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Homogeneity......to identify entities Spital Thun-Simmental AG Dr.med. Marc Oertle Head Clinic IT The completion of domain specific message and document.

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Presentation on theme: "Homogeneity......to identify entities Spital Thun-Simmental AG Dr.med. Marc Oertle Head Clinic IT The completion of domain specific message and document."— Presentation transcript:

1 Homogeneity......to identify entities Spital Thun-Simmental AG Dr.med. Marc Oertle Head Clinic IT The completion of domain specific message and document definitions by process control mechanisms: Project Idef-IS: Identification as Key for Quality Management Medinorma LLC Christian Hay Partner

2 Agenda Identifications as a Challenge: Background of Project Path to a Solution Standardisation with GS1 Deployment IdefIS

3 2.5.2006SGMI Dr.med. M. Oertle Background IOM Reports: –To Err is Human (2000) –Crossing the Quality Chasm (2001) Number of Errors in Hospitals is to high At first: –Drug Prescription and Administration –Mix ups in General IdefIS

4 Background (2) 5% of Inpatients suffer complications (mostly avoidable) Complications  Numerous Near-misses Averages (USA): –Longer Hospital Stay 2.9 – 4.5 Days when Medication erreor (even more for Transfusion errors) –Average additional cost by case $5400.- (Medication errors) International Strategies (IOM, WHO, JCAHO) –safe, efficient, effective, timely, patient centered, equitable IdefIS

5 Background (3) Transfusion (when declared...) Switzerland and U.K. : –near miss every 340 transfusion –Full incompatibel transfusion with morbidity: 1:100‘000 –Hospital Thun 5‘100 transfusions yearly, but : every ~ 18 Month a transfusion error –Thun:März 2005-März 2006 declared events: Laboratory: 26 (wrong blood tested....) Number of non-declared errors estimated as very important CIRS (Critical Incident Reporting System) additionnally: 13 Declaration in 3 Months (13/46) concerning wrong Patient, medication given to wrong Patient (Hypnotika, Morphium, …), near wrong site surgery, wrong Patient to Xray examination. It is said that weekly laboratories have to eliminate samples badly identified IdefIS

6 Objective: the 7 R Right patient Right time Right plan (to act): prescription e.g. Right action Right resource Right person Right place IdefIS

7 Landscape impacting Idef-IS CIS, PIS, LIS are in place WLAN access across all the Hospital, medical and nursing documentation electronically accessible; mobility with Laptops Requirement for efficient, user friendly and safe System Hospital Group with 4 Sites No Budget IdefIS

8 Pre-requisits for Idef-IS Existing Systems to be used Existing identifications to be used Standardisation of existing and new identifications to achieve uniqueness Role based identifications; management of attributes Generic Architecture, has to be used for all cross-matching All Resources to be handled similarly (Staff, Patient, Material, Locations) IdefIS

9 Example of blood transfusion IdefIS Electronic order entry Blood sample with reference to Patient Blood analysis Blood product dispensed Cross Match with Patient Clearance Feed-Back Accounting / re- ordering Transfusion

10 Uniqueness: GS1 Standard System with set of components (GSRN, GLN, GTIN...) Individual : Global Company Prefix Integration of existing (non-unique) Identifications Example: Global Service Relationship Number –18 digits Code (numeric) –Global Company Prefix + 10 digits + check digit –GCP+ (Attribut+existing identification)+C IdefIS

11 Concept IdefIS GS1: uniqueness; attributes for roles IdefIS

12 Identity Management GS1 –unique –Role Management –Standardisation IdefIS Identification: Who/What is it Mapping: belongs to ? Validiation: expiry date?  reading the 3 entities in free sequences Idef-IS Tabels 760100520022583166 GCP PAT FID p

13 RFID ISO 15693 Patient wristband RFID ISO 15693 Badge Staff GS1 GTIN Medication ISBT 128 Blood product Proprietary Barcode (migration to GS1 planed) Blood sample GS1 GTIN Materials AIDC (Automatic Identification Data Capture) IdefIS

14 Last but not least Incompetent people are, at most, 1% of the problem. The other 99% are good people trying to do a good job who make very simple mistakes and it's the processes that set them up to make these mistakes. Dr. Lucian Leape, Harvard School of Public Health IdefIS

15 Contact Dr.med. Marc Oertle marc.oertle@spitalstsag.ch Christian Hay hay@medinorma.ch IdefIS


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