8 results on '"Joshua W. Ohde"'
Search Results
2. 562 AI Translation Advisory Board: Mastering team science to facilitate implementation of AI into clinical practice
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Joshua W. Ohde, Momin M. Malik, Shauna M. Overgaard, Tracey A. Brereton, Lu Zheng, Kevin J. Peterson, and Lauren M. Rost
- Subjects
Medicine - Abstract
OBJECTIVES/GOALS: Healthcare sectors are rushing to develop AI models. Yet, a dearth of coordinated practices leaves many teams struggling to implement models into practice. The Enterprise AI Translation Advisory Board uses across-disciplinary team to facilitate AI translation. METHODS/STUDY POPULATION: The Mayo Clinic Enterprise AI Translation Advisory Board was established to assess AI solutions lever aging cross-disciplinary team science to accelerate AI innovation and translation. The 23-member board reflects expertise in data science, qualitative research, user experience, IT, human factors, informatics, regulatory compliance,ethics, and clinical care, with members spanning thought leadership, decision-making, and clinical practice. Taking an approach of respectful communication, transparency, scientific debate, and open discussion, the Board has consulted onover two dozen projects at various stages of the AI life cycle. RESULTS/ANTICIPATED RESULTS: Common issues identified for projects earlier in the AI life cycle, sometimes fatal but often address able once identified, include a lack of buy-in from potential product users, a lack of planningabout integration into clinical workflow, inadequately labeled data, and attempting to use machine learning when what is desired is really a causal model for intervening. Recommendations for projects later in the AI life cycle include details of a testing plan (silent evaluation, pragmatic clinical trials), advice about clinical integration, both post-hoc and on going auditing for performance disparities, and planning for regulatory clearance. DISCUSSION/SIGNIFICANCE: Advising is more valuable for projects at the ideation phase, when multi disciplinary interrogation can identify weaknesses. But at all phases, projects have gaps related to a lack of specific disciplinary expertise. A multi disciplinary cluster like the AI Translation Advisory Board seeks to address these gaps.
- Published
- 2024
- Full Text
- View/download PDF
3. Stakeholder perceptions of using 'opt-out' for tobacco use treatment in a cancer care setting: a qualitative evaluation of patients, providers, and desk staff
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Joshua W. Ohde, David O. Warner, Jason S. Egginton, and Hildi J. Hagedorn
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Opt-out ,Tobacco ,Cancer ,Tobacco treatment ,Medicine (General) ,R5-920 - Abstract
Abstract Background Continued tobacco use in cancer patients increases the risk of cancer treatment failure and decreases survival. However, currently, most cancer patients do not receive evidence-based tobacco treatment. A recently proposed “opt-out” approach would automatically refer all cancer patients who use tobacco to tobacco treatment, but its acceptability to cancer patients and providers is unknown. We aimed to understand stakeholder beliefs, concerns, and receptivity to using the “opt-out” approach for tobacco treatment referrals in a cancer care setting. Methods Semi-structured interviews were conducted with oncology patients, providers, and desk staff. The sample size was determined when theoretical saturation was reached. Given the differences among participant roles, separate interview guides were developed. Transcripts were analyzed using standard coding techniques for qualitative data using the Consolidated Framework for Implementation Research (CFIR) codebook. Emergent codes were added to the codebook to account for themes not represented by a CFIR domain. Coded transcripts were then entered into the qualitative analysis software NVivo to generate code reports for CFIR domains and emergent codes for each stakeholder group. Data were presented by stakeholder group and subcategorized by CFIR domains and emergent codes when appropriate. Results A total of 21 providers, 19 patients, and 6 desk staff were interviewed. Overall acceptance of the “opt out” approach was high among all groups. Providers overwhelmingly approved of the approach as it requires little effort from them to operate and saves clinical time. Desk staff supported the opt-out system and believed there are clinical benefits to patients receiving information about tobacco treatment. Many patients expressed support for using an opt-out approach as many smokers need assistance but may not directly ask for it. Patients also thought that providers emphasizing the benefits of stopping tobacco use to cancer treatment and survival would be an important factor motivating them to attend treatment. Conclusions While providers appreciated that the system required little effort on their part, patients clearly indicated that promotion of tobacco cessation treatment by their provider would be vital to enhance willingness to engage with treatment. Future implementation efforts of opt-out systems will require implementation strategies that promote provider engagement with their patients around smoking cessation while continuing to limit burden on providers.
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- 2023
- Full Text
- View/download PDF
4. User-Centered Design to Develop and Implement an ML-Based Asthma Management Tool (Preprint)
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Lu Zheng, Joshua W Ohde, Shauna M Overgaard, Tracey A Brereton, Kristelle A Jose, Chung-II Wi, Kevin J Peterson, and Young J Juhn
- Abstract
BACKGROUND Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. Transparency, accountability, suitability, and adaptability may be bolstered by clinician engagement through a direct empathetic approach aimed at determining complex user requirements of implementation, usability, and workflow integration. OBJECTIVE We aimed to utilize a structured user-centered design approach (double-diamond design framework) to 1) qualitatively explore clinicians' experience with the current asthma management system, 2) identify user requirements to improve algorithm explainability and A-GPS prototype, and 3) identify potential barriers to ML-based CDS system use. METHODS At the 'discovery' phase, we first shadowed to understand the practice context. Then, semi-structured interviews were conducted online with 14 clinicians who provide asthma care at two outpatient facilities. Participants were asked about their current difficulties in gathering information for pediatric asthma patients, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the 'define' phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical 'how might we' research questions to guide model development and implementation. RESULTS We identified user requirements and potential barriers associated with three overarching themes: 1) Usability and Workflow Aspects of the ML System, 2) User Expectations and Algorithm Explainability, and 3) Barriers to Implementation in Context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal machine-learning algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. CONCLUSIONS As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semi-structured interviews. Our focus on meeting the needs of the practice with machine learning technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.
- Published
- 2023
5. User-Centered Design to Develop and Implement an ML-Based Asthma Management Tool
- Author
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Lu Zheng, Joshua W. Ohde, Shauna M. Overgaard, Tracey A. Brereton, Kristelle A. Jose, Chung-Il Wi, Kevin J. Peterson, and Young J. Juhn
- Abstract
BackgroundPersonalized asthma management depends on a clinician’s ability to efficiently review patient’s data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. Transparency, accountability, suitability, and adaptability may be bolstered by clinician engagement through a direct empathetic approach aimed at determining complex user requirements of implementation, usability, and workflow integration.ObjectivesWe aimed to utilize a structured user-centered design approach (double-diamond design framework) to 1) qualitatively explore clinicians’ experience with the current asthma management system, 2) identify user requirements to improve algorithm explainability and A-GPS prototype, and 3) identify potential barriers to ML-based CDS system use.MethodsAt the ‘discovery’ phase, we first shadowed to understand the practice context. Then, semi-structured interviews were conducted online with 14 clinicians who provide asthma care at two outpatient facilities. Participants were asked about their current difficulties in gathering information for pediatric asthma patients, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the ‘define’ phase, a synthesis analysis was conducted to converge key results from interviewees’ insights into themes, eventually forming critical ‘how might we’ research questions to guide model development and implementation.ResultsWe identified user requirements and potential barriers associated with three overarching themes: 1) Usability and Workflow Aspects of the ML System, 2) User Expectations and Algorithm Explainability, and 3) Barriers to Implementation in Context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients’ high risks and take proactive actions to manage asthma efficiently and effectively. For optimal machine-learning algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants.ConclusionsAs part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semi-structured interviews. Our focus on meeting the needs of the practice with machine learning technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.
- Published
- 2022
6. Docosahexaenoic acid decreased inflammatory gene expression, but not 18-kDa translocator protein binding, in rat pup brain after controlled cortical impact
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Daniela F Requena, Sydney Maves, Joshua W Ohde, James R. Pauly, and Michelle Elena Schober
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medicine.medical_specialty ,biology ,Microglia ,business.industry ,Hippocampus ,030208 emergency & critical care medicine ,Inflammasome ,Hippocampal formation ,Critical Care and Intensive Care Medicine ,Proinflammatory cytokine ,03 medical and health sciences ,0302 clinical medicine ,Endocrinology ,medicine.anatomical_structure ,Docosahexaenoic acid ,Internal medicine ,Translocator protein ,biology.protein ,Medicine ,Surgery ,business ,Neuroinflammation ,medicine.drug - Abstract
Background Traumatic brain injury is the leading cause of acquired neurologic disability in children. In our model of pediatric traumatic brain injury, controlled cortical impact (CCI) in rat pups, docosahexaenoic acid (DHA) improved lesion volume and cognitive testing as late as postinjury day (PID) 50. Docosahexaenoic acid decreased proinflammatory messenger RNA (mRNA) in microglia and macrophages at PIDs 3 and 7, but not 30. We hypothesized that DHA affected inflammatory markers differentially relative to impact proximity, early and persistently after CCI. Methods To provide a temporal snapshot of regional neuroinflammation, we measured 18-kDa translocator protein (TSPO) binding using whole brain autoradiography at PIDs 3, 7, 30, and 50. Guided by TSPO results, we measured mRNA levels in contused cortex and underlying hippocampus for genes associated with proinflammatory and inflammation-resolving states at PIDs 2 and 3. Results Controlled cortical impact increased TSPO binding at all time points, most markedly at PID 3 and in regions closest to impact, not blunted by DHA. Controlled cortical impact increased cortical and hippocampal mRNA proinflammatory markers, blunted by DHA at PID 2 in hippocampus. Conclusion Controlled cortical impact increased TSPO binding in the immature brain in a persistent manner more intensely with more severe injury, not altered by DHA. Controlled cortical impact increased PIDs 2 and 3 mRNA levels of proinflammatory and inflammation-resolving genes. Docosahexaenoic acid decreased proinflammatory markers associated with inflammasome activation at PID 2. We speculate that DHA's salutary effects on long-term outcomes result from early effects on the inflammasome. Future studies will examine functional effects of DHA on microglia both early and late after CCI.
- Published
- 2021
7. Presumed Consent With Opt-Out: An Ethical Consent Approach to Automatically Refer Patients With Cancer to Tobacco Treatment Services
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David O. Warner, Jon C. Tilburt, Joshua W. Ohde, and Zubin Master
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Cancer Research ,medicine.medical_specialty ,business.industry ,MEDLINE ,Cancer ,medicine.disease ,Choice Behavior ,Opt-out ,Comments and Controversies ,Tobacco Use ,Oncology ,Family medicine ,Neoplasms ,Medicine ,Humans ,Smoking Cessation ,Presumed consent ,business ,Presumed Consent ,Referral and Consultation - Published
- 2021
8. Clinical Needs Assessment of a Machine Learning–Based Asthma Management Tool: User-Centered Design Approach
- Author
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Lu Zheng, Joshua W Ohde, Shauna M Overgaard, Tracey A Brereton, Kristelle Jose, Chung-Il Wi, Kevin J Peterson, and Young J Juhn
- Subjects
Medicine - Abstract
BackgroundPersonalized asthma management depends on a clinician’s ability to efficiently review patient’s data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. ObjectiveWe aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians’ experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. MethodsAt the “discovery” phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the “define” phase, a synthesis analysis was conducted to converge key results from interviewees’ insights into themes, eventually forming critical “how might we” research questions to guide model development and implementation. ResultsWe identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients’ high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. ConclusionsAs part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.
- Published
- 2024
- Full Text
- View/download PDF
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