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Wk 4, mha 599: capstone project: part 3 – program finances

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Part 3: Program Finances

Factors that determine the financial feasibility of a health care program include the cost of the facility, equipment, staff salaries, patient out-of-pocket expenses, and insurance reimbursement. Referring to the concepts practiced in the collaborative assignment with your group last week, work on the following:

1. Determine how many physicians and supporting staff are needed to operate the clinic, applying the appropriate ratio of clinical and support staff per physician.

2. Determine the direct costs of the program.
Direct costs in this case are patient expenses, and they increase as the number of patients increase. These include:

a. Physician and procedural support staff costs per patient/procedure

b. Gloves, sutures, gowns, equipment, lab supplies, other supplies

3. Determine the indirect costs of the program. Indirect costs are overhead costs that include but are not limited to:

a. General support staff and related costs

b. Electronic medical records

c. Insurance and taxes

d. Facility and administration

e. Employee benefits such as health and life insurance, retirement plans, and fringe benefits

4. Determine the equipment costs of the program. This includes items for the physician clinic that will be used for
more than one year. This is in contrast to variable costs which occur per patient and include such items as gloves, syringes, needles, and gauze, and would include items such as:

a. Examination tables

b. Workstations

c. Desks and chairs in the waiting room

d. Other equipment of this nature

5. Determine reimbursement for clinic services based on HCPCS/CPT codes with geographical adjustment.

6. Determine net reimbursement after variable costs per patient.

7. Determine how many patients the clinic will need to see (a) on average per day and (b) annually to break even on expenses.

Note: Refer to the health financial instructions and sample calculations document to help you with this part of the project.

Table 1. Staffing and Salaries

Complete Table 1 by adding the staff positions including physician specialty, nurses and support staff, and the number of positions in your clinic. Multiply the number of positions by average salary for those positions to obtain total salaries.

Staff Position

Number of Positions

Average Salary

Total Salaries

Primary Care Physician

Nurse

Support Staff

Total Salary:

Table 2. Fringe Benefits Expense and Total Salary Expense

These expenses have been established by the hospital system at 25% of salaries and added to salaries for Total Salary Expense.

Factors

Dollar Amount

Total Salaries

Fringe Benefit Expense 25% of Salaries

Total Salary Expense w/ Fringe Benefits:

Table 3. Overhead (Indirect) Costs and Total Fixed Practice Costs

Begin with total salaries including fringe benefits. Multiply that amount by 40% (0.40) and combine the 2 numbers for total fixed practice costs. These costs do not vary based on the number of patients.

Cost Factors

Dollar Amount

Overhead Costs 40% of Total Salary Expense

Total Salaries Expense

Overhead Costs 40% of Total Salary Expense

Total Overhead and Fixed Costs:

Table 4. Equipment Costs

These items will be used by the clinic for 5 years so the costs will be divided equally over 5 years (depreciation).

Cost Factors

Dollar Amount

Total Equipment Cost

Depreciated Equipment Cost (cost for year one)

Total Clinic Costs (for year one including salaries, overhead, and equipment costs):

Table 5. RVU and Reimbursement Calculation

The cost of physician care varies in different locations, as do the cost of operating a practice and the rates of physician malpractice claims. In addition, each year Medicare adjusts the conversion rate either up or down.

Category

RVU

GCI

Total RVU

Totals

· RVU:

· GCI:

· Total RVU:

Table 6. Variable Costs and Net Reimbursement

To complete Table 6, multiply reimbursement per visit by 10% (0.10) and subtract this number from reimbursement to obtain net reimbursement per patient.

Cost Factors

Dollar Amount

Reimbursement per visit

Variable cost per patient 10%

Net Reimbursement per patient:

7. Breakeven Analysis

To calculate the number of patients needed to break even, divide total practice costs by net reimbursement. To calculate the number of patients per day, divide the total number of patients for an annual breakeven number by the number of days the practice is open. Enter text below.

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July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

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