Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree

Development and Pilot Implementation of Neotree, a Digital Quality Improvement Tool Designed to Improve Newborn Care and Survival in 3 Hospitals in Malawi and Zimbabwe: Cost Analysis Study

Background

Two-thirds of the 2.4 million newborn deaths that occurred in 2020 within the first 28 days of life might have been avoided by implementing existing low-cost evidence-based interventions for all sick and small newborns. An open-source digital quality improvement tool (Neotree) combining data capture with education and clinical decision support is a promising solution for this implementation gap.

Objective

We present results from a cost analysis of a pilot implementation of Neotree in 3 hospitals in Malawi and Zimbabwe.

Methods

We combined activity-based costing and expenditure approaches to estimate the development and implementation cost of a Neotree pilot in 1 hospital in Malawi, Kamuzu Central Hospital (KCH), and 2 hospitals in Zimbabwe, Sally Mugabe Central Hospital (SMCH) and Chinhoyi Provincial Hospital (CPH). We estimated the costs from a provider perspective over 12 months. Data were collected through expenditure reports, monthly staff time-use surveys, and project staff interviews. Sensitivity and scenario analyses were conducted to assess the impact of uncertainties on the results or estimate potential costs at scale. A pilot time-motion survey was conducted at KCH and a comparable hospital where Neotree was not implemented.

Results

Total cost of pilot implementation of Neotree at KCH, SMCH, and CPH was US $37,748, US $52,331, and US $41,764, respectively. Average monthly cost per admitted child was US $15, US $15, and US $58, respectively. Staff costs were the main cost component (average 73% of total costs, ranging from 63% to 79%). The results from the sensitivity analysis showed that uncertainty around the number of admissions had a significant impact on the costs in all hospitals. In Malawi, replacing monthly web hosting with a server also had a significant impact on the costs. Under routine (nonresearch) conditions and at scale, total costs are estimated to fall substantially, up to 76%, reducing cost per admitted child to as low as US $5 in KCH, US $4 in SMCH, and US $14 in CPH. Median time to admit a baby was 27 (IQR 20-40) minutes using Neotree (n=250) compared to 26 (IQR 21-30) minutes using paper-based systems (n=34), and the median time to discharge a baby was 9 (IQR 7-13) minutes for Neotree (n=246) compared to 3 (IQR 2-4) minutes for paper-based systems (n=50).

Conclusions

Neotree is a time- and cost-efficient tool, comparable with the results from limited similar mHealth decision-support tools in low- and middle-income countries. Implementation costs of Neotree varied substantially between the hospitals, mainly due to hospital size. The implementation costs could be substantially reduced at scale due to economies of scale because of integration to the health systems and reductions in cost items such as staff and overhead. More studies assessing the impact and cost-effectiveness of large-scale mHealth decision-support tools are needed.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.

Effectiveness of a mobile app, KhunLook versus the maternal and child health handbook on Thai parent’s health literacy, accuracy of health assessments and convenience of use: A randomized controlled trial (Preprint)

BACKGROUND

Children of parents who have higher health literacy (HL) benefit more from preventive child health care. Digital interventions have been used to improve parents' HL with high satisfaction. KhunLook is a Thai mobile app conceived using strategies to improve HL. It was developed to assist parents in assessing and keeping track of their child's health in complement to the standard Maternal and Child Health Handbook (MCHH).

OBJECTIVE

This trial focuses on the effectiveness of using the KhunLook app with the MCHH and standard care (intervention) compared with the conventional MCHH and standard care (control) on parents' HL. Data on accuracy of parents' assessment of their child's health and growth as well as convenience of use of the tool (app or MCHH) in the well-child clinic were collected at 2 visits (immediate=visit 1, and intermediate=visit 2).

METHODS

Parents of children under 3 years of age who (1) had a smartphone or tablet and the MCHH and (2) could participate in 2 visits, 2-6 months apart at Srinagarind Hospital, Khon Kaen, Thailand, were enrolled in this 2-arm parallel randomized controlled trial between April 2020 and May 2021. Parents were randomized 1:1 to 2 groups. At visit 1, data on demographics and baseline HL (Thailand Health Literacy Scales) were collected. Parents in the app group used the KhunLook app and the control group used their child's handbook to assess their child's growth, development, nutrition and feeding, immunization status and rated the convenience of the tool they used. At visit 2, they repeated the assessments and completed the HL questionnaire.

RESULTS

A total of 358 parents completed the study (358/408, 87.7%). After the intervention, the number of parents with high total HL significantly increased from 94/182 (51.6%) to 109/182 (59.9%; 15/182; Δ 8.2%; P=.04), specifically in the health management (30/182; Δ 16.4%; P<.001) and child health management (18/182; Δ 9.9%; P=.01) domains in the app group, but not in the control group. Parents in the app group could correctly assess their child's head circumference (172/182, 94.5% vs 124/176, 70.5%; P<.001) and development (173/182, 95.1% vs 139/176, 79.0%; P<.001) better than those in the control group at both visits. A higher proportion of parents in the app group rated their tool as very easy or easy to use (174-181/182, 95.6%-99.5% vs 141-166/176, 80.1%-94.3%; P<.001) on every item since the first visit.

CONCLUSIONS

Our results suggest the potential of a smartphone app (KhunLook) to improve parents' HL as well as to promote superior accuracy of parents' assessment of their child's head circumference and development, with a similar effect on weight, height, nutrition and feeding, and immunization as in traditional interventions. Using the KhunLook app is useful and more convenient for parents in promoting a healthy child preventive care during early childhood.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20200312003; https://www.thaiclinicaltrials.org/show/TCTR20200312003.