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Exploiting HIS medical data resources to manage Emergency Department: Application to the study of el

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Title: Exploiting HIS medical data resources to manage Emergency Department: Application to the study of el


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
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 flow over time
  • To provide support decision tools to managers and
    clinicians

4
The teaching hospital, Rennes
2005 111628 inpatients
1919 beds
11/10/2005 to 31/05/2006 32883 patients
75 years old 4951 patients
5
The Emergency Department
Proximity ward lt 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)
12
Dynamics of flow
  • Admission rates over time

13
Results Admissions rate over time
Standard time-series data
CUSUM analysis
14
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
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17
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
18
Discussion
  • Limitations of the data exploitation due to ICD10
    coding

To relate semantically diagnoses within the
hospital
19
Thank you for your attention
Rennes Teaching Hospital
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