Similar outcomes for general medicine patients discharged on any day of the week

Holiday ratio of hospitalization and 30-day readmission rates among cancer patients after major surgery

Background

To determine the association of 30‐day readmission with weekend discharge and the number of holiday days during a hospital stay (holiday ratio).

Methods

This retrospective cohort study used the clinical research database and cancer registry data of our hospital from January 1, 2011 to December 31, 2017. Patient characteristics, tumor factors, clinical laboratory data, and proxies of continuity of care, such as weekend discharge or holiday ratio (holiday days/total hospitalization days), received statistical analysis. Multivariate logistic regression identified the independent factors for 30‐day potentially avoidable readmission rate (PAR).

Results

Of 1433 patients receiving tumor resection, 520 (36.29%) had colon cancer; 440 (30.70%) had head and neck cancer (HNC), and 473 (33.01%) had other cancers (lung, liver, and prostate). The rate of 30‐day PAR was 6.3% for those with colon cancer, 8.6% for HNC, and 3.6% for other cancers. The 30‐day PAR did not significantly differ by discharge on a weekend versus weekday for those with colon cancer (8.33% vs. 5.90%; p = 0.379), HNC (7.06% vs. 9.01%; p = 0.566), or other cancers (0.00% vs. 4.28%; p = 0.960). Colon cancer patients with holiday ratio >0.3 had a higher readmission rate (9.58% vs. 4.82%, p = 0.041). In multivariate analysis, a holiday ratio >0.3 (adjusted odds ratio 2.16; 95% Confidence Interval, 1.05–4.39) in those with colon cancer was an independent predictor of 30‐day PAR.

Conclusions

Weekend discharge after major surgery did not affect 30‐day readmission rates in cancer patients, but the holiday ratio did affect 30‐day PAR for those with colon cancer.

Risk factors for hospital readmission in older adults within 30 days of discharge - a comparative retrospective study

BACKGROUND

The area of hospital readmission in older adults within 30 days of discharge is extensively researched but few studies look at the whole process. In this study we investigated risk factors related, not only to patient characteristics prior to and events during initial hospitalisation, but also to the processes of discharge, transition of care and follow-up. We aimed to identify patients at most risk of being readmitted as well as processes in greatest need of improvement, the goal being to find tools to help reduce early readmissions in this population.

METHODS

This comparative retrospective study included 720 patients in total. Medical records were reviewed and variables concerning patient characteristics prior to and events during initial hospital stay, as well as those related to the processes of discharge, transition of care and follow-up, were collected in a standardised manner. Either a Student's t-test, χ²-test or Fishers' exact test was used for comparisons between groups. A multiple logistic regression analysis was conducted to identify variables associated with readmission.

RESULTS

The final model showed increased odds of readmission in patients with a higher Charlson Co-morbidity Index (OR 1.12, p-value 0.002), excessive polypharmacy (OR 1.66, p-value 0.007) and living in the community with home care (OR 1.61, p-value 0.025). The odds of being readmitted within 30 days increased if the length of stay was 5 days or longer (OR 1.72, p-value 0.005) as well as if being discharged on a Friday (OR 1.88, p-value 0.003) or from a surgical unit (OR 2.09, p-value 0.001).

CONCLUSION

Patients of poor health, using 10 medications or more regularly and living in the community with home care, are at greater risk of being readmitted to hospital within 30 days of discharge. Readmissions occur more often after being discharged on a Friday or from a surgical unit. Our findings indicate patients at most risk of being readmitted as well as discharging routines in most need of improvement thus laying the ground for further studies as well as targeted actions to take in order to reduce hospital readmissions within 30 days in this population.

Quality assessment of weekend discharge: a systematic review and meta-analysis

PURPOSE

Hospital bed utility and length of stay affect the healthcare budget and quality of patient care. Prior studies already show admission and operation on weekends have higher mortality rates compared with weekdays, which has been identified as the 'weekend effect.' However, discharges on weekends are also linked with quality of care, and have been evaluated in the recent decade with different dimensions. This meta-analysis aims to discuss weekend discharges associated with 30-day readmission, 30-day mortality, 30-day emergency department visits and 14-day follow-up visits compared with weekday discharges.

DATA SOURCES

PubMed, EMBASE, Cochrane Library and ClinicalTrials.gov were searched from January 2000 to November 2019.

STUDY SELECTION

Preferred reporting items for systematic reviews and meta-analyses guidelines were followed. Only studies published in English were reviewed. The random-effects model was applied to assess the effects of heterogeneity among the selected studies.

DATA EXTRACTION

Year of publication, country, sample size, number of weekday/weekend discharges, 30-day readmission, 30-day mortality, 30-day ED visits and 14-day appointment follow-up rate.

RESULTS OF DATA SYNTHESIS

There are 20 studies from seven countries, including 13 articles from America, in the present meta-analysis. There was no significant difference in odds ratio (OR) in 30-day readmission, 30-day mortality, 30-day ED visit, and 14-day follow-up between weekday and weekend. However, the OR for 30-day readmission was significantly higher among patients in the USA, including studies with high heterogeneity.

CONCLUSION

In the USA, the 30-day readmission rate was higher in patients who had been discharged on the weekend compared with the weekday. However, interpretation should be cautious because of data limitation and high heterogeneity. Further intervention should be conducted to eliminate any healthcare inequality within the healthcare system and to improve the quality of patient care.