The Changing Role of Healthcare Data Analysts—How Our Most Successful Clients Are Embracing Healthcare Transformation (Executive Report)

The Future Role of the Healthcare Data Analyst

Healthcare data analysts will become an even hotter commodity as their role becomes more central to quality improvement strategies of healthcare organizations. Historically, data analysts in health systems have not played visible roles. They have spent their days sifting through long report queues, and the flood of report requests has meant they don’t have time to do much else. Healthcare organizations can no longer afford to have analysts simply develop static reports from queues. Today, analysts need to move from gathering and collecting data to analyzing data and being part of performance improvement teams, where they will serve as data experts. Their role will be to work on collaborative, multidisciplinary teams with clinicians and operational leaders to review and analyze data and to help develop the best presentation of the data for consumption across the organization. As part of these teams, data analysts will also gain an understanding of workflows and evidence-base practices, which will help them in their analyses and recommendations. These data analysts will sit down with clinical experts, technical experts and workflow experts, reviewing analyses and visualizations that show statistically significant patterns. They will analyze data on a daily basis to understand which processes are working well and which processes are in need of improvement. Their analyses will help identify gaps and include recommended actions that help drive improved performance outcomes. An assessment similar to the previously cited HAS survey, administered via Health Catalyst’s weekly newsletter, revealed that most business intelligence leaders and data analysts support this future vision.1 Figure 6 shows that the majority of these professionals think the ideal time for analysts to spend in frontend work—analyzing data and being part of performance improvement teams—is 75 percent or more.

Figure 6: Newsletter survey results—BI and data analysts’ responses to ideal time spent in front-end workFigure 6: Newsletter survey results—BI and data analysts’ responses to ideal time spent in front-end work

In fact, Health Catalyst’s most successful clients have demonstrated that hospital systems that no longer segregate but rather embrace collaboration between technical and clinical experts consistently achieve better outcomes. In these same organizations, data analysts that embrace this change are finding their job is much more interesting and rewarding—and they can see the direct impact of their work. The newsletter survey asked respondents to describe a time when they or their team used data and analysis to make a positive impact on a patient or a process. It also asked them what they thought about their work because of this impact. Here are some verbatim examples of the feedback received:

Can you describe a time where you or your team used data and analysis to make a positive impact on a patient or a process? What inspired you?
We are currently using BI data for population health and outreach calls. Getting patients the care that is needed
We measured and ultimately reduced heart failure readmissions. We developed daily operational patient follow-up views to enhance communication between 64 teams to ensure patients receive timely follow up care. Able to see the relief of patients when they knew they had the critical medication information clarified by a pharmacist during a medication reconciliation encounter
We showed the team data and trend lines to assess effectiveness of their intervention to reduce readmissions. Getting buy-in from those most resistant to change
We often provide data used for analysis by performance improvement teams to help them develop better workflows. We frequently discovered things happening that were surprises
I began investigating our encounters (the percentage of encounters with claims vs. the percentage of encounters without claims). I found out that a big percentage of our encounters did not have claims created, which meant we couldn’t bill for those encounters. So we had our clinical coordinator meet with providers with the largest percentage of these encounters and found that in some cases the E&M codes were not assigned; the CPT were not coded; HPI were missing a few items—to name a few. We saw a tremendous increase in our receipts because billing had increased and, in return, a higher return from payers. Also, we were able to reduce our untimely filing rate. How using data to examine the ROI and the impact of making adjustments to a few processes could yield tremendous results. Additionally, getting providers to understand how what they do or don’t do may impact the bottom line.
We were working on a sepsis program and we provided data that was used to help with predictive analytics. The knowledge that we were saving people’s lives and helping our organization succeed
Utilized data points to improve moving the patient through delivery of care Improved staff and patient satisfaction
Recent orthopedic project where devices, blood usage, CPM usage and Foley catheter removal issues were analyzed and reductions in cost were received Ability to analyze provider practices that weren’t evidence-based
Tracking compliance with best practices around pressure ulcer minimization The actual measurable direct impact that BI had on patient care
Reducing the defect rate on patient home medication lists has greatly impacted patient safety in general and allowed competency feedback and improvement to front line staff. Seeing happy faces on patients, nurses, pharmacists, physicians. As the project produced positive results, senior leadership became more engaged and enthusiastic.
Chronic disease management and monitoring tools with data-driven modeling to: (1) identify non-compliance, not at goal parameters and at-risk populations, (2) help create population health-based care delivery processes to improve outcomes, and (3) create processes to help align workflows at the point of care. Enhancing patient and provider experience in healthcare delivery methods via improved technological interfaces
Improving outcomes for diabetic patients The patients’ appreciation

Not only are analysts happier with their roles and pleased with their contributions, clinicians are happier as well. Clinicians are key members of the multidisciplinary teams, and they value the healthcare data analysts and the role they play. A major satisfier for clinicians—and a factor that impacts clinicians’ perception of the data analyst role— is trusted information. Trusted information enables clinicians to act confidently on the information they receive from the data analyst. Patients receive improved care and a better experience—and data analysts find meaningful value in their work. When asked in the survey how analysts are helping the teams, we received numerous examples, including:

  • Our Patient Centered Medical Home team gets data and identifies gaps in care. We are reaching out to patients in need. Many, if not most, are thrilled!
  • We have implemented a team admission process through analytics. We reduced readmission rates and improved length of stay for most frequent diagnoses.
  • We pull data from patient satisfaction tools to monitor our improvement in communication with patients and families about delays.

Technology Solutions for Healthcare Data Analysts

Of course, data analysts can’t fill this new role without technology that can take over the heavy lifting of gathering and disseminating data. That’s why many organizations are evolving their technologies and processes to make this possible. Analytics platforms—like the Health Catalyst Late-BindingTM Data Warehouse and analytics applications have opened new frontiers for data analysis. BI teams and data analysts are aware of what these platforms can deliver—and they have high expectations for the problems an analytics solution can solve. In the Health Catalyst newsletter survey, we asked respondents to identify what they expected from a healthcare analytics system (see Figure 7). In rank order, the expected benefits are:

  • Identifying and correcting data quality issues
  • Moving their roles into front-end roles as part of a performance team
  • Spend less time pulling data (by enabling them to get data from one system versus multiple systems)
  • Meeting the demand of users by being able to deliver the data faster and in a scalable fashion
  • Providing insights to users in the form of trends lines, graphs, etc.

Figure 7: Newsletter survey results—expectations of analytics systemsFigure 7: Newsletter survey results—expectations of analytics systems

Our most successful clients are helping data analysts and BI teams achieve these benefits by implementing foundational analytics tools such as:

  • Source systems that support SQL queries. These allow data analysts to get to the data at a granular or atomic level themselves. They are no longer constrained by the canned reporting from a source system. A data analyst can go directly to the underlying tables that store the data.
  • A healthcare enterprise data warehouse (EDW). An EDW is a powerful analytics foundation that aggregates all source system data into a single source of truth for the organization—which means that analysts can truly spend their time analyzing data. The right EDW architecture is a scalable platform that can start small and then grow as needed.
  • Business intelligence development tools to build meaningful visualizations. BI tools such as Cognos, Crystal Reports, Tableau and Qlikview run on top of an EDW and enable data analysts to create visualizations that make data easily understood and consumed by a variety of audiences.

Common Concerns of Healthcare Data Analysts

Despite the availability of these new and powerful tools, many data analysts have trouble reconciling the enticing new vision of their role with the current realities of their workload. Many analysts feel like they can hardly keep their heads above water as they tackle their report queues.

Figure 8: Newsletter survey results—common BI concernsFigure 8: Newsletter survey results—common BI concerns

Adding new responsibilities seems impossible. Others simply feel uncertainty in the midst of change. The following are common concerns data analysts express: We will address each of those concerns here. Architecture won’t scale It is entirely understandable that data analysts and other BI professionals would worry that the analytics…

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