title: Predicting Unpanned Return to Hospital for Chronic Disease Patients.
abstract: Preventing unplanned returns, including readmissions and representations to the emergency department is increasingly becoming a performance target for hospitals across the globe. Significant successes have been reported from interventions put in to place by hospitals to reduce their incidence. However, despite several risk stratification algorithms being proposed in recent years, there is limited use of these algorithms in hospital services to identify patients for enrolment into these intervention programs. This study identifies constraints limiting the practical use of such algorithms. We also develop and validate models that focus on clinically relevant patient cohorts and are thus better suited to practical deployment in hospitals, while still offering good predictive ability.
authors.affiliation: CSIRO Australian e-Health Research Centre, Brisbane, Australia.
authors.forename: Sankalp
authors.initials: S
authors.lastname: Khanna
authors.affiliation: CSIRO Australian e-Health Research Centre, Brisbane, Australia.
authors.forename: Norm
authors.initials: N
authors.lastname: Good
authors.affiliation: CSIRO Australian e-Health Research Centre, Brisbane, Australia.
authors.forename: Justin
authors.initials: J
authors.lastname: Boyle
mesh_terms: D000367:Age Factors; D000465:Algorithms; D002908:Chronic Disease; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data* / Q000639:trends*; D011793:Queensland