RFID in chemotherapy production & mistake-proofing techniques for managing competencies in scheduling.

Transcription

1 Day on OR in Cancer Treatment & Operation Management. Nov. 17, 2010, Paris RFID in chemotherapy production & mistake-proofing techniques for managing competencies in scheduling. Sylvain Housseman, Nabil Absi, Rémi Collomp and Dominique Feillet.

2 2 / 23 RFID systems and utilization General supply-chains - Stock management; - Reduced resources utilization; - Errors reduction (production, order picking, deliveries ); Encouraging results, take-up lowered because of - Technology still very expensive; - Knowledgebase building and implementation (healthcare); Interest in maxing the potential of the technology - Generic software designs and implementation results; - Investment costs and benefits sharing throughout the supply-chain; - Design and optimize innovative working schemes / activities.

3 3 / 23 Problematic and assumptions Description of the tool being developed - Simulation - Scheduling - Performance measure First results Conclusions / Perspectives

4 4 / 23 Impacts of RFID technologies in healthcare Health systems objectives / performance measure: - Minimize risks; - Minimize costs; - Maximize Quality of Service. Generic impacts of RFID in chemotherapy production: - Stocks management; - Regulatory traceability (who, what, when, where, why, how); - Errors reduction (production, deliveries, transport conditions ). Specific impacts and implications of the previous: - Oncology pharmacy sharing, home treatment; - Production assistance; On-the-job training and competencies management.

5 5 / 23 RFID benefits for chemotherapies logistics Master thesis J. El Yousfi. (jamila.elyousfi@hotmail.fr) Configurable discrete-event simulation model (Arena) : Production Demand (quantity, types, delivery location); Human resources and production stations available; Re-dosing and dosage checking; Transport Predefined delivery policies; Administration Checking efficiency (date, patient). RFID implications : Operating costs (human resources); Delivery returns; Throughput time. Scenarios : Tags on raw and/or finished products; Sensitivity analysis.

6 Chemotherapies (Cytotoxics) concoction 6 / 23 Treatment containing several active molecules. Mostly perishable products, with some particular sensitiveness to light, heat, Low therapeutic index, dosages to measure; Respect of the protocol is important for efficient treatments.

7 7 / 23 Mistake-proofing devices and methods Double-checking 85% of the mistakes detected. (Facchinetti NJ, Med Care 1999;37:39-43) Post-production detection : «Reliable real-time analytical control of monoclonal antibodies chemotherapies preparations on Multispec automaton» (Bazin et al., 2010). Arranging the product requires another isolator lap Prospective trial : Insert the automaton within the isolator?

8 8 / 23 Mistake-proofing devices and methods Real-time production guidance, calculation of doses, gravimetric measurements Step by step guidance of the manipulator(s) Weight control Products identification (needed on primary package): Barcodes? RFID?

9 9 / 23 Study proposition and assumptions Use mistake-proofing techniques to authorize inexperienced staff to make concoctions Technologies and devices considerations: Initial products and actors must be identifiable (Barcode vs. RFID); Interest in wireless configurable pipette? Software: Production mistake-proofing / assistance. Objective : Maximize the overall performance level Reduce the risks drawn by staff unavailability sudden demand increase. Dacarbazine 895mg Dissoudre Kytril 3mg Mousse Glucose 450ml Precipite

10 10 / 23 Problematic and assumptions Description of the tool being developed - Simulation - Scheduling - Performance measure First results Conclusions / Perspectives

11 11 / 23 Study approach Simulation (Real system) Human resources Profile (learning curve) Experience level Stochastic parameters Schedule generator Resources estimated performance Based on X observations (importance of their accurateness) Estimation function (average, linear regression, ): Differs from simulation! Processing observations Time to process a task Impact of processing errors Observation variability Tasks arrivals Emergencies, cancellations technology Resources selection (strategies) Objectives Quality of Service (QoS) Performance improvement (purposed QoS reduction)

12 12 / 23 Study approach: Simulation Sequence of a day: Choose the isolator team (depending on the prospected activity); Generate 1 st schedule; Start observations; On events (stochastic?): schedule does not fit observation, patient cancelled, unexpected request, Update schedule and continue. Main parameters: Demand instances; Staff selection strategies; Resources estimated performance: frequency and function; Resources performance level and profile learning curve, consistency; Observations precision; Delayed release dates probability; Tasks abortion probability;

13 13 / 23 Simulation : The learning curve Wright (1936), planes manufacturing cost Whatever the tasks, the learning curve has an identical shape (Ritter et Schooler, 2001) Typical formulation :

14 14 / 23 Forgetting function Learn-Forget Curve Model, LFCM (Jaber et Bonney, 1996, 1997): - Forgetting depends on the accumulated experience when pause happens; - Competency loss depends on the amount of time needed to acquire it; - After a long enough length without practicing, experience is completely lost. Adapting this model to our problem: - Products are unique but can be sorted in few types (difficulties: foams / precipitates, complexity ); - An ideal processing length is associated to each preparation order (when performance = 1).

15 15 / 23 Schedule generation approach Integer linear programming: - Time-indexed model (bad results); - Schedule position indexed model (no experimentation results yet); Taboo heuristic: - Initial affectation to the earlier finishing resource Sorting by Late-Start, Due date, Processing time, priority - Step 1 : Minimize lateness (M * reached deadlines + Σ latenesses) (Neighborhood : 2 tasks exchange or 1 task reallocation); - Quality of service purposed degradation (%tage); - Step 2 : Maximize competencies evolution respecting the lateness score (considering the QoS degradation).

16 16 / 23 Performance indicators Quality of service: - Lateness as compared to the announced hour; - Reactivity (patient throughput time). Competencies / experience evolution - Inexperienced manipulators are present at the beguinning of any instancy; - Quality of the competencies estimates. Risks: Simultaneous unavailability, sudden demand augmentation - Same as above measured on «extreme scenarios» i.e. twice the usual demand with the less efficient resources Costs - Experimental system (devices + softwares) Cost hard to estimate.

17 17 / 23 Problematic and assumptions Description of the tool being developed - Simulation - Scheduling - Performance measure First results Conclusions / Perspectives

18 18 / 23 First results: 2,5 Without performance management approach 2 1,5 1 0,5 2,5 Série1 Série2 Série3 With performance management part 0 2 1, Série1 Série2 Série3 0,

19 19 / 23 First results: Staff selection ordomakerstrategy_00_randomselection,cfg initptimesum Random, whatever the workload Strategy using the sum of performance level ,01 0,13 0,19 0,26 0,36 1,1 1,18 1,24 1,36 2,07 2,14 2,21 2,28 2,37 3,11 3,19 3,28 3,37 4,09 4,15 4,23 4,3 4,38 5,13 5,21 5,35 6,1 ordomakerstrategy_00_str01,cfg initptimesum 6,16 6,22 6,34 7,08 7,14 7,2 7,27 7,36 8,07 8,17 8,29 9,06 9,14 9,22 9,34 Lowest on easy days Random on normal days Largest on hard days ,06 0,13 0,2 0,29 0,37 1,1 1,18 1,24 1,36 2,11 2,16 2,28 2,35 3,06 3,15 3,23 3,32 4,06 4,13 4,2 4,26 4,35 5,08 5,17 5,25 5,41 6,12 6,19 6,25 6,36 7,09 7,15 7,22 7,29 7,39 8,14 8,26 8,36 9,11 9,17 9,28 9,37 ordomakerstrategy_00_str02,cfg initptimesum Strategy using competencies details Lowest per (sorted) type on easy days Random on normal days Best per (sorted) type on hard days 0 0,06 0,14 0,2 0,28 0,36 1,09 1,16 1,23 1,33 2,05 2,13 2,21 2,3 2,36 3,08 3,16 3,22 3,34 4,06 4,12 4,18 4,23 4,31 4,38 5,12 5,19 5,35 6,09 6,16 6,23 6,33 7,05 7,13 7,19 7,25 7,34 8,06 8,16 8,28 9,05 9,13 9,2 9,31 9,4

20 20 / 23 Problematic and assumptions Description of the tool being developed - Simulation - Scheduling - Performance measure First results Conclusions / Perspectives

21 21 / 23 Conclusion / perspectives Simulation model Hard to configure realistically _ Data acquisition through RFID? Include performance shaping factors (stochastic performance) Stochastic studies will require a (very) long computation time Estimation of the impacts of RFID in chemotherapy production Regroup the different studies (multi-level simulation) New activities: Operational modifications (staff management, production site sharing) adds value to RFID projects Sensitivity analysis on resources - Different initial performance levels - Personal profiles (learning curve parameters)

22 22 / 23 Short-term perspective: Material scenarios Precise impact of preparation mistakes CATO : Multispec 1 : Multispec 2 : Production length augmentation, Solvent and/or raw medication wasted (low quantity); Preparation must go for a second round in isolator, Solvent and/or raw medication wasted (large quantity); Production length augmentation, Solvent and/or raw medication wasted (large quantity); Electro-Pipette : Specifications to be defined / adapted to cytotoxics, What leeway and competencies for manipulators?

23 23 / 23 Thanks for still being there Day on OR in Cancer Treatment & Operation Management. Nov. 17, 2010, Paris Questions / Suggestions?