Faculty Bibliography

BACKGROUND: Previous studies have suggested that weekend hospital care is inferior to weekday care and that this difference may be related to diminished care intensity. The purpose of this study was to determine whether a metric for measuring intensity of hospital care based on utilization of the electronic health record (EHR) was associated with patient-level outcomes. METHODS: We performed a cohort study of hospitalizations at an academic medical center. Intensity of care was defined as the hourly number of provider accessions of the electronic health record, termed "EHR interactions." Hospitalizations were categorized based on the mean difference in EHR interactions between the first Friday and Saturday of hospitalization. We used regression models to determine the association of these categories with patient outcomes after adjusting for covariates. RESULTS: EHR interactions decreased from Friday to Saturday in 77% of the 9,051 hospitalizations included in the study. As compared to hospitalizations with no change in Friday to Saturday EHR interactions, the relative lengths of stay for hospitalizations with a small, moderate, and large decrease in EHR interactions were 1.05 (95% CI 1.00-1.10), 1.11 (95% CI 1.05-1.17), and 1.25 (95% CI 1.15-1.35), respectively. Although a large decrease in EHR interactions was associated with in-hospital mortality, these findings were not significant after risk adjustment (odds ratio 1.74, 95% CI 0.93-3.25). CONCLUSIONS: Intensity of inpatient care, measured by EHR interactions, significantly diminished from Friday to Saturday, and this decrease was associated with length of stay. Hospitals should consider monitoring and correcting temporal fluctuations in care intensity.

Brain imaging and genetic studies over the past two decades suggest that substance use disorders are best considered chronic illnesses. The passing of the Affordable Care Act in the United States has set the occasion for integrating treatment of substance use disorders into mainstream healthcare; and for using the proactive, team-oriented Chronic Care Model (CCM). This paper systematically examines and compares whether and how well the CCM could be applied to the treatment of substance use disorders, using type 2 diabetes as a comparator. The chronic illness management approach is still new in the field of addiction and research is limited. However comparative findings suggest that most proactive, team treatment-oriented clinical management practices now used in diabetes management are applicable to the substance use disorders; capable of being implemented by primary care teams; and should offer comparable potential benefits in the treatment of substance use disorders. Such care should also improve the quality of care for many illnesses now negatively affected by unaddressed substance abuse.

ABSTRACT. Background: With rising rates of prescription drug abuse and associated overdose deaths, there is great interest in having accurate and efficient screening tools that identify nonmedical use of prescription drugs in health care settings. The authors sought to gain a better understanding of how patients interpret questions about misuse of prescription drugs, with the goal of improving the accuracy and acceptability of instruments intended for use in primary care. Methods: A total of 27 English-speaking adult patients were recruited from an urban safety net primary care clinic to complete a cognitive interview about a 4-item screening questionnaire for tobacco, alcohol, illicit drugs, and misuse of prescription drugs. Detailed field notes were analyzed for overall comprehension of the screening items on illicit drug use and prescription drug misuse, the accuracy with which participants classified drugs into these categories, and whether the screening response correctly captured the participant's substance use behavior. Results: Based on initial responses to the screening items, 6 (22%) participants screened positive for past-year prescription drug misuse, and 8 (30%) for illicit drug use. The majority (26/27) of participants correctly interpreted the item on illicit drug use, and appropriately classified drugs in this category. Eleven (41%) participants had errors in their understanding of the prescription drug misuse item. The most common error was classifying use of medications without abuse potential as nonmedical use. All cases of misunderstanding the prescription drug misuse item occurred among participants who screened negative for illicit drug use. Conclusions: The results suggest that terminology used to describe misuse of prescription medications may be misunderstood by many primary care patients, particularly those who do not use illicit drugs. Failure to improve upon the language used to describe prescription drug misuse in screening questionnaires intended for use in medical settings could potentially lead to high rates of false-positive results.

BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems. Journal of Hospital Medicine 2013;8:513-518. (c) 2013 Society of Hospital Medicine.

Global access to opioid agonist therapy and HIV/hepatitis C virus (HCV) treatment is expanding but when used concurrently, problematic pharmacokinetic and pharmacodynamic interactions may occur. Articles published from 1966 to 2012 in Medline were reviewed using the following keywords: HIV, AIDS, HIV therapy, HCV, HCV therapy, antiretroviral therapy, highly active antiretroviral therapy, drug interactions, methadone and buprenorphine. In addition, a review of abstracts from national and international meetings and conference proceedings was conducted; selected reports were reviewed as well. The metabolism of both opioid and antiretroviral therapies, description of their known interactions and clinical implications and management of these interactions were reviewed. Important pharmacokinetic and pharmacodynamic drug interactions affecting either methadone or HIV medications have been demonstrated within each class of antiretroviral agents. Drug interactions between methadone, buprenorphine and HIV medications are known and may have important clinical consequences. Clinicians must be alert to these interactions and have a basic knowledge regarding their management.

Background: The New York University- New York City Health and Hospitals Corporation (NYU-HHC) Clinical and Translational Science Institute (CTSI) used a community-based participatory research (CBPR) and consensus-building approach among its community advisory board (CAB) and steering committee (SC) members to formulate research priorities to foster shared research collaborations. Methods: The Delphi technique is a methodology used to generate consensus from diverse perspectives and organizational agendas through a multi-method, iterative approach to collecting data. A series of on-line surveys was conducted with CAB members to identify health and research priorities from the community perspective. Subsequently, CAB and SC members were brought together and the snow card approach was utilized to narrow to two priority areas for shared research collaborations. Results: Cardiovascular disease (CVD)/obesity and mental health were identified as health disparity areas for shared research collaborations within a social determinants framework. In response, two workgroups were formed with leadership provided by three co-chairs representing the three constituents of the NYU-HHC CTSI: NYU faculty, HHC providers, and community leaders Conclusions: The Delphi approach fostered ownership and engagement with community partners because it was an iterative process that required stakeholders' input into decision making. The snow card technique allowed for organizing of a large number of discrete ideas. Results have helped to inform the overall CTSI research agenda by defining action steps, and setting an organizing framework to tackle two health disparity areas. The process helped ensure that NYUHHC CTSI research and community engagement strategies are congruent with community priorities.