OBJECTIVES: Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward identifying and supporting trafficked persons in health-care settings, highlighting current technology roles, gaps, and future directions. MATERIALS AND METHODS: We developed an interview guide to investigate current human trafficking (HT) approaches, safety procedures, and HT education. Semistructured interviews were conducted via Zoom, iteratively coded in Dedoose, and analyzed using a thematic analysis approach. RESULTS: We interviewed 19 health-care and community group professionals and identified 3 themes: (1) participants described a responsibility to build trust with patients through compassionate communication, rapport, and trauma-informed approaches across different stages of care. (2) Technology played a dual role, as professionals navigated both benefits and challenges of tools such as Zoom, virtual interpreters, and cameras in trust building. (3) Safety and privacy concerns guided how participants documented patient encounters and shared community resources, ensuring confidentiality while supporting patient and community well-being. DISCUSSION: Technology can both support and hinder trust in health care, directly affecting trafficked patients and their safety. Informatics can improve care for trafficked persons, but further research is needed on technology-based interventions. We provide recommendations to strengthen trust, enhance safety, support trauma-informed care, and promote safe documentation practices. CONCLUSION: Effective sociotechnical approaches rely on trust, safety, and mindful documentation to support trafficked patients. Future research directions include refining the role of informatics in trauma-informed care to strengthen trust and mitigate unintended consequences.

OBJECTIVES: To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity. MATERIALS AND METHODS: We evaluated GPT-3.5, GPT-4, and traditional ML (as gradient-boosting trees) on clinical prediction tasks in EHR data from Vanderbilt University Medical Center (VUMC) and MIMIC IV. We measured predictive performance with area under the receiver operating characteristic (AUROC) and model calibration using Brier Score. To evaluate the impact of data privacy protections, we assessed AUROC when demographic variables are generalized. We evaluated algorithmic fairness using equalized odds and statistical parity across race, sex, and age of patients. We also considered the impact of using in-context learning by incorporating labeled examples within the prompt. RESULTS: Traditional ML [AUROC: 0.847, 0.894 (VUMC, MIMIC)] substantially outperformed GPT-3.5 (AUROC: 0.537, 0.517) and GPT-4 (AUROC: 0.629, 0.602) (with and without in-context learning) in predictive performance and output probability calibration [Brier Score (ML vs GPT-3.5 vs GPT-4): 0.134 vs 0.384 vs 0.251, 0.042 vs 0.06 vs 0.219)]. DISCUSSION: Traditional ML is more robust than GPT-3.5 and GPT-4 in generalizing demographic information to protect privacy. GPT-4 is the fairest model according to our selected metrics but at the cost of poor model performance. CONCLUSION: These findings suggest that non-fine-tuned LLMs are less effective and robust than locally trained ML for clinical prediction tasks, but they are improving across releases.

OBJECTIVE: The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels. METHODS: At the time of the study, the resource consisted of 329 084 participants. Systematic amendments were applied to the data to mitigate re-identification risk (eg, generalization of geographic regions, suppression of public events, and randomization of dates). We computed the re-identification risk for each participant using a state-of-the-art adversarial model specifically assuming that it is known that someone is a participant in the program. We confirmed the expected risk is no greater than 0.09, a threshold that is consistent with guidelines from various US state and federal agencies. We further investigated how risk varied as a function of participant demographics. RESULTS: The results indicated that 95th percentile of the re-identification risk of all the participants is below current thresholds. At the same time, we observed that risk levels were higher for certain race, ethnic, and genders. CONCLUSIONS: While the re-identification risk was sufficiently low, this does not imply that the system is devoid of risk. Rather, All of Us uses a multipronged data protection strategy that includes strong authentication practices, active monitoring of data misuse, and penalization mechanisms for users who violate terms of service.

The Supreme Court recently overturned settled case law that affirmed a pregnant individual's Constitutional right to an abortion. While many states will commit to protect this right, a large number of others have enacted laws that limit or outright ban abortion within their borders. Additional efforts are underway to prevent pregnant individuals from seeking care outside their home state. These changes have significant implications for delivery of healthcare as well as for patient-provider confidentiality. In particular, these laws will influence how information is documented in and accessed via electronic health records and how personal health applications are utilized in the consumer domain. We discuss how these changes may lead to confusion and conflict regarding use of health information, both within and across state lines, why current health information security practices may need to be reconsidered, and what policy options may be possible to protect individuals' health information.

OBJECTIVE: Human trafficking is a global problem taking many forms, including sex and labor exploitation. Trafficking victims can be any age, although most trafficking begins when victims are adolescents. Many trafficking victims have contact with health-care providers across various health-care contexts, both for emergency and routine care. MATERIALS AND METHODS: We propose 4 specific areas where medical informatics can assist with combatting trafficking: screening, clinical decision support, community-facing tools, and analytics that are both descriptive and predictive. Efforts to implement health information technology interventions focused on trafficking must be carefully integrated into existing clinical work and connected to community resources to move beyond identification to provide assistance and to support trauma-informed care. RESULTS: We lay forth a research and implementation agenda to integrate human trafficking identification and intervention into routine clinical practice, supported by health information technology. CONCLUSIONS: A sociotechnical systems approach is recommended to ensure interventions address the complex issues involved in assisting victims of human trafficking.

Objective: We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods: This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results: The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion: We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion: EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.

Objective: Biomedical science is driven by datasets that are being accumulated at an unprecedented rate, with ever-growing volume and richness. There are various initiatives to make these datasets more widely available to recipients who sign Data Use Certificate agreements, whereby penalties are levied for violations. A particularly popular penalty is the temporary revocation, often for several months, of the recipient's data usage rights. This policy is based on the assumption that the value of biomedical research data depreciates significantly over time; however, no studies have been performed to substantiate this belief. This study investigates whether this assumption holds true and the data science policy implications. Methods: This study tests the hypothesis that the value of data for scientific investigators, in terms of the impact of the publications based on the data, decreases over time. The hypothesis is tested formally through a mixed linear effects model using approximately 1200 publications between 2007 and 2013 that used datasets from the Database of Genotypes and Phenotypes, a data-sharing initiative of the National Institutes of Health. Results: The analysis shows that the impact factors for publications based on Database of Genotypes and Phenotypes datasets depreciate in a statistically significant manner. However, we further discover that the depreciation rate is slow, only ∼10% per year, on average. Conclusion: The enduring value of data for subsequent studies implies that revoking usage for short periods of time may not sufficiently deter those who would violate Data Use Certificate agreements and that alternative penalty mechanisms may need to be invoked.

OBJECTIVES: Improvements in electronic health record (EHR) system development will require an understanding of psychiatric clinicians' views on EHR system acceptability, including effects on psychotherapy communications, data-recording behaviors, data accessibility versus security and privacy, data quality and clarity, communications with medical colleagues, and stigma. DESIGN: Multidisciplinary development of a survey instrument targeting psychiatric clinicians who recently switched to EHR system use, focus group testing, data analysis, and data reliability testing. MEASUREMENTS: Survey of 120 university-based, outpatient mental health clinicians, with 56 (47%) responding, conducted 18 months after transition from a paper to an EHR system. RESULTS: Factor analysis gave nine item groupings that overlapped strongly with five a priori domains. Respondents both praised and criticized the EHR system. A strong majority (81%) felt that open therapeutic communications were preserved. Regarding data quality, content, and privacy, clinicians (63%) were less willing to record highly confidential information and disagreed (83%) with including their own psychiatric records among routinely accessed EHR systems. LIMITATIONS: single time point; single academic medical center clinic setting; modest sample size; lack of prior instrument validation; survey conducted in 2005. CONCLUSIONS: In an academic medical center clinic, the presence of electronic records was not seen as a dramatic impediment to therapeutic communications. Concerns regarding privacy and data security were significant, and may contribute to reluctances to adopt electronic records in other settings. Further study of clinicians' views and use patterns may be helpful in guiding development and deployment of electronic records systems.