Exploring health information system resilience during COVID-19 pandemic: case studies from Norway, Sri Lanka & Rwanda.

The study aims at exploring health system resilience by defining the scope on health information systems, one of the six building blocks of the health system. The empirical evidence is derived using qualitative data collection and analysis in the context of Norway, Sri Lanka and Rwanda during the COVID-19 pandemic. The case studies elicit bounce back and bounce forward properties as well as the agility as major attributes of resilience present across the countries. Existing local capacity, networking and collaborations, flexible digital platforms and enabling antecedent conditions are identified as socio-technical determinants of information system resilience based on the case studies across the countries.

Open Source Digital Health Software for Resilient, Accessible and Equitable Healthcare Systems.

OBJECTIVE: To assess the impact of open-source projects on making healthcare systems more resilient, accessible and equitable. METHODS: In response to the International Medical Informatics Association (IMIA) call for working group contributions for the IMIA Yearbook, the Open Source Working Group (OSWG) conducted a rapid review of current open source digital health projects to illustrate how they can contribute to making healthcare systems more resilient, accessible and equitable. We sought case studies from the OSWG membership to illustrate these three concepts and how open source software (OSS) addresses these concepts in the real world. These case studies are discussed against the background of literature identified through the rapid review. RESULTS: To illustrate the concept of resilience, we present case studies from the adoption of District Health Information Software version 2 (DHIS2) for managing the Covid pandemic in Rwanda, and the adoption of the OpenEHR open Health IT standard. To illustrate accessibility, we show how open source design systems for user interface design have been used by governments to ensure accessibility of digital health services for patients and healthy individuals, and by the OpenMRS community to standardise their user interface design. Finally, to illustrate the concept of equity, we describe the OpenWHO framework and two open source digital health projects, GNU Health and openIMIS, that both aim to reduce health inequities through the use of open source digital health software. CONCLUSION: This review has demonstrated that open source software addresses many of the challenges involved in making healthcare more accessible, equitable and resilient in high and low income settings.

Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

INTRODUCTION: Reliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents. METHODS: We conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF<0.90 or VF>1.10 indicated over- and under-reporting in HMIS, respectively. RESULTS: High proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits. CONCLUSION: There was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

The national burden of influenza-associated severe acute respiratory illness hospitalization in Rwanda, 2012-2014.

BACKGROUND: Estimates of influenza-associated hospitalization are severely limited in low- and middle-income countries, especially in Africa. OBJECTIVES: To estimate the national number of influenza-associated severe acute respiratory illness (SARI) hospitalization in Rwanda. METHODS: We multiplied the influenza virus detection rate from influenza surveillance conducted at 6 sentinel hospitals by the national number of respiratory hospitalization obtained from passive surveillance after adjusting for underreporting and reclassification of any respiratory hospitalizations as SARI during 2012-2014. The population at risk was obtained from projections of the 2012 census. Bootstrapping was used for the calculation of confidence intervals (CI) to account for the uncertainty associated with all levels of adjustment. Rates were expressed per 100 000 population. A sensitivity analysis using a different estimation approach was also conducted. RESULTS: SARI cases accounted for 70.6% (9759/13 813) of respiratory admissions at selected hospitals: 77.2% (6783/8786) and 59.2% (2976/5028) among individuals aged <5 and ≥5 years, respectively. Overall, among SARI cases tested, the influenza virus detection rate was 6.3% (190/3022): 5.7% (127/2220) and 7.8% (63/802) among individuals aged <5 and ≥5 years, respectively. The estimated mean annual national number of influenza-associated SARI hospitalizations was 3663 (95% CI: 2930-4395-rate: 34.7; 95% CI: 25.4-47.7): 2637 (95% CI: 2110-3164-rate: 168.7; 95% CI: 135.0-202.4) among children aged <5 years and 1026 (95% CI: 821-1231-rate: 11.3; 95% CI: 9.0-13.6) among individuals aged ≥5 years. The estimates obtained from both approaches were not statistically different (overlapping CIs). CONCLUSIONS: The burden of influenza-associated SARI hospitalizations was substantial and was highest among children aged <5 years.

Impact of a district-wide health center strengthening intervention on healthcare utilization in rural Rwanda: Use of interrupted time series analysis.

BACKGROUND: Evaluations of health systems strengthening (HSS) interventions using observational data are rarely used for causal inference due to limited data availability. Routinely collected national data allow use of quasi-experimental designs such as interrupted time series (ITS). Rwanda has invested in a robust electronic health management information system (HMIS) that captures monthly healthcare utilization data. We used ITS to evaluate impact of an HSS intervention to improve primary health care facility readiness on health service utilization in two rural districts of Rwanda. METHODS: We used controlled ITS analysis to compare changes in healthcare utilization at health centers (HC) that received the intervention (n = 13) to propensity score matched non-intervention health centers in Rwanda (n = 86) from January 2008 to December 2012. HC support included infrastructure renovation, salary support, medical equipment, referral network strengthening, and clinical training. Baseline quarterly mean outpatient visit rates and population density were used to model propensity scores. The intervention began in May 2010 and was implemented over a twelve-month period. We used monthly healthcare utilization data from the national Rwandan HMIS to study changes in the (1) number of facility deliveries per 10,000 women, (2) number of referrals for high risk pregnancy per 100,000 women, and (3) the number of outpatient visits performed per 1,000 catchment population. RESULTS: PHIT HC experienced significantly higher monthly delivery rates post-HSS during the April-June season than comparison (3.19/10,000, 95% CI: [0.27, 6.10]). In 2010, this represented a 13% relative increase, and in 2011, this represented a 23% relative increase. The post-HSS change in monthly rate of high-risk pregnancies referred increased slightly in intervention compared to control HC (0.03/10,000, 95% CI: [-0.007, 0.06]). There was a small immediate post-HSS increase in outpatient visit rates in intervention compared to control HC (6.64/1,000, 95% CI: [-13.52, 26.81]). CONCLUSION: We failed to find strong evidence of post-HSS increases in outpatient visit rates or referral rates at health centers, which could be explained by small sample size and high baseline nation-wide health service coverage. However, our findings demonstrate that high quality routinely collected health facility data combined with ITS can be used for rigorous policy evaluation in resource-limited settings.

Toward utilization of data for program management and evaluation: quality assessment of five years of health management information system data in Rwanda.

BACKGROUND: Health data can be useful for effective service delivery, decision making, and evaluating existing programs in order to maintain high quality of healthcare. Studies have shown variability in data quality from national health management information systems (HMISs) in sub-Saharan Africa which threatens utility of these data as a tool to improve health systems. The purpose of this study is to assess the quality of Rwanda's HMIS data over a 5-year period. METHODS: The World Health Organization (WHO) data quality report card framework was used to assess the quality of HMIS data captured from 2008 to 2012 and is a census of all 495 publicly funded health facilities in Rwanda. Factors assessed included completeness and internal consistency of 10 indicators selected based on WHO recommendations and priority areas for the Rwanda national health sector. Completeness was measured as percentage of non-missing reports. Consistency was measured as the absence of extreme outliers, internal consistency between related indicators, and consistency of indicators over time. These assessments were done at the district and national level. RESULTS: Nationally, the average monthly district reporting completeness rate was 98% across 10 key indicators from 2008 to 2012. Completeness of indicator data increased over time: 2008, 88%; 2009, 91%; 2010, 89%; 2011, 90%; and 2012, 95% (p<0.0001). Comparing 2011 and 2012 health events to the mean of the three preceding years, service output increased from 3% (2011) to 9% (2012). Eighty-three percent of districts reported ratios between related indicators (ANC/DTP1, DTP1/DTP3) consistent with HMIS national ratios. Conclusion and policy implications: Our findings suggest that HMIS data quality in Rwanda has been improving over time. We recommend maintaining these assessments to identify remaining gaps in data quality and that results are shared publicly to support increased use of HMIS data.