Evidence map›Paper›PMID 31127719›Full record

ArticleJMIR mHealth and uHealth2019

Using Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs.

Hannah Brown Amoakoh, Kerstin Klipstein-Grobusch, Diederick E Grobbee, Mary Amoakoh-Coleman, Ebenezer Oduro-Mensah, Charity Sarpong, Edith Frimpong, Gbenga A Kayode, Irene Akua Agyepong, Evelyn K Ansah

Abstract readMulticenter Study
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Exploration of Telemidwifery: An Initiation of Application Menu in Indonesia.International journal of environmental research and public health · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
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

10 authors.

Hannah Brown AmoakohDepartment of Epidemiology, School of Public Health, University of Ghana, Accra, Ghana.ORCID 0000-0002-6575-428X
Kerstin Klipstein-GrobuschJulius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands.ORCID 0000-0002-5462-9889
Diederick E GrobbeeJulius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands.ORCID 0000-0003-4472-4468
Mary Amoakoh-ColemanJulius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands.ORCID 0000-0002-0320-0396
Ebenezer Oduro-MensahLa General Hospital, Ghana Health Service, Accra, Ghana.ORCID 0000-0002-8888-3439
Charity SarpongRegional Health Directorate, Ghana Health Services, Koforidua, Ghana.ORCID 0000-0002-7132-6425
Edith FrimpongDodowa Research Centre, Ghana Health Service, Accra, Ghana.ORCID 0000-0002-9002-8415
Gbenga A KayodeJulius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands.ORCID 0000-0001-6313-010X
Irene Akua AgyepongResearch and Development Division, Ghana Health Service, Accra, Ghana.ORCID 0000-0002-0193-5882
Evelyn K AnsahCentre for Malaria Research, University of Health and Allied Sciences, Ho, Ghana.ORCID 0000-0003-0401-8606

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeveloping and maintaining resilient health systems in low-resource settings like Ghana requires innovative approaches that adapt technology to context to improve health outcomes. One such innovation was a mobile health (mHealth) clinical decision-making support system (mCDMSS) that utilized text messaging (short message service, SMS) of standard emergency maternal and neonatal protocols via an unstructured supplementary service data (USSD) on request of the health care providers. This mCDMSS was implemented in a cluster randomized controlled trial (CRCT) in the Eastern Region of Ghana.

objectiveThis study aimed to analyze the pattern of requests made to the USSD by health workers (HWs). We assessed the relationship between requests made to the USSD and types of maternal and neonatal morbidities reported in health facilities (HFs).

methodsFor clusters in the intervention arm of the CRCT, all requests to the USSD during the 18-month intervention period were extracted from a remote server, and maternal and neonatal health outcomes of interest were obtained from the District Health Information System of Ghana. Chi-square and Fisher exact tests were used to compare the proportion and type of requests made to the USSD by cluster, facility type, and location; whether phones accessing the intervention were shared facility phones or individual-use phones (type-of-phone); or whether protocols were accessed during the day or at night (time-of-day). Trends in requests made were analyzed over 3 6-month periods. The relationship between requests made and the number of cases reported in HFs was assessed using Spearman correlation.

resultsIn total, 5329 requests from 72 (97%) participating HFs were made to the intervention. The average number of requests made per cluster was 667. Requests declined from the first to the third 6-month period (44.96% [2396/5329], 39.82% [2122/5329], and 15.22% [811/5329], respectively). Maternal conditions accounted for the majority of requests made (66.35% [3536/5329]). The most frequently accessed maternal conditions were postpartum hemorrhage (25.23% [892/3536]), other conditions (17.82% [630/3536]), and hypertension (16.49% [583/3536]), whereas the most frequently accessed neonatal conditions were prematurity (20.08% [360/1793]), sepsis (15.45% [277/1793]), and resuscitation (13.78% [247/1793]). Requests made to the mCDMSS varied significantly by cluster, type of request (maternal or neonatal), facility type and its location, type-of-phone, and time-of-day at 6-month interval (P<.001 for each variable). Trends in maternal and neonatal requests showed varying significance over each 6-month interval. Only asphyxia and sepsis cases showed significant correlations with the number of requests made (r=0.44 and r=0.79; P<.001 and P=.03, respectively).

conclusionsThere were variations in the pattern of requests made to the mCDMSS over time. Detailed information regarding the use of the mCDMSS provides insight into the information needs of HWs for decision-making and an opportunity to focus support for HW training and ultimately improved maternal and neonatal health.

Indexed as

AdultDecision Support Systems, ClinicalFemaleGhanaHumansInfantInfant MortalityMaternal MortalityOutcome Assessment, Health CarePregnancyQuality of Health CareTelemedicinedecision-makingdeveloping countrieshealth care systemsinformation retrieval systemsmaternal healthmHealthneonatal health

Identifiers

PMID31127719
PMCPMC6555115

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.