Activity: Your case study includes an Excel dataset. Use the dataset to complete the following:

• Calculate the mean time for each of the patient visit components in the dataset (Columns M through S)
• Determine for each patient visit component whether the activity is a value-added, non-value-added, 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-value-added but essential. Also, indicate the mean time for each component in the patient visit. Calculate the average total value-added, non-value-added, and non-value-added but essential times and discuss the importance of each in relation to the mean total time patients spend in the clinic.
o Wait Time Analysis – Based on your findings in the Data Analysis Summary:
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.)

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 made-up test data. Keep in mind that all graphs must have descriptive titles and axis titles.

Figure 1 – Format of an 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. Your graph should look similar to Figure 1, but the day of the week (Monday through Friday) should be on the x-axis.

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 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 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 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. Very few grammatical or punctuation problems are present. All diagrams are professionally embedded.