Faculty Bibliography

IMPORTANCE/OBJECTIVE:Patient safety experts believe that patients/family members should be involved in adverse event review. However, it is unclear how aware patients/family members are about the causes of adverse events they experienced. OBJECTIVE:To determine whether patients/family members interviewed could identify at least one contributing factor for the event they experienced. Secondary objectives included understanding the way patients/family members became aware of adverse events, the types of contributing factors patients/family members identified for different types of adverse events, and recommendations provided by patients/family members to address the contributing factors. DESIGN/METHODS:We interviewed patients/family members using semistructured interviews to understand their perceptions about why these adverse events occurred. The adverse events occurred between 1991 and 2014. SETTING/METHODS:Participants described adverse events that occurred in various types of health care organizations (i.e., hospitals, ambulatory facilities/clinics, and dental clinics). PARTICIPANTS/METHODS:We interviewed 72 patients and family members who each described a unique adverse event. Eligibility requirements were that patients/family members spoke English or Spanish and were aware of an adverse event that happened to them or a loved one. INTERVENTION(S) FOR CLINICAL TRIALS OR EXPOSURE(S) FOR OBSERVATIONAL STUDIES: N/A. MAIN OUTCOME(S) AND MEASURE(S)/UNASSIGNED:The main outcome was determining whether patients/family members could identify at least one contributing factor they perceived as related to the adverse event they described. RESULTS:Each participant identified at least one contributing factor and on average identified 3.67 contributing factors for their event. The most frequently mentioned contributing factors were Staff Qualifications/Knowledge (79 percent), Safety Policies/Procedures (74 percent), and Communication (64 percent). Participants knew about the contributing factors from personal observation only (32 percent), personal reasoning (11 percent), personal research (7 percent), record review (either their own medical records or reports they received in their own investigation; 6 percent), and being told by a physician (5 percent). Finally, patients/family members were able to provide recommendations that address each of the nine contributing factors we examined. CONCLUSIONS AND RELEVANCE/CONCLUSIONS:Patients/family members identified contributing factors related to their adverse event. Given that these contributing factors might not be known to health care organizations because most participants stated that they were not involved in the analysis process, opportunities for organizational learning from patients are potentially being missed. Health care organizations should interview patients/family about the event that harmed them to help ensure a full understanding of the causes of the event.

OBJECTIVE:To describe the litigation experience in a state with strict tort reform of a large public university health system that has committed to transparency with patients and families in resolving medical errors. DATA SOURCES/STUDY SETTING/METHODS:Secondary data collected from The University of Texas System, which self-insures approximately 6,000 physicians at six health campuses across the state. We obtained internal case management data for all medical malpractice claims closed during 1 year before and 6 recent years following the enactment of state tort reform legislation. STUDY DESIGN/METHODS:We retrospectively reviewed information about malpractice claimants, malpractice claims, and the process and outcome of dispute resolution. DATA COLLECTION/EXTRACTION METHODS/METHODS:We accessed an internal case management database, supplemented by both electronic and paper records compiled by the university's Office of General Counsel. PRINCIPAL FINDINGS/RESULTS:Closed claims dropped from 244 in 2001-2002 to an annual mean of 96 in 2009-2015, closures following lawsuits from 136 in 2001-2002 to an annual mean of 28 in 2009-2015, and paid claims from 60 in 2001 to an annual mean of 20 in 2009-2015. Patterns of resolution suggest efforts by the university to provide some compensation to injured patients in cases that were no longer economically viable for plaintiffs' lawyers to litigate. The percentage of payments relating to cases in which lawsuits had been filed decreased from 82 percent in 2001-2002 to 47 percent in 2009-2012 and again to 29 percent in 2012-2015, although most paid claimants were represented by attorneys. Unrepresented patients received payment in 13 cases closed in 2009-2012 (22 percent of payments; mean amount $60,566) and in 24 cases closed in 2012-2015 (41 percent of payments; mean amount $109,410). Even after tort reform, however, claims that resulted in payment remained slow to resolve, which was worsened for claimants subject to Medicare secondary payer rules. Strict confidentiality became a more common condition of settlement, although restrictions were subsequently relaxed in order to further transparency and improve patient safety. CONCLUSIONS:Malpractice litigation risk diminished substantially for a public university health system in Texas following legal changes that reduced rights to sue and available damages. Health systems operating in a low-tort environment should work with policy makers, plaintiffs' attorneys, and patient groups to assist unrepresented patients, facilitate early mediation, limit nondisclosure obligations following settlement, and expedite the resolution of Medicare liens.

Tracing the evolution of political conversations about health care spending and their relationship to the formation of policy is a valuable exercise. Health care spending is about science and ethics, markets and government, freedom and community. By the late 1980s the unique upward trajectory of post-Medicare U.S. health care spending had been established, recessions and tax cuts were eroding federal and state budgets, and efforts to harness market forces to serve policy goals were accelerating. From the initial writings on "managed competition," through the failed Clinton health reform effort in the early 1990s, to the passage of the Affordable Care Act in 2010, the policy narrative of health spending acquired a superficial consistency. On closer examination, however, it becomes apparent that the cost problem has been repeatedly reframed in political discourse even during this relatively brief period. The clearest transition has been from a narrative centered on rationing necessary care to one committed to reducing wasteful care - although the role of accumulated law and regulation in perpetuating waste remains largely unrecognized and the recently articulated commitment to population health seems an imperfect proxy for explicitly developing social solidarity with respect to health and health care in the United States.

Propensity score methods, such as subclassification, are a common approach to control for confounding when estimating causal effects in non-randomized studies. Propensity score subclassification groups individuals into subclasses based on their propensity score values. Effect estimates are obtained within each subclass and then combined by weighting by the proportion of observations in each subclass. Combining subclass-specific estimates by weighting by the inverse variance is a promising alternative approach; a similar strategy is used in meta-analysis for its efficiency. We use simulation to compare performance of each of the two methods while varying (i) the number of subclasses, (ii) extent of propensity score overlap between the treatment and control groups (i.e., positivity), (iii) incorporation of survey weighting, and (iv) presence of heterogeneous treatment effects across subclasses. Both methods perform well in the absence of positivity violations and with a constant treatment effect with weighting by the inverse variance performing slightly better. Weighting by the proportion in subclass performs better in the presence of heterogeneous treatment effects across subclasses. We apply these methods to an illustrative example estimating the effect of living in a disadvantaged neighborhood on risk of past-year anxiety and depressive disorders among U.S. urban adolescents. This example entails practical positivity violations but no evidence of treatment effect heterogeneity. In this case, weighting by the inverse variance when combining across propensity score subclasses results in more efficient estimates that ultimately change inference. Copyright © 2016 John Wiley & Sons, Ltd.

Background: Substance use, a leading cause of illness and death, is underidentified in medical practice. Objective: The Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) tool was developed to address the need for a brief screening and assessment instrument that includes all commonly used substances and fits into clinical workflows. The goal of this study was to assess the performance of the TAPS tool in primary care patients. Design: Multisite study, conducted within the National Drug Abuse Treatment Clinical Trials Network, comparing the TAPS tool with a reference standard measure. (ClinicalTrials.gov: NCT02110693). Setting: 5 adult primary care clinics. Participants: 2000 adult patients consecutively recruited from clinic waiting areas. Measurements: Interviewer- and self-administered versions of the TAPS tool were compared with a reference standard, the modified World Mental Health Composite International Diagnostic Interview (CIDI), which measures problem use and substance use disorder (SUD). Results: Interviewer- and self-administered versions of the TAPS tool had similar diagnostic characteristics. For identifying problem use (at a cutoff of 1+), the TAPS tool had a sensitivity of 0.93 (95% CI, 0.90 to 0.95) and specificity of 0.87 (CI, 0.85 to 0.89) for tobacco and a sensitivity of 0.74 (CI, 0.70 to 0.78) and specificity of 0.79 (CI, 0.76 to 0.81) for alcohol. For problem use of illicit and prescription drugs, sensitivity ranged from 0.82 (CI, 0.76 to 0.87) for marijuana to 0.63 (CI, 0.47 to 0.78) for sedatives; specificity was 0.93 or higher. For identifying any SUD (at a cutoff of 2+), sensitivity was lower. Limitations: The low prevalence of some drug classes led to poor precision in some estimates. Research assistants were not blinded to participants' TAPS tool responses when they administered the CIDI. Conclusion: In a diverse population of adult primary care patients, the TAPS tool detected clinically relevant problem substance use. Although it also may detect tobacco, alcohol, and marijuana use disorders, further refinement is needed before it can be recommended broadly for SUD screening. Primary Funding Source: National Institute on Drug Abuse.

BACKGROUND: Intervention studies among individuals in diverse community settings are needed to reduce health disparities in colorectal cancer (CRC) screening and mortality rates. The current study compared the efficacy of 2 intervention conditions promoting CRC screening among black individuals. METHODS: Black individuals ages 50 to 75 years (N = 330) were recruited in community settings in 4 Tampa Bay counties. After obtaining consent and conducting a baseline interview to assess sociodemographic and health-related variables, participants received either a culturally targeted CRC photonovella booklet plus a fecal immunochemical test (FIT) kit or a standard CRC screening brochure plus an FIT kit. The primary outcome was FIT kit screening uptake. RESULTS: FIT screening uptake at 6 months was 86.7% overall (90.3% in the brochure group and 81.9% in the photonovella group). Controlling for baseline between-group differences, there was no influence of intervention on FIT kit uptake (P = .756). Significant predictors of not returning an FIT kit included being unable to work (P = .010), having higher religious belief scores (P = .015), and living farther from the cancer center (P = .015). CONCLUSIONS: Providing FIT kits and educational print materials to black individuals in community settings resulted in high rates of CRC screening. The study also identified subgroups of participants who were less likely to return an FIT kit and provides insight for future interventions. Cancer 2016;122:3288-3296. (c) 2016 American Cancer Society.