Simulation of Telemedicine Discussion Case

Simulation of Telemedicine Discussion Case

Instructions/Descriptions

Simulation of Telemedicine Discussion Case

Discussion: Simulation of Telemedicine

Classifying and clustering are important methods that may assist healthcare administrators in identifying specific issues or challenges in healthcare delivery or general management of a health services organization. For example, how would you determine if a patient is likely to pay on time, pay late, or not pay a bill? Using classification and clustering techniques can help administrators describe characteristics of their patients.

For this Discussion, review the resources for this week. Then, reflect on how you might apply classification and clustering techniques for your health services organization or one with which you are familiar.

By Day 3

Post a brief description of one of the techniques on classification and clustering examined this week. Explain how the technique you described might apply to your health services organization or one with which you are familiar. Be specific, and provide examples.

Discussion

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Continue the Discussion and respond to your colleagues in one or more of the following ways:

Each Colleagues 250 words or more (Colleague 1 250 words, Colleague 2 250 words, Total 500 words):

• Ask a probing question, substantiated with additional background information, evidence, or research.
• Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.
• Validate an idea with your own experience and additional research.
• Suggest based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.

Colleague 1

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This week we examined clustering.   Clustering is known as segmentation which groups entities into similar clusters such as cities like Chicago, Columbus, Cincinnati, and Cherokee.  Or companies like UPS, FEDEX,.  Clustering can be used in marketing, finance, and operations of the HSO.  The purpose of clustering is to discover characteristics of groups based on given data.  One technique of clustering studied was unsupervised data mining.  This type of method has no dependent variable. (Albright. 2017).  It searches for structure and patterns among all variables.

There are many uses of clustering in a healthcare services organization (HSO).  Based on the data, the HSO might want to discover the number of doctors and how many live within 25 miles of the hospital.  The HSO might need to discover how many EMS vehicles it owns and how many of them have mileage over 100,000.   It might want to discover how many different foods are being served in thE cafeteria and which ones need frozen.  They also might want to explore which employees are single men and use public transportation, where another cluster is married women.  Clustering would aid greatly in most decision making.

REFERENCE

Albright, S.C. & Winston, W.L. (2017).   Business Analytics.  Data Analysis and Decision Making.  (6th ed).  CENGAGE Learining.

Colleague 2

Classification refers to categorizing variables into categories, often binary (Albright & Winston, 2016). An example would be to classify patients as those with insurance coverage and those without. Clustering, on the other hand, is a categorization or grouping technique used by grouping based on the value of their variables (Albright & Winston, 2016). An example of clustering would be to group together a set of patients based on their general reason for admittance, such as ‘Cardiac’, ‘Respiratory’, ‘Fractures’, and ‘Mental Health’.

There are a variety of classification methods that can be used in analytics such as logistic regression, neural networks, and Naïve Bayes. Logistic regression is a methodology used to determine the probability that an individual is in a specific category (Albright & Winston, 2016). An example of how you could apply this would be if you were to research the relationship between the socioeconomic status of a community and the availability of telehealth as a mode of care delivery. You could use a logistic regression that would allow you to determine the probability of an individual receiving telecare based on their community’s socioeconomic status.

References

Albright, S., C. and Wayne L. Winston. Business Analytics: Data Analysis & Decision Making. Available from: MBS Direct, (6th Edition).

Simulation of Telemedicine Discussion Case

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