Sankalp KHANNA , Derek IRELAND , David ROLLS and Justin BOYLECSIRO Australian e-Health Research Centre, Brisbane, Australia

July 2018

Implementing a Risk Stratification Algorithm

HEALTH AND BIOSECURITYwww.csiro.au

Patient Flow @ CSIRO AEHRC

Implementing a Risk Stratification Algorithm

Aims : • Improving public hospital

performance through efficiency improvements

• Creating an evidence base to support policy and decision making

Science Areas : • Visualisation• Statistical Modelling• Machine Learning• Stochastic Optimisation• Distributed Constraint Reasoning• Discrete Event Simulation

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Risk Stratification for Hospital AvoidancePrimary Care Setting

Predictive Algorithm Driven Risk Stratification to inform Recruitment

Employed across up to 200 GP Practices and Aboriginal Health Services starting Dec 2017

Acute Care Setting

Predictive Algorithm Driven Risk Stratification to inform Discharge Planning

12 Month trial at Queensland Metropolitan Hospital commenced April 2018

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Project Brief• To develop, implement and evaluate a web-based risk

stratification algorithm that can be used in-hospital to identify chronic disease patients with a high risk of re-hospitalisation.

• What are we predicting

• Unplanned re-admission within 30 days of discharge from hospital

• Unplanned ED re-presentation within 30 days of discharge from hospital

• Timeline

Chronic Disease Patient Admitted to Hospital

Risk Score generated overnight

Risk score used by care teams for appropriate interventions and

care/discharge planning

Next morning

Trial Apr 2018 to Mar 2019

Post-trial evaluation Apr 2019 to Jun 2019

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Study Design

Patients who, over a 5 year period, as an Emergency or Admitted Patient :• Attended Logan Hospital, and• Had at least one Chronic Disease visit

(any QLD hospital)

Patient Cohort

• Emergency Data (EDIS/FirstNet)• Inpatient Data (QHAPDC/ePADT)• Mortality Data (Death Registry)• Pharmacy dispensing information (eLMS)• Pathology test results (AUSLAB)

Data Used for Modelling & Validation

• Patients stays in previous 180 days• ED presentations in previous 180 days• Marital status• Age• Indigenous status• SEIFA• Admission source• Admission unit• Care type• Elective status• Planned same day status• Binary flags for routine dialysis• Number of medication records• Binary flags for medication• Binary flags for abnormal pathology results

Predictor Variables in Final Models

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Trial MethodologyData Flows • Algorithm hosted on QLD Health Virtual Machine

• Risk profiling once per day (1 AM)

• Risk score provided for each patient with a chronic

disease history that is staying overnight

Evaluation Research QuestionsDoes the algorithm:

1. Improve the process of identifying patients at high risk

of unplanned re-hospitalisation?

2. Reduce re-hospitalisation rates?

3. Provide information to staff not readily available at the

time of discharge planning?

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The Web-based Decision Support Tool - Screenshots

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The Web-based Decision Support Tool - Screenshots

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The Web-based Decision Support Tool - Screenshots

Implementing a Risk Stratification Algorithm

Questions ?

For more information, please contact : Sankalp KhannaSenior Research Scientist

t +61 7 3253 3629e Sankalp.Khanna@csiro.auw www.aehrc.com

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