Evidence map›Paper›PMID 32456706›Full record

ArticleBMC health services research2020

Perceptions and experiences with district health information system software to collect and utilize health data in Bangladesh: a qualitative exploratory study.

Tahmina Begum, Shaan Muberra Khan, Bridgit Adamou, Jannatul Ferdous, Muhammad Masud Parvez, Mohammad Saiful Islam, Feroza Akhter Kumkum, Aminur Rahman, Iqbal Anwar

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 1 pooled it
17.0field-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

60 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.

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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

9 authors at 2 institutions in 2 countries.

Tahmina BegumHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh. tahminashakera@gmail.com.ORCID http://orcid.org/0000-0001-8106-2454
Shaan Muberra KhanHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh.
Bridgit AdamouCarolina Population Center, University of North Carolina, Chapel Hill, USA.
Jannatul FerdousUNICEF, Dhaka, Bangladesh.
Muhammad Masud ParvezUNICEF, Dhaka, Bangladesh.
Mohammad Saiful IslamHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh.
Feroza Akhter KumkumHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh.
Aminur RahmanHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh.
Iqbal AnwarHealth system and population studies division (HSPSD), icddr,b, Dhaka, Bangladesh.
International Centre for Diarrhoeal Disease Research · BDUniversity of North Carolina at Chapel Hill · US

Funding

United States Agency for International Development PRH Small Grants Program Round IV
6 · The paper itself

Abstract

backgroundAccurate and high-quality data are important for improving program effectiveness and informing policy. In 2009 Bangladesh's health management information system (HMIS) adopted the District Health Information Software, Version 2 (DHIS2) to capture real-time health service utilization data. However, routinely collected data are being underused because of poor data quality and reporting. We aimed to understand the facilitators and barriers to implementing DHIS2 as a way to retrieve meaningful and accurate data for reproductive, maternal, newborn, child, and adolescent health (RMNCAH) services.

methodsThis qualitative study was conducted in two districts of Bangladesh from September 2017 to 2018. Data collection included key informant interviews (n = 11), in-depth interviews (n = 23), and focus group discussions (n = 2). The study participants were involved with DHIS2 implementation from the community level to the national level. The data were analyzed thematically.

resultsDHIS2 could improve the timeliness and completeness of data reporting over time. The reported facilitating factors were strong government commitment, extensive donor support, and positive attitudes toward technology among staff. Quality checks and feedback loops at multiple levels of data gathering points are helpful for minimizing data errors. Introducing a dashboard makes DHIS2 compatible to use as a monitoring tool. Barriers to effective DHIS2 implementation were lack of human resources, slow Internet connectivity, frequent changes to DHIS2 versions, and maintaining both manual and electronic system side-by-side. The data in DHIS2 remains incomplete because it does not capture data from private health facilities. Having two parallel HMIS reporting the same RMNCAH indicators threatens data quality and increases the reporting workload.

conclusionThe overall insights from this study are expected to contribute to the development of effective strategies for successful DHIS2 implementation and building a responsive HMIS. Focused strategic direction is needed to sustain the achievements of digital data culture. Periodic refresher trainings, incentives for increased performance, and an automated single reporting system for multiple stakeholders could make the system more user-friendly. A national electronic health strategy and implementation framework can facilitate creating a culture of DHIS2 use for planning, setting priorities, and decision making among stakeholder groups.

Indexed as

Health Information SystemsSoftwareBangladeshData AccuracyData CollectionFocus GroupsHealth ServicesHumansQualitative ResearchBangladeshBarriersDistrict health information system software 2Electronic health management information systemFacilitators

Identifiers

PMID32456706
PMCPMC7249629
OpenAlexW3031591073

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.