Faculty Bibliography

Optimizing the health of populations, whether defined as persons receiving care from a health care delivery system or more broadly as persons in a region, is emerging as a core focus in the era of health care reform. To achieve this goal requires an approach in which preventive care is valued and "nonmedical" determinants of patients' health are engaged. For large, multimission systems such as academic medical centers, navigating the evolution to a population-oriented paradigm across the domains of patient care, education, and research poses real challenges but also offers tremendous opportunities, as important objectives across each mission begin to align with external trends and incentives. In clinical care, opportunities exist to improve capacity for assuming risk, optimize community benefit, and make innovative use of advances in health information technology. Education must equip the next generation of leaders to understand and address population-level goals in addition to patient-level needs. And the prospects for research to define strategies for measuring and optimizing the health of populations have never been stronger. A remarkable convergence of trends has created compelling opportunities for academic medical centers to advance their core goals by endorsing and committing to advancing the health of populations.

BACKGROUND AND OBJECTIVES: This analysis aims to: (1) compare induction experiences among participants who self-reported using one of the four most commonly reported POs, and (2) examine factors associated with difficult bup-nx induction. Our hypothesis, based on previous research and current guidelines, is that those on longer-acting opioids will have experienced more difficult inductions. METHODS: The Prescription Opioid Addiction Treatment Study (POATS) was a multi-site, randomized clinical trial, using a two-phase adaptive treatment research design. This analysis examines bup-nx induction of participants who self-reported primary PO use of methadone, ER-oxycodone, IR-oxycodone, and hydrocodone (n = 569). Analyses examined characteristics associated with difficult induction, defined as increased withdrawal symptoms measured by the Clinical Opiate Withdrawal Scale (COWS) after the first bup-nx dose with higher scores denoting greater withdrawal symptoms/severity. RESULTS: Contrary to our hypothesis, difficult induction experiences did not differ by primary PO type. Those who experienced a post-induction increase in COWS score had lower pre-dose COWS scores compared to those who did not experience a post-induction increase in COWS score (10.09 vs. 12.77, t(624) = -13.56, p < .001). Demographics characteristics, depression, and pain history did not predict a difficult induction. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Difficult bup-nx inductions were not associated with participants' primary PO. Severity of withdrawal, measured with the COWS, was an important variable, reminding clinicians that bup-nx should not be commenced prior to evidence of moderate opioid withdrawal. These findings add to the evidence that with careful procedures, bup-nx can used with few difficulties in PO-dependent patients. (Am J Addict 2013;XX:000-000).

The time required to conduct drug and alcohol screening has been a major barrier to its implementation in mainstream healthcare settings. Because patient self-administered tools are potentially more efficient, we translated the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) into an audio guided computer assisted self interview (ACASI) format. This study reports on the test-retest reliability of the ACASI ASSIST in an adult primary care population. Adult primary care patients completed the ACASI ASSIST, in English or Spanish, twice within a 1-4week period. Among the 101 participants, there were no significant differences between test administrations in detecting moderate to high risk use for tobacco, alcohol, or any other drug class. Substance risk scores from the two administrations had excellent concordance (90-98%) and high correlation (ICC 0.90-0.97) for tobacco, alcohol, and drugs. The ACASI ASSIST has good test-retest reliability, and warrants additional study to evaluate its validity for detecting unhealthy substance use.

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: 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.

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.

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.