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.

Differential Mortality Among Heart Failure Patients Across Different COVID-19 Surges in New York City.

ABSTRACT: Learning from the healthcare system's response to the COVID-19 pandemic is essential to better prepare for potential future crises. We sought to assess mortality rates for patients admitted for acute decompensated heart failure (HF) and to analyze which factors demonstrated a statistically significant correlation with this primary endpoint. We performed a retrospective analysis of patients hospitalized with a primary diagnosis of acute decompensated HF within the New York City Health and Hospitals 11-hospital system across the different COVID surge periods. Mortality information was collected in 4,405 participants (mean [SD] age 70.54 [14.44] years, 1885 [42.87%] female).The highest mortality existed in the first surge (9.02%), then improved to near prepandemic levels (3.65%) in the second (3.91%) and third surges (5.94%, p < 0.0001). In-hospital mortality inversely correlated with receipt of a COVID-19 vaccination, but had no correlation with left ventricular ejection fraction or the number of vaccination doses. Mortality for acute decompensated HF patients improved after the first surge, suggesting that hospitals adequately adapted to provide quality care. As future infectious outbreaks may occur, emergency preparedness must ensure that adequate focus and resources remain for other clinical entities, such as HF, to ensure optimal care is delivered across all areas of illness.

Costs of Care for Operative and Nonoperative Management of Emergency General Surgery Conditions.

OBJECTIVE: Many emergency general surgery (EGS) conditions can be managed operatively or nonoperatively, with outcomes that vary by diagnosis. We hypothesized that operative management would lead to higher in-hospital costs but to cost savings over time. BACKGROUND: EGS conditions account for $28 billion in health care costs in the United States annually. Compared with scheduled surgery, patients who undergo emergency surgery are at increased risk of complications, readmissions, and death, with accompanying costs of care that are up to 50% higher than elective surgery. Our prior work demonstrated that operative management had variable impacts on clinical outcomes depending on the EGS condition. METHODS: This was a nationwide, retrospective study using fee-for-service Medicare claims data. We included patients 65.5 years of age or older with a principal diagnosis for an EGS condition 7/1/2015-6/30/2018. EGS conditions were categorized as: colorectal, general abdominal, hepatopancreaticobiliary (HPB), intestinal obstruction, and upper gastrointestinal. We used near-far matching with a preference-based instrumental variable to adjust for confounding and selection bias. Outcomes included Medicare payments for the index hospitalization and at 30, 90, and 180 days. RESULTS: Of 507,677 patients, 30.6% received an operation. For HPB conditions, costs for operative management were initially higher but became equivalent at 90 and 180 days. For all others, operative management was associated with higher inpatient costs, which persisted, though narrowed, over time. Out-of-pocket costs were nearly equivalent for operative and nonoperative management. CONCLUSIONS: Compared with nonoperative management, costs were higher or equivalent for operative management of EGS conditions through 180 days, which could impact decision-making for clinicians, patients, and health systems in situations where clinical outcomes are similar.

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.

National Cohort Study of Resource Utilization in Older Adults With Emergency General Surgery Conditions.

INTRODUCTION: Prior studies have sought to describe Emergency General Surgery (EGS) burden, but a detailed description of resource utilization for both operative and nonoperative management of EGS conditions has not been undertaken. METHODS: Patient and hospital characteristics were extracted from Medicare data, 2015-2018. Operations, nonsurgical procedures, and other resources (i.e., radiology) were defined using Current Procedural Terminology codes. RESULTS: One million eight hundred two thousand five hundred forty-five patients were included in the cohort. The mean age was 74.7 y and the most common diagnoses were upper gastrointestinal. The majority of hospitals were metropolitan (75.1%). Therapeutic radiology services were available in 78.4% of hospitals and operating rooms or endoscopy suites were available in 92.5% of hospitals. There was variability in resource utilization across EGS subconditions, with hepatobiliary (26.4%) and obstruction (23.9%) patients most frequently undergoing operation. CONCLUSIONS: Treatment of EGS diseases in older adults involves several interventional resources. Changes in EGS models, acute care surgery training, and interhospital care coordination may be beneficial to the treatment of EGS patients.

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.

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.

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.

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.

Health System Structure: An Opportunity to Address Structural Racism and Discrimination.

OBJECTIVE: To examine the role of hub-and-spoke systems as a factor in structural racism and discrimination. BACKGROUND: Health systems are often organized in a "hub-and-spoke" manner to centralize complex surgical care to 1 high-volume hospital. Although the surgical health care disparities are well described across health care systems, it is not known how they seem across a single system's hospitals. METHODS: Adult patients who underwent 1 of 10 general surgery operations in 12 geographically diverse states (2016-2018) were identified using the Healthcare Cost and Utilization Project's State Inpatient Databases. System status was assigned using the American Hospital Association dataset. Hub designation was assigned in 2 ways: (1) the hospital performing the most complex operations (general hub) or (2) the hospital performing the most of each specific operation (procedure-specific hub). Independent multivariable logistic regression was used to evaluate the risk-adjusted odds of treatment at hubs by race and ethnicity. RESULTS: We identified 122,236 patients across 133 hospitals in 43 systems. Most patients were White (73.4%), 14.2% were Black, and 12.4% Hispanic. A smaller proportion of Black and Hispanic patient underwent operations at general hubs compared with White patients (B: 59.6% H: 52.0% W: 62.0%, P <0.001). After adjustment, Black and Hispanic patients were less likely to receive care at hub hospitals relative to White patients for common and complex operations (general hub B: odds ratio: 0.88 CI, 0.85, 0.91 H: OR: 0.82 CI, 0.79, 0.85). CONCLUSIONS: When White, Black, and Hispanic patients seek care at hospital systems, Black and Hispanic patients are less likely to receive treatment at hub hospitals. Given the published advantages of high-volume care, this new finding may highlight an opportunity in the pursuit of health equity.

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 patients who do not desire tech-first approaches.

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.

Prevalence of Third-party Tracking on Medical Journal Websites.

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