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How Data Analytics Can Impact Small Community Health Care Systems

Published on 4/27/2017

Every health care organization generates data — usually, more data than anyone knows what to do with. The key to unlocking the power of data and transforming it into actionable information is in analytics.

Data analytics can be especially useful when it is declassified and aggregated from a variety of institutions and used to identify patterns and trends. Taking the data that is created as a byproduct of workflows, for example, and analyzing it in order to improve operational efficiencies can ultimately impact the patient experience.

Often, we hear about the impact of data analytics in large-scale health care organizations and academic medical centers, but there's room to expand on that. In this blog, we examine this through the lens of small community and specialty hospitals, where increasing operational efficiencies could mean providing timely, life-saving care.

What do we mean when we talk about data analytics?

Where are organizations getting all this data to analyze? At almost every point. A vast amount of data are generated through patient demographic intake, patient care, compliance, billing and regulatory requirements. When we take this data and look at the implications they have, we can support a wide range of medical, health care and financial functions including clinical decision support, disease surveillance and population health management.

When we talk about the impact of data analytics in health care — and particularly a small community health care system — we're talking about a couple things. First, we're looking for insight into how rural and small community hospitals are providing patient care and managing their business. Second, based on those insights, we're asking how we can change the way these hospitals are providing patient care.

The connection between rural health care providers and data analytics

Small community health care systems mean smaller patient populations. A small census tends to de-emphasize the need to analyze data for possible opportunities for improvement, since the collected data are generally not perceived to be large enough for an effective sample study. In addition, many target benchmarks and workflows used across health care are not realistic for a rural hospital.

However, quality care for a patient in a small community setting is just as important for a patient in a more densely populated area. By having a robust data set available through a comprehensive electronic health record, we can answer different questions related to the process that analytics can answer. For example, rather than asking for mortality rates for patients with a heart attack diagnosis, we can look at door-to-transfer order time to determine a level of quality. These techniques can dramatically increase the number of encounters we're able to analyze and improve upon.

In addition, by equipping community care providers with comparisons aggregated from similar organizations across the country, they can better understand people and processes that can have life-saving implications for their own patient pool.

Case study: Troponin test times in small and rural communities

Let's take a look at a real-world example of how data analytics can directly improve the quality of patient care in a small community health care organization.

Every year, approximately 735,000 Americans have a heart attack. There's great interest in improving this number — and one of the ways we can contribute to that goal is by quickly identifying symptoms of and treating heart attacks. Troponin tests are commonly used in the emergency department (ED) to identify if a patient is experiencing a heart attack. In an ideal setting, the turnaround for a troponin test is about 35 minutes; most hospitals have a protocol setting of 60 minutes or less.

We recognized an opportunity for improvement with some of our clients around their troponin test rates. We pulled data on individual clients and compared it to industrywide data, and found that while some of our clients had fantastic numbers, others hadn't had a focus group around this topic and there was room for improvement. If a hospital's median turnaround time for a troponin test is 45 minutes, for example, that still means that approximately half their tests are taking longer than that.

Though there is currently no troponin test standard mandated by the Centers for Medicaid and Medicare Services (CMS), the turnaround time clearly impacts patient care. Think of it this way: The 25-minute difference in test results is akin to an ambulance arriving to pick up an individual with heart attack symptoms and then simply waiting in the driveway for over half an hour. A quicker turnaround could mean the difference between a life saved and a life lost.

One of the core missions of any critical access hospital is to increase the operational efficiency of the ED. When we pulled the data and started doing the math for some of our clients, we realized that there was an incredible opportunity to provide a realistic statistical analysis and impact patient care.

Using data analytics and taking ED processes, people and technology into account empowered us to partner with small community hospitals to make recommendations around improving this turnaround time. By optimizing troponin test times, our small community health care clients could not only improve operational efficiencies but also impact patient care and literally save lives.

CommunityWorks empowers small community, specialty and rural health care organizations to accomplish tasks with fewer resources and keep up with the ever-changing health care landscape. Learn more here.