Development of a Perioperative Medication-Related Clinical Decision Support Tool to Prevent Medication Errors: An Analysis of User Feedback

2021 ◽  
Vol 12 (05) ◽  
pp. 984-995
Author(s):  
Karen C. Nanji ◽  
Pamela M. Garabedian ◽  
Sofia D. Shaikh ◽  
Marin E. Langlieb ◽  
Aziz Boxwala ◽  
...  

Abstract Objectives Medication use in the perioperative setting presents many patient safety challenges that may be improved with electronic clinical decision support (CDS). The objective of this paper is to describe the development and analysis of user feedback for a robust, real-time medication-related CDS application designed to provide patient-specific dosing information and alerts to warn of medication errors in the operating room (OR). Methods We designed a novel perioperative medication-related CDS application in four phases: (1) identification of need, (2) alert algorithm development, (3) system design, and (4) user interface design. We conducted group and individual design feedback sessions with front-line clinician leaders and subject matter experts to gather feedback about user requirements for alert content and system usability. Participants were clinicians who provide anesthesia (attending anesthesiologists, nurse anesthetists, and house staff), OR pharmacists, and nurses. Results We performed two group and eight individual design feedback sessions, with a total of 35 participants. We identified 20 feedback themes, corresponding to 19 system changes. Key requirements for user acceptance were: Use hard stops only when necessary; provide as much information as feasible about the rationale behind alerts and patient/clinical context; and allow users to edit fields such as units, time, and baseline values (e.g., baseline blood pressure). Conclusion We incorporated user-centered design principles to build a perioperative medication-related CDS application that uses real-time patient data to provide patient-specific dosing information and alerts. Emphasis on early user involvement to elicit user requirements, workflow considerations, and preferences during application development can result in time and money efficiencies and a safer and more usable system.

Author(s):  
Muhammad Tahir Aziz ◽  
Toofeeq Ur Rehman ◽  
Sadia Qureshi ◽  
Kashif Sajjad

Background: Medication therapy management (MTM) continues to offer pharmacists the opportunity to use their knowledge, assist patients and caregiver in improving therapeutic outcomes, however the change is slow. Health information technology has been noted as an important driver in the success of MTM and has a potential role in improving therapeutic outcomes and reducing medication errors. Objective: This research aimed to design an integrated clinical pharmacist menu (CPM) software along with clinical decision support tools, optimizing MTM services and reducing medication errors. Methods: The integrated CPM software was designed abridged with decision support tools. A comparative study was conducted in a setting of integrated CPM software versus paper-based clinical pharmacy services (P-CPS) for the evaluation of MTM services. Clinical decision support systems (CDSS) and automated significant laboratory and medication alerts were analyzed for the improvement of MTM and impact on the identification and resolution of medication errors. Results: MTM improved after the application of the CPM software with a difference of 100% in “medication history generation” and “patient care plan,” with a reduction in medication errors by 39.8%. The identification of medication errors and verification of medication order significantly improved from 49% to 82% (p = 0.00) and from 4.5% to 7.0% (p = 0.00), respectively, in the CPM setting. The CDSS tool in the CPM software generated 730, 1802, and 198 auto alerts for “drug–drug interaction,” “inappropriate dose,” and “dose adjustment in an abnormal clinical laboratory test,” respectively, which improved the resolution and identification of medication errors. Conclusion: The CPM is user-friendly, which improved the MTM services. Medication error identification and resolution were significantly improved by the CPM software.


Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Panagiotis Bountris ◽  
Maria Haritou ◽  
Abraham Pouliakis ◽  
Niki Margari ◽  
Maria Kyrgiou ◽  
...  

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.


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