applied healthcare quality improvement analytics boot … course... · applied healthcare quality...
TRANSCRIPT
Applied Healthcare Quality Improvement Analytics Boot Camp
HIDI Analytics Academy
Saint Louis University Center for Health Outcomes Research
Course Purpose
• To enhance the hospital quality analyst’s data management and analysis skills in a manner that facilitates organizational quality improvement
Course Structure
• Applied Orientation – No mathematical formulas to use or remember – Use the software R to perform data management and
analysis that is relevant to the hospital quality analyst – Class examples, exercises and homework based on
tasks that are relevant to the hospital quality analyst – Focused on learning:
What analytical method to utilize to answer the question(s) at hand
How to execute the analytical method correctly using R How to interpret analytical output in a quality improvement
context
Data Analysis Process
1. Formulate question(s) to be answered 2. Formulate the null and alternate hypotheses 3. Specify the significance level 4. Select the analytical technique 5. Determine data requirements 6. Acquire data 7. Conduct data screening 8. Conduct data cleaning 9. Conduct data analysis 10. Interpret results 11. Communicate results
Presentation Structure
• Explanation of the analytical method
• Analytical method requirements
• Analytical method assumptions
• Analytical method example
• Analytical method class exercise
Multiple Logistic Regression Overview
• Answers the question about which factors – when considered together – are associated with the outcome of interest (i.e. the dependent variable) – For example, if the factors arrival shift and ECG
within 10 minutes are thought to impact whether AMI patients receive a thrombolytic within 30 minutes • The question could be formulated as:
– Which of the factors – arrival shift, ECG within 10 minutes – when considered together – are associated with a door-to-needle time that is less than or equal to 30 minutes?
Multiple Logistic Regression Requirements
• Data type required – Dependent Variable
• Categorical (Binary) – Data that is classified into two mutually exclusive categories
» For example:
Primary PCI Received Within 90 Minutes of Hospital Arrival: Yes vs. No Beta Blocker on Arrival: Yes vs. No ACEI or ARB at Discharge: Yes vs. No
– The data is formatted where 1=Yes and 0=No
– Independent Variable • Continuous variables can be used, but they require methods beyond this
course (See Regression Modeling Strategies by Frank Harrell for more information). • Categorical
– Data that is classified into two or more mutually exclusive categories » For example:
Sex – male or female Race/Ethnicity – Caucasian, African American, Asian Discharge Status – Home, SNF, Nursing Home, Expired
Multiple Logistic Regression Assumptions
• Assumptions:
– Patients are selected randomly
– The factor categories are independent
• The outcome of one factor category has no influence on the outcome of the other factor category
Multiple Logistic Regression Steps
1. Formulate the question(s) to be answered 2. Specify the significance level 3. Determine data requirements 4. Acquire data 5. Conduct data screening for each variable to be included in the analysis
a. Assess each categorical variable to be included in the analysis for the presence of observations in each factor category
6. Conduct data cleaning a. Categorical variables that contain only one factor category with observations are
excluded from the analysis
7. Conduct exploratory analysis for each variable to be included in the analysis 8. Conduct multiple logistic regression 9. Evaluate diagnostics 10. Correct issues identified in diagnostics 11. Conduct multiple logistic regression 12. Interpret and communicate results
Multiple Logistic Regression Class Exercise
• Answer the question:
– Which of the following factors, considered together, are associated with receiving an initial ECG within 10 minutes
Gender
Race
Arrival shift
Presence of dedicated of ECG technician
Use of ECG protocol
Presence of chest pain on arrival?
• Use a significance level of 0.05
• Import the data set ami_ecg.csv
• Assume that all the data screening/cleaning has been done and the data is ready for analysis
R Output Examples
Identify and Prioritize Quality Improvement Projects
Evaluate Multiple Factors
Identify Significant Factors Affecting Performance
------------------------------------------------------------------------------
lndoordr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pr_chst | -.0053153 .292718 -0.02 0.986 -.5857209 .5750904
d_stroke | .1849864 .2255869 0.82 0.414 -.2623107 .6322835
pr_stup | -.2964981 .196244 -1.51 0.134 -.6856138 .0926175
pr_lbbb | .2028425 .3729654 0.54 0.588 -.536679 .942364
d_hyper | .1304519 .113394 1.15 0.253 -.0943874 .3552912
d_sex | .1133893 .1272889 0.89 0.375 -.1390011 .3657796
d_age | .0035462 .0047204 0.75 0.454 -.0058134 .0129058
_Ishift_2 | -.1970292 .1357439 -1.45 0.150 -.4661842 .0721259
_Ishift_3 | -.0311062 .1427623 -0.22 0.828 -.3141774 .251965
_Iday_2 | .283393 .2206837 1.28 0.202 -.1541819 .7209679
_Iday_3 | .323035 .2267842 1.42 0.157 -.126636 .7727061
_Iday_4 | .2945226 .2088691 1.41 0.161 -.1196263 .7086715
_Iday_5 | .3075697 .2213839 1.39 0.168 -.1313937 .746533
_Iday_6 | .3429329 .2209213 1.55 0.124 -.0951133 .7809791
_Iday_7 | .1917197 .1869825 1.03 0.308 -.179032 .5624714
doorekg2 | .0222599 .0048575 4.58 0.000 .0126283 .0318915
_cons | 3.150323 .4134564 7.62 0.000 2.330515 3.970131
------------------------------------------------------------------------------
Course Benefits
• Advance your data analysis knowledge and skills:
– Conduct informative and insightful analytics in a manner that facilitates quality improvement
– Enhances your organizational contribution to meeting the value-based payment demands
• Learn the statistical software package R
• Promote your career growth
Why Learn R
• The capability to conduct analytics not possible with Excel
• Conduct analytics and produce graphs more efficiently compared to Excel
• Integrate data from various sources more effectively and efficiently compared to Excel
• R is gaining in popularity
Information
• For additional information about the Healthcare Quality Improvement Analytics Boot Camp contact: —Brian Waterman
Missouri Hospital Association
Email: [email protected]
Phone: 573-893-3700
– Robert Sutter Saint Louis University Center for Health Outcomes Research
Email: [email protected]
Phone: 314-977-9300