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An Overview of Emerging Real-World OpenHIE Use Cases: Successes, Challenges, and Future Opportunities Shaun Grannis, MD, MS, FACMI1, Eric-Jan Manders, PhD2, Carl Leitner, PhD3, Annah Ngaruro, MS4, Jack Bowie, ScD5 1 Indiana University and the Regenstrief Institute, Indianapolis, IN; 2 Centers for Disease Control and Prevention, Atlanta, GA, USA; 3 PATH, Chapel Hill, NC, USA; 4 ICF International Inc., Rockville, MD, USA; 5 Apelon Inc., Hartford, CT, USA.
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Discussion Questions What are the key elements of a component-based eHealth architecture supporting low and middle-income countries? How can elements in component-based eHealth architecture be orchestrated to support specific data exchange workflows? What are potential barriers to implementing specific data exchange use cases? What key non-technical factors can enable successful implementation of data exchange workflows? How can terminology services integrate into an overall eHealth architecture? What lessons learned from implementing Health Information Exchange in the US could translate to LMIC settings?
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What We’ll Cover OpenHIE Introduction (Grannis)
Integrating Standard Terminologies: OpenHIE Terminology Services (Bowie) The DATIM experience supporting indicator reporting (Ngaruro) mHero: Supporting health workers for routine care and Ebola crisis care (Leitner) HIV case-based surveillance (Manders)
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OpenHIE Introduction Shaun Grannis, MD MS FACMI
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Who we are OpenHIE is a diverse mission-driven community of practice including countries, organizations, individuals and donors working to promote sharing of health data across many different software products. We make and improve standards-based software to improve the exchange of health information.
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MISSION Our mission is to improve the health of the under-served through the open collaborative development and support of country driven, large scale health information sharing architectures. VISION We envision a world where all countries are empowered to pragmatically implement sustainable health information sharing architectures that measurably improve health outcomes.
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Architecture Framework
Sharing Data to Improve Health Outcomes
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OpenHIE Introduction to Terminology Services Jack Bowie, Apelon, Inc.
AMIA, 2017
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Agenda What Why How Where
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Terminology Services We are here
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Terminology Services “The objective of the Terminology Services component is to provide a central resource for the definitional assets of the HIE, i.e., terminologies, ontologies, dictionaries, code systems, value sets, etc., that can be used by other HIE components to achieve normalization of clinical data and consistent aggregation and reporting.” From the OHIE Terminology Services Planning and Implementation Guide
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Why Standard Terminologies?
Normalization of clinical data Consistent aggregation and reporting Counting Grouping
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Courtesy of Mary Morgan, LCS, Partners Healthcare
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Journal of the American Medical Informatics Association – September 1994
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Why Terminology Services?
Today’s HIT environment requires access to multiple “standard” terminologies. These terminologies are under constant revision. Maintain the official version of HIE datasets (the “gold- standard” or “single source of truth”) Eliminate multiple, duplicate, copies of these datasets in the HIE
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Terminology Services Interfaces
Originally, interfaces to other OHIE Components were idiosyncratic and proprietary Hard to use and weren’t portable Now moving to interfaces drawn from the HL7 FHIR Specifications Primary Resources CodeSystems ValueSets ConceptMaps
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Terminology Services Interfaces
FHIR Operations: Existence Membership Expansion Mapping Is ‘123XYZ’ an ICD-10 code? Is ‘123XYZ’ in the HIV ValueSet? Get the codes in the HIV ValueSet. What is the SNOMED code for ‘123XYZ’?
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Terminology Services Adoption
Implementations vary greatly in their initial terminology services adoption driven by their specific use-case: Maternal Health HIV Immunization tracking National organizations vary in their maturity for terminology governance Use-cases often must support specific terminologies, e.g., international standards, or historical local dictionaries
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National Selections Notes:
Domain Ethiopia Nepal Nigeria Philippines Rwanda South Africa Tanzania Thailand Dx ICD-10 Px local CCHI CPT ICD-9 Lab LOINC Local (map to LOINC) Rx ATC CVX SNOMED Devices UMDNS Billing Notes: All selections should be considered in development All Code Systems should be interpreted as national subsets
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Terminology Services A typical OHIE Terminology Services component consists of: A central repository/authority of the datasets needed by the HIE: Code Systems Value Sets A set of public interfaces (APIs) for accessing these datasets (the “services”) by: Other OHIE components External, e.g., PoS, applications
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The DATIM Experience Supporting Indicator Reporting
Annah Ngaruro, MS, CISSP, PMP Annah Ngaruro, MS, CISSP, PMP
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The President’s Emergency Plan for AIDS Relief (PEPFAR) Strategy
PEPFAR’s strategy goal: To reach sustainable control of the HIV/AIDS epidemic through the pillars of transparency, accountability, and impact How: By collecting and using data in the most granular manner (disaggregated by sex, age, and at the facility level) to do the right things, in the right places, and right now within the highest HIV-burdened populations and geographic locations ICF proprietary and confidential. Do not copy, distribute, or disclose.
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DATIM: Supporting PEPFAR’s Strategy
DATIM is the global system used by PEPFAR to collect data on monitoring, evaluation, and reporting; site improvement monitoring; and expenditure analysis. Collects data from 55 countries Went live in 2015 Has more than 10K users Provides annual, semi-annual, and quarterly reporting ICF proprietary and confidential. Do not copy, distribute, or disclose.
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Data Exchange: Addressing the Gap
Reduce data collection burden: Collect once, used by many Provide timely and accurate meta data necessary to perform data exchange. All data values in DATIM are associated with these four metadata dimensions: Where: Site What: indicator + disaggregation When: Period Who: U.S. Government funding mechanism Meet country-specific data needs: Going beyond PEPFAR data Lay the foundation for in-country data exchange infrastructure: Facilitate data exchange with respective Ministries of Health ICF proprietary and confidential. Do not copy, distribute, or disclose.
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Data Exchange Solution: DATIM4U
Is a standalone data exchange-enabled version of DATIM Stands for DATIM for Operating Units Designed for PEPFAR country teams (operating units) Supports monitoring, evaluation, and reporting results and targets data Transmits site and data sent to DATIM using the OpenHIE (OHIE) data exchange platform ICF proprietary and confidential. Do not copy, distribute, or disclose.
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DATIM4U: Out of the Box Key Features
Preconfigured components to meet PEPFAR reporting requirements Automated data exchange: (1) quarterly reporting of data to DATIM (the global reporting system) at the push of a button; (2) meta data synchronization of site lists, implementing mechanisms, and up-to-date versions of indicators and disaggregates Industry-standard health management information system platform (i.e., DHIS2) A data exchange infrastructure using OHIE Standards-based components using ADX, CSD, and ITI-73 ICF proprietary and confidential. Do not copy, distribute, or disclose.
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DATIM4U Status Conducted a successful year-long pilot that demonstrated the platform’s technical feasibility and utility Is fully operational in one country Used pilot feedback to optimize DHIS2 and OHIE components Developed extensive self-service documentation Built a data exchange community of practice Will be adopted by additional countries in 2018 ICF proprietary and confidential. Do not copy, distribute, or disclose.
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Challenges Documentation gap
Extensive hands-on support from U.S.-based team Tight coupling with DATIM Ever-changing PEPFAR reporting needs Local skills gap to support the variety of technology components Limited number of OHIE subject matter experts ICF proprietary and confidential. Do not copy, distribute, or disclose.
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Thank You! Annah Ngaruro, MS, CISSP, PMP Annah Ngaruro, MS, CISSP, PMP
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HIV Case-Based Surveillance
A use case for OpenHIE in global public health Eric-Jan Manders Center for Global Health, Division of Global HIV and TB Health Informatics, Data Management, and Statistics Branch Health Information Systems Team American Medical Informatics Association, Fall Symposium 7 Nov 2017, Washington DC Center for Global Health Division of Global HIV/AIDS and TB
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Outline HIV case-based surveillance Informatics challenges in HIV CBS
On the critical path to controlling the HIV epidemic Informatics challenges in HIV CBS Health Information Exchange to support HIV CBS Current Work Next Steps
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HIV care continuum: strategic information measures and the notion of epidemic control
Context of the global HIV epidemic in two slides. Not completely sure if that’s possible, we’ll find out from questions later WHO laid out a high level subpopulation grouping to help categorize how HIV program services are organized. Program delivery is then organized to reach programmatic targets for these groups, where the objective is to reach as many people as possible with the full range of services. In the implementation of the HIV program interventions some patients are not reached in the complete set of interventions, so this is a “leaky cascade.” program planning and implementation is driven by resource allocation and program management interventions to further target the gaps. UNAIDS has set targets for the different sub-population groups, with a timeline, based on modeling and projections on what is needed to reach epidemic control, a compound performance metric reflecting systematic and persistence trends in program response Target setting has been a part of the global response to HIV since the beginnings of the international response. It is worth noting that we are in a different phase now, bit different. Now, the final target is a true health outcome target, the result of availability of a direct measure of viral suppression, which in turn indicates that the patient is indeed living with a managed chronic condition. 100% of HIV+ people in the viral suppression box effectively interrupts HIV transmission at population level. There are of course challenges with this way of performance driven program planning. To direct the program interventions properly you need disaggregations for the indicators, so that you can target more specifically, eg. Based on geograhypy, age or sex Challenges, First, you need accurate patient counts to make sure you don’t count patients multiple times, in the global HIV context impacted by a life long episode of care, and a mobile population, making robust patient identification a challenge. Second, if you are not reaching your targets and you can’t determine which program intervention to tweak next. You need to look more closely at characteristics of the patients that don’t fall in your target bins. In short, this type of program monitoring doesn’t get you everything you need to know. World Health Organization, Consolidated strategic information guidelines for HIV in the health sector, 2015
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HIV Case-based surveillance On the path to epidemic-control
Tailor HIV program interventions to evolving dynamics of the epidemic Understand HIV disease progression over time Describe epidemic profile beyond performance indicators UNAIDS: HIV presents as multiple local/regional epidemics, not one global epidemic Surveillance challenge – detect and respond HIV Surveillance strategy HIV case-based surveillance is a component of a comprehensive strategy
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HIV Case-based surveillance On the path to epidemic-control (2)
What is HIV case-based surveillance Routine tracking of individual cases over time From confirmed HIV diagnosis Through a life-long episode of care Defined public health use of patient level data Detect and report events of public health interest, that may require public health action Use data for planning prevention and response What HIV case-based surveillance is not Exploratory data analysis Research Events of interest: We’ll look at those in a bit more detail next Evaluation criteria CDC bread and butter, there is a whole guideline on this What HIV case-based surveillance is not Important to distinguish because any information system could be defined to support multiple use cases, with different complexity The context of handling confidential information is different in different data use scenarios
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HIV care continuum Sentinel events in the “clinical cascade”
The care continuum has multiple events that are important to monitor at population level Recognize the corresponding subpopulation bins from the WHO groupings Reflects corresponding indicator definitions Can enhance with specific additional events Can enhance with time-to-event analysis This is a timeline but Can have multiple cycles of lack of viral suppression and back to suppression Partially ordered list of events, we may observe events out of order Can enhance with other key public health issues or co-morbitities that intersect with HIV program implementation Episodes of TB Interrelation of HIV care with maternal child health Effects of long term exposure to antiretroviral meds Emergence of drug resistance, and interventions. Note that the origins of this are in the separate inclusion of reporting conditions for HIV infection and HIV disease (what is more commonly known as AIDS)
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HIV case-based surveillance Informatics Challenges
Increasing volume and complexity of data Implementation scale & design Uptake of EMR in high burden settings Large scale databases of Viral Load data New program elements or design – test and start New technologies HIV testing assays – measure recency of infection HIV Viral load measurements decentralized Integration of other data sources Responsiveness How timely can / should we move case data for public health use Patient Identification Patient mobility challenges robust patient identification Here we are at the Informatics Challenges, finally Implementation scale and design Evidence based, Necessity driven Volume and complexity increasing Other data sources National laboratory database for Viral Load data
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WHO guidelines 2017 HIV Case based Surveillance Public Health Capacity
Policy environment Case reporting mandate Governance Informatics Component Leveraging EMR uptake Patient Identification Information Architecture Data repository Case report form Interoperability High level guidance Guidance evolves previously exisiting guidance on HIV specific patient monitoring systems Extends this with guidance on HIV case-based surveillance WHO guidance us very high level, starting point for a country level process on implementation.
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WHO guidance introduces the now familiar OpenHIE architecture figure as a recommended reference for achieving interoperable solutions for handling patient level data Including to enable and facilitate HIV case reporting. It is clear that Implementation of HIV services can benefit from HIE implementation in multiple ways Non clinical service elements, including logistics, and supply chain management, Program monitoring – currently in progress in the PEPFAR context Clinical service and care coordination From experience in country HIV programs where CDC provides technical assistance, current activities at implementing interoperable solutions begin to show essential services needed, and limitations of home grown point to point interoperable applications
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Leveraging Health Information Exchange
Address multiple interoperability challenges in a common framework EMR, Laboratory, CRVS, Testing services Stop short of implementing care coordination Accommodate robust patient identification The patient registry is key to establish longitudinal record Demands on matching accuracy are not the same as for care coordination Align investments in HIV information systems with country strategy for national health information infrastructure HIE is becoming a strategic aim for countries Creates a path for integrating HIV investments with emerging other public health priorities
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Current Work HIV CBS demonstration with OpenHIE
Informed by Country pilots, and preliminary implementation activities Country planning activities around existing national infrastructure elements Objectives Demonstrate EMR based HIV case based surveillance using OpenHIE Standards based message format for an HIV electronic CRF Constructing a longitudinal case record, with identify matching Status Outlined an approach for an EMR based HIV caser reporting module Influenced by the public health informatics institute “HER toolkit” See AMIA 2017 poster presentation: Global HIV Case Surveillance Using Electronic Medical Records (EMR) Data Lisa A. Murie et al Development work with Regenstrief Institute Implement a case reporting module in OpenMRS using a CDA template Configuration of OpenHIE to support case based surveillance workflow
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Minimal HIE supporting HIV case-based surveillance
Client Testing Care laboratory Client registry Person matching HIM Interoperability services ClinicalData Store Longitudinal Record Surveillance data mart Trigger and dataset definitions Control clinical data messaging Secure data environment controls access to patient identity
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Current Status Different country positions/approaches
Incorporating OpenHIE as one alternative to consider in national implementation guidance for implementation of HIV CBS no concrete decision on implementation plan Adopting OpenHIE for HIV program implementation where care coordination is the primary motivation Development/planning in early phase, no live pilot projects yet OpenHIE not yet considered Leveraging platform homogeneity on the EMR side Countries interested in HIE typically mention OpenHIE. In most cases these countries also have a presence of OPenMRS in country Range of maturity in technical capacity is not new Combined with range of maturity of policy environment needs careful dialogue
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Current opportunities and challenges emerging
Broad range of maturity in capability technical – OpenHIE introduces a complex technology stack Policy – Is there a national eHealth policy document? Does it adequately cover the HIV case based surveillance needs Existing approaches/investments to point-to-point interoperability difficult to dislodge Transition paths from legacy tools difficult to envision Demonstration system facilitates dialogue around what-if implementation scenarios
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Next Steps Develop more complete data use demonstrations
Use simulated data to demonstrate the HIV CBS workflow in a range of epidemic scenarios Establish the HIV CBS data mart/repository outside the HIE to demonstrate the data analytics Continue dialogue with country governments that intend to implement both HIE and HIV CBS on how to establish the right policy environment and needed informatics capacity.
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THANK YOU For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA Telephone: CDC-INFO ( )/TTY: Web: The findings and statements in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Center for Global Health Division of Global HIIV/AIDS (DGHA)
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