scholarly journals Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Pharmacy ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Adriana Matos ◽  
David L. Bankes ◽  
Kevin T. Bain ◽  
Tyler Ballinghoff ◽  
Jacques Turgeon
Keyword(s):  
Drug Interactions ◽  
Paradigm Shift ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Records ◽  
Technological Paradigm ◽  
Life Threatening ◽  
Drug Regimens ◽  
Manual Input ◽  
Complex Drug

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

scholarly journals Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Ramzi Shawahna
Keyword(s):  
Decision Support ◽  
Electronic Health Records ◽  
Analytic Hierarchy Process ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Analytic Hierarchy ◽  
Health Records ◽  
Hierarchy Process

Abstract Background Electronic health records (EHRs) with embedded clinical decision support systems (CDSSs) have the potential to improve healthcare delivery. This study was conducted to explore merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs. Methods A mixed-method combining the Delphi technique and Analytic Hierarchy Process was used. Potentially important items were collected after a thorough search of the literature and from interviews with key contact experts (n = 19). Opinions and views of the 76 panelists on the use of EHRs were also explored. Iterative Delphi rounds were conducted to achieve consensus on 122 potentially important items by a panel of 76 participants. Items on which consensus was achieved were ranked in the order of their importance using the Analytic Hierarchy Process. Results Of the 122 potentially important items presented to the panelists in the Delphi rounds, consensus was achieved on 110 (90.2%) items. Of these, 16 (14.5%) items were related to the demographic characteristics of the patient, 16 (14.5%) were related to prescribing medications, 16 (14.5%) were related to checking prescriptions and alerts, 14 (12.7%) items were related to the patient’s identity, 13 (11.8%) items were related to patient assessment, 12 (10.9%) items were related to the quality of alerts, 11 (10%) items were related to admission and discharge of the patient, 9 (8.2%) items were general features, and 3 (2.7%) items were related to diseases and making diagnosis. Conclusions In this study, merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs were explored. Considering items on which consensus was achieved might promote congruence and safe use of EHRs. Further studies are still needed to determine if these recommendations can improve patient safety and outcomes in Palestinian hospitals.

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 137-138
Keyword(s):  
Linked Data ◽  
Clinical Decision Support Systems ◽  
Computational Prediction ◽  
Clinical Decision ◽  
Cancer Drug ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach ◽  
Adjuvant Breast Cancer ◽  
Team Decision

Lin FP, Pokorny A, Teng C, Dear R, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer 2016 Dec 1;16(1):929 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131452/ Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Bellika JG. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. J Biomed Inform 2016 Aug;62:243-64 http://www.sciencedirect.com/science/article/pii/S153204641630065X?via%3Dihub McEvoy DS, Sittig DF, Hickman TT, Aaron S, Ai A, Amato M, Bauer DW, Fraser GM, Harper J, Kennemer A, Krall MA, Lehmann CU, Malhotra S, Murphy DR, O’Kelley B, Samal L, Schreiber R, Singh H, Thomas EJ, Vartian CV, Westmorland J, McCoy AB, Wright A. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017 Mar 1;24(2):331-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391726/ Zamborlini V, Hoekstra R, Da Silveira M, Pruski C, ten Teije A, van Harmelen F. Inferring recommendation interactions in clinical guidelines. Semantic Web 2016;7(4):421-46 https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwin39e8r77VAhXEaFAKHeLkAC0QFgg_MAI&url=http%3A%2F%2Fwww.semantic-web-journal.net%2Fsystem%2Ffiles%2Fswj891.pdf&usg=AFQjCNF7CEXY49F929iQL09sRbGCl2Lc2A

Information technology in antimicrobial stewardship

2016 ◽  
Author(s):  
Matthew Laundy
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Antimicrobial Stewardship ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Electronic Prescribing ◽  
Sources Of Information ◽  
Health Records ◽  
Online Educational Resources ◽  
It Revolution

The information technology (IT) revolution has totally changed the way we function in society, yet sometimes it appears that this revolution has bypassed healthcare, partially due to its inherently conservative nature and partially to justifiable concerns about patient safety and privacy. Information is essential for an effective antimicrobial stewardship programme. Stewardship programmes have been hampered by the lack of IT and informatics systems to monitor, measure, and support them. This chapter reviews the sources of information required for antimicrobial stewardship, electronic health records, including electronic prescribing, and clinical decision support systems. Barriers to the implementation of IT in stewardship are examined. Social media and online educational resources are discussed. Big Data and clinical intelligence systems are briefly investigated.

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