JMIR Publications

ABSTRACT

Background:

Despite an increase in hospital based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics there is a significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardising 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 healthcare facilities. The first phase of 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 (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn and hypothermia).

Objective:

To describe the next stage of co-development, implementation, and evaluation of digital neonatal clinical decision support algorithms in Malawi and Zimbabwe.

Methods:

11 diagnosis-specific online workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing: 1. Review of available evidence 2. Country-specific guideline review (Essential Medicines List and Standard Treatment Guidelines for Zimbabwe (EDLIZ); Care of the infant and newborn (COIN), Malawi) 3. Identification of uncertainties within the literature for future research After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital’s healthcare professionals (HCPs), and refined according to their feedback. Once finalised, 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 two usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and Systems Usability Scale (SUS).

Results:

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, e.g., birthweight thresholds for admissions to the neonatal unit, country-specific guidelines were created. Nine HCPs attended usability workshops and completed the SUS questionnaire, among them eight completed the PSSUQ. Both usability scores (SUS mean 75.8/100 [higher score better]; PSSUQ overall 2.28/7 [lower score better]) demonstrated high usability of the CDS function (comparable to previous SUS scores assessing usability of Neotree data capture and dashboard functionalities) 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. Real-world usability testing could further enhance implementation methodology. Effective local and international partnerships were key. Mixed methods, larger scale evaluation of impact on neonatal quality of care and survival is planned.