Evidence map›Paper›PMID 25612791›Full record

ArticleInternational journal of medical informatics2015

Usability and feasibility of a tablet-based Decision-Support and Integrated Record-keeping (DESIRE) tool in the nurse management of hypertension in rural western Kenya.

Rajesh Vedanthan, Evan Blank, Nelly Tuikong, Jemima Kamano, Lawrence Misoi, Deborah Tulienge, Claire Hutchinson, Deborah D Ascheim, Sylvester Kimaiyo, Valentin Fuster and 1 more

Abstract read
In one paragraph

Article in International journal of medical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 6 pooled it
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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

44 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  20. Scoping review of health information technology usability methods leveraged in Africa.Journal of the American Medical Informatics Association : JAMIA · 2023
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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

11 authors.

Rajesh VedanthanIcahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: rajesh.vedanthan@mssm.edu.
Evan BlankIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Nelly TuikongAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Jemima KamanoAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Lawrence MisoiAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Deborah TuliengeAcademic Model Providing Access to Healthcare, Eldoret, Kenya.
Claire HutchinsonIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Deborah D AscheimIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Sylvester KimaiyoAcademic Model Providing Access to Healthcare, Eldoret, Kenya; Moi University, College of Health Sciences, School of Medicine, Department of Medicine, Eldoret, Kenya.
Valentin FusterIcahn School of Medicine at Mount Sinai, New York, NY, USA; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
Martin C WereAcademic Model Providing Access to Healthcare, Eldoret, Kenya; Indiana University School of Medicine & Regenstrief Institute, Inc., Indianapolis, IN, USA.

Funding

Nurse Management of Hypertension in Rural Western KenyaK01TW009218 · FIC · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI VEDANTHAN, RAJESH · 2011 to 2015
$638k
FIC NIH HHS K01 TW009218FIC NIH HHS K01 TW 009218 - 04
6 · The paper itself

Abstract

backgroundMobile health (mHealth) applications have recently proliferated, especially in low- and middle-income countries, complementing task-redistribution strategies with clinical decision support. Relatively few studies address usability and feasibility issues that may impact success or failure of implementation, and few have been conducted for non-communicable diseases such as hypertension.

objectiveTo conduct iterative usability and feasibility testing of a tablet-based Decision Support and Integrated Record-keeping (DESIRE) tool, a technology intended to assist rural clinicians taking care of hypertension patients at the community level in a resource-limited setting in western Kenya.

methodsUsability testing consisted of "think aloud" exercises and "mock patient encounters" with five nurses, as well as one focus group discussion. Feasibility testing consisted of semi-structured interviews of five nurses and two members of the implementation team, and one focus group discussion with nurses. Content analysis was performed using both deductive codes and significant inductive codes. Critical incidents were identified and ranked according to severity. A cause-of-error analysis was used to develop corresponding design change suggestions.

resultsFifty-seven critical incidents were identified in usability testing, 21 of which were unique. The cause-of-error analysis yielded 23 design change suggestions. Feasibility themes included barriers to implementation along both human and technical axes, facilitators to implementation, provider issues, patient issues and feature requests.

conclusionsThis participatory, iterative human-centered design process revealed previously unaddressed usability and feasibility issues affecting the implementation of the DESIRE tool in western Kenya. In addition to well-known technical issues, we highlight the importance of human factors that can impact implementation of mHealth interventions.

Indexed as

Attitude of Health PersonnelAttitude to ComputersComputers, HandheldDecision Support Systems, ClinicalCooperative BehaviorElectronic Health RecordsEquipment Failure AnalysisFeasibility StudiesFocus GroupsHumansHypertensionKenyaNursesRural PopulationUser-Computer InterfaceClinical decision support systemsElectronic health recordsFeasibility studiesHypertensionNursesWorld health

Identifiers

PMID25612791
PMCPMC4314432

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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.