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Developing a Centralized Repository Strategy: The Top Three Success Factors



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Developing a Centralized Repository Strategy: The Top Three Success Factors

  1. 1. John Sharp, MSSA, PMP, FHIMSS Research Informatics Developing a Centralized Repository Strategy: The Top Three Success Factors
  2. 2. Introduction • As healthcare organizations seek ways to better predict the true transformation of healthcare, centralized data repositories hold the keys to unlocking the potential for predicting the impact of quality on patient care. • Learning Objectives: – Determine your healthcare quality challenge for leveraging data – Identify the top three critical success factors for a centralized repository strategy
  3. 3. Current State of Quality Reporting – Multiple Demands • Joint Commission • CMS • Get with the Guidelines – AHA • Meaningful Use • Core Measures
  4. 4. Current State of the Data Claims data EMR data – new for many health systems and reporting systems emerging Other clinical systems Public health, population data Reference data Genomic data
  5. 5. Three Success Factors of a Centralize Data Repository Buy in and governance Organizing Data Displaying data in a meaningful way
  7. 7. Buy-in and Governance Hazards of Decentralized approach – Many copies of the data – storage issues – Different data definitions – Confusion over public reporting – Are all data repositories up-to-date? – Multiple teams with different degrees of expertise – Different ways of transforming the data – Taxing systems which data is extracted from
  8. 8. Buy-in and Governance Complaints about a centralized approach • Give up control • Too IT centric • Slow response – queue is too long • Can’t get my request prioritized
  9. 9. Buy-in and Governance Solution • Involve key stakeholders from the beginning • Include institution leadership and clinical leadership • Include data stewards • Define end users of the data broadly
  10. 10. Buy-in and Governance Data Stewards • Develop consistent definitions and interpretations of data and data concepts so that information can be consistently interpreted • Document where data originates and the processes that act on it • Help ensure data quality, accuracy, consistency, timeliness, validity, and completeness • Define appropriate controls to address data security and privacy requirements • http://himss.files.cms- df
  11. 11. Buy-in and Governance Governance Committee Role Increasing consistency and confidence in decision making Decreasing the risk of regulatory fines Improving data security Maximizing the income generation potential of data Designating accountability for information quality Enable better planning by supervisory staff Minimizing or eliminating re-work
  12. 12. Buy-in and Governance Governance Committee tasks • Standards for defining – definitions and taxonomies • Processes for managing – data quality, change management • Organizational responsibilities – oversight, prioritization • Technologies for managing – data dictionaries, data discovery tools
  13. 13. Organizing Data Aggregate Normalize Analyze DATA
  14. 14. DataFlux Data Governance Maturity Model
  15. 15. Organizing Data – Data Quality • Data definitions – making sure that data are well understood across the organization • Data lineage – documenting how data are created, transformed and intermingled • Data accuracy – making sure that data accurately reflect the clinical and business transactions and activities of the organization HIMSS Clinical & Business Intelligence: Data Management – A Foundation for Analytics
  16. 16. Data Quality (2) • Data consistency – making sure that data within and across data stores provide a consistent representation facts • Data accessibility / availability – making sure those who need data to perform their job function have access to relevant data • Data security – making sure that data access is restricted to those who have a legitimate and legally allowable need for the data
  17. 17. Data Quality - Definitions How do you define a hospital admission? • > 24 hours • >= 24 hours • Include time in the ED? • What about direct admits after a procedure? • What if the patient dies in a hospital bed in less than 24 hours • Does CMS and private insurers have the same definition?
  18. 18. Data Definitions • ICD-9, ICD-10 • SNOMED-CT • LOINC • RxNorm • National Quality Forum measure – definitions – Numerators/Denominators
  20. 20. Basic Reports Simplest solution for many requirements Rows and columns Detail, summary or both Daily, weekly, monthly Must know source of data Validation
  21. 21. Basic Graphs  Bar, line, area – picking the appropriate display for the data Comparing trends – YTD, previous year, nursing units, product lines Making data actionable – how will this graph change what I am doing?
  22. 22. Big Data Visualization Assist in understanding greater degrees of complexity May be 3 dimensional Geocoding of data into maps Network diagrams Layers of data Must work with customers to find most effective display which is actionable
  23. 23. Disease prevalence and socioeconomic status across the US
  24. 24. Network Diagram showing relationships between concepts
  25. 25. Heatmaps
  26. 26. Future of Clinical Data Repositories Become familiar with big data tools, such as, Hadoop, NOSQL, and use in unstructured data Understand data growth – how big will your data be a year from now? Begin to do real-time or near real-time reporting to impact quality Stay on top of changing definitions
  27. 27. Three Success Factors of a Centralize Data Repository Buy in and governance Organizing Data Displaying data in a meaningful way