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D. Rossille a , M. Cuggia a,b , A. Arnault c , J. Bouget d , P. Le Beux a

Exploiting HIS medical data resources to manage Emergency Department: Application to the study of elderly patients’ clinical pathways. D. Rossille a , M. Cuggia a,b , A. Arnault c , J. Bouget d , P. Le Beux a a Medical Informatics Dept, Hospital of Rennes, France

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D. Rossille a , M. Cuggia a,b , A. Arnault c , J. Bouget d , P. Le Beux a

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  1. Exploiting HIS medical data resources to manage Emergency Department: Application to the study of elderly patients’ clinical pathways D. Rossillea, M. Cuggiaa,b, A. Arnaultc, J. Bougetd, P. Le Beuxa a Medical Informatics Dept, Hospital of Rennes, France b EA3888, University of Rennes, France c ENSAI, Bruz, France d Emergency Department, Hospital of Rennes, France

  2. Emergency Department Elderly Population • Supporting decisions on • Medical • Public-health • Management • issues -poly-pathology -frail • No admission scheduled • Broad spectrum of • Illnesses and injuries HIS • Electronic Medical • patient records Introduction

  3. Objective To provide comprehensive views of the activities according to three complementary analysis: • characterization of the population • the patients’ pathways within the medical and non medical wards • the patients’ flowover time To provide support decision tools to managers and clinicians

  4. Emergency The teaching hospital, Rennes 2005: 111628 inpatients 1919 beds 11/10/2005 to 31/05/2006: 32883 patients ≥ 75 years old 4951 patients

  5. Traumatology Surgery Medicine Emergency The Emergency Department Proximity ward < 24 h Entrance dispatching Arrival Departure

  6. Material • Hospital Database • Medico-economic data • Coded medical acts and diagnoses • ED Database – RESURGENCE • Clinical data • Arrival/departure data, general data • 11 Oct. 2005 – 31 May 2006: 4951 patients

  7. Methods • Patients’ characterization Descriptive analysis • Dynamics of flows  CUSUM chart • Patients’ flows within departmental wards Graph representation • SAS – Statistical Analysis • GRAPHVIZ – Graph Visualization

  8. Patients’ Characterization • Diagnoses distribution • ED length of stay

  9. Results: Diagnoses Distribution Chapter 18 Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified Chapter 19 Injury, poisoning and Certain other consequences Of external causes Chapter 9 Diseases of the circulatory system ICD 10 Chapter 10 Respiratory system 43,6 % Chapter 11 Digestive system 56,9 % 75,3 %

  10. Results: Diagnoses Distribution

  11. Results: Length of Stayvs. CCMU(Patients’ medical status) Length Of stay CCMU Stable prognosis Vital prognosis In danger, Immediate intensive care Likely To worsen

  12. Dynamics of flow • Admission rates over time

  13. Results: Admissions rate over time Standard time-series data CUSUM analysis

  14. APRIL FEB WINTER HOLIDAYS Results: Admissions rate over time

  15. Patients’ pathways • Visualization tool (GRAPHVIZ) • To provide insights on • The % w.r.t. the ED wards • The % w.r.t. the departure modes • The median waiting times at each step

  16. Conclusion Decision support tools: Patients’ characterization (DESCRIPTIVE ANALYSIS) Dynamics of flows (CUSUM) Patients’ pathways (GRAPHS) Multifaceted, synthesized and easily readable view Of the Emergency Department

  17. pathologies symptoms Emergency Discussion • Limitations of the data exploitation due to ICD10 coding To relate semantically diagnoses within the hospital

  18. Thank you for your attention Rennes Teaching Hospital

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