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.

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.

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.