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Breaking Down Barriers with Master Data Management

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Breaking Down Barriers with Master Data Management Breaking Down Barriers with Master Data Management and Data Governance Session #249, March 8th, 2018 1:00 PM – 2:00 PM Kim Jackson, Vice President, Strategy, Products and Governance, Providence St. Joseph Health 1 Conflict of Interest Kim Jackson Has no real or apparent conflicts of interest to report. 2 Agenda • Providence St Joseph Health Overview • Creating Funding for Data Governance and Master Data Management • Presenting a Clear ROI to Support Funding for Data Governance and Master Data Management • Challenges facing successful program 3 Learning Objectives • Define the stages of a master data management and data governance program • Illustrate key uses cases and metrics to prove hard ROI for engagement and funding • Evaluate some of the challenges and barriers encountered throughout the data governance process 4 About us 5 Data Governance Mission Developing TRUST in data to run a business that is consistent, timely and integrated Increase TRUST in the data Enhance data quality Master key data domains Encouraging more, not less, data Create a culture of healthy data Resolving analytic priorities access Establish a foundation for future Establishing standards for master information-based initiatives and Campaign for data literacy reference data innovations 6 Holistic Approach People and Technology Without Process and Process Technology Automated Chaos and Confusion Without People Poor Service Delivered Alienation and Turnover Underutilized Systems People and Process Without Technology Frustration and Inefficiency High Cost of Operation 7 PROCESS Manage Standardize Manage Measure,Data Monitor Quality & Automate Collaborate Data Quality Data Data Quality Fix Process On-going Process Data Quality Management People People People People Technology Process Process Process Process Technology Technology Establish Support internal Establish data Establish data Ensure data is enterprise process, and external quality standards quality report card created as close to policy and data across the and escalation the source as technology synchronization organization process possible architecture to manage business- critical data quality Leverage Utilize technology industry data to improve governance best manage data Establish a value practice to quality based roadmap manage data aligned with quality Analytic Governance Council strategy 8 Create Funding for Data Governance and Master Data Management Call for Action Voice of Determine the Real ROI Customer Funded Program Vision and Road map Goals Highlight pain points 9 Call for Action Determ Voice ine of the Real Custom Reason for Action ROI er Funded Program Vision Road and map Data governance is a set of processes that ensures that Goals Highlig ht pain important data assets are formally managed throughout the enterprise points 1 Need for more timely, accessible, consistent, high quality information for • Improving reimbursement, cost efficiency • Improving quality of care and patient safety (e.g., reduce readmissions) • Support at-risk payment models/ACO participation 2 Business Case: • Support strategic Initiatives– foundational IS component to support Portals, Big Data/Population Health, EMR Reginal efforts, HIE • Reduce time spent extracting, merging, validating, and cleaning data and refocus those FTEs on analyzing and using data • Improve timeliness and quality of information for strategic & operational decision making and performance improvement 3 In Scope: • Identify data governance needs (e.g., data quality, master data management, metadata management) to support Information Sophistication initiatives • Inpatient, Ambulatory, Home Health, Community Health Ministries • Propose tactical recommendations and implementation roadmap Out of Scope: • IT Governance; Implementation (this will be a subsequent phase) 10 10 Call for Action Determ Voice ine of the Real Custom Voice of the Customer ROI er Funded Program Vision Road and map Goals Highlig ht pain Representative Quotespoints Key Themes Significant barriers to accessing information Lack of data integration—need to access multiple systems Significant manual effort required to get needed data Lack of trust in the data Need for more timely data Need for standardization Lack of accountability Difficult to turn data into actionable information 11 Call for Action Determ Voice ine of the Real Custom Create Vision and Goals ROI er Funded Program Vision Road and map Goals Highlig ht pain points Vision Goals Establish processes that ensure data assets are Provide data that is: effectively managed across the enterprise Consistent – One enterprise-wide definition Accurate – Correctly represents underlying process Timely – Available to authorized users when needed Develop standards, policies, processes, guidelines, Accessible – Easy to find, understand and use framework and technology for management, use, Actionable – Relevant to business decisions distribution and protection of enterprise data and compliance with standards and policies Standardize and oversee how data is processed, Provide knowledge, skills, and tools to enable data-driven prioritized, and continually verified decision making at multiple levels of the organization Establish a foundation for Analytic Services and future information-based initiatives and innovations 12 Call for Action Determ Voice ine of the Real Custom ROI er Pain Points Funded Program Vision Road and map Goals Highlig ht pain Projects Analytics Team listed as too completed but could say ‘Yes’ to if they had Governance tools points EIM Module Tasks Supported/Benefits Example Data Integration • Shorten development/testing of extract/integration PICIS Integration to NHSN programs so DW and other teams can spend less Integration to EPSI and other 3PA time developing, more time analyzing Systems • EIM is for Batch/ as HL7 is to Clinical Master Data • Integrate med staff and community provider data from Managing IPC’s is manual process IP & Amb credentialing systems, MEDITECH where an Excel list is sent to Each Management instances, Allscripts, and Allscripts Home Health and Org, HH, IDX, ETC. hoping they will publish a ‘golden record’ to Portal and Explorys be integrated. • Integrate list of payors/payor types across contracting, MEDITECH instances, IDX, and Allscripts Home Health Data Quality • Publish a dashboard of data quality metrics to 1) CPT, POA, Complication Rates, - proactively identify and resolve problems Providers DOC? • Setup business rules to identify when # of encounters 2) Admission Types, Charge Cost changes by X% at any location and alert the data Ratio’s – Staff Complete Charges? steward of the potential issue 3) Reduction in RAD Reports – 13 Interface Breaks Call for Action Determ Voice ine of the Real Custom ROI er Pain Points Funded Program Vision Road and map Goals Highlig ht pain Projects Analytics Team listed as too completed but could say ‘Yes’ to if they had Governance tools points EIM Module Tasks Supported/Benefits Example Metadata • Provide a glossary to business users with Measures: Readmit , CPOE, MU, Management metric/field definitions and linage showing Central Line Days, NHSN, Pressure the source Ulcer Progression, Census, Surgical • Do an impact analysis of downstream Procedures, Algorithms: LACE, impact to Amalga and reports of changes Adjusted Pt Days to a MEDITECH field before making the DIAG Groups: CHF, SEPSIS, AIM, change Chief Complaint, Surgery pt, Obs Pts Terminology/ • Translation of ICD9 to ICD10 codes to Glucose tests, Critical Care Day, Ontology Services plan for the transition and for retrospective Lactose, Antibiotics, pressure ulcers, reporting of trends after the change over Social Risk Factors, Positive • Translation between LOINC, SNOMED, Culture, OR Charges. OBS ADMIT CPT, ICD codes for regulatory & quality Order reporting 14 Call for Action Determ Voice ine of the Real Custom ROI er Funded Program Vision Road and map Goals Highlig ht pain Most Common Road Map ROI Driven Road Map points MDM/ Reference Oversight Data Committee KPI standardization for Analytics KPI Standardization/ Data Glossary/ MDM/ Data Glossary Skills Assessment Reference Oversight Data Committee for Reporting Data Literacy Prioritize Reporting Demand Master Data Master15 Data Expand Reach Prioritize Domains Road Map Phase 0: Phase 1: Phase 2: Phase 3: FY13-14 FY15 FY16 Mature * Operational rollout *DG is part of business *Interviews *Focused pilots (JIT *Streamline Global codes operations *strategy involvement of business) (Reference Data) **Reference Data across *tool set selection **Narrow scope *Broader scope within all analytics/Reporting *Defined requirements for Provider/EMPI same data domains, *Additional data domains first two projects downstream consumption and problems *Proposed org structure *Technical infrastructure and process *Lean exec oversight *Business assumes data *Mature DG organization stewardship (with Analytics Governing * Documented current – IT/ Strategy Owner *Slightly broader exec Council, Data Standards state information *Global codes in EDW’s workgroup) architecture oversight – IT and Data *Data Literacy Owners *Data Literacy *Data Literacy *Data Literacy **Data Glossary Scope of data problems As scope is broadened, Data governance Define Scope, strategy, to be solved is narrow; exec oversight required to becomes embedded make prioritization decisions fund DG can’t be solely a into operational 16 and to enforce “complaint forum” accountability processes Data Literacy Analytics 30 Program: Domain Training and Documentation • 2X per month 30 Min Education
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