Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information.
To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system.
Two strategies can help organizations integrate and deliver data to end users when they need it:
1. Select infrastructure that fits most people’s needs.
2. Ask the right questions.
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This article is based on a 2020 Healthcare Analytics Summit (HAS 20 Virtual) presentation by Sy Johnson, Chief of Staff, Renown Health, titled, “Success in the New Normal: Integrated Data to the Masses.”
If easy access to comprehensive data wasn’t a priority for health systems before the pandemic, it is now. With the onset of the novel coronavirus, healthcare leaders everywhere turned to data to understand the impacts of COVID-19 (e.g., capacity planning and COVID-19 patient and staff tracking) and inform decision making such as directing patient flow and identifying vulnerable patients. A key part of the answer to these new challenges was—and still is—accurate, up-to-date data.
Data has always been critical for improving healthcare delivery, even before the pandemic. From operations to the clinical frontlines, health systems have relied on data to measure the care they deliver, identify care variation, and tailor care delivery to each patient’s needs. Without data, health systems resort to anecdotal evidence and then make their best guess when it comes to improvement efforts, often wasting precious resources along the way.
While data is the bedrock to meaningful change in any healthcare setting, it can only drive systemwide improvement if it is available across the health system and to decision makers at every level of the organization. A health system typically has access to data but doesn’t always aggregate it from all source systems and then make it available to its end users, the point at which data can drive real change. By enhancing healthcare data integration that increases access and availability to end users, health systems can support data-driven decisions and better frontline care.
Although data can be a powerful force for positive change and better health, poor data quality can lead to uniformed decisions and worse outcomes. When health systems lack efficient healthcare data integration—the process of compiling data from different sources and increasing access to decision makers—they are left to use whatever data sets they can find, including fragmented or incomplete data.
For example, if a nurse manager can’t access data from a central source, she might look in the EHR and pull a patient’s data from the past six months. However, because the EHR doesn’t include data from all of the hospital’s source systems, she would make care decisions for the patient based on piecemeal datasets. Although the nurse manager made the best decision with the data available to her, this example highlights the need for decision makers to have easy access to one, comprehensive source of data, ensuring that every person uses the same information to make decisions.
Many end users think of cumbersome processes, custom requests, and long turnaround times when it comes to requesting data reports. However, two strategies can help health systems simplify the healthcare data integration process and rapidly deliver the most relevant data to end users:
Health systems should invest in data infrastructure and capabilities that fit the needs of most of their decision makers rather than building a data solution for one person or one group of people. Data infrastructure that fits the decision makers’ broader needs allows more people to leverage data as soon as the infrastructure is in place, avoiding delays often associated with building customized options. Data teams can rapidly scale a commercial solution (e.g., the Health Catalyst Data Operating System (DOS™)) that still permits customization options as needed.
To find the best data infrastructure for the health system, leaders should identify a variety of the organization’s use cases and then explore analytics solutions they can swiftly apply to those use cases.
Delivering data to decision makers starts when data and analytic leaders ask the right questions to identify which data will be most useful. For example, data and analytic leaders should assess which data sets end users can currently access, then ask which additional data sets they are requesting from data support teams (e.g., what custom reports is IT generating). Asking questions about additional data requests will reveal which data sets team members need but can’t access in their decision-making process.
Once data teams have a deeper understanding of the organization’s data needs, they can create a data report “menu” with different data report options anyone in the organization can order. Leaders can think of a data menu as a fast food menu. For example, a restaurant provides a menu with different types of cheeseburgers that customers can order. Similarly, leaders can create a menu based on the decision makers’ most common requests/needs and use common language that end users and technical team members understand.
Creating a simplified data report menu decreases custom report requests and reduces delays in reports due to misunderstandings from technical jargon. Ordering data reports from a menu creates a smoother transaction between end users and technical team members.
By applying the above two strategies, health systems can understand gaps in data and leverage that information to increase data accessibility to team members in a crisis (such as COVID-19) or in everyday population health management. The following two use cases show these data integration strategies at work:
COVID-19 underscored the need for systemwide access to data in a crisis. When the pandemic’s initial impacts (e.g., halted elective procedures) took effect in early 2020, health systems needed a high-level view of the goings on throughout the hospital to understand the virus’s ripple effects. Without access to information from every data source, leaders couldn’t proactively manage the pandemic. They had to react to different areas based on disjointed data that failed to show how a change in one area (e.g., ICU capacity) would affect another area (e.g., emergency department capacity).
To provide more comprehensive data for the end users—especially during a pandemic—a health system can ask team members what information they need to make decisions during the pandemic, invest in data solutions that support these needs, and then combine information from all the appropriate sources. For example, data teams could aggregate the data from the EHR, the supply chain source system, and any other source systems, based on the datasets team members deem critical. Then, the leadership team could use these data sources to feed a central data dashboard living in the incident command center. This data display would serve as the main source of truth to drive pandemic-response decisions throughout the healthcare system.
Providing widespread access to the command center central display is a way to deliver data to the organization’s decision makers at every level. Not only do the decision makers have the information they need to make educated decisions about patient care, but they also see the value that leadership places on making data-informed decisions.
The health system can also add information to the incident command center data display that they consider vital for all team members to know, such as such as COVID-19 volumes and trends. Leaders might consider adding other COVID-19-related information, including amounts of personal protective equipment, staffing, lab supplies, and other information health systems cannot access from only one source system.
Another example that conveys the value of healthcare data integration is in population health management. To manage the health of patient groups, population health depends on widespread access to data to inform a unified approach to better health.
When elective care went on hold in March 2020, health systems needed to ask the right questions to identify the patients who were at the highest risk for worse health due to the unexpected changes in care. For example, when health leaders sought to understand how different departments were impacted, they would learn that patients receiving pulmonary rehabilitation couldn’t continue their regular rehabilitation visits. Health systems had to pivot in response to the sudden change and learned to modify services or find new ways to reach patients with telehealth.
In this all-too-common scenario, many health systems have turned to predictive models to identify patients at higher risk for worse outcomes or readmission based on the disruption in routine care. Ensuring the results from these predictive models are available to decision makers throughout the entire health system—with the right data infrastructure—allows every service line (e.g., cardiology and neurology) to identify their at-risk patients and quickly implement interventions, such as additional monitoring.
As data and data-based technologies (e.g., data displays) become more widely used in healthcare, health systems must make the data and supporting tools available to their team members. Data can’t reach its full potential—to inform decisions that improve and restore patient health—if end users can’t easily access it at the point of decision making.
By asking questions and exploring team members’ needs, leaders can invest in data infrastructure that benefits the majority of end users’ needs and create a simple menu that team members can use to request data reports. When leaders deliver data to the decision makers when needed, they maximize data in workflows, processes, and everyday care delivery.
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