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Dickinson H, Teltsch DY, Feifel J, Hunt P, Vallejo-Yagüe E, Virkud AV, Muylle KM, Ochi T, Donneyong M, Zabinski J, Strauss VY, Hincapie-Castillo JM. The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness. Drug Saf 2024; 47:117-123. [PMID: 38019365 DOI: 10.1007/s40264-023-01376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 11/30/2023]
Abstract
The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.
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Affiliation(s)
| | | | - Jan Feifel
- Merck Healthcare KGaA, Darmstadt, Germany
| | - Philip Hunt
- Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
| | - Enriqueta Vallejo-Yagüe
- AstraZeneca, Gaithersberg, MD, USA
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Arti V Virkud
- Kidney Center School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Taichi Ochi
- Department of PharmacoTherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
- Center for Innovation in Medicine, Bucharest, Romania
| | | | | | - Victoria Y Strauss
- Boehringer Ingelheim, Binger Str. 173, 55218, Ingelheim am Rhein, Germany
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Muylle KM, van Laere S, Pannone L, Coenen S, de Asmundis C, Dupont AG, Cornu P. Added value of patient- and drug-related factors to stratify drug-drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model. Br J Clin Pharmacol 2023; 89:1374-1385. [PMID: 36321834 DOI: 10.1111/bcp.15580] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 09/14/2022] [Accepted: 10/30/2022] [Indexed: 11/24/2022] Open
Abstract
AIMS Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors. METHODS We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference). RESULTS The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], padj = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI -17.7% to -4.7%], padj ≤ 0.001). CONCLUSION The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.
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Affiliation(s)
- Katoo M Muylle
- Department of Pharmaceutical and Pharmacological Sciences, Research Group Clinical Pharmacology and Clinical Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1090, Belgium
| | - Sven van Laere
- Department of Public Health, Research Group of Biostatistics and Medical Informatics, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1090, Belgium
| | - Luigi Pannone
- Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing, European Reference Networks Guard-Heart, Universitair Ziekenhuis Brussel - Vrije Universiteit Brussel, Laarbeeklaan 101, Brussels, 1090, Belgium
| | - Samuel Coenen
- Department of Family Medicine and Population Health, Faculty of Medicine and Health Sciences, Campus Drie Eiken, Gouverneur Kinsbergencentrum, University of Antwerp, Doornstraat 331, Antwerp, 2610, Belgium
| | - Carlo de Asmundis
- Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing, European Reference Networks Guard-Heart, Universitair Ziekenhuis Brussel - Vrije Universiteit Brussel, Laarbeeklaan 101, Brussels, 1090, Belgium
| | - Alain G Dupont
- Department of Pharmaceutical and Pharmacological Sciences, Research Group Clinical Pharmacology and Clinical Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1090, Belgium
| | - Pieter Cornu
- Department of Pharmaceutical and Pharmacological Sciences, Research Group Clinical Pharmacology and Clinical Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1090, Belgium.,Department of Medical Informatics, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, Brussels, 1090, Belgium
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Muylle KM, Van Laere S, Gentens K, Dupont AG, Grosber M, Cornu P. Usability of Graphical User Interfaces With Semiautomatic Delabeling Feature to Improve Drug Allergy Documentation. J Allergy Clin Immunol Pract 2023; 11:519-526.e3. [PMID: 36581072 DOI: 10.1016/j.jaip.2022.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND The quality of allergy documentation in electronic health records is frequently poor. OBJECTIVE To compare the usability of 3 graphical user interfaces (GUIs) for drug allergy documentation. METHODS Physicians tested 3 GUIs by means of 5 fictional drug allergy scenarios: the current GUI (GUI 0), using mainly free-text, and 2 new coded versions (GUI 1 and GUI 2) asking information on allergen category, specific allergen, symptom(s), symptom onset, timing of initial reaction, and diagnosis status with a semiautomatic delabeling feature. Satisfaction was measured by the System Usability Scale questionnaire, efficiency by time to complete the tasks, and effectiveness by a task completion score. Posttest interviews provided more in-depth qualitative feedback. RESULTS Thirty physicians from 7 different medical specialties and with varying degrees of experience participated. The mean System Usability Scale scores for GUI 1 (77.25, adjective rating "Good") and GUI 2 (78.42, adjective rating "Good") were significantly higher than for GUI 0 (56.58, adjective rating "OK") (Z, 6.27, Padj < .001 and Z, 6.62, Padj < .001, respectively). There was no significant difference in task time between GUIs. Task completion scores of GUI 1 and GUI 2 were higher than for GUI 0 (Z, 9.59, Padj < .001 and Z, 11.87, Padj < .001, respectively). Quantitative and qualitative findings were combined to propose a GUI 3 with high usability. CONCLUSIONS The usability and quality of allergy documentation was higher for the newly developed coded GUIs with a semiautomatic delabeling feature without being more time-consuming.
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Affiliation(s)
- Katoo M Muylle
- Department of Pharmaceutical and Pharmacological Sciences (FARM), Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium.
| | - Sven Van Laere
- Department of Public Health (GEWE), Research Group of Biostatistics and Medical Informatics (BISI), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Kristof Gentens
- Department of Medical Informatics, Laarbeeklaan 101, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Alain G Dupont
- Department of Pharmaceutical and Pharmacological Sciences (FARM), Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Martine Grosber
- Department of Gerontology (GERO), Research Group of Skin Immunology and Immune Tolerance (SKIN), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium; Department of Dermatology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, Brussels, Belgium
| | - Pieter Cornu
- Department of Pharmaceutical and Pharmacological Sciences (FARM), Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium; Department of Medical Informatics, Laarbeeklaan 101, Universitair Ziekenhuis Brussel, Brussels, Belgium
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Van Laere S, Muylle KM, Cornu P. Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper. Int J Health Policy Manag 2022; 11:3159-3163. [PMID: 34814678 PMCID: PMC10105190 DOI: 10.34172/ijhpm.2021.144] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 10/17/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Sven Van Laere
- Research Group of Biostatistics and Medical Informatics (BISI), Department of Public Health (GEWE), Vrije Universiteit Brussel, Brussel, Belgium
| | - Katoo M. Muylle
- Research Group Clinical Pharmacology and Clinical Pharmacy (KFAR), Centre for Pharmaceutical Research (CePhar), Vrije Universiteit Brussel, Brussel, Belgium
| | - Pieter Cornu
- Research Group Clinical Pharmacology and Clinical Pharmacy (KFAR), Centre for Pharmaceutical Research (CePhar), Vrije Universiteit Brussel, Brussel, Belgium
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Van Laere S, Muylle KM, Dupont AG, Cornu P. Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction. J Med Syst 2022; 46:100. [DOI: 10.1007/s10916-022-01890-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
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Muylle KM, Gentens K, Van Laere S, Hamza C, Grosber M, Cornu P. Usability of Three Graphical User Interfaces for Drug Allergy Documentation. Stud Health Technol Inform 2022; 290:991-992. [PMID: 35673171 DOI: 10.3233/shti220233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The current drug allergy documentation module in the electronic health record of our institution is in a free-text format. Two versions of a structured and coded drug allergy documentation module were developed. Twenty-five physicians tested the three interfaces via 3x5 test scenarios. The usability was measured for each interface with a system usability scale questionnaire. Both new versions scored significantly better than the current free-text version. User feedback will be used to further optimize the new module.
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Affiliation(s)
- Katoo M Muylle
- Centre for Pharmaceutical Research (CePhar), Research group Clinical Pharmacology and Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Kristof Gentens
- Department of Medical Informatics, Universitair Ziekenhuis Brussel, 1090 Brussels, Belgium
| | - Sven Van Laere
- Department of Public Health (GEWE), Research Group of Biostatistics and Medical Informatics (BISI), Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Chaïmae Hamza
- Centre for Pharmaceutical Research (CePhar), Research group Clinical Pharmacology and Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Martine Grosber
- Department of Dermatology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Pieter Cornu
- Centre for Pharmaceutical Research (CePhar), Research group Clinical Pharmacology and Clinical Pharmacy (KFAR), Vrije Universiteit Brussel, 1090 Brussels, Belgium
- Department of Medical Informatics, Universitair Ziekenhuis Brussel, 1090 Brussels, Belgium
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Muylle KM, Cornu P, Cools W, Barbé K, Buyl R, Van Laere S. Optimization of Performance by Combining Most Sensitive and Specific Models in Data Science Results in Majority Voting Ensemble. Stud Health Technol Inform 2022; 294:435-439. [PMID: 35612117 DOI: 10.3233/shti220496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Ensemble modeling is an increasingly popular data science technique that combines the knowledge of multiple base learners to enhance predictive performance. In this paper, the idea was to increase predictive performance by holding out three algorithms when testing multiple classifiers: (a) the best overall performing algorithm (based on the harmonic mean of sensitivity and specificity (HMSS) of that algorithm); (b) the most sensitive model; and (c) the most specific model. This approach boils down to majority voting between the predictions of these three base learners. In this exemplary study, a case of identifying a prolonged QT interval after administering a drug-drug interaction with increased risk of QT prolongation (QT-DDI) is presented. Performance measures included accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Overall performance was measured by calculating the HMSS. Results show an increase in all performance measure characteristics compared to the original best performing algorithm, except for specificity where performance remained stable. The presented approach is fairly simple and shows potential to increase predictive performance, even without adjusting the default cut-offs to differentiate between high and low risk cases. Future research should look at a way of combining all tested algorithms, instead of using only three. Similarly, this approach should be tested on a multiclass prediction problem.
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Affiliation(s)
- Katoo M Muylle
- Centre for Pharmaceutical Research (CePhar), Vrije Universiteit Brussel, Belgium
| | - Pieter Cornu
- Centre for Pharmaceutical Research (CePhar), Vrije Universiteit Brussel, Belgium
| | - Wilfried Cools
- Department of Public Health, Vrije Universiteit Brussel, Belgium
| | - Kurt Barbé
- Department of Public Health, Vrije Universiteit Brussel, Belgium
| | - Ronald Buyl
- Department of Public Health, Vrije Universiteit Brussel, Belgium
| | - Sven Van Laere
- Department of Public Health, Vrije Universiteit Brussel, Belgium
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Muylle KM, Van Laere S, Grosber M, Cornu P. Physicians' needs for drug allergy documentation in electronic health records and allergy alert systems: Results of an end user's survey. Clin Transl Allergy 2022; 12:e12141. [PMID: 35414889 PMCID: PMC8984674 DOI: 10.1002/clt2.12141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Katoo M. Muylle
- Department of Pharmaceutical and Pharmacological Sciences (FARM) Vrije Universiteit Brussel Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR) Brussels Belgium
| | - Sven Van Laere
- Department of Public Health (GEWE) Vrije Universiteit Brussel Research Group of Biostatistics and Medical Informatics (BISI) Brussels Belgium
| | - Martine Grosber
- Department of Gerontology (GERO) Vrije Universiteit Brussel Research Group of Skin Immunology and Immune Tolerance (SKIN) Brussels Belgium
- Department of Dermatology Universitair Ziekenhuis Brussel Brussels Belgium
| | - Pieter Cornu
- Department of Pharmaceutical and Pharmacological Sciences (FARM) Vrije Universiteit Brussel Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR) Brussels Belgium
- Department of Medical Informatics Universitair Ziekenhuis Brussel Brussels Belgium
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Muylle KM, Gentens K, Dupont AG, Cornu P. Evaluation of an optimized context-aware clinical decision support system for drug-drug interaction screening. Int J Med Inform 2021; 148:104393. [PMID: 33486355 DOI: 10.1016/j.ijmedinf.2021.104393] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/06/2020] [Accepted: 01/08/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Evaluation of the effect of six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and alert acceptance and description of clinical pharmacist intervention acceptance. METHODS Optimizations in the new CDSS were the customization of the knowledge base (with addition of 67 extra DDIs and changes in severity classification), a new alert design, required override reasons for the most serious alerts, the creation of DDI-specific screening intervals, patient-specific alerting, and a real-time follow-up system of all alerts by clinical pharmacists with interventions by telephone was introduced. The alert acceptance was evaluated both at the prescription level (i.e. prescription acceptance, was the DDI prescribed?) and at the administration level (i.e. administration acceptance, did the DDI actually take place?). Finally, the new follow-up system was evaluated by assessing the acceptance of clinical pharmacist's interventions. RESULTS In the pre-intervention period, 1087 alerts (92.0 % level 1 alerts) were triggered, accounting for 19 different DDIs. In the post-intervention period, 2630 alerts (38.4 % level 1 alerts) were triggered, representing 86 different DDIs. The relative risk forprescription acceptance in the post-intervention period compared to the pre-intervention period was 4.02 (95 % confidence interval (CI) 3.17-5.10; 25.5 % versus 6.3 %). The relative risk for administration acceptance was 1.16 (95 % CI 1.08-1.25; 54.4 % versus 46.7 %). Finally, 86.9 % of the clinical pharmacist interventions were accepted. CONCLUSION Six concurrently implemented CDSS optimization strategies resulted in a high alert acceptance and clinical pharmacist intervention acceptance. Administration acceptance was remarkably higher than prescription acceptance.
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Affiliation(s)
- Katoo M Muylle
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Kristof Gentens
- Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.
| | - Alain G Dupont
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Pieter Cornu
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.
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Muylle KM, Gentens K, Dupont AG, Cornu P. Evaluation of context-specific alerts for potassium-increasing drug-drug interactions: A pre-post study. Int J Med Inform 2019; 133:104013. [PMID: 31698230 DOI: 10.1016/j.ijmedinf.2019.104013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 10/04/2019] [Accepted: 10/14/2019] [Indexed: 10/25/2022]
Abstract
OBJECTIVE To investigate whether context-specific alerts for potassium-increasing drug-drug interactions (DDIs) in a clinical decision support system reduced the alert burden, increased alert acceptance, and had an effect on the occurrence of hyperkalemia. MATERIALS AND METHODS In the pre-intervention period all alerts for potassium-increasing DDIs were level 1 alerts advising absolute contraindication, while in the post-intervention period the same drug combinations could trigger a level 1 (absolute contraindication), a level 2 (monitor potassium values), or a level 3 alert (informative, not shown to physicians) based on the patient's recent laboratory value of potassium. Alert acceptance was defined as non-prescription or non-administration of the interacting drug combination for level 1 alerts and as monitoring of the potassium levels for level 2 alerts. RESULTS The alert burden decreased by 92.8%. The relative risk (RR) for alert acceptance based on prescription rates for level 1 alerts and monitoring rates for level 2 alerts was 15.048 (86.5% vs 5.7%; 95% CI 12.037-18.811; P < 0.001). With alert acceptance for level 1 alerts based on actual administration and for level 2 alerts on monitoring rates, the RR was 3.597 (87.6% vs 24.4%; 95% CI 3.192-4.053; P < 0.001). In the generalized linear mixed model the effect of the intervention on the occurrence of hyperkalemia was not significant (OR 1.091, 95% CI 0.172-6.919). CONCLUSION The proposed strategy seems effective to get a grip on the delicate balance between over- and under alerting.
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Affiliation(s)
- Katoo M Muylle
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Kristof Gentens
- Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium.
| | - Alain G Dupont
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Pieter Cornu
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090, Brussels, Belgium; Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium.
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