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Optimizing your mri practice with kaizen

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Practical experience and advice on how to optimize an MRI practice (or any other Radiology practice) by applying principles of Kaizen, Lean and Six Sigma.

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Optimizing your mri practice with kaizen

  1. 1. Optimizing your MRI practice with Kaizen events André van Est Founding Partner Care IQ Group September 2015
  2. 2. Outline 2 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  3. 3. Care IQ Group 150 years of experience in healthcare planning & engineering 3
  4. 4. Career theme: Healthcare innovation 4 •  1983 – MRI development (Philips Gyroscan) •  1988 - WS development (Gyroview) •  1992 – Healthcare IT Apps (EasyVision) •  1999 - MR Applications (Intera, Achieva, Ingenia) •  2004 - Professional Services o  2004 - NetForum Community o  2006 - Utilization Services o  2008 - Consulting Services •  2013 - Founding Partner Care IQ Courtesy)to)Philips)Healthcare) www.philips.com/ne7orum))))) NetForum)Community)
  5. 5. OHSU Portland OR NIC Las Vegas, NE The Netherlands (Rijnstate Arnhem, 2x Catharina Eindhoven, Antonius Nieuwegein, Viecuri Venlo, Elkerliek Helmond, 9x UMC Utrecht) Japan (Yaesu Clinics) Austria (Neusiedl, Klagenfurt, Kapfenberg) Denmark (2x Herlev Hosp. Kopenhagen, Herning Hosp.) Turkey (Yeditepe University ) Saudi Arabia (NGH Riyadh, Bakhsh Hospital Jeddah) UA Emirates (Al Zahra Hosp Sharjah) Southern Open MRI FL DMI Palm Springs CA 2x St Barnabas Livingston NJ China (Renji Hospital) Sweden (2x Lund Uni Hospital, 2x Unilabs Göteborg) France (La Porte Verte Versailles, La Pitié Paris) UK (North Staffs Univ Hosp. Germany (2x BrüderKH Trier, 2x Asklepios Barmbek Hamburg, MRT Schwerin) Norway (AHUS Oslo, Molde Hospital, Ålesund Hospital, 2x Unilabs Oslo) 60+ Kaizen Events, 300+ Quick Scans Spain (2x La Fé Valencia) 5 Switzerland (2x Unilabs Geneva)
  6. 6. 6 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  7. 7. “You can have data without information, but… you cannot have information without data.” Daniel Keys Moran 7
  8. 8. From “what goes wrong” to “what goes on!” and turning this into opportunities
  9. 9. Tracing the waste! 9 Examina1on)Times) • )many)or)long)scans) • )contrast)prep)in)room)) • )repeat)or)add:on)scans) • )non:coopera;ve)pat’s) Changeover)Times) • )pa;ent)late/no:shows) • )inefficient)changeover) • )inefficient)pat.)transport)) • )unplanned)slots) Most)1me)waste)occurs)when)the)scanner)is)not)running!)
  10. 10. Recognizing trends and patterns 10Keep)measuring)to)know)that)improvement)is)durable!)
  11. 11. DATA PROVIDE A GOOD START FOR A MORE OBJECTIVE DISCUSSION Lesson learned 11
  12. 12. 12 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  13. 13. Significant)performance)varia;on)despite)similar)technology) Top)performers)more)effec;ve)in)managing)org.)complexity) Global benchmark Philips 1.5T 13 )Philips)1.5T)Systems) )) )) )) )) )) )) )) )) )) )2013) BEL) CAN) CHI) DEN) FRA) GER) ITA) JAP) NL) SWI) SPA) SWE) UK) USA) )Procedure)1me) 26) 40) 25) 43) 26) 32) 36) 39) 34) 47) 39) 46) 35) 58) )Scan)1me) 17) 23) 13) 21) 16) 19) 21) 18) 18) 24) 22) 22) 20) 25) )Scans)per)exam) 7.8) 8.7) 7.6) 8.1) 8.2) 8.5) 9.1) 8.9) 8.0) 8.6) 9.0) 8.5) 9.9) Source:(Philips(U0liza0on(Services)) 1.5T)Germany)by) prac1ce)type) Procedure)1me) Mean) Best) Private) 31) 16) Hospital)<500)beds) 43) 21) Hospital)>500)beds) 52) 20) Academic) 64) 20)
  14. 14. PRACTICE OPTIMIZATION REQUIRES A SOUND MIX OF PEOPLE, PROCESS AND TECHNOLOGY, PLUS CAREFULLY FACILITATED CHANGE Lesson learned 14 change
  15. 15. 15 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  16. 16. Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom. Clifford Stoll 16
  17. 17. How to get started? Question the status quo! •  Measure – Voice of the data •  Interview – Voice of the people •  Observe – Voice of the process © Care IQ Group BV 17
  18. 18. How to get results that stick? •  Involve the team to inspire commitment •  Do it swiftly and focused ! Kaizen 18 :  Iden;fy)issues)) :  Analyze)root)causes) :  Iden;fy)solu;ons) :  Priori;ze)solu;ons) :  Implement)solu;ons)
  19. 19. 5 day Kaizen event 19 •  Team work •  Fact based •  Solution driven •  DMAIC )Deliver) )Report) )Interpret) )Exam) )Perform) )Exam) )Prepare) )Exam) )Receive) )Pa1ent) )Confirm) )Exam) )Schedule) )Exam) Receive) Order) Quality)) of)care) Cost) effec;veness) Pa;ent)&)Staff) experience) KAIZEN)EVENT)
  20. 20. THINK BIG, ACT SMALL KAIZEN & TEAMWORK Lesson learned 20
  21. 21. 21 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  22. 22. Keep the schedule simple! 22 Before:)li[le)flexibility)due)to) many)‘reserva;ons’) A^er:)more)flexibility)and)capacity,)) more)autonomy)for)scheduling)staff)
  23. 23. Clear space, clear mind! Issues •  Clutter in the scanner room, e.g. coil and accessory storage, sterile goods, contrast materials, blankets, etc. •  Excessive amounts •  Unnecessary materials •  Dirty laundry and waste Solution 23 Before) A^er)
  24. 24. Timely contrast prep outside room saves 3-5 min per exam! 24 Common issues •  Patient prep starts too late •  Patient being anxious •  Contrast prep inside room Opportunities to add value •  Tech-aide to help maximize technologist time •  Personal contact to educate patient about exam or handle anxiety •  Scan execution reports shows impact of in-room prep 5 min inter-scan delay
  25. 25. Tech aide - avoid being penny wise pound foolish Issue: •  High volume (30 ex/day) •  Single operator, stress! •  Invest in staff? Solution: •  Introduce tech aide to assist with patient prep (shared with CT suite) Results: •  Productivity +10% •  Quick ROI •  Staff satisfaction " 25
  26. 26. Preparation – two birds one stone or tackling no-shows effectively Issue: •  High no-show rate (9%) due to patient ‘shopping’ behavior Solution: •  Introduce ‘reminder’ calls & script •  Overcoming ‘no time’ syndrome Results: •  No-show rate from 9% to 3% •  Effective use of shift overlap 26 Pa;ent)present) Pa;ent)no:show)
  27. 27. Scanning - start optimizing most used not the most complex exams! 27
  28. 28. Utilize the technological advances 28 Before) A^er) Issue •  Knee ExamCard not updated after major system upgrade •  Untapped technological advances Solution •  New method technology applied (asymmetric-TSE) •  30% time gain, average scan time reduced by 5 min 50%) peak) 25%) peak)
  29. 29. Leverage the combined team skills 29 Before) Issue •  2D strategy often leads to add-on scans (i.e. extra spine levels) •  Lack of ownership and team play Solution •  3D strategy automatically covers larger field of view •  More robust and predictable ExamCard execution 20%) peak) 80%) peak) A^er)
  30. 30. 30 •  Introduction •  Data treasure •  Benchmark lessons •  Kaizen approach •  Examples •  Conclusion
  31. 31. Conclusion Data) Informa;on) Knowledge) Understanding) 31 Wisdom) Ac;on) experience) applica;on) Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom.) Clifford)Stoll)
  32. 32. Thank)you)for)your)a[en;on) ) more)info:) andre.van.est@care:iq.com)) www.care:iq.com))

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