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The ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagnosis after responding to a set of probes about their symptoms. Although the potential for their utility is great, little is known about such tools’ actual use and effects. We aimed to understand who uses a web-based artificial intelligence–powered symptom checker and its purposes, how they evaluate the experience of the web-based interview and quality of the information, what they intend to do with the recommendation, and predictors of future use. Cross-sectional survey of web-based health information seekers following the completion of a symptom checker visit (N=2437). Measures of comprehensibility, confidence, usefulness, health-related anxiety, empowerment, and intention to use in the future were assessed. ANOVAs and the Wilcoxon rank sum test examined mean outcome differences in racial, ethnic, and sex groups. The relationship between perceptions of the symptom checker and intention to follow recommended actions was assessed using multilevel logistic regression. Buoy users were well-educated (1384/1704, 81.22% college or higher), primarily White (1227/1693, 72.47%), and female (2069/2437, 84.89%). Most had insurance (1449/1630, 88.89%), a regular health care provider (1307/1709, 76.48%), and reported good health (1000/1703, 58.72%). Three types of symptoms—pain (855/2437, 35.08%), gynecological issues (293/2437, 12.02%), and masses or lumps (204/2437, 8.37%)—accounted for almost half (1352/2437, 55.48%) of site visits. Buoy’s top three primary recommendations split across less-serious triage categories: primary care physician in 2 weeks (754/2141, 35.22%), self-treatment (452/2141, 21.11%), and primary care in 1 to 2 days (373/2141, 17.42%). Common diagnoses were musculoskeletal (303/2437, 12.43%), gynecological (304/2437, 12.47%) and skin conditions (297/2437, 12.19%), and infectious diseases (300/2437, 12.31%). Users generally reported high confidence in Buoy, found it useful and easy to understand, and said that Buoy made them feel less anxious and more empowered to seek medical help. Users for whom Buoy recommended “Waiting/Watching” or “Self-Treatment” had strongest intentions to comply, whereas those advised to seek primary care had weaker intentions. Compared with White users, Latino and Black users had significantly more confidence in Buoy (P<.05), and the former also found it significantly more useful (P<.05). Latino (odds ratio 1.96, 95% CI 1.22-3.25) and Black (odds ratio 2.37, 95% CI 1.57-3.66) users also had stronger intentions to discuss recommendations with a provider than White users.

OpenAI's ChatGPT is a source of advanced online health information (OHI) that may be integrated into individuals' health information-seeking routines. However, concerns have been raised about its factual accuracy and impact on health outcomes. To forecast implications for medical practice and public health, more information is needed on who uses the tool, how often, and for what. This study aims to characterize the reasons for and types of ChatGPT OHI use and describe the users most likely to engage with the platform. In this cross-sectional survey, patients received invitations to participate via the ResearchMatch platform, a nonprofit affiliate of the National Institutes of Health. A web-based survey measured demographic characteristics, use of ChatGPT and other sources of OHI, experience characterization, and resultant health behaviors. Descriptive statistics were used to summarize the data. Both 2-tailed t tests and Pearson chi-square tests were used to compare users of ChatGPT OHI to nonusers. Of 2406 respondents, 21.5% (n=517) respondents reported using ChatGPT for OHI. ChatGPT users were younger than nonusers (32.8 vs 39.1 years, P<.001) with lower advanced degree attainment (BA or higher; 49.9% vs 67%, P<.001) and greater use of transient health care (ED and urgent care; P<.001). ChatGPT users were more avid consumers of general non-ChatGPT OHI (percentage of weekly or greater OHI seeking frequency in past 6 months, 28.2% vs 22.8%, P<.001). Around 39.3% (n=206) respondents endorsed using the platform for OHI 2-3 times weekly or more, and most sought the tool to determine if a consultation was required (47.4%, n=245) or to explore alternative treatment (46.2%, n=239). Use characterization was favorable as many believed ChatGPT to be just as or more useful than other OHIs (87.7%, n=429) and their doctor (81%, n=407). About one-third of respondents requested a referral (35.6%, n=184) or changed medications (31%, n=160) based on the information received from ChatGPT. As many users reported skepticism regarding the ChatGPT output (67.9%, n=336), most turned to their physicians (67.5%, n=349). This study underscores the significant role of AI-generated OHI in shaping health-seeking behaviors and the potential evolution of patient-provider interactions. Given the proclivity of these users to enact health behavior changes based on AI-generated content, there is an opportunity for physicians to guide ChatGPT OHI users on an informed and examined use of the technology.

Background Inappropriate antibiotic use is implicated in antibiotic resistance and resultant morbidity and mortality. Overuse is particularly prevalent for outpatient respiratory infections, and perceived patient expectations likely contribute. Thus, various educational programs have been implemented to educate the public. Methods We systematically identified public-directed interventions to promote antibiotic awareness in the United States. PubMed, Google Scholar, Embase, CINAHL, and Scopus were queried for articles published from January 1996 through January 2016. Two investigators independently assessed titles and abstracts of retrieved articles for subsequent full-text review. References of selected articles and three review articles were likewise screened for inclusion. Identified educational interventions were coded for target audience, content, distribution site, communication method, and major outcomes. Results Our search yielded 1,106 articles; 34 met inclusion criteria. Due to overlap in interventions studied, 29 distinct educational interventions were identified. Messages were primarily delivered in outpatient clinics ( N  = 24, 83%) and community sites ( N  = 12, 41%). The majority included clinician education. Antibiotic prescription rates were assessed for 22 interventions (76%). Patient knowledge, attitudes, and beliefs (KAB) were assessed for 10 interventions (34%). Similar rates of success between antibiotic prescription rates and patient KAB were reported (73 and 70%, respectively). Patient interventions that did not include clinician education were successful to increase KAB but were not shown to decrease antibiotic prescribing. Three interventions targeted reductions in Streptococcus pneumoniae resistance; none were successful. Conclusions Messaging programs varied in their designs, and many were multifaceted in their approach. These interventions can change patient perspectives regarding antibiotic use, though it is unclear if clinician education is also necessary to reduce antibiotic prescribing. Further investigations are needed to determine the relative influence of interventions focusing on patients and physicians and to determine whether these changes can influence rates of antibiotic resistance long-term.

Evidence-based medicine is the application of scientific evidence to clinical practice. This article discusses the difficulties of applying global evidence (\"average effects\" measured as population means) to local problems (individual patients or groups who might depart from the population average). It argues that the benefit or harm of most treatments in clinical trials can be misleading and fail to reveal the potentially complex mixture of substantial benefits for some, little benefit for many, and harm for a few. Heterogeneity of treatment effects reflects patient diversity in risk of disease, responsiveness to treatment, vulnerability to adverse effects, and utility for different outcomes. Recognizing these factors, researchers can design studies that better characterize who will benefit from medical treatments, and clinicians and policymakers can make better use of the results.

Background A concern before 2020, physician burnout worsened during the COVID-19 pandemic. Little empirical data are available on pandemic workplace support interventions or their influence on burnout. We surveyed a national sample of frontline physicians on burnout and workplace support during the pandemic. Methods We surveyed a stratified random sample of 12,833 US physicians most likely to care for adult COVID-19 patients from the comprehensive AMA Physician Professional Data ™ file. The sample included 6722 primary care physicians (3331 family physicians, 3391 internists), 880 hospitalists, 1783 critical care physicians (894 critical care physicians, 889 pulmonary intensivists), 2548 emergency medicine physicians, and 900 infectious disease physicians. The emailed survey elicited physicians’ perceptions of organizational interventions to provide workplace support and/or to address burnout. Burnout was assessed with the Professional Fulfillment Index Burnout Composite scale (PFI-BC). Proportional specialty representation and response bias were addressed by survey weighting. Logistic regression assessed the association of physician characteristics and workplace interventions with burnout. Results After weighting, respondents were representative of the total sample. Overall physician burnout was 45.4%, significantly higher than in our previous survey. Open-ended responses mentioned that staffing shortages (physician, nursing, and other staff) combined with the increased volume, complexity, and acuity of patients during the pandemic increased job demands. The most frequent workplace support interventions were direct pandemic control measures (increased access to personal protective equipment, 70.0%); improved telehealth functionality (43.4%); and individual resiliency tools (yoga, meditation, 30.7%). Respondents placed highest priority on workplace interventions to increase financial support and increase nursing and clinician staffing. Factors significantly associated with lower odds of burnout were practicing critical care (compared with emergency medicine) OR 0.33 (95% CI 0.12 – 0.93), improved telehealth functionality OR 0.47 (95% CI 0.23 – 0.97) and being in practice for 11 years or longer OR 0.44 (95% CI 0.19–0.99). Conclusions Burnout across frontline specialties increased during the pandemic. Physician respondents focused on inadequate staffing in the context of caring for more and sicker patients, combined with the lack of administrative efforts to mitigate problems. Burnout mitigation requires system-level interventions beyond individual-focused stress reduction programs to improve staffing, increase compensation, and build effective teams.

Background Specialty-specific data on career satisfaction may be useful for understanding physician workforce trends and for counseling medical students about career options. Methods We analyzed cross-sectional data from 6,590 physicians (response rate, 53%) in Round 4 (2004-2005) of the Community Tracking Study Physician Survey. The dependent variable ranged from +1 to -1 and measured satisfaction and dissatisfaction with career. Forty-two specialties were analyzed with survey-adjusted linear regressions Results After adjusting for physician, practice, and community characteristics, the following specialties had significantly higher satisfaction levels than family medicine: pediatric emergency medicine (regression coefficient = 0.349); geriatric medicine (0.323); other pediatric subspecialties (0.270); neonatal/prenatal medicine (0.266); internal medicine and pediatrics (combined practice) (0.250); pediatrics (0.250); dermatology (0.249);and child and adolescent psychiatry (0.203). The following specialties had significantly lower satisfaction levels than family medicine: neurological surgery (-0.707); pulmonary critical care medicine (-0.273); nephrology (-0.206); and obstetrics and gynecology (-0.188). We also found satisfaction was significantly and positively related to income and employment in a medical school but negatively associated with more than 50 work-hours per-week, being a full-owner of the practice, greater reliance on managed care revenue, and uncontrollable lifestyle. We observed no statistically significant gender differences and no differences between African-Americans and whites. Conclusion Career satisfaction varied across specialties. A number of stakeholders will likely be interested in these findings including physicians in specialties that rank high and low and students contemplating specialty. Our findings regarding \"less satisfied\" specialties should elicit concern from residency directors and policy makers since they appear to be in critical areas of medicine.

All the opinions in this article are those of the authors and should not be construed to reflect, in any way, those of the Department of Veterans Affairs. Our study purpose was to assess the predictive validity of reviewer quality ratings and editorial decisions in a general medicine journal. Submissions to the Journal of General Internal Medicine (JGIM) between July 2004 and June 2005 were included. We abstracted JGIM peer review quality ratings, verified the publication status of all articles and calculated an impact factor for published articles (Rw) by dividing the 3-year citation rate by the average for this group of papers; an Rw>1 indicates a greater than average impact. Of 507 submissions, 128 (25%) were published in JGIM, 331 rejected (128 with review) and 48 were either not resubmitted after revision was requested or were withdrawn by the author. Of 331 rejections, 243 were published elsewhere. Articles published in JGIM had a higher citation rate than those published elsewhere (Rw: 1.6 vs. 1.1, p = 0.002). Reviewer quality ratings of article quality had good internal consistency and reviewer recommendations markedly influenced publication decisions. There was no quality rating cutpoint that accurately distinguished high from low impact articles. There was a stepwise increase in Rw for articles rejected without review, rejected after review or accepted by JGIM (Rw 0.60 vs. 0.87 vs. 1.56, p<0.0005). However, there was low agreement between reviewers for quality ratings and publication recommendations. The editorial publication decision accurately discriminated high and low impact articles in 68% of submissions. We found evidence of better accuracy with a greater number of reviewers. The peer review process largely succeeds in selecting high impact articles and dispatching lower impact ones, but the process is far from perfect. While the inter-rater reliability between individual reviewers is low, the accuracy of sorting is improved with a greater number of reviewers.

Editorial peer review is universally used but little studied. We examined the relationship between external reviewers' recommendations and the editorial outcome of manuscripts undergoing external peer-review at the Journal of General Internal Medicine (JGIM). We examined reviewer recommendations and editors' decisions at JGIM between 2004 and 2008. For manuscripts undergoing peer review, we calculated chance-corrected agreement among reviewers on recommendations to reject versus accept or revise. Using mixed effects logistic regression models, we estimated intra-class correlation coefficients (ICC) at the reviewer and manuscript level. Finally, we examined the probability of rejection in relation to reviewer agreement and disagreement. The 2264 manuscripts sent for external review during the study period received 5881 reviews provided by 2916 reviewers; 28% of reviews recommended rejection. Chance corrected agreement (kappa statistic) on rejection among reviewers was 0.11 (p<.01). In mixed effects models adjusting for study year and manuscript type, the reviewer-level ICC was 0.23 (95% confidence interval [CI], 0.19-0.29) and the manuscript-level ICC was 0.17 (95% CI, 0.12-0.22). The editors' overall rejection rate was 48%: 88% when all reviewers for a manuscript agreed on rejection (7% of manuscripts) and 20% when all reviewers agreed that the manuscript should not be rejected (48% of manuscripts) (p<0.01). Reviewers at JGIM agreed on recommendations to reject vs. accept/revise at levels barely beyond chance, yet editors placed considerable weight on reviewers' recommendations. Efforts are needed to improve the reliability of the peer-review process while helping editors understand the limitations of reviewers' recommendations.

Background The COVID-19 pandemic spurred publication of a rapid proliferation of studies on potential therapeutic agents. While important for the advancement of clinical care, pressure to collect, analyze, and report data in an expedited manner could potentially increase the rate of important errors, some of which would be captured in published errata. We hypothesized that COVID-19 therapeutic studies published in the early years of the pandemic would be associated with a high rate of published errata and that, within these errata, there would be a high prevalence of serious errors. Methods We performed a review of published errata associated with empirical studies of COVID-19 treatments. Errata were identified via a MEDLINE and Embase search spanning January 2020 through September 2022. Errors located within each published erratum were characterized by location within publication, error type, and error seriousness. Results Of 47 studies on COVID-19 treatments with published errata, 18 met inclusion criteria. Median time from publication of the original article to publication of the associated erratum was 76 days (range, 12–511 days). A majority of errata addressed issues with author attribution or conflict of interest disclosures (39.5%) or numerical results (25.6%). Only one erratum contained a serious error: a typographical error which could have misled readers into believing that the treatment in question had serious adverse effects when in fact it did not. Conclusions Despite accelerated publication times, we found among studies of COVID-19 treatments the majority of errata (17/18) reported minor errors that did not lead to misinterpretation of the study results. Retractions, an indicator of scientific misdirection even more concerning than errata, were beyond the scope of this review.

Background Multiple myeloma (MM) is an incurable cancer with complex treatment options. Trusting patient–clinician relationships are essential to promote effective shared decision‐making that aligns best clinical practices with patient values and preferences. This study sought to shed light on the development of trust between MM patients and clinicians. Methods Nineteen individual semi‐structured interviews were conducted with MM patients within 2 years of initial diagnosis or relapse for this qualitative study. Interviews were recorded and transcripts were coded thematically. Results We identified three main themes: (1) externally validated trust describes patients’ predisposition to trust or distrust clinicians based on factors outside of patient–clinician interactions; (2) internally validated trust describes how patients develop trust based on interactions with specific clinicians. Internally validated trust is driven primarily by clinician communication practices that demonstrate competence, responsiveness, listening, honesty, and empathy; and (3) trust in relation to shared decision‐making describes how patients relate the feeling of trust, or lack thereof, to the process of shared decision‐making. Conclusion Many factors contribute to the development of trust between MM patients and clinicians. While some are outside of clinicians’ control, others derive from clinician behaviors and interpersonal communication skills. These findings suggest the possibility that trust can be enhanced through communication training or shared decision‐making tools that emphasize relational communication. Given the important role trust plays in shared decision‐making, clinicians working with MM patients should prioritize establishing positive, trusting relationships. Patient–clinician trust is influenced by both external factors and clinician behaviors in the context of multiple myeloma. Trust between patients with myeloma and their clinicians is an important aspect of shared decision‐making.