Evidence map›Paper›PMID 41538509›Full record

ArticleJMIR human factors2026

Designing a Substance Misuse Data Dashboard for Overdose Fatality Review Teams: User-Centered Design Approach.

Marie Pisani, Madeline K Oguss, Julia Dickson-Gomez, Constance Kostelac, Amy Parry, Starr Moss, Elizabeth Salisbury-Afshar, Brian Patterson, Michael Spigner, Megan Gussick and 6 more

Abstract read
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Observational
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

16 authors.

Marie PisaniDepartment of Medicine, University of Wisconsin, Madison, WI, United States.ORCID 0009-0007-0436-6631
Madeline K OgussSchool of Medicine and Public Health, University of Wisconsin, 610 Walnut St, Suite 517, Madison, WI, 53726, United States, 1 6082636400.ORCID 0000-0002-4983-8109
Julia Dickson-GomezInstitute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, United States.ORCID 0000-0002-9388-847X
Constance KostelacInstitute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, United States.ORCID 0000-0003-0775-6348
Amy ParryInstitute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, United States.ORCID 0009-0002-1528-9209
Starr MossUnited States Department of Justice, Madison, WI, United States.ORCID 0009-0000-5548-2148
Elizabeth Salisbury-AfsharDepartment of Family Medicine and Community Health, University of Wisconsin, Madison, WI, United States.ORCID 0000-0003-4096-6822
Brian PattersonBerbeeWalsh Department of Emergency Medicine, University of Wisconsin, Madison, WI, United States.ORCID 0000-0002-4584-3808
Michael SpignerBerbeeWalsh Department of Emergency Medicine, University of Wisconsin, Madison, WI, United States.ORCID 0000-0003-1325-4336
Megan GussickBerbeeWalsh Department of Emergency Medicine, University of Wisconsin, Madison, WI, United States.ORCID 0009-0008-4833-5877
Alison KrautkramerDepartment of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, United States.ORCID 0009-0006-4963-0697
Timothy GruenlohDepartment of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, United States.ORCID 0000-0002-8817-2637
Askar Safipour AfsharSchool of Medicine and Public Health, University of Wisconsin, 610 Walnut St, Suite 517, Madison, WI, 53726, United States, 1 6082636400.ORCID 0000-0002-8542-0471
Preeti GuptaSchool of Medicine and Public Health, University of Wisconsin, 610 Walnut St, Suite 517, Madison, WI, 53726, United States, 1 6082636400.ORCID 0000-0001-9972-2123
Anoop MayampurathSchool of Medicine and Public Health, University of Wisconsin, 610 Walnut St, Suite 517, Madison, WI, 53726, United States, 1 6082636400.ORCID 0000-0002-3010-6960
Majid AfsharSchool of Medicine and Public Health, University of Wisconsin, 610 Walnut St, Suite 517, Madison, WI, 53726, United States, 1 6082636400.ORCID 0000-0002-6368-4652

Funding

Data Driven Strategies for Substance Misuse Identification in Hospitalized PatientsR01DA051464 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI Majid Afshar · 2020 to 2026
$4.5M
NIDA NIH HHS R01 DA051464
6 · The paper itself

Abstract

Background: Overdose fatality review (OFR) is a public health process in which cases of fatal overdose are carefully reviewed to identify prevention strategies. Current OFR requires review of multiple unconnected data sources, which is a manually intensive process. The Substance Misuse Data Commons (SMDC) was created to link electronic health record data with data from local and state agencies into a single, cloud-based e-platform but does not currently have a data visualization tool. Objective: We aimed to use human factors design principles to develop a comprehensive dashboard for the SMDC that could facilitate enhanced processes to support OFR. Methods: We first surveyed OFR leaders in Wisconsin using the National Aeronautics and Space Administration-Task Load Index to understand the cognitive workload of 3 tasks: (1) analysis of population-level overdose trends, (2) selection and preparation of individual cases for review, and (3) abstraction of data from individual causes. We then conducted semistructured interviews to identify targets for workflow optimization. Next, we developed a prototype dashboard for evaluation using a synthetic dataset built with GPT-4. We subsequently performed iterative design sessions with heuristic evaluations and collected end-user feedback on the final prototype via a second round of semistructured interviews and targeted surveys, including the Unified Theory of Acceptance and Use of Technology and the Perceived Usefulness Questionnaire. Results: The National Aeronautics and Space Administration-Task Load Index revealed a moderately high mental workload with the current workflow for all 3 tasks, with mean scores of 12.60 (SD 3.31), 11.90 (SD 3.57), and 12.43 (SD 5.41) for tasks 1, 2, and 3, respectively. Interviews pointed to causes including technological challenges and a reliance on manual processes. The prototype dashboard addressed these concerns by integrating multiple data sources to generate population-level visualizations and patient-level event timelines. End users reported the potential for improved efficiency and data accessibility compared to antecedent processes. The Unified Theory of Acceptance and Use of Technology results indicated the dashboard would likely be adopted if made available, with a mean of 4.07 out of 5.00 (SD 0.65). The Perceived Usefulness Questionnaire results suggested moderate usefulness for both the aggregate and individual-level data, with means of 3.61 (SD 0.82) and 3.64 (SD 0.85) out of 5.00, respectively. Conclusions: OFR is a data-intensive process that traditionally demands substantial cognitive and manual effort, and there are multiple barriers to efficiently collecting data and presenting them for review. The dashboard offers a user-centered, informatics-based approach to streamline data aggregation and presentation, potentially enhancing the efficiency of case reviews. Implementing a dashboard that consolidates and visualizes disparate data sources has the potential to alleviate the manual workload in OFR. Ultimately, our aim is to deliver a finalized data dashboard with real-world SMDC data, giving OFR leaders additional tools to aid in their rigorous work shaping interventions to reduce overdose fatalities.

Indexed as

Dashboard SystemsDrug OverdoseSubstance-Related DisordersUser-Centered DesignElectronic Health RecordsHumansWisconsindata linkageoverdosepublic health informaticsqualitative researchsubstance use disorders

Identifiers

PMID41538509
PMCPMC12806594

What OpenQuestion holds

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LicenceCC BY
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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.