Critically Thinking about Quality Control Measures Utilized by HIIN. Brian Guinn

Transcription

1 Critically Thinking about Quality Control Measures Utilized by HIIN Brian Guinn

2 STATISTICS

3 Statistics

4 Statistics Not Statistics All the fun!!

5 Statistics Not Statistics All the fun!!

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8 Empower

9 Entertain

10 Entertain

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12 Presentation Overview History Hidden Perspectives Airplanes Exposures Trends K-HIIN

13 Why are we here?

14 Hippocrates 460 bce bce Humoral Theory

15 Hippocrates 460 bce bce Humoral Theory Celsus 25 bce - 50 ce Celsus Tetrad

16 Hippocrates 460 bce bce Humoral Theory Celsus 25 bce - 50 ce Celsus Tetrad Galen 129 ce bce Anatomist

17 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine

18 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Philip von Hohenheim People like Philip von Hohenheim ( ) Trial and error to treat the sick Famous for utilizing observation in lieu of ancient texts

19 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Philip von Hohenheim

20 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Better known as? Philip von Hohenheim

21 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Better known as? Paracelsus Philip von Hohenheim

22 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Better known as? Paracelsus Philip von Hohenheim

23 Hippocrates Celsus Galen Ibn-Sina 460 bce bce Humoral Theory 25 bce - 50 ce Celsus Tetrad 129 ce bce Anatomist 980 ce bce Canon of Medicine Better known as? Paracelsus Philip von Hohenheim Beyond Celsus

24 Paracelsus Observational Medicine Father of Modern Demography John Graunt

25 Hippocrates Ancient Celsus Medical Galen Texts Ibn-Sina 460 bce bce 25 bce - 50 ce 129 ce ce 980 ce ce Observational Medicine Paracelsus Numerical Medicine Statistics /Epidemiolog John Graunt y

26 Observational Medicine In-Hospital Surveillance Trained Staff HIIN EOM Numerical Medicine Statistics /Epidemiolog y Counting Cases Incidence Prevalence Trends Kentucky Quality Counts Harm Across the Board / Control Charts

27 Observational Medicine In-Hospital Surveillance Trained Staff HIIN EOM Numerical Medicine Statistics /Epidemiolog y Counting Cases Incidence Prevalence Trends Kentucky Quality Counts Harm Across the Board / Control Charts

28 Observational Medicine In-Hospital Surveillance Trained Staff HIIN EOM Numerical Medicine Statistics /Epidemiolog y Counting Cases Incidence Prevalence Trends Kentucky Quality Counts Harm Across the Board / Control Charts

29 We are here because

30 Ancient Medical Texts Observational Medicine Numerical Medicine Statistics /Epidemiolog y Trained Staff HIIN EOM Kentucky Quality Counts Harm Across the Board / Control Charts

31 Ancient Medical Texts Observational Medicine Numerical Medicine Statistics /Epidemiolog y Trained Staff HIIN EOM Kentucky Quality Counts Harm Across the Board / Control Charts

32 Hippocrates 460 bce bce Humoral Theory HIIN EOM

33 Presentation Overview History Hidden Perspectives Airplanes Exposures Trends K-HIIN

34 World War II (Example 1)

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38 Where do you put the armor?

39 Abraham Wald

40 Risk Assessment (Example 2)

41 Exposure A Relative Risk 3.00 Disease X

42 Exposure A Relative Risk 3.00 Disease X Exposure B Relative Risk 1.35 Disease Q

43 Exposure A Relative Risk 3.00 Disease X Baseline Incidence: 1 Person per 2 Million Exposure B Relative Risk 1.35 Disease Q Baseline Incidence: 340 per 100,000

44 Exposure B Relative Risk 1.35 Disease Q Baseline Incidence: 340 per 100,000 Exposure B Removing Exposure B will eliminate 35% of Disease Q!!

45 Exposure B Relative Risk 1.35 Disease Q Baseline Incidence: 340 per 100,000 Exposure B Removing Exposure B will eliminate 35% of Disease Q!!

46 Exposure B Relative Risk 1.35 Disease Q Baseline Incidence: 340 per 100,000 Exposure B

47 Exposure B Relative Risk 1.35 Disease Q Baseline Incidence: 340 per 100,000 Exposure B Competing Risks Exposure C Exposure D Exposure E

48 Surveillance (Example 3)

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51 What is Maternal Mortality?

52 What is Maternal Mortality? Definitions Matter

53 What is Maternal Mortality? Definitions Matter Globally - Lots of Variation

54 ?

55 ? States Revised Death Certificates

56 ? States Revised Death Certificates

57 Standardized Death Certificates

58 Unknown Truth Standardized Death Certificates

59 Unknown Truth Standardized Death Certificates

60 How do you know where you are going, if you don t know where you have been?

61 United States Begins Systematic Surveillance of Maternal Mortality

62 United States Begins Systematic Surveillance of Maternal Mortality New Standardized Death Certificates show the Incidence of Maternal Mortality Higher than Previously Thought Or

63 HIIN Encyclopedia of Measures (EOM)

64 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts

65 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015)

66 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios

67 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts Underlying theme in statistics: Observed vs. Expected

68 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts Underlying theme in statistics: Observed vs. Expected

69 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts Underlying theme in statistics: Observed vs. Expected = Observed vs. Baseline

70 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts What are we Observing? Numerator Data Denominator Data = # Event(s) Total Population

71 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts What are we Observing? Numerator Data Denominator Data = # Event(s) Total Person Time

72 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts What are we Observing? - Example

73 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts Numerator What are we Observing? - Example We observe 169 patients Pressure Ulcers that met inclusion criteria Denom. There were 100,000 patients that met inclusion criteria for the source pop.

74 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts What are we Observing? - Example Numerator Denom ,000 X 1000 = 1.69

75 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts What are we Observing? - Example Numerator Denom ,000 X 1000 = 1.69 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.

76 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) We know what we Observed But 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.

77 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) We know what we Observed But 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?

78 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline?

79 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun-16 Expected Rate

80 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun-16 40% Less than Baseline Rate

81 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET The Rate for June 2016

82 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET X 100 = 28%

83 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET The closer the Current Rate gets to 0, the better our Current % Improved X 100 = 28%

84 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET For example if our Current Rate is 0.75, our Current % Improved gets better (68% vs. 28%) X 100 = 68%

85 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET Aggregate rate from beginning of initiative through the most recent month

86 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET X 100 = 44.7%

87 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Has our new Observed Rate improved upon the Expected baseline? Harm Measure Baseline rate Target Rate Current Month Current Rate Curr % Improv Cumul Rate Cumul % Improv Status HAPU Jun % % AT TARGET In theory there is still 55.3 % left to try to improve upon X 100 = 44.7%

88 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) WARNING Has our new Observed Rate improved upon the Expected baseline? Harm Baseline Target Current Current Curr. % Cumul. Cumul % Measure rate Rate Month Rate Improv Rate Improv. Status HAPU Jun % % AT TARGET In theory there is still 55.3 % left to try to improve upon X 100 = 44.7%

89 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline Measure Run Chart Facility State Baseline

90 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

91 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

92 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios What is SIR? Where does the Expected Number of Cases come from? Observed vs. Expected Hospital Specific Cases Hospital Specific Cases Expected # Cases Expected # Cases

93 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios What is SIR? Expected cases are the pooled average from a stratum of hospitals Hospital Specific Cases Observed vs. Expected Hospital Specific Cases Expected # Cases Expected # Cases

94 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

95 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 , / 1000 Medical Major Teaching Medical Surgical > 15 Beds Medical Surgical 15 Beds , / , / , / 1000 Neurosurgical , / 1000

96 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)

97 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)

98 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)

99 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)

100 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios How to interpret SIR? SIR = 1 The number of cases is around what would be expected SIR > 1 The number of cases is higher than expected SIR < 1 The number of cases is lower than expected

101 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios How to interpret SIR? SIR = 1 The number of cases is around what would be expected SIR > 1 The number of cases is higher than expected SIR < 1 The number of cases is lower than expected

102 HIIN Encyclopedia of Measures (EOM) Kentucky Quality Counts A Calculated Baseline (2015) Standardized Incidence Ratios How to interpret SIR? SIR = 1 The number of cases is around what would be expected SIR > 1 The number of cases is higher than expected SIR < 1 The number of cases is lower than expected

103 Like fire, the chi-squared test is an excellent servant and a bad master. Sir Bradford Hill