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OBJECTIVE: To determine whether trauma patients managed by an admitting or consulting service with a high proportion of physicians exhibiting patterns of unprofessional behaviors are at greater risk of complications or death. SUMMARY BACKGROUND DATA: Trauma care requires high-functioning interdisciplinary teams where professionalism, particularly modeling respect and communicating effectively, is essential. METHODS: This retrospective cohort study used data from 9 level I trauma centers that participated in a national trauma registry linked with data from a national database of unsolicited patient complaints. The cohort included trauma patients admitted January 1, 2012 through December 31, 2017. The exposure of interest was care by 1 or more high-risk services, defined as teams with a greater proportion of physicians with high numbers of patient complaints. The study outcome was death or complications within 30âdays. RESULTS: Among the 71,046 patients in the cohort, 9553 (13.4%) experienced the primary outcome of complications or death, including 1875 of 16,107 patients (11.6%) with 0 high-risk services, 3788 of 28,085 patients (13.5%) with 1 high-risk service, and 3890 of 26,854 patients (14.5%) with 2+ highrisk services (P < 0.001). In logistic regression models adjusting for relevant patient, injury, and site characteristics, patients who received care from 1 or more high-risk services were at 24.1% (95% confidence interval 17.2% to 31.3%; P < 0.001) greater risk of experiencing the primary study outcome. CONCLUSIONS: Trauma patients who received care from at least 1 service with a high proportion of physicians modeling unprofessional behavior were at an increased risk of death or complications.
Subject(s)
Professionalism , Wounds and Injuries , Cohort Studies , Hospitalization , Humans , Retrospective Studies , Trauma Centers , Wounds and Injuries/therapyABSTRACT
OBJECTIVE: Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients. DESIGN: Retrospective cohort study. SETTING: Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015. PATIENTS: Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less. EXPOSURE: ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles. MEASUREMENTS AND MAIN RESULTS: We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p < 0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs). CONCLUSIONS: Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.
Subject(s)
Intensive Care Units/statistics & numerical data , APACHE , Female , Hospital Mortality , Humans , Male , Middle Aged , Retrospective Studies , Risk Factors , Treatment OutcomeABSTRACT
OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. DESIGN: We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. SETTING: Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members. PATIENTS: ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. CONCLUSIONS: A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.
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Cluster Analysis , Critical Care , Intensive Care Units , Machine Learning , Aged , California , Female , Humans , Male , Middle Aged , Needs AssessmentABSTRACT
Surgical interventions are common among seriously ill older patients, with nearly one-third of older Americans facing surgery in their last year of life. Despite the potential benefits of palliative care among older surgical patients undergoing high-risk surgical procedures, palliative care in this population is underutilized and little is known about potential disparities by race/ethnicity and how frailty my affect such disparities. The aim of this study was to examine disparities in palliative care consultations by race/ethnicity and assess whether patients' frailty moderated this association. Drawing on a retrospective cross-sectional study of inpatient surgical episodes using the National Inpatient Sample of the Healthcare Cost and Utilization Project from 2005 to 2019, we found that frail Black patients received palliative care consultations least often, with the largest between-group adjusted difference represented by Black-Asian/Pacific Islander frail patients of 1.6 percentage points, controlling for sociodemographic, comorbidities, hospital characteristics, procedure type, and year. No racial/ethnic difference in the receipt of palliative care consultations was observed among nonfrail patients. These findings suggest that, in order to improve racial/ethnic disparities in frail older patients undergoing high-risk surgical procedures, palliative care consultations should be included as the standard of care in clinical care guidelines.
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BACKGROUND: Intercostal nerve cryoablation (IC) offers potential for targeted and durable analgesia for patients with traumatic rib fractures. Our pilot study aimed to investigate thoracoscopic IC's safety, feasibility, and preliminary efficacy for patients undergoing surgical stabilization of rib fractures (SSRF). We hypothesized that concurrent surgical stabilization of rib fractures and intercostal nerve cryoablation (SSRF-IC) is a safe and feasible procedure without immediate or long-term complications. METHODS: We retrospectively evaluated patients 18 years or older who underwent SSRF (with or without IC) for acute rib fractures at our level I trauma center between September 1, 2019, and September 30, 2020. We performed IC under thoracoscopic visualization (-70°C for 2 minutes per intercostal nerve bundle). Among patients whose only operative procedure during hospitalization was SSRF, we evaluated post-SSRF length of stay, operative times, opioid requirements (oral morphine equivalents), and pain scores (Numerical Rating Scale). Generalized estimating equations compared SSRF and SSRF-IC group outcomes (population mean [robust standard error]). We assessed long-term outcomes of patients who underwent SSRF-IC. RESULTS: Thirty-four patients (144 ribs) underwent SSRF; of these, 20 patients (135 ribs) underwent SSRF-IC. Patients who did and did not undergo concurrent IC had no significant difference demographic, injury, or hospitalization characteristics. Among 20 patients who did not undergo other operations, 12 underwent SSRF-IC. We did not find significant difference between SSRF and SSRF-IC groups' median operative times or post-SSRF length of stay. Compared with SSRF group, SSRF-IC group did not have statistically significant change in pain score (0.2 [1.5] lower) or opioid use (43.9 [86.1] mg/d greater) between 12 hours before SSRF and last 24 admission hours. Among 17 SSRF-IC patients who followed-up postdischarge (median [range], 160 [9-357] days), one reported mild chest wall paresthesia; no other complications were reported. CONCLUSION: This pilot study performing 135 intercostal nerve cryoablations on 20 patients suggests that IC is safe and feasible for patients undergoing SSRF. Evaluating IC's analgesic efficacy for rib fractures requires further study. LEVEL OF EVIDENCE: Therapeutic, Level V.
Subject(s)
Analgesia/methods , Cryosurgery/methods , Intercostal Nerves/surgery , Rib Fractures , Thoracic Injuries , Thoracoscopy/methods , Female , Fracture Fixation/adverse effects , Fracture Fixation/methods , Hospitalization/statistics & numerical data , Humans , Length of Stay , Male , Middle Aged , Operative Time , Outcome and Process Assessment, Health Care , Pain Measurement/methods , Pilot Projects , Rib Fractures/complications , Rib Fractures/physiopathology , Rib Fractures/surgery , Thoracic Injuries/diagnosis , Thoracic Injuries/surgeryABSTRACT
Introduction Patient care in the trauma-surgical intensive care unit (SICU) requires trust and effective communication between nurses and physicians. Our SICU suffered from poor communication and trust between nurses and physicians, negatively impacting the working environment and, potentially, patient care. Methods A SICU Task Force studied communication practices and identified areas for improvement, leading to several interventions. The daily physician rounding was altered to improve communication and to enhance the role of the registered nurses (RN) in rounds. Additionally, a formal night resident rounding time was implemented. Results A post-intervention survey focusing on cooperation, teamwork, and appreciation between nurses and physicians revealed improvement in these domains. Informal feedback from nurses and physicians indicated improved working relationships and satisfaction with the SICU environment. However, results of a national survey performed after the intervention did not show the same level of improvement. Conclusions A Task Force consisting of SICU nurses and physicians can effectively study a widespread communication issue and implement targeted interventions. While informal feedback may indicate improvement, it can be difficult to demonstrate improvement using formal surveys.
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INTRODUCTION: There is substantial variability in intensive care unit (ICU) utilisation and quality of care. However, the factors that drive this variation are poorly understood. This study uses a novel adaptation of positive deviance approach-a methodology used in public health that assumes solutions to challenges already exist within the system to detect innovations that are likely to improve intensive care. METHODS AND ANALYSIS: We used the Philips eICU Research Institute database, containing 3.3 million patient records from over 50 health systems across the USA. Acute Physiology and Chronic Health Evaluation IVa scores were used to identify the study cohort, which included ICU patients whose outcomes were felt to be most sensitive to organisational innovations. The primary outcomes included mortality and length of stay. Outcome measurements were directly standardised, and bootstrapped CIs were calculated with adjustment for false discovery rate. Using purposive sampling, we then generated a blinded list of five positive outliers and five negative comparators.Using rapid qualitative inquiry (RQI), blinded interdisciplinary site visit teams will conduct interviews and observations using a team ethnography approach. After data collection is completed, the data will be unblinded and analysed using a cross-case method to identify themes, patterns and innovations using a constant comparative grounded theory approach. This process detects the innovations in intensive care and supports an evaluation of how positive deviance and RQI methods can be adapted to healthcare. ETHICS AND DISSEMINATION: The study protocol was approved by the Stanford University Institutional Review Board (reference: 39509). We plan on publishing study findings and methodological guidance in peer-reviewed academic journals, white papers and presentations at conferences.
Subject(s)
Critical Care/standards , Hospital Mortality , Intensive Care Units/statistics & numerical data , Organizational Innovation , Databases, Factual , Double-Blind Method , Focus Groups , Humans , Length of Stay/statistics & numerical data , Outcome Assessment, Health Care , Qualitative Research , Research Design , United States/epidemiologySubject(s)
Artificial Intelligence , Computers , Humans , Intelligence , Neural Networks, Computer , Reference StandardsSubject(s)
Elective Surgical Procedures/standards , Patient Safety/standards , Quality Improvement/standards , Surgical Procedures, Operative/standards , Emergency Service, Hospital , Emergency Treatment , Humans , Practice Guidelines as Topic , Quality Assurance, Health Care , Quality Improvement/organization & administration , United StatesABSTRACT
This paper examines several different queuing models for intensive care units (ICU) and the effects on wait times, utilization, return rates, mortalities, and number of patients served. Five separate intensive care units at an urban hospital are analyzed and distributions are fitted for arrivals and service durations. A system-based simulation model is built to capture all possible cases of patient flow after ICU admission. These include mortalities and returns before and after hospital exits. Patients are grouped into 9 different classes that are categorized by severity and length of stay (LOS). Each queuing model varies by the policies that are permitted and by the order the patients are admitted. The first set of models does not prioritize patients, but examines the advantages of smoothing the operating schedule for elective surgeries. The second set analyzes the differences between prioritizing admissions by expected LOS or patient severity. The last set permits early ICU discharges and conservative and aggressive bumping policies are contrasted. It was found that prioritizing patients by severity considerably reduced delays for critical cases, but also increased the average waiting time for all patients. Aggressive bumping significantly raised the return and mortality rates, but more conservative methods balance quality and efficiency with lowered wait times without serious consequences.