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The Best Organizational Structure for Healthcare AnalyticsBy John Wadsworth
The Best Organizational Structure for Healthcare Analytics Your goal is to build or implement an effective and robust enterprise data warehouse (EDW) for your healthcare organization. But did you realize the success of your goal may hinge upon your healthcare’s organizational structure? 2
Why You Need Both: An EDW and a Supportive Organizational Structure An EDW integrates data from across your enterprise. In healthcare, for example, you could tie cost accounting data, hospital billing data, clinical data, and patient satisfaction data. Done correctly, this level of integration could help your organization analyze the following scenarios: 3
Challenges for Traditional Clinical Data Management The challenge healthcare organizations wrestle with today is under-standing the appropriate quid pro quo relationship between IT and data-dependent departments to demonstrate ROI on an EDW investment. 4 Without this understanding of symbiotic dependency, IT is viewed as a parasitic cost center.
Creating Cross-Functional Teams to Drive Change Through Healthcare Analytics Cross-functional teams will simultaneously improve clinical and financial outcomes and demonstrate ROI. By following this approach, you’ll experience the following advantages: 5 Removal of organizational barriers between team members Prioritization of BI and analytic efforts according to institutional readiness and need Engagement and prioritization from appropriate leadership Buy in from each level of the organization to improvement goals
Creating Cross-Functional Teams to Drive Change Through Healthcare Analytics 6 Here is a basic diagram of the organizational structure we recommend:
7 The content and analytics team is primarily comprised of data architects and outcome analysts. CONTENT AND ANALYTICS TEAM The content and analytics team is primarily comprised of data architects and outcome analysts. Under the direction of senior leadership, this team mines data to identify opportunities for improvement.
8 Small, representative sampling of clinical staff responsible for a given clinical work process, such as a C-section, a hip surgery, etc. WORK GROUP This team typically consists of a physician lead, an operations lead, and a seasoned RN. This group analyzes clinical process data to uncover opportunities for improvement across the patient treatment spectrum.
9 Consists of practicing clinicians who own a clinical process within an organization. The CIT roughly aligns with clinical practice specialties. CLINICALIMPLEMENTATION TEAM The CIT role is crucial to widespread adoption of clinical improvements. They hear findings and recommendations for improvement from the work group and then champions adoption of these improvements.
10 Provides governance over all the workgroups and clinical implementation teams under a clinical program. GUIDANCE TEAM The guidance team takes into account resources, organizational readiness, and political climate to determine which workgroups receive priority. This team reports to senior executive leadership.
11 Senior leadership retains the final word on which efforts are prioritized and executed upon SENIOR EXECUTIVELEADERSHIP TEAM Senior executives consider the well-informed recommendations of the guidance teams to make decisions about where best to focus resources. The hierarchy of workgroups supports greater transparency and visibility.
Done right, we consistently find that the culture changes and IT staff and data-dependent departments enjoy healthy, symbiotic growth within their health care organization Structuring Your Healthcare Analytics Workgroups 12 Analyze a sample data set to determine the best two or three opportunities for improvement and structure the workgroups accordingly. Once these teams mature senior leadership can begin to govern the workgroups.
Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com John Wadsworth joined Health Catalyst in September 2011 as a senior data architect. Prior to Catalyst, he worked for Intermountain Healthcare and for ARUP Laboratories as a data architect. John has a Master of Science degree in biomedical informatics from the University of Utah, School of Medicine. Finding $5.7 Million with a Healthcare Data Warehouse Also by John Wadsworth