Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study

Is it legitimate to use unplanned hospitalizations as a quality indicator for cancer patients? A retrospective French cohort study with special attention to the influence of social deprivation

PURPOSE

Readmission indicators are used around the world to assess the quality of hospital care. We aimed to assess the relevance of this type of indicator in oncology, especially for socially deprived patients. Our objectives were (1) to assess the proportion of unplanned hospitalizations (UHs) in cancer patients, (2) to assess the proportion of UHs that were avoidable, i.e., related to poor care quality, and (3) to analyze cancer patients the effect of patients' deprivation level on the type of UH (avoidable UHs vs. unavoidable UHs).

METHODS

In a French university hospital, we selected all hospitalizations over a year for a random sample of cancer patients. Based on medical records, we identified those among UHs due to avoidable health problems. We assessed the association between social deprivation, home-to-hospital distance, or home-to-general practitioner with the type of UH (avoidable vs. unavoidable) via a multivariate binary logit estimation.

RESULTS

Among 2349 hospitalizations (355 patients), there were 383 UHs (16 %), among which 38% were avoidable. Among UHs, the European Deprivation Index was significantly associated with the risk of avoidable UHs, with a lower risk of avoidable UH for patients with medium or high social deprivation.

CONCLUSION

Our results suggest that the use of UHs rate as a quality indicator is questionable in oncology. Indeed, the majority of UHs were not avoidable. Furthermore, within UHs, those involving patients with medium or high social deprivation are more often unavoidable in comparison with other patients.

Does a Community Care programme reach a high health need population and high users of acute care hospital services in Melbourne, Australia? An observational cohort study

OBJECTIVE

The Community Care programme is an initiative aimed at reducing hospitalisations and emergency department (ED) presentations among patients with complex needs. We aimed to describe the characteristics of the programme participants and identify factors associated with enrolment into the programme.

DESIGN

This observational cohort study was conducted using routinely collected data from the National Centre for Healthy Ageing data platform.

SETTING

The study was carried out at Peninsula Health, a health service provider serving a population in Melbourne, Victoria, Australia.

PARTICIPANTS

We included all adults with unplanned ED presentation or hospital admission to Peninsula Health between 1 November 2016 and 31 October 2017, the programme's first operational year.

OUTCOME MEASURES

Community Care programme enrolment was the primary outcome. Participants' demographics, health factors and enrolment influences were analysed using a staged multivariable logistic regression.

RESULTS

We included 47 148 adults, of these, 914 were enrolled in the Community Care programme. Participants were older (median 66 vs 51 years), less likely to have a partner (34% vs 57%) and had more frequent hospitalisations and ED visits. In the multivariable analysis, factors most strongly associated with enrolment included not having a partner (adjusted OR (aOR) 1.83, 95% CI 1.57 to 2.12), increasing age (aOR 1.01, 95% CI 1.01 to 1.02), frequent hospitalisations (aOR 7.32, 95% CI 5.78 to 9.24), frequent ED visits (aOR 2.0, 95% CI 1.37 to 2.85) and having chronic diseases, such as chronic pulmonary disease (aOR 2.48, 95% CI 2.06 to 2.98), obesity (aOR 2.06, 95% CI 1.39 to 2.99) and diabetes mellitus (complicated) (aOR 1.75, 95% CI 1.44 to 2.13). Residing in aged care home and having high socioeconomic status) independently associated with reduced odds of enrolment.

CONCLUSIONS

The Community Care programme targets patients with high-readmission risks under-representation of individuals residing in residential aged care homes warrants further investigation. This study aids service planning and offers valuable feedback to clinicians about programme beneficiaries.

Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study

OBJECTIVE

To develop a predictive model for identifying patients at high risk of all-cause unplanned readmission within 30 days after discharge, using administrative data available before discharge.

MATERIALS AND METHODS

Hospital administrative data of all adult admissions in three tertiary metropolitan hospitals in Australia between July 01, 2015, and July 31, 2016, were extracted. Predictive performance of four mixed-effect multivariable logistic regression models was compared and validated using a split-sample design. Diagnostic details (Charlson Comorbidity Index CCI, components of CCI, and primary diagnosis categorised into International Classification of Diseases chapters) were added gradually in the clinically simplified model with socio-demographic, index admission, and prior hospital utilisation variables.

RESULTS

Of the total 99 470 patients admitted, 5796 (5.8%) were re-admitted through the emergency department of three hospitals within 30 days after discharge. The clinically simplified model was as discriminative (C-statistic 0.694, 95% CI [0.681-0.706]) as other models and showed excellent calibration. Models with diagnostic details did not exhibit any substantial improvement in predicting 30-days unplanned readmission.

CONCLUSION

We propose a 10-item predictive model to flag high-risk patients in a diverse population before discharge using readily available hospital administrative data which can easily be integrated into the hospital information system.