Dr. Matt Hoffman, Chief Medical Informatics Officer
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1 Dr. Matt Hoffman, Chief Medical Informatics Officer
2 Interoperability and Population Health How interoperability between systems can assist in population health and risk management.
3 OVERVIEW Intro to UHIN Value added by an HIE - Providers, Payers, Public Health Improved Population Health Increased Risk Assessment Accuracy Specific Use Cases in Utah w/ Demos
4
5 25th Anniversary Founded in 1993 Convened by community Non-profit
6 Lines of Business:
7 TODAY We are a successful non-profit clearinghouse 3 million + 3 million + 2 million + Claims/payment remittances monthly Eligibility requests Remittance advices We are considered a gold-standard in HIE 5 million 53 million Western hub Unique lives from UT and around the world Records in the CHIE Connected to other western states
8 Providers get paid. Payers get solutions. Patients get care. Concierge Service Electronic eligibility Claim submissions / status / scrubbing Remittance advices Coming down the pike Electronic attachment web-based tool Better reporting tools
9 25 years of experience processing millions of HIPAA transactions every day / /
10 Helps providers and payers see their patient s whole story Patient s data follows them no matter where they go Tools ADT alert notifications Direct messaging Public health reporting Data normalizations Last handful of independent Utah hospitals coming on line in 2018 ADT alerts from other states AZ, CO, ID, NE, NV CA, OR, WA coming in 2018
11 State-designated HIE 5 million 53 million unique lives in MPI medical records 500+ Western hub data sources Connected to other western states
12 Newest UHIN solution. Combines claims and clinical data. Offering Real-time data visualization dashboards Custom, actionable reports Filterable dashboards Heat maps Geomapping Tables And more!
13 ARCHITECTURE
14 Reporting Engine Interface Engine MPI Engine Standardization Engine Data Replication Statewide HIE - Clinical Data Infrastructure Alerts Engine Data Warehouse (DW) Hospitals Providers Central Data Repository (CDR) UHIN Analytics Labs Dashboards Analytics Reports Provider Portal Web-based access
15 UHIN Firewall Python Engine Staging DB Data Warehouse MSG DB Indexing MPI ADT LAB MPI DB NLP REC Once data is inside UHIN firewall, Mirth channel feeds it to a Staging Database Different feed jobs run nightly - Read data from Staging DB - Populate it into individual data stores in the DW During Extract, Transform, Load steps - Standard HL7 fields are indexed - From the unformatted text, Natural Language Processing Engine extracts - Problems - Procedures - Medications - Vitals Custom Python scripts are developed - Read from the DW data stores - Generate data extract, which populated dashboards
16 Adding Value Providers Payers Hospitals Public Health Others
17 PROVIDERS Knowledge of the patient s whole story Value adds Comprehensive view of patient record Follow up on referrals Measure outcomes Fill gaps in care Decrease repeat exams / labs
18 PAYERS Access, improved ratings and progress tracking Value adds Access to clinical data Lab Transcriptions Radiology Admissions / Discharges Improved outcome measures / star ratings Track progress not just adherence Better case management
19 HOSPITALS Better data, improved feeds Value adds Maintain / monitor fewer feeds Improved follow up after discharge via alerts Better readmission data Identity management assistance
20 PUBLIC HEALTH Monitoring and preparation Value adds Chronic disease monitoring Epidemiology Disaster preparation Registries Cost of connections
21 OTHERS Long-term care Home health Behavioral health EMS Poison control Etc.
22 OBSTACLES TO POPULATION HEALTH ANALYSES Complete sets of data (including from outside the health system) Clean data Normalized data Unstructured data Duplicate data
23 POPULATION HEALTH SOLUTIONS Comprehensive view of patient population Data normalization layer Combination of clinical and claims Natural language processing (NLP) Actionable tools
24 RISK ASSESSMENT Comprehensive view of patient history Claims vs clinical information NLP vs structured data Actionable tools
25 USE CASES Risk assessment Diabetes Syndromic Surveillance Readmissions
26 Accurate Complete Risk Assessment Risk Adjustment Analysis Architecture TRN HL7 RAPS X12: 837 HIE Data Warehouse X12: 834 APCD Enrollment Jay Z
27 ICD-X from Clinical Notes
28 Harvesting Diagnoses from Clinical Notes X12 (837) Claim 1234 Clinical Note Assessment and Plan Problem 1 Problem 2 Problem 3 Problem 4 Problem 5 Problem 6 Problem 7 Problem 8 Problem 9 Problem 10 Clinical Review SNOMED/ICD 10 Diagnosis 1 Diagnosis 2 Diagnosis 3 Diagnosis 4 Diagnosis 5 X12 (837 Supplemental) Claim 1234 Diagnosis 6 Diagnosis 7 Diagnosis 8 Diagnosis 9 Diagnosis 10
29 DEMONSTRATION
30 QUESTIONS / THUNDEROUS APPLAUSE
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