Decreasing Emergency Department Walkout Rate and Boarding Hours by Improving Inpatient Length of Stay

Driving in the Wrong Direction: Exploring the Unintended Consequence of an Early Discharge Program on Length of Stay in Hospital Setting

IMPORTANCE

Early discharge of patients has become standard work in acute care settings to reduce inpatient length of stay (LOS), improve patient flow, and reduce boarding in the emergency department (ED).

OBJECTIVE

Retrospective analysis of outcomes from a discharge by 11 am program at an academic medical center from January 1, 2020, to June 30, 2022. The analysis addresses the effects of a discharge by 11 am goal on time from discharge order release to patient discharge, ED boarding, LOS, and observed-to-expected LOS.

DESIGN, SETTING, AND PARTICIPANTS

Patient-level electronic health record data included discharge order entry time, discharge time, LOS, and diagnosis-related group geometric LOS (GMLOS). Additional unit-level data for ED boarding volumes and hours were included. Analyses were conducted at the hospital and unit levels where indicated.

RESULTS

Patients with a discharge order by 9 am have longer mean hours from order to discharge than patients without a discharge order by 9 am (9.04 vs 2.48 hours, P < .001) ED boarding total (R2 = 46.2%, P ≤ .001), percentage (R2 = 50.4%, P ≤ .001), median minutes (R2 = 24.6%, P = .005), and total minutes (R2 = 40.8%, P ≤ .001) all increased as discharge by 11 am performance improved. The mean LOS is longer for the discharge by 11 am group than the non-discharge by 11 am group -1.67; 95% CI, -2.03 to -1.28, P < .001). Discharge by 11 am patients had a LOS/GMLOS ratio 21.9% higher than the non-discharge by 11 am cohort (difference -0.31; 95% CI, -0.36 to -0.26, P < .001).

CONCLUSIONS

Discharge order entry and release by 9 am and patient physically discharged by 11 am initiatives demonstrate a statistical increase in time from discharge order to discharge time, ED boarding, LOS, and observed-to-expected LOS.

Lean and/or Six Sigma for process optimization in the perioperative period: an integrative review

OBJECTIVE

To analyze the evidence on the influence of Lean and/or Six Sigma for process optimization in the perioperative period.

METHODS

Integrative review carried out in the MEDLINE (PubMed), Web of Science, EMBASE, CINAHL, Scopus and LILACS databases on the use of Lean and/or Six Sigma to optimize perioperative processes. The studies included were analyzed in three thematic categories: flow of surgical patients, work process and length of stay.

RESULTS

The final sample consisted of ten studies, which covered all operative periods. Lean and/or Six Sigma make a significant contribution to optimizing perioperative processes.

FINAL CONSIDERATIONS

Lean and/or Six Sigma optimize perioperative processes to maximize the achievement of system stability indicators, making it possible to identify potential problems in order to recognize them and propose solutions that can enable the institution of patient-centered care.

Prospective application of the interdisciplinary bedside rounding checklist 'TEMP' is associated with reduced infections and length of hospital stay

Protocols that enhance communication between nurses, physicians and patients have had a variable impact on the quality and safety of patient care. We combined standardised nursing and physician interdisciplinary bedside rounds with a mnemonic checklist to assure all key nursing care components were modified daily. The mnemonic TEMP allowed the rapid review of 11 elements. T stands for tubes assuring proper management of intravenous lines and foleys; E stands for eating, exercise, excretion and sleep encouraging a review of orders for diet, exercise, laxatives to assure regular bowel movements, and inquiry about sleep; M stands for monitoring reminding the team to review the need for telemetry and the frequency of vital sign monitoring as well as the need for daily blood tests; and P stands for pain and plans reminding the team to discuss pain medications and to review the management plan for the day with the patient and family. Faithful implementation eliminated central line-associated bloodstream infections and catheter-associated urinary tract infections and resulted in a statistically significant reduction in average hospital length of stay of 13.3 hours, one unit achieving a 23-hour reduction. Trends towards reduced 30-day readmissions (20% down to 10%-11%) were observed. One unit improved the percentage of patients who reported nurses and doctors always worked together as a team from a 56% baseline to 75%. However, the combining of both units failed to demonstrate statistically significant improvement. Psychologists well versed in implementing behavioural change were recruiting to improve adherence to our protocols. Following training physicians and nurses achieved adherence levels of over 70%. A high correlation (r²=0.69) between adherence and reductions in length of stay was observed emphasising the importance of rigorous training and monitoring of performance to bring about meaningful and reliable improvements in the efficiency and quality of patient care.

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the massive medical needs of patients. In this process, the hospital length of stay (LOS) of different patients is a crucial reference to the management department. Therefore, building a model to predict LOS is of great significance. Five machine learning (ML) algorithms named Lasso regression (LR), ridge regression (RR), random forest regression (RFR), light gradient boosting machine (LightGBM), and extreme gradient boosting regression (XGBR) and six feature encoding methods named label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method are used to construct the regression prediction model. The Scikit-Learn toolbox on the Python platform builds the prediction model. The input is the dataset named Hospital Inpatient Discharges (SPARCS De-Identified) 2017 with 2343569 instances provided by the New York State Department of Health verify the model after removing 2.2% of the missing data, and the model ultimately uses mean squared error (MSE) and coefficient of determination (R2) as the performance measurement. The results show that the model with the LightGBM algorithm and the proposed encoding method has the best R2 (96.0%) and MSE score (2.231).

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean–Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications.

Critical Success Factors for Addressing Discharge Inefficiency at a Large Academic Medical Center: A Lean Six Sigma Approach

BACKGROUND

Delayed discharges can be a systemic issue. Understanding the systemic factors that contribute to discharge inefficiencies is essential to addressing discharge inefficiencies.

PURPOSE

This article reports on a Lean Six Sigma approach and the process to identifying inefficiencies and systemic barriers to early discharge in a large US academic medical center.

METHODS

A qualitative methodology guided this project. In particular, direct observation methods were used to help the project team identify factors contributing to discharge inefficiencies.

RESULTS

Overall, findings suggest that establishing consistent multidisciplinary team communication processes was a contributing factor to reducing the inefficiencies around discharges. On a more granular level, key barriers included disparate communication systems, disruptors (specifically Kaizen bursts), and unique role challenges.

CONCLUSIONS

This article provides a framework for addressing discharge inefficiencies. Because the output of the process, a critical contributor to the overall outcome, is often not analyzed, this analysis provides value to others contemplating the same or similar process toward discharge efficiency.

Using Accountable Care Teams to Improve Timely Discharge: A Pilot Study

BACKGROUND AND OBJECTIVE

Hospitals worldwide are faced with the problem of discharging patients on time. Delayed discharge creates domino effects with significant implications for hospitals. The accountable care team (ACT) is a multidisciplinary, unit-based approach to identifying opportunities to improve patient care and address inefficiencies in care delivery and throughput, including assuring timely discharges. In response to concerns about emergency department boarding times and delays in timely discharge, the ACT recommended a set of strategies to improve communication across team members and to reduce wait times for transportation within and outside the hospital. Collectively these strategies were thought to increase the proportion of patients discharged on time. In this article, we describe and assess changes in timely discharge resulting from the implementation of strategies recommended by the ACT.

METHODS

This study uses a retrospective, quasi-experimental design to compare the percentage of discharges by 1 pm of hospital units implementing the ACT intervention to those units not implementing the intervention. Median discharge time was compared pre- and post-implementation using the Wilcoxon rank sum test. Difference-in-differences modeling was employed to assess whether changes in the percentage of discharges by 1 pm differed between units implementing the intervention and units not implementing the intervention.

RESULTS

One month post-implementation, the percentage discharged by 1 pm was statistically significantly higher for units implementing the intervention (53.6%) compared with comparison units (22.5%, t = -4.48, P < .01). Statistically significant differences in the percentage discharged by 1 pm were also seen at 3 and 6 months post-implementation. The median discharge time showed a statistically significant decrease by 77 minutes from the baseline to intervention period ( P < .01).

CONCLUSION

The result from the study suggests that ACTs can be used to develop approaches aimed at improving patient care in general, and discharge efficiencies in particular. Health care organizations are encouraged to utilize and then evaluate the specific activities of multidisciplinary teams aimed at developing recommendations for practice improvement.

The Impact of Hospitalist Switch Day on Length of Stay

BACKGROUND AND OBJECTIVE

Hospitalist practices around the country switch service on different days of the week. It is unclear whether switching clinical service later in the week is associated with an increase in length of stay (LOS). The aim of this study was to examine the association between service switch day for hospitalists at an academic medical center and LOS.

METHODS

A single-center, cross-sectional study examined 4284 discharges from hospitalist staffed general internal medicine ward teams over a 1-year period between July 2018 and June 2019. Hospitalist service switch day changed from Tuesday to Thursday on January 1, 2019. The period between July 1, 2018, and December 31, 2018, was defined as the pre-switch time, while January 1, 2019, to June 30, 2019, was defined as the post-switch period. We calculated the LOS in days for patients discharged from hospitalist general internal medicine teams in the 2 periods. Generalized linear models were used to examine the association between attending switch day and LOS while adjusting for demographic factors, payer status, markers of severity of illness, and hospital or discharge-level confounders.

RESULTS

There was no difference in mean LOS for patients discharged in the pre-switch time (6 days) period versus patients discharged in the post-switch time (6.03 days) (difference of means 0.03 days, 95% confidence interval -0.04 to 0.09, P value .37).

CONCLUSIONS

Change in attending switch day from earlier in the week to later in the week is not associated with an increase in LOS. Other factors such as group preference and institutional needs should drive service switch day selection for hospitalist groups.

The Effect of a Nursing Hold Team on Patient Satisfaction for Admitted Patients Discharged Directly From the Emergency Department

Objectives: Emergency departments (ED) across the United States face challenges related to patient volume, available capacity, and patient throughput. Patient satisfaction is adversely affected by crowding and lengthy boarding times. This study aimed to determine whether the implementation of a dedicated nursing hold team (NHT) would improve patient satisfaction scores for admitted patients discharged directly from the ED.

Methods: This was a retrospective, observational study with a pre-/post-test design. All admitted adult patients who returned a Press Ganey (PG) survey were included in the study. There were two twelve-month study periods before and after implementing an ED NHT. The primary outcome was the percentage of patients who gave top box scores for all questions in the Nursing Communication Domain.

Results: During the pre-implementation period, 108 patients (59%) gave an overall top box rating for the Nursing Communication Domain versus the post-implementation period, where 99 patients (66%) provided a top box rating (OR 1.375, p = 0.16). There was a trend toward increased satisfaction for individual categories. However, these differences were not statistically significant.

Conclusions: Implementing a dedicated NHT showed an increase in the overall top box PG Nursing Communication Domain score and several of the individual domain questions. Future studies should examine other potential benefits from a dedicated NHT, such as the rate of adverse events and medication delays.

Applying health-six-sigma principles helps reducing the variability of length of stay in the emergency department

BACKGROUND

Reducing length of stay (LOS) is one of the urgent problems in healthcare systems worldwide. Popular methods that are used to reduce LOS are the Lean and the 6 Sigma, which in practice result in limited improvements. In this paper, we introduce and test a tailored method for implementing the 6 Sigma principles in healthcare (we call H-6S).

OBJECTIVE

To reduce the variability in the time between admission and discharge of patients in the emergency department.

METHODS

The study took place within the emergency department (ED) of Josephtal Medical Center in Eilat, Israel. Our analysis focused on the processes of examining and treating patients from admission to ED until discharge home. The analysis was done during the second quarter of 2018. The implementation of the recommendations took place during Q3 2018. The reported results are from Q3 2018 to Q2 2019, compared to the corresponding period in 2017 (experienced team).

RESULTS

In Q2 2017, LOS was 2.42 ± 2.07 h (experienced team, n = 9928). In Q2 2018, the LOS was 2.62 ± 7.04 h (before the H-6S, inexperienced team, n = 9484). In Q2 2019 following the intervention, it reached 2.3 ± 1.74 h (n = 7647). The differences between the standard deviations of the three periods are significant.

CONCLUSION

Implementing H-6S dropped the variance of LOS within 3 months and remained low for the whole year. Each new team of physicians who enter the ED should be thoroughly instructed as to the routines and expectations of the system from them, which should narrow the differences of previous education between them.

Assessing the Impact of Lean Healthcare on Inpatient Care: A Systematic Review

Healthcare services are facing challenges in increasing their efficiency, quality of care, and coping with surges in demand. To this end, some hospitals have implemented lean healthcare. The aim of this systematic review is to evaluate the effects of lean healthcare (LH) interventions on inpatient care and determine whether patient flow and efficiency outcomes improve. The review was performed according to PRISMA. We used six databases to search for studies published from 2002 to 2019. Out of 5732 studies, 39 measuring one or more defined outcomes were included. Hospital length of stay (LOS) was measured in 23 studies, 16 of which reported a reduction, turnover time (TOT) decreased in six out of eight studies, while the turnaround time (TAT) and on-time starts (OTS) improved in all five and seven studies, respectively. Moreover, eight out of nine studies reported an earlier discharge time, and the boarding time decreased in all four cases. Meanwhile, the readmission rate did not increase in all nine studies. Lastly, staff and patient satisfaction improved in all eight studies. Our findings show that by focusing on reducing non-value-added activities, LH contributed to improving patient flow and efficiency within inpatient care.

The Distributions of Weekday Discharge Times at Acute Care Hospitals in the State of Florida were Static from 2010 to 2018

When hospital capacity is near census, either due to limits on the number of physical or staffed beds, delays in patients' discharge can result in domino effects of congestion for the emergency department, the intensive care units, the postanesthesia care unit, and the operating room. Hospital administrators often promote increasing the percentage of patients discharged before noon as mitigation. However, benchmark data from multiple hospitals are lacking. We studied the time of weekday inpatient discharges from all 202 acute care hospitals in the state of Florida between 2010 and 2018 using publicly available data. Statewide, the average length of stay (4.63 days) did not change, but hospital discharges increased 6.1%. There was no change over years in the percentage of patients discharged before 12 noon (13.0% ± 0.28% standard error [SE]) or before 3 PM (42.2% ± 0.25% SE). For every year, the median hour of patient discharge was 3 PM. Only 9 of the 202 hospitals (4.5%) reliably achieved a morning weekday discharge rate ≥ 20.0%. Only 19 hospitals (9.4%) in the state reliably achieved a ≥ 50.0% weekday discharge rate before 3 PM. Hospital administrators seeking to achieve earlier patient discharges can use our provided data as realistic benchmarks to guide efforts. Alternatively, administrators could plan based on a model that beds will not be reliably available for new patients until late in the afternoon and apply other well-developed operational strategies to address bottlenecks affecting the internal transfer of patients within the hospital.

Impact of Emergency Department Phlebotomists on Left-Before-Treatment-Completion Rates

Introduction

The emergency department (ED) serves as the primary access point to the healthcare system. ED throughput efficiency is critical. The percentage of patients who leave before treatment completion (LBTC) is an important marker of department efficiency. Our study aimed to assess the impact of an ED phlebotomist, dedicated to obtaining blood specimen collection on waiting patients, on LBTC rates.

Methods

This study was conducted as a retrospective observational analysis over approximately 18 months (October 5, 2015–March 31, 2017) for patients evaluated by a triage provider with a door-to-room (DtR) time of > 20 minutes (min). LBTC rates were compared in 10-min DtR increments for when the ED phlebotomist collected the patient’s specimen vs not.

Results

Of 71,942 patient encounters occurring during the study period, 17,349 (24.1%) met study inclusion criteria. Of these, 1842 (10.6%) had blood specimen collection performed by ED phlebotomy. The overall LBTC rate for encounters included in the analysis was 5.26% (95% confidence interval [CI], 4.94%–5.60%). Weighting the LBTC rates for each 10-min DtR interval using the fixed effects model led to an overall LBTC rate of 2.74% (95% CI, 2.09%–3.59%) for patient encounters with ED phlebotomist collection vs 5.31% (95% CI, 4.97%–5.67%) in those which did not, yielding a relative reduction of 48% (95% CI, 34%–63%). The effect of the phlebotomist on LBTC rates increased as DtR times increased. The difference in the rate of the rise of LBTC percentages, per 10-min interval, was 0.50% (95% CI, 0.19%–0.81%) higher for non-ED phlebotomist encounters vs phlebotomist encounters.

Conclusion

ED phlebotomy demonstrated a significant reduction in ED LBTC rates. Further, as DtR times increased, the impact of ED phlebotomy became increasingly significant. Adult EDs with increased rates of LBTC patient encounters may want to consider the implementation of ED phlebotomy.