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188


Trial Participants' Perceptions of the Impact of Ecological Momentary Assessment on Smoking Behaviors: Qualitative Analysis

Stevens, Elizabeth R; Li, Rina; Xiang, Grace; Wisniewski, Rachel; Rojas, Sidney; O'Connor, Katherine; Wilker, Olivia; Vojjala, Mahathi; El-Shahawy, Omar; Sherman, Scott E
BACKGROUND/UNASSIGNED:Ecological momentary assessment (EMA) is an increasingly used tool for data collection in behavioral research, including smoking cessation studies. As previous addiction research suggests, EMA has the potential to elicit cue reactivity by triggering craving and increasing behavioral awareness. However, there has been limited evaluation of its potential influence on behavior. OBJECTIVE/UNASSIGNED:By examining the perspectives of research participants enrolled in a tobacco treatment intervention trial, this qualitative analysis aims to understand the potential impact that EMA use may have had on smoking behaviors that may not have otherwise been captured through other study measures. METHODS/UNASSIGNED:We performed a qualitative analysis of in-depth interviews with participants enrolled in a pilot randomized controlled trial of a tobacco treatment intervention that used SMS text messaging to collect EMA data on smoking behaviors. In the pilot randomized controlled trial, combustible cigarette and e-cigarette use and smoking-related cravings were measured as part of an EMA protocol, in which SMS text messaging served as a smoking diary. SMS text messaging was intended for data collection only and not designed to serve as part of the intervention. After a baseline assessment, participants were asked to record daily nicotine use for 12 weeks by responding to text message prompts that they received 4 times per day. Participants were prompted to share their experiences with the EMA text messaging component of the trial but were not directly asked about the influence of EMA on their behaviors. Transcripts were coded according to the principles of the framework for applied research. The codes were then examined, summarized, and grouped into themes based on the principles of grounded theory. RESULTS/UNASSIGNED:Interviews were analyzed for 26 participants. The themes developed from the analysis suggested the potential for EMA, in the form of an SMS text messaging smoking diary, to influence participants' smoking behaviors. The perceived impacts of EMA text messaging on smoking behaviors were polarized; some participants emphasized the positive impacts of text messages on their efforts to reduce smoking, while others stressed the ways that text messaging negatively impacted their smoking reduction efforts. These contrasting experiences were captured by themes reflecting the positive impacts on smoking behaviors, including increased awareness of smoking behaviors and a sense of accountability, and the negative impacts on emotions and smoking behaviors, including provoking a sense of guilt and triggering smoking behaviors. CONCLUSIONS/UNASSIGNED:The collection of EMA smoking behavior data via SMS text messaging may influence the behaviors and perceptions of participants in tobacco treatment interventions. More research is needed to determine the magnitude of impact and mechanisms, to account for the potential effects of EMA. A broader discussion of the unintended effects introduced by EMA use is warranted among the research community.
PMID: 38270520
ISSN: 2291-5222
CID: 5625222

Implementing a Clinical Decision Support Tool to Improve Physical Activity

McCarthy, Margaret M; Szerencsy, Adam; Taza-Rocano, Leslie; Hopkins, Stephanie; Mann, Devin; D'Eramo Melkus, Gail; Vorderstrasse, Allison; Katz, Stuart D
BACKGROUND:Currently, only about half of U.S. adults achieve current physical activity guidelines. Routine physical activity is not regularly assessed, nor are patients routinely counseled by their health care provider on achieving recommended levels. The three-question physical activity vital sign (PAVS) was developed to assess physical activity duration and intensity and identify adults not meeting physical activity guidelines. Clinical decision support provided via a best practice advisory in an electronic health record (EHR) system can be triggered as a prompt, reminding health care providers to implement the best practice intervention when appropriate. Remote patient monitoring of physical activity can provide objective data in the EHR. OBJECTIVES/OBJECTIVE:This study aimed to evaluate the feasibility and clinical utility of embedding the PAVS and a triggered best practice advisor into the EHR in an ambulatory preventive cardiology practice setting to alert providers to patients reporting low physical activity and prompt health care providers to counsel these patients as needed. METHODS:Three components based in the EHR were integrated for the purpose of this study: patients completed the PAVS through their electronic patient portal prior to an office visit; a best practice advisory was created to prompt providers to counsel patients who reported low levels of physical activity; and remote patient monitoring via Fitbit synced to the EHR provided objective physical activity data. The intervention was pilot-tested in the Epic EHR for 1 year (July 1, 2021-June 30, 2022). Qualitative feedback on the intervention from both providers and patients was obtained at the completion of the study. RESULTS:Monthly assessments of the use of the PAVS and best practice advisory and remote patient monitoring were completed. Patients' completion of the PAVS varied from 35% to 48% per month. The best practice advisory was signed by providers between 2% and 65% and was acknowledged by 2% to 22% per month. The majority (58%) of patients were able to sync a Fitbit device to their EHR for remote monitoring. DISCUSSION/CONCLUSIONS:Although uptake of each component needs improvement, this pilot demonstrated the feasibility of incorporating a PA promotion intervention into the EHR. Qualitative feedback provided guidance for future implementation.
PMID: 38207172
ISSN: 1538-9847
CID: 5631332

Virtual-first care: Opportunities and challenges for the future of diagnostic reasoning

Lawrence, Katharine; Mann, Devin
SCOPUS:85182482557
ISSN: 1743-4971
CID: 5629652

Remote Patient Monitoring for Management of Diabetes Mellitus in Pregnancy Is Associated With Improved Maternal and Neonatal Outcomes

Kantorowska, Agata; Cohen, Koral; Oberlander, Maxwell; Jaysing, Anna R.; Akerman, Meredith B.; Wise, Anne Marie; Mann, Devin M.; Testa, Paul A.; Chavez, Martin R.; Vintzileos, Anthony M.; Heo, Hye J.
SCOPUS:85180013996
ISSN: 0029-7828
CID: 5620962

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial

Stevens, Elizabeth R; Agbakoba, Ruth; Mann, Devin M; Hess, Rachel; Richardson, Safiya I; McGinn, Thomas; Smith, Paul D; Halm, Wendy; Mundt, Marlon P; Dauber-Decker, Katherine L; Jones, Simon A; Feldthouse, Dawn M; Kim, Eun Ji; Feldstein, David A
BACKGROUND:Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model. METHODS:Following qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout. DISCUSSION:This study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .
PMCID:10644670
PMID: 37964232
ISSN: 1472-6947
CID: 5631732

Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening

Stevens, Elizabeth R; Caverly, Tanner; Butler, Jorie M; Kukhareva, Polina; Richardson, Safiya; Mann, Devin M; Kawamoto, Kensaku
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
PMID: 37844677
ISSN: 1532-0480
CID: 5609662

Implementing Remote Patient Monitoring of Physical Activity in Clinical Practice

McCarthy, Margaret; Jevotovsky, David; Mann, Devin; Veerubhotla, Akhila; Muise, Eleanor; Whiteson, Jonathan; Rizzo, John Ross
PURPOSE/OBJECTIVE:Remote patient monitoring (RPM) is a tool for patients to share data collected outside of office visits. RPM uses technology and the digital transmission of data to inform clinician decision-making in patient care. Using RPM to track routine physical activity is feasible to operationalize, given contemporary consumer-grade devices that can sync to the electronic health record. Objective monitoring through RPM can be more reliable than patient self-reporting for physical activity. DESIGN AND METHODS/METHODS:This article reports on four pilot studies that highlight the utility and practicality of RPM for physical activity monitoring in outpatient clinical care. Settings include endocrinology, cardiology, neurology, and pulmonology settings. RESULTS:The four pilot use cases discussed demonstrate how RPM is utilized to monitor physical activity, a shift that has broad implications for prediction, prevention, diagnosis, and management of chronic disease and rehabilitation progress. CLINICAL RELEVANCE/CONCLUSIONS:If RPM for physical activity is to be expanded, it will be important to consider that certain populations may face challenges when accessing digital health services. CONCLUSION/CONCLUSIONS:RPM technology provides an opportunity for clinicians to obtain objective feedback for monitoring progress of patients in rehabilitation settings. Nurses working in rehabilitation settings may need to provide additional patient education and support to improve uptake.
PMID: 37723623
ISSN: 2048-7940
CID: 5591172

Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory

Boyd, Andrew D; Gonzalez-Guarda, Rosa; Lawrence, Katharine; Patil, Crystal L; Ezenwa, Miriam O; O'Brien, Emily C; Paek, Hyung; Braciszewski, Jordan M; Adeyemi, Oluwaseun; Cuthel, Allison M; Darby, Juanita E; Zigler, Christina K; Ho, P Michael; Faurot, Keturah R; Staman, Karen L; Leigh, Jonathan W; Dailey, Dana L; Cheville, Andrea; Del Fiol, Guilherme; Knisely, Mitchell R; Grudzen, Corita R; Marsolo, Keith; Richesson, Rachel L; Schlaeger, Judith M
Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges-incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology-that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.
PMID: 37364017
ISSN: 1527-974x
CID: 5540142

Putting ChatGPT's Medical Advice to the (Turing) Test: Survey Study

Nov, Oded; Singh, Nina; Mann, Devin
BACKGROUND:Chatbots are being piloted to draft responses to patient questions, but patients' ability to distinguish between provider and chatbot responses and patients' trust in chatbots' functions are not well established. OBJECTIVE:This study aimed to assess the feasibility of using ChatGPT (Chat Generative Pre-trained Transformer) or a similar artificial intelligence-based chatbot for patient-provider communication. METHODS:A survey study was conducted in January 2023. Ten representative, nonadministrative patient-provider interactions were extracted from the electronic health record. Patients' questions were entered into ChatGPT with a request for the chatbot to respond using approximately the same word count as the human provider's response. In the survey, each patient question was followed by a provider- or ChatGPT-generated response. Participants were informed that 5 responses were provider generated and 5 were chatbot generated. Participants were asked-and incentivized financially-to correctly identify the response source. Participants were also asked about their trust in chatbots' functions in patient-provider communication, using a Likert scale from 1-5. RESULTS:A US-representative sample of 430 study participants aged 18 and older were recruited on Prolific, a crowdsourcing platform for academic studies. In all, 426 participants filled out the full survey. After removing participants who spent less than 3 minutes on the survey, 392 respondents remained. Overall, 53.3% (209/392) of respondents analyzed were women, and the average age was 47.1 (range 18-91) years. The correct classification of responses ranged between 49% (192/392) to 85.7% (336/392) for different questions. On average, chatbot responses were identified correctly in 65.5% (1284/1960) of the cases, and human provider responses were identified correctly in 65.1% (1276/1960) of the cases. On average, responses toward patients' trust in chatbots' functions were weakly positive (mean Likert score 3.4 out of 5), with lower trust as the health-related complexity of the task in the questions increased. CONCLUSIONS:ChatGPT responses to patient questions were weakly distinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower-risk health questions. It is important to continue studying patient-chatbot interaction as chatbots move from administrative to more clinical roles in health care.
PMCID:10366957
PMID: 37428540
ISSN: 2369-3762
CID: 5537462

Leveraging Electronic Health Record Technology and Team Care to Address Medication Adherence: Protocol for a Cluster Randomized Controlled Trial

Blecker, Saul; Schoenthaler, Antoinette; Martinez, Tiffany Rose; Belli, Hayley M; Zhao, Yunan; Wong, Christina; Fitchett, Cassidy; Bearnot, Harris R; Mann, Devin
BACKGROUND:Low medication adherence is a common cause of high blood pressure but is often unrecognized in clinical practice. Electronic data linkages between electronic health records (EHRs) and pharmacies offer the opportunity to identify low medication adherence, which can be used for interventions at the point of care. We developed a multicomponent intervention that uses linked EHR and pharmacy data to automatically identify patients with elevated blood pressure and low medication adherence. The intervention then combines team-based care with EHR-based workflows to address medication nonadherence. OBJECTIVE:This study aims to describe the design of the Leveraging EHR Technology and Team Care to Address Medication Adherence (TEAMLET) trial, which tests the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence among patients with hypertension. METHODS:TEAMLET is a pragmatic, cluster randomized controlled trial in which 10 primary care practices will be randomized 1:1 to the multicomponent intervention or usual care. We will include all patients with hypertension and low medication adherence who are seen at enrolled practices. The primary outcome is medication adherence, as measured by the proportion of days covered, and the secondary outcome is clinic systolic blood pressure. We will also assess intervention implementation, including adoption, acceptability, fidelity, cost, and sustainability. RESULTS:As of May 2023, we have randomized 10 primary care practices into the study, with 5 practices assigned to each arm of the trial. The enrollment for the study commenced on October 5, 2022, and the trial is currently ongoing. We anticipate patient recruitment to go through the fall of 2023 and the primary outcomes to be assessed in the fall of 2024. CONCLUSIONS:The TEAMLET trial will evaluate the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence. If successful, the intervention could offer a scalable approach to address inadequate blood pressure control among millions of patients with hypertension. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/47930.
PMCID:10362494
PMID: 37418304
ISSN: 1929-0748
CID: 5539452