Healthcare process optimization research University Twente

1. Healthcare process optimization research University Twente Prof. Wim H. van Harten Prof. Quality management & healthcare technology.(UT) Exec. Board Netherlands Cancer Institute Prof. Erwin W. Hans, MSc, PhD Associate prof. Operations Management and Process Optimization in Healthcare Center for Healthcare Operations Improvement & Research

3. A modern framework for health care planning & control(Hans, Houdenhoven, Hulshof, 2010) Resource capacity Material Medical Financial ß planning planning planning planning Society Research planning, Case mix planning, Agreements with Supply chain and Strategic introduction of new layout planning, insurance companies, warehouse design treatment methods capacity dimensioning capital investments hierarchical decomposition Allocation of time and Supplier selection, Care pathway Budget and cost Tactical resources to tendering, forming planning allocation specialties, rostering purchasing consortia Diagnosis and Purchasing, Operational Elective patient scheduling DRG billing, cash flow planning of an determining order workforce planning analysis offline individual treatment sizes Expenditure monitoring, Operational Triage, diagnosing Monitoring, emergency Rush ordering, handling billing à complications rescheduling inventory replenishing online complications managerial areas ß à 3 e.w.hans@utwente.nl 8/20/2014

4. Research development 2003 - 2007: Focus on single departments Operating rooms (planning, scheduling, etc.) Radiology (CT, MRI) 2008 - 2012: Focus on care pathways within hospitals STW funded project “LogiDOC” 12 hospitals, 7 PhD students PhD students are at hospitals 2-3 days per week 2010 - 2016: Optimization of complex clinical pathways Optimization of the “transmural” care pathway Optimization of rehabilitation processes 4 e.w.hans@utwente.nl 8/20/2014

5. Utilizationvs. Lead-time 5 e.w.hans@utwente.nl 8/20/2014

6. Tactical resource allocation in hospitals Minimize waiting for resources Minimize access time Minimize care pathway lead time Maximize utilization of resources Equitable distribution amongst patient types  Prototype “dashboard” / tactical planner based on MILP Peter Hulshof 6 e.w.hans@utwente.nl 8/20/2014

7. Tactical resource allocation in hospitals Static vs. dynamic problem MILP: Decision variables Weights Queues, patient types, resources, time waiting Peter Hulshof Details: p.j.h.hulshof@utwente.nl 7 e.w.hans@utwente.nl 8/20/2014

8. Analytical Comparison of the Patient-to-Doctor Policy and the Doctor-to-Patient Policy in Outpatient Clinics Peter Hulshof 8 e.w.hans@utwente.nl 8/20/2014

9. Analytical Comparison of the Patient-to-Doctor Policy and the Doctor-to-Patient Policy in Outpatient Clinics Analytical stochastic model, based on the recursion of the moment that the doctor finishes a consultation Discrete-event simulation to observe behavior when general assumptions are relaxed Peter Hulshof doctor travel time patient preparation time 9 e.w.hans@utwente.nl 8/20/2014

10. Access to CT scannersPatient preferences (2009) Ranking of patient preferences (Analytic Hierarchy Process method): One-stop shop 43% Access time 22% Waiting time 19% Autonomy in selection of appointment time 16% Majority of patients prefer regular hours for appointments “One-stop shop” can be obtained by: Planning combination appointments Walk-in Patients accept a higher waiting time in case of walk-in Maartje Zonderland NikkyKortbeek 10 10 e.w.hans@utwente.nl e.w.hans@utwente.nl 8/20/2014

11. Combine appointments with walk-in Walk-in: Extra service Zero access time  contributes to lead-time optimization Not always possible Medical reasons Demand fluctuates  Combine walk-in with appointments Maartje Zonderland NikkyKortbeek 11 e.w.hans@utwente.nl 8/20/2014

12. Combine appointments with walk-in Given walk-in demand, when should appointments be planned? When on 2 levels: On which day? When on the day? Trade-off: access time (app) & acceptance rate (walk-in) Maartje Zonderland NikkyKortbeek 12 e.w.hans@utwente.nl 8/20/2014

13. Algorithm: Balance Maartje Zonderland Result: Appointment plan: number of appointments per day in the cycle (satisfy access time norm)  Day plan with appointment slots (maximize walk-in acceptance rate) Model I:Access TimeAppointments Algorithm:Balance! Model II:Acceptance RateWalk-in Patients Tuesday’s plan 13 e.w.hans@utwente.nl 8/20/2014 Details: m.e.zonderland@utwente.nl

14. Emergency OR, or not? Concept: “emergency ORs” Concept: “No emergency ORs” 14 e.w.hans@utwente.nl 8/20/2014

15. Simulation results operational problem Case mix Academic Hospital 15 e.w.hans@utwente.nl 8/20/2014

16. Results after simulation “Emergency surgery in elective program” instead of “emergency ORs” yields: Improved OR utilization (3.1%) Less overtime (21%) Break-in-moment optimization yields: Reduced waiting time for emergency surgery, especially for the first arrival (patients helped within 10 minutes: from 28.8%  48.6%) 16 e.w.hans@utwente.nl 8/20/2014

17. Evidence based management (HBR 2006) • Weak evidence • Many self proclaimed experts • Wide array of sources • Variation in company configuration

18. Evidence based management (HBR 2006) • Trust of own experience above research • Capitalise on own strenghts • Hype and marketing • Dogma and belief • Casual benchmarking

19. Evidence based management (HBR 2006) • Examine the logic • Always Pilot your Programs • The art of implementation cf EBM

20. Survey Operations management in Dutch Hospitals To explore the use of business improvement approaches to improve patient logistics in Dutch hospitals Used business improvement approaches Tools that hospitals used Results achieved with these approaches Respons 46 – 6 UMC, 13 STZ, 27 Alg.

21. Frequency of methods used and combinations of methods used (n=46)

22. Combination of type of hospital and priority (N=46) Diverse ziekenhuizen hebben meerdere prioriteiten

23. Results per performance aspect + = objectives achieved or results above objectives - = objectives were not achieved • 35 hospitals answered this question on different aspects • General hospitals report more successes • Top clinical hospitals report least successful • No relation with method; with consultants less successful

24. Ontwikkeling ziekenhuismanagement • 1980 Professional Quality • 1985 Continuous improvement (audit ) • 1990 Quality systems(EFQM/TQM) • (2005 “seperate track” Safety) • 2000 Operations management/logistics • 2015 Evidence and formats OM/OR?

26. Antoni van Leeuwenhoek Hospital - Medical & Surgical Oncology, Radiotherapy Budget (2011): 135 million euro Number of employees ca. 1400 (1600 pers.) First contacts 29.000 Admissions 6600 Chemotherapy Day care 15.000 180 beds; 30 Chemo day care ; 10 linacs; 6 Operating rooms 120 medical specialists (employed)

28. Framework voor planning en control in ziekenhuizen

29. Alignment of CDU and Pharmacy processes Queue: Make in advance Queue: Make on demand Lab Green Light Patient will wait No Wastage CDU Medicine Orders Pharmacy Leftovers No wait Risk of Wastage Decision Criteria? Modelled with stochastic programming

30. Alignment of CDU and Pharmacy processes Prepare nothing in advance: There is no medicine wasted Patients must wait while medicine is prepared Prepare everything in advance Patient do not have to wait for their medicine to be prepared There is a risk that expensive medicine will be wasted Prepare some in advance

31. Results Waiting Time per patient Cost of wasted medicines per day Prepare nothing in advance Prepare some in advance

32. Results mathematical analysis • The outcome from this study resulted in a new policy at the cancer centre which is expected to decrease the waiting time by half, while only increasing pharmacy’s costs by 1-2%

33. Lit. review successful simulation Defined by Robinson and Pidd 1998 • The study achieves its objectives and/or shows a benefit. • The results of the study are accepted • The results are implemented • Implementation proved the results of the study to be correct

34. 161 abstracts from Pubmed 125 abstracts from BSE 277 different abstracts 70 relevant papers 2 papers inaccessible 21 cross references of papers reporting implementation 89 papers included in literature review Figure 2 Overview selected papers for literature review

35. Simulation review Results Section II: implementation

36. Results of the electronic survey of the authors

37. Conclusions review & survey • Implementation rate of literature review and the survey was 18% and 44%  actual implementation occurred more often than reported in literature • Quality of the research methods used for evaluation is higher in reality (17%) than is reported in the literature (3%) • Availability and Technical quality of the data • Culture, knowledge and “Contingency” (what to apply when)

38. throughput time radiologist’s report access time CT scanner access time outpatient consult throughput time diagnostic track Process analysis physician physician radiologist

39. Data collection: Variation in access time 1. Variation 2. High average Variation is influenced by capacity outage

40. Design of experiments

41. Modeled results

42. Implemented changes X • Set service levels: not formally done. Growth causes problems • Change allocation of capacity • Separate slots for long and short term patients. • Plan short term max 7 working days ahead • Slots based on model: 19 long term, 19 short term, 3 inpatients, rest is urgency. • But after short term group contained ‘long term’ patients  22, 17,2 • Set service levels for throughput time radiologist report • Improve maintenance management • Labor regulations X

43. Results

44. Conclusions CT track simulation • OR shows that significant improvements are possible • The best solution is not always implemented in reality, other factors play a role • Unexpected differences between model and reality complicate the use of a strict before and after design. iterative approach preferred • Continuous measurement of data is needed to maintain the results

45. A case from : Peter T. Vanberkel et al Published in Anasthesia & Analgesia Netherlands Cancer Institute- Antoni van Leeuwenhoek Hospital (NKI-AVL) Expand OR capacity from 5 to 6 with limited additional bed use Design new OR schedule in combination with optimal ward occupancy ACCOUNTING FOR INPATIENTS WARDS WHEN DEVELOPING A MASTER SURGICAL SCHEDULE

46. Project Description • Developprognostic model thatrelates OR production to wardoccupancy • Calculateeffects of various OK scheme’s • Verification of alternativescheduleswithobservedlimitations in practice • Scenario’s werelimited to changingsessionsontactical level. • Implementation of modelled OR schedule . Pre modelled data werecomparedwithobserved data in practice

47. Model: Ward workload as a function of the MSS Batches of patients arrive daily according to the MSS Ward Discharge Recovery Conceptual Model Scheme Assumptions • No cancelations due to lack of ward space (extra nurses will be called in) • Acceptable Risk of “calling in a nurse” is ~10% • Time scales is days. • Count patients on the day of admission, not on the day of discharge

48. Model: Ward workload as a function of the MSS Conceptual Model Scheme Metrics • Recovering Patients in the Hospital • Ward occupancy • Rates of admissions and discharges • Patients in recovery day n Ward Batches of patients arrive daily according to the MSS Discharge Recovery

49. Model: Ward workload as a function of the MSS Conceptual Model Scheme Data • For each surgical specialty • Empirical Distributions of Cases/Block (batch size) • Empirical Distribution of Length of Stay (LOS) Ward Batches of patients arrive daily according to the MSS Discharge Recovery

50. Baseline situation • 2 Surgical wards, total capacity 100 beds • Objective: sufficient beds to serve upto 90th percentile of patient load on every weekday