Background: The US residency application, interview, and match processes are costly and time-intensive. We sought to quantify the importance of an applicant being from the same-state as a residency program in terms of how this impacted the number of interviews needed to match. METHODS: We examined data from interview scheduling software used by 32 programs located in 31 US states and 1300 applicants for the US anesthesiology recruitment cycles from 2015 to 2018. Interviewee data (distance from program, region, numbers of interviews, and program at which interview occurred) were analyzed to quantify the effect of the interviewee being from the same state as the residency program on the odds of matching to that program. Other variables of interest (medical school, current address, US Medical Licensing Exam [USMLE] Step 1 and 2 clinical knowledge [CK] scores, Alpha Omega Alpha [AOA] status, medical school ranking) were also examined as controls. Confidence intervals (CI) were calculated for the ratios of odds ratios. RESULTS: An interviewee living in the same state as the interviewing program could have 5.42 fewer total interviews (97.5% CI, 3.02–7.81) while having the same odds of matching. The same state effect had an equivalent value as an approximately 4.14 USMLE points-difference from the program’s mean (97.5% CI was 2.34–5.94 USMLE points). Addition of whether the interviewee belonged to an affiliated medical school did not significantly improve the model; same-state remained significant (P < .0001) while affiliated medical school was not (P = .40). CONCLUSIONS: Our analysis of anesthesiology residency recruitment using previously unstudied interview data shows that same-state locality is a viable predictor of residency matching and should be strongly considered when evaluating whether to interview an applicant.
BACKGROUND: Intraoperative hypotension is common and associated with organ injury and death, although randomized data showing a causal relationship remain sparse. A risk-adjusted measure of intraoperative hypotension may therefore contribute to quality improvement efforts. METHODS: The measure we developed defines hypotension as a mean arterial pressure <65 mm Hg sustained for at least 15 cumulative minutes. Comparisons are based on whether clinicians have more or fewer cases of hypotension than expected over 12 months, given their patient mix. The measure was developed and evaluated with data from 225,389 surgeries in 5 hospitals. We assessed discrimination and calibration of the risk adjustment model, then calculated the distribution of clinician-level measure scores, and finally estimated the signal-to-noise reliability and predictive validity of the measure. RESULTS: The risk adjustment model showed acceptable calibration and discrimination (area under the curve was 0.72 and 0.73 in different validation samples). Clinician-level, risk-adjusted scores varied widely, and 36% of clinicians had significantly more cases of intraoperative hypotension than predicted. Clinician-level score distributions differed across hospitals, indicating substantial hospital-level variation. The mean signal-to-noise reliability estimate was 0.87 among all clinicians and 0.94 among clinicians with >30 cases during the 12-month measurement period. Kidney injury and in-hospital mortality were most common in patients whose anesthesia providers had worse scores. However, a sensitivity analysis in 1 hospital showed that score distributions differed markedly between anesthesiology fellows and attending anesthesiologists or certified registered nurse anesthetists; score distributions also varied as a function of the fraction of cases that were inpatients. CONCLUSIONS: Intraoperative hypotension was common and was associated with acute kidney injury and in-hospital mortality. There were substantial variations in clinician-level scores, and the measure score distribution suggests that there may be opportunity to reduce hypotension which may improve patient safety and outcomes. However, sensitivity analyses suggest that some portion of the variation results from limitations of risk adjustment. Future versions of the measure should risk adjust for important patient and procedural factors including comorbidities and surgical complexity, although this will require more consistent structured data capture in anesthesia information management systems. Including structured data on additional risk factors may improve hypotension risk prediction which is integral to the measure’s validity.
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
Operating room schedules and plans for resource utilization should recognize that the same bariatric procedure will require more time for patients with BMI >60 kg/m(2) than for smaller bariatric patients.
While a number of studies have examined efficiency metrics in the operating rooms (ORs), there are few studies addressing non-operating room anesthesia (NORA) metrics. The standards established in the realm of OR studies may not apply to ongoing investigations of NORA efficiency. We hypothesize that there are significant differences in these commonly used metrics. Using retrospective data from a single tertiary care hospital in the 2015 calendar year, we measured turnover times, cancellation rates, first case start delays, and scheduling error (actual time minus scheduled time) for the OR and NORA settings. On average, TOTs for NORA cases were approximately 50% shorter than OR cases (16.21 min vs. 37.18 min), but had a larger variation (11.02 min vs. 8.12 min). NORA cases were 64% as likely to be cancelled compared to OR cases. In contrast, NORA cases had an average first case start delay that was two times greater than that of OR cases (24.45 min vs. 10.58 min), along with over double the standard deviation (11.97 min vs. 5.90 min). Case times for NORA settings tended to be overestimated (-4.07 min versus -2.12 min), but showed less variation (8.61 min vs. 17.92 min). In short, there are significant differences in common efficiency metrics between OR and NORA cases. Future studies should elucidate and validate appropriate efficiency benchmarks for the NORA setting.
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