Pharmacy & Sterilisation

Transcription

1 HUG Pharmacy & Sterilisation Prof. Pascal BONNABRY M. Hervé NEY Berner Fachhochschule Geneva, September 13, 2013 Agenda 13h45 14h00 15h00 17h00 18h00 HUG presentation Theoretical introduction, processes at the: - Pharmacy - Sterilisation Visit in 2 groups (1 hour per visit) - Pharmacy - Sterilisation Debriefing and use cases End 1

2 Geneva university hospitals (HUG) Geneva university hospitals (HUG) 1 out of 5 swiss university hospitals Consortium of public hospitals in the Geneva county 1 central pharmacy 1 central sterilisation Annual report

3 Medical activity Annual report 2011 Finances Expenditure Income Drugs CHF 60 millions Annual report 2011 Budget CHF 1,7 billion 3

4 Human resources The larger employer in the Geneva county Annual report 2011 Men at work Facts and figures of HUG,

5 IT to improve the safety of the medication process at the hospital Prof. Pascal BONNABRY Berner Fachhochschule Geneva, September 13, 2013 Pharmacy strategic priorities Optimize the safety, the efficiency and the traceability of the physical circuit of drugs Optimize the information flow during prescription, dispensing, preparation and administration of drugs 5

6 Medication process organisation Existing models Global distribution The pharmacy delivers boxes of drugs and nurses dispense individual treatments from the ward stock Nominal or individual distribution Drug dispensing is performed at the pharmacy, for each patient, based on the prescription Global or individual? Individual distribution is more convenient in some conditions Few prescription modifications (chronic care) Pharmacy close to the wards At the HUG, the global model was selected Acute care in majority Long distance between the pharmacy and some wards (multi-sites hospital) 6

7 The medication process Prescription Industry stock Cytos TPN MP Pharmacy stock Ward stock Dispensation Production Production stock EPR End-product analysis Production Raw-materials analysis Administration to patients Safety problems? 7

8 The addition of two errors Commission error AND Control failure Selection Calculation Counting Check Double-check Check-list Electronic Error rate= 1 % Distribution errors (real life) 24% 56% 20% Gschwind L, Carrez L, François O, HUG, Counting Omission Selection 8

9 Error rate= 3 % Dispensing errors (experimental) 20% 6% 74% Garnerin P, Eur J Clin Pharmacol 2007;63:769 Selection error Repartition error Counting error Administration errors Error rate= 19 % Barker KN, Arch Intern Med 2002;162:1897 Observation study in 36 institutions 9

10 Limited performance of controls Introduction of errors during unit dose dispensing Detection ability during human-performed control: Pharmacists: 87.7% Nurses: 82.1% Facchinetti NJ, Med Care 1999;37:39-43 Efficiency 85% (known value in the industry) Do not be too confident with the double-checks! Implementation of IT in the medication process Diogène

11 Potential interests of IT To improve The safety by reducing the rate of errors and improving the reliability of controls The traceability by facilitating the registration of logs The efficiency by increasing the working performance The communication by connecting the different steps of the processes Many questions before to start Positive impacts? New risks? Return on investment? System selection? Commercial or homemade? Interoperability? User s s training strategy? Acceptability? 11

12 Industry stock Electronic systems to catch errors Prescription Cytos Delivery TPN MP ( ) Pharmacy stock Ward stock Dispensation Production Production stock EPR End-product analysis Production Raw-materials analysis Administration to patients Industry stock Electronic systems to avoid errors Prescription CPOE / Cytos CDSS TPN MP EDI Pharmacy stock Ward stock Dispensation Production EPR Production stock End-product analysis Production Raw-materials analysis Administration to patients 12

13 Robotisation of drug distribution Safety 4500 avoided errors/yr François O et al, HUG, 2013 Selection 0% Conveyor 0.27% Manual finalisation 0% Robotisation of drug distribution Efficiency François O et al, HUG, FTE ROI 4.5 years 13

14 Automation of drug dispensing Safety Experimental Error rate [%] without with Du Pasquier C, Riberdy L, HUG, 2003 Automation of drug dispensing Efficiency Real life (digestive surgery ward) Minutes/week sans without armoire au beginning début en routine routine -8h +4h +1h François O et al, HUG, Nurse 3AL Assistante Technician Pharmacien Pharmacist 14

15 Administration to patients Bedside scanning Cytostatics Nurse Drug Physician Database Patient Administration to patients Safety Benefit of bedside scanning Wrong drug - 75% Wrong dose - 62% Wrong patient - 93% Wrong administration time - 87% Globally - 80% Johnson, J Healthcare Inf Manag 2002;16:1 15

16 The medication process Robotized distribution Final perspective Automated dispensing system Manufacturer stock EDI Pharmacy stock Ward stock CPOE Clinical information system Distribution with scanning Logistic information system Bedside scanning Computer-assisted production management Objectives To support any type of preparation by IT Batches Cytostatics TPN Other individualized prescriptions To implement in-process electronic controls during the most critical steps To link individualized preparation to their prescription and their administration 16

17 Production management Automation Nutrition (Baxa) Cytostatics (CytoCare) CIVAS (Smartfiller) PharmaHelp (Medical Dispensing Systems), Riva, Production management Gravimetry Cytostatics (Cato, Cypro ) Batch production 17

18 Production management Efficiency Production Parenteral nutrition (10/day) - 60% Manual 2 x 3h = 6 h Automated (BAXA) 1 x 2.5h = 2.5 h Prerequisite to successful IT implementation Electronic management of processes (CPOE, stocks, ) Technical infrastructure (hard-, soft-) Actors identification (caregivers, patients, drugs) Acceptability (patients, caregivers) Adaptation to processes Project leadership Financing 18

19 Actors identification The patient The caregiver The drug Drug identification? Reconditioned by the pharmacy Identified by the industry 19

20 Drug identification Hierarchy Unit dose Secondary package Hospital package Box Pallet = international standard GS1 codification of pharmaceuticals at HUG Product ID (cytostatics) GTIN - cytos EXP (date and time) Serial cyt

21 GS1 codification of pharmaceuticals at HUG Product ID (batch production) GTIN EXP date Batch PDS How to progress? Determine an institutional strategy and an implementation schedule, taking into account the local organisation the local culture the expected return on investment Involve the different partners Re-think the process organisation (re-engineering) Manage each projet independently, without loosing the global vision 21

22 Conclusion The medication process at the hospital is complex and involves many different professionals A clever organisation contributes to improve the safety, the efficiency, the communication and the traceability IT take more and more importance in the process improvement approaches: their implementation is necessary but is a challenge Each hospital must determine a strategy, based on the local context On the presentation? On the use case? Questions? 22