Enabling Clinical Interoperability through a Primary Health Care Electronic Medical Record Content Standard

Transcription

1 Enabling Clinical Interoperability through a Primary Health Care Electronic Medical Record Content Standard Infoway Fall 2015 Partnership November 24, 2015 Charisa Flach, Project Lead, Primary Health Care Information Ginette Therriault, Program Lead, Classifications & Terminology 1

2 Session Objectives Discuss how PHC EMR Content Standard offers a pan- Canadian solution to facilitate the capture of structured and standardized data Highlight some of CIHI initiatives to date on advancing PHC EMR Content Standard: Jurisdictional Readiness Assessment Demonstration Projects Considerations for the future 2

3 CIHI s Journey working with EMR data How Did We Get Here? Earlier Versions of EMR CS (2.1) and PHC VRS 500+ Clinicians over 3 years 106 Data Elements Across 3 Jurisdictions Environmental Assessment and Data Quality Review Focus on smaller set of data elements Clinician- Friendly Pick Lists Revised Strategy and new Version of EMR CS (3.0) 45 EMR Data Elements Jurisdictional Advisory Group Clinician Input and Review Conference of Deputy Ministers of Health Endorsement, Dec 2014 Readiness Assessment to Inform CDM report back Demonstration Project Partnerships Initiated 3

4 Primary Health Care Electronic Medical Record Content Standard (PHC EMR CS) v3.0 Version 3.0 is focused on a smaller set of EMR data elements Priority Subset: 45 priority data elements that are commonly captured in EMRs and meaningful for clinical and health system use. CFPLs: 8 constrained lists of clinician-friendly terms mapped to appropriate code systems ICD, CCI, SNOMED-CT 20 PHC Reference Sets (from Infoway) Clinician-Friendly Pick-Lists (CFPL) Health Concern Clinician Assessment Reason for Visit Social Behaviour Intervention Diagnostic Imaging Test Ordered Referral Vaccine Administered 4

5 PHC EMR Content Standard v3.0 Priority Subset and Clinician-Friendly Pick-lists DE # Data Element Name DE # Data Element Name A1 Patient Identifier E29 Height Unit of Measure* A2 Patient Identifier Type* E30 Weight A3 Patient Identifier Assigning Authority* E31 Weight Unit of Measure* A4 Patient Date of Birth E34 Clinician Assessment A5 Patient Gender* F1 Intervention A9 Patient Status* F2 Intervention Date A14 Patient Postal/Zip Code G1 Lab Test Ordered* B4 Clinician Identifier G2 Lab Test Ordered Date B5 Clinician Identifier Type* H1 Lab Test Performed Date B6 Clinician Identifier Assigning Authority* H2 Lab Test Name* B7 Clinician Role* H3 Lab Test Result Value C1 Service Delivery Identifier H4 Lab Test Result Unit of Measure* C4 Service Delivery Postal Code I1 Diagnostic Imaging Test Ordered D1 Appointment Creation Date I2 Diagnostic Imaging Test Ordered Date D2 Reason for Visit J1 Diagnostic Imaging Test Performed Date D3 Visit Date K1 Referral* D4 Visit Type K2 Referral Requested Date E11 Health Concern L1 Referral Occurred Date E12 Health Concern Date of Onset M1 Prescribed Medication E14 Social Behaviour M2 Prescription Date E23 Systolic Blood Pressure O1 Vaccine Administered* E24 Diastolic Blood Pressure O2 Vaccine Administered Date E28 Height Notes: Data elements highlighted have CFPLs * Data elements with PHC ref sets 5

6 Mapping Parameters Phase 1 Supporting HSU CFPL Code System(s) Rationale/Remarks Health Concern Clinician Assessment ICD10CA - ICD9 ICD10CA - ICD9 Support secondary use of data (e.g. priority indicators, HSU, organizational performance monitoring) Social Behaviour Reason for Visit ICD10CA - ICD9 ICD10CA - ICD9 ICD9 in most billing systems Future linkage to ambulatory & acute care data Facilitates regional/ provincial and international comparison Intervention CCI Supports indicator calculation Alignment to ambulatory and acute DI Test Ordered CCI care data Referral SNOMED Leverage constrained scope of PHC Ref Sets Vaccine Administered SNOMED Confirm Vaccine brand name for inclusion and mapping and maintenance impact 6

7 Mapping Parameters Phase 2 Bridge to SNOMED CFPL Code System Rationale/Remarks Health Concern SNOMED Leverage constrained scope of PHC Ref Sets Clinician Assessment Social Behaviour SNOMED SNOMED Granularity supports clinical use and research needs Reason for Visit SNOMED Intervention DI Test Ordered Referral SNOMED SNOMED SNOMED Vaccine Administered SNOMED 7

8 Comparison Between Historical Efforts and Content Standard Approach Historical Efforts* Focused on larger set of data elements (PHC EMR CS v elements) Individual practices EMR data as-is Data extraction format are practicespecific Analysis based on case definition methodology for identification of patients with chronic conditions Reports disseminated to individual clinicians Standardized Approach Focus on priority subset of data elements (PHC EMR CS v elements) PHC organizations (data aggregators) with sufficient volume of patients Structured EMR data through use of picklists at point of care or back-end transformation to standardized terms Data extraction format is consist across entire organization Analysis based on structured terms that are mapped to code system Reports are disseminated to organization for distribution * Based on CIHI s Primary Health Care Prototype Voluntary Reporting System (PHC VRS) 8

9 PHC EMR CS Demonstration Projects Purpose: CIHI and Partners to pilot implementations of the PHC EMR CS 3.0 to 1. Test its usefulness in generating structured EMR data for clinical and health system use 2. Provide further input and recommendations for future directions based on use by clinicians at the point of care and organizations supporting these clinicians 3. Understand changes required to improve the PHC EMR CS products, implementation and data collection before scaling up with a pan-canadian approach. 9

10 PHC EMR CS Demo Projects: Context Demo Project Type Project Goal 1. Existing EMRs 2. New EMRs 3. Terminology To use the EMR CS in a point-of-care clinical situation to assess the viability and usability as well as inform future development and evolution To influence the development of an emerging EMR Program and incorporate key EMR CS considerations from the initial implementation, and attempt to encourage use and enable analytics from program initiations To focus on the terminology and mapping components and to expand capability and assess potential for future evolution ON Demo Projects 10

11 PHC EMR CS Demonstration Project Framework Project insights, lessons learned & knowledge transfer Project Agreement & Initiation Implementation & Testing Data Extraction & Analysis Project Evaluation Partnership agreement Data sharing agreement Privacy Joint Project Charter Point of care vs. back-end Existing terms vs. CFPL terms Types of users (physicians, NPs, nurses) Extraction format (template) Mapping to code system Generation feedback reports Clinician assessment to inform EMR CS evolution Ability to generate structured data Implications for scale-up activities 11

12 Overview Ontario Demo Projects Team-based Demo Project A 22 of 45 EMR data elements 6 of 8 Clinician-Friendly Pick -Lists Back-end implementation approach transformation of existing terms to CIHI terms Data extracted from business intelligence tool (data mart) No impact on clinicians (no change in EMRs or clinician workflow) Single data submission to CIHI Feedback reports Team-based Demo Project B 30 of 45 EMR data elements 3 of 8 Clinician-Friendly Pick -Lists Implementation of CIHI pick-list terms at point-of-care Data extracted from EMR Minimal change in clinician workflow due to similar past project using Pick-Lists and structured data Multiple data collection period to CIHI Feedback reports

13 PHC EMR CS Considerations for the Future EMR Data standardization supports clinical interoperability Examples: Referral: Clinician Friendly Pick List, Related EMR data elements Immunization: Clinician Friendly Pick List, Related EMR data elements 13

14 Summary: Supporting Data Standardization and Enabling Clinical Interoperability Offers a pan-canadian solution to facility capture of structured and standardized health information Improves data quality and availability of information Supports comparable EMR data and allows for the calculation of performance measures at the clinic level and system level Potential outcomes, in the longer term, include highquality structured data to support clinical interoperability and ability to track patients across the health system 14

15 Questions or comments? Contact: Charisa Flach, Project Lead, CIHI s PHCi Program cflach@cihi.ca or Primary Health Care Information phc@cihi.ca 15