Faculty Bibliography

BACKGROUND:The design and integration of technology within inpatient hospital rooms has a critical role in supporting nursing workflows, enhancing provider experience, and improving patient care. As health care technology evolves, there is a need to design "future-proofed" physical environments that integrate technology in ways that support workflows and maintain clinical performance. Assessing how current technologies affect nursing workflows can help inform the development of these future environments. OBJECTIVE:We assessed the current challenges nursing staff face in inpatient rooms, gather insights on technology, and build environment interactions to envision the design of a technology-integrated "Inpatient Room of the Future." METHODS:A qualitative study was conducted involving semistructured interviews, shadowing, and focus groups among nursing staff in the inpatient setting. Methods including horizon scanning, scenario analysis, technology assessment, and backcasting facilitated a comprehensive qualitative analysis of current technology use and needs in inpatient nursing workflows to inform exploratory design considerations for technology-integrated envisioned futures solutions. RESULTS:In total, 26 nursing staff across 4 inpatient hospital units participated in this study. Analysis identified four major themes considered central to designing a technology-integrated inpatient room that enhances nursing workflow and experience: (1) the need for seamless integration of technologies advocating for a unified system that minimizes fragmented technology use and enhances efficiency; (2) the potential for technology to reduce cognitive load, alleviate mental strain, and streamline complex workflows; (3) a focus on enhancing interpersonal communication with specific emphasis on tools that facilitate clear and efficient communication among clinicians and with patients; and (4) the importance of improved staff well-being with design considerations aimed at promoting both physical and mental health for health care workers in the inpatient setting. Envisioned future solutions included enhanced patient monitoring with automated measurements and actions through computer vision and data triangulation, a smart electronic health record-integrated supply management system using computer vision to detect supply shortages and auto-delivery of needed supplies, and a personal tech smart assistant capable of real-time patient monitoring and escalation, task prioritization, and hands-free clinical documentation and communication. CONCLUSIONS:While current technologies address specific tasks, there are significant opportunities for better technology integration, reducing cognitive load, enhancing communication, and promoting the physical and mental well-being of nursing staff. Future research should focus on seamless technology integration aligned with clinical workflows and implementing supportive technologies that do not interfere with clinician judgment and critical thinking. Policy recommendations include oversight mechanisms for evaluating artificial intelligence-enabled devices, safeguarding patient information, and ensuring nurses are actively involved at every stage of technology development and implementation. Future inpatient unit designs should actively engage input from both nursing professionals and technologists in developing future-proofed clinical spaces to ensure the creation of integrated systems that foster a cohesive and harmonious user experience.

BACKGROUND:E-cigarettes may serve as a safer alternative to combustible cigarettes and may be more effective than currently available nicotine replacement therapy (NRT). Little is known about the perceptions of using e-cigarettes as part of a smoking treatment program. The objective of this study was to gain insight into patient-level factors to consider when developing smoking treatment programs that incorporate e-cigarettes. METHODS:Qualitative analysis of in-depth interviews with 14 participants enrolled in the e-cigarette treatment arm of a tobacco treatment intervention pilot randomized trial comparing the impact of behavioral counseling paired with e-cigarettes or NRT on smoking outcomes. Participants were prompted to share their experiences with the products and the study overall. Transcripts were coded according to the principles of framework analysis for applied research. Codes were organized into themes using the principles of grounded theory. RESULTS:Themes suggest that while there is an eagerness to try e-cigarettes as a new tool for smoking cessation, there is apprehension regarding what it means to "quit" if switching to e-cigarettes. Reflecting on the transitional purpose of e-cigarettes and potential health concerns associated with their use, many participants differentiated between the short-term goal to quit combustible cigarettes and the long-term goal to quit e-cigarettes. CONCLUSIONS:Including e-cigarettes as an option in smoking treatment regimens may be an opportunity to re-engage people who smoke who have tried and failed to quit with other forms of treatment. Participants found it challenging to establish what it means to quit cigarettes with e-cigarettes due to addiction and other health concerns. Clear guidelines are needed for integrating e-cigarettes into smoking cessation programs. TRIAL REGISTRATIONS/BACKGROUND:ClinicalTrials.gov Identifier: NCT04465318.

BACKGROUND/UNASSIGNED:Ecological momentary assessment (EMA) provides insight into the effectiveness and feasibility of smoking-related interventions. OBJECTIVE/UNASSIGNED:The objective of this paper was to assess adherence to an EMA protocol and compare EMA-derived responses with measures collected through multiple surveys. METHODS/UNASSIGNED:A subanalysis was conducted using data from a 12-week, open-label, and 2-arm pilot randomized clinical trial among adult smokers with chronic obstructive pulmonary disease, coronary artery disease, peripheral vascular disease, or asthma in the last 12 months (n=109). Participants were randomized to either electronic cigarette (EC) or nicotine replacement therapy (NRT) treatment arms. We compared EMA data collected through automated SMS text message prompts sent to participants 4 times daily for 12 weeks, including cigarettes smoked per day (CPD), craving, and satisfaction, to survey data collected at 12 weeks. Convergent validity between survey- and EMA-reported measures was evaluated using Pearson correlation and paired t tests. CPD was modeled using negative binomial regression. Relative rates (RRs) of reaching at least 50%, 75%, and 100% CPD reduction between two arms were calculated using both EMA and survey data. RESULTS/UNASSIGNED:The majority of participants were non-Hispanic White (63/109, 58%) and female (60/109, 55%), and had a median age of 60 (IQR 54-65) years. Among the 109 participants, 59.6% (n=65) were consistently adherent to the EMA protocol over the 12-week period. Median weekly EMA response rate remained high over the 12-week study period even though a modest decline was observed (week 1, 97.8% and week 12, 89.4%). The mean CPD declined significantly (week 1, mean 14.2, SD 9.9 and week 12, mean 4.6, SD 6.7; P<.001). EMA-derived and survey-based CPD measurements were positively correlated (r=0.73, 95% CI 0.6-0.82) as were measures of craving (r=0.38, 95% CI 0.17-0.56). No significant paired difference in CPD was observed between EMA measurements and surveys. A significant effect of time on CPD EMA data (incidence rate ratio [IRR] 1-week change 0.93; P<.01) and survey data was found (IRR 12-week change 0.36; P<.01). However, the treatment effect was not significant, which aligned with the RR results. An increase in the EC consumption was observed over time in the EC arm, with 12.1% (7/58) fully switched to EC (defined as CPD=0 and EC use>0) and 20.7% (12/58) mostly switched (defined as a reduction in CPD>75% and EC use>0) in week 12. CONCLUSIONS/UNASSIGNED:EMA is a suitable method to collect recall-based smoking-related data. Though results from mixed effect modeling and RR comparisons were similar using EMA or survey data, EMA provides unique advantages, namely greater granularity in the time and the capability to detect switching patterns in near real time. These findings provide the feasibility of using EMA in developing smoking cessation interventions in future tobacco harm reduction research.

INTRODUCTION/BACKGROUND:E-cigarettes (ECs) may be an effective harm reduction strategy for individuals with conditions like chronic obstructive pulmonary disease (COPD), asthma, coronary artery disease (CAD), and peripheral arterial disease (PAD) who smoke combustible cigarettes (CCs). Our aim was to examine how individuals with chronic conditions transition from CCs to ECs and its impact on health outcomes. METHODS:In a pilot randomized controlled trial (RCT), patients with COPD, asthma, CAD/PAD who currently smoke CCs and have not used nicotine replacement therapy (NRT) or ECs in the past 14 days were randomized to receive ECs or combination NRT with behavioral counselling. Disease symptoms, acceptability/satisfaction (TSQM-9) and feasibility, and cigarettes per day (CPD), and/or EC use were collected at baseline, 3-, and 6-months. Descriptive statistics and a linear regression were conducted to explore changes in CPD and chronic condition-specific assessments (CAT, SAQ-7, ACT) that assess COPD, asthma, and CAD/PAD symptom change. RESULTS:At 3-months, the EC group (n=63, mean CPD=9±11) reduced their CPD by 54% vs. 60% in the NRT group (n=58, mean CPD=7±6), p=0.56. At 6-months, 17.5% had switched completely to ECs while 23% quit smoking in the NRT arm. CAT scores showed a significant 6-point reduction in the EC arm (p=0.03). Participants scored an average of 69±27 for EC effectiveness, 87±23 for convenience, and 75±27 for overall satisfaction. CONCLUSIONS:This pilot study suggests that ECs may be a safer alternative for chronic condition patients using CCs and warrants further research on expected smoking cessation/reduction among individuals who use ECs. IMPLICATIONS/CONCLUSIONS:The findings from this pilot RCT hold significant implications with chronic conditions such as COPD, asthma, CAD and PAD who smoke CCs. The observed reduction in cigarettes per day and improvement in respiratory symptoms suggest that switching to ECs appears feasible and acceptable among those with chronic diseases. These results suggest that ECs may offer an alternative for individuals struggling to quit CC smoking through existing pharmacotherapies. This study supports further exploration of switching to ECs as a harm reduction strategy among CC users who have been unsuccessful at quitting by other means.

INTRODUCTION/BACKGROUND:Early lung cancer screening (LCS) through low-dose CT (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centred intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision-making. METHODS AND ANALYSIS/METHODS:MyLungHealth is a multisite pragmatic trial, involving University of Utah Health and New York University Langone Health primary care clinics. The MyLungHealth intervention was developed using a user-centred design process, informed by patient and provider focus groups and interviews. The intervention's effectiveness will be evaluated through a patient-randomised trial, comparing the combined use of MyLungHealth and DecisionPrecision+ (a provider-focused shared decision-making intervention) against DecisionPrecision+ alone. The first study hypothesis is that among patients aged 50-79 with uncertain LCS eligibility (eg, 10-19 pack-years or unknown pack-years or unknown quit date for individuals who used to smoke), MyLungHealth eligibility questionnaires will result in increased identification of LCS-eligible patients (n~26 729 patients). The second study hypothesis is that among patients aged 50-79 with documented LCS eligibility (20+ pack-years, quit within the last 15 years if individuals who used to smoke, and no recent screening or screening discussion), MyLungHealth education will result in increased LDCT ordering (n~4574 patients). Primary outcomes will be identification of LCS-eligible patients among individuals with uncertain LCS eligibility and LDCT ordering rates among individuals with documented LCS eligibility. ETHICS AND DISSEMINATION/BACKGROUND:The protocol was approved by the University of Utah Institutional Review Board (# 00153806). The patient data collected for this study will not be shared publicly due to the sensitive nature of the patient health information and the fact that we will not be obtaining written informed consent to allow public sharing of their data. Results will be disseminated through peer-reviewed publications. TRIAL REGISTRATION NUMBER/BACKGROUND:Clinicaltrials.gov, NCT06338592.

INTRODUCTION/BACKGROUND:Failure to document colonoscopy follow-up needs postpolypectomy can lead to delayed detection of colorectal cancer (CRC). Automating the update of a unified follow-up date in the electronic health record (EHR) may increase the number of patients with guideline-concordant CRC follow-up screening. METHODS:Prospective pre-post design study of an automated rules engine-based tool using colonoscopy pathology results to automate updates to documented CRC screening due dates was performed as an operational initiative, deployed enterprise-wide May 2023. Participants were aged 45-75 years who received a colonoscopy November 2022 to November 2023. Primary outcome measure is rate of updates to screening due dates and proportion with recommended follow-up < 10 years. Multivariable log-binomial regression was performed (relative risk, 95% confidence intervals). RESULTS:Study population included 9,824 standard care and 19,340 intervention patients. Patients had a mean age of 58.6 ± 8.6 years and were 53.4% female, 69.6% non-Hispanic White, 13.5% non-Hispanic Black, 6.5% Asian, and 4.6% Hispanic. Postintervention, 46.7% of follow-up recommendations were updated by the rules engine. The proportion of patients with a 10-year default follow-up frequency significantly decreased (88.7%-42.8%, P < 0.001). The mean follow-up frequency decreased by 1.9 years (9.3-7.4 years, P < 0.001). Overall likelihood of an updated follow-up date significantly increased (relative risk 5.62, 95% confidence intervals: 5.30-5.95, P < 0.001). DISCUSSION/CONCLUSIONS:An automated rules engine-based tool has the potential to increase the accuracy of colonoscopy follow-up dates recorded in patient EHR. The results emphasize the opportunity for more automated and integrated solutions for updating and maintaining EHR health maintenance activities.

Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.

Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40-69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and "observing patient involvement in decision making" (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0-67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening.

OBJECTIVE:Both diabetes and smoking significantly increase the risk of cardiovascular disease (CVD). Understanding whether a diagnosis of diabetes can be leveraged to promote smoking cessation is a gap in the literature. METHODS:We used data from the US National Health Interview Survey, 2006 to 2018, to investigate the relationship between self-report of diagnosis of diabetes and subsequent smoking abstinence among 142,884 respondents who reported regular smoking at baseline. Effect sizes were presented as hazard ratios (HRs) derived from multivariable Cox regression models adjusted for potential confounders using diabetes as a time-dependent covariate. Subgroup-specific estimates were obtained using interaction terms between diabetes and variables of interest. RESULTS:A self-reported diagnosis of diabetes was associated with smoking abstinence (HR: 1.21; 95% CI: 1.16 to 1.27). The strength of the association varied based on race (P for interaction: 0.004), where it was strongest in African Americans (HR: 1.44; 95% CI: 1.29 to 1.60); income (P for interaction <0.001), where it was strongest in those with a yearly income less than $35,000 (HR: 1.45; 95% CI: 1.36 to 1.53); and educational attainment (P for interaction <0.001), where it was strongest in those who did not attend college (HR: 1.48; 95% CI: 1.40 to 1.57). CONCLUSION/CONCLUSIONS:Among adults who smoke, a diagnosis of diabetes is significantly associated with subsequent smoking abstinence. The association is strongest in socially disadvantaged demographics, including African Americans, low-income individuals, and those who did not attend college.

INTRODUCTION/BACKGROUND:Six hospitals within the New York City public hospital system implemented the Consult for Addiction Treatment and Care in Hospitals (CATCH) program, an interprofessional addiction consult service. A stepped-wedge cluster randomized controlled trial tested the effectiveness of CATCH for increasing initiation and engagement in post-discharge medication for opioid use disorder (MOUD) treatment among hospital patients with opioid use disorder (OUD). The objective of this study was to identify facility characteristics that were associated with stronger performance of CATCH. METHODS:This study used a mixed methods multiple-case study design. The six hospitals in the CATCH evaluation were each assigned a case rating according to intervention reach. Reach was considered high if ≥50 % of hospitalized OUD patients received an MOUD order. Cross-case rating comparison identified attributes of high-performing hospitals and inductive and deductive approaches were used to identify themes. RESULTS:Higher-performing hospitals exhibited attributes that were generally absent in lower-performing hospitals, including (1) complete medical provider staffing; (2) designated office space and resources for CATCH; (3) existing integrated OUD treatment resources; and (4) limited overlap between the implementation period and COVID-19 pandemic. CONCLUSIONS:Hospitals with attributes indicative of awareness and integration of OUD services into general care were generally higher performing than hospitals that had siloed OUD treatment programs. Future implementations of addiction consult services may benefit from an increased focus on hospital- and community-level buy-in and efforts to integrate MOUD treatment into general care.