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Objectives: There are no widely accepted metrics to determine the optimal number and geographic distribution of trauma centers (TCs). We propose a Performance-based Assessment of Trauma System (PBATS) model to optimize the number and distribution of TCs in a region using key performance metrics., Methods: The proposed PBATS approach relies on well-established mathematical programming approach to minimize the number of level I (LI) and level II (LII) TCs required in a region, constrained by prespecified system-related under-triage (srUT) and over-triage (srOT) rates and TC volume. To illustrate PBATS, we collected 6002 matched (linked) records from the 2012 Ohio Trauma and EMS registries. The PBATS-suggested network was compared to the 2012 Ohio network and also to the configuration proposed by the Needs-Based Assessment of Trauma System (NBATS) tool., Results: For this data, PBATS suggested 14 LI/II TCs with a slightly different geographic distribution compared to the 2012 network with 21 LI and LII TC, for the same srUT≈.2 and srOT≈.52. To achieve UT ≤ .05, PBATS suggested 23 LI/II TCs with a significantly different distribution. The NBATS suggested fewer TCs (12 LI/II) than the Ohio 2012 network., Conclusion: The PBATS approach can generate a geographically optimized network of TCs to achieve prespecified performance characteristics such as srUT rate, srOT rate, and TC volume. Such a solution may provide a useful data-driven standard, which can be used to drive incremental system changes and guide policy decisions.

Background: Limited work has been done in predicting discharge disposition in trauma patients; most studies use single institutional data and have limited generalizability. This study develops and validates a model to predict, at admission, trauma patients' discharge disposition using NTDB, transforms the model into an easy-to-use score, and subsequently evaluates its generalizability on institutional data., Methods: NTDB data were used to build and validate a binary logistic regression model using derivation-validation (ie, train-test) approach to predict patient disposition location (home vs non-home) upon admission. The model was then converted into a trauma disposition score (TDS) using an optimization-based approach. The generalizability of TDS was evaluated on institutional data from a single Level I trauma center in the U.S., Results: A total of 614 625 patients in the NTDB were included in the study; 212 684 (34.6%) went to a non-home location. Patients with a non-home disposition compared to home had significantly higher age (69 ± 19.7 vs 48.3 ± 20.3) and ISS (11.2 ± 8.2 vs 8.2 ± 6.3); P < .001. Older age, female sex, higher ISS, comorbidities (cancer, cardiovascular, coagulopathy, diabetes, hepatic, neurological, psychiatric, renal, substance abuse), and Medicare insurance were independent predictors of non-home discharge. The logistic regression model's AUC was 0.8; TDS achieved a correlation of 0.99 and performed similarly well on institutional data (n = 3161); AUC = 0.8., Conclusion: We developed a score based on a large national trauma database that has acceptable performance on local institutions to predict patient discharge disposition at the time of admission. TDS can aid in early discharge preparation for likely-to-be non-home patients and may improve hospital efficiency.

Background: Prior studies of the impact of the Affordable Care Act on reimbursement for inpatient trauma care do not include disproportionate share hospital (DSH) funding. Because trauma centers and other safety-net hospitals are sensitive to any changes in financial support, it is essential to include DSH funding in evaluating overall reimbursement. This study analyzes the long-term financial trends, including DSH, of a level I trauma center in Ohio, a state that expanded Medicaid., Methods: Charges, reimbursement, sources of insurance coverage, Injury Severity Scores, and DSH funding for the trauma patient population of an Ohio American College of Surgeons level 1 trauma center were studied from 2012 to 2017. Data were collected from Transition Systems, Inc., Results: During 2012-2017, self-pay patient cases decreased from 15.0% to 4.1% and commercial insurance patients decreased from 34.2% to 27.6%. The percentage of Medicaid patients increased from 15.5% to 27.1%; however, Medicaid reimbursement average per case declined from $17,779 in 2012 to $10,115 in 2017 (a decline of 43.1%). Self-pay charges decreased from $22.0 million to $6.7 million. Total DSH funding, compensation given to hospitals that disproportionately treat underserved populations, decreased 17.4%., Conclusions: Self-pay charges and self-pay patients decreased dramatically; Medicaid patients and charges increased substantially in the years after the implementation of the Affordable Care Act at our trauma center. However, there was a decrease in commercial insurance, which had the highest reimbursement for our hospital, and a significant decline in DSH, a critical supplemental source of funding for safety-net hospitals., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: Efforts to understand interruptions now span much of the last decade and a half. Often thought to negatively impact patient safety, some now acknowledge that interruptions may be beneficial and actually necessary for safety and high quality care. This study seeks a framework for differentiating between interruptions that are detrimental and those that are beneficial., Methods: A mixed-methods approach at a US Level 1 trauma centre included direct observation of 13 registered nurses (RNs), survey of 47 RNs, retrospective observation of hands-free communication devices, and modelling of observed interruptions to key performance measures., Results: On average, RNs were interrupted every 11 min, with 20.3% of their workload triggered by interruptions. While 85% of RNs agreed that interruptions place their patients at risk, only 21% agreed that all should be eliminated. During one 90-min period, 18 original events spawned 68 interruptions, 50 of these repeat messages. A statistical model, with patient measures of time and comfort, revealed that alarms and call lights returning RN's attention to the patient outside the patient room are beneficial, while interruptions in the patient room are generally detrimental. Triangulating the results, we present an emerging framework for differentiating between beneficial and detrimental interruptions based on the impact of interruptions on the RN's steady treatment and attention to the patient., Conclusions: A mixed-methods approach can help distinguish between detrimental and beneficial interruptions. While interruptions breaking the delivery of steady treatment and attention to the patient are detrimental, those returning the RN's focus to the patient, as well as those supporting patient-clinician and clinician-clinician communications are beneficial. This insight may be helpful to healthcare delivery teams tasked with improving interruption-laden processes., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Background: Hospital financial pressures and inadequate reimbursement contribute to the closure of trauma centers. Uninsured patients contribute significantly to the burden of trauma center costs. The Affordable Care Act implemented changes in 2014 to provide health care coverage for all Americans. This study analyzes the impact of the recent health care changes on an Ohio Level I trauma center financials., Methods: We conducted an analysis of trauma charges, reimbursement, and supplemental payments at an Ohio Level I trauma center. A 3-year trauma patient cohort (2012-2014) was selected and grouped by reimbursement source (Medicare, Medicaid, other government, commercial, and self-pay/charity). A total of 9,655 patients were reviewed. Data were collected with the Transition Systems Inc. accounting system and analyzed with IBM SPSS Statistics 22.0., Results: For trauma cases, the percentage of self-pay/charity patients decreased during the 2012 to 2014 period (15.1%, 15%, to 6.4%, respectively), while the percentage of Medicaid decreased from 2012 to 2013 followed by a large increase in 2014 (15.4%, 13.9%, to 24.3%, respectively). The percentage of commercially insured patients decreased slightly from 2012 to 2014 (34.2%, 32.3%, to 30.7%, respectively). Uninsured charges decreased notably (approximately $22.5 million and $21 million for 2012-2013 to approximately $8.6 million in 2014). Medicaid charges decreased from 2012 to 2013, followed by a rebound in 2014 ($50.7 million in 2012 to $37.3 million in 2013 to $54.3 million in 2014). The percentage of total charges for self-pay/charity decreased (9.5%, 10.1%, to 4.1%). The percentage of total charges for Medicaid increased (21.4%, 18.0%, to 25.9%). Mean Medicaid reimbursement per patient decreased ($19,000, $14,000, to $13,000). Mean reimbursement per uninsured patient did not vary significantly among years. Total hospital supplemental payments (trauma and nontrauma combined) decreased ($47.6 million, $49 million, to $39.2 million)., Conclusion: In the first year following the changes implemented by the Affordable Care Act, our hospital saw self-pay/charity charges decrease, Medicaid charges increase, and total hospital supplemental payments decrease. In addition, there was a small, yet noteworthy, downward trend in the number of commercially insured patients. Although more data collection and analysis are needed, this initial financial evaluation of a Level I trauma center following the Affordable Care Act provides insight into insurance trends.

Background: Advanced practice providers (APPs) are essential to the provision of trauma care services, particularly in the wake of residency hour restrictions. Demand for these APPs fluctuates with cyclic patient arrivals; however, most trauma teams continue to staff APPs in a linear fashion. Failure to plan for variable arrivals may contribute to excessive patient wait times and emergency department overcrowding. This study used both qualitative and quantitative approaches to evaluate the impact of APP scheduling on patient wait time and to find schedules minimizing delays in reaching the needed care at the right time., Methods: A retrospective observation of the availability of APPs and the flow of 2,249 trauma patients at a Level 1 trauma center, using both visual overlays and computer modeling, allowed us to evaluate the baseline condition, two what-if schedules, and two model-generated schedules minimizing patient time without any additional APP hours., Results: A visual overlay of APP staffing on 2010 patient arrivals indicated substantial times of mismatch. Trauma managers considered adding an APP during weekday evenings that would have resulted in a 14.8% increase in APP hours and yielded a 27% reduction in patient wait times according to our model. An alternate schedule was developed and implemented in 2012 with a 10.5% increase in APP hours and yielding a 73% reduction in wait times. We also delineated two schedule options with 57% and 78% reductions in wait time and no increase in APP work hours., Conclusion: Evaluating alternate shift times and assignments using visual overlays and computer modeling can provide APP staffing solutions with up to 78% reduction in trauma patient wait time without additional APP labor. Knowing that care at the right time is crucial to arriving patients, making sure APP staffing is synchronized with arriving patients is something trauma center managers cannot ignore., Level of Evidence: Care management study, level IV.

Background: Motor vehicle-related trauma remains the leading cause of adolescent injury and death in the United States. We previously reported results from the Drive Alive (DA) program-a comprehensive juvenile prevention program that highlights risky driving behavior and consequences-and demonstrated a reduction in recidivism 6 months after its completion. We further evaluated the results of the original and subsequent participants on a long-term basis., Methods: Bureau of Motor Vehicle records of all individuals who had completed the DA program were prospectively reviewed. This 4-week, Level I trauma center-based program provides 10 contact hours of exposure to mock trauma sessions, drug and alcohol education, former trauma patients and their families, state troopers and other pertinent driving safety, educational, and prevention topics. The County Juvenile Court ordered participation after driving-related convictions. The driving records were compared with a control group consisting of adolescents convicted of similar driving offenses in the same period, not referred to the DA program. Comparisons were made at 6 monthly intervals up to 60 months using Fischer's exact test., Results: A total of 488 teens (346 male and 142 female) completed the DA program between May 2003 and October 2008. Mean participant age was 17.4 years. Speeding and driving under the influence of alcohol were the most frequent reasons for referral., Conclusion: Consistent with our prior results, this interactive intervention for juvenile driving offenders resulted in a statistically significant reduction in driving-related offenses for the 6-month periods after its completion. This effect is lost in the long term. The role of booster interventions at 6 months and beyond, as adjuncts to initial interventional prevention initiatives, needs to be explored to aid sustained positive effects in this population of drivers.