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

The Gap Between Daily Hospital Bed Supply and Demand: Design, Implementation, and Impact of Data-Driven Pre-Noon Discharge Targets

BACKGROUND

Hospitals have sought to increase pre-noon discharges to improve capacity, although evidence is mixed on the impact of these initiatives. Past interventions have not quantified the daily gap between morning bed supply and demand. The authors quantified this gap and applied the pre-noon data to target a pre-noon discharge initiative.

METHODS

The study was conducted at a large hospital and included adult and pediatric medical/surgical wards. The researchers calculated the difference between the average cumulative bed requests and transfers in for each hour of the day in 2018, the year prior to the intervention. In 2019 an intervention on six adult general medical and two surgical wards was implemented. Eight intervention and 14 nonintervention wards were compared to determine the change in average cumulative pre-noon discharges. The change in average hospital length of stay (LOS) and 30-day readmissions was also calculated.

RESULTS

The average daily cumulative gap by noon between bed supply and demand across all general care wards was 32.1 beds (per ward average, 1.3 beds). On intervention wards, mean pre-noon discharges increased from 4.7 to 6.7 (p < 0.0000) compared with the nonintervention wards 14.0 vs. 14.6 (p = 0.19877). On intervention wards, average LOS decreased from 6.9 to 6.4 days (p < 0.001) and readmission rates were 14.3% vs 13.9% (p = 0.3490).

CONCLUSION

The gap between daily hospital bed supply and demand can be quantified and applied to create pre-noon discharge targets. In an intervention using these targets, researchers observed an increase in morning discharges, a decrease in LOS, and no significant change in readmissions.

Logistic Regression and Machine Learning Models for Predicting Whether Intensive Care Patients Who Are Alert and Without Delirium Remain As Such for at Least Two More Days

Background Some intensive care unit patients are alert and without delirium for at least two consecutive days. These patients, like other critically ill individuals, are at risk for dignity-related distress. An interval of at least two days would provide for a palliative care multidisciplinary team to be consulted in the late morning or afternoon of day one and visit the next day. An assessment would include the administration of the validated Patient Dignity Inventory in a reflective manner. To determine whether dignity-related distress can be identified and treated during patients' intensive care unit stay, we evaluated whether a substantive fraction of such patients (≥5%) have a substantial (>90%) probability of remaining alert and without delirium in the intensive care unit for at least four consecutive days. Methods The retrospective cohort study used data from one large teaching hospital in the United States of America, from 2012 to June 2022. The inclusion criteria were: a) adults, b) present in an intensive care unit at 12 PM one day and continually so for the next 48 hours, c) during those two days had every Riker sedation-agitation scale score "4, calm and cooperative," and d) during those two days had all Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) scores negative (i.e., no delirium) and all Delirium Observation Screening Scale (DOS) scores less than three (i.e., no delirium). Results Among the 10,314 patients alert and without delirium in an intensive care unit over two-day periods that included three successive 12 PMs, 3,826 (37%) maintained this status for at least two successive 12 PMs. Six patient characteristics (e.g., hemodynamic infusion or ventilatory support) had value in predicting those 37% of patients. However, logistic regression and classification models each predicted a few (≈0.2%) patients with >90% probability of maintaining these criteria. Forecasts were inaccurate for nearly all patients remaining alert and without delirium in the intensive care unit (≈37%) because the models predicted no patient alert, without delirium, and in the intensive care unit for two days would remain so for at least four days. That ≈63% accuracy was improved upon by random forest machine learning, but only with ≈3% improvement. Conclusion Although many intensive care unit patients remain alert and without delirium for several consecutive days, each patient has a high daily probability of intensive care unit discharge or deterioration in medical condition. Therefore, the results of our prediction modeling show that care models for the assessment and treatment of patients with intensive care unit-associated dignity-related distress should not rely solely on the intensive care unit team but instead should be taken from the perspective of the entire hospitalization.

Rhythmicity of patient flow in an acute medical unit: relationship to hospital occupancy, 7-day working and the effect of COVID-19

BACKGROUND

The Acute Medical Unit (AMU) provides care for unscheduled hospital admissions. Seven-day consultant presence and morning AMU discharges have been advocated to improve hospital bed management.

AIMS

To determine whether a later time of daily peak AMU occupancy correlates with measures of hospital stress; whether 7-day consultant presence, for COVID-19, abolished weekly periodicity of discharges.

DESIGN

Retrospective cohort analysis.

METHODS

: Anonymised AMU admission and discharge times were retrieved from the Profile Information Management System (PIMS), at a large, urban hospital from 14 April 2014 to 31 December 2018 and 20 March to 2 May 2020 (COVID-19 peak). Minute-by-minute admission and discharge times were combined to construct a running total of AMU bed occupancy. Fourier transforms were used to determine periodicity. We tested association between (i) average AMU occupancy and (ii) time of peak AMU occupancy, with measures of hospital stress (total medical bed occupancy and 'medical outliers' on non-medical wards).

RESULTS

: Daily, weekly and seasonal patterns of AMU bed occupancy were evident. Timing of AMU peak occupancy was unrelated to each measure of hospital stress: total medical inpatients (Spearman's rho, rs = 0.04, P = 0.24); number of medical outliers (rs = -0.06, P = 0.05). During COVID-19, daily bed occupancy was similar, with continuation of greater Friday and Monday discharges than the weekend.

CONCLUSIONS

: Timing of peak AMU occupancy did not alter with hospital stress. Efforts to increase morning AMU discharges are likely to have little effect on hospital performance. Seven-day consultant presence did not abolish weekly periodicity of discharges-other factors influence weekend discharges.

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases' Contributions to Hospital Occupancy

Introduction When the hospital census is high, perioperative medical directors or operating room (OR) managers may need to consider postponing some surgical cases scheduled to be performed within the next three workdays. This scenario has arisen at hospitals in regions with large increases in admissions due to coronavirus disease 2019 (COVID-19). We compare summary measures for hospital length of stay (LOS) to guide the OR manager having to decide which cases may need to be postponed to ensure a sufficient reserve of available inpatient beds. Methods We studied the 1,201,815 ambulatory and 649,962 inpatient elective cases with a major therapeutic procedure performed during 2018 at all 412 non-federal hospitals in Florida. The data were sorted by the hospital, and then by procedure category. Statistical comparisons of LOS were made pairwise among all procedure categories with at least 100 cases at (the) each hospital, using the chi-square test (LOS ≤ 1 day versus LOS > 1 day), Student's t-test with unequal variances, and the Wilcoxon-Mann-Whitney test. The comparisons among the three tests then were repeated having sorted the data by procedure category and making statistical comparisons among all hospitals with at least 100 cases for the procedure category. Results Whether using a criterion for statistical significance of P < 0.05 or P < 0.01, and whether compared with Student's t-test with unequal variances or Wilcoxon-Mann-Whitney test, the chi-square test had greater odds (i.e., greater statistical power) to detect differences in LOS (all four with P < 0.0001 and all 95% lower confidence limits for odds ratios ≥ 3.00). The findings were consistent when the data, first sorted by procedure category and then by probability distributions of LOS, were compared between hospitals (all P < 0.0001 and the 95% lower confidence limits for odds ratio ≥ 3.72). Conclusions For purposes of comparing procedure categories pairwise at the same hospital, there was no loss of information by summarizing the probability distributions using single numbers, the percentages of cases among patients staying longer than overnight. This finding substantially simplifies the mathematics for constructing dashboards or summaries of OR information system data to help the perioperative OR manager or medical director decide which cases may need to be postponed, when the hospital census is high, to provide a sufficient reserve of inpatient hospital beds.

Policy Implications for the COVID-19 Pandemic in Light of Most Patients (≥72%) Spending at Most One Night at the Hospital After Elective, Major Therapeutic Procedures

A large number of inpatients with Coronavirus disease 2019 (COVID-19) in some regions of the United States may interfere with the ability of hospitals to take care of patients requiring treatment for other conditions. Nonetheless, many patients need surgery to improve their quality of life and to prevent deterioration in health. Curtailment of services also negatively affects the financial health of hospitals and health systems. Broad policies to prohibit all "elective" surgical procedures to ensure that there is sufficient hospital capacity for pandemic patients may be unnecessarily restrictive because, for many such procedures, patients are rarely admitted following surgery or only stay overnight. We studied all elective inpatient and ambulatory cases involving major therapeutic procedures performed in the state of Florida in 2018. We mapped the primary procedure to the corresponding Clinical Classification Software (CCS) category. We determined the distributions of lengths of stay overall and as stratified by CCS category, then calculated the percentage of cases that had a hospital length of stay of ≤1 night (i.e., 0 or 1 day). A threshold of one night was selected because patients discharged home on the day of surgery have no effect on the inpatient census, and those staying overnight would either have a transient effect or no effect if observed overnight in the postoperative care unit. Among the 1,852,391 elective cases with one or more major therapeutic procedures, 65.2% (95% lower confidence limit [LCL] = 65.1%) of cases had a length of stay of 0 days and 72.9% (95% LCL = 72.8%) had stay ≤1 day. There were 38 different CCS categories for which at least 95% of patients had a length of stay of ≤1 day. There were 28 CCS codes that identified 80% of the patients who were discharged with a length of stay ≤1 day, showing representation of multiple surgical specialties. Our results show that even in the face of constraints imposed by a high hospital census, many categories of major therapeutic elective procedures could be performed without necessarily compromising hospital capacity. Most patients will be discharged on the day of surgery. If overnight admission is required, there would be an option to care for them in the postanesthesia care unit, thus not affecting the census. Thus, policies can reasonably be based on allowing cases with a substantial probability of at most an overnight stay rather than a blanket ban on "elective" surgery or creating a carve-out for specified surgical subspecialties. Such policies would apply to at least 72% of elective, major therapeutic surgical procedures.