1. EVALUATING HOSPITAL PHARMACY STAFFING AND WORK SCHEDULING USING SIMULATION Charles W. Spry Mark A. Lawley School of Industrial Engineering Purdue University West Lafayette, I N 47907, U.S.A. Presented by Anderson John, Midwestern State University

2. Overview Introduction Introduction Objective Objective Inspection of the Actual System Inspection of the Actual System Approach Approach Simulation Output Simulation Output Conclusion Conclusion

3. Introduction Dilemma: Dilemma: 1. The cost of drugs and healthcare has increased over the years. 1993 to 2000 drug costs as a percentage of total healthcare cost doubled 1993 to 2000 drug costs as a percentage of total healthcare cost doubled 2. There is currently a shortage of licensed pharmacists

4. Introduction Why Simulate? Why Simulate? to find the best utilization of pharmacy staff to find the best utilization of pharmacy staff To save money To save money To improve healthcare delivery To improve healthcare delivery

5. Introduction BroMenn Regional Medical Center (BMRC) BroMenn Regional Medical Center (BMRC) Non-profit Non-profit 224 bed 224 bed Operating in Normal Illinois Operating in Normal Illinois 24 hour inpatient pharmacy 24 hour inpatient pharmacy

6. Objective The goal is to help BroMenn Regional Medical Center offset: The goal is to help BroMenn Regional Medical Center offset: High healthcare costs High healthcare costs Insufficient qualified pharmacists Insufficient qualified pharmacists

7. Inspection of the Actual System The actual system: The actual system: BMRC In-patient pharmacy Prescription medication orders for patients admitted Prescription medication orders for patients admitted A goal of 120 minutes to process a prescription order and have it delivered A goal of 120 minutes to process a prescription order and have it delivered At least 1 pharmacist 24 hours per day At least 1 pharmacist 24 hours per day

8. Inspection of the Actual System Classification of orders Classification of orders 1. Med 2. IV 3. SureMed Each can have a priority status of Normal or Stat (higher priority) Each can have a priority status of Normal or Stat (higher priority) Orders of the same priority are FIFO Orders of the same priority are FIFO

9. Approach Data Collection Data Collection Order type distribution Order type distribution Inter-arrival distribution Inter-arrival distribution Processing times Processing times Phone call data Phone call data

10. Approach Data Collection Data Collection

11. Approach The Model: The Model: Modeling Software: AutoMod 11.1 Modeling Software: AutoMod 11.1 Entities: Entities: Processes Processes Resources (pharmacy staff) Resources (pharmacy staff) Queues Queues Loads (prescription orders, dummy loads) Loads (prescription orders, dummy loads) Rx orders Attributes: Rx orders Attributes: Arrival time, Type (Med/IV), Priority

12. Approach The Model The Model Each process is triggered by an arriving load Each process is triggered by an arriving load Load’s attributes are assigned at arrival to the system Load’s attributes are assigned at arrival to the system The load goes through some operations The load goes through some operations Sent to the next process Sent to the next process

13. Approach One process responsible for keeping the hour of the day One process responsible for keeping the hour of the day One process brings up and takes down resources based on the staff schedule One process brings up and takes down resources based on the staff schedule Other Processes: SureMed, Filling cart Other Processes: SureMed, Filling cart

14. Approach The variables of the model are altered to evaluate options of pharmacy management The variables of the model are altered to evaluate options of pharmacy management Staffing Plans: Staffing Plans: 1. Current 2. Add 8hrs to an IV tech in the evening 3. Add an additional technician in the evening

15. Simulation Output Performance measurement: Performance measurement: Average time an order spends in system Average time an order spends in system Charts were created to aid management in interpreting results Charts were created to aid management in interpreting results Data collected was based on a typical week Data collected was based on a typical week Model was ran for an order volume of 150% of the week that was collected Model was ran for an order volume of 150% of the week that was collected

16. Simulation Output

17. Conclusion BroMenn pharmacy management is using the results to help justify changing their operational policy BroMenn pharmacy management is using the results to help justify changing their operational policy Forthcoming for the project: Forthcoming for the project: To create a simulation model robust enough to be used by any hospital in- patient pharmacy To create a simulation model robust enough to be used by any hospital in- patient pharmacy

18. Questions?

19. References Spry, Charles, Mark A. Lawley. “EVALUATING HOSPITAL PHARMACY STAFFING AND WORK SCHEDULING USING SIMULATION.” informs-cs.org. 24 Jan. 2007

20. Thank You!