Data Governance: Foundation to Leveraging Data as an Asset Paula J Edwards, Ph.D. Jim Gaddis, MBA, FHIMSS

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1 Data Governance: Foundation to Leveraging Data as an Asset Paula J Edwards, Ph.D. Jim Gaddis, MBA, FHIMSS DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Paula J Edwards, Ph.D. And Jim Gaddis, MBA, FHIMSS Have no real or apparent conflicts of interest to report HIMSS

3 Learning Objectives Describe key components of a data governance program Identify organizational benefits from data governance Reveal risks from not having a data governance program Analyze the role data governance should play on key initiatives like EHR implementation, IDC10 compliance, ACO participation and clinical/business intelligence

4 Topics What is data governance? Case studies: It s not just for analytics Benefits of data governance Putting data governance into practice

5 What is Data Governance? Processes that ensure that data assets are effectively managed across the enterprise Develops and institutes standards, policies, processes, guidelines, framework and technology for management, usage, distribution and protection of enterprise data Standardizes and oversees how data is processed, prioritized, and continually verified 5

6 Managing data as an asset The goal of data governance is to provide data that is: Consistent One enterprise-wide definition Accurate Correctly represents underlying process Timely Available to authorized users when needed Accessible Easy to find, understand and use Actionable Relevant to business decisions

7 Managing data as an asset To be effective, your data governance program needs to: Promote collaboration between business, clinical & IT groups Clarify roles, responsibilities, & privileges related to data Provide transparency into decision making processes Address change management needs

8 Key Components of a Data Governance Program Data Quality Change Management Data Integration Master Data Management Standards Data Distribution Data Stewardship Security Metadata Management 8

9 Data Integration Automating and stream-lining business rules for integrating data from disparate sources Standardizing and cleansing data as it is integrated and aggregated Standards Standards tool sets & policies for data management Standard definitions for metrics and terms Standard tool sets and templates for information delivery Data Quality Monitoring and continuous improvement of data quality including identifying data quality problems, cleaning bad data, and implementing processes/controls to avoid future bad data.

10 Master Data Management Data Stewardship Defining the source of truth, standard codes and descriptions for entities used across the organization like physician list, patient list, organization hierarchy for reporting, patient type, payor and payor type. Business owners of a particular subject are who provide expertise on what data mean and how data should/should not be used. They take an active role in managing data quality & master data and defining enterprise-standard metrics and definitions. Metadata Management Data dictionary encompassing both business and technical definitions and data lineage User access to the data dictionary

11 Organizational Perspective Executive-level Strategy & Oversight Set strategy & priorities Fund & resource initiatives Oversee & monitor Approve policy/procedures Approve standards Communicate Operations-level Data Management Data Stewardship Database administration Manage security Data integration Performance monitoring & management Monitor & improve data quality Manage & publish metadata Develop common views Establish standard metrics & definitions Define data access rules Educate & Communicate

12 Data Governance: It s not just for Business Intelligence & Analytics Case Studies

13 EHR Implementation Master data management across EMR, ancillary systems Avoid problems during integration testing Prevent production issues by ensuring master data stays in sync over time Data standards & metadata: everyone talking the same language, using the same business rules for metrics Data integration: build interface once, leverage many times Avoid implementation & support costs Less interfaces = less opportunity for production problems

14 Example: IDN Physician Identity Challenge Problem: Multiple physician data stores that did not agree Solution: Create core / additional structure and manage input privileges

15 Accountable Care Organizations Security Reduce barriers to participation due to security/privacy concerns Reduce HIPPA risks & associate costs Standards, Metadata, & Data integration Improve comparability of data across patient and provider groups More consistent, complete, and reliable reporting across a divergent organization Data quality & data integration More accurate & timely cost-accounting data

16 Example: Community Hospital Charge Description Master (CDM) Problem: Multiple departments managing their portion of the CDM individually poor master data! Descriptions, pricing, etc. not standardized Led to confusion in billing and profitability analysis Solution: Centralize and standardize CDM maintenance Standardize descriptions for billing Minimize duplicates and errors Enable more complete, accurate data analysis

17 Example: Academic Medical Center Location /Function Schedule Pre-Certify Pre- Register Verify Insurance Register Create Encounter Arrival Cleanup Patient Access Call Center Main Admissions Point of Service Emergency Back Office Registration Core Clinical Specialty Insurance Problem: Data originating from many functional areas in systems specific to each area Solution: Split data into patient and encounter data and manage input quality

18 ICD10 Conversion Data quality: profile data prior to conversion Prioritize training & data clean-up efforts Data integration: mapping ICD9 to ICD10 values for historical reporting after conversion Support operations management and decision making Facilitate early identification of revenue problems during the cutover

19 Health Information Exchange (HIE) Master data management & standards: Enable structured data exchange for care coordination and clinical decision support Master data management & data Integration Automate matching incoming data to the appropriate patient, provider, organization Security Remove barriers to adoption due to security/privacy concerns Comply with HIPPA regulations

20 Business Intelligence/Analytics Master data management, data integration & data quality Reduce effort required to generate reports through reduced need to manually map, merge, & clean data Data quality & data integration Consistent, timely results means better information to support tactical & strategic decision making and quality improvement More complete, timely information on quality and cost to negotiate pricing with payors More reliable reporting for regulatory programs (e.g., Meaningful Use, Core Measures, PQRS)

21 Benefits of Data Governance Reduce costs Reduced cost & complexity of data-related initiatives resulting in reduced time-to-market and FTEs for integrating and cleaning data Reduce duplicate efforts in data management & reporting Increase revenue & value to the organization Through specific data-driven initiatives Avoid bad decisions based on incomplete or inaccurate data Manage risks related to data access HIPPA & other privacy/security regulations Intellectual property/proprietary knowledge

22 Putting Data Governance into Practice Identify strategic priorities that are driving the need for data governance Build a business case based on these Build executive support & sponsorship Identify existing data governance assets you can build on Formal & informal processes, policies, procedures Organizational structures; informal roles/responsibilities IT tools from your current vendors

23 Putting Data Governance into Practice Identify gaps in your data governance program Develop a roadmap for incrementally filling those gaps Educate the organization Develop a culture of collaborative information management Develop skills in managing and using information effectively

24 Thank You! Paula J Edwards, Ph.D. (678) Jim Gaddis, MBA, FHIMSS (404)

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