Interoperability: Linking RHIS and Other Data Sources

RHINO Forum

Michael Edwards, PhDRHINO Forum Webinar, September 20, 2016

Objectives and Topics CoveredObjectives• Explain Interoperability and data linkage terms • Describe the role of information and communication

technology (ICT) in integration and interoperability of the RHIS• Introduce Forum Discussion TopicsTopics Covered• RHIS Fragmentation• Integration of the Health System • Interoperability of Information Systems• Data Linkage Terminology• Health Information Exchange• Data Warehouse• Enterprise Architecture

Data Linkage Terminology• Fragmentation of the Health System• Integration• Interoperability• Electronic Medical Records• Aggregate Systems• MFL (master facility list)• Health information exchange (HIE)• Sharing metadata • Triangulation• Enterprise Architechture

RHIS Fragmentation

Fragmentation of RHIS refers to the absence, or

underdevelopment, of connections among the data collected by the various systems and subsystems.

Source: Heywood A; Boone D. (2015). Guidelines for RHIS data management standards. Chapel Hill, NC: MEASURE Evaluation, University of North Carolina.

HIS Fragmentation

Source: Adapted by MEASURE Evaluation from University of Oslo presentation

Causes of Fragmentation• Relating to poor governance, weak

oversight and supervision, differing organizational and programmatic interests, political maneuvering, donor pressure, and/or geographic rivalry

Institutional

• Reflecting poor HIS design, lack of technical interoperability among existing systems, and the absence of common metadata (i.e., data definitions, data sources, frequency of reporting, levels of use, targets)

Technical

• Resulting from narrow programmatic interests, inadequate training, and the lack of appropriate HIS skills of health managers and providers

Behavioral

What is “integration” of the health system

• The act of forming, coordinating, or blending several subsystems into a functioning or unified whole

• The ultimate purpose is to create a health system where integrated service delivery leads to holistic health improvements

Integration of the Data Management System

A number of definitions apply to integration:• In engineering: Bringing together of the

components into a single system and ensuring that subsystems function together as a unit

• In information technology: Process of linking together different computing systems and software applications physically or functionally, to act as a coordinated whole.

Source: Heywood A; Boone D. Guidelines for RHIS Data Management Standards. February 2015. MEASURE Evaluation

Best Practices• Standard Indicators and Data Sets

o Agreeing on and developing standard indicators and related data sets is an essential first step in developing an integrated RHIS.

• Beneficiary-Centered Integrationo Integration of Paper Recordso Integration of Electronic Records

• Facility-Level Integrationo Summary reports from different programs are combined into one integrated

monthly facility report

• System-Level Integrationo Integration of the RHISo Interoperability of data sourceso Integrated data warehouse

Linking Data Sources

Linking of data sources leads to a strengthened health information system (HIS)• Linking various EMR systems• Linking EMRs to aggregate systems• Linking various RHIS subsystems such as

HMIS, LMIS, HRIS, Laboratory, Financial, etc.• Linking the routine health information system

(RHIS) with population census and data from the Demographic and Health Surveys (DHS)

Electronic Medical Records (EMR)

• Contain data related to a single patient, such as diagnosis, name, age, and earlier medical history

• Data typically based on a single patient/healthcare worker interaction

• Systems used largely by clinicians for diagnosis and treatment, but also by administrative staff for accounting and file management

• EMR is not just one system; it may include interfaces with multiple other systems and applications

Aggregate Information Systems

• Contain consolidated data relating to multiple patients, and therefore cannot be traced back to a specific patient. They are merely counts, such as incidences of malaria, TB, or other diseases.

• Aggregated data are used for the generation of routine reports and indicators, and for strategic planning and guidance within the health system.

Interoperability

Ability of health information systems to work together within and across organizational boundaries in order to advance the effective and integrated delivery of healthcare for individuals and communities

Ability of different information technology systems and software applications to communicate, exchange data, and use the information that has been exchanged for improved service delivery and health.

Source: HMISS Interoperability and Standards Toolkit. Retrieved fromhttp://www.himss.org/library/interoperability-standards/toolkit.

Sharing Metadata

• Metadata: “data about data"• Metadata describe how, when, and by whom a

particular set of data was collected, and how the data are formatted

• Metadata: Essential for understanding information stored in data warehouses; has become increasingly important in XML-based web applications (most recent IEP)

Health Information ExchangeElectronic health information exchange (HIE) allows doctors, nurses, pharmacists, other healthcare providers, and patients to appropriately access and securely share a patient’s vital medical information electronically, thus improving the speed, quality, safety, and cost of patient care.• Directed Exchange: Ability to send and receive secure

information electronically between care providers to support coordinated care

• Query-Based Exchange: Ability for providers to find and/or request information on a patient from other providers, often used for unplanned care

• Consumer Mediated Exchange: Ability for patients to aggregate and control the use of their health information among providers

Open HIE

Community of Practice (CoP) dedicated to improve the health of the underserved through open and collaborative development and support of country-driven, large-scale health-information-sharing architectures.• Enabling large-scale health information

interoperability• Offering freely available standards-based

approaches and reference technologies• Supporting each other’s needs through peer

technical assistance communities

HIS Architecture and Health Information Exchange

Master Facility List (MFL)• A comprehensive, up-to-date, and accurate list of

all the health facilities (public and private, including community services) in the country

• Each health facility is uniquely identified using a set of identifiers (the signature domain)

• Links health services data and other core health-system data (financing, human resources, commodities, and infrastructure) through the unique identifiers defined in the MFL:o Is useful for administrative purposeso Allows better analysis and synthesis of informationo Improves health systems reporting and planning

Linking Data Using Master Facility List

• Data harmonization: comparing and contrasting data across different data sources and across time

• Data linkages and collaboration between departments and ministries with related data

• Health facility surveys: comprehensive lists for sampling

• Health information system strengthening: combining data from multiple sources to generate facility, regional, and national profiles for effective planning

RHIS Linkage Examples

Linking HMIS with a census• Coverage rates

Linking logistics management information systems (LMIS) and HMIS:

• Relationship between stockouts and services• Composite indicators, such as couple years of

protection (CYP)Linking human resource information systems (HRIS) and HMIS

• Workload analysis (patient visits per doctor)

Linking Family PlanningService Data with Census Data• Intervention, restructuring maternal and

child health (MCH)/family planning (FP) facility-based information system

• Before linking RHIS and census data, the only contraceptive prevalence rates available to an MOH were national estimates from DHS every 5 years

• After linkages, calculations from RHIS data provided the needed annual district- and national-level CPR estimates

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Routine FP data: Case Study on Morocco

An Example of System Interoperability in Eritrea

Linking LMIS and HMIS Data forImproved Use of Information

• Intervention: General restructuring of the facility-based RHIS• Before the improved RHIS, vaccine stockouts went unreported• After linking LMIS and HMIS, vaccine stockouts could be

monitored monthly, and the relationship between stockouts and children vaccinated could be tracked

• Evidence of an elevated stockout percentage alerted MOH to request additional vaccines from donors

Linking LMIS and HMIS Data for Improved Use of Information

Before: Vaccine stockouts went unreported

After: Tracked relationship between stockouts and children vaccinated

Evidence of elevated stockout percentage and lower number of children vaccinated alerted MOH to request additional vaccines from donors.

ANSEBA

DEBUB

DEBUBAWI KEYHI BAHRI

GASH-BARKA

MAAKEL

NATIONAL REFERRAL

SEMENAWI KEYHI BAHRI

0 200 400 600 800 1000 1200

Admissions per Doctor

Eritrea: Linking HRIS and HMIS:Calculation of New Indicators

What Is a Decision Support System (DSS)?

A computerized application allowing health managers to visualize RHIS health indicators and data elements in graphic and geographic presentations

Comparison is one of the most powerful analytic methods

• Spatial: by health facility, district, province• Time: trends by week, month, year • Indicators: between inputs and outputs• Benchmark: expected versus achieved

Decision Support System

Why a Decision Support System (DSS)?

• Enables health managers to promptly and efficiently analyze data for decision making

• Allows health managers with limited data analysis skills to better interpret aggregate information from the RHIS

• Is well-suited to health managers at national, regional, district, and local levels, because is user-friendly for lowly ICT educated health workers

Data Warehouse Definitions

“A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.”

– Barry Devlin, IBM consultant

“A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management's decision-making process.”

– W. H. Inmon, computer scientist

Data Warehouse Concepts

Distinction between data and information• Data are observable and recordable facts that

are often found in operational or transactional systems

• Data only have value to end-users when they are organized and presented as information

• Information is an integrated collection of facts and is used as the basis for decision making

Data Warehouse Concepts

• Data warehouse is designed for query and analysis rather than for transaction processing

• Data warehouse separates analysis workload from transaction workload. This helps:o Maintain historical recordso Analyze data to better understand the businesso Improve the business

Data Warehouse Architecture

Need for Enterprise Architecture (EA)?

To give management the big picture. EA gives a “systems thinking” view that combines vision and strategy, business architecture, information systems, and technology domains. To align IT investments with business goals.Creating a platform for business-ICT stakeholder collaboration is essential. Effective enterprise architecture supports strategy, analysis and planning by providing stakeholders a blueprint of the current state of the business and IT landscape, and of the desired future state (vision).To provide IT developers with specific requirements for software applicationsThe business architecture provides the IT developer with the specific software requirements of an application.

Enterprise Architecture

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HIS Architecture Principles

HIS Architecture

• Can align and leverage investments to build stronger and better integrated HIS supporting better health policy and local health services management, and ultimately stronger health systems

• To be built on a coherent set of best practices for promoting data integration

• To foster stakeholder groups to collaboratively build on common components and a common architecture within the HIS

• Helps identify and create interoperability between the components of the system

RHINO Forum

This presentation was produced with the support of the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and Tulane University. The views expressed in this presentation do not necessarily reflect the views of USAID or the United States government.