Utility of Probability Models in Healthcare Administration

Discussion: Utility of Probability Models in Healthcare Administration

You are the healthcare administration leader for a health services organization and are interested in achieving a standard, whereby 90% of all patients are screened within the initial 15 minutes of arriving for a family practice appointment. In a random sample of 30 patients, you find that 25 were screened within 15 minutes. The probability that this event (or one more extreme) would occur, might be modeled as a binomial with the following probability statement:

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P(X≤25 | N=30, p=0.9)

To solve, you use =binom.dist(25, 30, 0.9, TRUE) in Excel and find that you would expect 25 or fewer screenings in 30 trials when the success rate should be 0.9 about 17.5% of the time.

For this Discussion, review the resources for this week, and consider how one of the distributions presented might be useful for healthcare administration leaders.

Post an example of how one of the distributions presented might be used in your health services organization or one with which you are familiar. Then, generate a representative probability statement based on the scenario and solve using fictitious data. Be specific in your probability statement.

Discussion

Continue the Discussion and respond to 2 of 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.
  • Make a suggestion 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.

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Colleague 1

No-show appointments frequently happen in healthcare. No-show appointments account for billions in revenue loss for healthcare in the U.S., resulting in negative issues surrounding quality of care and revenue (Ruiz-Hernández et al., 2020). Using binomial probability can help healthcare leaders find ways to shore up ways to lower probability in their organization and see less loss of revenue. A lower probability of no-shows enables to serve more patients.

Binomial probability looks specifically at two populations and seen visually as a bell curve (Albright& Winston, 2017). It can support populations in healthcare when leaders look specifically at two values. An example of binomial distribution is seen in seminar courses here at Walden University where there are only two grade options, met or unmet. But in healthcare, leaders could use it with no-shows.

As a healthcare leader, you have 75 patient appointments for the week with no more than (2) no-shows per day, which leaves you with 65 or more appointments to see. The probability of no-shows for the week is 10%.

P(X≤65 | N=75, p=0.10), TRUE

References

Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.

Ruiz-Hernández, D., García-Heredia, D., Delgado-Gómez, D., & Baca-García, E. (2020). A probabilistic patient scheduling model for reducing the number of no-shows. Journal of the Operational Research Society71(7), 1102–1112. https://doi-org.ezp.waldenulibrary.org/10.1080/01605682.2019.1658552

 

Colleague 2

 

Scheduling has always been an issue for healthcare facilities of different kinds and specialties. Creating an improvement within that healthcare facility will require making the appropriate changes in care and service delivery (Benneyan, Lloyd, & Plsek, 2003). The issues may arise from different reasons, such as lack of staffing to the wrong scheduling techniques. The way to figure these out is using probability distribution to utilize information of the possible random variables when and how we can support a busy waiting room so that patients would not have to wait to be seen by their doctors (Albright & Winston, 2017). During flu and cold seasons are the times of the year when offices need a quick turnaround with patients to reduce the number of sick people in the office and not infect those who are just visiting for another reason.

For a situation where we have several patients come into the facility with 30 patients, screening patients should not take more than 5 minutes per patient, and the sample will then compare 5 minutes for each of the 30 patients that need to be seen. The result of this should be that all 30 patients should be seen within two and a half hours for 30 patients. Intake should not take more than 5 minutes per person unless other issues arise or there are difficulties. We also have to include that many of these patients might not take longer than the allotted time, covering for the additional time used for other patients.

 

References

Albright, S. C., & Winston, W. L. (2017). Business Analytics: Data Analysis and Decision Making (6th ed.). Stamfort, CT, USA: Cengage Learning.

Benneyan, J., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. BMJ Journals, 12, 458-464. Retrieved from https://qualitysafety.bmj.com/content/qhc/12/6/458.full.pdf