Source of Truth Becoming a Data Driven Organization

Transcription

1 Source of Truth Becoming a Data Driven Organization Shawn S. Sutherland, CPHIMS Manager, Patient/Member Outcomes Shawn_Sutherland@IBI.com Not all data are created equal 2 Data we must treat them as a valued asset, but why? YOU must achieve the Quadruple AIM! Quality Satisfaction Productivity Cost 3

2 Where is Data Utopia? Can the EMR do it all? 4 Can the EMR do it all? One system s analysis 5 6

3 I did NOT see that coming! What s your pick: 4X AIM, STEEEP Care, Pillars, Other? Understand population cost and outcomes by chronic conditions Specific Healthcare Data Challenges Lack of Flexibility to Consume, Harmonize, Govern and Understand Imaging Ambulatory Surgery Rehab Skilled Nursing Disparate EMRs, Cost, Billing, ERPS Patient Matching Errors Productivity Loss Incorrect Billing Lack of Timely Care Higher Costs Poor Decision Making Pharmacy Clinic Outpatient Rehab External Data Needed Patient Demographics Where Do Patients Go for Care Where Patients Live Device Data Devices Inpatient Care Home Care Bi-Directional Data Exchange Consume All Formats Push Information Downstream 9

4 Data Value Chain Sequence is Important Avoid the Don t worry about the mule going blind, just load the cart! mentality! Data Business Value Connect RDBMS Application E-Business Legacy SaaS Big Data Move Batch Transactional Event Driven SOA Automate Orchestrate Fix Profile Clean Enrich DQ Firewall Relate Master Data Organize Synchronize 360 View Govern Monitor Visualize Alert Remediate Report History Business Intelligence Dashboards Analytics Ad Hoc Reports Enterprise Search Mobile Visualize Predictive Social Intelligence Performance Mgt Integration Integrity Intelligence 10 The Chain of Value for Data Management Capabilities needed to deliver healthcare industry metrics Data Canonical Healthcare Information Model Reference Data Management Business Value Analysis Consumption Source Data Mastering Clinical Data Code Sets Code Maps Algorithms Presentation Complexity: Medium Complexity: High Complexity: Medium Complexity: High Complexity: High Complexity: Very High Complexity: Medium Loaded from multiple source systems, either in batch or transactionally; from databases, flat files, HL7 messages or HIPAA transactions, etc. Cleanse, match and merge to resolve differences in data across systems. Provide a process for data stewards to implement continuous data quality improvement of patient, provider, facility, organization, member and worker data. Longitudinal, 360-degree view of patient s clinical history, tied back to the mastered patients, providers and facilities is materialized in health data marts for ease of consumption. Must have the latest versions of industry code sets such as ICD- 9/10, CPT, HCPCS, LOINC, RXNORM, NDC, SNOMED, etc. and map custom organizational codes to these and add code metadata. Industry value sets from CMS, NIH, CDC, NCQA, AHRQ should be used to interpret codes into meanings. These are indispensable for implementing standard care quality metrics. Complex query processes are needed to implement industry algorithms and use the data to create patient and episode cohorts, apply inclusion / exclusion rules and aggregate into annual and rolling 12 month materialized metrics views/marts. Build views specific to their presentation and consumption needs and display those data through a business intelligence system. 11 Healthcare Data Challenges easy? Example #1 Chronic disease data by gender and marital status Patient Gender(22) Omni-Patient 01 M F T I N MALE FEMALE A B C D UNKNOWN 09 N/A TRANSGENDER OTHER <NULL> Marital Status (37) MRD LSP DIV OTHR NULL MRD LSP DIV ,02S ALD OTR SGL A01 D01 M00 MS_001 DPR UNK MS_002 W04 D02 I01 WDW MS_003 S01 R _M1 MS_ _M2 Validation Harmonization Standardization Omni-Codes Other Reference Medical Terminology Patient Gender (6) A Ambiguous F Female M Male N Not applicable O Other U Unknown Marital Status (16) A Separated B Unmarried C Common law D Divorced E Legally Separated G Living together I Interlocutory M Married N Annulled O Other P Domestic partner R Registered domestic partner S Single T Unreported U Unknown W Widowed 12

5 Healthcare Data Challenges kind of easy? Example #2 CNO Nursing Report by Hospital, Unit, and Bed Hospital Name IBH of New York IB and NY Medical Center IBHealth Hospital of New York IBNY Med Center The IBHealth Medical Center, NY Unit 4 North 4-North 4_North 04North 4N Bed 4N 420 4N-420 RM_ N420 In other systems: IBNY Medical Center IBHealth Medical Center IB NY CMS Hospital Name TIN Hospital Name 13 Healthcare Data Challenges OK, that s hard! Example #3 develop my own cohorts by various clinical ontology hierarchies SNOMED CT ICD-9-CM ICD-10-CM/PCS CPT-4 Medical necessity Age/gender edits HCPCS APC MS-DRG, AP-DRG LOINC DSM IV Medications: RXNorm, FDB, NDC, NDF-RT, Medi-Span, Multum Terminology Sets (partial list) HLI Medical Specialty Subsets PQRS Subsets HLI Medical Specialty Subsets Nursing: NIC, NOC, NANDA HL7 CDT UCUM UNI HRG CCI CVX, MVX Rev codes Multiple languages Mappings (partial list) SNOMED CT to ICD-9-CM SNOMED CT to CPT SNOMED CT to MeSH SNOMED CT to ICD-10-CM/PCS ICD-9-CM to ICD-10-CM/PCS ICD-10-CM/PCS to ICD-9-CM ICD-9-CM to SNOMED CT ICD-10-CM to SNOMED CT CPT to SNOMED CT DSM IV to SNOMED CT RxNorm to NDF-RT CPT to CVX 14 Modernize Data Management Putting it All Together EMR Data Epic, Cerner, AllScripts, etc.. Payer Data Claims, Payer system, etc.. HR/Provider Data Lawson, KRONOS, etc.. Financial Data Accounting, GL, Cost, etc.. Externally Fed Data HIE, Census, Location, Death Index, Benchmarks, Blockchain, IoT, etc.. Data On-ramps HL7/CCD, EDI, relational, XML, etc.. Reference data Integrate, Cleanse, Correlate, Govern Code sets: HLI HealthViews Relational Repository Downstream Apps empi EMR/EHR Registry Analytics Decision Support Self-Service Predictive Data Repositories External Benchmarks HIE Portals Blockchain 15

6 A Note about Data Governance (and technology for lunch) 16 Data As An Asset HUMAN RESOURCES FACILITIES/ EQUIPMENT MONEY DATA QUALITY Performance Reviews Applicant Screening Training Preventative Maintenance Scheduled Testing Balancing Totals Threshold Alerts Audits Proactive Profiling Defining Rules Assigning Ownership META DATA Demographics Employment History Education Inventories Balance Sheet Income Statement Business Glossary Application Inventory Code & Reference Sets Titles Standardized Employee ID MASTER DATA (and technology for lunch) Equipment Standardized Vendors Standardized Currency Currency Conversions Patients/Members Workforce Organization & Facility Provider & Clinical Payer & Financials POLICY/ PROCESS Job Descriptions Reporting Structures Defined Processes Clear Accountability Clearly Defined Processes Clear Accountability Decision Rights Regulated Processes What does this mean? Who needs to fix this? Whose decision is it? 17 Data Governance Organization By Subjects, Departments and Source Systems LEADERSHIP COMMITTEE Business Rules, Standards, Design & Tools Chair: Executive Sponsor Quality Management Physician Clinical Effort Reporting Operations Patient Access Ambulatory Rev Cycle Patient Satisfaction Hospital Finance Ambulatory Quality Hospital Quality Surgical Services ED/IP Ambulatory Revenue Expense Service Lines Work Flow/ Process Data Analysis 18

7 Achieving Speed to Value Capitalize on the Right Approach and Technology Time to Market Quickly Onboard Data from Disparate Systems Clinical and Business Integration Harmonize Data from Disparate Clinical and Business Systems Ensure Data Accuracy and Automate Codeset Matching Match and Merge Diagnosis, Procedure Codes and other prebuilt code-sets Establish Enterprise Data Governance Consistency from Source to Enterprise Analytics Automate Data Feeds to HIEs, Payers, Providers, Portals, EMR & DSS Complete with History and Data Archiving Features Improve Decision Making Across Care Settings and Drive Value 19 Data Driven Decision Making Enabled! Strategic Analytical What Happened? o Clinical-Operational-Financial KPIs Why did it happen? o Discover Patterns and Outliers o Visualize Improvement Opportunities Performance Scorecards Business Discovery Operational Do something about it! o Take Action with Fit for Purpose Analysis o Reactive to Real Time to Prescriptive Push Externally - Consumerism! o Payers, Providers, Patients, SNF, Services Operationalize! Healthcare is collaborative by nature Think Possible 21

8 Q&A successes, failures and landmines Credit 22 Source of Truth Becoming a Data Driven Organization Shawn S. Sutherland, CPHIMS Manager, Patient/Member Outcomes Shawn_Sutherland@IBI.com