initiatives and research.
But it seems the US health care system is designed to prevent this kind of data. Most patients see multiple doctors, often in different health systems, and their data is not centralized or easily accessible.
Federal regulations governing how data can be collected and shared and how patients have control over their information can be another obstacle. And hospitals often have different technologies and standards they use 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 be. Otherwise, it is like trying to run a high-performance engine on contaminated fuel. You simply won’t get the results you need. Providers who want to participate in population health management need to continually monitor, optimize, and improve their data processes because no single technology is the perfect answer.
Q. How can public health interventions improve outcomes and reduce health care spending?
ONE. At this point, most in the industry understand that prevention is more personal, simple, and affordable than addressing acute or chronic conditions. More broadly, caring for individuals with preventable conditions in later stages can overwhelm an organization’s ability to serve the needs of those with non-preventable 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 patients presenting with early-stage symptoms of this chronic condition?”
In addition to simply asking the right questions, it takes dedicated individuals to ensure the successful implementation of a new approach. Then it is important to continually measure and evaluate the effectiveness of the approach.
Too often, it is difficult to get data-driven answers to behavioral questions because organizations lack the right staff and process infrastructure to effectively implement discoveries. And often, when we spend our resources and energy early on to gain initial insights, we are no longer able to capitalize on those insights by implementing changes and measuring results.
As an industry, we need to invest more time, resources, and focus on a thoughtful approach to healthcare, rather than a reactive one. By empowering smart, thoughtful clinicians to quickly and easily ask questions using rich organizational data, we can take real steps to implement changes that can improve outcomes and reduce costs across the population.
Q. What are the data-related barriers to implementing public health initiatives, and how can hospitals and health systems overcome them?
ONE. 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 the data to see the problems or they are used to dealing with data shortcomings. We do a lot of testing and quality discovery at the beginning of a contract and often the client’s eyes are opened to how messy the data is.
The customers with the best data quality are those with data governance structures in place. For example, small teams monitor emerging quality issues, such as new employees entering incorrect data or broken interfaces. They catch these issues and fix them before the entire system breaks down.
Of course, system interoperability and the fragmented nature of the U.S. health care system are also major obstacles. Hospitals should focus on ensuring that all of their internal systems can interact with each other and with vendors and partners.
Q. What do hospitals and health systems need from analytics to ensure successful public health initiatives?
ONE. Hospitals and health systems need three things from their analytics platforms to help their population health initiatives succeed. These elements alone do not guarantee success, but the absence of any one of them makes it difficult, if not impossible, to make significant improvements in population health.
First is data quality and maintaining data quality. I keep coming back to this because it is so fundamental and yet so many organizations lack it. Missing or erroneous data makes accurate analysis impossible. And data quality is not a one-time entry proposition; it is an ongoing process that requires dedicated resources.
The second need is an analytics platform that allows analysts and clinicians to access data quickly and easily. Ideally, these queries should be able to be performed without the assistance of database experts. If it takes too much time or effort for users to get answers, they will not engage with the platform, which is detrimental to public health initiatives.
Third is open, frequent, high-quality communication between clinicians and IT. This must be based on trust, shared goals, and a mutual understanding of what the public health initiative is designed to achieve.
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