I’m working on a health & medical discussion question and need an explanation to help me learn.
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:
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.
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
Expert Solution Preview
Introduction:
In healthcare administration, setting standards and achieving them is crucial in providing quality care to patients. Monitoring and evaluating these standards is essential to ensure that they are met consistently. Probability distributions can be used to model and analyze data to make informed decisions.
Answer:
One way a distribution might be used in a health services organization is to analyze patient wait times in the emergency department. This information can be used to set a standard for wait times and evaluate if the standard is being met. A representative probability statement could be:
P(X ≤ 30 | N=50, p=0.8)
This probability statement represents the probability of 30 or fewer patients waiting for emergency care out of 50 patients when the expected success rate of meeting the standard is 0.8. Using fictitious data, if the actual number of patients waiting is 25, we can calculate the probability using the binomial distribution formula:
=binom.dist(25, 50, 0.8, TRUE)
The result is the probability of observing 25 or fewer patients waiting in 50 trials when the expected success rate is 0.8. This information can then be used to evaluate if the standard of wait times is being met and make any necessary adjustments to improve patient care.
