3 research outputs found

    Implemention of a Laboratory Information System in Zimbabwe

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    Objective: Understand the challenges that exist in the Zimbabwe health systems, that could be addressed through the integration of a Laboratory Information Management System (LIMS).Understand key aspects for consideration when selecting and adapting a LIMS in a resource limited setting.Showcase improvements in laboratory information management processes following adoption of a LIMS.Introduction: Zimbabwe's National Health Laboratory Services faces multiple challenges related to inadequate financial support and skilled human resources, insufficient infrastructure, and inefficient tracking of clinical samples collected by health facilities. The slow turnaround time and poor management of the sample testing process, as well as delivery of results remain critical challenges. Compounding these problems further is a manual system for tracking large volumes of samples. This laborious and time-consuming process is inefficient for management of high amounts of incoming medical samples, frequently resulting in incomplete and inaccurate data. Additionally, health facilities are unable to monitor clinical samples and results in transit, leading to misplaced samples and missing results. Furthermore, although the laboratory service runs on a tiered network system - with lower level laboratories referring surveillance samples to higher level laboratories, processing of samples is not fulfilled promptly. The solutions to these challenges are divergent - sometimes even pointing in different directions. To this end, the Zimbabwe Ministry of Health and Child Care (MoHCC) has identified and integrated a LIMS to improve tracking of samples from the time of collection through results delivery.Methods: Our methods included an environmental needs assessment, user requirement analysis, followed by a LIMS customization and integration. The overarching aim has been to integrate the electronic open source BIKA LIMS into Zimbabwe’s national health information systems (HIS), to improve laboratory information management.The user requirements gathering exercise, included focus group discussion meetings with potential LIMS users, and direct observations, to guide the establishment of LIMS specifications. The needs assessment focused on the system functionality. Specifically, it investigated those aspects that would improve the ability: to track clinical samples such as creating and activating an ‘alerting’ capability when results are not reported within the set turnaround time; for users to see lists and counts of clinical samples at various testing levels; to uniquely identify samples received in the laboratories. Guided by these requirements, an environmental scan of off-the-shelf and open source LIMS platforms was conducted to identify a few options for the Zimbabwe context. Primary factors for shortlisting included: an existing community of practice for support; interoperability; customizability and configurability; and local awareness of the platform. In a LIMS national user’s meeting, involving relevant levels of the health system (Laboratories, Central, Provincial and District hospitals), a review of LIMS platform options was performed to narrow down selections. It evaluated the extent to which the user requirements (Workflow, equipment interface, result management, inter-operable, quality control, and stock management) were being met. Based on the evaluation, a single system (LIMS) was selected, adopted and adapted for use at six representative laboratories, including Zimbabwe’s National Microbiology Reference Laboratory.On-Site classroom and desk-side training, for knowledge transfer to local LIMS users, characterised the implementation phase. Local champions were identified from laboratory technicians and equipped to offer first line support. Both on-site and remote support was provided to LIMS users. The monitoring phase is ongoing, using interview guides and LIMS user meetings to understand challenges and ways to improve the system.Results: A LIMS was successfully customized and integrated into Zimbabwe’s national health information system infrastracture in six regional laboratories, to improve overall laboratory information management, timeliness of reporting and quality control. Since its full implementation between 2013 and 2017, average turnaround time for results improved significantly from 10 to 21 days in 2013 to only 3 days in 2017. Data quality improved; the number of untested clinical samples reduced from an average of 6 in 100 in 2013, to average of less or equal to 1 in 100, in 2017 . Also, there have been observed improvements in Zimbabwe's laboratory information management workflow and results reporting. High user satisfaction and increased LIMS use have led to the demand for LIMS expansion to additional laboratories. The LIMS has also managed to reduce the time required to produce disease notification reports.Conclusions: LIMS are proving to be an effective method for tracking samples and laboratory results in low resource settings like Zimbabwe. LIMS has provided an efficient way for record, store, and track timely reporting of laboratory data, allowing for improved quality of data. Overall, LIMS has increased efficiency in laboratory workflow and introduced the ability to adequately track samples from time of collection

    Advancing GHSA: Lessons learned about strengthening HIS and disease surveillance

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    ObjectiveThe objective is to discuss two decades of international experiencein health information and disease surveillance systems strengtheningand synthesize lessons learned as applicable to implementation of theGlobal Health Security Agenda (GHSA).IntroductionRTI International has worked on enhancing health informationand disease surveillance systems in many countries, includingThe Democratic Republic of the Congo (DRC), Guinea, Indonesia,Kenya, Nepal, Philippines, Tanzania, Zambia, and Zimbabwe.Strengthening these systems is critical for all three of the Prevent,Detect and Respond domains within the Global Health SecurityAgenda.We have deep experience in this area, ranging from implementingDistrict Health Information Software (DHIS), electronic medicalrecords, health facility registries, eHealth national strategies,electronic Integrated Disease Surveillance and Response system(eIDSR), mobile real-time malaria surveillance and response, nationalweekly disease surveillance, patient referral system, and communitybased surveillance. These experiences and lessons learned can informwork being done to advance the GHSA.We will discuss several examples, including activities in Zimbabweand Tanzania. RTI has been working in Zimbabwe for over six yearsto strengthen the national health information system. This workhas included the configuration and roll-out of DHIS 2, the nationalelectronic health information system. In doing so, RTI examinedand revitalized the weekly disease surveillance system, improvingdisease reporting timeliness and completeness from 40% to 90%.Additionally, RTI has integrated mobile technology to help morerapidly communicate laboratory test results, a laboratory informationmanagement systems to manage and guide test sample processing,and various other patient level systems in support of health servicedelivery at the local level. This work has involved capacity buildingwithin the ministry of health to allow for sustainable support of healthinformation systems practices and technology and improvements todata dissemination and use practices.Similarly, RTI has worked for more than five years to helpstrengthening the National HIS in Tanzania. These activities haveincluded stakeholder coordination, developing national eHealthstrategy and enterprise architecture, harmonizing indicators,redesigning routine reporting instruments, national DHIS 2 roll-out,information technology infrastructure management and user helpdesk support, reducing the number of parallel information systems,data dissemination and use, development of district health profiles,development of the national health facility registry, and supportingroll-out of the electronic integrated disease surveillance system.MethodsWe will profile selected projects and synthesize critical lessonslearned that pertain to implementation of the GHSA in resourceconstrained countries.ResultsWe will summarize our experience and lessons learned withhealth information and disease surveillance systems strengthening.Topics such as those that relate to advancing the GHSA RealTime Surveillance and Reporting Action Package areas will bediscussed, including: indicator and event based surveillance systems;interoperable, interconnected, electronic real-time reporting system;analysis of surveillance data; syndromic surveillance systems;systems for efficient reporting to WHO, FAO and OIE; and reportingnetwork and protocols in country.ConclusionsOur experience working over the past 14 years in 9 countrieson different HIS and disease surveillance system strengtheningprojects has led to a deep understanding of the challenges aroundimplementation of these systems in limited resource settings. Theseexperiences and lessons learned can inform initiatives and programsto advance the GHSA

    Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

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    Background In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. Objectives This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied. Methods The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken. Results Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application. Conclusions Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems’ weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches
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