The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Evaluation of an optimized context-aware clinical decision support system for drug-drug interaction screening
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Different studies have shown a substantial reduction in DDI alert burden by applying patient and context-specific factors.
“Different studies have shown a substantial reduction in DDI alert burden by applying patient and context-specific factors [14–18].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [15]
A study by Muylle et al. recently showed a significant increase in alert acceptance, i.e. from 6.3 to 25.5%, by including DDI-specific screening intervals and contextual factors.
“A study by Muylle et al. recently showed a significant increase in alert acceptance, i.e. from 6.3 to 25.5%, by including DDI-specific screening intervals and contextual factors [15].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [15]
Citations
Cites
with the HITEC Investigators, Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0430-8 · Cited by 709
Tiering Drug-Drug Interaction Alerts by Severity Increases Compliance Rates
Paterno, M. D., Maviglia, S. M., Gorman, P. N., Seger, D. L., Yoshida, E., Seger, A. C., Bates, D. W., & Gandhi, T. K. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2808 · Cited by 263
Drug interaction alert override rates in the Meaningful Use era: No evidence of progress
Bryant, A. D., Fletcher, G. S., & Payne, T. H. (2014) · Applied Clinical Informatics · doi:10.4338/aci-2013-12-ra-0103 · Cited by 172
Van de Velde, S., Heselmans, A., Delvaux, N., Brandt, L., Marco-Ruiz, L., Spitaels, D., Cloetens, H., Kortteisto, T., Roshanov, P., Kunnamo, I., et al. (2018) · Implementation Science · doi:10.1186/s13012-018-0790-1 · Cited by 156
Slight, S. P., Seger, D. L., Nanji, K. C., Cho, I., Maniam, N., Dykes, P. C., & Bates, D. W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0085071 · Cited by 86
Flagged first, then by citations
Research works printed but not linked: 39
- Poudel, 2017, Burden of hospitalizations related to adverse drug events in the USA: a retrospective analysis from large inpatient database, Pharmacoepidemiol. Drug Saf., 26, 635, 10.1002/pds.4184 · DOI
- El Morabet, 2018, Prevalence and preventability of drug-related hospital readmissions: a systematic review, J. Am. Geriatr. Soc., 66, 602, 10.1111/jgs.15244 · DOI
- Bouvy, 2015, Epidemiology of adverse drug reactions in Europe: a review of recent observational studies, Drug Saf., 38, 437, 10.1007/s40264-015-0281-0 · DOI
- Moja, 2014, Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis, Am. J. Public Health, 104, e12, 10.2105/AJPH.2014.302164 · DOI
- Varghese, 2018, Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review, J. Am. Med. Inform. Assoc., 25, 593, 10.1093/jamia/ocx100 · DOI
- Jia, 2016, The effects of clinical decision support systems on medication safety: an overview, PLoS One, 11, 10.1371/journal.pone.0167683 · DOI
- Reis, 2017, Impact on process results of clinical decision support systems (CDSSs) applied to medication use: overview of systematic reviews, Pharm. Pract., 15
- Roumeliotis, 2019, Effect of electronic prescribing strategies on medication error and harm in hospital: a systematic review and meta-analysis, J. Gen. Intern. Med., 34, 2210, 10.1007/s11606-019-05236-8 · DOI · Paper page
- Prgomet, 2017, Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis, J. Am. Med. Inform. Assoc., 24, 413, 10.1093/jamia/ocw145 · DOI
- Carli, 2018, Quality of decision support in computerized provider order entry: systematic literature review, JMIR Med. Inform., 6, e3, 10.2196/medinform.7170 · DOI
- Kane-Gill, 2017, Technologic distractions (Part 1): summary of approaches to manage alert quantity with intent to reduce alert fatigue and suggestions for alert fatigue metrics, Crit. Care Med., 45, 1481, 10.1097/CCM.0000000000002580 · DOI
- Hussain, 2019, Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review, J. Am. Med. Inform. Assoc. JAMIA, 26, 1141, 10.1093/jamia/ocz095 · DOI
- Roshanov, 2013, Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials, BMJ (Clin. Res. ed.), 346
- Nabovati, 2017, Information technology-based interventions to improve drug-Drug interaction outcomes: a systematic review on features and effects, J. Med. Syst., 41, 12, 10.1007/s10916-016-0649-4 · DOI
- Cornu, 2015, Pilot evaluation of an optimized context-specific drug–drug interaction alerting system: a controlled pre-post study, Int. J. Med. Inform., 84, 617, 10.1016/j.ijmedinf.2015.05.005 · DOI
- Muylle, 2020, Evaluation of context-specific alerts for potassium-increasing drug-drug interactions: a pre-post study, Int. J. Med. Inform., 133, 104013, 10.1016/j.ijmedinf.2019.104013 · DOI
- Strom, 2010, Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial, Arch. Intern. Med., 170, 1578, 10.1001/archinternmed.2010.324 · DOI · Paper page
- McCoy, 2012, A framework for evaluating the appropriateness of clinical decision support alerts and responses, J. Am. Med. Inform. Assoc. JAMIA, 19, 346, 10.1136/amiajnl-2011-000185 · DOI · Paper page
- Woods, 2014, Clinical decision support for atypical orders: detection and warning of atypical medication orders submitted to a computerized provider order entry system, J. Am. Med. Inform. Assoc. JAMIA, 21, 569, 10.1136/amiajnl-2013-002008 · DOI
- Beeler, 2013, Use of an on-demand drug-drug interaction checker by prescribers and consultants: a retrospective analysis in a Swiss teaching hospital, Drug Saf., 36, 427, 10.1007/s40264-013-0022-1 · DOI
- Cornu, 2014, Performance of a clinical decision support system and of clinical pharmacists in preventing drug-drug interactions on a geriatric ward, Int. J. Clin. Pharm., 36, 519, 10.1007/s11096-014-9925-x · DOI
- Guignard, 2015, Drug-related problems identification in general internal medicine: the impact and role of the clinical pharmacist and pharmacologist, Eur. J. Intern. Med., 26, 399, 10.1016/j.ejim.2015.05.012 · DOI
- Bosma, 2018, Pharmacist interventions during patient rounds in two intensive care units: clinical and financial impact, Neth. J. Med., 76, 115
- Lombardi, 2018, Evaluation of the implementation of a clinical pharmacy service on an acute internal medicine ward in Italy, BMC Health Serv. Res., 18, 10.1186/s12913-018-2988-y · DOI
- Renaudin, 2018, Clinical, economic, and organizational impact of the clinical pharmacist in an orthopedic and trauma surgery department, J. Patient Saf., 1
- Jaspers, 2011, Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings, J. Am. Med. Inform. Assoc., 18, 327, 10.1136/amiajnl-2011-000094 · DOI · Paper page
- Bright, 2012, Effect of clinical decision-support systems: a systematic review, Ann. Intern. Med., 157, 29, 10.7326/0003-4819-157-1-201207030-00450 · DOI · Paper page
- Cook, 2017, Context-sensitive decision support (infobuttons) in electronic health records: a systematic review, J. Am. Med. Inform. Assoc., 24, 460, 10.1093/jamia/ocw104 · DOI
- Page, 2017, A systematic review of the effectiveness of interruptive medication prescribing alerts in hospital CPOE systems to change prescriber behavior and improve patient safety, Int. J. Med. Inform., 105, 22, 10.1016/j.ijmedinf.2017.05.011 · DOI · Paper page
- Powers, 2018, Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review, J. Am. Med. Inform. Assoc. JAMIA, 25, 1556, 10.1093/jamia/ocy112 · DOI · Paper page
- Zenziper Straichman, 2017, Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients, Int. J. Med. Inform., 107, 70, 10.1016/j.ijmedinf.2017.08.008 · DOI
- Humphrey, 2018, An investigation of drug-drug interaction alert overrides at a pediatric hospital, Hosp. Pediatr., 8, 293, 10.1542/hpeds.2017-0124 · DOI
- Wong, 2018, Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit, BMJ Qual. Saf., 27, 718, 10.1136/bmjqs-2017-007531 · DOI · Paper page
- Wright, 2019, Structured override reasons for drug-drug interaction alerts in electronic health records, J. Am. Med. Inform. Assoc. JAMIA, 26, 934, 10.1093/jamia/ocz033 · DOI · Paper page
- Phansalkar, 2010, A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems, J. Am. Med. Inform. Assoc., 17, 493, 10.1136/jamia.2010.005264 · DOI · Paper page
- Horsky, 2013, Design of decision support interventions for medication prescribing, Int. J. Med. Inform., 82, 492, 10.1016/j.ijmedinf.2013.02.003 · DOI
- Duke, 2011, A successful model and visual design for creating context-aware drug-drug interaction alerts, AMIA Annu. Symp. Proc., 2011, 339
- Russ, 2014, Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation, J. Am. Med. Inform. Assoc. JAMIA, 21, e287, 10.1136/amiajnl-2013-002045 · DOI
- Tolley, 2018, Improving medication-related clinical decision support, Am. J. Health. Syst. Pharm., 75, 239, 10.2146/ajhp160830 · DOI · Paper page
Cited by
Sorted by citations
Van De Sijpe, G., Quintens, C., Walgraeve, K., Van Laer, E., Penny, J., De Vlieger, G., Schrijvers, R., De Munter, P., Foulon, V., Casteels, M., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01783-z · Cited by 54
“5%, by including DDI-specific screening intervals and contextual factors [15].”
Background
Metadata
- Authors
- Muylle, K. M., Gentens, K., Dupont, A. G., & Cornu, P.
- Affiliations
- Vrije Universiteit Brussel (BE)
- Universitair Ziekenhuis Brussel (BE)
- Corresponding author
- Muylle, Katoo M.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 148
- Pages
- 104393
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W3119058389
- Cited by
- 45
- References
- 5
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 7 · 2023: 10 · 2022: 13 · 2021: 1
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- clinical decision support systems, drug-drug interactions, alert burden, clinical pharmacist intervention, alert design, severity classification
- MeSH terms
- Drug Interactions, Pharmaceutical Preparations, Humans, Pharmacists, Decision Support Systems, Clinical, Medical Order Entry Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review