With mounting pressures to deliver quality care with fixed resources, data-driven healthcare is pivotal to organizations’ well-being. From operations to the front lines of clinical care, data can drive the best outcome if decision makers have relevant information when they need it. However, many organizations simply use data in one-off situations rather than integrating it into systemwide processes and workflows. To understand what it means to become data driven and take the right steps forward, organizations can apply four key elements:
1. Invest in one source of data truth.
2. Apply a data governance strategy.
3. Promote systemwide data literacy.
4. Implement a cybersecurity framework.
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Healthcare organizations constantly face new pressures that challenge their approach to care delivery and financial sustainability and demand a data-informed strategy. Data-driven health systems—those that rely on accurate, up-to-date data to guide and determine decision making—are more likely to succeed than organizations that simply use data to make decisions. A data-driven healthcare organization uses data to maximize its limited resources and meet its goals, from clinical outcomes to charge capture processes.
Although many healthcare organizations strive to be data driven, barriers, including a lack of leadership support, data-driven culture, or sufficient data access, keep them from fully leveraging data. Organizations can navigate these barriers by investing in data infrastructure that supports systemwide data reach, including one source of data truth, enabling data to inform processes and culture.
To determine where they are on the journey to become data driven, health systems can consider four foundational elements of a data-driven organization:
Data-driven healthcare organizations have to manage increasing amounts of data (inside and outside of the hospital) and make these large amounts of data available to team members. Systemwide access means one source of data truth, such as the Health Catalyst Data Operating System (DOS™), allows all users to reference the same data and avoid partial data sets when making decisions.
Many health systems rely on EHRs as their source of data truth, but EHRs are limited platforms that can’t aggregate multiple data sources, organize the data, and then distribute it. A robust enterprise data platform (e.g., DOS) with supporting analytic infrastructure delivers relevant data to the right team member at the right time. Delivering analytic insight to end users when they need it enables and supports data-informed decision making, a core part of a data-driven organization.
To effectively leverage data to improve operational, clinical, and financial processes, organizations need a data governance strategy. Data-driven organizations implement a data governance strategy to monitor the use of data, oversee the quality of the data, and distribute relevant data to team members. Because data is arguably a healthcare organization’s most valuable asset, a data governance strategy that ensures data veracity is key. However, data governance can be overwhelming and with increased access to data, many health systems don’t know where to begin a governance strategy.
Organizations can start their data governance strategy to optimize data use by following five steps:
Health systems can improve data use among team members by promoting data literacy. Data literacy means that users understand the role data plays in decision making and rely on that data to guide the best decision. If team members value and prioritize data in decision making, organizations are more likely to be data driven. On the contrary, organizations can’t become data driven if their team members don’t trust data or lack understanding about the value of data in decision making. With many team members at differing levels of data competency, organizations can improve data literacy by generating leadership support and creating data literacy programs.
High-level support allows team members to see that data is a priority, enabling the data-centric mindset to trickle down. Data literacy programs assess data literacy levels and allow improvement teams to create custom programs to increase data literacy among team members, and therefore, the likelihood that users will leverage data in everyday decisions.
Lastly, data-driven healthcare organizations protect their data and ensure the highest quality data with a strong cybersecurity framework. Interoperability and rising amounts of data leave more opportunities for cyberattacks and data theft. A cyberattack could result in an organization losing data, a disruption in the enterprise data platform, and compromised patient privacy and safety.
Organizations can start their cybersecurity approach by considering who is responsible for cybersecurity: the vendor (e.g., Health Catalyst) or the health system? Usually, cybersecurity is a shared responsibility between the vendor and the health system. Next, security leaders can perform third-party audits and obtain certifications (e.g., Systems and Organizations Controls compliance) to assess and understand the current level of cybersecurity and opportunities to strengthen it.
From patient outcomes to operational processes, health systems need data to implement meaningful improvements. However, simply using data in one-off situations is different from being data driven. Organizations can be more effective if they achieve the latter. Becoming a data-driven healthcare organization requires more investment and resources—a data infrastructure, data-driven processes, a data-centric culture, and a supporting cybersecurity framework—allowing team members to make the most informed decision and the organization to reach its goals.
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