Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study.


Journal

JMIR human factors
ISSN: 2292-9495
Titre abrégé: JMIR Hum Factors
Pays: Canada
ID NLM: 101666561

Informations de publication

Date de publication:
09 Sep 2024
Historique:
received: 02 08 2023
accepted: 05 05 2024
revised: 08 11 2023
medline: 9 9 2024
pubmed: 9 9 2024
entrez: 9 9 2024
Statut: epublish

Résumé

Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings. Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making. We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors. Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders. We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders. ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

Sections du résumé

BACKGROUND BACKGROUND
Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings.
OBJECTIVE OBJECTIVE
Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making.
METHODS METHODS
We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors.
RESULTS RESULTS
Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders.
CONCLUSIONS CONCLUSIONS
We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders.
TRIAL REGISTRATION BACKGROUND
ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

Identifiants

pubmed: 39250216
pii: v11i1e51525
doi: 10.2196/51525
doi:

Banques de données

ClinicalTrials.gov
['NCT04111939']

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

e51525

Informations de copyright

©Naleef Fareed, Ramona G Olvera, Yiting Wang, Michael Hayes, Elizabeth Liz Larimore, Peter Balvanz, Ronald Langley, Corinna A Noel, Peter Rock, Daniel Redmond, Jessica Neufeld, Sarah Kosakowski, Daniel Harris, Marc LaRochelle, Timothy R Huerta, LaShawn Glasgow, Emmanuel Oga, Jennifer Villani, Elwin Wu. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 09.09.2024.

Auteurs

Naleef Fareed (N)

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States.

Ramona G Olvera (RG)

Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, OH, United States.

Yiting Wang (Y)

Department of Research Information Technology, College of Medicine, The Ohio State University, Columbus, OH, United States.

Michael Hayes (M)

Research Triangle Institute, Research Triangle Park, NC, United States.

Elizabeth Liz Larimore (EL)

Center for Drug and Alcohol Research, University of Kentucky, Lexington, KY, United States.

Peter Balvanz (P)

Clinical Addiction Research and Evaluation Unit, Section of General Internal Medicine, Boston Medical Center, Boston, MA, United States.

Ronald Langley (R)

Center for Drug and Alcohol Research, University of Kentucky, Lexington, KY, United States.

Corinna A Noel (CA)

Department of Public and Ecosystem Health, Cornell University, Ithaca, NY, United States.

Peter Rock (P)

Center for Drug and Alcohol Research, University of Kentucky, Lexington, KY, United States.

Daniel Redmond (D)

Institute for Biomedical Informatics, University of Kentucky, Kentucky, KY, United States.

Jessica Neufeld (J)

Social Intervention Group, School of Social Work, Columbia University, New York, NY, United States.

Sarah Kosakowski (S)

Clinical Addiction Research and Evaluation Unit, Section of General Internal Medicine, Boston Medical Center, Boston, MA, United States.

Daniel Harris (D)

Institute for Pharmaceutical Outcomes and Policy, University of Kentucky, Lexington, KY, United States.

Marc LaRochelle (M)

Clinical Addiction Research and Evaluation Unit, Section of General Internal Medicine, Boston Medical Center, Boston, MA, United States.

Timothy R Huerta (TR)

Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, OH, United States.
Department of Research Information Technology, College of Medicine, The Ohio State University, Columbus, OH, United States.

LaShawn Glasgow (L)

Research Triangle Institute, Research Triangle Park, NC, United States.

Emmanuel Oga (E)

Department of Research Information Technology, College of Medicine, The Ohio State University, Columbus, OH, United States.

Jennifer Villani (J)

National Institute on Drug Abuse, Bethesda, MD, United States.

Elwin Wu (E)

Social Intervention Group, School of Social Work, Columbia University, New York, NY, United States.

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Classifications MeSH