7 Essential Practices for Data Governance in Healthcare

1. 7 Essential Practices for Data Governance in Healthcare By Dale Sanders

2. The Value of Healthcare Data 2 As healthcare has become a more analytically driven industry, data is now one of the most valuable assets outliving facilities, devices and people.

3. Governance of Healthcare Data 3 Data governance describes the concept of managing and influencing the collection and utilization of data in an organization. Demand for data governance growing due to increased data demand for ACO and population health Tendency to operate in extremes, either too much or too little governance

4. Healthcare Analytic Adoption Model In the  Healthcare Analytic Adoption Model , a robust data governance function is required in order to achieve the conditions of Level 5 maturity. 4 Level 8 Level 7 Level 6 Level 5 Level 4 Level 3 Level 2 Level 1 Level Personalized Medicine & Prescriptive Analytics Clinical Risk Intervention & Predictive Analytics Population Health Management & Suggestive Analytics Waste & Care Variability Reduction Automated External Reporting Automated Internal Reporting Standardized Vocabulary & Patient Registries Enterprise Data Warehouse Fragmented Point Solutions Tailoring patient care based on population outcomes and generic data. Fee-for-quality rewards health maintenance. Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment. Tailoring patient care based on population metrics. Fee-for-quality includes bundled per case payment. Reducing variability in care processes. Focusing on internal optimization and waste reduction. Efficient, consistent production of reports & adaptability to changing requirements. Efficient, consistent production of reports & widespread availability in the organization. Relating and organizing the core data content. Collecting and integrating the core data content. Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

5. Balanced, Lean Governance 5 Start with a broad vision and framework but limited application. Expand governance function only as needed Govern to the least extent required for the common good. Essentials of DATA GOVERNANCE 1 Enhancing data quality Increasing data content in the EDW Encouraging more, not less data access Resolving analytic priorities Campaigning for data literacy Establishing standards for master reference data Base your committee charter on… Data governance committee needs data steward SMEs When in doubt, govern less, not more. Keep it lean.

6. Data Quality 6 Essentials of DATA GOVERNANCE 2 Data Quality = Completeness x Validity x Timeliness of Data. . Data quality is probably the single most important function of data governance . Low data quality negatively impacts decision accuracy or timeliness

7. Data Access 7 Essentials of DATA GOVERNANCE 3 Increasing access to data, across all members of the enterprise – external stakeholders, community members and especially patients, is a critical Committee function. The Committee bridges internal stakeholders to streamline decision making and departmental reconciliation.

8. Data Literacy 8 Essentials of DATA GOVERNANCE 4 Data literacy can be increased by: Education – good data from bad data Data analysis tools Data driven process improvement Applying statistical techniques to improve decision making process Deliberate collection and dissemination of metadata Data serves no purpose if intended beneficiaries cannot interpret or use the data.

9. Data Content 9 Essentials of DATA GOVERNANCE 5 For example, activity-based-costing data, genetic and familial data, bedside devices data, and patient reported observations and outcomes data are all critically important to the evolution of analytics in the industry. Building and acquiring the systems to collect this data is the first step in the analytic journey and can take as long as five years to complete. The Data Governance Committee should plot a multi-year strategy for data provisioning and acquisition

10. Analytic Prioritization 10 Essentials of DATA GOVERNANCE 6 The Data Governance Committee should play a major role in developing and implementing the strategic analytic plan for the C-level suite Analytic resource allocation should use 60/40 mix to balance top-down corporate priorities with bottom-up requests from clinical and business units. Top-down: 60% Bottom-up: 40%

11. Master Data Management 11 Essentials of DATA GOVERNANCE 7 Over time, the Data Governance Committee defines, encourage use, and resolves conflicts in master data management. Local data standards (facility codes, department codes, etc.); Regional and industry standards (CPT, ICD, SNOMED, LOINC, etc .). Defining algorithms such as readmission criteria, or attributing patients to providers in accountable care arrangements.

12. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com A Landmark, 12-Point Review of Population Health Management Companies (Also by Dale Sanders) Prior to his healthcare experience, Dale Sanders worked for fourteen years in the military, national-intelligence, and manufacturing sectors, specializing in analytics and decision support. In addition to his role at Health Catalyst, he continues to serve as the senior technology advisor and CIO for the National Health System in the Cayman Islands. Previously, he was CIO with the Northwestern University Medical Center, and regional director of Medical Informatics at Intermountain Healthcare where he served in a number of capacities, including chief architect of Intermountain’s enterprise data warehouse. Dale is a founder of the Healthcare Data Warehousing Association. He holds Bachelor of Science degrees in Chemistry and in Biology from Ft. Lewis College, Durango Colorado, and is a graduate of the U.S. Air Force Information Systems Engineering program.