Prof. Dr. med. Andreas Becker
CLINOTEL Krankenhausverbund gGmbH
Riehler Str. 36 | 50668 Köln | Germany
becker@clinotel.de | www.clinotel.de
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Structure of the webinar
 Some management ideas and „DID“
 The data we use
 Bar charts
 Excursus: Analysis of Clinical Processes
 Funnel Plot
 Variable Life Adjusted Display (VLAD)
 Conclusion
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http://www.clinotel-journal.de/article-id-017e.html
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http://www.clinotel-journal.de/article-id-003e.html
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Data to Information to Decisions (DID)
Data
Mortality rate
Complications
…
 Meaning
Information
Adequate?
Actual ≥ Target value?
Actual < Target value?
…
 Meaning
Decision
“No intervention”
“Need for
improvement”
Investigations
Analyses
Dialogue
Projects
…
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Data to Information to Decisions (DID)
Data
Mortality rate
Complications
…
 Meaning
Information
Adequate?
Actual ≥ Target value?
Actual < Target value?
…
 Meaning
Decision
“No intervention”
“Need for
improvement”
Investigations
Analyses
Dialogue
Projects
…
The informal equation which links total variance between health-care providers to the components of
variance (V) can be written as:
V(outcome)= V(definitions/data quality) + V(case mix) + V(clinical quality of care) + V(chance)
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Data
 Hospital administrative case data
 1,809,643 discharged in-patients (2010 to 2012)
 24,689 in-patients with community-acquired pneumonia (CAP),
age ≥ 18 years, 2010-2012
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In-hospital mortality rate (%) for CAP: Bar chart
Data 2012 | 10,379 patients
20,7
13,6
12,0
5,8
B
Grey bar
Grey line
A
CLINOTEL
12.7% (Overall value German hospitals, External Quality Assurance 2011, all risk groups in accordance with CRB-65, all patients whether or not it has
been documented that a specific therapy was stopped
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Pyramid Model for Investigating Hospital Performance
Staff
Process of care
Structure or resource
Patient characteristics (Case Mix)
Data
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Observed and expected in-hospital mortality rate (%) for CAP
Data 2012 | 10,379 patients
B
C
x D
x
E
A
Bar
Diamond
Observed (not risk-adjusted)
Expected (based on a model for risk adjustment, with 95% CI)
x
Number of cases for calculating the expected in-hospital mortality rate critical or insufficient
x
x x
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Risk-adjusted in-hospital mortality rate (%) for CAP
Data 2012 | 10,379 patients
17,8
15,4
11,8
9,8
4,3
A
B
Grey bar
CLINOTEL
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Risk-adjusted in-hospital mortality rate (%) for CAP: Funnel Plot
Data 2012 | 10,379 patients
Confidence interval
Confidence interval
B (N = 786)
A (N = 1,295)
CAP case volume
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Risk-adjusted in-hospital mortality rate (%) for CAP: Funnel Plot
Data 2012 | 10,379 patients
Confidence interval
Confidence interval
B (N = 786)
A (N = 1,295)
CAP case volume
Dimick JB, Welch HG, Birkmeyer JD (2004). Surgical Mortality as an Indicator of Hospital
Quality. The Problem With Small Sample Size. JAMA. 2004; 292 (7): 847-85. PubMed-ID:
15315999
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December
November
October
September
August
July
June
May
April
March
February
January
“Lives lost”
“Lives saved”
Variable Life Adjusted Display (VLAD)
Hospital A | data 2012 | 432 patients
Upper limit
Lower limit
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Variable Life Adjusted Display (VLAD)
Hospital A | data 2012 | 432 patients
“Lives saved”
!!!
!!!
Upper limit
December
November
October
September
August
July
June
May
April
March
February
January
“Lives lost”
Lower limit
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“Lives saved”
Variable Life Adjusted Display (VLAD)
Hospital A | data 2012 | 432 patients
Upper limit
December
November
October
September
August
July
June
May
April
March
February
January
“Lives lost”
Lower limit
● Upper limit / patient survived at an expected value of ≥ 0.460
● Lower limit / patient died at an expected value of ≤ 0.015
● Adverse event (acute kidney failure, acute myocardial infarction, acute cystitis, urinary tract infection – site not defined in more detail)
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Conclusion
 Statistical graphics can effectively support “DID” transformation
 Complex process – do not try to reduce it to traffic light colours, thumbs
up/down or series of stars
 Involve professional staff
 Solutions must be repetitive operational measures
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Conclusion
 Statistical graphics can effectively support “DID” transformation
 Complex process – do not try to reduce it to traffic light colours, thumbs
up/down or series of stars
 Involve professional staff
 Solutions must be repetitive operational measures
 Understand why change of processes works - and why not (Dixon-Woods et al.!!!)
 For further analysis, use a model with high face-validity: Analysis Pyramid
 The next steps: Morbidity and Mortality Conference / Process Audit
 If you evaluate clinical processes: Make a difference that makes a difference!
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Prof. Dr. med. Andreas Becker
CLINOTEL Krankenhausverbund gGmbH
Riehler Str. 36 | 50668 Köln | Germany
becker@clinotel.de | www.clinotel.de
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