SFINX—a drug-drug interaction database designed for clinical decision support systems

Böttiger, Ylva; Laine, Kari; Andersson, Marine L.; Korhonen, Tuomas; Molin, Björn; Ovesjö, Marie‐Louise; Tirkkonen, Tuire; Rane, Anders; Gustafsson, Lars L.; Eiermann, Birgit

doi:10.1007/s00228-008-0612-5

Cited by 125 publications

(146 citation statements)

References 23 publications

(21 reference statements)

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“…Such concepts may offer promising strategies to improve the specificity of computer-triggered alerts. [11,20,27,29] Among the 10 most frequent level 1 and level 2 DDIs identified by the knowledge base (cf. table V), the "prescription" ethanol needs further explanation.…”

Section: Discussionmentioning

confidence: 99%

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Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

Beeler

Eschmann

Rosen³

et al. 2013

Drug Saf

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BACKGROUND: Offering a drug-drug interaction (DDI) checker on-demand instead of computertriggered alerts is a strategy to avoid alert fatigue. OBJECTIVE: The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients. METH-ODS: The study was conducted at the University Hospital Zurich, an 850-bed teaching hospital. The hospital-wide use of the on-demand DDI checker was measured for prescribers and consulting pharmacologists. The number of DDIs identified on-demand was compared to the number that would have resulted by computer-triggering and this was compared to patient-specific recommendations by a consulting pharmacist. RESULTS: The on-demand use was analyzed during treatment of 64,259 inpatients with 1,316,884 prescriptions. The DDI checker was popular with nine consulting pharmacologists (648 checks/consultant). A total of 644 prescribing physicians used it infrequently (eight checks/prescriber). Among prescribers, internists used the tool most frequently and obtained higher numbers of DDIs per check (1.7) compared to surgeons (0.4). A total of 16,553 DDIs were identified on-demand, i.e., <10 % of the number the computer would have triggered (169,192). A pharmacist visiting 922 patients on a medical ward recommended 128 adjustments to prevent DDIs (0.14 recommendations/patient), and 76 % of them were applied by prescribers. In contrast, computer-triggering the DDI checker would have resulted in 45 times more alerts on this ward (6.3 alerts/patient). CONCLUSIONS: The on-demand DDI checker was popular with the consultants only. However, prescribers accepted 76 % of patient-specific recommendations by a pharmacist. The prescribers' limited on-demand use indicates the necessity for developing improved safety concepts, tailored to suit these consumers. Thus, different approaches have to satisfy different target groups. Running TitleUse of an On-demand DDI Checker Keywords (MeSH terms)

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Section: Discussionmentioning

confidence: 99%

“…DDI reports should be concise, user friendly and customizable. [29] Personal options such as "remind me in one week" and "don't show this message again" could be part of this approach. In order to attract prescribers, DDI reports providing alternative management recommendations have been advocated.…”

Section: Discussionmentioning

confidence: 99%

Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

Beeler

Eschmann

Rosen³

et al. 2013

Drug Saf

View full text Add to dashboard Cite

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“…In the web application, contrary to when SFINX is integrated in the EHR, the user has to manually enter all drugs by either their trade name or active substances. SFINX delivers colour coded alerts when a drug list containing drugs with potentially harmful DDIs is entered [8]. The code is related to the SFINX-classification of interactions [8;14], which rates the clinical relevance of a possible interaction from A to D. Red colour stands for DDIs which are clinically relevant and should be avoided (D); yellow for clinically relevant interactions which could be handled by e.g.…”

Section: Setting Of the Sfinx Web Sitementioning

confidence: 99%

“…Additionally, alerts should be tiered by severity to increase user compliance and avoid alert fatigue of the users [6]. To maximize the usefulness of a DDI alert system, it should not only point out potentially harmful combinations of drugs, but also provide information on alternative strategies to meet the therapeutic needs of the patient [7][8].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of usage patterns and user perception of the drug–drug interaction database SFINX

Andersson

Böttiger

Bastholm‐Rahmner

et al. 2015

International Journal of Medical Informatics

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PurposeThe aim of the present study was to investigate how prescribers and pharmacists use and perceive the drug-drug interaction database SFINX in their clinical work. MethodsA questionnaire was developed with questions aimed at the usage of SFINX, and the perceptions of the database. The questionnaire was sent out to all registered users of the web application of SFINX.The anonymous answers from the target users, prescribers and pharmacists were summarized using descriptive statistics. Statistical analysis was performed on age and gender differences for some questions regarding different usage patterns. ResultsThe questionnaire was sent to 11 763 registered SFINX users. The response rate was 23 %, including 1871 answers from prescribers or pharmacists. SFINX was reported to be used at least weekly or more often by 45% of the prescribers and 51% of the pharmacists. Many prescribers reported using the database during the patient consultation (60%) or directly before or after (56%). Among the prescribers, 74% reported that the information received made them change their action at least sometimes. About 20% of the prescribers and 25% of the pharmacists considered the information as irrelevant sometimes or more often. ConclusionMost prescribers and pharmacists reported using SFINX in direct association with a patient consultation. Information received by using SFINX makes prescribers and pharmacists change their handling of patients. DDI databases with relevant information about patient handling might improve drug treatment outcome.

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Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

et al. 2019

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IMPORTANCE Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. OBJECTIVE To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. DESIGN, SETTING, AND PARTICIPANTS Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. INTERVENTIONS Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. MAIN OUTCOMES AND MEASURES The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. RESULTS In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. (continued) Key Points Question Can a multispecialty computerized clinical decision support system (CDSS) reduce inappropriate prescribing in a general hospital? Findings In this randomized clinical trial of 6480 patients, the CDSS alerted to a median of 3 reminders for prescription problems per patient per hospital stay. The CDSS led to a change in practice in approximately 4 of 100 patients, an...

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SFINX—a drug-drug interaction database designed for clinical decision support systems

Cited by 125 publications

References 23 publications

Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

Evaluation of usage patterns and user perception of the drug–drug interaction database SFINX

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

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