Newer Developments in EHRs and Research. SD Fihn MD MPH Office of Analytics and Business Intelligence Veterans Health Administration

Transcription

1 Newer Developments in EHRs and Research SD Fihn MD MPH Office of Analytics and Business Intelligence Veterans Health Administration May 17, 2013

2 EHR Today What is there Provider and machine entry of data Efficient storage and retrieval of single encounter data Very limited trending, e.g., individual lab values or vital signs Limited, rule-driven reminders and alerts Limited communication among health care team Limited communication with patients and care-givers Rudimentary portals Limited capacity for patients to enter, retrieve, manage information 2

3 EHR Today Some of was is NOT There High level search capability Intelligent aggregation of data Customized presentation of information based upon context (User/Patient) and importance Integration of data sources including pt.-generated data Filtering of dense data streams Routine presentation of high-level analytic, modeled information 3

4 Further Shortcomings of the Modern EHR Too expensive Purchase price, maintenance, upgrades Hardware Personnel Proprietary Inflexible and difficult to customize Steep learning curve Lack of interoperability Difficult to integrate with other applications Too political 4

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8 The Future EHR Integrated Information Management Systems Platform Independent (cloud-based) High-performance transactional data systems Highly customizable (easy integration of compatible apps, including open source) Multiple data flows (many continuous, real-time) Patient entered data Biometrics, Monitors Specialized software (e.g., Surgery, ICU) Devices (including RFID) Powerful background analytics 8

9 What will be needed Standardized data of dependable quality Real-time time data flows Data collection integrated into workflow and process improvement (transactional quality) Flexible, customizable, context-sensitive reports Data extracted from text-based sources Population-based data Routine display of modeled data showing probabilistic assessments of important trends & events 9

10 10 Evolving VA-DoD iehr User Experience (UX) Common Functional Community Service Definitions (35) DoD Unique (1) Operational Medicine Enabling Services Direct Clinical Services Genomics Emergency Department Care Patient Consent Credentialing Teleconsultation Disease Management Laboratory / Anatomic Pathology Occupational Health Encounter Coding Patient Identity Management Anesthesia Documentation Consult and Referral Mgmt Registries Barcoding (Information & Data Capture) Disability Evaluation Specialty Care* iehr Core (Clinical) Pharmacy Scheduling Appointment Orders Service Documentation Registration/ Enrollment Eligibility Clinical Decision Support/Alerts Document Management Mental Health Blood Management Radiology and Imaging Mgmt & Avail. Patient Safety Reports Patient Portal / Personal Health Record Secure Messaging Patient Education Dental Care Immunization Utilization Management Business Intelligence Operating Room Management Nutrition Care Care Management Medical Device Management Private Sector Data Access Supporting Infrastructure iehr Core (Supporting) Portal Framework Network and Security Architecture Patient Portal Infrastructure Info Mgmt & Terminology Services Access Control Fed Data Repository / Data Warehouse SOA Architecture Workflow Engine Single Sign On / Context Management iehr Core Joint DoD/VA DoD Only VA Only Open HDD Common DoD-VA Measures of Effectiveness, Measures of Performance and Key Performance Parameters iehr Platform Services Broker (includes Enterprise Service Bus (ESB) and Infrastructure Services) Data Centers NwHIN/VLER External Gateway

11 CART Prototypical App Standardized data capture at all VA Cath Labs natl data repository Core data elements conform to ACC- NCDR standards Extensible database structure fqcilitates modification & expansion Operations Research If specific data are needed for patient care, to improve quality and increase patient safety, these data should be available and accessible at the point of care. Robert Jesse, VHA Principal Deputy Undersecretary

12 CART Capabilities Standard local and national reporting Patient Safety Device Safety monitoring (w/ FDA) PCI-only sites Medication adherence clopidogrel Bleeding risk Risk for contrast-induced kidney injury Quality/Value National adverse event reporting and peer review Appropriateness evaluation Patient reported outcomes (SAQ pre- and post-procedure) Stent selection Occupational safety Radiation exposure monitoring Asset management RFID 12

13 VA Analytic Ecosystem Common Data Common Infrastructure Common Tools Common Security REGION 1 REGION 2 REGION 3 REGION 4 CDW System Facts: CDW Vx System: Vy Vn VISN:18-22 VISTAs: 33 VISTA source systems: 130 Extract tools: Active Users: 20,000 R1 R1 RPT Vx VISTA source systems: 130 Extract tools: Data facts: Production Journal Reader Regional Journal Reader Batch Extractor Data facts: Processing CDW Sample Data Facts: cores Domains of information: 51 Unique patients: 20 million Rows of data: 80 billion 1.5 Petabytes Outpatient storage encounters: 1.6 billion Columns of data: 20,000 Inpatient admissions: 9 million Tables of data: Active 840 Users: 20,000 Clinical orders: 3.2 billion Lab tests: 5.6 billion xdw Hardware Facts: Pharmacy fills: 1.5 billion Processing cores Radiology procedures: 162 million 1.5 Petabytes storage Vital signs: 2.3 billion Text notes: 2.0 billion Hardware: Vy Vn Production Journal Reader Regional Journal Reader Batch Extractor Domains of information: 51 Rows of data: 80 billion Columns of data: 20,000 Tables of data: 840 Vx Vy Vn R2 VISN:12-17 VISTAs: 29 R2 RPT Vx Vy Vn Unique patients: 20 million Outpatient encounters: 1.6 billion Inpatient admissions: 9 million Clinical orders: 3.2 billion Lab tests: 5.6 billion Pharmacy fills: HPC 1.5 billion Grid Radiology procedures: 162 million R&D CDW RPT Vital signs: 2.3 billion Text notes: Ana Enterprise GIS 2.0 billion App Store Apps VISN:6-11 VISTAs: 41 CDW Corporate Data Warehouse (Production, Raw, Static, R&D Mirror) HPC Grid Analytic Compute Grid (SAS, SPSS, R, HADOOP Cluster) RPT Reporting Farm (SharePoint) Ana Apps Analytical Applications (Mobile, Web, NLP) App Store Central store of shareable analytic applications GIS Geospatial Intelligence System (ESRI) R&D Clinical and Operational Research (e.g. VINCI Project) Vx Vy Vn Vx Vy Vn VISN:1-5 VISTAs: 28 R3 R3 RPT R4 Production VISTA Vx Vy Vn Vx Vy Vn R4 RPT Vx Vy Vn VISTA Analytic Shadow Rx Regional Data Warehouse Rx RPT Reporting Farm VISN Data Warehouse Vx Vy Vn 5

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15 Developing Context Sensitive Orders 30 million records 20 years of data mined to identify relationships between Problems Laboratory results Prescriptions Procedures Orders Location Provider Patient 15,000 conditions and 10,000 orders

16 Common Procedure Orders Epigastric pain CBC with diff Chemistry panel Helicobacter pylori Lipase Amylase Upper GI endoscopy Abdominal ultrasound CT abdomen w/o contrast Omeprazole Ranitidine Amphogel Rabeprazole Promethazine Sucralfate Metronidazole Metoclopramide Docusate

17 Predicted and Observed Likelihood of Death/Admission 4,505,501 primary care patients 17

18 95 th %ile 39% 96 th %ile 42% 97 th %ile -- 49% 98 th %ile -- 57% 99 th %ile -- 72% Patients in highest percentile of risk have 62% probability of admission, 30% probability of death, and 72% probability of either event 18

19 Risk Data Updated Weekly About 1000 users monthly

20 Use of High Level Analytic Data for Population Management and Resource Planning 1-yr likelihood of admission or death 1-yr likelihood of admission 1.86% % 5.94% % 7.01% % 7.98% % 9.22% % Dynamic Geospatial Mapping 2.37% % 9.04% % 10.02% % 10.97% % 12.19% % 12

21 Specific Events / Decisions Increased risk of renal failure, e.g., with contrast Increased risk of C. difficele infection with antibiotic High risk for suicide High likelihood of MRSA colonization

22 Research Opportunities Traditional clinical epidemiology/health services research (but with much bigger data ) Comparative effectiveness assessments Predictive analytics Data Mining -- linkages Patient engagement (Built-in) Platform to evaluate health care interventions (will become standard) 22

23 Caveats Privacy and security Human subjects protections (operations vs. research) Data quality/validity Signal to noise New competencies e.g., Bayesian statistics, NLP 23