Evidence map›Paper›PMID 33906650›Full record

ArticleMalaria journal2021

Leveraging phone-based mobile technology to improve data quality at health facilities in rural Malawi: a best practice project.

Tinashe A Tizifa, William Nkhono, Spencer Mtengula, Michele van Vugt, Zachary Munn, Alinune N Kabaghe

Abstract read
In one paragraph

Article in Malaria journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. 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

6 authors.

Tinashe A TizifaDivision of Internal Medicine, Department of Infectious Diseases, Center for Tropical Medicine and Travel Medicine, University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. t.tizifa@amsterdamumc.nl.ORCID http://orcid.org/0000-0001-5040-2807
William NkhonoTraining and Research Unit of Excellence (TRUE), School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi.
Spencer MtengulaTraining and Research Unit of Excellence (TRUE), School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi.
Michele van VugtDivision of Internal Medicine, Department of Infectious Diseases, Center for Tropical Medicine and Travel Medicine, University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Zachary MunnJBI, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia.
Alinune N KabagheTraining and Research Unit of Excellence (TRUE), School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo further reduce malaria burden, identification of areas with highest burden for targeted interventions needs to occur. Routine health information has the potential to indicate where and when clinical malaria occurs the most. Developing countries mostly use paper-based data systems however they are error-prone as they require manual aggregation, tallying and transferring of data. Piloting was done using electronic data capture (EDC) with a cheap and user friendly software in rural Malawian primary healthcare setting to improve the quality of health records.

methodsAudit and feedback tools from the Joanna Briggs Institute (Practical Application of Clinical Evidence System and Getting Research into Practice) were used in four primary healthcare facilities. Using this approach, the best available evidence for a malaria information system (MIS) was identified. Baseline audit of the existing MIS was conducted in the facilities based on available best practice for MIS; this included ensuring data consistency and completeness in MIS by sampling 25 random records of malaria positive cases. Implementation of an adapted evidence-based EDC system using tablets on an OpenDataKit platform was done. An end line audit following implementation was then conducted. Users had interviews on experiences and challenges concerning EDC at the beginning and end of the survey.

resultsThe existing MIS was paper-based, occupied huge storage space, had some data losses due to torn out papers and were illegible in some facilities. The existing MIS did not have documentation of necessary parameters, such as malaria deaths and treatment within 14 days. Training manuals and modules were absent. One health centre solely had data completeness and consistency at 100% of the malaria-positive sampled records. Data completeness and consistency rose to 100% with readily available records containing information on recent malaria treatment. Interview findings at the end of the survey showed that EDC was acceptable among users and they agreed that the tablets and the OpenDataKit were easy to use, improved productivity and quality of care.

conclusionsImprovement of data quality and use in the Malawian rural facilities was achieved through the introduction of EDC using OpenDataKit. Health workers in the facilities showed satisfaction with the use of EDC.

Indexed as

Data AccuracyCell PhoneHealth FacilitiesMalariaMalawiPrimary Health CareRural PopulationTechnologyBaseline auditElectronic data captureEndline auditEvidence-based implementationGRiP matrixInformation systemsMalaria

Identifiers

PMID33906650
PMCPMC8077781

What OpenQuestion holds

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Registered trials

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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.