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METHODOLOGY Open Access

Improving health information systems fordecision making across five sub-Saharan Africancountries: Implementation strategies from theAfrican Health InitiativeWilbroad Mutale1,2*, Namwinga Chintu1, Cheryl Amoroso3, Koku Awoonor-Williams4, James Phillips5,Colin Baynes5,6, Cathy Michel7, Angela Taylor1, Kenneth Sherr8,with input from the Population Health Implementation and Training – Africa Health Initiative Data Collaborative

Abstract

Background: Weak health information systems (HIS) are a critical challenge to reaching the health-relatedMillennium Development Goals because health systems performance cannot be adequately assessed or monitoredwhere HIS data are incomplete, inaccurate, or untimely. The Population Health Implementation and Training (PHIT)Partnerships were established in five sub-Saharan African countries (Ghana, Mozambique, Rwanda, Tanzania, andZambia) to catalyze advances in strengthening district health systems. Interventions were tailored to the setting inwhich activities were planned.

Comparisons across strategies: All five PHIT Partnerships share a common feature in their goal of enhancing HISand linking data with improved decision-making, specific strategies varied. Mozambique, Ghana, and Tanzania allfocus on improving the quality and use of the existing Ministry of Health HIS, while the Zambia and Rwandapartnerships have introduced new information and communication technology systems or tools. All partnershipshave adopted a flexible, iterative approach in designing and refining the development of new tools andapproaches for HIS enhancement (such as routine data quality audits and automated troubleshooting), as well asimproving decision making through timely feedback on health system performance (such as through summarydata dashboards or routine data review meetings). The most striking differences between partnership approachescan be found in the level of emphasis of data collection (patient versus health facility), and consequently the levelof decision making enhancement (community, facility, district, or provincial leadership).

Discussion: Design differences across PHIT Partnerships reflect differing theories of change, particularly regardingwhat information is needed, who will use the information to affect change, and how this change is expected tomanifest. The iterative process of data use to monitor and assess the health system has been heavilycommunication dependent, with challenges due to poor feedback loops. Implementation to date has highlightedthe importance of engaging frontline staff and managers in improving data collection and its use for informingsystem improvement. Through rigorous process and impact evaluation, the experience of the PHIT teams hope tocontribute to the evidence base in the areas of HIS strengthening, linking HIS with decision making, and its impacton measures of health system outputs and impact.

* Correspondence: [email protected] for Infectious Disease Research in Zambia, ZambiaFull list of author information is available at the end of the article

Mutale et al. BMC Health Services Research 2013, 13(Suppl 2):S9http://www.biomedcentral.com/1472-6963/13/S2/S9

© 2013 Mutale et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

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BackgroundHealth Information Systems (HIS) are one of the sixessential and interrelated building blocks of a health sys-tem. A well-functioning HIS should produce reliable andtimely information on health determinants, health statusand health system performance, and be capable of ana-lyzing this information to guide activities across allother health system building blocks [1]. Thus, an HISenables decision-makers at all levels of the health systemto identify progress, problems, and needs; make evi-dence-based decisions on health policies and programs;and optimally allocate scarce resources [2-4] – all ofwhich are key elements in the success of large-scaleefforts to achieve health improvements [5].Weak HIS are a critical challenge to reaching the health-

related Millennium Development Goals [6,7]. Evaluationsof routine health facility data have identified consistentproblems in HIS completeness, accuracy and timeliness inlow- and middle-income country (LMIC) health settings[8,9], which limit HIS use for routine primary health care(PHC) planning, monitoring, and evaluation [10-12].Other factors associated with poor quality data in resourceconstrained settings include duplicate, parallel reportingchannels and insufficient capacity to analyze and use datafor decision making [13].Improving HIS functioning is a priority given its cen-

tral role in the delivery of equitable and high qualityhealth services, though approaches to improving HISvary. Simple data quality assessments that engage front-line health workers and data managers have been usedto verify, standardize, and improve routine HIS data[14-16]. Other approaches have focused on technologi-cal interventions such as information communicationtechnologies (ICT) designed to reduce errors throughreducing data bulkiness and automating data collection,validation, and analysis [4,17,18].To ensure that HIS contribute to improved health ser-

vices, it is essential that policy makers and health systemmanagers utilize available information for ongoing mon-itoring of plans and programs, as well as for resourceallocation purposes. Information management is a basisfor the production of knowledge and its translation forhealth system decision making [19-21]. Further evidenceis needed on effective strategies for linking data systemimprovements with decision making, including itsimpact on the delivery of health services and populationhealth.The Doris Duke Charitable Foundation launched the

African Health Initiative to catalyze significant advances inhealth systems strengthening through supporting Popula-tion Health and Implementation Training (PHIT) Partner-ships in five sub-Saharan African countries (Ghana,Mozambique, Rwanda, Tanzania and Zambia) [29]. Allfive PHIT Partnerships include approaches to strengthen

the HIS building block as a means of improving healthservice delivery and, ultimately, population level health.Despite the common goal of improving data capture tosupport timely decision making, each partnership usesproject-specific strategies to strengthen HIS and improvedecision making and to target different levels of the healthsystem, including health managers, clinicians, and thecommunity. The full description of each partnership’smethodology is described elsewhere [30-35].This paper describes, compares, and contrasts the five

PHIT Partnership approaches to strengthen HIS andpromote the use of data for decision making, focusingon the designs, activities, and the adaptations during theimplementation process.

PHIT Partnership approaches to improve HIS anddecision makingTable 1 summarizes the range of models to improveHIS across the five PHIT countries, focusing on integra-tion approaches with the MOH’s HIS, strategies forimproving data quality, procedures for handling andmanipulating data, strategies for linking data to decisionmaking, and sustainability plans.

GhanaThe Ghana PHIT Partnership (the Ghana Essential HealthIntervention Project, or GEHIP), has two intervention stra-tegies to strengthen the HIS and link information withimproved health system operations. The first is to imple-ment a simplified information capturing system as part ofthe District Health Information Management System(DHIMS-2) that focuses on essential information for dis-trict level planning, thereby reducing the reporting burdenin primary care settings (Figure 1). The second is theadoption of a District Health Planning and ReportingToolkit (DiHPART) for use by district health leadership toidentify and allocate resources based on the district levelburden of disease profile.

Rationale and contextual appropriatenessData capture for DHIMS-2Simplified registers were introduced to standardize datasources, and to ensure consistent supply of registers forcommunity health officers (CHOs). The simplified regis-ters also allow health facilities to rapidly tally figures formonthly summary reports in order to address complexdata capture responsibilities that occupied more frontlinestaff time than clinical service delivery [22]. Prior to theadoption of the simplified registers, maintaining patientencounter registers was complex and cumbersome, invol-ving 27 register books to collect information on patientattendance at outpatient consults, maternity, well-childcare, family planning, and home visits. Collating andreporting health information was particularly tedious for


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Table 1 PHIT Partnership health information system innovations

HealthInformationSystem Domain

PHIT Partnership Country

Ghana Mozambique Rwanda Tanzania Zambia

Summary Registersimplification.

Improving quality of MOH’sroutine HIS.

EMR. Community healthinformation system.

EMR using mobile phonetechnology.

Integration withnational HIS

Harmonizes datafrom routine MOHfacility forms.

Focuses on national MOHinformation system(Módulo Básico).

Integrated into healthinformation system,national roll-out ongoing.

Not currentlyintegrated.

Not currently integrated.

Strategy for dataqualityimprovement

Simplified datacapture andstreamlinedreporting designedto lead to moretime to focus onquality.

Ongoing feedback onmissing data and outliers,and ongoing data qualityassessments across facility,district and provinciallevels.

Quarterly data qualityaudits and automateddata quality report basedon logic errors generatedwhen administrative andclinical reports aredeveloped.

Facility supervisorsreview communityhealth agent reportsand provide datafeedback.

Standardized protocols fordata capture with real-time query of data gaps;subsequent follow-upduring monitoring visits.

Levels at whichdata are used

Community, healthfacility and districtlevels.

Health facility, district andprovincial levels.

Community, health facility,district and national levels.

Community, healthfacility and districtlevels.

Community, health facilityand district levels.

Datamanipulation

Data areaggregated at sub-district, district, andregional levels, andreported to thenational level.

Facility and district levelgraphs and tables routinelyupdated for Primary HealthCare services.

Data are aggregated andsummarized to providesummary indicators.

Data aresummarized intables and graphicforms to facilitatetrend analysis.

Data are aggregated andsummarized into reportsand graphics for easyinterpretation.

Linkage withdecision making

Data used toidentify priorityareas, and guideplanning andresource allocation.

Trend analysis at facility,district and provincial levelsto identify priorityproblems, monitorimplementation ofmodifications, and link withdistrict activity plans andbudgets.

Data used by clinicians toplan patient management,as well as district andhealth facility managers toidentify service qualitygaps.

Data used forcommunityproblem-solving andplanning, andincorporated intofacility and districtplanning.

Focus on data use byCommunity HealthWorkers to identifypatients for follow-up, aswell as clinicians andfacility managers forperformance assessmentand improvement.

Sustainabilityplans

Routine use byMOH managersfacilitatesownership andcontinuity.

Integration with currentMOH HIS facilitatesadoption and continueduse of tools and approach.

The EMR has beenincorporated into thenational HIS.

Demonstratingfeasibility and utilityof approachexpected togenerate support forsustaining theapproach.

Training all health workersin the intervention areaand close relationship withdistrict managers to buildHIS ownership.

Policy Planning Monitoring & Evaluation Division

NationalMCH Unit

CentreFor

HealthInformation

Management

DHIMS data entry

27 Sub-district clinic

registers

CHPS HMIS

Reproductive. Health forms

Public Health Division

27 District Hospital forms & registers

BEFORE REFORM: Ghana Health Service District Health Information Management System (DHIMS-1)

Reproductive Health forms

Reproductive Health forms

District hospital

Community Health Compound

Sub-District Clinic

27 DHIMSregisters & forms

Regional Hospital

DHIMS-1

Regional Hospital reports

Feedback to Region

District Hospital

Sub-district Health Center

Regional Health Administration

District Health Administration

Sub-District Health Centre

“Doorstep” Community Health workers (CHO)

Ghana Health Service Policy Planning Monitoring & Evaluation Division

District Health Administration

District data entry

Clinic DHIMS & health insurance data capture

Reformed Ghana Health Service District Health Information Management System (DHIMS-2)

NHMIS

A

B

C

D

District Hospital DHIMS data capture

“Doorstep” Community Health workers (CHO)

Regional Health Administration

Sub-District Health Centre

National DHIMS

5 Simplified registers

Community Health

Compound

Feedback to all levels

Regional Hospital

Centre For Health Information

Management (CHIM)

Figure 1 Visual framework for the health information intervention - Ghana


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CHOs, who record, compile, and report client encountersto sub-district and district levels.Planning and budgeting with DiHPARTBased on the observation that national decentralizationpolicies lack appropriate training and tools for districtleaders to base priorities on need, the DiHPART toolwas developed to assist managers with planning. As ameans of basing decision-making on known patterns ofrisk, DiHPART removes the guesswork from budgeting,simplifying the task of strategic leadership for healthresource allocation.

Activities and feedback mechanismData capture for DHIMS-2The GEHIP team worked with district and sub-districtmanagers and CHOs to review, redesign, and implementthe improved versions of the simplified registers over aone-year period. A detailed review was carried out toinventory baseline data collection (data fields collected,registers used), identify redundant information, andassess data collection for appropriateness and relevancefor district health managers and CHOs. The physical sizeof the simplified registers was reduced to make themeasier to carry during outreach activities. In the course ofthis iterative process, the simplified registers were pilotedin one district and subsequently adapted to the need ofall three GEHIP districts after feedback from CHOs anddistrict health information officers. The data fields col-lected are regularly reviewed to keep them up to datewith those collected by the Ghana Health Service. Pro-curement, distribution, and content revision functionshave been fully integrated into the Upper East RegionalHealth Information Unit, which facilitates rapid adapta-tion, adoption, and continued use.In their final format, the simplified registers include five

registers for CHOs to gather data on facility consults foroutpatient, maternal and child care services, and outreachservices in homes and schools. Although the initial goalwas to develop a single register, delineation of functionswithin health facilities required five registers to collectclinical data when staff were deployed to outreach activ-ities. To ensure data quality and its use, monthly andquarterly data validation meetings are held by CHOs, sub-district, and district teams to review data collected andidentify gaps. Subsequently, the data are compiled andsubmitted to the regional and national levels.Planning and budgeting with DiHPARTDiHPART’s introduction included an orientation for dis-trict health management teams to provide an overview ofthe disease burden and its implications for current plansand activities, followed by identification of adaptations toalign spending priorities with risk patterns. Disease bur-den models for DiHPART were based on cause of death

data from locally derived data provided by the NavrongoHealth Research Centre.

Adaptation and learning during implementationData capture for DHIMS-2Qualitative appraisal of reactions to the simplified registersystem suggests that CHOs welcome the reduced docu-mentation burden and additional time for service and out-reach. Essential for the register simplification process hasbeen coordination with national HIS reform (Figure 1),including streamlining data collection and aggregationoperations (pathway A) , simplifying and computerizingfeedback to all levels (pathway C), and enabling healthworkers to view data feedback and compare performancewith counterparts (pathway D).GEHIP experience has identified additional areas for

improvement. Efforts to use cell phone technology fordata entry encountered technical problems. In addition,district and regional funds are insufficient to indepen-dently cover the recurrent supply cost, including CHOregisters. This problem may be resolved when the simpli-fied registers are adopted for nationwide implementation.Planning and budgeting with DiHPARTThe experience with implementing DiHPART has differedfrom expectations in multiple ways. The lack of flexiblefunds due to earmarked wages and donor requirementshas led to a disconnect between DiHPART plans andactual expenditure, which has impeded implementation ofDiHPART guided decision making. However, during itsimplementation, DiHPART has become an influentialresource mobilization tool, providing district managerswith evidence to lobby political officials for additionalresources.

MozambiqueThe Mozambique PHIT strategy focuses on strengtheningthe MOH’s established HIS through applying innovativeapproaches to improve HIS quality and foment its use forresource allocation, program monitoring, and servicedelivery improvements at the facility, district, and provin-cial levels (Figure 2). The Mozambique project has intro-duced simplified tools based on routine HIS data tohighlight service delivery performance success and pro-blems at the facility and district levels. The project teammentors district and facility health managers to use thesetools for identifying, implementing and evaluating effortsto improve health system performance.

Rationale and contextual appropriatenessThe PHIT strategy is designed to work within the MOHpriorities, specifically to strengthen the quality and use ofthe existing information system (Módulo Básico). The part-nership has adopted and modified nationally developed


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training modules and data assessment approaches in devel-oping an intervention that is contextually appropriate fordistrict managers.The PHIT strategy endeavors to improve HIS quality

from the facility, district, and provincial levels in Sofala pro-vince. Strengthening data for decision making focuses onthe district level – the key management unit to support andmonitor service delivery improvements at the facility level.Under the government of Mozambique’s decentralizationprogram, district managers are increasingly responsible forresource allocation (including financial and non-financialresources, such as human resources), as well as monitoringand evaluating program activities. The PHIT strategy there-fore builds district capacity for using data for decision mak-ing and supports their linkages with health facilities to leadto health system improvements.

Activities and feedback mechanismData quality includes training and supporting district andprovincial statistics personnel to continuously monitorthe performance of the HIS and the provision of timelyfeedback to facility and district managers to lead to incre-mental improvements in HIS quality. Furthermore, anannual data quality assessment (DQA) for primary health

care (PHC) services is carried out in all districts in thePHIT intervention province, with feedback provided todistrict and health facility managers via a summary dataquality ranking tool that acknowledges facilities with highdata quality and identifies facilities with poor data qualityfor follow-up by health system managers and PHIT-supported personnel [32]. After health facilities with glar-ing or persistent data quality problems are identified(those in the lowest category of the ranking process), dis-trict and provincial health managers provide supportivesupervision to facility managers and staff that includes are-introduction to the HIS and associated tools, clarifica-tion of timing and procedures for reporting, and reinfor-cement of the importance of the HIS. Technical andfinancial support is also provided to develop and main-tain infrastructural capacity to computerize facilitysummary reports at the district level and send them elec-tronically for monthly collation at the provincial level.Identifying problems and making informed decisions

based on up-to-date data from the HIS is promoted atthe facility, district, and provincial levels. District andfacility managers are trained and mentored to buildcompetencies and routine practices for basic data analy-sis, including indicator development and secular trend

Health System Level

Province

District

Health

Facility

Data System

Summarized facility and district-level data

(Módulo Básico, weekly disease surveillance,

program reports, service provision assessment)

Summarized facility and district-level data

(Módulo Básico, weekly disease surveillance,

program reports, service provision assessment)

Service registries, monthly facility reports,

service provision assessment

Data System Improvement

Approach

Training on new data systems, supervision

DQA (registers monthly

report)

Training on new data systems, monitoring

for outliers with feedback,

supervision, DQA (monthly paper report Módulo

Básico)

Tools for Data Driven Decision

Making

Monitoring for outliers, imbedded technical assistance

Dashboard comparing district-level PHC

indicators, operations research

Dashboard comparing facility-level PHC

indicators, annual district planning for resource allocation, operations research

Dashboard comparing facility-level PHC

indicators, annual sector specific data

review and action plan development,

operations research

Figure 2 Visual framework for the health information intervention - Mozambique


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analysis. Simple tools and graphical representations usingroutinely collected data have been developed, field tested,and implemented for health system managers to use formonitoring primary health care indicators, target inter-ventions, target resources at the district (to improve facil-ity performance), and provincial levels (to improvedistrict performance) [32] and evaluate whether interven-tions have led to intended service delivery improvements.

Adaptation and learning during implementationDuring the six-month planning grant, the MozambiquePHIT Partnership piloted and refined a province-specificDQA methodology, which are now in use [14]. Annualassessment results are disseminated to health facility,district, and provincial managers using a simplifiedranking system that was developed based on suggestionsfrom a provincial data quality feedback session. Tools tosummarize and regularly compare key PHC indicatorsacross facilities and districts have evolved in design andcontent over the first three years of implementation toinclude fewer indicators and focus on secular trend ana-lysis and graphic comparisons among peer facilities anddistricts. Efforts to promote use of data for decisionmaking have also evolved to go beyond training healthmanagers in data systems, indicator development, andanalysis approaches. Periodic district-level review andplanning meetings bring together peer facility staff withdistrict and provincial leadership to promote active datareview combined with planning and monitoring of planimplementation with key stakeholders.

RwandaIn Rwanda, the Ministry of Health (MOH) and PartnersIn Health (PIH) have co-developed an electronic medicalrecord (EMR) system (OpenMRS)[23] and are imple-menting an enhanced version as part of the PHIT Part-nership (Figure 3). In the three PIH-supported districtsof Rwanda the EMR holds patient records for 33 healthcenters, including a catchment area of approximately800,000 people. The EMR system includes comprehen-sive medical records for all patients with HIV, tuberculo-sis, heart failure, epilepsy, hypertension, asthma, chronicobstructive pulmonary disease, diabetes, and cancer. Inaddition, a medical record system has been developedand is being implemented for acute outpatient consults,including registration, presentation, diagnosis, laboratorytests, and treatment. The EMR supports patient care byproviding clinicians with summaries of patient visits andlaboratory test results; through reports of at-risk patients(including those with missed visits, low CD4 counts,unsuppressed viral load, and high HBA1c) [24] andthrough administrative reports to support clinic manage-ment, resource allocation, and quality improvement (QI).

Rationale and contextual appropriatenessThough hospitals have paper patient charts recordingprior admissions and emergency room visits, the pri-mary care facilities in the project area do not have astandardized comprehensive outpatient paper-basedrecord. As a result, acute and chronic medical history isnot always immediately available to clinicians duringpatient consultation, and information does not alwaysflow optimally between the levels of care. The EMR sys-tem allows for synthesis and access to patient historyfrom chronic and acute outpatient encounters at bothlevels of care. In addition to the nationally required HISreports, key EMR outputs include customized reportsfor QI, administration, and infectious disease monitor-ing. At present, patient registration data have been usedto identify geographic areas with low access to acuteoutpatient services, while chronic care reports guidecare for patients with chronic conditions (includingHIV, TB, diabetes, hypertension, heart disease, asthma/COPD and cancer).The MOH has commenced implementation of a

nationwide comprehensive electronic medical recordsystem, based partly on the partnership’s work. Corework for this included agreement on standard terminol-ogy for national use, including symptoms and diagnoseslinked to international standards and development of atested and refined user interface. This collaborationensures that parallel systems are not created, with onenational information system that integrates across EMRcomponents and feeds into national HIS reportingrequirements.

Activities and feedback mechanismTools that are being introduced include an electronicpatient registration system and an acute patient visitrecord. Each of these have reports as part of the feed-back loop that aggregate data at the facility and districtlevels (for reporting and administrative purposes), aswell as the individual patient level for QI and patienttracking purposes. Training is conducted for data offi-cers and coordinators on a quarterly basis, just prior tothe quarterly software releases that deliver new content.Clinicians receive both formal and on-the-job trainingon using the systems and have a point person from theEMR team to support them.

Adaptation and learning during implementationIn order to allow for integration with the nationalimplementation, the health information model wasrevised after the terminology standards were discussedwith the national e-Health Technical Working Group.Additionally, a training schedule based around softwarereleases and accompanied by more formalized training


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materials has been developed based on identified fieldneeds.

TanzaniaThe Connect Project aims to improve community-levelavailability, accessibility, and quality of primary health careservices using community health agents (CHA) in threedistricts in rural Tanzania [34]. The Connect Project hasadapted and adopted existing community-level healthinformation data capture tools and is working with CHAsto collect and integrate community-level data with theroutine HIS at facility and district levels (Figure 4), withdata feedback targeting workers at the community, dispen-sary, health center, and hospital levels.

Rationale and contextual appropriatenessAlthough the MOH has developed community-level datacollection tools, integrating collected data into the MOHHIS (MTUHA) has been challenging. Facility-basedhealth workers are intended to use the community-levelmodule (MTUHA III) to collect information on a rangeof community health indicators and report to their corre-sponding council health management teams (CHMT),who use this information to design an accurate profile oftheir district and develop Comprehensive Council Health

Management Plans. Currently, MTUHA III is not fully oruniformly operative throughout the country owing to arange of systems factors, including workforce shortagesthat prevent timely and frequent community outreach.The CHA represents an opportunity to pilot and refineapproaches to integrate community health informationto the MTUHA system.The Connect project supports integration of commu-

nity data in the national MTUHA in order to improvethe comprehensiveness and quality of health informationin general and prompt data interpretation, discussion,and problem solving in community settings. Integrationefforts have focused on working with CHA clinical super-visors, village leaders, and CHMT MTUHA coordinatorsto facilitate their administrative ownership over reportingand utilization of service delivery information fromCHAs. As health system and community stakeholdersupport is built, the Connect HIS system will be custo-mized to reflect the data and reporting requirements ofthe MTUHA HIS.

Activities and feedback mechanismConnect staff worked with MTUHA supervisors todevelop two community registers (one for service deliv-ery outputs, a second for community mobilization and

Symptoms and findings

Diagnoses (confirmed, suspected, primary

secondary)

Laboratory /radiology tests ordered and results

Treatment (medications, procedures, referral)

Prior acute care history

Prior chronic care history (HIV, TB, Diabetes, Heart Failure,

Hypertension, Asthma/COPD, cancer)

National HIS monthly report (acute outpatient section)

Clinical reports for patient follow-up

Aggregate reports for Quality Improvement and

administration (as defined by District Hospital)

Data available for use in nationally approved research

studies

Current and past medications

Vital signs

Infectious disease and outbreak monitoring

Existing data available to support decision-making

Acute Clinical visit data input Acute Clinical data output

Figure 3 Visual framework for the health information intervention - Rwanda


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health education activities) that provide simple projectindicators aligned with the MTUHA III modules. Addi-tional health information summary forms were devel-oped for CHAs to record aggregate data from theirregisters and report each month to supervisors fromtheir community, the health system, and the Connectteam.CHAs and supervisors from both health facilities and

village governments meet regularly to review monthly out-puts, identify and troubleshoot problems, and plan jointlywith the health system. Connect project coordinators, dis-trict MTUHA coordinators, and CHA supervisors holdsimilar meetings quarterly and transfer CHA health infor-mation to district and project managers for planning andprogram improvement.

Adaptation and learning during implementationData feedback to the CHAs was initially delayed due tothe evolving nature of the intervention, the large numberand geographic dispersion of study clusters, and variationin CHA supervisor leadership qualities and motivation.

To overcome these barriers, the Connect team workswith CHA supervisors to motivate their involvement andcover transportation costs incurred while making suppor-tive supervision visits to CHA.There are notable challenges in collecting and using

community-based health information. Supervision visitsto all CHAs following initial deployment revealed minorproblems concerning the uniformity and proper use ofthe registers. Project staff and supervisors compiledfindings from these visits and convened CHAs in therespective study areas in a joint review of the registersto clarify register use. Management of community-basedhealth information has also been a challenge. Thoughregisters are appropriate for recording service deliveryinformation and aggregating data, they did not facilitateCHAs data use for improving client-focused care as theydid not capture household and client information, norqualitative aspects of service encounters that would beuseful for follow-up service encounters. Therefore, theproject introduced booklets that remain in each villagehousehold where CHAs can log more detailed notes

Figure 4 Visual framework for the health information intervention - Tanzania


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from each visit, which has come at a high financial andlogistical cost. Patient referrals from CHAs has alsobeen a challenge, as post-referral feedback from healthfacilities to guide CHA follow-up services has been erra-tic. To facilitate the CHA/health facility communication,CHAs, supervisors, and referral providers have beenprovided closed-user phone groups to communicatewithout incurring costs.

ZambiaThe Better Health through Mentorship and Assessment(BHOMA) project is using an Electronic Data CaptureSystem (EDCS) and mobile technology to improve thequality of data captured in the target districts. TheBHOMA system includes a dedicated low-wattage Linuxclient terminal (powered by solar panels and a 12-voltbattery pack) with touch screen data entry terminalsattached to a miniature data processing server, intowhich patient visit information is entered (Figure 5).The system automatically generates performance reportsbased on predetermined performance indicators thatidentify facility-level performance gaps and are used byclinical QI teams to mentor facility staff on improvingclinical care quality. The EDCS system also automati-cally generates and sends follow-up messages via generalpacket radio service (GPRS) technology to CHWs (viamobile phones) to indicate a need for patient follow-up.Using modems and cellular networks, BHOMA clinicsaccess the internet to securely synchronize records to acentral server, housed at CIDRZ headquarters in Lusaka,

which, in turn, transmits the data to BHOMA districtoffices, and the MOH’s District Health Offices.

Rationale and contextual appropriatenessPoor quality data has been a source of concern through-out Zambia and data are frequently not used for evi-dence-based planning. Furthermore, community-leveldata are often not collected or used. The expansion ofHIV care and treatment in Zambia brought EMR sys-tems to some rural health facilities, which demonstratedtheir feasibility for capturing patient-level data in realtime and their utility in guiding decision making byhealth system managers. Increases in mobile technologycoverage in Zambia has made internet widely available,providing an opportunity to leverage ICT for collectionof patient and community level data in real time and touse these data for evidence-based decision making.

Activities and feedback mechanismThere are six data entry screens (patient registration, adult,pediatric, sick antenatal care (ANC), normal ANC, andlabor and delivery) that follow the flow of information onclinical forms. Data are entered and locally and availablein real time. To date, BHOMA has trained 72 clinic sup-porters to enter data for each patient visit and run reports.The five reports include 1) Clinic report (summarizing thenumber of patient visits at each facility, including follow-up visits for patients with danger signs or severe symp-toms who missed their appointment); 2) Patient reviewreport (listing patient charts for the QI teams to review

Figure 5 Visual framework for the health information intervention - Zambia


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with clinic staff); 3) Clinic performance reports (summar-izing twelve clinical care measures for QI teams and clinicstaff to use as a snapshot of clinical care quality); 4) CHWperformance report (summarizing follow-up and assess-ment activity levels for CHWs at the health facility); and5) HIS reports (to remove duplicate burden of tallyingdata).Each clinic has a GPRS modem that uses Zambia’s cell

phone networks to synchronize de-identified patientrecords to a central district database every 15 minuteswhen the system is on. Each district office has a serverthat aggregates information from all clinics in that district,allowing the QI teams to print patient review and clinicperformance reports in preparation for each supportivementoring visit.

Adaption and learning during implementationThe BHOMA HIS model has been deployed in largelyrural, remote, and understaffed facilities and lessons havebecome clear during implementation. First, reviewing andclarifying data entry fields reduced the data entry work-load. Second, computers with low-power requirementsthat run on solar power with battery back-up systems areimportant due to the unreliability of power. Third, using adedicated client that runs only the BHOMA softwareavoids viruses, facilitates updates, and simplifies replace-ment. Fourth, it is essential that clinic performance reportsare immediately available at the clinic level — rather thancycling first through the district — for health facility man-agers to identify areas requiring improvements and tocheck whether the corrective measures are working.Finally, patient-level information (rather than aggregatedata) is used for flagging specific patient charts for follow-up with targeted intervention.

Comparisons across the PHIT strategiesAlthough the five PHIT Partnerships have designed dif-ferent approaches to strengthen health systems in theirrespective countries, they share common features inenhancing HIS and linking data with improved decisionmaking. Recognizing the complexity and context-specificnature of the intervention settings, PHIT Partnershipshave adopted a flexible, iterative approach in designingand refining the development of new tools for HISenhancement and improved decision making. Across thepartnerships, the tools and approaches are designed toactively provide health system performance summaries toenable health system personnel to make informed deci-sion on where to focus their efforts and limited resources.A second common feature is the use of feedback systemsto improve data quality, though the error detection andcorrection approach varies across PHIT Partnerships.Error-detection approaches include automated trouble-shooting mechanisms, routine review of aggregate

reports for outliers and missing data, or periodic DQAs.A final similarity across PHIT Partnership approaches isthe recognition of the importance of MOH informationsystems to ensure that HIS strengthening efforts arealigned with national priorities and to increase the likeli-hood of sustained project approaches beyond the life ofthe African Health Initiative. However, approaches acrossPartnerships vary in terms of pace and degree of align-ment, which can be best described as either front-endintegration (Mozambique), progressive integration(Rwanda), current harmonization (Ghana), and potentialfuture harmonization or integration (Tanzania andZambia).Despite these similarities, there are notable differences

in the PHIT Partnership approaches to HIS strengthen-ing and improved decision making. One difference isthe level of focus for data collection, and by extension,its use. The Rwanda, Tanzania and Zambia PHIT Part-nerships begin with intensive collection of patient-leveldata, while the Ghana and Mozambique Partnershipsfocus on facility, district and provincial-level aggregatedata. In addition, the Ghana, Tanzania and Zambia datasystems incorporate data from community service provi-sion to direct outreach services from either formal orcommunity health cadres. All systems, however, havesufficient flexibility to manipulate data according to fre-quency of aggregation (daily, monthly, quarterly,annual), and level of aggregation (health facility, districtor province). A second difference is the type of data col-lection system, with the Rwanda and Zambia Partner-ships implementing new EMR systems, while the Ghana,Mozambique, and Tanzania partnerships focus onpaper-based HIS that are computerized at the healthfacility or district levels.

DiscussionThrough the African Health Initiative, the five PHITPartnerships have designed and are testing novelapproaches to enhancing data systems and using HISresults as a driver for decision making and health systemperformance improvements. Design differences describedacross the PHIT Partnerships reflect the different the-ories of change for each project, particularly with regardsto what information is needed, who will use the informa-tion to affect change, and how this change is expected tomanifest. Ghana and Tanzania have simplified paperregistries that incorporate data on community serviceprovision, and in Ghana a resource allocation tool pio-neered in Tanzania intends to support district managersin decision making. Mozambique focuses on strengthen-ing the existing national HIS, and provides data summa-ries for health system managers to identify problems,evaluate solutions, and allocate resources. Zambia andRwanda are implementing ICT approaches to improve


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data quality, and provide timely information to guidedecision making for clinicians and managers. Thoughimplementation of the PHIT interventions is ongoing,there has been significant country-level enthusiasm forbuilding on the HIS innovations of the African HealthInitiative, with elements of the programs being adoptednationally in PHIT Partnership countries.The first three years of PHIT implementation has

highlighted a number of elements important forstrengthening HIS and linked decision making. First,though an important starting point, training alone isinsufficient to engage and build capacity for facility andcommunity health workers. Stakeholder meetings, datareviews, and mentored use of data as a basis for deci-sions have been utilized to engage health workers andmanagers and demonstrate the value of data, HIS qual-ity, and ownership of tools to summarize data and guidedecision making. A second lesson learned is that it iscritical for HIS interventions to be developed in thecontext of the national HIS, which has been feasibleacross PHIT Partnerships and is crucial to ensuring sus-tainability of the programs beyond the project lifespan.Finally, in two of the PHIT Partnerships, the increasedavailability of mobile phone technology has facilitatedthe introduction of EMR systems in rural, resource con-strained environments. These ICT innovations havecome at a high initial financial cost to build infrastruc-ture, modify software, and build human resource capa-city for their use.Like many complex health system interventions, suc-

cess of the PHIT HIS and decision-making approacheswill hinge on whether frontline health workers andmanagers value, adopt and own the tools and proce-dures introduced by the country Partnerships [19,21].For HIS to have an impact on health system function-ing, and ultimately population health, it will be the insti-tutionalization of habits and norms around data that willmake the difference, such that prioritizing and usingquality data is as much a part of routine practice asstocking a pharmacy or immunizing a child. Thoughexploring different approaches, all PHIT Partnershipsare working towards the goal of standardized and routi-nely used procedures to improve data quality, its avail-ability, and use.The PHIT Partnerships have both a common evalua-

tion framework and project specific evaluation plan inplace to assess their impact on health system function-ing and population health [36]. Identifying effective andappropriate strategies for improving data availability,quality and its use, as well as the role of HIS in improv-ing the health service delivery (including the quality andcoverage of these services), will contribute to the limitedevidence on this health system building block. Taking

lessons learned to scale, however, will require substantialinvestment in general PHC information systems ratherthan disease specific information systems that can frag-ment, distort, and weaken country HIS at all levels ofthe health system [25].Without a well-functioning HIS,it is unlikely that the remaining five building blocks of ahealth system can reach their full potential in improvingpopulation health [26-28].

List of abbreviations usedBHOMA: Better Health through Mentorship and Assessment; CHA:Community health agent; CHMT: Community Health Management Team;CHO: Community health officer; CHW: Community health worker; CIDRZ:Center for Infectious Disease Research in Zambia; DHIMS-2: District HealthInformation Management System; DiHPART: District Health Planning andReporting Toolkit; DQA: Data quality assurance; EDCS: Electronic data capturesystem; EMR: Electronic medical record; GEHIP: Ghana Essential HealthIntervention Project; GPRS: General packet radio service; HIS: Healthinformation system; HIV: Human immunodeficiency virus; ICT: Informationcommunication technologies; LMIC: Low and middle income country; MOH:Ministry of Health; MTUHA: MOH health information system in Tanzania;MTUHA III: MOH HIS community-level module in Tanzania; PHC: Primaryhealth care; PHIT: Population Health and Implementation Training; PIH:Partners In Health; QI: Quality Improvement; TB: Tuberculosis.

Competing interestsThe authors declare that they have no competing interests.

AcknowledgementsThis work was supported by the African Health Initiative of the Doris DukeCharitable Foundation. K Sherr was supported by Grant NumberK02TW009207 from the Fogarty International Center; the content is solelythe responsibility of the authors and does not necessarily represent theofficial views of the National Institutes of Health. We would also like tothank the members of the Population Health Implementation and Training –African Health Initiative Data Collaborative for their contributions to thismanuscript. Members include: Cheryl Amoroso, Manzi Anatole, John KokuAwoonor-Williams, Helen Ayles, Paulin Basinga, Ayaga A. Bawah, ColinBaynes, Harmony F. Chi, Roma Chilengi, Namwinga Chintu, AngelaChisembele-Taylor, Jeanine Condo, Fatima Cuembelo, Felix RwabukwisiCyamatare, Peter Drobac, Karen Finnegan, Sarah Gimbel, Stephen Gloyd,Jessie Hamon, Ahmed Hingora, Lisa Hirschhorn, Marina Kariaganis, HandsonManda, João Luis Manuel, Wendy Mazimba, Mark Micek, Cathy Michel,Megan Murray, Fidele Ngabo, Anthony Ofosu, James Pfeiffer, James F.Phillips, Alusio Pio, Ab Schaap, Kenneth Sherr, Ntazana Sindano, AllisonStone, Jeffrey S. A. Stringer.

DeclarationsThis article has been published as part of BMC Health Services ResearchVolume 13 Supplement 2, 2013: Improving primary health care to achievepopulation impact: the African Health Initiative. The full contents of thesupplement are available online at http://www.biomedcentral.com/bmchealthservres/supplements/13/S2. Publication of this supplement wassupported by the African Health Initiative of the Doris Duke CharitableFoundation.

Author details1Centre for Infectious Disease Research in Zambia, Zambia. 2School ofMedicine, University of Zambia, Zambia. 3Partners In Health/Inshuti MuBuzima, Rwanda. 4Upper East Regional Health Directorate, Ministry of Health,Ghana. 5Heilbrunn Department of Population and Family Health, MailmanSchool of Public Health, Columbia University, NY, USA. 6Ifakara HealthInstitute , Mikocheni, Dar-es-Salaam, Tanzania. 7Health Alliance International,Direcçao Provincial de Saúde, Beira, Sofala, Mozambique. 8Department ofGlobal Health, University of Washington, Seattle, USA.

Published: 31 May 2013


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