RESUMEN

Background: HIV viral suppression requires sustained engagement in care. The COVID-19 pandemic challenged care accessibility for many people living with HIV (PLWH). We used health information exchange data to evaluate the effect of pandemic-related disruptions in HIV care on viral load suppression (VLS) and to examine racial/ethnic disparities in VLS. Methods: We performed a retrospective observational cohort study of PLWH using data from a regional health information exchange in the New York City region between 1 January 2018 and 31 December 2022. We established 2 cohorts: PLWH who received HIV care in 2020 (cohort A) and PLWH who did not receive HIV care in 2020 (cohort B). We categorized HIV VLS outcomes as suppressed or not suppressed and calculated the prevalence of VLS between 2018 and 2022. We compared proportions using chi-square tests and used unadjusted and adjusted logistic regression to estimate the association among variables, including race/ethnicity, cohort, and VLS. Results: Of 5 301 578 patients, 34 611 met our inclusion criteria for PLWH, 11 653 for cohort A, and 3141 for cohort B. In 2019, cohort B had a lower prevalence of VLS than cohort A (86% vs 89%, P < .001). Between 2019 and 2021, VLS dropped significantly among cohort B (86% to 81%, P < .001) while staying constant in cohort A (89% to 89%, P = .62). By 2022, members of cohort B were less likely than cohort A to be receiving HIV care in New York City (74% vs 88%, P < .001). Within both cohorts, Black and Hispanic patients had lower odds of VLS than White patients. Conclusions: In New York City, VLS remained high among PLWH who continued to receive care in 2020 and dropped among PLWH who did not receive care. VLS was lower among Black and Hispanic patients even after controlling for receipt of care.

RESUMEN

Real-world clinical practice commonly veers from formal drug approvals in off-label use, accounting for 21% of prescriptions for common drugs. Due to its ad hoc nature, off-label use typically goes undocumented, evading the safety and efficacy scrutiny of clinical trials. A systematic and automated approach to detection of these uses in the electronic health record (EHR) would enable improved safety monitoring, provide insight into prescribing patterns, and support real-world evidence appraisal. Domain knowledge provided by medication-indication knowledge bases has been shown to improve the accuracy of EHR-based automated detection of off-label use, but remains limited due to diverse concept representations and granularities across data sources. We present a method to leverage hierarchical concept knowledge to align medication-indication knowledge with EHR data for automated detection of off-label drug use in clinical practice. We demonstrate an over two-fold increase in detected off-label diagnoses when leveraging hierarchical knowledge relative to direct concept matching alone.

RESUMEN

OBJECTIVE: To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-text RCT evidence in PubMed into the structured representation. MATERIALS AND METHODS: Our representation, EvidenceMap, consists of 3 levels of abstraction: Medical Evidence Entity, Proposition and Map, to represent the hierarchical structure of medical evidence composition. Randomly selected RCT abstracts were annotated following EvidenceMap based on the consensus of 2 independent annotators to train an NLP pipeline. Via a user study, we measured how the EvidenceMap improved evidence comprehension and analyzed its representative capacity by comparing the evidence annotation with EvidenceMap representation and without following any specific guidelines. RESULTS: Two corpora including 229 disease-agnostic and 80 COVID-19 RCT abstracts were annotated, yielding 12 725 entities and 1602 propositions. EvidenceMap saves users 51.9% of the time compared to reading raw-text abstracts. Most evidence elements identified during the freeform annotation were successfully represented by EvidenceMap, and users gave the enrollment, study design, and study Results sections mean 5-scale Likert ratings of 4.85, 4.70, and 4.20, respectively. The end-to-end evaluations of the pipeline show that the evidence proposition formulation achieves F1 scores of 0.84 and 0.86 in the adjusted random index score. CONCLUSIONS: EvidenceMap extends the participant, intervention, comparator, and outcome framework into 3 levels of abstraction for transforming free-text evidence from the clinical literature into a computable structure. It can be used as an interoperable format for better evidence retrieval and synthesis and an interpretable representation to efficiently comprehend RCT findings.

Asunto(s)

RESUMEN

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.