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
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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).
Conclusion
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.
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).
Conclusion
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.
Date Issued
2023
Date Acceptance
2023-11-07
Citation
JMIR mHealth and uHealth, 2023, 11
ISSN
2291-5222
Publisher
JMIR Publications
Journal / Book Title
JMIR mHealth and uHealth
Volume
11
Copyright Statement
© Hassan Haghparast-Bidgoli, Tim Hull-Bailey, Deliwe Nkhoma, Tarisai Chiyaka, Emma Wilson, Felicity Fitzgerald,
Gwendoline Chimhini, Nushrat Khan, Hannah Gannon, Rekha Batura, Mario Cortina-Borja, Leyla Larsson, Msandeni
Chiume, Yali Sassoon, Simbarashe Chimhuya, Michelle Heys. Originally published in JMIR mHealth and uHealth (https://
mhealth.jmir.org), 22.12.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete
bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license
information must be included.
Gwendoline Chimhini, Nushrat Khan, Hannah Gannon, Rekha Batura, Mario Cortina-Borja, Leyla Larsson, Msandeni
Chiume, Yali Sassoon, Simbarashe Chimhuya, Michelle Heys. Originally published in JMIR mHealth and uHealth (https://
mhealth.jmir.org), 22.12.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete
bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license
information must be included.
License URL
Subjects
babies
baby
child
children
clinical decision support
cost
costing
costs
decision support
DECISION-SUPPORT-SYSTEMS
economic
economics
expenditure
Health Care Sciences & Services
INTERVENTIONS
Life Sciences & Biomedicine
maternal
Medical Informatics
mHealth
mobile health
neonatal
newborn
newborns
paediatric
paediatrics
pediatric
pediatrics
premature
preterm
quality improvement tool
Science & Technology
Publication Status
Published
Article Number
e50467
Date Publish Online
2023-12-22