Appendix 5_Assessment tool- Use of Data for QI DRAFT 10.4.2023.docx
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This draft checklist outlines criteria for assessing quality improvement contracts related to the use of data dashboards and other data products. It addresses whether dashboards are actionable and relevant to the problem being addressed. Usability factors considered include ease of understanding concepts, appropriate number of data visualization elements per page, and labeling of axes and titles. Accuracy criteria focus on computational errors, distortion in visualizations, and selection of appropriate comparison data. The related federal contract opportunity is for the 13th Statement of Work under the Quality Improvement Network-Quality Improvement Organization program. It involves developing data dashboards and other data products to support quality improvement efforts driven by Medicare and Medicaid data.
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| QIN-QIO DRAFT SOD 10.4.23.docx | DOCX document | |
| Appendix 4_Detailed List of Measures with Specifications DRAFT.docx | DOCX document | |
| Appendix 7_ROI_template DRAFT 10.4.2023.xlsx | XLSX spreadsheet | |
| Appendix 6_Success Story Template DRAFT 10.4.2023.docx | DOCX document | |
| Appendix 3_AHQT Technical Readiness Assessment Survey DRAFT.docx | DOCX document | |
| Appendix 10_13thSOW Terminology DRAFT 10.4.2023.docx | DOCX document | |
| Appendix 9_CMS Security Privacy Requirements DRAFT 10.4.2023.docx | DOCX document | |
| Appendix 2_IDFs DRAFT 10.4.2023.pptx | PPTX presentation | |
| Appendix 8_Additional Standards Guidelines and Best Practices for Data Submission Management Analytics and Reporting DRAFT 10.4.2023.docx | DOCX document | |
| Appendix 1_A3C Example and Template DRAFT 10.4.2023.docx | DOCX document | |
| Question Submission Format.xlsx | XLSX spreadsheet | |
| QIN-QIO Draft SOW - 13th Scope.docx | DOCX document |
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Appendix 5 Draft Checklist/Score sheet for Qualitative Assessment Tool of QI Contracts: Use of Data for Quality Improvement Item 1. Dashboard/data product check list
| Criterion |
| Notable Features |
ACTIONABLE and Relevant: Are Data Products relevant and actionable?
· Did the contractor select data appropriate to the problem to be solved?
· Does the dashboard effectively distinguish between providers with and without quality issues related to this problem? Can each provider easily recognize whether or not it has a problem?
· Does the dashboard support the exploration of provider-specific trends and variation in quality (i.e., filtering)?
· Did the contractor include a way to collect feedback for data visualization improvements?
· Did the contractor act upon data in the dashboard to improve performance?
· Did the contractor transparently share dashboard/data products with its partners including CMS?
USABILITY: Are Dashboards and other data products easy to use?
Conceptual
· Is the main idea easily understood by viewing it?
· Did the contractor make the dashboard accessible and design it to be useful for its audience (to its own staff and to its providers with quality issues)?
· If a dashboard or analysis is purely descriptive, would a benchmark or comparison group lead the viewer to a different conclusion about the data (e.g. if equitable outcomes is a key issue, is it possible to disaggregate by demographic group) ?
USABILITY: Are Dashboards and other data products easy to use?
Operational
· Are there an appropriate number of widgets on a single page? If a dashboard has too many widgets for a single page, is it easy to locate the relevant widget across different pages or dashboards?
· Is there an instructions/definitions page to orient the user?
· Is it easy to navigate between dashboards?
Are the axes and titles appropriately labelled and consistent with each other?
· Are visualizations free from unnecessary clutter (e.g. would it make sense to create two separate visualizations to communicate information more clearly, is there too much text, etc.)?
ACCURACY: Are dashboards and other data products accurate?
· Are visualizations accurate and free from computational errors?
· Do graphics distort, exaggerate, or minimize impacts (e.g. data visualization are appropriate for data type, scales used and axis truncations do not distort magnitude of differences)?
· If the dashboard/data product includes a comparison group or benchmarks, are these selected comparison data accurate and appropriate?
For more on dashboard design see: Schwabish J, “Better data visualizations”, Columbia University Press, New York, 2021.
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