Evidence map›Paper›PMID 34784293›Full record

ArticleJMIR human factors2022

Developing a User-Centered Digital Clinical Decision Support App for Evidence-Based Medication Recommendations for Type 2 Diabetes Mellitus: Prototype User Testing and Validation Study.

Kevin Larsen, Bilikis Akindele, Henry Head, Rick Evans, Purvi Mehta, Quinn Hlatky, Brendan Krause, Sydney Chen, Dominic King

Registry-linked trialAbstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06410755 (Research on Evaluation of Home-based Rehabilitation Monitoring System With Wearable Devices and Self-Report Application), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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.

NCT06410755 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

Research on Evaluation of Home-based Rehabilitation Monitoring System With Wearable Devices and Self-Report Application

TypeinterventionalSponsorYonsei UniversityRan2024 to 2026Enrolled120ConditionsGait Disorders, NeurologicArmsIntegrated Wearable devices Monitoring sys-Assisted Home Rehabilitation Program
3 · Its place in the literature

Who cites it

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

  1. Pooled it
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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.

Kevin LarsenCenter for Advanced Clinical Solution, OptumHealth, Optum, Washington, DC, United States.ORCID https://orcid.org/0000-0003-0460-5800
Bilikis AkindeleCenter for Advanced Clinical Solution, OptumHealth, Optum, Boston, MA, United States.ORCID https://orcid.org/0000-0001-7349-3350
Henry HeadCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0002-3912-0080
Rick EvansCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0001-5149-6964
Purvi MehtaCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0003-1078-3443
Quinn HlatkyCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0002-4920-3680
Brendan KrauseOptumLabs, UnitedHealth Group, Minnetoka, MN, United States.ORCID https://orcid.org/0000-0002-0909-9903
Sydney ChenCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0003-1664-7522
Dominic KingCenter for Advanced Clinical Solution, OptumHealth, Optum, Raleigh, NC, United States.ORCID https://orcid.org/0000-0002-1898-842X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClosing the gap between care recommended by evidence-based guidelines and care delivered in practice is an ongoing challenge across systems and delivery models. Clinical decision support systems (CDSSs) are widely deployed to augment clinicians in their complex decision-making processes. Despite published success stories, the poor usability of many CDSSs has contributed to fragmented workflows and alert fatigue.

objectiveThis study aimed to validate the application of a user-centered design (UCD) process in the development of a standards-based medication recommender for type 2 diabetes mellitus in a simulated setting. The prototype app was evaluated for effectiveness, efficiency, and user satisfaction.

methodsWe conducted interviews with 8 clinical leaders with 8 rounds of iterative user testing with 2-8 prescribers in each round to inform app development. With the resulting prototype app, we conducted a validation study with 43 participants. The participants were assigned to one of two groups and completed a 2-hour remote user testing session. Both groups reviewed mock patient facts and ordered diabetes medications for the patients. The Traditional group used a mock electronic health record (EHR) for the review in Period 1 and used the prototype app in Period 2, while the Tool group used the prototype app during both time periods. The perceived cognitive load associated with task performance during each period was assessed with the National Aeronautics and Space Administration Task Load Index. Participants also completed the System Usability Scale (SUS) questionnaire and Kano Survey.

resultsAverage SUS scores from the questionnaire, taken at the end of 5 of the 8 user testing sessions, ranged from 68-86. The results of the validation study are as follows: percent adherence to evidence-based guidelines was greater with the use of the prototype app than with the EHR across time periods with the Traditional group (prototype app mean 96.2 vs EHR mean 72.0, P<.001) and between groups during Period 1 (Tool group mean 92.6 vs Traditional group mean 72.0, P<.001). Task completion times did not differ between groups (P=.23), but the Tool group completed medication ordering more quickly in Period 2 (Period 1 mean 130.7 seconds vs Period 2 mean 107.7 seconds, P<.001). Based on an adjusted α level owing to violation of the assumption of homogeneity of variance (Ps>.03), there was no effect on screens viewed and on perceived cognitive load (all Ps>.14).

conclusionsThrough deployment of the UCD process, a point-of-care medication recommender app holds promise of improving adherence to evidence-based guidelines; in this case, those from the American Diabetes Association. Task-time performance suggests that with practice the T2DM app may support a more efficient ordering process for providers, and SUS scores indicate provider satisfaction with the app.

Indexed as

clinical decision supportdecision supportdesigndiabeteselectronic health recordevidence-based guidelinestype 2 diabetes mellitususer-centered designuser testingvalidationworkflows

Identifiers

PMID34784293
PMCPMC8808349

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Registered trials

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.