Evidence map›Paper›PMID 31906929›Full record

ArticleBMC medical informatics and decision making2020

Development of a targeted client communication intervention to women using an electronic maternal and child health registry: a qualitative study.

Binyam Bogale, Kjersti Mørkrid, Brian O'Donnell, Buthaina Ghanem, Itimad Abu Ward, Khadija Abu Khader, Mervett Isbeih, Michael Frost, Mohammad Baniode, Taghreed Hijaz and 3 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
32.9field-weighted citation impact, top 1% of its field
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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 238 citations in OpenAlex.

  1. Pooled it
  2. Meaningful patient and public involvement in digital health innovation, implementation and evaluation: A systematic review.Health expectations : an international journal of public participation in health care and health policy · 2022
    Pooled it
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  8. Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.Interdisciplinary sciences, computational life sciences · 2022
    Article
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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

13 authors at 3 institutions in 2 countries.

Binyam BogaleGlobal Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Kjersti MørkridGlobal Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Brian O'DonnellGlobal Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Buthaina GhanemThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Itimad Abu WardThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Khadija Abu KhaderThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Mervett IsbeihThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Michael FrostHealth Information Systems Program, Department of Informatics, University of Oslo, Oslo, Norway.
Mohammad BaniodeThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Taghreed HijazThe Palestinian Ministry of Health, Ramallah, Palestine.
Tamara AwwadThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
Yousef RabahThe Palestinian National Institute of Public Health, World Health Organization, Ramallah, Palestine.
J Frederik FrøenGlobal Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway. frederik.froen@fhi.no.ORCID 0000-0001-9390-8509
National Institute of Public Health · SINorwegian Institute of Public Health · NOUniversity of Oslo · NO

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundTargeted client communication (TCC) using text messages can inform, motivate and remind pregnant and postpartum women of timely utilization of care. The mixed results of the effectiveness of TCC interventions points to the importance of theory based interventions that are co-design with users. The aim of this paper is to describe the planning, development, and evaluation of a theory led TCC intervention, tailored to pregnant and postpartum women and automated from the Palestinian electronic maternal and child health registry.

methodsWe used the Health Belief Model to develop interview guides to explore women's perceptions of antenatal care (ANC), with a focus on high-risk pregnancy conditions (anemia, hypertensive disorders in pregnancy, gestational diabetes mellitus, and fetal growth restriction), and untimely ANC attendance, issues predefined by a national expert panel as being of high interest. We performed 18 in-depth interviews with women, and eight with healthcare providers in public primary healthcare clinics in the West Bank and Gaza. Grounding on the results of the in-depth interviews, we used concepts from the Model of Actionable Feedback, social nudging and Enhanced Active Choice to compose the TCC content to be sent as text messages. We assessed the acceptability and understandability of the draft text messages through unstructured interviews with local health promotion experts, healthcare providers, and pregnant women.

resultsWe found low awareness of the importance of timely attendance to ANC, and the benefits of ANC for pregnancy outcomes. We identified knowledge gaps and beliefs in the domains of low awareness of susceptibility to, and severity of, anemia, hypertension, and diabetes complications in pregnancy. To increase the utilization of ANC and bridge the identified gaps, we iteratively composed actionable text messages with users, using recommended message framing models. We developed algorithms to trigger tailored text messages with higher intensity for women with a higher risk profile documented in the electronic health registry.

conclusionsWe developed an optimized TCC intervention underpinned by behavior change theory and concepts, and co-designed with users following an iterative process. The electronic maternal and child health registry can serve as a unique platform for TCC interventions using text messages.

Indexed as

Child HealthWomenCommunicationElectronicsFamilyFemaleHumansPatientsPregnancyPrenatal CareRegistriesAntenatal careDHIS2Digital healthElectronic registryMaternal and child healthmHealthSMSTargeted client communicationText messages

Identifiers

PMID31906929
PMCPMC6945530
OpenAlexW2998445959

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.