Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada

Yang, Cui; Metge, Colleen; Ye, Xibiao; Moffatt, Michael; Oppenheimer, L.; Forget, Evelyn L.

doi:10.1177/1355819614565498

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Many studies have a cross-sectional rather than a longitudinal design [19,20]. Several are also restricted to a specific healthcare institution [6,14,21] or condition [11,12,16]. This gap presents a need for findings from reliable study data for the general population of older people.…”

Section: Introductionmentioning

confidence: 99%

Unplanned Readmission within 28 Days of Hospital Discharge in a Longitudinal Population-Based Cohort of Older Australian Women

Shebeshi

Dolja‐Gore

Byles

2020

IJERPH

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This study aimed to estimate the incidence of 28-day unplanned readmission among older women, and associated factors. Data were used from the 1921–1926 birth cohort of the Australian Longitudinal Study on Women’s Health. Linkage of self-reported survey data with the Admitted Patient Data Collection allowed the identification of hospital admissions for each woman and the corresponding baseline characteristics. The Cox proportional-hazards model was used to identify factors associated with time to unplanned readmission, using SAS software V 9.4. (SAS Institute, Cary, NC, USA). Of 2056 women with index unplanned admission, 363 (17.5%) were readmitted within 28 days of discharge, and of these 229 (11.14%) had unplanned readmission. Among women with unplanned readmission, 24% were for the same condition as for the index hospitalisation. Cardiovascular diseases were the main diagnoses for the index admission and readmission. Unplanned readmission risk was higher if not partnered (hazard ratio (HR) = 1.43, 95% confidence interval (CI): 1.05–1.95), of non-English speaking background (HR = 1.62%, 95% CI: 1.07–2.47), more than three days length of stay on index admission (HR = 1.41%, 95% CI: 1.04–1.90) and one or two of the assessed chronic diseases (HR = 1.68, 95% CI: 1.19–2.36). At least one in ten women had unplanned readmission at some time between ages 75–95 years. Women who are not partnered, not of English-speaking background, with longer hospital stay and those with multi-morbidity, may need further efforts during their stay and on discharge to mitigate unplanned readmission.

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Section: Introductionmentioning

confidence: 99%

Unplanned Readmission within 28 Days of Hospital Discharge in a Longitudinal Population-Based Cohort of Older Australian Women

Shebeshi

Dolja‐Gore

Byles

2020

IJERPH

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“…The discrimination of the risk score was comparable if not better than those that are commonly used in other countries. 24,32,33,[80][81][82] The model can also be replicated by other hospitals in Singapore given that all the predictors can be obtained in a similar fashion upon admission at any acute hospital in Singapore. The risk score was developed on the NTFGH cohort and was adopted for implementation across the NUHS and NHG clusters' EMR system.…”

Section: Discussionmentioning

confidence: 99%

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

Suresh

Phng

et al. 2021

Appl Clin Inform

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Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

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“…To tackle the issue of re-admissions, a lot of research is g. Several techniques and predictive models, which consider several patient factors, including socioeconomic status, marital status, sex, and age, among others, have been developed to predict readmission [24,25]. Some recent research studies have used administrative data to be predict patient readmission within a year [26][27][28] or even within a month after being discharged from the hospital [24,[29][30][31][32]. A few studies have only concentrated on a select group of patients [25,27,29,31] or only on a single hospital [24,25,33].…”

Section: Introductionmentioning

confidence: 99%

“…Some recent research studies have used administrative data to be predict patient readmission within a year [26][27][28] or even within a month after being discharged from the hospital [24,[29][30][31][32]. A few studies have only concentrated on a select group of patients [25,27,29,31] or only on a single hospital [24,25,33]. Despite all the work, most of the predictive models have poor predictive capability and are too complex to be utilized in daily practice [26].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients

Lodhi

Ansari

Yao

et al. 2017

Lecture Notes in Computer Science

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Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.

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Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada

Abstract: This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.

Cited by 6 publications

References 17 publications

Unplanned Readmission within 28 Days of Hospital Discharge in a Longitudinal Population-Based Cohort of Older Australian Women

Unplanned Readmission within 28 Days of Hospital Discharge in a Longitudinal Population-Based Cohort of Older Australian Women

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients

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