User Information Sharing and Hospital Website Privacy Policies.

Importance: Hospital websites frequently use tracking technologies that transfer user information to third parties. It is not known whether hospital websites include privacy policies that disclose relevant details regarding tracking. Objective: To determine whether hospital websites have accessible privacy policies and whether those policies contain key information related to third-party tracking. Design, Setting, and Participants: In this cross-sectional content analysis of website privacy policies of a nationally representative sample of nonfederal acute care hospitals, hospital websites were first measured to determine whether they included tracking technologies that transferred user information to third parties. Hospital website privacy policies were then identified using standardized searches. Policies were assessed for length and readability. Policy content was analyzed using a data abstraction form. Tracking measurement and privacy policy retrieval and analysis took place from November 2023 to January 2024. The prevalence of privacy policy characteristics was analyzed using standard descriptive statistics. Main Outcomes and Measures: The primary study outcome was the availability of a website privacy policy. Secondary outcomes were the length and readability of privacy policies and the inclusion of privacy policy content addressing user information collected by the website, potential uses of user information, third-party recipients of user information, and user rights regarding tracking and information collection. Results: Of 100 hospital websites, 96 (96.0%; 95% CI, 90.1%-98.9%) transferred user information to third parties. Privacy policies were found on 71 websites (71.0%; 95% CI, 61.6%-79.4%). Policies were a mean length of 2527 words (95% CI, 2058-2997 words) and were written at a mean grade level of 13.7 (95% CI, 13.4-14.1). Among 71 privacy policies, 69 (97.2%; 95% CI, 91.4%-99.5%) addressed types of user information automatically collected by the website, 70 (98.6%; 95% CI, 93.8%-99.9%) addressed how collected information would be used, 66 (93.0%; 95% CI, 85.3%-97.5%) addressed categories of third-party recipients of user information, and 40 (56.3%; 95% CI, 44.5%-67.7%) named specific third-party companies or services receiving user information. Conclusions and Relevance: In this cross-sectional study of hospital website privacy policies, a substantial number of hospital websites did not present users with adequate information about the privacy implications of website use, either because they lacked a privacy policy or had a privacy policy that contained limited content about third-party recipients of user information.

Patient perceptions of behavioral flags in the emergency department: A qualitative analysis.

BACKGROUND: To combat increasing levels of violence in the emergency department (ED), hospitals have implemented several safety measures, including behavioral flags. These electronic health record (EHR)-based notifications alert future clinicians of past incidents of potentially threatening patient behavior, but observed racial disparities in their placement may unintentionally introduce bias in patient care. Little is known about how patients perceive these flags and the disparities that have been found in their placement. OBJECTIVE: This study aims to investigate patient perceptions and perceived benefits and harms associated with the use of behavioral flags. METHODS: Twenty-five semistructured qualitative interviews were conducted with a convenience sample of patients in the ED of a large, urban, academic medical center who did not have a behavioral flag in their EHR. Interviews lasted 10-20 min and were recorded then transcribed. Thematic analysis of deidentified transcripts took place in NVivo 20 software (QSR International) using a general inductive approach. RESULTS: Participant perceptions of behavioral flags varied, with both positive and negative opinions being shared. Five key themes, each with subthemes, were identified: (1) benefits of behavioral flags, (2) concerns and potential harms of flags, (3) transparency with patients, (4) equity, and (5) ideas for improvement. CONCLUSIONS: Patient perspectives on the use of behavioral flags in the ED vary. While many saw flags as a helpful tool to mitigate violence, concerns around negative impacts on care, transparency, and equity were also shared. Insights from this stakeholder perspective may allow for health systems to make flags more effective without compromising equity or patient ideals.

Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).

OBJECTIVE: To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates. DATA SOURCES: We used secondary data on ED visits from the National Hospital Ambulatory Medical Survey (NHAMCS), from 2016 to 2020. STUDY DESIGN: We established baseline performance metrics with seven published algorithms consisting of International Classification of Disease, Tenth Revision codes used to identify low acuity ED visits. We then trained logistic regression, random forest, and gradient boosting (XGBoost) models to predict low acuity ED visits. Each model was trained on five different covariate sets of demographic and clinical data. Model performance was compared using a separate validation dataset. The primary performance metric was the probability that a visit identified by an algorithm as low acuity did not experience significant testing, treatment, or disposition (positive predictive value, PPV). Subgroup analyses assessed model performance across age, sex, and race/ethnicity. DATA COLLECTION: We used 2016-2019 NHAMCS data as the training set and 2020 NHAMCS data for validation. PRINCIPAL FINDINGS: The training and validation data consisted of 53,074 and 9542 observations, respectively. Among seven rule-based algorithms, the highest-performing had a PPV of 0.35 (95% CI [0.33, 0.36]). All model-based algorithms outperformed existing algorithms, with the least effective-random forest using only age and sex-improving PPV by 26% (up to 0.44; 95% CI [0.40, 0.48]). Logistic regression and XGBoost trained on all variables improved PPV by 83% (to 0.64; 95% CI [0.62, 0.66]). Multivariable models also demonstrated higher PPV across all three demographic subgroups. CONCLUSIONS: Machine learning models substantially outperform existing algorithms based on ICD codes in predicting low acuity ED visits. Variations in model performance across demographic groups highlight the need for further research to ensure their applicability and fairness across diverse populations.

Medical journals and advertiser tracking-Consequences for patients, clinicians, and editors.

Medical journal websites frequently contain tracking code that transfers data about journal readers to third parties. These data give drug, device, and other medical product companies a potentially powerful resource for targeting advertisements and other marketing materials to journal readers based on unique attributes and medical interests that can be inferred from the articles they read. Thus, while editors may strictly regulate the content of advertisements that such companies place in their journals' pages, they simultaneously provide those companies with the means to target readers in other forums, possibly in ways that subvert editorial guidelines. We examine the implications of third-party tracking on medical journal webpages, and recommend actions that publishers, editors, and academic societies can take to curb it.

The Scope and Legal Implications of Tracking Technologies on Hospital Websites.

This Viewpoint analyzes the scope and legal implications of tracking on hospital websites, including potential HIPAA and state privacy law violations, and suggests that hospitals limit such tracking.

Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs.

COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients' preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or for patients who do not desire tech-first approaches.

Widespread Third-Party Tracking On Hospital Websites Poses Privacy Risks For Patients And Legal Liability For Hospitals.

Computer code that transfers data to third parties (third-party tracking) is common across the web and is subject to few federal privacy regulations. We determined the presence of potentially privacy-compromising data transfers to third parties on a census of US nonfederal acute care hospital websites, and we used descriptive statistics and regression analyses to determine the hospital characteristics associated with a greater number of third-party data transfers. We found that third-party tracking is present on 98.6 percent of hospital websites, including transfers to large technology companies, social media companies, advertising firms, and data brokers. Hospitals in health systems, hospitals with a medical school affiliation, and hospitals serving more urban patient populations all exposed visitors to higher levels of tracking in adjusted analyses. By including third-party tracking code on their websites, hospitals are facilitating the profiling of their patients by third parties. These practices can lead to dignitary harms, which occur when third parties gain access to sensitive health information that a person would not wish to share. These practices may also lead to increased health-related advertising that targets patients, as well as to legal liability for hospitals.

Qualitative Perspectives of Emergency Nurses on Electronic Health Record Behavioral Flags to Promote Workplace Safety.

Importance: Emergency nurses experience high levels of workplace violence during patient interactions. Little is known about the efficacy of behavioral flags, which are notifications embedded within electronic health records (EHRs) as a tool to promote clinician safety. Objective: To explore the perspectives of emergency nurses on EHR behavioral flags, workplace safety, and patient care. Design, Setting, and Participants: In this qualitative study, semistructured interviews were conducted with emergency nurses at an academic, urban emergency department (ED) between February 8 and March 25, 2022. Interviews were audio recorded, transcribed, and analyzed using thematic analysis. Data analysis was performed from April 2 to 13, 2022. Main Outcomes and Measures: Themes and subthemes of nursing perspectives on EHR behavioral flags were identified. Results: This study included 25 registered emergency nurses at a large academic health system, with a mean (SD) tenure of 5 (6) years in the ED. Their mean (SD) age was 33 (7) years; 19 were women (76%) and 6 were men (24%). Participants self-reported their race as Asian (3 [12%]), Black (3 [12%]), White (15 [60%]), or multiple races (2 [8%]); 3 participants (12%) self-reported their ethnicity as Hispanic or Latinx. Five themes (with subthemes) were identified: (1) benefits of flags (useful advisory; prevents violence; engenders compassion), (2) issues with flags (administrative and process issues; unhelpful; unenforceable; bias; outdated), (3) patient transparency (patient accountability; damages patient-clinician relationship), (4) system improvements (process; built environment; human resources; zero-tolerance policies), and (5) difficulties of working in the ED (harassment and abuse; unmet mental health needs of patients; COVID-19-related strain and burnout). Conclusions and Relevance: In this qualitative study, nursing perspectives on the utility and importance of EHR behavioral flags varied. For many, flags served as an important forewarning to approach patient interactions with more caution or use safety skills. However, nurses were skeptical of the ability of flags to prevent violence from occurring and noted concern for the unintended consequences of introducing bias into patient care. These findings suggest that changes to the deployment and utilization of flags, in concert with other safety interventions, are needed to create a safer work environment and mitigate bias.

Prevalence of Behavioral Flags in the Electronic Health Record Among Black and White Patients Visiting the Emergency Department.

Importance: Behavioral flags in the electronic health record (EHR) are designed to alert clinicians of potentially unsafe or aggressive patients. These flags may introduce bias, and understanding how they are used is important to ensure equitable care. Objective: To investigate the incidence of behavioral flags and assess whether there were differences between Black and White patients and whether the flags were associated with differences in emergency department (ED) clinical care. Design, Setting, and Participants: This was a retrospective cohort study of EHR data of adult patients (aged ≥18 years) from 3 Philadelphia, Pennsylvania, EDs within a single health system between January 1, 2017, and December 31, 2019. Secondary analyses excluded patients with sickle cell disease and high ED care utilization. Data were analyzed from February 1 to April 4, 2022. Main Outcomes and Measures: The primary outcome of interest was the presence of an EHR behavioral flag. Secondary measures included variation of flags across sex, race, age, insurance status, triage status, ED clinical care metrics (eg, laboratory, medication, and radiology orders), ED disposition (discharge, admission, or observation), and length of key intervals during ED care. Results: Participating EDs had 195 601 eligible patients (110 890 [56.7%] female patients; 113 638 Black patients [58.1%]; 81 963 White patients [41.9%]; median [IQR] age, 42 [28-60] years), with 426 858 ED visits. Among these, 683 patients (0.3%) had a behavioral flag notification in the EHR (3.5 flags per 1000 patients), and it was present for 6851 ED visits (16 flagged visits per 1000 visits). Patient differences between those with a flag and those without included male sex (56.1% vs 43.3%), Black race (71.2% vs 56.7%), and insurance status, particularly Medicaid insurance (74.5% vs 36.3%). Flag use varied across sites. Black patients received flags at a rate of 4.0 per 1000 patients, and White patients received flags at a rate of 2.4 per 1000 patients (P < .001). Among patients with a flag, Black patients, compared with White patients, had longer waiting times to be placed in a room (median [IQR] time, 28.0 [10.5-89.4] minutes vs 18.2 [7.2-75.1] minutes; P < .001), longer waiting times to see a clinician (median [IQR] time, 42.1 [18.8-105.5] minutes vs 33.3 [15.3-84.5] minutes; P < .001), and shorter lengths of stay (median [IQR] time, 274 [135-471] minutes vs 305 [154-491] minutes; P = .01). Black patients with a flag underwent fewer laboratory (eg, 2449 Black patients with 0 orders [43.4%] vs 441 White patients with 0 orders [36.7%]; P < .001) and imaging (eg, 3541 Black patients with no imaging [62.7%] vs 675 White patients with no imaging [56.2%]; P < .001) tests compared with White patients with a flag. Conclusions and Relevance: This cohort study found significant differences in ED clinical care metrics, including that flagged patients had longer wait times and were less likely to undergo laboratory testing and imaging, which was amplified in Black patients.

Prevalence of Third-Party Tracking on Abortion Clinic Web Pages.

This cross-sectional study assesses how often third-party domains use tracking data from visitors to abortion clinic web pages.

The combined effects of physical frailty and cognitive impairment on emergency department- versus direct-admission hospitalizations.

BACKGROUND: We aimed to study whether physical frailty and cognitive impairment (CI) increase the risk of recurrent hospitalizations in older adults, independent of comorbidity, and disability. METHODS: Two thousand five hundred forty-nine community-dwelling participants from the National Health and Aging Trends Study (NHATS) with 3 + years of continuous Medicare coverage from linked claims data were included. We used the marginal means/rates recurrent events model to investigate the association of baseline CI (mild CI or dementia) and physical frailty, separately and synergistically, with the number of all-source vs. Emergency Department (ED)-admission vs. direct admission hospitalizations over 2 years. RESULTS: 17.8% of participants had at least one ED-admission hospitalization; 12.7% had at least one direct admission hospitalization. Frailty and CI, modeled separately, were both significantly associated with risk of recurrent all-source (Rate Ratio (RR) = 1.24 for frailty, 1.21 for CI; p < .05) and ED-admission (RR = 1.49 for frailty, 1.41 for CI; p < .05) hospitalizations but not direct admission, adjusting for socio-demographics, obesity, comorbidity and disability. When CI and frailty were examined together, 64.3% had neither (Unimpaired); 28.1% CI only; 3.5% Frailty only; 4.1% CI + Frailty. Compared to those Unimpaired, CI alone and CI + Frailty were predictive of all-source (RR = 1.20, 1.48, p < .05) and ED-admission (RR = 1.36, 2.14, p < .05) hospitalizations, but not direct admission, in our adjusted model. CONCLUSIONS: Older adults with both CI and frailty experienced the highest risk for recurrent ED-admission hospitalizations. Timely recognition of older adults with CI and frailty is needed, paying special attention to managing cognitive impairment to mitigate preventable causes of ED admissions and potentiate alternatives to hospitalization.

Prevalence of Third-party Tracking on Medical Journal Websites.

This cross-sectional study investigates web tracking prevalence and characteristics on medical journal websites.

Addressing Online Health Privacy Risks for Older Adults: A Perspective on Ethical Considerations and Recommendations.

The rise in online health information seeking among older adults promises significant benefits but also presents potentially serious privacy risks. In light of these risks, we argue that ongoing research and advocacy aimed at promoting online health information seeking among older adults must be coupled with efforts to identify and address threats to their online privacy. We first detail how internet users reveal sensitive health information to third parties through seemingly innocuous web browsing. We then describe ethical concerns raised by the inadvertent disclosure of health information, which include the potential for dignitary harms, subjective injuries, online health scams, and discrimination. After reviewing ways in which existing privacy laws fail to meet the needs of older adults, we provide recommendations for individual and collective action to protect the online privacy of older adults.

Algorithms identifying low-acuity emergency department visits: A review and validation study.

OBJECTIVE: To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low-acuity emergency department (ED) visits. DATA SOURCES: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). STUDY DESIGN: We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low-acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low-acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. DATA COLLECTION: We used the 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk. PRINCIPAL FINDINGS: We identified 15 papers with an original list of ICD codes used to identify preventable or low-acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization. CONCLUSIONS: Existing algorithms that identify low-acuity ED visits lack congruence and are imperfect predictors of visit acuity.

Telemedicine catches on: changes in the utilization of telemedicine services during the COVID-19 pandemic.

OBJECTIVES: To determine the degree of telemedicine expansion overall and across patient subpopulations and diagnoses. We hypothesized that telemedicine visits would increase substantially due to the need for continuity of care despite the disruptive effects of COVID-19. STUDY DESIGN: A retrospective study of health insurance claims for telemedicine visits from January 1, 2018, through March 10, 2020 (prepandemic period), and March 11, 2020, through October 31, 2020 (pandemic period). METHODS: We analyzed claims from 1,589,777 telemedicine visits that were submitted to Independence Blue Cross (Independence) from telemedicine-only providers and providers who traditionally deliver care in person. The primary exposure was the combination of individual behavior changes, state stay-at-home orders, and the Independence expansion of billing policies for telemedicine. The comparison population consisted of telemedicine visits in the prepandemic period. RESULTS: Telemedicine increased rapidly from a mean (SD) of 773 (155) weekly visits in prepandemic 2020 to 45,632 (19,937) weekly visits in the pandemic period. During the pandemic period, a greater proportion of telemedicine users were older, had Medicare Advantage insurance plans, had existing chronic conditions, or resided in predominantly non-Hispanic Black or African American Census tracts compared with during the prepandemic period. A significant increase in telemedicine claims containing a mental health-related diagnosis was observed. CONCLUSIONS: Telemedicine expanded rapidly during the COVID-19 pandemic across a broad range of clinical conditions and demographics. Although levels declined later in 2020, telemedicine utilization remained markedly higher than 2019 and 2018 levels. Trends suggest that telemedicine will likely play a key role in postpandemic care delivery.

Evaluation and disposition of older adults presenting to the emergency department with abdominal pain.

BACKGROUND: Abdominal pain is the most common chief complaint in US emergency departments (EDs) among patients over 65, who are at high risk of mortality or incident disability after the ED encounter. We sought to characterize the evaluation, management, and disposition of older adults who present to the ED with abdominal pain. METHODS: We performed a survey-weighted analysis of the National Hospital Ambulatory Medical Care Survey (NHAMCS), comparing older adults with a chief complaint of abdominal pain to those without. Visits from 2013 to 2017 to nationally representative EDs were included. We analyzed 81,509 visits to 1211 US EDs, which projects to 531,780,629 ED visits after survey weighting. We report the diagnostic testing, evaluation, management, additional reasons for visit, and disposition of ED visits. RESULTS: Among older adults (≥65 years), 7% of ED visits were for abdominal pain. Older patients with abdominal pain had a lower probability of being triaged to the "Emergent" (ESI2) acuity on arrival (7.1% vs. 14.8%) yet were more likely to be admitted directly to the operating room than older adults without abdominal pain (3.6% vs. 0.8%), with no statistically significant differences in discharge home, death, or admission to critical care. Ultrasound or CT imaging was performed in 60% of older adults with abdominal pain. A minority (39%) of older patients with abdominal pain received an electrocardiogram (EKG). CONCLUSIONS: Abdominal pain in older adults presenting to EDs is a serious condition yet is triaged to "emergent" acuity at half the rate of other conditions. Opportunities for improving diagnosis and management may exist. Further research is needed to examine whether improved recognition of abdominal pain as a syndromic presentation would improve patient outcomes.