Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree

Progress with the Learning Health System 2.0: a rapid review of Learning Health Systems' responses to pandemics and climate change

BACKGROUND

Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0.

METHODS

Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance.

RESULTS

The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges.

CONCLUSIONS

An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events.

TRIAL REGISTRATION

PROSPERO pre-registration: CRD42023408896.

Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study

BACKGROUND

Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms.

OBJECTIVE

This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe.

METHODS

Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital's health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS).

RESULTS

Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively.

CONCLUSIONS

This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings.

Effects of the COVID-19 pandemic on the outcomes of HIV-exposed neonates: a Zimbabwean tertiary hospital experience

INTRODUCTION

The COVID-19 pandemic has globally impacted health service access, delivery and resources. There are limited data regarding the impact on the prevention of mother to child transmission (PMTCT) service delivery in low-resource settings. Neotree ( www.neotree.org ) combines data collection, clinical decision support and education to improve care for neonates. Here we evaluate impacts of COVID-19 on care for HIV-exposed neonates.

METHODS

Data on HIV-exposed neonates admitted to the neonatal unit (NNU) at Sally Mugabe Central Hospital, Zimbabwe, between 01/06/2019 and 31/12/2021 were analysed, with pandemic start defined as 21/03/2020 and periods of industrial action (doctors (September 2019-January 2020) and nurses (June 2020-September 2020)) included, resulting in modelling during six time periods: pre-doctors' strike (baseline); doctors' strike; post-doctors' strike and pre-COVID; COVID and pre-nurses' strike; nurses' strike; post nurses' strike. Interrupted time series models were used to explore changes in indicators over time.

RESULTS

Of 8,333 neonates admitted to the NNU, 904 (11%) were HIV-exposed. Mothers of 706/765 (92%) HIV-exposed neonates reported receipt of antiretroviral therapy (ART) during pregnancy. Compared to the baseline period when average admissions were 78 per week (95% confidence interval (CI) 70-87), significantly fewer neonates were admitted during all subsequent periods until after the nurses' strike, with the lowest average number during the nurses' strike (28, 95% CI 23-34, p < 0.001). Across all time periods excluding the nurses strike, average mortality was 20% (95% CI 18-21), but rose to 34% (95% CI 25, 46) during the nurses' strike. There was no evidence for heterogeneity (p > 0.22) in numbers of admissions or mortality by HIV exposure status. Fewer HIV-exposed neonates received a PCR test during the pandemic (23%) compared to the pre-pandemic periods (40%) (RR 0.59, 95% CI 0.41-0.84, p < 0.001). The proportion of HIV-exposed neonates who received antiretroviral prophylaxis during admission was high throughout, averaging between 84% and 95% in each time-period.

CONCLUSION

While antiretroviral prophylaxis for HIV-exposed neonates remained high throughout, concerning data on low admissions and increased mortality, similar in HIV-exposed and unexposed neonates, and reduced HIV testing, suggest some aspects of care may have been compromised due to indirect effects of the pandemic.

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.

Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model

OBJECTIVE

To develop a clinical prediction model to diagnose neonatal sepsis in low-resource settings.

DESIGN

Secondary analysis of data collected by the Neotree digital health system from 1 February 2019 to 31 March 2020. We used multivariable logistic regression with candidate predictors identified from expert opinion and literature review. Missing data were imputed using multivariate imputation and model performance was evaluated in the derivation cohort.

SETTING

A tertiary neonatal unit at Sally Mugabe Central Hospital, Zimbabwe.

PATIENTS

We included 2628 neonates aged <72 hours, gestation ≥32+0 weeks and birth weight ≥1500 g.

INTERVENTIONS

Participants received standard care as no specific interventions were dictated by the study protocol.

MAIN OUTCOME MEASURES

Clinical early-onset neonatal sepsis (within the first 72 hours of life), defined by the treating consultant neonatologist.

RESULTS

Clinical early-onset sepsis was diagnosed in 297 neonates (11%). The optimal model included eight predictors: maternal fever, offensive liquor, prolonged rupture of membranes, neonatal temperature, respiratory rate, activity, chest retractions and grunting. Receiver operating characteristic analysis gave an area under the curve of 0.74 (95% CI 0.70-0.77). For a sensitivity of 95% (92%-97%), corresponding specificity was 11% (10%-13%), positive predictive value 12% (11%-13%), negative predictive value 95% (92%-97%), positive likelihood ratio 1.1 (95% CI 1.0-1.1) and negative likelihood ratio 0.4 (95% CI 0.3-0.6).

CONCLUSIONS

Our clinical prediction model achieved high sensitivity with low specificity, suggesting it may be suited to excluding early-onset sepsis. Future work will validate and update this model before considering implementation within the Neotree.

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.