Critical risk and success factors for sustainability of an electronic health data capture, processing and dissemination platform for Uganda.

Several studies have investigated challenges that have marred success or even caused the failure of eHealth implementations in Uganda; however, none has focused on the risks and success factors of their sustainability. This study explored critical risk and success factors for the sustainability of an electronic health data capture, processing and dissemination platform for Uganda. A mixed-method research design was followed involving collecting empirical data from all four regions of Uganda. A purposive sampling strategy was used to select the study districts per region, health facilities per district, and respondents/participants per facility or district. Findings revealed several risks and success factors for sustainability, including; bad leadership, corruption, lack of sustainable maintenance programs, lack of suitable sustainability plans, lack of ICT infrastructure investment, poor management systems, funds, stakeholder buy-ins, data sharing and access rights. The success factors included reinvestments as a partial sustainability plan for ICT infrastructure. These factors can be leveraged to ensure the continued operation of eHealth implementations in Uganda. Every electronic health project aiming at success should always make due consideration/sustainability plan at the onset of project conceptualisation; as lack of such a plan has often resulted in failed projects after the initial funds have been withdrawn.

Feasibility and utility of Point-of-Care electronic clinical data capture in Uganda's healthcare system: a qualitative study.

OBJECTIVE: This study aimed to assess Uganda's readiness for implementing a national Point-of-Care (PoC) electronic clinical data capture platform that can function in near real-time. METHODS: A qualitative, cross-sectional design was adopted to obtain a snapshot of Uganda's eHealth system landscape with an aim to assess the readiness for implementing PoC platform. A purposive sampling strategy was used to select the study districts per region, health facilities per district, and participants per facility or district. RESULTS: Nine facilitators were identified, including health worker motivation to serve the community, affirmative action on eHealth financing, improved integrating information and communication technology (ICT) infrastructure, Internet and electricity power connectivity, improved human resource skills and knowledge, the culture of sensitizing and training of stakeholders on eHealth interventions, the perceived value of the platform, health workers' motivation to improve health data quality, interest to improve data use, and continuous improvement in the eHealth regulatory environment. Other suggestions entailed several requirements that must be met, including infrastructure, eHealth governance, human resources, as well as functional and data requirements. DISCUSSION: Uganda, like other low-income countries, has adopted ICT to help solve some of its health system challenges. Although several challenges face eHealth implementations in Uganda, this study revealed facilitators that can be leveraged and requirements that, if met, would facilitate the successful implementation of a near real-time data capture platform capable of improving the country's health outcomes. CONCLUSION: Other countries with eHealth implementations similar to those faced in Uganda can also leverage identified facilitators and address the stakeholders' requirements.

How to do (or not to do) using causal loop diagrams for health system research in low and middle-income settings.

Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors' own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs.

Viewing the global health system as a complex adaptive system - implications for research and practice.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield.   We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

Understanding the maternal and child health system response to payment for performance in Tanzania using a causal loop diagram approach.

Payment for performance (P4P) has been employed in low and middle-income (LMIC) countries to improve quality and coverage of maternal and child health (MCH) services. However, there is a lack of consensus on how P4P affects health systems. There is a need to evaluate P4P effects on health systems using methods suitable for evaluating complex systems. We developed a causal loop diagram (CLD) to further understand the pathways to impact of P4P on delivery and uptake of MCH services in Tanzania. The CLD was developed and validated using qualitative data from a process evaluation of a P4P scheme in Tanzania, with additional stakeholder dialogue sought to strengthen confidence in the diagram. The CLD maps the interacting mechanisms involved in provider achievement of targets, reporting of health information, and population care seeking, and identifies those mechanisms affected by P4P. For example, the availability of drugs and medical commodities impacts not only provider achievement of P4P targets but also demand of services and is impacted by P4P through the availability of additional facility resources and the incentivisation of district managers to reduce drug stock outs. The CLD also identifies mechanisms key to facility achievement of targets but are not within the scope of the programme; the activities of health facility governing committees and community health workers, for example, are key to demand stimulation and effective resource use at the facility level but both groups were omitted from the incentive system. P4P design considerations generated from this work include appropriately incentivising the availability of drugs and staffing in facilities and those responsible for demand creation in communities. Further research using CLDs to study heath systems in LMIC is urgently needed to further our understanding of how systems respond to interventions and how to strengthen systems to deliver better coverage and quality of care.

Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models.

BACKGROUND: Mathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM. METHODS: We searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature. RESULTS: We identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes. CONCLUSIONS: Future work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.

Implementation of USSD Technology to Improve Quality of Routinely Reported Health Data in a Resource-Limited Setting.

Health decision-making is heavily premised on routinely reported data from lower levels of healthcare delivery to the national level. The reported data are of best use if their quality is high. Unfortunately, in many resource-limited settings in sub-Saharan Africa, the quality of reported data is often poor. Among the reasons attributed for poor data quality is use of sub-optimal modalities for collecting and transmitting data, such as paper-based and Short Message Service (SMS). Through a user-centered approach, we developed and implemented an Unstructured Supplementary Service Data (USSD)-based health data reporting intervention in a district in Uganda. The impact of the developed system on report accuracy, timeliness and completeness was evaluated against the expected 100% rates by the Ministry of Health (MoH). A total of 224 reports were submitted over the two-month study period. Of the submitted reports, 171 (76.3%) were complete (p<0.0001) compared to MoH's required 100%). 161 (71.9%) were accurate (P<0.0001), and 158 (70.5%) of the reports were submitted on time (p<0.0001). The deficiencies were largely attributed to a few facilities, as only 17.9% of facilities had data discrepancies with a mean of - 2.11 (P=0.38), 96.4% (0.130) of the facilities had complete reports and 87.4% (0.100) of the facilities reported on time. Poor network coverage was an outstanding challenge to reporting.

Applying a system dynamics modelling approach to explore policy options for improving neonatal health in Uganda.

BACKGROUND: The most recent reports on global trends in neonatal mortality continue to show alarmingly slow progress on improvements in neonatal mortality rates, with sub-Saharan Africa still lagging behind. This emphasised the urgent need to innovatively employ alternative solutions that take into account the intricate complexities of neonatal health and the health systems in which the various strategies operate. METHODS: In our first paper, we empirically explored the causes of the stagnating neonatal mortality in Uganda using a dynamic synthesis methodology (DSM) approach. In this paper, we completed the last three stages of DSM, which involved the development of a quantitative (simulation) model, using STELLA modelling software. We used statistical data to populate the model. Through brainstorming sessions with stakeholders, iterations to test and validate the model were undertaken. The different strategies and policy interventions that could possibly lower neonatal mortality rates were tested using what-if analysis. Sensitivity analysis was used to determine the strategies that could have a great impact on neonatal mortality. RESULTS: We developed a neonatal health simulation model (NEOSIM) to explore potential interventions that could possibly improve neonatal health within a health system context. The model has four sectors, namely population, demand for services, health of the mothers and choices of clinical care. It tests the effects of various interventions validated by a number of Ugandan health practitioners, including health education campaigns, free delivery kits, motorcycle coupons, kangaroo mother care, improving neonatal resuscitation and labour management skills, and interventions to improve the mothers health, i.e. targeting malaria, anaemia and tetanus. Among the tested interventions, the package with the highest impact on reducing neonatal mortality rates was a combination of the free delivery kits in a setting where delivery services were free and motorcycle coupons to take women to hospital during emergencies. CONCLUSIONS: This study presents a System Dynamics model with a broad and integrated view of the neonatal health system facilitating a deeper understanding of its current state and constraints and how these can be mitigated. A tool with a user friendly interface presents the dynamic nature of the model using 'what-if' scenarios, thus enabling health practitioners to discuss the consequences or effects of various decisions. Key findings of the research show that proposed interventions and their impact can be tested through simulation experiments thereby generating policies and interventions with the highest impact for improved healthcare service delivery.