Quality registries in ICU Nicolette de Keizer PhD Dept Medical - - PowerPoint PPT Presentation

quality registries in icu
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Quality registries in ICU Nicolette de Keizer PhD Dept Medical - - PowerPoint PPT Presentation

Quality registries in ICU Nicolette de Keizer PhD Dept Medical Informatics Academic Medical Center Amsterdam, The Netherlands Knowledge No standard treatment Effective and Efficient? Very expensive Shortage of beds Evaluating


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Quality registries in ICU

Nicolette de Keizer PhD Dept Medical Informatics Academic Medical Center Amsterdam, The Netherlands

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Knowledge

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  • No standard treatment
  • Very expensive
  • Shortage of beds

Effective and Efficient?

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Evaluating Quality of Care

  • Randomized Controlled Trials

– Not ethical

  • Benchmarking

– Compare to national average or best ICU – Monitor performance in time – Quality registry

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Quality registry

  • Continuous data collection
  • Predefined data set
  • Multiple centers
  • Feedback and benchmark
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Examples of quality registries

Project Impact e.g. TRACER

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State of the art registries

  • Many national quality registries
  • Some international initiatives

– Project-based

  • Overlap in data collection
  • Differences in data items and data

definitions

  • All have the same goal
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Goal of ICU quality registries

  • To get insight into quality of intensive care

– Quality indicators

  • To foster improvements in the organization

and practice of intensive care

– Benchmarking – Case mix correction – Statistical Process Control – Collaborative network

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Goal of ICU quality registries

  • To get insight into quality of intensive care

– Quality indicators

  • To foster improvements in the organization

and practice of intensive care

– Benchmarking – Case mix correction – Statistical Process Control – Collaborative network

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Quality management

Structure Process Outcome

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Quality Indicator

“An measurable variable (or characteristic) that is used to determine the level of quality achieved“

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Selection of Quality Indicators (1)

  • Association with outcome or a process/ structure

indicator that is related to outcome

  • Relevant for clinical practice

– frequency

  • Results in quality improvement activities
  • Easily measurable
  • Fast available
  • Applicable in several institutions
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Example DMV

  • Mean duration of mechanical ventilation (DMV)
  • f patients admitted at the ICU
  • Interpretation:
  • Mechanical ventilation?
  • Calculation:

1. Mean difference in fractional days between start and end time of (non-) invasive mechanical ventilation during an ICU admission 2. Same as 1, but also including non-ventilated patients (DMV=0) 3. Same as 1, but using calendar days instead of fractional days

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Example indicator mean duration

  • f mechanical ventilation

fractional days*

  • incl. DMV=0*

calendar days* p-value ** DMV (days) 1.95 [0.59] 0.08 [0.13] 3.86 [0.70] <0.001

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State of the art indicators

  • Many quality indicators
  • Ambigously defined
  • Indicators should be easily measurable, fast

available, applicable in several institutions

  • -> Requires formalisation and automatic

derivation of indicators based on routinely collected data

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Goal of ICU quality registries

  • To get insight into quality of intensive care

– Quality indicators

  • To foster improvements in the organization and

practice of intensive care

– Benchmarking – Case mix correction – Statistical Process Control

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Measuring quality of care by indicators

  • External benchmark

– Compare to peers

  • Internal benchmark

– Compare own performance in time

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Measuring quality of care by indicators

  • External benchmark
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Measuring quality of care by indicators

  • External benchmark
  • Standardized mortality ratio=
  • bserved mortality / expected mortality

Case-mix corrected

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Case mix adjustment

  • Many different prognostic models:

– APACHEII,III, IV, SAPSII,III, MPM0/24, LODS, SOFA, Euroscore etc. – Logistic regression models – Data of first 24 hours of ICU admission

  • Measure performance (discrimination,

calibration, accuracy)

  • Recalibrate if needed
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Statistical Process Control

  • Internal benchmark

– Compare own performance in time – Variation in proces is noise or special cause – Detection rules e.g. 1 point cross UCL/LCL, 8 consequetive points below or under mean

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MIE 2011, 30 Aug, Oslo, Norway Reza.Shahpori@AlbertaHealthServices.Ca

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$2.338.000 $1.344.000 $1.190.000 $574.000 $434.000 $378.000

$0 $500.000 $1.000.000 $1.500.000 $2.000.000 $2.500.000 $0 $50.000 $100.000 $150.000 $200.000 $250.000 $300.000 apr./05 jun./05 aug./05

  • kt./05

dec./05 feb./06 apr./06 jun./06 aug./06

  • kt./06

dec./06 feb./07 apr./07 jun./07 aug./07

  • kt./07

dec./07 feb./08 apr./08 jun./08 aug./08

  • kt./08

dec./08 feb./09 apr./09 jun./09 aug./09

  • kt./09

dec./09 feb./10 apr./10 jun./10 aug./10 Total Case Cost (Cnd$) / Fiscal Year VAP Case Cost (Cnd$)

Months of the Year

Cost of VAP in Calgary Health Region

Each case of VAP = $14,000 extra expense for Health Care (Safer Healthcare Now)

VAP Cost VAP Total Cost / Year

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State of the art SPC

  • Many types of charts

– EWMA, CUSUM, RSPRT, P-charts etc – Risk adjusted or not

  • Limitly applied in critical care
  • Unknown what is the best chart to detect a

decrease or increase in quality

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Points of interest / discussion

  • Share datasets / indicator sets

– Formalisation – Terminology

  • How to move from quality assessment to

quality improvement

– Internal or external benchmarking – Statistical process control – Collaborative networks