Electronic prescribing in paediatric secondary care: are harmful errors prevented?

2019 ◽  
Vol 104 (9) ◽  
pp. 895-899 ◽  
Author(s):  
Andy Fox ◽  
Jane Portlock ◽  
David Brown

ObjectiveThe aim of this research was to ascertain the effectiveness of current electronic prescribing (EP) systems to prevent a standardised set of paediatric prescribing errors likely to cause harm if they reach the patient.DesignSemistructured survey.SettingUK hospitals using EP in the paediatric setting.Outcome measuresNumber and type of erroneous orders able to be prescribed, and the level of clinical decision support (CDS) provided during the prescribing process.Results90.7% of the erroneous orders were able to be prescribed across the seven different EP systems tested. Levels of CDS varied between systems and between sites using the same system.ConclusionsEP systems vary in their ability to prevent harmful prescribing errors in the hospital paediatric setting. Differences also occur between sites using the same system, highlighting the importance of how a system is set up and optimised.

2018 ◽  
Vol 103 (2) ◽  
pp. e1.29-e1
Author(s):  
Asif Yusuf ◽  
Choudhury Camrul

AimTo develop an electronic prescribing system (EPS), in a tertiary care paediatric hospital.MethodOne of the many benefits of electronic prescribing (EP) in secondary care, is the reduction in prescribing error rates.1 However, implementing EP in paediatrics, presents many challenges such as the increased complexity of medication dosing2 and varying doses of drugs depending on indication.2 An EPS was acquired from a local adult secondary care hospital and developed to include a specialist paediatric drug library with clinical decision support. The pharmacy department used a dispensing patient medication record system that was incompatible with the EPS, so the latter had to work side-by- side with the former, as the drug chart. A smaller training team was deployed with external trainers, from the hospital where the system was acquired from and they were enlisted for the pilot.ResultsThe pilot was launched in April 2017, on the hepatology ward, consisting of 14 beds. All patients that were treated under the hepatology medical and surgical teams were placed on the e-prescribing system and this accounted for 95 patients, from the launch over a period of 3 months. Although the benefits of an EPS became a reality, which included a reduction in medication and administration errors, many drawbacks still existed that hindered a more complete EPS. Certain drugs were found to be missing from the drug library and drug monographs lacked the appropriate clinical decision support for prescribers and administrators alike. This was observed by the sharp rise in incident reporting from 20 reports, in the 3 months prior to the launch, to 55 reports, in the 3 months post-launch. Pharmacy processes, that proved effortless on drug charts and discharge prescriptions, became complex for pharmacists and technicians, as the EPS lacked the necessary features including insufficient message functionality to document patient’s own medicines and supply from pharmacy, discharge prescription alerting and modification of prescriptions once printed. The absence of sufficient and relevant clinical support staff became apparent soon after external trainers returned to their respective bases; with only one support member remaining that had held a clinical position previously. Difficulties quickly became apparent when attempting to explain specific clinical EP functions to non-clinical support staff.ConclusionIn preparation for rollout across the trust, many areas could be improved upon to ensure substantial progress could be made, from the pilot. Developing a more robust system to build and review drug monographs to include both medical and nursing input, from their respective clinical specialities and ensuring that all drugs whether supplied with or without pharmacy involvement are included in the paediatric drug library. Observing the work of pharmacists and other healthcare professionals, to ensure their day-today tasks, on drug charts or discharge prescriptions, are replicated successfully on the EPS. Increased pharmacy involvement in training and support, would benefit the EPS greatly, from a clinical perspective.ReferencesFranklin G, O’Grady K, Donyai P, Jacklin A, Barber N. The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: A before-and-after study. QualSaf Health Care2007;16:279–84.Johnson KB, Lehmann CU. Council on clinical information technology. Technical report: Electronic prescribing in paediatrics: Toward safer and more effective medication management. Paediatrics2013;131(4):e1350–e1356.


2005 ◽  
Vol 12 (4) ◽  
pp. 403-409 ◽  
Author(s):  
Randolph A. Miller ◽  
Reed M. Gardner ◽  
Kevin B. Johnson ◽  
George Hripcsak

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rung-Ching Chen ◽  
Hui Qin Jiang ◽  
Chung-Yi Huang ◽  
Cho-Tsan Bau

Introduction. Although a number of researchers have considered the positive potential of Clinical Decision Support System (CDSS), they did not consider that patients’ attitude which leads to active treatment strategies or HbA1c targets. Materials and Methods. We adopted the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) published to propose an HbA1c target and antidiabetic medication recommendation system for patients. Based on the antidiabetic medication profiles, which were presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE), we use TOPSIS to calculate the ranking of antidiabetic medications. Results. The endocrinologist set up ten virtual patients’ medical data to evaluate a decision support system. The system indicates that the CDSS performs well and is useful to 87%, and the recommendation system is suitable for outpatients. The evaluation results of the antidiabetic medications show that the system has 85% satisfaction degree which can assist clinicians to manage T2DM while selecting antidiabetic medications. Conclusions. In addition to aiding doctors’ clinical diagnosis, the system not only can serve as a guide for specialty physicians but also can help nonspecialty doctors and young doctors with their drug prescriptions.


2016 ◽  
Vol 24 (2) ◽  
pp. 432-440 ◽  
Author(s):  
Clare L Brown ◽  
Helen L Mulcaster ◽  
Katherine L Triffitt ◽  
Dean F Sittig ◽  
Joan S Ash ◽  
...  

Objective: To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems. Materials and Methods: We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken. Results: A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and auto-population, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users’ work processes, and clinical decision support systems. Displaying an incomplete list of a patient’s medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users’ misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors. Discussion and Conclusions: Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users’ workflow expectations.


2019 ◽  
Vol 10 (01) ◽  
pp. 066-076 ◽  
Author(s):  
David Lyell ◽  
Farah Magrabi ◽  
Enrico Coiera

Objective Clinicians using clinical decision support (CDS) to prescribe medications have an obligation to ensure that prescriptions are safe. One option is to verify the safety of prescriptions if there is uncertainty, for example, by using drug references. Supervisory control experiments in aviation and process control have associated errors, with reduced verification arising from overreliance on decision support. However, it is unknown whether this relationship extends to clinical decision-making. Therefore, we examine whether there is a relationship between verification behaviors and prescribing errors, with and without CDS medication alerts, and whether task complexity mediates this. Methods A total of 120 students in the final 2 years of a medical degree prescribed medicines for patient scenarios using a simulated electronic prescribing system. CDS (correct, incorrect, and no CDS) and task complexity (low and high) were varied. Outcomes were omission (missed prescribing errors) and commission errors (accepted false-positive alerts). Verification measures were access of drug references and view time percentage of task time. Results Failure to access references for medicines with prescribing errors increased omission errors with no CDS (high-complexity: χ 2(1) = 12.716; p < 0.001) and incorrect CDS (Fisher's exact; low-complexity: p = 0.002; high-complexity: p = 0.001). Failure to access references for false-positive alerts increased commission errors (low-complexity: χ 2(1) = 16.673, p < 0.001; high-complexity: χ 2(1) = 18.690, p < 0.001). Fewer participants accessed relevant references with incorrect CDS compared with no CDS (McNemar; low-complexity: p < 0.001; high-complexity: p < 0.001). Lower view time percentages increased omission (F(3, 361.914) = 4.498; p = 0.035) and commission errors (F(1, 346.223) = 2.712; p = 0.045). View time percentages were lower in CDS-assisted conditions compared with unassisted conditions (F(2, 335.743) = 10.443; p < 0.001). Discussion The presence of CDS reduced verification of prescription safety. When CDS was incorrect, reduced verification was associated with increased prescribing errors. Conclusion CDS can be incorrect, and verification provides one mechanism to detect errors. System designers need to facilitate verification without increasing workload or eliminating the benefits of correct CDS.


2017 ◽  
Vol 74 (11) ◽  
pp. 774-776 ◽  
Author(s):  
Jashvant Poeran ◽  
Ksenia Gorbenko ◽  
Jason Babby ◽  
Madhu Mazumdar ◽  
Rosanne M. Leipzig

2017 ◽  
pp. 1501-1527 ◽  
Author(s):  
Jane Dominique Moon ◽  
Mary P. Galea

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of self-management using social computing technologies, in particular in the management of chronic disease.


Author(s):  
Jane Dominique Moon ◽  
Mary P. Galea

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of self-management using social computing technologies, in particular in the management of chronic disease.


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