Comparing Rates of Undiagnosed Hypertension and Diabetes in Patients With and Without Substance Use Disorders.

BACKGROUND: Individuals with substance use disorders (SUDs) have increased risk for developing chronic conditions, though few studies assess rates of diagnosis of these conditions among patients with SUDs. OBJECTIVE: To compare rates of undiagnosed hypertension and diabetes among patients with and without an SUD. DESIGN: Cross-sectional analysis using electronic health record (EHR) data from 58 primary care clinics at a large, urban, healthcare system in New York. PARTICIPANTS: Patients who had at least two primary care visits from 2019-2022 were included in our patient sample. Patients without an ICD-10 hypertension diagnosis or prescribed hypertension medications and with at least two blood pressure (BP) readings ≥ 140/90 mm were labeled 'undiagnosed hypertension,' and patients without a diabetes diagnosis or prescribed diabetes medications and with A1C/hemoglobin ≥ 6.5% were labeled 'undiagnosed diabetes.' MAIN MEASURES: We calculated the mean number of patients with and without an ICD-10 SUD diagnosis who were diagnosed and undiagnosed for each condition. We used multivariate logistic regression to assess the association between being undiagnosed for each condition, and having an SUD diagnosis, patient demographic characteristics, clinical characteristics (body mass index, Elixhauser comorbidity count, diagnosed HIV and psychosis), the percentage of visits without a BP screening, and the total number of visits during the time period. KEY RESULTS: The percentage of patients with undiagnosed hypertension (2.74%) and diabetes (22.98%) was higher amongst patients with SUD than patients without SUD. In multivariate models, controlling for other factors, patients with SUD had significantly higher odds of having undiagnosed hypertension (OR: 1.81; 95% CI: 1.48, 2.20) and undiagnosed diabetes (OR: 1.93; 1.72, 2.16). Being younger, female, and having an HIV diagnosis was also associated with significantly higher odds for being undiagnosed. CONCLUSIONS: We found significant disparities in rates of undiagnosed chronic diseases among patients with SUDs, compared with patients without SUDs.

Assessing Differences in Social Determinants of Health Screening Rates in a Large, Urban Safety-Net Health System.

INTRODUCTION/OBJECTIVE: Previous studies have evaluated the implementation of standardized social determinants of health (SDOH) screening within healthcare settings, however, less is known about where screening gaps may exist following initial implementation based on facility characteristics. The objective of this study is to assess differences in screening rates for SDOH at a large, urban healthcare system. METHODS: We used electronic health record data obtained from NYC Health + Hospitals primary care sites from 2019 to 2022. We calculated the mean number of visits that were SDOH screened by visit type, facility size, and the percentages of community characteristics. We conducted 4 logistic regression models predicting the odds of screening for any SDOH and for specific SDOH needs (housing, food, and medical cost assistance) based on facility type, facility size, and the socioeconomic characteristics of the surrounding community. RESULTS: Among the 3 212 650 visits included, 16.90% were SDOH screened. Across all 4 multivariate logistic regression models predicting SDOH screening, a visit had significantly lower odds of being screened if based at a midsize or small facility, if it was a telemedicine visit, or based at a facility located in a zip-code with a higher percentage of SDOH needs. CONCLUSIONS: Our study found important differences in SDOH screening rates at a large, NYC-based health system based on size, visit type, and community level characteristics. In particular, our findings point to barriers related to facility size and telemedicine workflow that should be addressed to increase uptake of SDOH screening within different visits and facility types.

People with disability and privacy in precision medicine research: what's at stake?

Re-identification from data used in precision medicine research is presumed to create minimal risk but may disproportionately impact health disparity populations. We consider plausible privacy risks and the negative ramifications thereof for people with disabilities, the largest health disparity population in the USA, and suggest measures to address these concerns.

Perspectives of Remote Volunteer Palliative Care Consultants During COVID: A Qualitative Study.

CONTEXT: A novel remote volunteer program was implemented in response to the initial COVID-19 surge in New York City, allowing out-of-state palliative care specialists to serve patients and families in need. No study has detailed the perceptions of these consultants. OBJECTIVES: To understand the experiences of remote volunteer palliative care consultants during the initial COVID-19 surge. METHODS: This qualitative study utilized a thematic analysis approach. During January and February 2021, we conducted one-on-one semi-structured interviews with 15 board-certified palliative care physicians who participated in the program. Codes and emerging themes were identified through iterative discussion and comparison. RESULTS: Five overarching themes (with sub-themes in parentheses) were identified: 1) motivations for participating in the program, 2) logistical evaluation of the program (integration, telehealth model, dyad structure and debriefing sessions), 3) barriers to delivery (language and cultural differences, culture of high-intensity care, legal and administrative differences), 4) emotional burden (moral distress, burnout), and 5) ideas for improvements. Notably, participants observed institutional and cultural differences that posed challenges to delivery of care. Many expressed feelings of distress related to the uncertainty and scarcity caused by the pandemic, although volunteering may have been protective against burnout. CONCLUSION: This study provides an in-depth look at the experiences of remote volunteer palliative care consultants during the initial COVID-19 surge from the unique perspectives of the consultants themselves. Participants expressed overall positive and meaningful experiences and felt that the model was appropriate given the circumstances. Additionally, participants provided recommendations that could guide future implementations of similar programs.

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers. Recently, machine learning algorithms have been developed for detecting PrCa in whole slide images (WSIs) with high test accuracy. However, the impact of these artificial intelligence systems on pathologic diagnosis is not known. To address this, we investigated how pathologists interact with Paige Prostate Alpha, a state-of-the-art PrCa detection system, in WSIs of prostate needle core biopsies stained with hematoxylin and eosin. Three AP-board certified pathologists assessed 304 anonymized prostate needle core biopsy WSIs in 8 hours. The pathologists classified each WSI as benign or cancerous. After ~4 weeks, pathologists were tasked with re-reviewing each WSI with the aid of Paige Prostate Alpha. For each WSI, Paige Prostate Alpha was used to perform cancer detection and, for WSIs where cancer was detected, the system marked the area where cancer was detected with the highest probability. The original diagnosis for each slide was rendered by genitourinary pathologists and incorporated any ancillary studies requested during the original diagnostic assessment. Against this ground truth, the pathologists and Paige Prostate Alpha were measured. Without Paige Prostate Alpha, pathologists had an average sensitivity of 74% and an average specificity of 97%. With Paige Prostate Alpha, the average sensitivity for pathologists significantly increased to 90% with no statistically significant change in specificity. With Paige Prostate Alpha, pathologists more often correctly classified smaller, lower grade tumors, and spent less time analyzing each WSI. Future studies will investigate if similar benefit is yielded when such a system is used to detect other forms of cancer in a setting that more closely emulates real practice.