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Capturing and Using Data from Quality Improvement University of Texas MD Anderson Cancer Center Margaret Bell, DNP, MPH, RN, CMPE Susan McBride PhD, RN-BC, CPHIMS 713-745-1201 [email protected]
de Lusignan, Simon de. (2005). Codes, classifications, terminologies and nomenclatures: definition, development and app;lication in practice. Informatics in Primary Care. 13:65-69.
de Lusignan. S., Liaw, S., Michalakidis, G., Jones, S. (2011). Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach. Informatics in Primary Care. 19: 127-134.
Selman, L., & Harding, R. (2010, January). How can we improve outcomes for patients and families under palliative care? Implementing clinical audit for quality improvement in resource limited settings. Indian Journal of Palliative Care, 16, 8–15.
van Vlymen, J., & Lusignan, S. (2005). A system of metadata to control the process of query, aggregating, cleaning and analyzing large datasets of primary care data. Informatics in Primary Care, 13, 281–291.
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Identify components (factors) associated with quality improvement projects within a large healthcare system. Utilize prior quality improvement projects to inform the design of a data repository strategy that enables easy retrieval and use of data.
Why capture data from quality improvement projects?
Data from quality projects can be leveraged to improve care across a hospital or system: Minimize data loss
• Confirm previous findings • Reduce time • Decrease repetition • Decrease variability • Increase efficiency and effectiveness • Explore projects with similar aims
Aim 1
Reasoning 2
Data 3
We can “improve outcomes for patients through data collection and analysis, designing and implementing service improvement strategies, and then reevaluating the quality of the care” (Selman & Harding, 2010, p. 8).
Solution
Technology Solution
Defining specific and unique metadata (data that describes other data) enables the aggregation, query and analysis of large datasets Metadata which links the data elements enables traceability because the original data can be identified (de Lusignan, 2005 ; de Lusignan, 2011; van Vlymen & de Lusignan, 2005).
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4 Workflow Design
Data mart would enable others to: • Build on successful methods • Improve return on investment (ROI) for quality
improvement • Use common language or taxonomy • Leads to standardization • Reduces variability, decreased cost • Leads to sustainable care improvements • Advances the science of quality improvement • Provide leadership with information impacting
decisions on deployment of resources • Set appropriate priorities • Supports evidence-based practice
Margaret A Bell 2014
Imagine a journey to a new world with ….…………..a powerful tool in your arsenal. • Utilizing the data mart to manage all quality
projects • Accessing a tool to allocate resources • Ability to focus on specific areas of
improvement
Design & Rationale: This retrospective analysis identified two important characteristics: 1. Common characteristics and patterns within the retrospective projects to inform strategies for the design of data structures. 2. Data points to store in metadata (detailed structured and unstructured data fields).
Data Sample: Quality Improvement Project Database: n = 1,878
Margaret A Bell 2014
Margaret A Bell 2014
Strategy & Design Phase Complete