Special Issue on Teaching and Training Future Health Informaticians: Increasing generative artificial intelligence competency among students enrolled in doctoral nursing research coursework.

BACKGROUND: Generative AI tools may soon be integrated into healthcare practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way. OBJECTIVE: The objective of this study was to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in healthcare. METHODS: We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat GPT 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in healthcare, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in healthcare practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n=10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis. RESULTS: Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. They also all reported increasing their self-rated competency in generative AI by one to two points on a 5-point rating scale. CONCLUSIONS: This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.

Advancing Data Science Competencies for Nursing PhD Students.

Given the evolving importance of data science approaches in nursing research, we developed a 3-credit, 15-week course that is integrated into the second year PhD curriculum at Columbia University School of Nursing. As a complement to didactic content, the students address a research question of their choice using a big data source, Jupyter Notebook, and R programming language. The course evolved over time with generative AI tools being added in 2023. Student self-evaluations of their data science competencies improved from baseline. This case study adds to the evolving body of literature on data science and AI competences in nursing.

Returning value from the All of Us research program to PhD-level nursing students using ChatGPT as programming support: results from a mixed-methods experimental feasibility study.

OBJECTIVE: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench. MATERIALS AND METHODS: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process. RESULTS: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate. DISCUSSION: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources. CONCLUSION: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

User-Centered Design of a Preference-Driven Patient Activation Tool for Optimizing Depression Treatment in Integrated Primary Care Settings (The Transform DepCare Study).

BACKGROUND: Few patient engagement tools incorporate the complex patient experiences, contexts, and workflows that limit depression treatment implementation. OBJECTIVE: Describe a user-centered design (UCD) process for operationalizing a preference-driven patient activation tool. DESIGN: Informed by UCD and behavior change/implementation science principles, we designed a preference-driven patient activation prototype for engaging patients in depression treatment. We conducted three usability cycles using different recruitment/implementation approaches: near live/live testing in primary care waiting rooms (V1-2) and lab-based think aloud testing (V3) oversampling older, low-literacy, and Spanish-speaking patients in the community and via EHR algorithms. We elicited clinician and "heuristic" expert input. MAIN MEASURES: We administered the system usability scale (SUS) all three cycles and pre-post V3, the patient activation measure, decisional conflict scale, and depression treatment barriers. We employed descriptive statistics and thematically analyzed observer notes and transcripts for usability constructs. RESULTS: Overall, 43 patients, 3 clinicians, and 5 heuristic (a usability engineering method for identifying usability problems) experts participated. Among patients, 41.9% were ≥ 65 years old, 79.1% female, 23.3% Black, 62.8% Hispanic, and 55.8% Spanish-speaking and 46.5% had ≤ high school education. We described V1-3 usability (67.2, 77.3, 81.8), treatment seeking (92.3%, 87.5%, 92.9%), likelihood/comfort discussing with clinician (76.9%, 87.5%, 100.0%), and pre vs. post decisional conflict (23.7 vs. 15.2), treatment awareness (71.4% vs. 92.9%), interest in antidepressants (7.1% vs. 14.3%), and patient activation (66.8 vs. 70.9), with fewer barriers pertaining to cost/insurance, access/coordination, and self-efficacy/stigma/treatment efficacy. Key themes included digital literacy, understandability, high acceptability for aesthetics, high usefulness of patient/clinician videos, and workflow limitations. We adapted manual entry/visibility/content; added patient activation and a personalized algorithm; and proposed flexible, care manager delivery leveraging clinic screening protocols. DISCUSSION: We provide an example of leveraging UCD to design/adapt a real-world, patient experience and workflow-aligned patient activation tool in diverse populations.

Perspectives on the role of industry in informatics research and authorship.

OBJECTIVES: Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship. PROCESS: We convened an expert panel at the American Medical Informatics Association 2022 Annual Symposium to explore the role of industry in informatics research and authorship with community input. The panel summarized session themes and prepared recommendations. CONCLUSIONS: Authorship for informatics research, regardless of affiliation, should be determined by International Committee of Medical Journal Editors uniform requirements for authorship. All authors meeting criteria should be included, and categorical rejection based on author affiliation is unethical. Informatics research should be evaluated based on its scientific rigor; all sources of bias and conflicts of interest should be addressed through disclosure and, when possible, methodological mitigation.

Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study.

Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results. Across stakeholder groups, our findings indicate support for PRS development and a strong interest in having PRS results returned to research participants. However, we also found multi-level barriers and significant differences in stakeholders' views about what is needed and possible for successful implementation. These include researcher-participant interaction formats, health and genomic literacy, and a range of structural barriers, such as financial instability, insurance coverage, and the absence of health-supporting infrastructure and affordable healthy food options in poorer neighborhoods. Our findings highlight the need to revisit and implement measures in PRS studies (e.g., incentives and resources for follow-up care), as well as system-level policies to promote equity in genomic research and health outcomes.

Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles.

Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes as well as notable gaps. We also compared trends in studied conditions to their relative prevalence using insurance claims (42 million Americans). HDMH publications represent 1% of articles in Medical Literature Analysis and Retrieval System Online (MEDLINE). Most studies are observational in nature, although randomized trial reporting has increased fivefold in the past 20 years. Half of HDMH articles concentrate on only three disease groups (cancer, mental health, and endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well represented despite substantial prevalence. To support further investigation, we present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.

Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study.

OBJECTIVE: Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments. MATERIALS AND METHODS: Guided by the Smerecnik framework that theorizes understanding of multifactorial genetic disease through 3 knowledge types, we developed English and Spanish infographics for individuals enrolled in the Electronic Medical Records and Genomics Network. Infographics were developed to explain concepts in lay language and visualizations. We conducted iterative sessions using a modified "think-aloud" process with 10 participants (6 English, 4 Spanish-speaking) to explore comprehension of and attitudes towards the infographics. RESULTS: We found that all but one participant had "awareness knowledge" of genetic disease risk factors upon viewing the infographics. Many participants had difficulty with "how-to" knowledge of applying genetic risk factors to specific monogenic and polygenic risks. Participant attitudes towards the iteratively-refined infographics indicated that design saturation was reached. DISCUSSION: There were several elements that contributed to the participants' comprehension (or misunderstanding) of the infographics. Visualization and iconography techniques best resonated with those who could draw on prior experiences or knowledge and were absent in those without. Limited graphicacy interfered with the understanding of absolute and relative risks when presented in graph format. Notably, narrative and storytelling theory that informed the creation of a vignette infographic was most accessible to all participants. CONCLUSION: Engagement with the intended audience who can identify strengths and points for improvement of the intervention is necessary to the development of effective infographics.

A pilot test of an infographic-based health communication intervention to enhance patient education among Latino persons with HIV.

OBJECTIVE: To pilot test an infographic-based health communication intervention that our team rigorously designed and explore whether its implementation leads to better health outcomes among Latino persons with HIV (PWH). MATERIALS AND METHODS: Latino PWH (N = 30) living in New York City received the intervention during health education sessions at 3 study visits that occurred approximately 3 months apart. At each visit, participants completed baseline or follow-up assessments and laboratory data were extracted from patient charts. We assessed 6 outcomes (HIV-related knowledge, self-efficacy to manage HIV, adherence to antiretroviral therapy, CD4 count, viral load, and current and overall health status) selected according to a conceptual model that describes pathways through which communication influences health outcomes. We assessed changes in outcomes over time using quantile and generalized linear regression models controlling for the coronavirus disease 2019 (COVID-19) research pause and new patient status (new/established) at the time of enrollment. RESULTS: Most participants were male (60%) and Spanish-speaking (60%); 40% of participants identified as Mixed Race/Mestizo, 13.3% as Black, 13.3% as White, and 33.3% as "other" race. Outcome measures generally improved after the second intervention exposure. Following the third intervention exposure (after the COVID-19 research pause), only the improvements in HIV-related knowledge and current health status were statistically significant. DISCUSSION AND CONCLUSION: Our infographic-based health communication intervention may lead to better health outcomes among Latino PWH, but larger trials are needed to establish efficacy. From this work, we contribute suggestions for effective infographic use for patient-provider communication to enhance patient education in clinical settings.