BACKGROUND: Variability in flow of patients through operating rooms has a dramatic impact on a hospital's performance and finances. Natural variation (uncontrollable) and artificial variation (controllable) differ and require different resources and management. The aim of this study was to use variability methodology for a hospital's surgical services to improve operational performance. STUDY DESIGN: Over a 3-month period, all operations at a referral center were classified as either scheduled (artificial variation) or unscheduled (natural variation). Data regarding patient flow were collected for all cases. From these data, mathematical models determined explicit resources to be allocated for scheduled and unscheduled cases, with isolation of the 2 flow streams. Services were allocated block time based on 80% prime time use, and scheduled cases were capped at 5:00 PM. Guidelines for operating room access were implemented to smooth the daily schedule and minimize artificial variation on the day of surgery. After implementation of this redesign, 12 months of data were compared with the previous 12-month period. Metrics analyzed included prime time use, overtime minutes, access for urgent or emergent cases, the number of room changes to the elective schedule on the day of surgery, and variation of daily schedules. RESULTS: Surgical volume and surgical minutes increased by 4% and 5%, respectively. Prime time use increased by 5%. Overtime staffing decreased by 27%. Day-to-day variability decreased by 20%. The number of elective schedule same day changes decreased by 70%. Staff turnover rate decreased by 41%. Net operating income and margin improved by 38% and 28%, respectively. CONCLUSIONS: Variability management results in improvement in operating room operational and financial performance. This optimization may have a significant impact on a hospital's ability to adapt to health care reform.
BACKGROUND: Variability in flow of patients through operating rooms has a dramatic impact on a hospital's performance and finances. Natural variation (uncontrollable) and artificial variation (controllable) differ and require different resources and management. The aim of this study was to use variability methodology for a hospital's surgical services to improve operational performance. STUDY DESIGN: Over a 3-month period, all operations at a referral center were classified as either scheduled (artificial variation) or unscheduled (natural variation). Data regarding patient flow were collected for all cases. From these data, mathematical models determined explicit resources to be allocated for scheduled and unscheduled cases, with isolation of the 2 flow streams. Services were allocated block time based on 80% prime time use, and scheduled cases were capped at 5:00 PM. Guidelines for operating room access were implemented to smooth the daily schedule and minimize artificial variation on the day of surgery. After implementation of this redesign, 12 months of data were compared with the previous 12-month period. Metrics analyzed included prime time use, overtime minutes, access for urgent or emergent cases, the number of room changes to the elective schedule on the day of surgery, and variation of daily schedules. RESULTS: Surgical volume and surgical minutes increased by 4% and 5%, respectively. Prime time use increased by 5%. Overtime staffing decreased by 27%. Day-to-day variability decreased by 20%. The number of elective schedule same day changes decreased by 70%. Staff turnover rate decreased by 41%. Net operating income and margin improved by 38% and 28%, respectively. CONCLUSIONS: Variability management results in improvement in operating room operational and financial performance. This optimization may have a significant impact on a hospital's ability to adapt to health care reform.
Authors: Daniel I McIsaac; Karim Abdulla; Homer Yang; Sudhir Sundaresan; Paula Doering; Sandeep Green Vaswani; Kednapa Thavorn; Alan J Forster Journal: CMAJ Date: 2017-07-10 Impact factor: 8.262
Authors: Ji Hwan Lee; Ji Hoon Kim; Incheol Park; Hyun Sim Lee; Joon Min Park; Sung Phil Chung; Hyeon Chang Kim; Won Jeong Son; Yun Ho Roh; Min Joung Kim Journal: Yonsei Med J Date: 2022-05 Impact factor: 3.052
Authors: Matthew F Toerper; Eleni Flanagan; Sauleh Siddiqui; Jeff Appelbaum; Edward K Kasper; Scott Levin Journal: J Am Med Inform Assoc Date: 2015-09-05 Impact factor: 4.497
Authors: Simone Barbagallo; Luca Corradi; Jean de Ville de Goyet; Marina Iannucci; Ivan Porro; Nicola Rosso; Elena Tanfani; Angela Testi Journal: BMC Med Inform Decis Mak Date: 2015-05-17 Impact factor: 2.796
Authors: Frank J Overdyk; Oonagh Dowling; Sheldon Newman; David Glatt; Michelle Chester; Donna Armellino; Brandon Cole; Gregg S Landis; David Schoenfeld; John F DiCapua Journal: BMJ Qual Saf Date: 2015-12-11 Impact factor: 7.035