How are risk priority numbers (RPN) useful for health care administration leaders
Week 10- DUE 10/27/2018
Assignment: Textbook Problems: Failure Modes Effects Analysis Project
How are risk priority numbers (RPN) useful for health care administration leaders?
As you continue your examination of the use of and purposes for FMEA, you will begin to critically evaluate the numbers associated with your analyses. That is, the rates with which certain processes may be failing in your health services organization will allow you to strategically assess and implement efforts aimed to reduce errors and to promote quality health care delivery.
For this Assignment, review the resources for this week regarding chi-square, ANOVA, ANOM, and regression. Pay particular attention to the examples shown in the textbook. Consider how these tools may contribute to the value-chain perspective.
The Assignment: (3- pages)
· Using SPSS and Microsoft Word, complete problems 1 through 4 on pages 405–406 in the Ross textbook. Show all work. Submit both your SPSS and Word files for grading.
Summary
PROBLEMS
1. An FMEA will be performed on postoperative infections. Data is available by surgery type on the frequency, severity, and detectability of these infections. The director of quality improvement wants you to prepare a priority list giving the order in which the various sur- geries should be examined, to produce the greatest potential benefit for the organization. Severity is ranked from most severe (4) to least severe (1), and detectability is ranked from difficult to detect (4) to easy to detect (1). Calculate a frequency ranking from very high (5) to very low (1) based on the number of infections and cases shown in the following and the RPNs for each type of surgery. Which surgery should be examined first? Which surgery should be examined last? What is the maximum possible risk score for this analysis?
Surgery Types
Number of Infections
Number of Cases
Frequency
Severity of Infections
Detectability
Risk Priority Number
Thoracic 5
Neuro 1
Orthopedic 50
Plastic 32
Oral 25
General 100
200 3 3
100 4 3
2,000 2 2
800 1 2
500 1 1
5,000 2 1
2. Instead of the ordinal ranking of frequency, 1 to 5, used in problem 10.1, the director of quality improvement has collected data on the number of cases for each type of surgery and the num- ber of patients that contracted a postoperative infection. The director wants you to recalculate the RPN using the actual incidence rates of infection. Infection severity and detectability are ranked the same as in problem 10.1. Compare your new RPNs with those calculated in 10.1. How does the use of the incidence rate change the improvement work list? Which surgery should be examined last? What is the maximum possible risk score for this analysis?
Surgery Types
Number of Cases
Number of Infections
Rate of Infection
Severity of Infections
Detectability
Risk Priority Number
Thoracic 200
Neuro 100
Orthopedic 2,000
Plastic 800
Oral 500
General 5,000
5 3 3
1 4 3
50 2 2
32 1 2
25 1 1
100 2 1
Chapter : Failure Mode and Effects Analysis
3. Assume that in problem 10.2 strict adherence to antibiotic administration one hour prior to thoracic surgery has reduced the rate of infection to 1.0% and severity to 2.0. How did this improvement affect the priority list? Which surgery should be the primary target for improvement?
4. A hospital is setting its quality improvement agenda for the upcoming year and will per- form an FMEA in one area. The hospital has rated sentinel events for severity, probability, and detectability on the following scale: very high, high, moderate, low, very low. Calculate the risk priority number for each type of event. Which sentinel event should be the focus of the hospital’s FMEA effort?
Event
Severity
Probability
Detectability
Hospital-acquired High Moderate Infection
Medication Error Moderate High
Patient Fall Low High
Pressure Ulcer Low High
Treatment Delay Low High
High
Moderate
Very high
Very high
High
References
Apkon M, Leonard J, Probst L, DeLizio L, and Vitale R, 2004, Design of a Safer Approach to Intravenous Drug Infusions: Failure Mode Effects Analysis, Quality and Safety in Health Care 13 (4): 265–271.
Camp RC and DeToro IJ, 1999, Benchmarking, in Juran’s Quality Handbook, 5th ed., edited by JM Juran and AB Godfrey, McGraw-Hill, New York, NY, 12.1–12.20.
Cleary P and Edgman-Levitan S, 1997, Health Care Quality: Incorporating Consumer Perspectives, JAMA 278 (19): 1608–1612.
Cohen H, 2007, Protecting Patients from Harm: Reduce the Risk of High-Alert Drugs, Nursing (September 2007): 49–54.
Coronary Drug Project Research Group, 1980, Influence of Adherence to Treatment and Response of Cholesterol on Mortality in the Coronary Drug Project, New England Journal of Medicine 303 (18): 1038–1041.
Curran TA and Ladd A, 2000, SAP R/3 Business Blueprint, Prentice Hall PTR, Upper Saddle River, NJ.
DeToro IJ, 1995, Business Process Benchmarking Workshop, The Quality Network Inc., Rochester, NY.
Duwe B, Fuchs B, and Hansen-Flaschen, J, 2005, Failure Mode and Effects Analysis Application to Critical Care Medicine, Critical Care Clinics 21 (1): 21–30.
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