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

Abstract

ObjectivesDevelop 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.DesignRetrospective healthcare utilisation analysis with multivariate logistic regression models.DataA 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 measuresModels 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.ResultsAmong 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%.ConclusionsWe 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.

Assessing Adverse Events of Postprostatectomy Radiation Therapy for Prostate Cancer: Evaluation of Outcomes in the Regione Emilia-Romagna, Italy

“…To estimate comorbidities, we mapped available administrative data during the year before prostate cancer diagnosis to comorbidity categories based primarily on organ system, including data from home health care, pharmacy, and hospital discharge abstracts (Table E2, available at www.redjournal.org). This comorbidity classification system incorporates Body System Etiology Groups, as has been described previously with the RER Italian Longitudinal Health Care Utilization Database (33,34). The total number of comorbidities was added for each patient.…”

“…The total number of comorbidities was added for each patient. A local tumor extension algorithm using administrative data was applied to assign a tumor stageddisease staging method that has been described previously (33). The disease stage of 3.01 and higher indicates local tumor extension beyond the prostate gland, or Fig.…”

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy

“…The outcome was defined as the occurrence of hospitalisation that could have potentially been prevented or delayed with appropriate patient care or death by any cause. 11 We developed a list of hospitalisations that are potentially preventable with appropriate patient care using a three-step process. First, we conducted a literature search to evaluate paediatric studies that defined potentially avoidable disease in paediatrics that could require hospitalisation.…”

“…We mapped diseases defined primarily by the affected body system with the exceptions of cancer, genetic conditions and trauma, which were based on aetiology 11 using 2014 hospital discharge data, outpatient prescription information and specialty visit claims. A total of 24 groups were defined.…”

“…The RER has therefore developed and implemented a population-based model to predict risk of hospitalisation or death for adult residents in the region. 11 The results of the model are presented to physicians in Patient-Centered Medical Homes as patient profiles to support care management and the identification of patients who may benefit from additional outreach such as home healthcare, disease management or case management.…”