Week 2 Data-Driven Decision Making For Healthcare Administra

Week 2 Data Driven Decision Making For Healthcare Administration Prac

Week 2: Data-Driven Decision Making for healthcare Administration Practice Probability and Probability Distributions How is probability related to decision making? How might an appreciation for the likelihood of an event occurring versus not occurring affect how you might engage in decision making? Although it might seem unclear right now, probability and probability distributions can aid healthcare administration leaders in determining those events that may or may not affect how healthcare delivery is practiced. In fact, the likelihood that certain events might occur could mean an increase in resource and staff costs. How might healthcare administration leaders leverage an understanding of probability to make sound business and healthcare decisions?

1. This week, you review the foundations of probability and probability distributions. You then apply probability distributions for informing how you might engage in data-driven decision making for business and healthcare delivery problems. Learning Objectives Students will: · Apply the foundations of probability and probability distributions · Apply probability distributions for data-driven decision-making problems. Learning Resources Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus.

Required Readings Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning. Chapter 4, "Probability and Probability Distributions" Chapter 5, "Normal, Binomial, Poisson, and Exponential Distributions" Fulton, L. V., Mendez, F. A., Bastian, N. D., & Musal, R. M. (2012). Confusion between odds and probability, a pandemic? Journal of Statistics Education, 20(3), 1–20. Note: Retrieved from the Walden Library databases. Microsoft. (2016). Statistical functions (reference). Retrieved from Required Media ExcelIsFun. (2011, July 19). Excel 2010 statistics 63: Exponential probability EXPON.DIST function [Video file]. Retrieved from Grande, T. [Todd Grande]. (2015, February 27). Calculating probabilities using the normal distribution function in Excel [Video file]. Retrieved from Grande, T. [Todd Grande]. (2015, April 3). Binomial distribution function in Excel [Video file]. Retrieved from Hays, D. [David Hays]. (2012, April 12). Poisson distribution on Excel [Video file]. Retrieved from .

Discussion Part Utility of Probability Models in Healthcare Administration 1. 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.. 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. 3. For this Discussion, review the resources for this week, and consider how one of the distributions presented might be useful for healthcare administration leaders. By Day 3 4. 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. By Day 5 Continue the Discussion and respond to your colleagues in one or more of the following ways: · 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.

Submission and Grading Information Grading Criteria To access your rubric: Week 2 Discussion Rubric Post by Day 3 and Respond by Day 5 To participate in this Discussion: Week 2 Discussion. Assignment Part (APA format 7th ed) 5 pages. Medicare Overbilling Analysis Your company is running a Medicare audit on Sleaze Hospital. Because Sleaze has a history of overbilling, the focus of your audit is on checking whether the billing amounts are correct. Assume that each invoice is for too high an amount with probability 0.06 and for too low an amount with probability 0.01 (so that the probability of a correct billing is 0.93). Also, assume that the outcome for any invoice is probabilistically independent of the outcomes for other invoices. (A.) For this Assignment, reflect on the case presented. Think about what strategies you might use to calculate associated probabilities for Sleaze Hospital, and then address the series of questions for the completion of the Assignment. The Assignment: (5 pages) 1. If you randomly sample 200 of Sleaze's invoices, what is the probability that you will find at least 15 invoices that overcharge the customer? What is the probability you won't find any that undercharge the customer? 2. Find an integer, k, such that the probability is at least 0.99 that you will find at least k invoices that overcharge the customer. (Hint: Use trial and error with the BINOMDIST function to find k.) 3. Suppose that when Sleaze overcharges Medicare, the distribution of the amount overcharged (expressed as a percentage of the correct billing amount) is normally distributed with mean 15% and standard deviation 4%. 4. What percentage of overbilled invoices are at least 10% more than the legal billing amount? 5. What percentage of all invoices are at least 10% more than the legal billing amount? 6. If your auditing company samples 200 randomly chosen invoices, what is the probability that it will find at least five where Medicare was overcharged by at least 10%? By Day 7 Submit your answers and embedded Excel analysis as a Microsoft Word management report.

Paper For Above instruction

Probability plays a fundamental role in healthcare administration decision-making by providing quantifiable insights into the likelihood of various events affecting healthcare operations, costs, and patient outcomes. An understanding of probability distributions enables healthcare leaders to predict potential scenarios, optimize resource allocation, and develop strategic responses to uncertainties inherent in healthcare delivery. This paper explores how probability and probability distributions can be practically applied within healthcare management contexts, illustrating their value through specific scenarios and analyses.

Fundamentally, probability allows healthcare administrators to assess risks associated with different operational decisions. For example, estimating the likelihood that a certain percentage of patients will delay seeking care can influence staffing levels and resource distribution. Similarly, understanding the probability of readmission rates exceeding a threshold can guide quality improvement initiatives and financial planning. These applications demonstrate that appreciating the likelihood of events shapes proactive decision-making, minimizing adverse outcomes and optimizing resource use.

One core concept in healthcare decision-making involves probability distributions such as the binomial, normal, Poisson, and exponential distributions. Each serves a specific purpose based on the nature of the data and the decision context. The binomial distribution models scenarios with a fixed number of independent trials and two possible outcomes, such as the occurrence of overbilling in an audit. The normal distribution approximates the likelihood of continuous variables, like the percentage overcharged in billing errors, assuming a symmetric spread around the mean. The Poisson distribution models the occurrence of rare but repetitive events, such as equipment failures or patient arrivals, while the exponential distribution estimates waiting times between events.

Healthcare leaders leverage these probability models to inform various operational decisions. For example, in a billing audit, the binomial distribution helps estimate the likelihood of detecting a certain number of overcharges among a sample of invoices. Using binomial probability calculations, leaders can determine the probability of surpassing penalties or thresholds, aiding in resource prioritization. In patient flow management, the Poisson distribution can predict the number of arrivals in a given period, enabling better staffing and facility utilization.

A practical scenario involves an audit similar to the one described for Sleaze Hospital. Suppose an auditor randomly samples invoices and aims to detect overbilling frequency. If the probability of overbilling is known from prior data, the binomial distribution can estimate the probability of detecting a certain number of overcharges in the sample. Executing this, the hospital's management might find that the probability of observing at least 15 overbilled invoices in a sample of 200 is significant, prompting further investigation or policy adjustments. These calculations inform risk assessments and strategic decisions about compliance and financial integrity.

Furthermore, probability models assist in estimating the extent of overbilling. For instance, assuming overcharges follow a normal distribution with a mean of 15% over the correct amount and a standard deviation of 4%, administrators can calculate the proportion of invoices exceeding certain thresholds (e.g., 10% or 20% overcharge). Such insights help prioritize auditing efforts and identify high-risk invoices that require detailed review. Additionally, by computing the probability that at least a specific number of invoices are overcharged by a certain margin, healthcare leaders can set data-backed benchmarks for investigation and resource deployment.

In conclusion, understanding and applying probability and statistical distributions equips healthcare administrators with tools to make informed, data-driven decisions amid uncertain conditions. These probabilistic models contribute to effective management strategies, risk mitigation, and compliance assurance. By integrating probability analytics into routine decision-making processes, healthcare organizations can enhance operational efficiency, ensure financial integrity, and improve patient care outcomes, ultimately supporting a resilient and adaptive healthcare system.

References

• Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
• Fulton, L. V., Mendez, F. A., Bastian, N. D., & Musal, R. M. (2012). Confusion between odds and probability, a pandemic? Journal of Statistics Education, 20(3), 1–20.
• Hays, D. (2012). Poisson distribution on Excel [Video]. Retrieved from YouTube.
• ExcelIsFun. (2011). Excel 2010 statistics 63: Exponential probability EXPON.DIST function [Video]. Retrieved from YouTube.
• Grande, T. (2015). Calculating probabilities using the normal distribution function in Excel [Video]. Retrieved from YouTube.
• Grande, T. (2015). Binomial distribution function in Excel [Video]. Retrieved from YouTube.
• Microsoft. (2016). Statistical functions in Excel. Retrieved from Microsoft Office support documentation.
• Sullivan, M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279-282.
• Liu, F., & Kavalieratos, D. (2019). Evidence-based decision making in healthcare. American Journal of Managed Care, 25(2), 65-72.
• Friedman, L. M., Furberg, C., & DeMets, D. L. (2010). Fundamentals of clinical trials. Springer.