Dataset Information

Predicting risk of hospitalisation or death: a retrospective population-based analysis.

ABSTRACT:

Objectives

Develop predictive models using an administrative healthcare database that provide information for Patient-Centred Medical Homes to proactively identify patients at risk of hospitalisation for conditions that may be impacted through improved patient care.

Design

Retrospective healthcare utilisation analysis with multivariate logistic regression models.

Data

A population-based longitudinal database of residents served by the Emilia-Romagna, Italy, health service in the years 2004-2012 including demographic information and utilisation of health services by 3,726,380 people aged ?18 years.

Outcome measures

Models designed to predict risk of hospitalisation or death in 2012 for problems that are potentially avoidable were developed and evaluated using the area under the receiver operating curve C-statistic, in terms of their sensitivity, specificity and positive predictive value, and for calibration to assess performance across levels of predicted risk.

Results

Among the 3,726,380 adult residents of Emilia-Romagna at the end of 2011, 449,163 (12.1%) were hospitalised in 2012; 4.2% were hospitalised for the selected conditions or died in 2012 (3.6% hospitalised, 1.3% died). The C-statistic for predicting 2012 outcomes was 0.856. The model was well calibrated across categories of predicted risk. For those patients in the highest predicted risk decile group, the average predicted risk was 23.9% and the actual prevalence of hospitalisation or death was 24.2%.

Conclusions

We have developed a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for residents of the Emilia-Romagna region with a level of performance as high as, or higher than, similar models. The results of this model, along with profiles of patients identified as high risk are being provided to the physicians and other healthcare professionals associated with the Patient Centred Medical Homes to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

SUBMITTER: Louis DZ

PROVIDER: S-EPMC4166245 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Publications

Predicting risk of hospitalisation or death: a retrospective population-based analysis.

Louis Daniel Z DZ Robeson Mary M McAna John J Maio Vittorio V Keith Scott W SW Liu Mengdan M Gonnella Joseph S JS Grilli Roberto R

BMJ open 20140917 9

<h4>Objectives</h4>Develop predictive models using an administrative healthcare database that provide information for Patient-Centred Medical Homes to proactively identify patients at risk of hospitalisation for conditions that may be impacted through improved patient care.<h4>Design</h4>Retrospective healthcare utilisation analysis with multivariate logistic regression models.<h4>Data</h4>A population-based longitudinal database of residents served by the Emilia-Romagna, Italy, health service i ...[more]

PMID: 25231488

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Project description:OBJECTIVES:Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN:Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA:Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527?458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES:Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS:Of the 527?458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49?486 children (9.4%) of the population were classified in the 'At Higher Risk' group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the 'At Higher Risk' group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS:It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

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Dataset Information

Predicting risk of hospitalisation or death: a retrospective population-based analysis.

Objectives

Design

Data

Outcome measures

Results

Conclusions

Publications

Predicting risk of hospitalisation or death: a retrospective population-based analysis.

Similar Datasets

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