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Productivity and staffing analysis of a sterile processing department
Computers ind. EaSaS VoL 31, No. 1/2, pp. 459 - 461, 1996
Pergamon S0360-8352(96) 00174-X
Copyright O 1996Ih~blishedby Elsevier Science Printed in Ca,mr Britain All righw reserv~l 0360- 8352/96 $15.00 + 0.00
PRODUCTIVITY AND STAFFING ANALYSIS OF A STERILE PROCESSING DEPARTMENT
Chentsau Chris Ying South Dakota School of Mines and Technology 501 E. St. Joseph Street Rapid City, SD 57701
ABSTRACT This paper describes a management engineering project initiated by the Director of the Surgery Department of a major hospital in Columbus, Ohio. The Study resulted in an improved sterile processing procedure, the identified system capacity, and a quantified staffing plan and sensitivity analysis.
KEYWORDS Health care, Staffing analysis, Productivity, Sensitivity analysis
INTRODUCTION
The director of the Surgery/Sterile Processing Department of a major hospital in Columbus, Ohio requested a Management Engineering project involving a productivity audit and staffing analysis. The study identified areas for potential process improvements, determined the department work load and processing capability, and analyzed/developed a staffing plan with sensitivity analysis. The problems and opportunities identified include: lack of communication between groups of employees, uneven utilization of tunnel washers, unreliable cart washer operations, unused steri-peel instruments in trays, stains on some instruments after sterile processing, inefficient drying cycle of the tunnel washers, uneven work load among staff, inefficient inventory control function, and inefficient steri-peel item preparation. A set of recommendations were provided for each problem or opportunity identified. After some recommendations on the process improvement were implemented, a work load and capacity study was performed. Based on the work load and capacity study results, a productivity and staffing analysis was carried out. One of the challenges for conducting staffing analysis was to determine the assemble time required for each type of instrument sets. In this hospital, there existed more than six hundred different instrument sets. It was necessary to classify instrument sets into four different categories based on the processing requirements. Time studies, operator self-logging, and interview with supervisors were conducted to determine an estimate of the processing time for each category of instruments. Category mix were determined from historical data. Based on these data and identifying other tasks performed by the staff, a staffing plan was developed and a set of sensitivity analysis tables were provided. 459
460
19th International Conference on Computers and Industrial Engineering
WORK LOAD AND PROCESSING CAPABILITY In order to facilitate the study, instrument trays are classified into four categories, namely, A, B, C, and D based on their processing requirements. A indicates the hardest set in terms of processing requirements. B indicates the intermediate set while C being the easiest set. Steri-peeled instruments are classified as D. Work Load Anaivsis 1. Based on the staff's self logging from 8/3/94 to 8/10/94, the instrument mix processed consists of category A (8.7%), category B (24.7%), and category C (66.6%). For every 100 instrument sets (A, B, and C), there are also 22 steri-peel items (category D) processed. .
Based on two weeks of ESI data in August, the average scheduled number of cases per day is 70 excluding weekends.
.
Based on two weeks of ESI data in August, the average number of trays ordered per case is 5.2 and the average number of steri-peel items is 1.1.
.
Work load varies from day to day. Fridays usually have more overall volume than other days during the week.
.
Based on observations in June, case carts arrive in the decontam area in the following pattern: Before 08:30am Few 08:30-10:30am 25% of the day's volume. 10:30-12:30pm 30% 12:30-02:30pm 20% 02:30-04:30pm 25% After 04:30pm Few
F.~uipment Processing Capability 1. Cart washer: I available cycle time: 6 minutes Output per cycle: 1 cart .
Tunnel washer: 3 available cycle time: 20 minutes Output per cycle: two baskets
PRODUCTIVITY AND STAFFING ANALYSIS Tasks performed in both the SPD decontam area and the instrument room are classified into two major categories: Tray-Volume Dependent Tasks and Routine Tasks. Task times were determined by either direct time study, staffs self logging, or supervisors estimates. Volume and frequency of each task performed were determined from the discussion with shift supervisors and managers. Examples of tray-volume dependent tasks in the decontam area include: remove instrument from containers, wash instruments, sort/load instrument to conveyor, transport cart to washer, etc. Examples of routine tasks in the decontam area include: clean equipment/area, attend staffing meeting, daily shift report and communication, train new hire, etc. Examples of tray-volume dependent tasks in the instrument room include: assemble trays, unload/shelf empty containers, return cart washer, etc. Examples of routine tasks in the instrument room include: keep steri-peel items inventory, pull out-dated instruments, update instrument pictures, attend project meetings, train new hires, etc.
19th International Conference on Computers and Industrial Engineering
461
Based on scheduled volume in August '94, 70 cases per day and 5.2 trays per case, the following shows the staff requirements to cover the work load 24 hours a day, Monday through Friday: SPD Decontamination Area: Instrument Room:
4.2 FTEs (Surgical Instruments only) 20.0 FTEs
Note that these numbers are based on a set of assumptions such as instrument tray processing times, number of trays per case, and number of cases per day, etc. The sensitivity analysis tables generated using Lotus 12-3 show how the FTE requirements in these work areas can vary according to various scenarios. For example, one sensitivity table shows that if the volume increases to 80 cases per day, 22.0 FTEs will be needed in the instrument room. The same table also shows that if the number of trays per case increases from 5.2 to 7, 24.9 FTEs will be required to meet the capacity.
CONCLUSIONS This paper describes a management engineering project that involved process improvement, productivity audit, and staffing analysis. Processes should be improved or re-engineered before any productivity audit can be performed. Productivity audit and staffing analysis performed on an unstable or unacceptable process will likely yield numbers that do not reflect the true mean or need to be redone right after a process re-engineering project is completed. Results from a staffing analysis should be analyzed using the sensitivity analysis tables.
Pergamon S0360-8352(96) 00174-X
Copyright O 1996Ih~blishedby Elsevier Science Printed in Ca,mr Britain All righw reserv~l 0360- 8352/96 $15.00 + 0.00
PRODUCTIVITY AND STAFFING ANALYSIS OF A STERILE PROCESSING DEPARTMENT
Chentsau Chris Ying South Dakota School of Mines and Technology 501 E. St. Joseph Street Rapid City, SD 57701
ABSTRACT This paper describes a management engineering project initiated by the Director of the Surgery Department of a major hospital in Columbus, Ohio. The Study resulted in an improved sterile processing procedure, the identified system capacity, and a quantified staffing plan and sensitivity analysis.
KEYWORDS Health care, Staffing analysis, Productivity, Sensitivity analysis
INTRODUCTION
The director of the Surgery/Sterile Processing Department of a major hospital in Columbus, Ohio requested a Management Engineering project involving a productivity audit and staffing analysis. The study identified areas for potential process improvements, determined the department work load and processing capability, and analyzed/developed a staffing plan with sensitivity analysis. The problems and opportunities identified include: lack of communication between groups of employees, uneven utilization of tunnel washers, unreliable cart washer operations, unused steri-peel instruments in trays, stains on some instruments after sterile processing, inefficient drying cycle of the tunnel washers, uneven work load among staff, inefficient inventory control function, and inefficient steri-peel item preparation. A set of recommendations were provided for each problem or opportunity identified. After some recommendations on the process improvement were implemented, a work load and capacity study was performed. Based on the work load and capacity study results, a productivity and staffing analysis was carried out. One of the challenges for conducting staffing analysis was to determine the assemble time required for each type of instrument sets. In this hospital, there existed more than six hundred different instrument sets. It was necessary to classify instrument sets into four different categories based on the processing requirements. Time studies, operator self-logging, and interview with supervisors were conducted to determine an estimate of the processing time for each category of instruments. Category mix were determined from historical data. Based on these data and identifying other tasks performed by the staff, a staffing plan was developed and a set of sensitivity analysis tables were provided. 459
460
19th International Conference on Computers and Industrial Engineering
WORK LOAD AND PROCESSING CAPABILITY In order to facilitate the study, instrument trays are classified into four categories, namely, A, B, C, and D based on their processing requirements. A indicates the hardest set in terms of processing requirements. B indicates the intermediate set while C being the easiest set. Steri-peeled instruments are classified as D. Work Load Anaivsis 1. Based on the staff's self logging from 8/3/94 to 8/10/94, the instrument mix processed consists of category A (8.7%), category B (24.7%), and category C (66.6%). For every 100 instrument sets (A, B, and C), there are also 22 steri-peel items (category D) processed. .
Based on two weeks of ESI data in August, the average scheduled number of cases per day is 70 excluding weekends.
.
Based on two weeks of ESI data in August, the average number of trays ordered per case is 5.2 and the average number of steri-peel items is 1.1.
.
Work load varies from day to day. Fridays usually have more overall volume than other days during the week.
.
Based on observations in June, case carts arrive in the decontam area in the following pattern: Before 08:30am Few 08:30-10:30am 25% of the day's volume. 10:30-12:30pm 30% 12:30-02:30pm 20% 02:30-04:30pm 25% After 04:30pm Few
F.~uipment Processing Capability 1. Cart washer: I available cycle time: 6 minutes Output per cycle: 1 cart .
Tunnel washer: 3 available cycle time: 20 minutes Output per cycle: two baskets
PRODUCTIVITY AND STAFFING ANALYSIS Tasks performed in both the SPD decontam area and the instrument room are classified into two major categories: Tray-Volume Dependent Tasks and Routine Tasks. Task times were determined by either direct time study, staffs self logging, or supervisors estimates. Volume and frequency of each task performed were determined from the discussion with shift supervisors and managers. Examples of tray-volume dependent tasks in the decontam area include: remove instrument from containers, wash instruments, sort/load instrument to conveyor, transport cart to washer, etc. Examples of routine tasks in the decontam area include: clean equipment/area, attend staffing meeting, daily shift report and communication, train new hire, etc. Examples of tray-volume dependent tasks in the instrument room include: assemble trays, unload/shelf empty containers, return cart washer, etc. Examples of routine tasks in the instrument room include: keep steri-peel items inventory, pull out-dated instruments, update instrument pictures, attend project meetings, train new hires, etc.
19th International Conference on Computers and Industrial Engineering
461
Based on scheduled volume in August '94, 70 cases per day and 5.2 trays per case, the following shows the staff requirements to cover the work load 24 hours a day, Monday through Friday: SPD Decontamination Area: Instrument Room:
4.2 FTEs (Surgical Instruments only) 20.0 FTEs
Note that these numbers are based on a set of assumptions such as instrument tray processing times, number of trays per case, and number of cases per day, etc. The sensitivity analysis tables generated using Lotus 12-3 show how the FTE requirements in these work areas can vary according to various scenarios. For example, one sensitivity table shows that if the volume increases to 80 cases per day, 22.0 FTEs will be needed in the instrument room. The same table also shows that if the number of trays per case increases from 5.2 to 7, 24.9 FTEs will be required to meet the capacity.
CONCLUSIONS This paper describes a management engineering project that involved process improvement, productivity audit, and staffing analysis. Processes should be improved or re-engineered before any productivity audit can be performed. Productivity audit and staffing analysis performed on an unstable or unacceptable process will likely yield numbers that do not reflect the true mean or need to be redone right after a process re-engineering project is completed. Results from a staffing analysis should be analyzed using the sensitivity analysis tables.