Design and implementation of a mobile health electronic data capture platform that functions in fully-disconnected settings: a pilot study in rural Liberia

Crossing the digital divide: The workload of manual data entry for integration between mobile health applications and eHealth infrastructure

Background

Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to "integrate" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood.

Methodology

As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel.

Results

We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone.

Conclusion

Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.

Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study

BACKGROUND

An interventional study named Malakit was implemented between April 2018 and March 2020 to address malaria in gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, referred to in the project as facilitators.

OBJECTIVE

This paper aims to describe the process by which the information system was designed, developed, and implemented to achieve the monitoring and evaluation of the Malakit intervention.

METHODS

The intervention was implemented in challenging conditions at five cross-border distribution sites, which imposed strong logistical constraints for the design of the information system: isolation in the Amazon rainforest, tropical climate, and lack of reliable electricity supply and internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source software, commercial services, and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK (Open Data Kit) Collect. A custom R package and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor, and clean data according to feedback from facilitators and supervision visits on the field.

RESULTS

Between April 2018 and March 2020, nine facilitators generated a total of 4863 form records, corresponding to an average of 202 records per month. Facilitators' feedback was essential for adapting and improving mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was 5 (IQR 3-7) minutes, suggesting that electronic data capture was not taking more time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory, with only 3.03% (147/4849) of form records requiring correction.

CONCLUSIONS

The development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that even in a challenging environment, it is possible to produce good-quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators' experience.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03695770; https://clinicaltrials.gov/ct2/show/NCT03695770.

Availability and Accessibility of Primary Care for the Remote, Rural, and Poor Population of Indonesia

Background: Adopting Universal Health Coverage for implementation of a national health insurance system [Jaminan Kesehatan Nasional (JKN)/Badan Penyelenggara Jaminan Sosial or the Indonesian National Social Health Insurance Scheme (BPJS)] targets the 255 million population of Indonesia. The availability, accessibility, and acceptance of healthcare services are the most important challenges during implementation. Referral behavior and the utilization of primary care structures for underserved (rural/remote regions) populations are key guiding elements. In this study, we provided the first assessment of BPJS implementation and its resulting implications for healthcare delivery based on the entire insurance dataset for the initial period of implementation, specifically focusing on poor and remote populations. Methods: Demographic, economic, and healthcare infrastructure information was obtained from public resources. Data about the JKN membership structure, performance information, and reimbursement were provided by the BPJS national head office. For analysis, an ANOVA was used to compare reimbursement indexes for primary healthcare (PHC) and advanced healthcare (AHC). The usage of primary care resources was analyzed by comparing clustered provinces and utilization indices differentiating poor [Penerima Bantuan Iur (PBI) membership] and non-poor populations (non-PBI). Factorial and canonical discrimination analyses were applied to identify the determinants of PHC structures. Results: Remote regions cover 27.8% of districts/municipalities. The distribution of the poor population and PBI members were highly correlated (r2 > 0.8; p < 0.001). Three clusters of provinces [remote high-poor (N = 13), remote low-poor (N = 15), non-remote (N = 5)] were identified. A discrimination analysis enabled the >82% correct cluster classification of infrastructure and human resources of health (HRH)-related factors. Standardized HRH (nurses and general practitioners [GP]) availability showed significant differences between clusters (p < 0.01), whereas the availability of hospital beds was weakly correlated. The usage of PHC was ~2-fold of AHC, while non-PBI members utilized AHC 4- to 5-fold more frequently than PBI members. Referral indices (r2 = 0.94; p < 0.001) for PBI, non-PBI, and AHC utilization rates (r2 = 0.53; p < 0.001) were highly correlated. Conclusion: Human resources of health availability were intensively related to the extent of the remote population but not the numbers of the poor population. The access points of PHC were mainly used by the poor population and in remote regions, whereas other population groups (non-PBI and non-Remote) preferred direct access to AHC. Guiding referral and the utilization of primary care will be key success factors for the effective and efficient usage of available healthcare infrastructures and the achievement of universal health coverage in Indonesia. The short-term development of JKN was recommended, with a focus on guiding referral behavior, especially in remote regions and for non-PBI members.