Today’s wider distribution of healthcare data and information empowers even non-expert data users to derive analytic insight on their own through self-service analytics. Gartner defines self-services analytics as “a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.”
In other words, rather than face the typical IT or centralized analytics team bottleneck associated with waiting for long report turnaround times, self-service analytics in healthcare allows team members to run their own reports, free to gain analytic insight when and where they need it without delay. With a robust data platform that aggregates multiple data sources into one place, all end users have access to the latest, most accurate data sets. For example, if a chief quality officer wants to know how COVID-19 has impacted operating room (OR) volumes, she can use self-service capabilities to run a same-day report. If the data also reveals patient safety concerns associated with the change in OR volumes, she can run additional reports targeting those patient safety areas without waiting for a follow-up report.
Despite the clear benefits of self-service analytics in healthcare, many leaders delay making data and analytics directly accessible to team members for a variety of reasons. However, by addressing their hesitations and embracing self-service, organizations can empower decision makers with targeted, timely insight into areas of concern and opportunities for outcomes improvement.
From data infrastructure limitations to cultural inertia, the uncertainties about self-service analytics in healthcare can feel overwhelming. By addressing the following five common hesitations, leaders can effectively implement self-service analytics, increasing access to valuable insights and delivering data to the frontlines of clinical and business decisions:
Effective self-service analytics requires preparing and standardizing data, a time-consuming process for analysts, if done manually. Data preparation, including cleaning, normalizing, and accurately labeling data sets with consistent terminology, is critical for mass distribution and analysis because it reduces inaccuracy and confusion. However, a lack of data standardization and inconsistent labeling is commonplace in healthcare due to so many available data sources and data sets, making insufficient standardization a common reason leaders resist self-service analytics. Without standardized data, various team members can receive different data sets that can lead to inaccurate answers.
Solution: To relieve the burden of data standardization, healthcare organizations can invest in advanced data platforms that ingest data from varied sources and automatically—not manually—organize, normalize, and correctly label that data. For example, the Health Catalyst Data Operating System (DOS™) aggregates data from sources inside and outside the hospital (e.g., EHR, ancillary systems, community clinics, etc.) into one place. With one source of truth for a system’s data, self-service analytics tools allow end users across the organization to access standardized data to inform decision making. Organizations looking for a good starting point can leverage out-of-the box data models, including Health Catalyst DOS Marts.
Distributing self-service analytics to team members who lack data literacy—the ability to aggregate, organize, and leverage data in different use cases—turns promising data tools into a burden. Typically, data experts, such as data analysts and data scientists, would interpret data, then deliver answers to end users. With self-service analytics, decision makers can run the query and derive their own answers. But how do they know they are making the right decisions and interpreting the data correctly?
Solution: Instead of unleashing data and self-service analytics tools to the frontlines and hoping team members make the right decisions, health systems should teach analytics best practices. For example, organizations should leverage their central IT and analytics teams to teach users how to use the data, identify and train data advocates or champions across different departments, and invest in educators. For example, a seasoned data and analytics vendor can ensure that health systems maximize every aspect of self-service analytics in healthcare by providing data expertise, a breadth of analytics experience, and knowledge about which strategies have proven successful at other organizations.
Additionally, health systems can provide pre-built content and queries (e.g., Health Catalyst DOS Marts™) for common health conditions so that users don’t have to start from scratch when leveraging self-service analytics. For example, most health systems screen for patients who are at risk for diabetes. Therefore, the organization can provide pre-built queries for people who meet specific criteria for pre-diabetes. Providing pre-built content helps team members develop confidence with self-service analytics by providing guidance while also empowering end users to discover answers for themselves.
Organizations typically associate self-service analytics in healthcare with descriptive, or historical, analytics. And, because systems already have a breadth of historical statistics, they might feel like self-service analytics that reflect past performance aren’t useful, unless they invest in technologies that can forecast and predict future activity, behavior, trends, etc.
Solution: To move beyond descriptive analytics, organizations should look to incorporate an artificial intelligence tool or platform. For example, Healthcare.AI powers self-service analytics with descriptive data as well as predictive and prescriptive data, allowing health systems to take their data further than historical analytics. For example, a health system can use descriptive analytics (e.g., number of diagnoses and patient details) to recognize an increase in patients with Type 2 diabetes diagnoses. Then, healthcare leaders can use predictive and prescriptive analytics that recognize trends and patterns to forecast a future surge in this at-risk population. Based on these predictions, health systems can make appropriate changes including appropriate screening for the condition.
Data analysts and experts have historically “owned” the data within an organization. Self-service analytics challenges this status quo and requires a culture change to allow every team member as data owners and stewards. Many data analysts might view self-service analytics as losing power and giving up a primary role. They also might worry about their influence or control over the data if it is widely available.
Solution: A mindset shift plays a critical role in self-service analytics success. Rather than relinquishing data power, data analysts and experts should see themselves as still leading from a strong data governance perspective, while empowering other decision makers throughout the system to do analytics work at their level. This frees data analysts up to work at the top of their license. When data experts start seeing themselves through this new lens, they will feel excited about self-service analytics and the ability to share data through these advanced tools.
This change of mindset takes time, but it is possible with data leaders’ support. For example, if data leaders involve members of the care management team in discussions about which tools will be the most useful in a clinical setting, other data experts will be more likely to model this collaborative behavior and take the same teamwork approach. Leaders’ example of sharing data and supporting self-service analytics in healthcare can be the catalyst for this necessary culture change.
The more people who have access to data, the more opportunities for a data breach. Naturally, some healthcare leaders choose to limit data use to decrease the possibility of a security event. Additionally, misunderstandings often exist about which data is appropriate to share and with whom. Lack of data infrastructure also makes it difficult to segment data, so health systems view self-service analytics as access to all data or none of it.
Solution: Withholding mission-critical information to avoid a possible data breach is not the answer to security concerns. While there is always a risk of data compromise, the benefits of sharing critical health data outweigh the risks of a data breach, if organizations implement self-service analytics correctly. By following data security policies and procedures (e.g., HIPAA, the Electronic Healthcare Network Accreditation Commission, and Health Information Technology for Economic and Clinical Health Act), health systems can greatly reduce the risk of a data breach. Additionally, using advanced data tools and services, such as the Health Catalyst analytics offerings, allows leaders to safely segment data, distributing only the necessary data to the right people.
By addressing the five hesitations to self-service analytics in healthcare listed above, health systems can ensure their data reaches its full potential. Self-service analytics lowers the technical barriers to entry to data and analytics use and thus expands the pool of users with access to critical information, placing relevant data and analytics insight at the frontlines of clinical care and healthcare business decision making.
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