Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

Implementing and Evaluating a Discharge Before Noon Initiative in a Large Tertiary Care Urban Hospital

BACKGROUND

Discharging clinically ready patients before noon on their discharge day may influence overall discharge process quality, emergency department (ED) boarding times, and length of stay (LOS). This study evaluated the effectiveness of a discharge before noon (DBN) initiative.

METHODS

Many DBN components were refined or added during a pilot, including incorporating the DBN process into daily rounds, an electronic tracking system, and other elements for possible DBN patients such as a car service when appropriate and expedited lab results and physical therapy consults. DBN was evaluated through a retrospective pre-post study (12-month periods). Study patients were from Maimonides Medical Center's medicine units. Kaplan-Meier estimates and a log-rank test characterized and compared the discharge time probabilities in pre-DBN and post-DBN groups. Log-logistic accelerated failure time (AFT) analysis assessed the influence of DBN on discharge time. Secondary analyses examined the relationship between LOS and readmission within 30 days for any cause and DBN.

RESULTS

The percentage of patients discharged before noon increased from 5.0% to 11.4% pre/post-DBN (p < 0.001). The AFT analysis estimated that post-DBN patients had discharge times 41.5% earlier (p < 0.001). DBN as an independent factor was not associated with LOS or subsequent readmissions within 30 days for any cause. Despite an increase in the percentage of patients admitted during the daytime (8:00 a.m. to 5:00 p.m.), the median ED boarding time increased by 41 minutes in post-DBN patients (p < 0.001).

CONCLUSION

The DBN initiative was associated with an increased percentage of patients discharged before noon. Further research is needed to identify strategies that reliably improve discharge timeliness while reducing ED boarding.

A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong

BACKGROUND

Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants.

METHODS

A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses.

RESULTS

The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status.

CONCLUSIONS

A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time.

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes

PURPOSE OF REVIEW

Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

RECENT FINDINGS

Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.