Business Question

Jun 15, 2024 | Business Finance

Description

Read the attached prompt and use the excel sheet attached to answer the prompt.

Unformatted Attachment Preview

Patient Throughput Analysis
Activity: Your case study includes an Excel dataset. Use the dataset to complete the following:
•
•
•
•
Calculate the mean time for each of the patient’s visit components in the dataset (Columns M
through S)
Determine for each patient visit component whether the activity is a value-added, non-valueadded, or non-value-added but essential activity (Hint – you will find the definition of these
categories in your module readings).
Calculate the average throughput time of a patient (Hint – throughput is the total average time
spent from the start of the visit to the end of the visit)
Assuming the data provided, calculate the average throughput time using Little’s Law as
discussed in the text (Hint – Chapter #11 in the textbook).
Your task is to analyze the patient throughput flow using the data, tools, and techniques learned in
this module and the information above. Your assignment is to create a Microsoft Word document
(your document must be Microsoft Word compatible) including all of the following:
o Data Analysis Summary – Discuss each patient visit component in the dataset and summarize
your findings. Include whether or not each activity is value-added, non-value-added, or non-valueadded but essential. Also, indicate the mean time for each component in the patient visit.
Calculate the average total value-added (VA), non-value-added(NVA), and non-value-added but
essential (NVAE) times and discuss the importance of each in relation to the mean total time
patients spend in the clinic (every activity must be identified as VA, NVA, or NVAE_.
o Wait Time Analysis – Based on your findings in the Data Analysis Summary, address each of the
following items:
o Identify and discuss areas of the patient process about which Dr. Millwood should be
concerned (i.e., where are outliers or mean times that can impact the patient experience).
o Identify bottlenecks and constraints as evidenced by data from your analysis.
o What are the mean total and individual component wait times for patients in the clinic across
the patient experience (i.e., are there specific process steps where wait times are higher,
indicating a bottleneck)?
o Identify specific concerns about individual physicians. Drawing conclusions per physician will
require you to analyze wait times by physician. There are multiple ways you can perform this
analysis in Excel, such as the subtotal function or a pivot table.
o What is the mean time physicians and MAs spend with patients delivering care? How are
these metrics important to the patient experience?
o Do average patient wait and care times differ significantly by physician? (Additional credit is
available for statistical analysis but is not required.)
1
o Patient Throughput Analysis
o Assume that the average number of patients in the clinic (waiting room, exam rooms, etc.) is
4.81 patients, and the clinic serves ten patients per hour, according to Little’s Law (found in
your text in chapter #11); what is the calculated average throughput time?
o Compare and contrast your observed average throughput time (from the data) and your
calculated average throughput time using Little’s law. Discuss possible reasons for the
difference between the two values (Hint – think about the flow of patients hourly and daily in
your observation dataset).
o Create a visual (graph) of the average hourly patient flow (hourly trends). Discuss your findings
concerning the hourly trends. See the format example in Figure 1 using example test data.
Your data line will look different. Keep in mind that all graphs must have descriptive titles
and axis titles.
Figure 1 – Format of an Average Hourly Trend Graph
o Create a visual (graph) of the average daily patient flow (Monday through Friday trends).
Discuss your findings concerning the trends by day of the week. See the format example in
Figure 2 using example test data. Your data line will look different. Keep in mind that all
graphs must have descriptive titles and axis titles.
Figure 2 – Format of an Average Daily Trend Graph
2
Submit one(1) Microsoft Word-compatible document to the proper Dropbox for this activity (other file
formats are not acceptable). You should not submit any PowerPoint or other format files. You may
use a tool such as PowerPoint or Excel to create your diagrams, but all of your content (e.g., text,
graphs, diagrams) should be in your Word-compatible document.
An exemplary student submission includes all of the following criteria:
•
•
•
•
•
The document is well-articulated and structured, with three distinct sections with the correct
titles as indicated in the instructions.
The Data Analysis Summary section
o Addresses each item outlined for this section above
o Discusses each visit component in detail
o Identifies each component as value-added, non-value-added, or non-value-added but
essential
o Provides the mean time for each component
o Provides the mean total value-added, non-value-added, and non-value-added but
essential times
o Discusses Each of the mean total time’s importance concerning the mean total time
patients spend in the clinic
The Wait Time Analysis section
o Addresses each item outlined for this section above
o Discusses a minimum of two areas from the data analysis where Dr. Millwood should be
concerned using supporting evidence and explaining why a concern is warranted
o Identifies a minimum of two potential bottlenecks and or constraints in the process
supported by evidence
o Discusses and analyzes mean patient wait times per physician and examines how the
metric is essential to the patient experience
o Discusses and analyzes mean patient care times per physician and examines how the
metric is essential to the patient experience.
o Discusses whether or not average patient wait and care times differ significantly by
physician
The Patient Throughput Analysis section
o Addresses each item outlined for this section above
o Discusses the average throughput time calculated using Little’s Law, comparing and
contrasting the observed average patient throughput time calculated from the data.
o Provides a logical discussion of the possible reasons for the difference between the
Little’s Law value and the observed value of average patient throughput
o Includes a logical visual (graph) of the average hourly patient flow (hourly trends)
o Includes a logical visual (graph) of the average daily patient flow (Monday through
Friday trends)
The document is professional in appearance, with no formatting issues. No grammatical or
punctuation problems are present. All diagrams are professionally embedded.
3
Activity: Your case study includes an Excel dataset. Use the dataset to complete the following:
•
•
•
•
Calculate the mean time for each of the patient’s visit components in the dataset (Columns M
through S)
Determine for each patient visit component whether the activity is a value-added, non-valueadded, or non-value-added but essential activity (Hint – you will find the definition of these
categories in your module readings).
Calculate the average throughput time of a patient (Hint – throughput is the total average time
spent from the start of the visit to the end of the visit)
Assuming the data provided, calculate the average throughput time using Little’s Law as
discussed in the text (Hint – Chapter #11 in the textbook).
Your task is to analyze the patient throughput flow using the data, tools, and techniques learned in
this module and the information above. Your assignment is to create a Microsoft Word document
(your document must be Microsoft Word compatible) including all of the following:
o Data Analysis Summary – Discuss each patient visit component in the dataset and summarize
your findings. Include whether or not each activity is value-added, non-value-added, or non-valueadded but essential. Also, indicate the mean time for each component in the patient visit.
Calculate the average total value-added (VA), non-value-added(NVA), and non-value-added but
essential (NVAE) times and discuss the importance of each in relation to the mean total time
patients spend in the clinic (every activity must be identified as VA, NVA, or NVAE_.
o Wait Time Analysis – Based on your findings in the Data Analysis Summary, address each of the
following items:
o Identify and discuss areas of the patient process about which Dr. Millwood should be
concerned (i.e., where are outliers or mean times that can impact the patient experience).
o Identify bottlenecks and constraints as evidenced by data from your analysis.
o What are the mean total and individual component wait times for patients in the clinic across
the patient experience (i.e., are there specific process steps where wait times are higher,
indicating a bottleneck)?
o Identify specific concerns about individual physicians. Drawing conclusions per physician will
require you to analyze wait times by physician. There are multiple ways you can perform this
analysis in Excel, such as the subtotal function or a pivot table.
o What is the mean time physicians and MAs spend with patients delivering care? How are
these metrics important to the patient experience?
o Do average patient wait and care times differ significantly by physician? (Additional credit is
available for statistical analysis but is not required.)
1
o Patient Throughput Analysis
o Assume that the average number of patients in the clinic (waiting room, exam rooms, etc.) is
4.81 patients, and the clinic serves ten patients per hour, according to Little’s Law (found in
your text in chapter #11); what is the calculated average throughput time?
o Compare and contrast your observed average throughput time (from the data) and your
calculated average throughput time using Little’s law. Discuss possible reasons for the
difference between the two values (Hint – think about the flow of patients hourly and daily in
your observation dataset).
o Create a visual (graph) of the average hourly patient flow (hourly trends). Discuss your findings
concerning the hourly trends. See the format example in Figure 1 using example test data.
Your data line will look different. Keep in mind that all graphs must have descriptive titles
and axis titles.
Figure 1 – Format of an Average Hourly Trend Graph
o Create a visual (graph) of the average daily patient flow (Monday through Friday trends).
Discuss your findings concerning the trends by day of the week. See the format example in
Figure 2 using example test data. Your data line will look different. Keep in mind that all
graphs must have descriptive titles and axis titles.
Figure 2 – Format of an Average Daily Trend Graph
2
Submit one(1) Microsoft Word-compatible document to the proper Dropbox for this activity (other file
formats are not acceptable). You should not submit any PowerPoint or other format files. You may
use a tool such as PowerPoint or Excel to create your diagrams, but all of your content (e.g., text,
graphs, diagrams) should be in your Word-compatible document.
An exemplary student submission includes all of the following criteria:
•
•
•
•
•
The document is well-articulated and structured, with three distinct sections with the correct
titles as indicated in the instructions.
The Data Analysis Summary section
o Addresses each item outlined for this section above
o Discusses each visit component in detail
o Identifies each component as value-added, non-value-added, or non-value-added but
essential
o Provides the mean time for each component
o Provides the mean total value-added, non-value-added, and non-value-added but
essential times
o Discusses Each of the mean total time’s importance concerning the mean total time
patients spend in the clinic
The Wait Time Analysis section
o Addresses each item outlined for this section above
o Discusses a minimum of two areas from the data analysis where Dr. Millwood should be
concerned using supporting evidence and explaining why a concern is warranted
o Identifies a minimum of two potential bottlenecks and or constraints in the process
supported by evidence
o Discusses and analyzes mean patient wait times per physician and examines how the
metric is essential to the patient experience
o Discusses and analyzes mean patient care times per physician and examines how the
metric is essential to the patient experience.
o Discusses whether or not average patient wait and care times differ significantly by
physician
The Patient Throughput Analysis section
o Addresses each item outlined for this section above
o Discusses the average throughput time calculated using Little’s Law, comparing and
contrasting the observed average patient throughput time calculated from the data.
o Provides a logical discussion of the possible reasons for the difference between the
Little’s Law value and the observed value of average patient throughput
o Includes a logical visual (graph) of the average hourly patient flow (hourly trends)
o Includes a logical visual (graph) of the average daily patient flow (Monday through
Friday trends)
The document is professional in appearance, with no formatting issues. No grammatical or
punctuation problems are present. All diagrams are professionally embedded.
3
Provider, Appointment, Visit
Provider
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
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Baker, Anthony
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Baker, Anthony
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Appt Date Scheduled
& Time Visit Length
1/5/2021
15
1/5/2021
15
1/5/2021
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1/5/2021
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1/5/2021
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1/5/2021
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1/5/2021
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1/7/2021
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1/7/2021
15
Appt Sch Time
1/5/21 8:45 AM
1/5/21 8:30 AM
1/5/21 9:30 AM
1/5/21 9:45 AM
1/5/21 10:00 AM
1/5/21 10:15 AM
1/5/21 11:00 AM
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1/5/21 12:00 PM
1/5/21 12:15 PM
1/5/21 12:45 PM
1/5/21 12:30 PM
1/5/21 1:00 PM
1/5/21 11:45 AM
1/5/21 2:30 PM
1/5/21 3:30 PM
1/5/21 3:45 PM
1/5/21 4:00 PM
1/5/21 3:00 PM
1/5/21 1:45 PM
1/5/21 2:00 PM
1/5/21 2:45 PM
1/7/21 9:45 AM
1/7/21 8:45 AM
1/7/21 9:00 AM
1/7/21 9:15 AM
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1/7/21 12:45 PM
1/7/21 2:15 PM
1/7/21 1:30 PM
1/7/21 1:00 PM
1/7/21 1:15 PM
Begin Check-In
1/5/21 8:01 AM
1/5/21 8:13 AM
1/5/21 9:10 AM
1/5/21 9:13 AM
1/5/21 9:41 AM
1/5/21 10:31 AM
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1/7/21 8:34 AM
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1/7/21 8:49 AM
1/7/21 9:19 AM
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1/7/21 11:29 AM
1/7/21 11:34 AM
1/7/21 11:41 AM
1/7/21 11:56 AM
1/7/21 12:34 PM
1/7/21 12:48 PM
1/7/21 12:50 PM
1/7/21 1:01 PM
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
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Baker, Anthony
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Baker, Anthony
Baker, Anthony
1/7/2021
1/7/2021
1/7/2021
1/7/2021
1/7/2021
1/12/2021
1/12/2021
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1/26/2021
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1/7/21 1:45 PM
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1/12/21 8:15 AM
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1/12/21 8:16 AM
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Baker, Anthony
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Baker, Anthony
Baker, Anthony
1/26/2021
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1/28/2021
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
2/2/2021
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
2/11/2021
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
2/18/2021
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3/2/2021
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3/2/21 8:21 AM
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
3/2/2021
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3/2/21 10:45 AM
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Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Anthony
Baker, Antho