Evidence map›Paper›PMID 33084581›Full record

ArticleJMIR mHealth and uHealth2020

Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App: Digital Perinatal Outcome Audit.

Caroline Crehan, Erin Kesler, Indira Angela Chikomoni, Kristi Sun, Queen Dube, Monica Lakhanpaul, Michelle Heys

Registry-linked trialAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05127070 (Evaluating the NeoTree), which is not on this map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05127070 unknown statusnot on this map

Evaluating the NeoTree: An eHealth Solution to Reduce Neonatal Mortality in Two Low Income Countries: Malawi and Zimbabwe

TypeobservationalSponsorUniversity College, LondonRan2019 to 2022Enrolled19,000ConditionsPrematurity, Neonatal Encephalopathy, Neonatal Sepsis, Neonatal DeathArmsNeotree
3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Caroline CrehanUCL-Great Ormond Street Hospital Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0002-3655-6954
Erin KeslerUCL-Great Ormond Street Hospital Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0002-1616-6772
Indira Angela ChikomoniZomba Central Hospital, Zomba, Malawi.ORCID 0000-0003-2582-9432
Kristi SunWhittington Hospital, London, United Kingdom.ORCID 0000-0002-3571-1684
Queen DubePaediatric Department, College of Medicine, University of Malawi, Blantyre, Malawi.ORCID 0000-0002-7587-6133
Monica LakhanpaulUCL-Great Ormond Street Hospital Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0002-9855-2043
Michelle HeysUCL-Great Ormond Street Hospital Institute of Child Health, University College London, London, United Kingdom.ORCID 0000-0002-1458-505X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile health (mHealth) is showing increasing potential to address health outcomes in underresourced settings as smartphone coverage increases. The NeoTree is an mHealth app codeveloped in Malawi to improve the quality of newborn care at the point of admission to neonatal units. When collecting vital demographic and clinical data, this interactive platform provides clinical decision support and training for the end users (health care professionals [HCPs]), according to evidence-based national and international guidelines.

objectiveThis study aims to examine 1 month's data collected using NeoTree in an outcome audit of babies admitted to a district-level neonatal nursery in Malawi and to demonstrate proof of concept of digital outcome audit data in this setting.

methodsUsing a phased approach over 1 month (November 21-December 19, 2016), frontline HCPs were trained and supported to use NeoTree to admit newborns. Discharge data were collected by the research team using a discharge form within NeoTree, called NeoDischarge. We conducted a descriptive analysis of the exported pseudoanonymized data and presented it to the newborn care department as a digital outcome audit.

resultsOf 191 total admissions, 134 (70.2%) admissions were completed using NeoTree, and 129 (67.5%) were exported and analyzed. Of 121 patients for whom outcome data were available, 102 (84.3%) were discharged alive. The overall case fatality rate was 93 per 1000 admitted babies. Prematurity with respiratory distress syndrome, birth asphyxia, and neonatal sepsis contributed to 25% (3/12), 58% (7/12), and 8% (1/12) of deaths, respectively. Data were more than 90% complete for all fields. Deaths may have been underreported because of phased implementation and some families of babies with imminent deaths self-discharging home. Detailed characterization of the data enabled departmental discussion of modifiable factors for quality improvement, for example, improved thermoregulation of infants.

conclusionsThis digital outcome audit demonstrates that data can be captured digitally at the bedside by HCPs in underresourced newborn facilities, and these data can contribute to a meaningful review of the quality of care, outcomes, and potential modifiable factors. Coverage may be improved during future implementation by streamlining the admission process to be solely via digital format. Our results present a new methodology for newborn audits in low-resource settings and are a proof of concept for a novel newborn data system in these settings.

Indexed as

Mobile ApplicationsFemaleHospitalizationHumansInfantInfant MortalityInfant, NewbornMalawiPregnancyclinical auditdata collectiondigital healthinfant, newbornlow income populationmHealthmobile phone

Identifiers

PMID33084581
PMCPMC7641784

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.