## Benchmarking & Clinician Profiles## Assigned ReadingUse data balancing to benchmark clinicians (use instructor's last name as password) Read► ## Presentations## AssignmentsStart with a summary page. Start each question in a separate page, sheet, or file. In the summary page write statements comparing your work to answers given or videos. For example, "I got the same answers as the Teach One video for question 1."
- What is the expected length of stay for each of the clinicians?
- What is the expected length of stay for Dr. Smith if he were to take care of patients of Dr. Jones?
- What is the expected length of stay for Dr. Jones if he were to take of patients of Dr. Smith?
- Regress "Cared for by Dr. Smith" on the HCC and other patient charcteristics. What type of patients are more likely to be cared for by Dr. Smith. Regression Results►
- Using regression results, identify which variables are likely to be in the Markov Blanket of the variable "Cared for by Dr. Smith".
- Use SQL to determine if the clinician is more efficient than his peer group. SQL► Mai's Teach One►
- Check that all variables are positively and monotonely related to prevalence of diabetes in the county. Monotone?►
- Assign a binary variable to each variable in such a manner that when the variable is 1, diabetes is more likely.
- Drop from analysis covariates that are not parents on Markov
Blanket of diabetes. Accomplish this task using the following
steps:
- Regress diabetes on all variables (with no interaction terms in the model), identify variables that are signficant predictors of diabetes and have a large effect size
- Do a second regression, verifying that no interaction terms that involve the signficant variables are predictors of diabetes (have a statistically signficant and large effect size). Include on the list of parents of Markov Blanket, any variable whose interactions is predictive of diabetes.
- Calculate the impact of access to food sources on diabetes, while controlling for other variables. Accomplish this task by stratifying the variables identified as parents in the Markov Blanket, then switch the distribution of controls (low-diabetic counties) with distribution of cases (high diabetic counties).
- Report overlap and impact of food access on diabetes.
- Verify that there are less than 30 cases in the database of 80 year olds who only cannot walk and toilet.
- Verify that all variables
lead to increased mortality. If not, re-name the variables so when the
variable is assigned the value of 1, it has higher probability of
mortality.
- Excessive Match: Add one additional disability to the list until you find 30 cases. These additional cases are excessive matches in the sense that these cases have additional disabilities that increase probability of mortality. For example, when you add unable to urine to 1 then you are including cases that also have urine incontinence. For another example, setting unable to eat to 1 will examine patients who cannot walk, toilet or eat. For each excessive match calculate the number of cases and the probability of being dead in 6 months.
- Partial Match: Remove one of the disabilities of the patient and calculate number of cases and average probability of mortality in 6 months. In this example we are looking at 80 year old who cannot walk or toilet. Remove toilet and see if you get 30 cases. Calculate number of cases you can match. Calculate probability of mortality of these cases in 6 months.
- Calculate the probability of mortality for the synthetic case as average of the minimum of excessive matches and maximum of partial matches.
- SQL►
Use the following dictionary of variables to create a header for the data. Data► Adel's Teach One►
- Data►
- Lina's Solution►
- Ayesha Bibi's Teach One Slides► Python► HTML►
## More- Practice profiling PubMed►
- Importance of risk adjustment in measuring performance in primary care PubMed►
Prepared by Farrokh Alemi, Ph.D. This page is part of the course on Statistical Process Improvement |