Day of the Week Variation in Emergency Department Arrivals, Chest Pain, and Acute Myocardial Infarction Throughout 2016-2019.

Overcrowding in the Emergency department (ED) necessitates a major challenge in delivering high-quality care in acute settings. This study presents a novel approach to modeling the relationship between the day of the week, ED arrivals, chest pain (CP), and acute myocardial infarction (AMI) using regression analysis. We analyzed data from 2016 to 2019 across three platforms: a nationwide representative sample (NHAMCS), a federated data network (TriNetX), and a regional medical center. For the stated three outcomes, the number of patients in that category on each day of the week was calculated; these were then calculated separately for each year, as well as across all four years. In line with prior studies, this study demonstrates the highest percentage of ED arrival on Mondays and the lowest on the weekends. Similarly, chest pain was more prevalent on Mondays, with similar patterns for TriNetX and the regional medical center. Analyzing NHAMCS data demonstrated Wednesdays as the busiest day for AMI-related ED arrivals, although this observation was not statistically significant. This knowledge will better aid us in resource allocation and system awareness, paving a path toward better patient care, improving disease management, and reducing healthcare costs.

The role of the lower extremity functional scale in predicting surgical outcomes for total joint arthroplasty patients.

BACKGROUND: The purpose of this study was to evaluate the relationship between lower extremity functional scale (LEFS) scores with postoperative functional outcomes for total joint arthroplasty (TJA) patients and to investigate the utility of this tool to create an individualized plan of care perioperatively. METHODS: Patients undergoing primary TJA at a single institution from 2016 to 2019 was retrospectively reviewed by a univariate analysis in terms of patient characteristics and outcomes across LEFS quartiles. Multivariate regression models were constructed to evaluate the association between the LEFS quartile and outcomes after controlling for confounding factors. RESULTS: A total of 1389 patients were included. All patients had a documented LEFS pre- and postoperatively with the last value documented at least 60 days to a maximum of 1 year after surgery. The following cutoffs for LEFS quartiles were observed: quartile 1 preoperative LEFS ≤27, quartile 2 ranges from 28 to 35, quartile 3 ranges from 36 to 43, and quartile 4 ≥ 44. Patients with a higher comorbidity burden and ASA score were more likely to have a lower LEFS. Higher levels of preoperative function were significantly associated with shorter LOS and higher rates of same day discharge, independent ambulation, mobility and activity scores, and rates of discharge home. CONCLUSION: These findings suggest that LEFS is a useful tool for aiding clinical resource allocation decisions, and incorporation of the measure into existing predictive models may improve their accuracy.

Preoperative Predictors of Same-Day Discharge After Total Knee Arthroplasty.

BACKGROUND: In January 2020, The Centers for Medicare and Medicaid Services approved total knee arthroplasty (TKA) to be performed in ambulatory surgery centers (ASCs). This study aims to develop a predictive model for targeting appropriate patients for ASC-based TKA. METHODS: A retrospective review of 2266 patients (205 same-day discharge [SDD; 9.0%] and 2061 one-day length of stay [91.0%]) undergoing TKA at a regional medical center between July 2016 and September 2020 was conducted. Multiple logistic regression was used to evaluate predictors of SDD, as these patients represent those most likely to safely undergo TKA in an ASC. RESULTS: Controlling for other demographics and comorbidities, patients with the following characteristics were at reduced odds of SDD: increased age (odds ratio [OR] = 0.935, P < .001), body mass index ≥35 (OR = 0.491, P = .002), female (OR = 0.535, P < .001), nonwhite race (OR = 0.456, P = .003), primary hypertension (OR = 0.710, P = .032), ≥3 comorbidities (OR = 0.507, P = .002), American Society of Anesthesiologists score ≥3 (OR = 0.378, P < .001). The model was deemed to be of adequate fit using the Hosmer and Lemeshow test (χ2 = 12.437, P = .112), and the area under the curve was found to be 0.773 indicating acceptable discrimination. CONCLUSION: For patients undergoing primary TKA, increased age, body mass index ≥35, female gender, nonwhite race, primary hypertension, ≥3 comorbidities, and American Society of Anesthesiologists score ≥3 decrease the likelihood of SDD. A predictive model based on readily available patient presentation and comorbidity characteristics may aid surgeons in identifying patients that are candidates for SDD or ASC-based TKA.

Preoperative Predictors of Patients Requiring Inpatient Admission for Total Hip Arthroplasty Following Removal From the Medicare Inpatient-Only List.

BACKGROUND: The Centers for Medicare and Medicaid Services has removed total hip arthroplasty from the inpatient-only (IO) list in January 2020. Given the confusion created when total knee arthroplasty came off the IO list in 2018, this study aims to develop a predictive model for guiding preoperative inpatient admission decisions based upon readily available patient demographic and comorbidity data. METHODS: This is a retrospective review of 1415 patients undergoing elective unilateral primary THA between January 2018 and October 2019. Multiple logistic regression was used to develop a model for predicting LOS ≥2 days based on preoperative demographics and comorbidities. RESULTS: Controlling for other demographics and comorbidities, increased age (odds ratio [OR], 1.048; P < .001), female gender (OR, 2.284; P < .001), chronic obstructive pulmonary disorder (OR, 2.249; P = .003), congestive heart failure (OR, 8.231; P < .001), and number of comorbidities (OR, 1.216; P < .001) were associated with LOS ≥2 days while patients with increased body mass index (OR, 0.964; P = .007) and primary hypertension (OR, 0.671; P = .008) demonstrated significantly reduced odds of staying in the hospital for 2 or more days. The area under the curve was found to be 0.731, indicating acceptable discriminatory value. CONCLUSION: For patients undergoing primary THA, increased age, female gender, chronic obstructive pulmonary disorder, congestive heart failure, and multiple comorbidities are risk factors for inpatient hospital LOS of 2 or more days. Our predictive model based on readily available patient presentation and comorbidity characteristics may aid surgeons in preoperatively identifying patients requiring inpatient admission with removal of THA from the Medicare IO list.

Decreasing Postanesthesia Care Unit to Floor Transfer Times to Facilitate Short Stay Total Joint Replacements.

PURPOSE: We describe a process for studying and improving baseline postanesthesia care unit (PACU)-to-floor transfer times after total joint replacements. DESIGN: Quality improvement project using lean methodology. METHODS: Phase I of the investigational process involved collection of baseline data. Phase II involved developing targeted solutions to improve throughput. Phase III involved measured project sustainability. FINDINGS: Phase I investigations revealed that patients spent an additional 62 minutes waiting in the PACU after being designated ready for transfer. Five to 16 telephone calls were needed between the PACU and the unit to facilitate each patient transfer. The most common reason for delay was unavailability of the unit nurse who was attending to another patient (58%). Phase II interventions resulted in transfer times decreasing to 13 minutes (79% reduction, P < .001). Phase III recorded sustained transfer times at 30 minutes, a net 52% reduction (P < .001) from baseline. CONCLUSIONS: Lean methodology resulted in the immediate decrease of PACU-to-floor transfer times by 79%, with a 52% sustained improvement. Our methods can also be used to improve efficiencies of care at other institutions.

Assessment of Opioid Prescribing Practices Before and After Implementation of a Health System Intervention to Reduce Opioid Overprescribing.

Importance: Overprescribing of opioids has generated and sustains the opioid overdose epidemic. Health systems have a responsibility to lead the effort to reduce overprescribing. Objective: To measure the effects of multilevel interventions on opioid prescribing within a health system. Design, Setting, and Participants: Quality improvement study comparing a 6-month preintervention baseline with a 16-month postintervention period ending in April 2018. Inpatient and outpatient clinical activity within a regional health system including an acute care hospital, same-day surgery, and outpatient clinics. Opioid prescribing activity by hundreds of clinicians involving over a million clinical encounters was measured using a health system's electronic medical record. Interventions: Multiple parallel interventions in different domains, including prescriber education and accountability, enhanced oversight via measurement of individual prescribers, tools to right-size postoperative discharge prescriptions, reduction of default amounts on standard opioid prescription orders, and professionally written patient and public education about opioid risks and alternatives. Main Outcomes and Measures: Morphine milligram equivalents (MME) per encounter per month, MME per opioid prescription, and rate of opioid prescriptions (opioid prescriptions per encounter per month). Results: More than 44 000 clinical encounters per month were recorded. All baseline trends were not significantly different from 0. Total health system MME per encounter decreased 1.0 MME per encounter per month. At the end of the postintervention observation period, the monthly MME per encounter was 58% lower than the average of the 6-month baseline, the MME per opioid prescription per month was 34% less than the average of the baseline, and the opioid prescription rate was 38% lower than the average of the baseline. Conclusions and Relevance: Opioid overprescribing was reduced with multifocal interventions targeting patient and public demand, creating prescriber awareness and accountability, and creating tools for clinical leadership accountability. The interventions described are adoptable by most organized health systems. Reducing total opioid supply within communities should be given high priority by those with a mission to protect and improve public health.