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Background Failure Mode and Effect Analysis (FMEA) is a tool to identify, assess and prevent possible failures that could occur in a process. Purpose To describe FMEA as a method to identify weaknesses in the process of prescription and transcription of medical orders. To isolate the key steps according to their risk priority number (RPN). To report the steps taken. Materials and Methods A multidisciplinary study group was assembled. Possible errors in the prescription/transcription workflow were identified and classified according to their RPN score (calculated by multiplying the severity, occurrence, and detection). Strategies for improvement were established. Results Errors in the prescription were classified as follows: (1) Patient-and-history identification, (2) Clinical and laboratory data checkout, (3) Treatment conciliation, (4) Allergies, (5) Verbal prescription, (6) Handwritten prescription. Errors in transcription: (7) Patient identification (nurse), (8) Internally mailed prescriptions, (9) Paper transcription, (10) Check in pharmacy, (11) Patient identification (pharmacist), (12) Prescription validation, (13) Prescription printing, and (14) Acknowledgement of errors by the pharmacist. Top-ranked item (number), suggested solution, and indicator, respectively were: (5) Verbal prescription (288), storage of verbal prescriptions in pharmacy, % of verbal prescriptions; (9) Failure in paper transcription (288), computerised physician order entry (CPOE), % of electronic prescriptions; (14) Error report to the pharmacist (288), implementation of a two-way communication protocol, number of reports; (8) Paper-based prescriptions sent to pharmacy (243), CPOE, % of electronic prescriptions; (10) Check in pharmacy (216), CPOE, % of electronic prescriptions. The pharmacy, medical director, and Quality Unit were responsible for the changes undertaken in all cases. Conclusions Verbal prescription, failure in paper transcription, error report and mailed prescriptions to pharmacy were the steps with the highest risk of error. For most cases, CPOE was implemented, whereas the percentage of electronic prescriptions was the key indicator to measure the overall improvement in these processes. In conclusion, further efforts and pharmacy policies should focus on the implementation of CPOE in all inpatient areas, thus preventing failure of prescription/transcription and validation loops. No conflict of interest.

Background Ours is a 110-bed regional hospital with electronic prescribing throughout. The electronic prescription programme offers allergy alert systems and the need for dosage adjustment in renal and hepatic impairment. However, no warning of potential drug interactions is included. Purpose To establish a drug interaction screening system to complement the electronic prescribing programme. Materials and Methods Prospective one-year study (Sept 2011–Sept 2012). Computerized drug interaction alerts can improve the safety, quality and efficiency of care processes and reduce the rate of medicines errors, but sending mass alerts can generate what is known as ‘alert fatigue’. For this reason we selected those drugs categorised in Micromedex® 2.0 as contraindicated for simultaneous and significant interactions (the interaction can cause death and/or require medical intervention to minimise or avoid serious side effects); drugs classified as D in Lexi-Comp OnlineTM (consider modifying the treatment and X (avoid combination); those classified as clinically important by the Arizona CERT. The interaction was detected by crossing data in Access 2003 with the pharmacotherapeutic profile of patients obtained in the electronic prescribing programme (eOsabide) and a proprietary database which contains a total of 3,133 pairs of interactions. The report was written in the patient’s medical history (Osabide-Global) and acceptance was verified in 24–48 hours. Results We detected a total of 1996 interactions and 25% of them were reported, 27% of which led to changes in medical treatment. The main cause of non-notification (36%) was that one drug was prescribed if needed. Conclusions The project was very well accepted among medical professionals and has improved the quality of prescribing. The biggest drawback is the delay in detecting the interaction; it would be helpful if the system generated the warning at the time of prescription. No conflict of interest.

Background Electronic prescribing (EP) systems have been recognised as successful in reducing chemotherapy prescribing errors. However, electronic prescriptions are unlikely to prevent all errors, and new types of errors may emerge. Purpose To assess prescribing error rates and identify new error types and their causes with the implementation of a electronic prescribing system for ambulatory cancer patients at a London Cancer Centre. Materials and Methods A service evaluation was conducted in two parts, covering two different strategies for interception of prescribing errors – prospectively by pharmacists during a 2-week period, and retrospectively using data from the pharmacy EP telephone helpline service, over 41 weeks. Results The overall rate of error-containing prescriptions was estimated to be 6%. In the prospective part, 32 errors were identified from 571 electronic chemotherapy prescriptions. Most commonly committed errors were chemotherapy drug dose adjustments (13; 41%) and weight omissions (11; 34%). In the retrospective analysis, 95 of 141 errors (67%) were ‘e-selection errors’, classified mainly as ‘work-arounds’ (26; 18%), ‘wrong commands’ (35; 25%), or ‘wrong fields’ (27; 19%). 63 errors (45%) were related to scheduling a chemotherapy or supportive drug or regimen. Electronic system-related causes of prescribing errors were recognised in 4 of 32 cases (13%) in the prospective part, and in 89 of 141 cases (63%) in the retrospective part. It was estimated that with implementation of technical solutions and additional prescriber training, 85% of these errors could be prevented in the future. Conclusions The estimated rate of chemotherapy prescribing errors was 6%. A number of different errors, specific for electronic prescribing, were identified, with a thorough explanation of how various errors may have occurred. Future larger scale studies are needed to confirm prescribing error rates, and to possibly identify other, previously unrecognised, types of chemotherapy prescribing errors. No conflict of interest.

Background Potassium (K+) is the principal intracellular cation and is essential to maintain the function of multiple organs. It is a critical component of cardiac conduction and has a narrow therapeutic/toxic range. Purpose To investigate the effect of pharmaceutical intervention through computerised prescription order entry (CPOE) in hospitalised patients with K+ disorders. Materials and Methods Prospective study carried out over 7 weeks. Pharmacists first added information about drugs that affect the K+ level as a support in the prescription programme. We then identified patients with abnormal K+ levels using a link with laboratory data ( 5.3 mmol/l). Pharmacists reviewed the pharmacotherapy daily in order to detect possible medication errors related to K+ disorders. Lastly we analysed the effect of pharmaceutical recommendations and physician acceptance rate. Results 183 patients were included (67 ± 17 years old on average), 128 patients (69.9%) with hypokalaemia and 55 (30.1%) with hyperkalaemia. A total of 3,380 electronic prescriptions were selected. Of them, 540 (16.0%) could affect K+ levels mainly through furosemide, piperacillin-tazobactam and meropenem; pharmacists checked 383 orders thoroughly to prevent possible medication errors. 232 (60.6%) required pharmaceutical recommendations, 130 of them (56.0%) were related to optimising K+ therapy in hypokalaemic patients and 35 (15.0%) were safety recommendations for closer monitoring. Clinicians accepted 72.4% of recommendations. Conclusions There is a high rate of prescription errors related to K+ disorders that could jeopardise patient safety. Pharmaceutical intervention through CPOE helps to minimise them and increases physician awareness of the necessity of closer K+ monitoring in these patients. No conflict of interest.

The e-health sector in Germany is currently focused on the e-health card concept. This e-health card will totally change the current system, since it will allow several new functions. Because enrollees have concerns about their data, the health insurance companies have to ensure that enrollees have the possibility to check and manage their data. Therefore, electronic kiosk systems (e-kiosks) are being developed and must be useable by all citizens. Hence, this paper serves to identify possibilities pertaining to how these terminals can be adapted to ensure that all people can use these e-kiosks effectively and efficiently.

[Figure][1] FIGURE. Proponents believe e-prescribing will reduce paperwork and health care system costs. Photos.com The United States government is telling doctors it’s time to set aside their legendarily lousy handwriting and convert to electronic prescriptions. To drive home the

ObjectivesTo assess the legibility and completeness of handwritten prescriptions and compare with electronic prescription system for medication errors.DesignProspective study.SettingKing Khalid University Hospital (KKUH), Riyadh, Saudi Arabia.Subjects and methodsHandwritten prescriptions were received from clinical units of Medicine Outpatient Department (MOPD), Primary Care Clinic (PCC) and Surgery Outpatient Department (SOPD) whereas electronic prescriptions were collected from the pediatric ward. The handwritten prescription was assessed for completeness by the checklist designed according to the hospital prescription and evaluated for legibility by two pharmacists. The comparison between handwritten and electronic prescription errors was evaluated based on the validated checklist adopted from previous studies.Main outcome measuresLegibility and completeness of prescriptions.Results398 prescriptions (199 handwritten and 199 e-prescriptions) were assessed. About 71 (35.7%) of handwritten and 5 (2.5%) of electronic prescription errors were identified. A significant statistical difference (P