Evidence map›Paper›PMID 34933992›Full record

ArticleGlobal health, science and practice2021

Development of an Innovative Digital Data Collection System for Routine Mental Health Care Delivery in Rural Haiti.

Alexandra L Rose, Darius L Fenelon, J Reginald Fils-Aimé, Wilder Dubuisson, Sarah F C Singer, Stephanie L Smith, Gregory Jerome, Eddy Eustache, Giuseppe Raviola

Open access · diamondAbstract read
In one paragraph

Article in Global health, science and practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 73% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 1 country.

Alexandra L RoseDepartment of Psychology, University of Maryland, College Park, MD, USA. alrose@umd.edu.
Darius L FenelonZanmi Lasante, Croix-des-Bouquets, Haiti.
J Reginald Fils-AiméZanmi Lasante, Croix-des-Bouquets, Haiti.
Wilder DubuissonZanmi Lasante, Croix-des-Bouquets, Haiti.
Sarah F C SingerPartners In Health, Boston, MA, USA.
Stephanie L SmithPartners In Health, Boston, MA, USA.
Gregory JeromeZanmi Lasante, Croix-des-Bouquets, Haiti.
Eddy EustacheZanmi Lasante, Croix-des-Bouquets, Haiti.
Giuseppe RaviolaPartners In Health, Boston, MA, USA.
Partners In Health · USHarvard Global Health Institute · US

Funding

Effectiveness and implementation of pre-training interpersonal skills assessment of community health workers in South Africa to predict post-training competence in mental health careF31MH123020 · NIMH · UNIV OF MARYLAND, COLLEGE PARK · PI ROSE, ALEXANDRA LEAH · 2021 to 2023
$127k
NIMH NIH HHS F31 MH123020
6 · The paper itself

Abstract

introductionEffective digital health management information systems (HMIS) support health data validity, which enables health care teams to make programmatic decisions and country-level decision making in support of international development targets. In 2015, mental health was included within the Sustainable Development Goals, yet there are few applications of HMIS of any type in the practice of mental health care in resource-limited settings. Zanmi Lasante (ZL), one of the largest providers of mental health care in Haiti, developed a digital data collection system for mental health across 11 public rural health facilities. PROGRAM

interventionWe describe the development, implementation, and evaluation of the digital system for mental health data collection at ZL. To evaluate system reliability, we assessed the number of missing monthly reports. To evaluate data validity, we calculated concordance between the digital system and paper charts at 2 facilities. To evaluate the system's ability to inform decision making, we specified and then calculated 4 priority indicators.

resultsThe digital system was missing 5 of 143 monthly reports across all facilities and had 74.3% (55/74) and 98% (49/50) concordance with paper charts. It was possible to calculate all 4 indicators, which led to programmatic changes in 2 cases. In response to implementation challenges, it was necessary to use strategies to increase provider buy-in and ultimately to introduce dedicated data clerks to keep pace with data collection and protect time for clinical work. LESSONS LEARNED: While demonstrating the potential of collecting mental health data digitally in a low-resource rural setting, we found that it was necessary to consider the ongoing roles of paper records alongside digital data collection. We also identified the challenge of balancing clinical and data collection responsibilities among a limited staff. Ongoing work is needed to develop truly sustainable and scalable models for mental health data collection in resource-limited settings.

Indexed as

Delivery of Health CareRural PopulationData CollectionHaitiHumansReproducibility of Results

Identifiers

PMID34933992
PMCPMC8691881
OpenAlexW3215133637

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.