Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

BACKGROUND: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored. OBJECTIVE: This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care. METHODS: We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values. RESULTS: A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all P<.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients' information. CONCLUSIONS: Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI's accuracy, factors that drive patients' mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient-health professional relationship is preserved when AI is used.

Public perspectives on increased data sharing in health research in the context of the 2023 National Institutes of Health Data Sharing Policy.

The National Institutes of Health (NIH) is the largest public research funder in the world. In an effort to make publicly funded data more accessible, the NIH established a new Data Management and Sharing (DMS) Policy effective January 2023. Though the new policy was available for public comment, the patient perspective and the potential unintended consequences of the policy on patients' willingness to participate in research have been underexplored. This study aimed to determine: (1) participant preferences about the types of data they are willing to share with external entities, and (2) participant perspectives regarding the updated 2023 NIH DMS policy. A cross-sectional, nationally representative online survey was conducted among 610 English-speaking US adults in March 2023 using Prolific. Overall, 50% of the sample identified as women, 13% as Black or African American, and 7% as Hispanic or Latino, with a mean age of 46 years. The majority of respondents (65%) agreed with the NIH policy, but racial differences were noted with a higher percentage (28%) of Black participants indicating a decrease in willingness to participate in research studies with the updated policy in place. Participants were more willing to share research data with healthcare providers, yet their preferences for data sharing varied depending on the type of data to be shared and the recipients. Participants were less willing to share sexual health and fertility data with health technology companies (41%) and public repositories (37%) compared to their healthcare providers (75%). The findings highlight the importance of adopting a transparent approach to data sharing that balances protecting patient autonomy with more open data sharing.

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.

Nurses' Roles in Artificial Intelligence Implementation: Results from a Mixed-Methods Study.

We aimed to understand nursing informaticists' perspectives on key challenges, questions, and opportunities for the nursing profession as it prepares for an era of healthcare delivery enriched by artificial intelligence (AI). We found that nursing practice is currently, and will continue to be, directly influenced by AI in healthcare. Educating and training nurses so that they may safely and effectively use AI in their clinical practice and engage in implementation planning and evaluation will help overcome predicted challenges. Defining the key tenets of AI literacy for nurses and re-envisioning nursing models of care in the context of AI-enriched healthcare are important next steps for nursing informaticists. If embraced, AI has the potential to support the existing nursing workforce in the context of major shortages and augment the safe and high-quality care that nurses can deliver.

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.

Reach, Acceptability, and Patient Preferences of a Mobile Health-Based Survey to Assess COVID-19 Vaccine Hesitancy Among Patients Receiving Dialysis.

Rationale & Objective: The majority of patients with kidney failure receiving dialysis own mobile devices, but the use of mobile health (mHealth) technologies to conduct surveys in this population is limited. We assessed the reach and acceptability of a short message service (SMS) text message-based survey that assessed coronavirus disease 2019 (COVID-19) vaccine hesitancy among patients receiving dialysis. Study Design & Exposure: A cross-sectional SMS-based survey conducted in January 2021. Setting & Participants: Patients receiving in-center hemodialysis, peritoneal dialysis, or home hemodialysis in a nonprofit dialysis organization in New York City. Outcomes: (1) Reach of the SMS survey, (2) Acceptability using the 4-item Acceptability of Intervention Measure, and (3) Patient preferences for modes of survey administration. Analytical Approach: We used Fisher exact tests and multivariable logistic regression to assess sociodemographic and clinical predictors of SMS survey response. Qualitative methods were used to analyze open-ended responses capturing patient preferences. Results: Among 1,008 patients, 310 responded to the SMS survey (response rate 31%). In multivariable adjusted analyses, participants who were age 80 years and above (aOR, 0.49; 95% CI, 0.25-0.96) were less likely to respond to the SMS survey compared with those aged 18 to 44 years. Non-Hispanic Black (aOR, 0.58; 95% CI, 0.39-0.86), Hispanic (aOR, 0.31; 95% CI, 0.19-0.51), and Asian or Pacific Islander (aOR, 0.46; 95% CI, 0.28-0.74) individuals were less likely to respond compared with non-Hispanic White participants. Participants residing in census tracts with higher Social Vulnerability Index, indicating greater neighborhood-level social vulnerability, were less likely to respond to the SMS survey (fifth vs first quintile aOR, 0.61; 95% CI, 0.37-0.99). Over 80% of a sample of survey respondents and nonrespondents completely agreed or agreed with the Acceptability of Intervention Measure. Qualitative analysis identified 4 drivers of patient preferences for survey administration: (1) convenience (subtopics: efficiency, multitasking, comfort, and synchronicity); (2) privacy; (3) interpersonal interaction; and (4) accessibility (subtopics: vision, language, and fatigue). Limitations: Generalizability, length of survey. Conclusions: An SMS text message-based survey had moderate reach among patients receiving dialysis and was highly acceptable, but response rates were lower in older (age ≥ 80), non-White individuals and those with greater neighborhood-level social vulnerability. Future research should examine barriers and facilitators to mHealth among patients receiving dialysis to ensure equitable implementation of mHealth-based technologies.

We conducted a short message service (SMS) text message-based survey that assessed coronavirus disease 2019 (COVID-19) vaccine hesitancy among patients receiving dialysis in New York City. Overall response rate was 31%, and those with age ≥ 80, non-White individuals, and participants with greater neighborhood-level social vulnerability were less likely to respond to the survey. Over 80% of participants found SMS-based surveys to be highly acceptable. Qualitative analysis showed that participants cared about the convenience, privacy, interpersonal interaction, and accessibility of surveys. Our results suggest that SMS text message surveys are a promising strategy to collect patient-reported data among patients receiving dialysis.

Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.

The electronic health record contains valuable patient data and offers opportunities to administer and analyze patients' individual needs longitudinally. However, most information in the electronic health record is currently stored in unstructured text notations. Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, interpret, and generate human language, can be used to delve into unstructured text data to uncover valuable insights and knowledge. This article discusses different types of NLP, the potential of NLP for cardiovascular nursing, and how to get started with NLP as a clinician.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

Characterizing atrial fibrillation symptom improvement following de novo catheter ablation.

AIMS: Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but AF symptom resolution is not well characterized. The study objective was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms. METHODS AND RESULTS: We conducted a retrospective cohort study using EHRs of patients with AF (n = 1293), undergoing ablation in a large, urban health system from 2010 to 2020. We extracted symptom data from clinical notes using a natural language processing algorithm (F score: 0.81). We used Cochran's Q tests with post-hoc McNemar's tests to determine differences in symptom prevalence pre- and post-ablation. We used logistic regression models to estimate the adjusted odds of symptom resolution by personal or clinical characteristics at 6 and 12 months post-ablation. In fully adjusted models, at 12 months post-ablation patients, patients with heart failure had significantly lower odds of dyspnoea resolution [odds ratio (OR) 0.38, 95% confidence interval (CI) 0.25-0.57], oedema resolution (OR 0.37, 95% CI 0.25-0.56), and fatigue resolution (OR 0.54, 95% CI 0.34-0.85), but higher odds of palpitations resolution (OR 1.90, 95% CI 1.25-2.89) compared with those without heart failure. Age 65 and older, female sex, Black or African American race, smoking history, and antiarrhythmic use were also associated with lower odds of resolution of specific symptoms at 6 and 12 months. CONCLUSION: The post-ablation symptom patterns are heterogeneous. Findings warrant confirmation with larger, more representative data sets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

OBJECTIVES: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes). MATERIALS AND METHODS: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. RESULTS: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). DISCUSSION AND CONCLUSION: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Perceptions of patient-reported outcome data access and sharing among patients with heart failure: ethical implications for research.

AIMS: In the face of growing expectations for data transparency and patient engagement in care, we evaluated preferences for patient-reported outcome (PRO) data access and sharing among patients with heart failure (HF) using an ethical framework. METHODS AND RESULTS: We conducted qualitative interviews with a purposive sample of patients with HF who participated in a larger 8-week study that involved the collection and return of PROs using a web-based interface. Guided by an ethical framework, patients were asked questions about their preferences for having PRO data returned to them and shared with other groups. Interview transcripts were coded by three study team members using directed content analysis. A total of 22 participants participated in semi-structured interviews. Participants were mostly male (73%), White (68%) with a mean age of 72. Themes were grouped into priorities, benefits, and barriers to data access and sharing. Priorities included ensuring anonymity when data are shared, transparency with intentions of data use, and having access to all collected data. Benefits included: using data as a communication prompt to discuss health with clinicians and using data to support self-management. Barriers included: challenges with interpreting returned results, and potential loss of benefits and anonymity when sharing data. CONCLUSION: Our interviews with HF patients highlight opportunities for researchers to return and share data through an ethical lens, by ensuring privacy and transparency with intentions of data use, returning collected data in comprehensible formats, and meeting individual expectations for data sharing.

Health literacy, numeracy, graph literacy, and digital literacy: An overview of definitions, evaluation methods, and best practices.

Health literacy is an important skill for people receiving care. Those with limited literacy face disparities in their care and health outcomes when strategies for addressing literacy are not used when delivering health information. In this article, we introduce the importance of considering health literacy, defining it and related concepts including numeracy, graph literacy, and digital literacy, and discuss open questions about measuring health literacy in clinical care. Finally, we present best practices, including assuming "universal precautions," carefully considering wording, leveraging visualizations, recognizing cultural differences in interpretation, guidance on pilot testing, and considering digital literacy when developing electronic materials.

Characterising symptom clusters in patients with atrial fibrillation undergoing catheter ablation.

OBJECTIVE: This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters. METHODS: We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward's hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher's exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status. RESULTS: A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients. CONCLUSIONS: We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients' symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

Mobile Integrated Health Interventions for Older Adults: A Systematic Review.

Background and Objectives: Mobile integrated health (MIH) interventions have not been well described in older adult populations. The objective of this systematic review was to evaluate the characteristics and effectiveness of MIH programs on health-related outcomes among older adults. Research Design and Methods: We searched Ovid MEDLINE, Ovid EMBASE, CINAHL, AgeLine, Social Work Abstracts, and The Cochrane Library through June 2021 for randomized controlled trials or cohort studies evaluating MIH among adults aged 65 and older in the general community. Studies were screened for eligibility against predefined inclusion/exclusion criteria. Using at least 2 independent reviewers, quality was appraised using the Downs and Black checklist and study characteristics and findings were synthesized and evaluated for potential bias. Results: Screening of 2,160 records identified 15 studies. The mean age of participants was 67 years. The MIH interventions varied in their focus, community paramedic training, types of assessments and interventions delivered, physician oversight, use of telemedicine, and post-visit follow-up. Studies reported significant reductions in emergency call volume (5 studies) and immediate emergency department (ED) transports (3 studies). The 3 studies examining subsequent ED visits and 4 studies examining readmission rates reported mixed results. Studies reported low adverse event rates (5 studies), high patient and provider satisfaction (5 studies), and costs equivalent to or less than usual paramedic care (3 studies). Discussion and Implications: There is wide variability in MIH provider training, program coordination, and quality-based metrics, creating heterogeneity that make definitive conclusions challenging. Nonetheless, studies suggest MIH reduces emergency call volume and ED transport rates while improving patient experience and reducing overall health care costs.

'Replace uncertainty with information': shared decision-making and decision quality surrounding catheter ablation for atrial fibrillation.

AIMS: As a first step in developing a decision aid to support shared decision-making (SDM) for patients with atrial fibrillation (AF) to evaluate treatment options for rhythm and symptom control, we aimed to measure decision quality and describe decision-making processes among patients and clinicians involved in decision-making around catheter ablation for AF. METHODS AND RESULTS: We conducted a cross-sectional, mixed-methods study guided by an SDM model outlining decision antecedents, processes, and outcomes. Patients and clinicians completed semi-structured interviews about decision-making around ablation, feelings of decision conflict and regret, and preferences for the content, delivery, and format of a hypothetical decision aid for ablation. Patients also completed surveys about AF symptoms and aspects of decision quality. Fifteen patients (mean age 71.1 ± 8.6 years; 27% female) and five clinicians were recruited. For most patients, decisional conflict and regret were low, but they also reported low levels of information and agency in the decision-making process. Most clinicians report routinely providing patients with information and encouraging engagement during consultations. Patients reported preferences for an interactive, web-based decision aid that clearly presents evidence regarding outcomes using data, visualizations, videos, and personalized risk assessments, and is available in multiple languages. CONCLUSION: Disconnects between clinician efforts to provide information and bolster agency and patient experiences of decision-making suggest decision aids may be needed to improve decision quality in practice. Reported experiences with current decision-making practices and preferences for decision aid content, format, and delivery can support the user-centred design and development of a decision aid.

Returning study results to research participants: Data access, format, and sharing preferences.

INTRODUCTION: Research participants have a growing expectation for transparency with their collected information; however, there is little guidance on participant preferences for receiving health information and how researchers should return this information to participants. METHODS: We conducted a cross-sectional online survey with a representative sample of 502 participants in the United States. Participants were asked about their preferences for receiving, sharing, and the formatting of health information collected for research purposes. RESULTS: Most participants wanted their health information returned (84 %) to use it for their own knowledge and to manage their own health. Email was the most preferred format for receiving health data (67 %), followed by online website (44 %), and/or paper copy (32 %). Data format preferences varied by age, education, financial resources, subjective numeracy, and health literacy. Around one third of Generation Z (25 %), Millennials (30 %), and Generation X (29 %) participants preferred to receive their health information with a mobile app. In contrast, very few Baby Boomers (12 %) and none from the Silent Generation preferred the mobile app format. Having a paper copy of the data was preferred by 38 % of participants without a college degree compared to those with a college degree. Preferences were highest for sharing all health information with doctors and nurses (77 %), and some information with friends and family (66 %). CONCLUSION: Study findings support returning research information to participants in multiple formats, including email, online websites, and paper copy. Preferences for whom to share information with varied by stakeholders and by sociodemographic characteristics. Researchers should offer multiple formats to participants and tailor data sharing options to participants' preferences. Future research should further explore combinations of individual characteristics that may further influence data sharing and format preferences.

Detecting early physiologic changes through cardiac implantable electronic device data among patients with COVID-19.

Background: Cardiac implantable electronic devices (CIEDs) may enable early identification of COVID-19 to facilitate timelier intervention. Objective: To characterize early physiologic changes associated with the onset of acute COVID-19 infection, as well as during and after acute infection, among patients with CIEDs. Methods: CIED sensor data from March 2020 to February 2021 from 286 patients with a CIED were linked to clinical data from electronic health records. Three cohorts were created: known COVID-positive (n = 20), known COVID-negative (n = 166), and a COVID-untested control group (n = 100) included to account for testing bias. Associations between changes in CIED sensors from baseline (including HeartLogic index, a composite index predicting worsening heart failure) and COVID-19 status were evaluated using logistic regression models, Wilcoxon signed rank tests, and Mann-Whitney U tests. Results: Significant differences existed between the cohorts by race, ethnicity, CIED device type, and medical admissions. Several sensors changed earlier for COVID-positive vs COVID-negative patients: HeartLogic index (mean 16.4 vs 9.2 days [P = .08]), respiratory rate (mean 8.5 vs 3.9 days [P = .01], and activity (mean 8.2 vs 3.5 days [P = .008]). Respiratory rate during the 7 days before testing significantly predicted a positive vs negative COVID-19 test, adjusting for age, sex, race, and device type (odds ratio 2.31 [95% confidence interval 1.33-5.13]). Conclusion: Physiologic data from CIEDs could signal early signs of infection that precede clinical symptoms, which may be used to support early detection of infection to prevent decompensation in this at-risk population.