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Open Access Publications from the University of California

Scholarly Works (27 results)

  • Thesis
  • Peer Reviewed
UC Davis Electronic Theses and Dissertations (2024)

BackgroundThe advent of Artificial Intelligence (AI) in healthcare marks a transformative era characterized by enhanced diagnostic tools, personalized treatments, and efficient patient care. However, the integration of AI into healthcare systems introduces complex ethical dilemmas, necessitating the development of robust frameworks to navigate these challenges effectively. This thesis explores the intricacies of establishing ethical maturity frameworks in biomedical research, aiming to bridge the gap between technological advancement and ethical considerations. Objective The primary objective is to develop a comprehensive understanding of the current ethical frameworks in health AI, identify existing gaps and limitations, and propose solutions through a scoping review of literature, gap analysis, and summarizing findings from the Research Data Ethics Maturity Model Project (README) workshop. We used a two-step process. Initially, we conducted a scoping review of 94 papers on AI ethics in healthcare to assess the existing landscape. Next, we pinpointed where these ideas are missing the mark and identified the deficiencies within these frameworks. Finally, we held a workshop where people could come together to think of new ways to deal with these ethical issues. This whole process helped us get a full picture of what’s going on with AI and ethics in healthcare right now. Methods We found that there’s not enough research on how AI and ethics cross paths in healthcare, which means we need to look into this more. The literature review highlighted key areas requiring attention, such as mitigating AI biases, achieving consensus on AI governance, and developing comprehensible and actionable regulations. Our deep dive showed us key areas that need work, like making sure AI doesn’t have biases, getting people to agree on how AI should be governed, and making rules that everyone can understand and use. The README workshop we had was a great place for people to share ideas; participants developed six use cases related to health AI, focusing on practical applications and ethical considerations like making sure AI is fair, improving health safety, and helping hospitals run better. Conclusion AI has the potential to improve healthcare, but we must be careful about its ethical use. By creating strong guidelines for using AI responsibly, we can ensure that it’s used in ways that are open, fair, and respect everyone’s rights. This study adds to the conversation on how to use AI in healthcare responsibly and sets the stage for more research and guidelines in the future. Our findings underline the necessity for continuous dialogue and collaboration among stakeholders to foster an ethically sound integration of AI in healthcare systems.

    Cover page: Requirements for Developing an Ethical AI Policy Framework in Healthcare Research: A Case Study
    • Thesis
    • Peer Reviewed
    UC Davis Electronic Theses and Dissertations (2024)

    Background: Data reported from clinical trials plays a significant role in providing resources for developing evidence-based medicine, and robust and accurate study data must be accessible, reproducible and transparently available to maintain accountability with the public. Discrepancies in how clinical trial data are reflected in national resources such as Clinicaltrials.gov compromise the credibility and reliability of studies conducted under the auspices of a clinical research institution. To improve the process that supports scientific integrity, public trust and adherence to NIH requirements for systematic and timely submission of federally funded clinical trial reporting, we sought to leverage LLMs to complement the manual processes in this extraction and validation of IRB-approved study protocols. We aimed to analyze the data work involved in protocol registration with ClinicalTrials.gov using a new system. We also aimed to assess the accuracy of the system's response against approved data formats and to suggest ways to enhance the quality and consistency of clinical trial registration processes.Methods: Matched data collection from IRB-approved protocols from irbnet.org and corresponding registrations on ClinicalTrials.gov were acquired. Following a scrum-inspired methodology, we designed, developed, and tested a pilot analytic architecture that extracts data summary elements from raw IRB protocol documents and generates responses approximating the Protocol Registration and Results System (PRS) format of ClinicalTrials.gov. Results: Our architecture successfully extracted and formatted the data elements from IRB-approved protocols to meet the specific requirements of the ClinicalTrials.gov registration system. Conclusion: Our architecture with large language models is capable of streamlining data and has the potential to enhance operational efficiency, reduce documentation time, and ensure regulatory compliance. Additionally, it can address persistent issues related to data quality, completeness, and consistency in registering results on ClinicalTrials.gov. Accurate study results will serve as a valuable resource for patient care delivery.

      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2016)

      Objective

      Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results.

      Methods

      Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages.

      Results

      We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility.

      Conclusion

      An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.
        Cover page: Implementing partnership-driven clinical federated electronic health record data sharing networks
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2016)
        Health leaders in the US and abroad are seeking to aggregate diverse health data from millions of people to enable new architectures for research. The integration of large health data sets raises significant social and ethical questions around the control of health information, human subjects research protection, and access to treatments. Because of the social stakes involved, policy-makers have begun to consult and engage publics with the idea that this might improve the quality, credibility, or relevance of big data health research. While policy-makers aim to engage publics, they might learn from experiments in patient engagement emanating from the private sector and patient advocacy organizations. Three modes of engagement have co-evolved out of new information technology and the cultures of disease advocacy: crowdsourcing, social networking platforms, and dynamic consenting. These modes of engagement are promising avenues of responsible innovation. Together, they project an alternative and more democratic vision of health research emphasizing citizen science, communicative reason, and the engaged research participant.
          • Article
          • Peer Reviewed
          UC Davis Previously Published Works (2012)
          Clinical genomic research faces increasing challenges in establishing participant privacy and consent processes that facilitate meaningful choice and communication capacity for longitudinal and secondary research uses. There are an evolving range of participant-centric initiatives that combine web-based informatics tools with new models of engagement and research collaboration. These emerging initiatives may become valuable approaches to support large-scale and longitudinal research studies. We highlight and discuss four types of emerging initiatives for engaging and sustaining participation in research.
            Cover page: Participant-centric initiatives: Tools to facilitate engagement in research
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2006)

            Background

            Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.

            Methods

            We manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998-2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2).

            Results

            For Method 1 we found that since 2001, only 71 - 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication.

            Conclusion

            We conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data.
              Cover page: On the persistence of supplementary resources in biomedical publicationsCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Davis Previously Published Works (2014)
              There is increasing interest in medical text messaging interventions being used to achieve positive patient outcomes across a range of clinical research and health practice environments. Short messaging service (SMS) is a low-cost tool that provides an easy communication route to engage potentially broad populations through text messaging, and is part of the growing social trend toward increased adoption of personal communication technologies by patient populations. Testing the effectiveness and impact of various communication strategies requires navigation of a complex web of clinical and research regulations and oversight mechanisms. We describe a case study of the implementation of SMS to provide bidirectional communications between physicians and patients involved in routine care reminders to illustrate the review processes and governance structures needed. By mapping the regulatory and approval processes required to manage and steward a research study across clinical and community boundaries, we provide a guide for other translational health researchers who may utilize similar kinds of personally owned technology interventions as research tools.
                Cover page: Governance strategies for conducting text messaging interventions in clinical research.
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2015)
                The purpose of this article is to describe feasibility, safety, and outcome results from a treatment development trial of the SAFETY Program, a brief intervention designed for integration with emergency services for suicide-attempting youths. Suicide-attempting youths, ages 11 to 18, were enrolled in a 12-week trial of the SAFETY Program, a cognitive-behavioral family intervention designed to increase safety and reduce suicide attempt (SA) risk (N = 35). Rooted in a social-ecological cognitive-behavioral model, treatment sessions included individual youth and parent session-components, with different therapists assigned to youths and parents, and family session-components to practice skills identified as critical in the pathway for preventing repeat SAs in individual youths. Outcomes were evaluated at baseline, 3-month, and 6-month follow-ups. At the 3-month posttreatment assessment, there were statistically significant improvements on measures of suicidal behavior, hopelessness, youth and parent depression, and youth social adjustment. There was one reported SA by 3 months and another by 6 months, yielding cumulative attempt rates of 3% and 6% at 3 and 6 months, respectively. Treatment satisfaction was high. Suicide-attempting youths are at high risk for repeat attempts and continuing mental health problems. Results support the value of a randomized controlled trial to further evaluate the SAFETY intervention. Extension of treatment effects to parent depression and youth social adjustment are consistent with our strong family focus and social-ecological model of behavior change.
                  Cover page: The SAFETY Program: A Treatment-Development Trial of a Cognitive-Behavioral Family Treatment for Adolescent Suicide Attempters
                  • Article
                  • Peer Reviewed
                  UC Davis Previously Published Works (2020)
                  Introduction: Informed consent for research biospecimen donations is traditionally obtained through a face-to-face interaction with research staff and by signing an Institutional Review Board (IRB)-approved printed form. Electronic signatures (eSign) are routinely used in the electronic medical record (EMR) for the consenting of clinical services after patients review printed documentation. Our goal was to develop an electronic self-consenting workflow that mimicked clinical services. Specifically, we tested a research consent process for the biobanking of remnant clinical samples that relies solely on clinical resources in a busy outpatient practice. Materials and Methods: The Biorepositories Core Resource (BCR) unit initiated a new enterprise-wide biobanking infrastructure for consenting patients, termed Biospecimen Use for Research-Related Investigations and Translational Objectives (BURRITO). BURRITO is modeled after an established clinical process called Terms and Conditions of Service (TACOS). The TACOS requires patients to annually review printed documentation and self-consent electronically for clinical services. BURRITO also requires patients to review printed documentation and self-consent with eSign to opt-in for remnant biospecimen banking, but patients must complete this process only once. We captured eSign for consents directly into the EMR without research staff. Results: Patients reviewed the IRB-approved documents and self-consented during their cardiology clinic visit. At checkout, their participation preferences were electronically documented by clinic staff. During a 6-month period, 123 patients agreed to donate. After a review of process, a second 3-month period identified 202 patients agreeing to donate. BURRITO did not require face-to-face interactions with research staff, used a "no-paper" eSign for consent, and created discrete fields in the clinical EMR of the patient's preference. Conclusions: BURRITO electronically documents informed consent using an EMR functionality and the least amount of clinical and research resources. Our results show promise for developing institutionally adopted processes, which could leverage existing clinical workflows for universal research consenting and scalability.
                    Cover page: Modeling Clinical Processes to Consent Research Donors of Remnant Biospecimens in an Outpatient Cardiology Clinic
                    • Article
                    • Peer Reviewed
                    UC Davis Previously Published Works (2021)

                    Objective

                    To explore multi-clinician perspectives on intensive care early mobility, monitoring and to assess the perceived value of technology-generated mobility metrics to provide user feedback to inform research, practice improvement, and technology development.

                    Methods

                    We performed a qualitative descriptive study. Three focus groups were conducted with critical care clinicians, including nurses (n = 10), physical therapists (n = 8) and physicians (n = 8) at an academic medical centre that implemented an intensive care early mobility programme in 2012. Qualitative thematic analysis was used to code transcripts and identify overarching themes.

                    Findings

                    Along with reaffirming the value of performing early mobility interventions, four themes for improving mobility monitoring emerged, including the need for: 1) standardised indicators for documenting mobility; 2) inclusion of both quantitative and qualitative metrics to measure mobility 3) a balance between quantity and quality of data; and 4) trending mobility metrics over time.

                    Conclusion

                    Intensive care mobility monitoring should be standardised and data generated should be high quality, capable of supporting trend analysis, and meaningful. By improving measurement and monitoring of mobility, future researchers can examine the arc of activity that patients in the intensive care unit undergo and develop models to understand factors that influence successful implementation.
                      Cover page: Standardisation, multi-measure, data quality and trending: A qualitative study on multidisciplinary perspectives to improve intensive care early mobility monitoring
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