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Critically Thinking about Quality Control Measures Utilized by HIIN Brian Guinn
Critically Thinking about Quality Control Measures Utilized by HIIN
Brian Guinn
STATISTICS
Statistics
Statistics Not Statistics
All the fun!!
Statistics Not Statistics
All the fun!!
Empower
Entertain
Entertain
Presentation Overview
• History
• Hidden Perspectives–Airplanes
–Exposures
–Trends
• K-HIIN
Why are we here?
Hippocrates
460bce - 370bce
Humoral Theory
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
People like Philip von Hohenheim(1493 – 1591) Trial and error to treat the sickFamous for utilizing observationin lieu of ancient texts
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
Better known as?
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
Better known as?
Paracelsus
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
Better known as?
Paracelsus
Hippocrates
460bce - 370bce
Humoral Theory
Celsus
25bce - 50ce
Celsus Tetrad
Galen
129ce - 210bce
Anatomist
Ibn-Sina
980ce - 1037bce
Canon of Medicine
Philip von Hohenheim
Better known as?
ParacelsusBeyond Celsus
Paracelsus
1493 – 1591Observational Medicine
John Graunt
1620 – 1674“Father of Modern Demography”
Hippocrates
460bce - 370bce
Celsus
25bce - 50ce
Galen
129ce - 210ce
Ibn-Sina
980ce - 1037ce
Paracelsus John Graunt
1493 -1591 1620 - 1674
Observational Medicine
Numerical Medicine
Ancient Medical Texts
Statistics /Epidemiolog
y
Observational Medicine
Numerical Medicine
Statistics /Epidemiolog
y
In-Hospital Surveillance
Counting Cases
IncidencePrevalence
Trends
Trained StaffHIIN EOM
Kentucky Quality Counts
Harm Across the Board /
Control Charts
Observational Medicine
Numerical Medicine
Statistics /Epidemiolog
y
In-Hospital Surveillance
Counting Cases
IncidencePrevalence
Trends
Trained StaffHIIN EOM
Kentucky Quality Counts
Harm Across the Board /
Control Charts
Observational Medicine
Numerical Medicine
Statistics /Epidemiolog
y
In-Hospital Surveillance
Counting Cases
IncidencePrevalence
Trends
Trained StaffHIIN EOM
Kentucky Quality Counts
Harm Across the Board /
Control Charts
We are here because
Ancient Medical Texts
Observational Medicine
Numerical Medicine
Statistics /Epidemiolog
y
Trained StaffHIIN EOM
Kentucky Quality Counts
Harm Across the Board /
Control Charts
Ancient Medical Texts
Observational Medicine
Numerical Medicine
Statistics /Epidemiolog
y
Trained StaffHIIN EOM
Kentucky Quality Counts
Harm Across the Board /
Control Charts
HIIN EOM
Hippocrates
460bce - 370bce
Humoral Theory
Presentation Overview
• History
• Hidden Perspectives–Airplanes
–Exposures
–Trends
• K-HIIN
World War II (Example 1)
Where do you put the armor?
Abraham Wald
Risk Assessment (Example 2)
Exposure A Disease “X”Relative Risk 3.00
Exposure A Disease “X”Relative Risk 3.00
Exposure B Disease “Q”Relative Risk 1.35
Exposure A Disease “X”Relative Risk 3.00
Baseline Incidence: 1 Person per 2 Million
Exposure B Disease “Q”Relative Risk 1.35
Baseline Incidence: 340 per 100,000
Exposure B Disease “Q”Relative Risk 1.35
Baseline Incidence: 340 per 100,000
Removing Exposure B will eliminate 35% of Disease
“Q”!!
Exposure B
Exposure B Disease “Q”Relative Risk 1.35
Baseline Incidence: 340 per 100,000
Removing Exposure B will eliminate 35% of Disease
“Q”!!
Exposure B
Exposure B Disease “Q”Relative Risk 1.35
Baseline Incidence: 340 per 100,000
Exposure B
Exposure B Disease “Q”Relative Risk 1.35
Baseline Incidence: 340 per 100,000
Exposure B Competing Risks
Exposure C
Exposure D
Exposure E
Surveillance (Example 3)
What is Maternal Mortality?
What is Maternal Mortality?
Definitions Matter
What is Maternal Mortality?
Definitions Matter
Globally - Lots of Variation
2000
2014
2003
?
2000
2014
2003
?
44 States –Revised Death Certificates
2000
2014
2003
?
44 States –Revised Death Certificates
Standardized Death Certificates
Standardized Death Certificates
Unknown Truth
Standardized Death Certificates
Unknown Truth
How do you know where you are going, if you don’t know where you have been?
United States Begins Systematic Surveillance of Maternal Mortality
United States Begins Systematic Surveillance of Maternal Mortality
New Standardized Death Certificates show the Incidence of Maternal Mortality Higher than Previously Thought
Or
HIIN Encyclopedia of Measures (EOM)
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
Underlying theme in statistics:
Observed vs. Expected
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
Underlying theme in statistics:
Observed vs. Expected
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
Underlying theme in statistics:
Observed vs. Expected
Observed vs. Baseline=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”?
Numerator Data
Denominator Data
# Event(s)
Total Population =
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”?
Numerator Data
Denominator Data
# Event(s)
Total Person Time=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”? - Example
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”? - Example
We observe 169 patients – Pressure Ulcers – that met inclusion criteria
There were 100,000 patients – that met inclusion criteria for the source pop.
Numerator
Denom.
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”? - Example
169
100,000X 1000 = 1.69
Numerator
Denom.
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
What are we “Observing”? - Example
169
100,000X 1000 = 1.69
Numerator
Denom. Given our source population as defined by the HIIN EOM, during the (time period), we observed 1.69 patients with pressure ulcers per 1000 patients.
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
We know what we ObservedBut what are we “EXPECTING”?
If we do nothing to reduce HARM, we are EXPECTING our current OBSERVED Rates to be approximately equal to the BASELINE RATE. The BASELINE is the EXPECTED Rate.
A Calculated Baseline (2015)
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
We know what we ObservedBut what are we “EXPECTING”?
Observed Pressure Ulcer Rate = 1.69/1000
In 2015 our baseline rate was 2.35/1000
Has our new Observed Rate improved upon the Expected baseline?
A Calculated Baseline (2015)
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baselinerate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 Jun-16
Expected Rate
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baselinerate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16
40% Less than Baseline Rate
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current
RateCurr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 28% 1.30 44.7% ATTARGET
The Rate for June 2016
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 28% 1.30 44.7% ATTARGET
2.35-1.69
2.35X 100 = 28%
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 28% 1.30 44.7% ATTARGET
2.35-1.69
2.35X 100 = 28%
The closer the Current Rate gets to 0, the better our Current % Improved
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 0.75 68% 1.30 44.7% ATTARGET
2.35-0.75
2.35X 100 = 68%
For example if our Current Rate is 0.75, our Current % Improved gets better (68% vs. 28%)
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 68% 1.30 44.7% ATTARGET
Aggregate rate from beginning of initiative through the most recent month
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 68% 1.30 44.7% ATTARGET
2.35-1.30
2.35X 100 = 44.7%
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr %Improv
CumulRate
Cumul % Improv
Status
HAPU 2 2.35 1.41 Jun-16 1.69 68% 1.30 44.7% ATTARGET
2.35-1.30
2.35X 100 = 44.7%
In theory there is still 55.3 % left to try to improve upon.
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Has our new Observed Rate improved upon the Expected baseline?
HarmMeasure
Baseline rate
TargetRate
CurrentMonth
Current Rate
Curr. %Improv
Cumul.Rate
Cumul % Improv.
Status
HAPU 2 2.35 1.41 Jun-16 1.69 68% 1.30 44.7% ATTARGET
2.35-1.30
2.35X 100 = 44.7%
In theory there is still 55.3 % left to try to improve upon.
WARNING
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline – Measure Run Chart
Facility
State
Baseline
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
Observed vs. Expected
Hospital Specific Cases Expected # Cases
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
Observed vs. Expected
Hospital Specific Cases Expected # Cases
Hospital Specific Cases
Expected # Cases
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
Observed vs. Expected
Hospital Specific Cases Expected # Cases
Hospital Specific Cases
Expected # Cases
Where does the Expected Number of Cases come from?
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
Observed vs. Expected
Hospital Specific Cases Expected # Cases
Hospital Specific Cases
Expected # Cases
Expected cases are the pooled average from a stratum of hospitals
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
170 CLABSI cases / 100,000 Central Line Days
Expected # Cases
Standardized Incidence Ratios
What is SIR?
* Critical Care Units Central Line Associated -BSI
Type of Facility No. of Locations
No. of CLABSI Central Line Days
Pooled Mean
Medical Cardiac 228 876 436, 409 2.0 / 1000
Medical Major Teaching
125 1410 549,088 2.5 / 1000
Medical Surgical >15 Beds
280 1449 986,982 1.5 / 1000
Medical Surgical ≤15 Beds
718 1130 755,437 1.5 / 1000
Neurosurgical 72 396 160,879 2.5 / 1000
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
(170 cases / 100,000 days)
(2.5 cases / 1,000 days)
(0.0017)
(0.0025)
=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
(170 cases / 100,000 days)
(2.5 cases / 1,000 days)
SIR = (0.68)=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
(170 cases / 100,000 days)
(2.5 cases / 1,000 days)
=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
What is SIR?
(170 cases observed)
(250 cases expected)
SIR = (0.68)=
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
How to interpret SIR?
• SIR = 1The number of cases is around what would be expected
• SIR > 1The number of cases is higher than expected
• SIR < 1The number of cases is lower than expected
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
How to interpret SIR?
• SIR = 1The number of cases is around what would be expected
• SIR > 1The number of cases is higher than expected
• SIR < 1The number of cases is lower than expected
HIIN Encyclopedia of Measures (EOM)
Kentucky Quality Counts
A Calculated Baseline (2015)
Standardized Incidence Ratios
How to interpret SIR?
• SIR = 1The number of cases is around what would be expected
• SIR > 1The number of cases is higher than expected
• SIR < 1The number of cases is lower than expected
“Like fire, the chi-squared test is an excellent servant and a bad master.”
Sir Bradford Hill
