Promoting perinatal safety through discrete data interoperability across multiple EHRs Vish Anantraman, MD, MS Michael I.

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1 Promoting perinatal safety through discrete data interoperability across multiple EHRs Vish Anantraman, MD, MS Michael I. Oppenheim, MD DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Disclosure Vish Anantraman, MD,MS Michael I. Oppenheim, MD Have no real or apparent conflicts of interest to report HIMSS

3 Learning Objectives Understand clinical and informatics considerations in implementing a standards-based interoperability solution between multiple EMRs Identify clinical workflow considerations in the design and deployment of complex interoperability interfaces Appreciate the benefits and shortcoming of using a standards based interoperability approach for data exchange (web services, messaging based), data representation (CCDs, HL7) and data vocabularies (SNOMED, ICD9, LOINC) Recognize the benefits of a "push" based data exchange in the context of health information exchange

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5 North Shore-LIJ Clinical Strategy Embrace goals of IHI Triple Aim and ACO-like coordinated care approach including PCMH Recognize that patient tracking and coordination of care across delivery environment is critical prerequisite to high-quality, efficient management of individuals and populations Believe that interoperability across EHRs is key enabler of the above

6 Why OB as Test Case? Predictably, patients will transition between outpatient & inpatient environments High-risk patients will have specialist visits; timely communication between outpatient providers is necessary Information from prenatal period is CRITICAL for appropriate management during labor & delivery Labor & delivery information important to assure appropriate outpatient follow-up for both mother and baby Documented incidents related to absent prenatal data during L&D

7 A Little Help

8 High Level Overview of Goals Interfaces PRE-NATAL Community Practices EHR(Allscripts) Prohealth Practice EHR(Centricity EMR) Faculty Practice EHR(Allscripts) Prenatal summary (200+ discrete data elements) RHIO(LIPIX) Intersystems Healthshare Centricity Perinatal LABOR AND DELIVERY INPATIENT POSTNATAL Sunrise Clinical Manager POST-PARTUM Discharge summary/plan Labor and Delivery summary

9 Technical Architecture Prohealth Allscripts Community Multiple Interfaces Sunrise Clinical Manager (NSHS and LIJ) Post delivery mother and baby in hospital care. Pre labor in hospital mother care HL7 L&D Summary HL7 AS Order Result LIPIX GE Centricity Perinatal (NSHS and LIJ) Intra partum (labor and delivery) of mother AS Imaging NSLIJ IDX NSLIJ Invision Faculty Allscripts EHR

10 The Medical Neighborhood 3 Outpatient EHRs Allscripts Enterprise EHR for NSLIJ Medical Group (x practices) Allscripts Enterprise EHR for Community MDs (y practices) Centricity Office for large community multi-specialty group 2 Prenatal Imaging Centers AS Prenatal Ultrasound reporting system 2 Inpatient EHRs Centricity Perinatal (Enterprise deployment, 2 hospitals) for L&D Allscripts Acute Care (Enterprise deployment, 3 hospitals) for postpartum maternal care, newborn (well baby, NICU) care

11 Technical Goals of Integration Adhere to IHE standards as much as possible (CCD, XDS-b, PIX-PDQ) Discrete data interfaces especially when it can result in downstream clinical decision support Standardize content and terminology mappings across multiple vendor EMRs Event-based push of data to destinations EMRs (vs. pull interface) Cover all possible workflows for perinatal

12 Discrete Data Adapting well established paper ACOG forms to electronic format Establish Common data (~200 data elements) OB history, Active Problems, Current Pregnancy history, Previous Pregnancy History, Past Medical/Surgical History, Medication, Allergies, Pregnancy Risk factors, Follow up visit vitals, labs, fetal vitals Common data elements mapped to available fields and structures in each EMR Each EMR forms were mapped to common data fields (2 outpatient EMRs and 1 inpatient EMR) Particular challenge was the inpatient L&D EMR that does not have standard problem list or medication dictionaries (custom dictionaries were created) Downstream system was able to leverage discrete data for clinical decision support Alerting admitting physician of Gram B Streptococcal infection result being positive Alerting admitting physician of high risk pregnancy Need for descriptive data was also accommodated Physician annotation of problem details Physician descriptive visit summary

13 Content Standards and Mapping All data was mapped to either LOINC (labs/vitals), ICD (diagnoses) or SNOMED (all other observations) SNOMED mapping allowed the automatic translation of concepts between EMRs E.g. Outpatient EMR ICD9 problem were mapped to generic disease categories for use in the L&D system using SNOMED-ICD crosswalk Extensive mapping tables were stored in the interface engine of the RHIO for realtime data translation

14 Data exchange standards Continuity of care document (C32 standard) was selected for exchange of data with the RHIO Needed to adapt the CCD to accommodate obstetric observations, which were ultimately modeled as results Different systems implement CCD protocols in different ways. Challeges required custom coding to account for patient summary v/s encounter summary Standard vendor CCDs only deal with a subset of OB data custom code was required to extract observation data and incorporate into CCD Non-CCD enabled systems (2 of the 3 EMRs) required translation of CCD to HL7 v2 (ORU) messages. Extensive mapping tables were designed to map each of the CCD segments to HL7 messages

15 Key Workflow Consideration Pushing data to destination system for seamless user experience Defining triggers for automatics sending of data to destination EMRs triggers based on ADT events Understanding the different documentation paradigms in outpatient setting necessary Event monitoring for entry of specific ICD9 codes in patient problem list (need to limit transmission only for pregnant patients) Document completion as trigger for pushing CCD to RHIO Automatic nightly push of CCD from EMR to RHIO Accounts for data captured outside of visit documentation (e.g. lab results)

16 Key Obstetric Workflows Scenario 1 Scenario 2 Scenario 3 Initial Pregnancy Visit to Obstetrician office (NSLIJ Outpatient EMR) Scenario 4 Emergency Room (Non NSLIJ Hospital with access to RHIO) Scenario 5 12 week Visit to OB office Order and Result back from Ultrasound System Scenario 6 Referral to a community high risk OB provider (Community EMR) Labor and Delivery (Perinatal Natal EMR) Post Natal Outpatient Visit (Outpatient EMR)

17 Screenshots from Outpatient EMR

18 Initial Visit OB history

19 Follow up flowsheet

20 Screenshots from RHIO Portal

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23 Screenshots from Inpatient L&D System

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26 Post natal CPN L&D Documents

27 Results (Average Monthly Statistics) 5500 CCDs per month automatically generated from Faculty EMR 1800 ORU messages with discrete data generated from Community EMR 650 documents per month automatically delivered to inpatient L&D system 315 discharge summaries per month sent to outpatient EMRs

28 What Worked Creation of custom CCD to handle prenatal elements Mapping of data elements to support discrete exchange between disparate EHRs Activation of decision support in EHR based on receipt of electronically transmitted data Workflow-based event triggers to assure most up-todate information delivered to user s primary system when needed

29 Challenges Patient matching by HIE Matching criteria stringent since no human review, automatic matching only Adoption of discrete charting of prenatal data in office EHR Critical information charted in free-text sections

30 Lessons Learned Complex interop projects need buy in from application teams very different development philosophy than typical point to point interfaces Ongoing monitoring of the data in the interfaces (quantity and quality) is critical for ongoing success Market, market, market

31 Thank You! Vish Anantraman, MD, MS Michael I. Oppenheim, MD