The way MDTs view a prescription is noticeably altered on implementation of electronic prescribing and these results emphasise that the system design must take into account MDTs’ visual needs to facilitate selleck products quality care for the patient. 1. General Medical Council (2013). Good Practice in Prescribing and Managing Medicines and Devices [Internet]. p. 1–11. http://www.gmc-uk.org/static/documents/content/Prescribing_Guidance_(2013).pdf.
2. Cornford T, Dean B, Savage I, Barber N, Jani Y (2009). Electronic Prescribing in Hospitals – Challenges and Lessons Learned. NHS Connecting for Health. http://www.connectingforhealth.nhs.uk/systemsandservices/eprescribing/challenges/Final_report.pdf (accessed 29 Feb 2012). L. Holmstocka, E. Verellena, B. D. Franklinb,c, M. McLeodb,c aCatholic selleck kinase inhibitor University of Leuven, Leuven, Belgium, bImperial College Healthcare NHS Trust, London, UK, cUniversity College London, London, UK This study aimed to assess the appropriateness of a time
series method for evaluating the implementation of an electronic prescribing and medication administration (EPMA) system by examining data variation with time. Weekly variation in all five safety-related measures including prescribing error rates was identified on two wards. This study supports the use of an interrupted time series analysis for the evaluation of an EPMA system on the studied medication safety related measures. Electronic prescribing and medication administration (EPMA) systems may reduce medication errors and increase patient safety1,2; however many evaluation studies use an uncontrolled before and after study design which limits any inference about cause and effect. We therefore aimed to examine the appropriateness of a time-series method and develop a tool for evaluating the impact of an EPMA system on: (1) prescribing
error rates, (2) pharmacist intervention rates, (3) completeness of allergy documentation, (4) dose omission rates, and (5) drug administration rate by nurses. We also aimed to quantify time spent by pharmacists and nurses on routine tasks, and with whom the tasks were carried out. The study was conducted by two pharmacy students, both on one medical and one surgical inpatient ward in a NHS London teaching hospital. Data were collected on the same day each week over 6 weeks in April/May AZD9291 clinical trial 2013 on each ward; one student shadowed pharmacists during their ward visit (typically in the morning); the other shadowed nurses’ morning drug rounds and reviewed patients’ drug charts (post drug round). Both students also recorded the task and with whom the task involved each time their random interval signal generator produced an alert which was set at 32 alerts per hour. Task lists were developed through review of the literature and pilot work. Students were trained to carry out observations by a senior pharmacist researcher as part of the pilot study.