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Van De Sijpe G, Walgraeve K, Van Laer E, Quintens C, Machiels C, Foulon V, Casteels M, Van der Linden L, Spriet I. The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis. J Med Syst 2024; 48:93. [PMID: 39347841 DOI: 10.1007/s10916-024-02113-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 09/25/2024] [Indexed: 10/01/2024]
Abstract
Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.
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Affiliation(s)
- Greet Van De Sijpe
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
| | - Karolien Walgraeve
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Eva Van Laer
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Charlotte Quintens
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
| | | | - Veerle Foulon
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Minne Casteels
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Lorenz Van der Linden
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Isabel Spriet
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
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Graafsma J, Klopotowska JE, Derijks HJ, van de Garde EMW, Hoge RHL, Kruip MJHA, Meijer K, Karapinar-Carkit F, van den Bemt PMLA. Adoption of antithrombotic stewardship and utilization of clinical decision support systems-A questionnaire-based survey in Dutch hospitals. PLoS One 2024; 19:e0306033. [PMID: 38905283 PMCID: PMC11192363 DOI: 10.1371/journal.pone.0306033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/10/2024] [Indexed: 06/23/2024] Open
Abstract
Antithrombotics require careful monitoring to prevent adverse events. Safe use can be promoted through so-called antithrombotic stewardship. Clinical decision support systems (CDSSs) can be used to monitor safe use of antithrombotics, supporting antithrombotic stewardship efforts. Yet, previous research shows that despite these interventions, antithrombotics continue to cause harm. Insufficient adoption of antithrombotic stewardship and suboptimal use of CDSSs may provide and explanation. However, it is currently unknown to what extent hospitals adopted antithrombotic stewardship and utilize CDSSs to support safe use of antithrombotics. A semi-structured questionnaire-based survey was disseminated to 12 hospital pharmacists from different hospital types and regions in the Netherlands. The primary outcome was the degree of antithrombotic stewardship adoption, expressed as the number of tasks adopted per hospital and the degree of adoption per task. Secondary outcomes included characteristics of CDSS alerts used to monitor safe use of antithrombotics. All 12 hospital pharmacists completed the survey and report to have adopted antithrombotic stewardship in their hospital to a certain degree. The median adoption of tasks was two of five tasks (range 1-3). The tasks with the highest uptake were: drafting and maintenance of protocols (100%) and professional's education (58%), while care transition optimization (25%), medication reviews (8%) and patient counseling (8%) had the lowest uptake. All hospitals used a CDSS to monitor safe use of antithrombotics, mainly via basic alerts and less frequently via advanced alerts. The most frequently employed alerts were: identification of patients using a direct oral anticoagulant (DOAC) or a vitamin K antagonist (VKA) with one or more other antithrombotics (n = 6) and patients using a VKA to evaluate correct use (n = 6), both reflecting basic CDSS. All participating hospitals adopted antithrombotic stewardship, but the adopted tasks vary. CDSS alerts used are mainly basic in their logic.
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Affiliation(s)
- Jetske Graafsma
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Joanna E. Klopotowska
- Department of Medical Informatics Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Institute, Amsterdam, the Netherlands
| | | | - Ewoudt M. W. van de Garde
- Department of Pharmacy, St. Antonius Hospital, Nieuwegein, Utrecht, the Netherlands
- Division Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, the Netherlands
| | - Rien H. L. Hoge
- Department of Pharmacy, Wilhelmina Hospital, Assen, the Netherlands
- Gaston Medical, Eindhoven, the Netherlands
| | - Marieke J. H. A. Kruip
- Department of Hematology, Erasmus MC, Erasmus University medical center, Rotterdam, the Netherlands
| | - Karina Meijer
- Department of Hematology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Fatma Karapinar-Carkit
- Department of Clinical Pharmacy & Toxicology, Maastricht University Medical Center+, Maastricht, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Patricia M. L. A. van den Bemt
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Graafsma J, Murphy RM, van de Garde EMW, Karapinar-Çarkit F, Derijks HJ, Hoge RHL, Klopotowska JE, van den Bemt PMLA. The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review. J Am Med Inform Assoc 2024; 31:1411-1422. [PMID: 38641410 PMCID: PMC11105146 DOI: 10.1093/jamia/ocae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 04/21/2024] Open
Abstract
OBJECTIVE Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. MATERIALS AND METHODS We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. RESULTS Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. DISCUSSION AND CONCLUSION AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.
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Affiliation(s)
- Jetske Graafsma
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, 9713GZ, The Netherlands
| | - Rachel M Murphy
- Department of Medical Informatics Amsterdam UMC, University of Amsterdam, Amsterdam, 1000GG, The Netherlands
- Amsterdam Public Health Institute, Digital Health and Quality of Care, Amsterdam, 1105AZ, The Netherlands
| | - Ewoudt M W van de Garde
- Department of Pharmacy, St Antonius Hospital, Utrecht, 3430AM, The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, 3584CS, The Netherlands
| | - Fatma Karapinar-Çarkit
- Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center, Maastricht, 6229HX, The Netherlands
- Department of Clinical Pharmacy, CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, 6229ER, The Netherlands
| | - Hieronymus J Derijks
- Department of Pharmacy, Jeroen Bosch Hospital, Den Bosch, 5200ME, The Netherlands
| | - Rien H L Hoge
- Department of Pharmacy, Wilhelmina Hospital, Assen, 9401RK, The Netherlands
| | - Joanna E Klopotowska
- Department of Medical Informatics Amsterdam UMC, University of Amsterdam, Amsterdam, 1000GG, The Netherlands
- Amsterdam Public Health Institute, Digital Health and Quality of Care, Amsterdam, 1105AZ, The Netherlands
| | - Patricia M L A van den Bemt
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, 9713GZ, The Netherlands
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Chen Q, Wang L, Lin M, Chen W, Wu W, Chen Y. Development and implementation of medication-related clinical rules for obstetrics, gynaecology, and paediatric outpatients. Eur J Hosp Pharm 2024; 31:101-106. [PMID: 35523537 PMCID: PMC10895191 DOI: 10.1136/ejhpharm-2021-003170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/12/2022] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Prescription errors can cause serious adverse drug events. Clinical decision support systems prevent prescription errors; however, real-time clinical rules in obstetrics, gynaecology, and paediatric outpatients remain unexplored. We evaluated the effects of localised, real-time clinical rules on alert rates and acceptance rates compared with manual prescription review. METHODS We developed real-time clinical rules that incorporate information systems to obtain characteristic information and laboratory values. We conducted a retrospective cohort study to compare the alert and recommendation acceptance rates of all prescription error types before and after clinical rule implementation in obstetrics, gynaecology, and paediatrics. Clinical rules, prescription error types, and alerts were determined by a prescribing review committee comprising physicians, pharmacists, nurses, and administrators. The difference in alert and acceptance rates between the groups was analysed using relative risk. RESULTS The number of alerts increased after clinical rules implementation; the number of on-duty pharmacists for review decreased from 10 to 2. Compared with those with manual review, the alert rates for paediatrics and obstetrics and gynaecology increased with the clinical rules by 3.97- and 11.26-fold, respectively, and the alert rates for drug-drug interactions (DDIs) and combined medication errors in obstetrics and gynaecology increased with the clinical rules by 26.10- and 26.54-fold, respectively. In paediatrics, the alert rate for all prescription error types was higher with the clinical rules review than with the manual review; the alert rates for DDI, dosage, and combination medication errors were significantly different between the clinical rules and the manual review. However, there was no difference in the recommendation acceptance rate between the manual review and the clinical rules. CONCLUSIONS Clinical rules can identify prescription errors that manual review cannot detect and ensure real-time review efficiency in high-volume outpatient prescription settings. The high acceptance rate and modification of prescriptions may be relevant to highly customised and localised clinical rules.
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Affiliation(s)
- Quanyao Chen
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Luwei Wang
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Min Lin
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Weida Chen
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Wen Wu
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Yao Chen
- Department of Pharmacy, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, China
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Amaral ACKB, Cuthbertson BH. The efficiency of computerised clinical decision support systems. Lancet 2024; 403:410-411. [PMID: 38262431 DOI: 10.1016/s0140-6736(23)02839-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Affiliation(s)
| | - Brian H Cuthbertson
- Department of Critical Care Medicine, Sunnybrook Health Sciences Centres, Toronto, ON M4G 2T9, Canada.
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Potier A, Ade M, Demoré B, Divoux E, Dony A, Dufay E. Enhancing pharmaceutical decision support system: evaluating antithrombotic-focused algorithms for addressing drug-related problems. Eur J Hosp Pharm 2024:ejhpharm-2023-003944. [PMID: 38233119 DOI: 10.1136/ejhpharm-2023-003944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/26/2023] [Indexed: 01/19/2024] Open
Abstract
OBJECTIVES To evaluate the efficacy of integrating antithrombotic-focused pharmaceutical algorithms (PAs) into a pharmaceutical decision support system (PDSS) for detecting drug-related problems (DRPs) and facilitating pharmaceutical interventions. METHODS A set of 26 PAs (12.4%) out of a total of 210 were created to model patient situations involving antithrombotics, and their contributions were compared with the entire PDSS system.The observational prospective study was conducted between November 2019 and June 2023 in two health facilities with 1700 beds. Pharmacists, who followed a DRP resolution strategy to support human supervision, analysed alerts generated by these encoded PAs. They registered their interventions and the acceptance by physicians. RESULTS From 3290 alerts analysed targeting antithrombotics, the pharmacists issued 1170 interventions of which 676 (57.8%) were accepted by physicians. With the 184 other PAs, from 9484 alerts the pharmacists issued 3341 interventions of which 1785 were accepted (53.4%).Results indicate that the detection of DRPs related to antithrombotics usage represents a high proportion of those detected by the PDSS, highlighting the importance of incorporating tailored PA elements at the modelling stage. CONCLUSIONS The system evolves alongside the physiological changes associated to the patient situations, adapts the alerts and complements the current care. Therefore, we recommend that all PDSS should integrate specific algorithms targeting DRPs associated with antithrombotics to enhance pharmaceutical interventions and improve patient safety.
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Affiliation(s)
- Arnaud Potier
- Pharmacy, Centre Hospitalier de Lunéville, Lunéville, France
| | - Mathias Ade
- Pharmacy, Centre Psychothérapique de Nancy, Laxou, France
| | - Béatrice Demoré
- Pharmacy, Centre Hospitalier Universitaire de Nancy, Vandoeuvre-lès-Nancy, France
- APEMAC, Université de Lorraine, Nancy, France
| | | | - Alexandre Dony
- Service de Pharmacie, Centre Hospitalier de Lunéville, Lunéville, France
| | - Edith Dufay
- Pharmacy, Centre Hospitalier de Lunéville, Lunéville, France
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Ledger TS, Brooke-Cowden K, Coiera E. Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review. J Am Med Inform Assoc 2023; 30:2064-2071. [PMID: 37812769 PMCID: PMC10654862 DOI: 10.1093/jamia/ocad193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023] Open
Abstract
OBJECTIVES A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization. MATERIALS AND METHODS A structured search was undertaken in the MEDLINE and Embase databases, from inception to August 2023. Articles providing sufficient information to determine whether an intervention was conducted to optimize alerts were included in the analysis. Snowball analysis was conducted to identify additional studies. RESULTS Sixteen studies were identified. Most were based in the United States and used a wide range of clinical software. Many studies used inpatient cohorts and conducted more than one intervention during the trial period. Alert types studied included drug-drug interactions, drug dosage alerts, and drug allergy alerts. Six types of interventions were identified: alert inactivation, alert severity reclassification, information provision, use of contextual information, threshold adjustment, and encounter suppression. The majority of interventions decreased alert quantity and enhanced alert acceptance. Alert quantity decreased with alert inactivation by 1%-25.3%, and with alert severity reclassification by 1%-16.5% in 6 of 7 studies. Alert severity reclassification increased alert acceptance by 4.2%-50.2% and was associated with a 100% acceptance rate for high-severity alerts when implemented. Clinical errors reported in 4 studies were seen to remain stable or decrease. DISCUSSION Post-implementation medication optimization interventions have positive effects for clinicians when applied in a variety of settings. Less well reported are the impacts of these interventions on the clinical care of patients, and how endpoints such as alert quantity contribute to changes in clinician and pharmacist perceptions of alert fatigue. CONCLUSION Well conducted alert optimization can reduce alert fatigue by reducing overall alert quantity, improving clinical acceptance, and enhancing clinical utility.
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Affiliation(s)
| | - Kalissa Brooke-Cowden
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, NSW 2109, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, NSW 2109, Australia
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Calvo-Cidoncha E, Verdinelli J, González-Bueno J, López-Soto A, Camacho Hernando C, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study. JMIR Med Inform 2023; 11:e45850. [PMID: 37477131 PMCID: PMC10366962 DOI: 10.2196/45850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/05/2023] Open
Abstract
Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.
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Affiliation(s)
| | - Julián Verdinelli
- Clinical Informatics Service, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Javier González-Bueno
- Pharmacy Service, Hospital Dos de Maig, Consorci Sanitari Integral, Barcelona, Spain
| | - Alfonso López-Soto
- Geriatric Unit, Department of Internal Medicine, Hospital Clínic of Barcelona, Barcelona, Spain
| | | | - Xavier Pastor-Duran
- Clinical Informatics Service, Hospital Clínic of Barcelona, Barcelona, Spain
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van de Burgt BWM, Wasylewicz ATM, Dullemond B, Grouls RJE, Egberts TCG, Bouwman A, Korsten EMM. Combining text mining with clinical decision support in clinical practice: a scoping review. J Am Med Inform Assoc 2022; 30:588-603. [PMID: 36512578 PMCID: PMC9933076 DOI: 10.1093/jamia/ocac240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/17/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation. MATERIALS AND METHODS A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice. RESULTS Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n = 26) or prognosis (n = 11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n = 16) and free-text narratives (36%, n = 14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals. DISCUSSION/CONCLUSIONS The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice.
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Affiliation(s)
- Britt W M van de Burgt
- Corresponding Author: Britt W.M. van de Burgt, MSc, Department Healthcare Intelligence, Catharina Hospital Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, The Netherlands;
| | - Arthur T M Wasylewicz
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Bjorn Dullemond
- Department of Mathematics and Computer Science, Technical University of Eindhoven, Eindhoven, The Netherlands
| | - Rene J E Grouls
- Department of Clinical Pharmacy, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Toine C G Egberts
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, the Netherlands,Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Arthur Bouwman
- Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands,Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Erik M M Korsten
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands,Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands
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Calvo-Cidoncha E, Camacho-Hernando C, Feu F, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors. BMC Med Inform Decis Mak 2022; 22:238. [PMID: 36088328 PMCID: PMC9463735 DOI: 10.1186/s12911-022-01979-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Clinical decision support systems (CDSS) have been shown to reduce medication errors. However, they are underused because of different challenges. One approach to improve CDSS is to use ontologies instead of relational databases. The primary aim was to design and develop OntoPharma, an ontology based CDSS to reduce medication prescribing errors. Secondary aim was to implement OntoPharma in a hospital setting.
Methods
A four-step process was proposed. (1) Defining the ontology domain. The ontology scope was the medication domain. An advisory board selected four use cases: maximum dosage alert, drug-drug interaction checker, renal failure adjustment, and drug allergy checker. (2) Implementing the ontology in a formal representation. The implementation was conducted by Medical Informatics specialists and Clinical Pharmacists using Protégé-OWL. (3) Developing an ontology-driven alert module. Computerised Physician Order Entry (CPOE) integration was performed through a REST API. SPARQL was used to query ontologies. (4) Implementing OntoPharma in a hospital setting. Alerts generated between July 2020/ November 2021 were analysed.
Results
The three ontologies developed included 34,938 classes, 16,672 individuals and 82 properties. The domains addressed by ontologies were identification data of medicinal products, appropriateness drug data, and local concepts from CPOE. When a medication prescribing error is identified an alert is shown. OntoPharma generated 823 alerts in 1046 patients. 401 (48.7%) of them were accepted.
Conclusions
OntoPharma is an ontology based CDSS implemented in clinical practice which generates alerts when a prescribing medication error is identified. To gain user acceptance OntoPharma has been designed and developed by a multidisciplinary team. Compared to CDSS based on relational databases, OntoPharma represents medication knowledge in a more intuitive, extensible and maintainable manner.
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Lapp L, Egan K, McCann L, Mackenzie M, Wales A, Maguire R. Decision Support Tools in Adult Long-Term Care Facilities: A Scoping Review (Preprint). J Med Internet Res 2022; 24:e39681. [PMID: 36066928 PMCID: PMC9490521 DOI: 10.2196/39681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/14/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Digital innovations are yet to make real impacts in the care home sector despite the considerable potential of digital health approaches to help with continued staff shortages and to improve quality of care. To understand the current landscape of digital innovation in long-term care facilities such as nursing and care homes, it is important to find out which clinical decision support tools are currently used in long-term care facilities, what their purpose is, how they were developed, and what types of data they use. Objective The aim of this review was to analyze studies that evaluated clinical decision support tools in long-term care facilities based on the purpose and intended users of the tools, the evidence base used to develop the tools, how the tools are used and their effectiveness, and the types of data the tools use to contribute to the existing scientific evidence to inform a roadmap for digital innovation, specifically for clinical decision support tools, in long-term care facilities. Methods A review of the literature published between January 1, 2010, and July 21, 2021, was conducted, using key search terms in 3 scientific journal databases: PubMed, Cochrane Library, and the British Nursing Index. Only studies evaluating clinical decision support tools in long-term care facilities were included in the review. Results In total, 17 papers were included in the final review. The clinical decision support tools described in these papers were evaluated for medication management, pressure ulcer prevention, dementia management, falls prevention, hospitalization, malnutrition prevention, urinary tract infection, and COVID-19 infection. In general, the included studies show that decision support tools can show improvements in delivery of care and in health outcomes. Conclusions Although the studies demonstrate the potential of positive impact of clinical decision support tools, there is variability in results, in part because of the diversity of types of decision support tools, users, and contexts as well as limited validation of the tools in use and in part because of the lack of clarity in defining the whole intervention.
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Affiliation(s)
- Linda Lapp
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Kieren Egan
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Lisa McCann
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Moira Mackenzie
- Digital Health & Care Innovation Centre, Glasgow, United Kingdom
| | - Ann Wales
- Digital Health & Care Innovation Centre, Glasgow, United Kingdom
| | - Roma Maguire
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
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12
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Wasylewicz ATM, van de Burgt BWM, Manten T, Kerskes M, Compagner WN, Korsten EHM, Egberts TCG, Grouls RJE. Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients. Clin Pharmacol Ther 2022; 112:382-390. [PMID: 35486411 PMCID: PMC9540177 DOI: 10.1002/cpt.2624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/07/2022] [Indexed: 11/23/2022]
Abstract
Drug–drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context‐dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI‐CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.
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Affiliation(s)
- Arthur T M Wasylewicz
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.,Department of Signal Processing Systems, Faculty of Electronic Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Thomas Manten
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
| | - Marieke Kerskes
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
| | - Wilma N Compagner
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands
| | - Erik H M Korsten
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.,Department of Signal Processing Systems, Faculty of Electronic Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Toine C G Egberts
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.,Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Rene J E Grouls
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
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13
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Chien SC, Chen YL, Chien CH, Chin YP, Yoon CH, Chen CY, Yang HC, Li YC(J. Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis. Healthcare (Basel) 2022; 10:healthcare10040601. [PMID: 35455779 PMCID: PMC9028311 DOI: 10.3390/healthcare10040601] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/10/2022] Open
Abstract
A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.
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Affiliation(s)
- Shuo-Chen Chien
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Ya-Lin Chen
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Chia-Hui Chien
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Office of Public Affairs, Taipei Medical University, Taipei 110, Taiwan
| | - Yen-Po Chin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Chang Ho Yoon
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK;
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Chun-You Chen
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Radiation Oncology, Taipei Municipal Wan Fang Hospital, Taipei 110, Taiwan
- Information Technology Office in Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei 110, Taiwan
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (S.-C.C.); (Y.-L.C.); (C.-H.C.); (Y.-P.C.); (C.-Y.C.); (H.-C.Y.)
- International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Dermatology, Taipei Municipal Wan Fang Hospital, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-27361661 (ext. 7600)
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14
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Potier A, Dufay E, Dony A, Divoux E, Arnoux LA, Boschetti E, Piney D, Dupont C, Berquand I, Calvo JC, Jay N, Demoré B. Pharmaceutical algorithms set in a real time clinical decision support targeting high-alert medications applied to pharmaceutical analysis. Int J Med Inform 2022; 160:104708. [DOI: 10.1016/j.ijmedinf.2022.104708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/15/2021] [Accepted: 01/24/2022] [Indexed: 11/25/2022]
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15
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Damoiseaux-Volman BA, Medlock S, van der Meulen DM, de Boer J, Romijn JA, van der Velde N, Abu-Hanna A. Clinical validation of clinical decision support systems for medication review: A scoping review. Br J Clin Pharmacol 2021; 88:2035-2051. [PMID: 34837238 PMCID: PMC9299995 DOI: 10.1111/bcp.15160] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 01/04/2023] Open
Abstract
The aim of this scoping review is to summarize approaches and outcomes of clinical validation studies of clinical decision support systems (CDSSs) to support (part of) a medication review. A literature search was conducted in Embase and Medline. In total, 30 articles validating a CDSS were ultimately included. Most of the studies focused on detection of adverse drug events, potentially inappropriate medications and drug‐related problems. We categorized the included articles in three groups: studies subjectively reviewing the clinical relevance of CDSS's output (21/30 studies) resulting in a positive predictive value (PPV) for clinical relevance of 4–80%; studies determining the relationship between alerts and actual events (10/30 studies) resulting in a PPV for actual events of 5–80%; and studies comparing output of CDSSs to chart/medication reviews in the whole study population (10/30 studies) resulting in a sensitivity of 28–85% and specificity of 42–75%. We found heterogeneity in the methods used and in the outcome measures. The validation studies did not report the use of a published CDSS validation strategy. To improve the effectiveness and uptake of CDSSs supporting a medication review, future research would benefit from a more systematic and comprehensive validation strategy.
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Affiliation(s)
- Birgit A Damoiseaux-Volman
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Stephanie Medlock
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Delanie M van der Meulen
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Jesse de Boer
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Johannes A Romijn
- Department of Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Nathalie van der Velde
- Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
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16
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Cuvelier E, Robert L, Musy E, Rousselière C, Marcilly R, Gautier S, Odou P, Beuscart JB, Décaudin B. The clinical pharmacist's role in enhancing the relevance of a clinical decision support system. Int J Med Inform 2021; 155:104568. [PMID: 34537687 DOI: 10.1016/j.ijmedinf.2021.104568] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 08/18/2021] [Accepted: 08/31/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Clinical decision support systems (CDSSs) can improve the quality of patient care by helping physicians to review their prescriptions and thus to optimize drug treatments. Nevertheless, the "alert fatigue" brought on by a large number of irrelevant alerts can decrease a CDSS's effectiveness and thus clinical value. Involving a clinical pharmacist in the development and management of a CDSS can reduce the number of irrelevant alerts presented to physicians. Clinical pharmacists screen alerts and suggest PIs for physicians, corresponding to any proposed therapeutic change about health products, only for relevant alerts could improve the relevance and the acceptance of the information given to physicians about the risks faced by their patients. OBJECTIVE To assess the value of involving clinical pharmacists in the development and maintenance of decision support rules for generating alerts and pharmaceutical interventions (PIs) and to describe the level of acceptance of these PIs by the physicians. METHOD In a retrospective, single-centre study, we evaluated the number of PIs accepted from alerts generated by the CDSS when a clinical pharmacist had developed and managed this tool. During the first 7 months of development of the CDSS, a clinical pharmacist analyzed alerts triggered by the CDSS according to its technical validity and pharmaceutical relevance. Lastly, for alerts that led to a PI, the level of acceptance by physicians was documented. RESULTS During the study, 1430 alerts were analysed: 186 (13%) were considered to be technically invalid - mainly due to the characteristics of the interface. Of the 1244 (87.0%) technically valid alerts, 353 (24.6%) were pharmaceutically relevant and led to a PI. The three main causes of pharmaceutical irrelevance were a lack of specificity in the CDSS (70.8%), lack of relevance with regard to the ward's habits (15.6%), and the pharmacist's decision to recommend monitoring for the patient rather than sending a PI immediately (10.8%). 64.6% of the submitted PIs were accepted by the physicians. CONCLUSION The standardized analysis of alerts by a clinical pharmacist appears to be a good way of improving the development of CDSS by limiting the generation of irrelevant alerts and the latter's transmission to physicians. The involvement of a clinical pharmacist in the development and implementation of a CDSS appears to be novel and may help to optimize drug treatment.
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Affiliation(s)
- E Cuvelier
- Univ. Lille, CHU Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, F-59000 Lille, France.
| | - L Robert
- CHU Lille, Institut de Pharmacie, F-59000 Lille, France.
| | - E Musy
- CHU Lille, Institut de Pharmacie, F-59000 Lille, France.
| | - C Rousselière
- CHU Lille, Institut de Pharmacie, F-59000 Lille, France.
| | - R Marcilly
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; INSERM, CIC-IT 1403, F-59000 Lille, France.
| | - S Gautier
- Univ. Lille, CHU Lille, INSERM U1171 - Centre Régional de Pharmacovigilance, F-59000 Lille, France.
| | - P Odou
- Univ. Lille, CHU Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, F-59000 Lille, France.
| | - J-B Beuscart
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France.
| | - B Décaudin
- Univ. Lille, CHU Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, F-59000 Lille, France.
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17
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Diesveld MME, de Klerk S, Cornu P, Strobach D, Taxis K, Borgsteede SD. Management of drug-disease interactions: a best practice from the Netherlands. Int J Clin Pharm 2021; 43:1437-1450. [PMID: 34273048 DOI: 10.1007/s11096-021-01308-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 07/12/2021] [Indexed: 11/28/2022]
Abstract
Background Drug-disease interactions are situations where pharmacotherapy may have a negative effect on patients' comorbidities. In these cases, it can be necessary to avoid that drug, adjust its dose or monitor therapy. In the Netherlands, pharmacists have developed a best practice how to systematically evaluate drug-disease interactions based on pharmacological considerations and implement recommendations for specific drug-disease interactions. Aim To describe the development of recommendations for drug-disease interactions and the implementation in prescribing and dispensing practice in the Netherlands. Setting Pharmacies and physicians' practices in primary care and hospitals in the Netherlands. Development A multi-disciplinary expert panel assessed if diseases had clinically relevant drug-disease interactions and evaluated drug-disease interactions by literature review and expert opinion, and subsequently developed practice recommendations. Implementation The recommendations were implemented in all clinical decision support systems in primary care and hospitals throughout the Netherlands. Evaluation Recommendations were developed for 57 diseases and conditions. Cardiovascular diseases have the most drug-disease interactions (n = 12, e.g. long QT-syndrome, heart failure), followed by conditions related to the reproductive system (n = 7, e.g. pregnancy). The number of drugs with recommendations differed between 6 for endometriosis and tympanostomy tubes, and up to 1171 in the case of porphyria or even all drugs for pregnancy. Conclusion Practice recommendations for drug-disease interactions were developed, and implemented in prescribing and dispensing practice. These recommendations support both pharmacists and physicians by signalling clinically relevant drug-disease interactions at point of care, thereby improving medication safety. This practice may be adopted and contribute to safer medication use in other countries as well.
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Affiliation(s)
- Maaike M E Diesveld
- Department of Clinical Decision Support, Health Base Foundation, Papiermolen 36, 3994DK, Houten, the Netherlands
| | - Suzanne de Klerk
- Department of Clinical Decision Support, Health Base Foundation, Papiermolen 36, 3994DK, Houten, the Netherlands
| | - Pieter Cornu
- Research Group Clinical Pharmacology and Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium.,Department of Medical Informatics, UZ Brussel, Brussels, Belgium
| | - Dorothea Strobach
- Hospital Pharmacy and Doctoral Programme Clinical Pharmacy, University Hospital Munich, Munich, Germany
| | - Katja Taxis
- Department of Pharmacy, Unit of Pharmacotherapy, Epidemiology and Economics, University of Groningen, Groningen, the Netherlands
| | - Sander D Borgsteede
- Department of Clinical Decision Support, Health Base Foundation, Papiermolen 36, 3994DK, Houten, the Netherlands.
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18
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Yan L, Reese T, Nelson SD. A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists. Appl Clin Inform 2021; 12:199-207. [PMID: 33730757 PMCID: PMC7968988 DOI: 10.1055/s-0041-1722916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. METHODS We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. CONCLUSION These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.
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Affiliation(s)
- Liang Yan
- University of Utah College of Pharmacy, University of Utah Health, Salt Lake City, Utah, United States
| | - Thomas Reese
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, United States
| | - Scott D. Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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19
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Berger FA, van der Sijs H, van Gelder T, van den Bemt PMLA. The use of a clinical decision support tool to assess the risk of QT drug-drug interactions in community pharmacies. Ther Adv Drug Saf 2021; 12:2042098621996098. [PMID: 33708374 PMCID: PMC7907715 DOI: 10.1177/2042098621996098] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/22/2021] [Indexed: 01/05/2023] Open
Abstract
Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.
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Affiliation(s)
- Florine A Berger
- Department of Hospital Pharmacy, Erasmus University Medical Centre, Department of Hospital Pharmacy, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Heleen van der Sijs
- Department of Hospital Pharmacy, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Teun van Gelder
- Department of Hospital Pharmacy, Erasmus University Medical Centre, Rotterdam, The Netherlands
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Chen J, Chokshi S, Hegde R, Gonzalez J, Iturrate E, Aphinyanaphongs Y, Mann D. Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination. J Med Internet Res 2020; 22:e16848. [PMID: 32347813 PMCID: PMC7221637 DOI: 10.2196/16848] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/19/2020] [Accepted: 02/21/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general. OBJECTIVE This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. METHODS We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. RESULTS During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). CONCLUSIONS All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.
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Affiliation(s)
- Ji Chen
- Department of Population Health, New York University School of Medicine, New York, NY, United States
| | - Sara Chokshi
- Department of Population Health, New York University School of Medicine, New York, NY, United States
| | - Roshini Hegde
- Department of Population Health, New York University School of Medicine, New York, NY, United States
| | - Javier Gonzalez
- Medical Center Information Technology, New York University Langone Health, New York, NY, United States
| | - Eduardo Iturrate
- Clinical Informatics, New York University School of Medicine, New York, NY, United States
| | - Yin Aphinyanaphongs
- Department of Population Health, New York University School of Medicine, New York, NY, United States
| | - Devin Mann
- Department of Population Health, New York University School of Medicine, New York, NY, United States
- Medical Center Information Technology, New York University Langone Health, New York, NY, United States
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21
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Reducing Inappropriate Drug Use in Older Patients by Use of Clinical Decision Support in Community Pharmacy: A Mixed-Methods Evaluation. Drugs Aging 2019; 37:115-123. [DOI: 10.1007/s40266-019-00728-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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22
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Quintens C, De Rijdt T, Van Nieuwenhuyse T, Simoens S, Peetermans WE, Van den Bosch B, Casteels M, Spriet I. Development and implementation of "Check of Medication Appropriateness" (CMA): advanced pharmacotherapy-related clinical rules to support medication surveillance. BMC Med Inform Decis Mak 2019; 19:29. [PMID: 30744674 PMCID: PMC6371500 DOI: 10.1186/s12911-019-0748-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/22/2019] [Indexed: 11/18/2022] Open
Abstract
Background To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called “Check of Medication Appropriateness” (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians. Methods CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Pharmacists’ actions are performed by adding an electronic note in the patients’ medical record or by contacting the physician by phone. A retrospective observational study was performed to evaluate the primary outcomes during an 18-month study period. Results 39,481 clinical rule alerts were checked by pharmacists for which 2568 (7%) electronic notes were sent and 637 (1.6%) phone calls were performed. 37,782 (96%) alerts were checked within four pharmacotherapeutic categories: drug use in renal insufficiency (25%), QTc interval prolonging drugs (11%), drugs with a restricted indication or dosing (14%) and overruled very severe drug-drug interactions (50%). The emergency department was a frequently involved ward and anticoagulants are the drug class for which actions are most frequently carried out. From the 458 actions performed for the four abovementioned categories, 69% were accepted by physicians. Conclusions These results demonstrate the added value of CMA to support medication surveillance in synergy with already integrated basic clinical decision support and bedside clinical pharmacy. Otherwise, the study also highlighted a number of limitations, allowing improvement of the service. Electronic supplementary material The online version of this article (10.1186/s12911-019-0748-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Charlotte Quintens
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium. .,Pharmacy Department, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium.
| | - Thomas De Rijdt
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.,Pharmacy Department, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Tine Van Nieuwenhuyse
- Pharmacy Department, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Steven Simoens
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Willy E Peetermans
- Department of Microbiology and Immunology, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.,Department of General Internal Medicine, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Bart Van den Bosch
- Department of Public Health and Primary Care, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.,Department of Information Technology, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Minne Casteels
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Isabel Spriet
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.,Pharmacy Department, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
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23
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Jefferies C, Rhodes E, Rachmiel M, Agwu JC, Kapellen T, Abdulla MA, Hofer SE. ISPAD Clinical Practice Consensus Guidelines 2018: Management of children and adolescents with diabetes requiring surgery. Pediatr Diabetes 2018; 19 Suppl 27:227-236. [PMID: 30039617 DOI: 10.1111/pedi.12733] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 07/10/2018] [Indexed: 12/13/2022] Open
Affiliation(s)
- Craig Jefferies
- Starship Children's Health, Auckland District Health Board, Auckland, New Zealand
| | - Erinn Rhodes
- Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts
| | - Marianna Rachmiel
- Assaf Haroffeh Medical Center, Zerifin, Sackler School of Medicine, Tel Aviv University, Israel
| | - Juliana C Agwu
- Department of Paediatrics, Sandwell and West Birmingham NHS Trust, Birmingham, UK
| | - Thomas Kapellen
- Department for Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
| | | | - Sabine E Hofer
- Department of Pediatrics, Medical University of Innsbruck, Innsbruck, Austria
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Kuipers E, Wensing M, De Smet PA, Teichert M. Considerations of prescribers and pharmacists for the use of non-selective β-blockers in asthma and COPD patients: An explorative study. J Eval Clin Pract 2018; 24:396-402. [PMID: 29319215 PMCID: PMC5901013 DOI: 10.1111/jep.12869] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 11/11/2017] [Accepted: 11/13/2017] [Indexed: 11/28/2022]
Abstract
RATIONALE, AIMS, AND OBJECTIVES Despite recommendations in prevailing guidelines to avoid the use of non-selective (NS) β-blockers in patients with asthma or COPD, on average, 10 patients per community pharmacy receive NS β-blockers monthly. The aim of our study was to identify the reasons of prescribers and pharmacists to treat asthma and COPD patients with NS β-blockers. METHODS Fifty-three community pharmacists in the Netherlands selected patients with actual concurrent use of inhalation medication and NS β-blockers. For at least 5 patients, each pharmacist screened all medication surveillance signals and actions taken at first dispensing. Each pharmacist selected 3 different initial prescribers for a short interview to explore their awareness of the co-morbidity and reasons to apply NS β-blockers. RESULTS Pharmacists identified 827 asthma/COPD patients with actual use of NS β-blockers. From these, 153 NS β-blocker prescribers were selected and interviewed (64 general practitioners, 45 ophthalmologists, 24 cardiologists, and 20 other prescribers). One hundred seven prescribers were aware of the drug-disease interaction of the asthma or COPD co-morbidity when initiating the NS β-blocker, and 46 were not. From these, 40 prescribers did not consider the contraindication to be relevant. For 299 patients, medication surveillance signals and actions at first dispensing were retrieved. Patients used predominantly ocular timolol (39.8%), and the oral preparations propranolol (30.8%) and carvedilol (15.1%). In 154 cases, the pharmacy system generated a warning alert. CONCLUSIONS A substantial number of prescribers was unaware of the co-morbidity or did not regard NS β-blockers contraindicated, despite prevailing clinical guidelines. Improvement programs should target prescribers' awareness and knowledge of NS β-blockers in patients with asthma or COPD.
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Affiliation(s)
- Esther Kuipers
- Department of IQ Healthcare, Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenThe Netherlands
- BENU Apotheek Zeist WestZeistThe Netherlands
| | - Michel Wensing
- Department of IQ Healthcare, Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenThe Netherlands
- Department of General Practice and Health Services ResearchUniversity Hospital HeidelbergHeidelbergGermany
| | - Peter A.G.M. De Smet
- Department of IQ Healthcare, Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenThe Netherlands
- Department of Clinical Pharmacy, Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenThe Netherlands
| | - Martina Teichert
- Department of IQ Healthcare, Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenThe Netherlands
- Department of Clinical Pharmacy & ToxicologyLeiden University Medical CentreLeidenThe Netherlands
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25
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Heringa M, Floor-Schreudering A, De Smet PAGM, Bouvy ML. Clinical Decision Support and Optional Point of Care Testing of Renal Function for Safe Use of Antibiotics in Elderly Patients: A Retrospective Study in Community Pharmacy Practice. Drugs Aging 2018; 34:851-858. [PMID: 29119468 PMCID: PMC5705753 DOI: 10.1007/s40266-017-0497-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Objective The aim was to investigate the management of drug therapy alerts on safe use of antibiotics in elderly patients with (potential) renal impairment and the contribution of optional creatinine point of care testing (PoCT) in community pharmacy practice. Methods Community pharmacists used a clinical decision support system (CDSS) for seven antibiotics. Alerts were generated during prescription processing in the case of previously registered renal impairment and when no information on renal function was available for patients aged 70 and over. Pharmacists could perform PoCT when renal function could not be retrieved from other health care professionals. Actions were registered in the CDSS. A retrospective descriptive analysis of alert management, performed PoCT and medication dispensing histories was performed. Results A total of 351 pharmacists registered the management of 88,391 alerts for 64,763 patients. For 68,721 alerts (77.7%), the pharmacist retrieved a renal function above the threshold for intervention. 1.7% of the alerts (n = 1532) led to a prescription modification because of renal impairment; in 3.0% of the alerts (n = 2631), the patient had renal impairment, but the pharmacist judged that no intervention was needed. Pharmacists performed 1988 PoCTs (2.2% of the alerts), which led to 15 prescription modifications (0.8% of the PoCT). Conclusion Community pharmacists performed CDSS-based interventions to prevent potentially inappropriate (dosing of) antibiotics in elderly patients with renal impairment. Pharmacists were well able to retrieve information on renal function, using PoCT in a limited number of cases. The intervention rate could be greatly increased by better registration of information on renal function. Performing PoCT seems especially worthwhile in the highest age groups.
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Affiliation(s)
- Mette Heringa
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5B, 2331 JE, Leiden, The Netherlands. .,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands. .,Health Base Foundation, Houten, The Netherlands.
| | - Annemieke Floor-Schreudering
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5B, 2331 JE, Leiden, The Netherlands.,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Peter A G M De Smet
- Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, Nijmegen, The Netherlands
| | - Marcel L Bouvy
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5B, 2331 JE, Leiden, The Netherlands.,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
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26
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Carli D, Fahrni G, Bonnabry P, Lovis C. Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review. JMIR Med Inform 2018; 6:e3. [PMID: 29367187 PMCID: PMC5803531 DOI: 10.2196/medinform.7170] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 08/25/2017] [Accepted: 09/16/2017] [Indexed: 02/03/2023] Open
Abstract
Background Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts. Objective The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE). Methods A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated. Results Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%. Conclusions There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.
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Affiliation(s)
- Delphine Carli
- Division of Pharmacy, University Hospitals of Geneva, Geneva, Switzerland.,School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland
| | - Guillaume Fahrni
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
| | - Pascal Bonnabry
- Division of Pharmacy, University Hospitals of Geneva, Geneva, Switzerland.,School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland
| | - Christian Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.,School of Medicine, University of Geneva, Geneva, Switzerland
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Jung M, Hackl WO, Kirrane F, Borbolla D, Jaspers MW, Oertle M, Koutkias V, Ferret L, Massari P, Lawton K, Riedmann D, Darmoni S, Maglaveras N, Lovis C, Ammenwerth E, Hoerbst A. Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems. Methods Inf Med 2018. [DOI: 10.3414/me12-02-0007] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
SummaryObjectives: To analyze the attitude of physicians towards alerting in CPOE systems in different hospitals in different countries, addressing various organizational and technical settings and the view of physicians not currently using a CPOE.Methods: A cross-sectional quantitative and qualitative questionnaire survey. We invited 2,600 physicians in eleven hospitals from nine countries to participate. Eight of the hospitals had different CPOE systems in use, and three of the participating hospitals were not using a CPOE system.Results: 1,018 physicians participated. The general attitude of the physicians towards CPOE alerting is positive and is found to be mostly independent of the country, the specific organizational settings in the hospitals and their personal experience with CPOE systems. Both quantitative and qualitative results show that the majority of the physicians, both CPOE-users and non-users, appreciate the benefits of alerting in CPOE systems on medication safety. However, alerting should be better adapted to the clinical context and make use of more sophisticated ways to present alert information. The vast majority of physicians agree that additional information regarding interactions is useful on demand. Around half of the respondents see possible alert overload as a major problem; in this regard, physicians in hospitals with sophisticated alerting strategies show partly better attitude scores.Conclusions: Our results indicate that the way alerting information is presented to the physicians may play a role in their general attitude towards alerting, and that hospitals with a sophisticated alerting strategy with less interruptive alerts tend towards more positive attitudes. This aspect needs to be further investigated in future studies.
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Heringa M, van der Heide A, Floor-Schreudering A, De Smet PAGM, Bouvy ML. Better specification of triggers to reduce the number of drug interaction alerts in primary care. Int J Med Inform 2017; 109:96-102. [PMID: 29195711 DOI: 10.1016/j.ijmedinf.2017.11.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 11/07/2017] [Accepted: 11/09/2017] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Drug interaction alerts (drug-drug and drug-disease interaction alerts) for chronic medications substantially contribute to alert fatigue in primary care. The aim of this study was to determine which events require (re)assessment of a drug interaction and whether using these events as triggers in clinical decision support systems (CDSSs) would affect the alert rate. METHODS Two random 5% data samples from the CDSSs of 123 community pharmacies were used: dataset 1 and 2. The top 10 of most frequent drug interaction alerts not involving laboratory values were selected. To reach consensus on events that should trigger alerts (e.g. first time dispensing, dose modification) for these drug interactions, a two-step consensus process was used. An expert panel of community pharmacists participated in an online survey and a subsequent consensus meeting. A CDSS with alerts based on the consensus was simulated in both datasets. RESULTS Dataset 1 and 2 together contained 1,672,169 prescriptions which led to 591,073 alerts. Consensus on events requiring alerts was reached for the ten selected drug interactions. The simulation showed a reduction of the alert rate of 93.0% for the ten selected drug interactions (comparable for dataset 1 and 2), corresponding with a 28.3% decrease of the overall drug interaction alert rate. CONCLUSION By consensus-based better specification of the events that trigger drug interaction alerts in primary care, the alert rate for these drug interactions was reduced by over 90%. This promising approach deserves further investigation to assess its consequences and applicability in daily practice.
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Affiliation(s)
- Mette Heringa
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands; Health Base Foundation, Papiermolen 36, 3994 DK Houten, The Netherlands.
| | - Annet van der Heide
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands.
| | - Annemieke Floor-Schreudering
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands.
| | - Peter A G M De Smet
- Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Marcel L Bouvy
- SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands.
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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 2017; 45:1481-1488. [PMID: 28682835 DOI: 10.1097/ccm.0000000000002580] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVE To provide ICU clinicians with evidence-based guidance on tested interventions that reduce or prevent alert fatigue within clinical decision support systems. DESIGN Systematic review of PubMed, Embase, SCOPUS, and CINAHL for relevant literature from 1966 to February 2017. PATIENTS Focus on critically ill patients and included evaluations in other patient care settings, as well. INTERVENTIONS Identified interventions designed to reduce or prevent alert fatigue within clinical decision support systems. MEASUREMENTS AND MAIN RESULTS Study selection was based on one primary key question to identify effective interventions that attempted to reduce alert fatigue and three secondary key questions that covered the negative effects of alert fatigue, potential unintended consequences of efforts to reduce alert fatigue, and ideal alert quantity. Data were abstracted by two reviewers independently using a standardized abstraction tool. Surveys, meeting abstracts, "gray" literature, studies not available in English, and studies with non-original data were excluded. For the primary key question, articles were excluded if they did not provide a comparator as key question 1 was designed as a problem, intervention, comparison, and outcome question. We anticipated that reduction in alert fatigue, including the concept of desensitization may not be directly measured and thus considered interventions that reduced alert quantity as a surrogate marker for alert fatigue. Twenty-six articles met the inclusion criteria. CONCLUSION Approaches for managing alert fatigue in the ICU are provided as a result of reviewing tested interventions that reduced alert quantity with the anticipated effect of reducing fatigue. Suggested alert management strategies include prioritizing alerts, developing sophisticated alerts, customizing commercially available alerts, and including end user opinion in alert selection. Alert fatigue itself is studied less frequently, as an outcome, and there is a need for more precise evaluation. Standardized metrics for alert fatigue is needed to advance the field. Suggestions for standardized metrics are provided in this document.
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Meystre SM, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann CU. Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearb Med Inform 2017; 26:38-52. [PMID: 28480475 PMCID: PMC6239225 DOI: 10.15265/iy-2017-007] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Indexed: 12/30/2022] Open
Abstract
Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.
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Affiliation(s)
- S. M. Meystre
- Medical University of South Carolina, Charleston, SC, USA
| | - C. Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Switzerland
| | - T. Bürkle
- University of Applied Sciences, Bern, Switzerland
| | - G. Tognola
- Institute of Electronics, Computer and Telecommunication Engineering, Italian Natl. Research Council IEIIT-CNR, Milan, Italy
| | - A. Budrionis
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
| | - C. U. Lehmann
- Departments of Biomedical Informatics and Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
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de Wit HAJM, Hurkens KPGM, Mestres Gonzalvo C, Smid M, Sipers W, Winkens B, Mulder WJ, Janknegt R, Verhey FR, van der Kuy PHM, Schols JMGA. The support of medication reviews in hospitalised patients using a clinical decision support system. SPRINGERPLUS 2016; 5:871. [PMID: 27386320 PMCID: PMC4920784 DOI: 10.1186/s40064-016-2376-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 05/20/2016] [Indexed: 12/24/2022]
Abstract
Objectives First, to estimate the added value of a clinical decision support system (CDSS) in the performance of medication reviews in hospitalised elderly. Second, to identify the limitations of the current CDSS by analysing generated drug-related problems (DRPs). Methods Medication reviews were performed in patients admitted to the geriatric ward of the Zuyderland medical centre. Additionally, electronically available patient information was introduced into a CDSS. The DRP notifications generated by the CDSS were compared with those found in the medication review. The DRP notifications were analysed to learn how to improve the CDSS. Results A total of 223 DRP strategies were identified during the medication reviews. The CDSS generated 70 clinically relevant DRP notifications. Of these DRP notifications, 63 % (44) were also found during the medication reviews. The CDSS generated 10 % (26) new DRP notifications and conveyed 28 % (70) of all 249 clinically relevant DRPs that were found. Classification of the CDSS generated DRP notifications related to ‘medication error type’ revealed that ‘contraindications/interactions/side effects’ and ‘indication without medication’ were the main categories not identified during the manual medication review. The error types ‘medication without indication’, ‘double medication’, and ‘wrong medication’ were mostly not identified by the CDSS. Conclusions The CDSS used in this study is not yet sufficiently advanced to replace the manual medication review, though it does add value to the manual medication review. The strengths and weaknesses of the current CDSS can be determined according to the medication error types.
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Affiliation(s)
- Hugo A J M de Wit
- Department of Clinical Pharmacy and Toxicology, Zuyderland Medical Centre, Henri Dunantstraat 5, 6419 PC Heerlen, The Netherlands
| | - Kim P G M Hurkens
- Department of Internal Medicine, Section of Geriatric Medicine, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Carlota Mestres Gonzalvo
- Department of Clinical Pharmacy and Toxicology, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Machiel Smid
- Department of Internal Medicine, Section of Geriatric Medicine, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Walther Sipers
- Department of Internal Medicine, Section of Geriatric Medicine, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, CAPHRI-School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
| | - Wubbo J Mulder
- Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Rob Janknegt
- Department of Clinical Pharmacy and Toxicology, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Frans R Verhey
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg/School for Mental Health and Neurosciences, Maastricht University, Maastricht, The Netherlands
| | - Paul-Hugo M van der Kuy
- Department of Clinical Pharmacy and Toxicology, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Jos M G A Schols
- Department of General Practice and Department of Health Services Research, CAPHRI-School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
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Heringa M, Siderius H, Floor-Schreudering A, de Smet PAGM, Bouvy ML. Lower alert rates by clustering of related drug interaction alerts. J Am Med Inform Assoc 2016; 24:54-59. [PMID: 27107437 DOI: 10.1093/jamia/ocw049] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/29/2016] [Accepted: 03/05/2016] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE We aimed to investigate to what extent clustering of related drug interaction alerts (drug-drug and drug-disease interaction alerts) would decrease the alert rate in clinical decision support systems (CDSSs). METHODS We conducted a retrospective analysis of drug interaction alerts generated by CDSSs in community pharmacies. Frequently generated combinations of alerts were analyzed for associations in a 5% random data sample (dataset 1). Alert combinations with similar management recommendations were defined as clusters. The alert rate was assessed by simulating a CDSS generating 1 alert per cluster per patient instead of separate alerts. The simulation was performed in dataset 1 and replicated in another 5% data sample (dataset 2). RESULTS Data were extracted from the CDSSs of 123 community pharmacies. Dataset 1 consisted of 841 572 dispensed prescriptions and 298 261 drug interaction alerts. Dataset 2 was comparable. Twenty-two frequently occurring alert combinations were identified. Analysis of these associated alert combinations for similar management recommendations resulted in 3 clusters (related to renal function, electrolytes, diabetes, and cardiovascular diseases). Using the clusters in alert generation reduced the alert rate within these clusters by 53-70%. The overall number of drug interaction alerts was reduced by 11% in dataset 1 and by 12% in dataset 2. This corresponds to a decrease of 21 alerts per pharmacy per day. DISCUSSION AND CONCLUSION Using clusters of drug interaction alerts with similar management recommendations in CDSSs can substantially decrease the overall alert rate. Further research is needed to establish the applicability of this concept in daily practice.
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Affiliation(s)
- Mette Heringa
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands .,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands.,Health Base Foundation, Houten, the Netherlands
| | - Hidde Siderius
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
| | - Annemieke Floor-Schreudering
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands.,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
| | - Peter A G M de Smet
- Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, Nijmegen, the Netherlands
| | - Marcel L Bouvy
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands.,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
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Heringa M, Floor-Schreudering A, Tromp PC, de Smet PAGM, Bouvy ML. Nature and frequency of drug therapy alerts generated by clinical decision support in community pharmacy. Pharmacoepidemiol Drug Saf 2015; 25:82-9. [DOI: 10.1002/pds.3915] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Revised: 09/17/2015] [Accepted: 10/19/2015] [Indexed: 11/11/2022]
Affiliation(s)
- Mette Heringa
- SIR Institute for Pharmacy Practice and Policy; Leiden The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
- Health Base Foundation; Houten The Netherlands
| | - Annemieke Floor-Schreudering
- SIR Institute for Pharmacy Practice and Policy; Leiden The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
| | | | - Peter A. G. M. de Smet
- Departments of Clinical Pharmacy and IQ Healthcare; University Medical Centre St Radboud; Nijmegen The Netherlands
| | - Marcel L. Bouvy
- SIR Institute for Pharmacy Practice and Policy; Leiden The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
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Shelton JB, Ochotorena L, Bennett C, Shekelle P, Kwan L, Skolarus T, Goldzweig C. Reducing PSA-Based Prostate Cancer Screening in Men Aged 75 Years and Older with the Use of Highly Specific Computerized Clinical Decision Support. J Gen Intern Med 2015; 30:1133-9. [PMID: 25740462 PMCID: PMC4510234 DOI: 10.1007/s11606-015-3249-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Revised: 10/10/2014] [Accepted: 02/10/2015] [Indexed: 11/28/2022]
Abstract
INTRODUCTION In 2012, the Veterans Health Administration (VHA) implemented guidelines seeking to reduce PSA-based screening for prostate cancer in men aged 75 years and older. OBJECTIVES To reduce the use of inappropriate PSA-based prostate cancer screening among men aged 75 and over. SETTING The Veterans Affairs Greater Los Angeles Healthcare System (VA GLA) PROGRAM DESCRIPTION: We developed a highly specific computerized clinical decision support (CCDS) alert to remind providers, at the moment of PSA screening order entry, of the current guidelines and institutional policy. We implemented the tool in a prospective interrupted time series study design over 15 months, and compared the trends in monthly PSA screening rate at baseline to the CCDS on and off periods of the intervention. RESULTS A total of 30,150 men were at risk, or eligible, for screening, and 2,001 men were screened. The mean monthly screening rate during the 15-month baseline period was 8.3%, and during the 15-month intervention period, was 4.6%. The screening rate declined by 38% during the baseline period and by 40% and 30%, respectively, during the two periods when the CCDS tool was turned on. The screening rate ratios for the baseline and two periods when the CCDS tool was on were 0.97, 0.78, and 0.90, respectively, with a significant difference between baseline and the first CCDS-on period (p < 0.0001), and a trend toward a difference between baseline and the second CCDS-on period (p = 0.056). CONCLUSION Implementation of a highly specific CCDS tool alone significantly reduced inappropriate PSA screening in men aged 75 years and older in a reproducible fashion. With this simple intervention, evidence-based guidelines were brought to bear at the point of care, precisely for the patients and providers for whom they were most helpful, resulting in more appropriate use of medical resources.
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Affiliation(s)
- Jeremy B Shelton
- Veterans Administration Greater Los Angeles Healthcare System, Los Angeles, CA, USA,
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de Wit HA, Mestres Gonzalvo C, Cardenas J, Derijks HJ, Janknegt R, van der Kuy PHM, Winkens B, Schols JM. Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients. Int J Med Inform 2015; 84:396-405. [DOI: 10.1016/j.ijmedinf.2015.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 02/07/2015] [Accepted: 02/10/2015] [Indexed: 11/25/2022]
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Heringa M, Floor A, Meijer WM, De Smet PAGM, Bouvy ML. Nature and management of duplicate medication alerts. J Am Med Inform Assoc 2015; 22:831-7. [PMID: 25862764 DOI: 10.1093/jamia/ocv021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Accepted: 02/25/2015] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE To investigate the nature of duplicate medication (DM) alerts, their management by community pharmacists, and potential characteristics of DM alerts that lead to interventions by pharmacists. METHODS Observational study in 53 community pharmacies. Each pharmacist registered the nature and management of 24 DM alerts on a structured form. RESULTS On average, the clinical decision support systems generated 20.4 DM alerts per 100 dispensed drugs. In half of the 1272 registered alerts, the pharmacists judged that there was no risk for concurrent use of both prescriptions. In 32% of the alerts, the DM alert was generated for an intentional combination. In 17% of the alerts, there was a risk for unintentional concurrent use. In 32% of the alerts the pharmacists decided that one or more actions were needed: the electronic patient record was updated in 15% of the alerts and in 19% of the alerts the pharmacists performed an external action-for example, informing the patient or modifying the prescription (including 5 therapeutic prescription modifications and 22 logistic prescription modifications). Alerts concerning first dispensing were more likely to be followed by an external action than alerts concerning refills (40% vs 14%, P < .001). DISCUSSION AND CONCLUSION In community pharmacy, prescription modifications based on DM alerts are rare, but DM alerts lead with some regularity to other actions-for example, patient instruction and update of the electronic patient record. As the current DM alerts are diverse and nonspecific in detecting situations where external action is considered relevant, other ways of alerting should therefore be considered.
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Affiliation(s)
- Mette Heringa
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
| | - Annemieke Floor
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
| | | | - Peter A G M De Smet
- Royal Dutch Pharmacists Association (KNMP), The Hague, the Netherlands Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, Nijmegen, the Netherlands
| | - Marcel L Bouvy
- SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands
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de Wit HAJM, Winkens B, Mestres Gonzalvo C, Hurkens KPGM, Janknegt R, Schols JMGA, van der Kuy PHM. Clinical practice of medication reviews in institutional care settings for older people in the Netherlands: an explorative survey. Eur J Hosp Pharm 2014. [DOI: 10.1136/ejhpharm-2014-000503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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Woods AD, Mulherin DP, Flynn AJ, Stevenson JG, Zimmerman CR, Chaffee BW. 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 2013; 21:569-73. [PMID: 24253195 DOI: 10.1136/amiajnl-2013-002008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The specificity of medication-related alerts must be improved to overcome the pernicious effects of alert fatigue. A systematic comparison of new drug orders to historical orders could improve alert specificity and relevance. Using historical order data from a computerized provider order entry system, we alerted physicians to atypical orders during the prescribing of five medications: calcium, clopidogrel, heparin, magnesium, and potassium. The percentage of atypical orders placed for these five medications decreased during the 92 days the alerts were active when compared to the same period in the previous year (from 0.81% to 0.53%; p=0.015). Some atypical orders were appropriate. Fifty of the 68 atypical order alerts were over-ridden (74%). However, the over-ride rate is misleading because 28 of the atypical medication orders (41%) were changed. Atypical order alerts were relatively few, identified problems with frequencies as well as doses, and had a higher specificity than dose check alerts.
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Affiliation(s)
- Allie D Woods
- American Society of Health-System Pharmacists, Wisconsin Avenue, Bethesda, Maryland, USA
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Beeler PE, Eschmann E, Rosen C, Blaser J. Use of an on-demand drug-drug interaction checker by prescribers and consultants: a retrospective analysis in a Swiss teaching hospital. Drug Saf 2013; 36:427-34. [PMID: 23516005 DOI: 10.1007/s40264-013-0022-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Offering a drug-drug interaction (DDI) checker on-demand instead of computer-triggered 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. METHODS 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.
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Affiliation(s)
- Patrick Emanuel Beeler
- Research Center for Medical Informatics, Directorate of Research and Teaching, University Hospital Zurich, Sonneggstrasse 6, D5, 8091 Zurich, Switzerland
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Floor-Schreudering A, Heringa M, Buurma H, Bouvy ML, De Smet PAGM. Missed drug therapy alerts as a consequence of incomplete electronic patient records in Dutch community pharmacies. Ann Pharmacother 2013; 47:1272-9. [PMID: 24259691 DOI: 10.1177/1060028013501992] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Complete and up-to-date medical and pharmaceutical information in the electronic patient record (EPR) is a prerequisite for risk management in community pharmacy. OBJECTIVES To analyze which information is missing in the EPR and which drug therapy alerts, therefore, fail to appear. METHODS Pharmacy students selected patients who were dispensed a prescription drug and enlisted for >3 months in the participating pharmacies. Patients received a questionnaire in which they were asked to verify their medication history, and to provide additional patient information. For each enrolled patient, the students collected all relevant information from the EPR. Self-reported data from the patient were compared with data retrieved from the EPR. Missed information in the EPR was evaluated based on national professional guidelines. RESULTS Questionnaires were received from 67% of the selected patients (442/660). Prescription drugs were missing in the EPR of 14% of the 442 patients, nonprescription drugs in 44%, diseases in 83%, and intolerabilities in 16%. In 38% of the patients (166/442), drug therapy alerts failed to appear because of missing information: drug-disease interactions in 34% of the patients, duplicate medications in 4%, drug-drug interactions (DDIs) in 4%, and drug intolerabilities in 2%. Among the (non-)prescription drugs missing, NSAIDs were most frequently responsible for the missed alerts. Diseases most frequently associated with missed alerts were gastroesophageal reflux disease, renal insufficiency, asthma/chronic obstructive pulmonary disease, and heart failure. CONCLUSIONS Relevant patient information was frequently missing in the EPRs. The nonappearance of drug therapy alerts may have had clinical consequences for patients.
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Geerts AFJ, De Koning FHP, De Vooght KMK, Egberts ACG, De Smet PAGM, van Solinge WW. Feasibility of point-of-care creatinine testing in community pharmacy to monitor drug therapy in ambulatory elderly patients. J Clin Pharm Ther 2013; 38:416-22. [PMID: 23808548 DOI: 10.1111/jcpt.12081] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 06/05/2013] [Indexed: 11/30/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVE It is often necessary to adjust drug therapy if renal function is impaired in elderly patients taking drugs for diabetes and/or cardiovascular disease that are cleared by the kidneys. Although clinical guidelines recommend regular monitoring of renal function in these patients, in practice adherence to these recommendations varies from 28% to 75%. To determine whether drug dosing is appropriate, pharmacists need have up-to-date information about patients' renal function. In this study, the feasibility of point-of-care creatinine testing (POCCT) in a community pharmacy was evaluated as part of monitoring the drug therapy of ambulatory elderly patients. METHODS Elderly patients on maintenance therapy with renally excreted drugs for diabetes or cardiovascular disease were eligible for POCCT. After informed consent was obtained, POCCT was performed by trained personnel. A pharmacist assessed the clinical relevance of electronically generated drug alerts based on the patient's calculated renal function and the Dutch guidelines for adjusting drug dosage in patients with chronic kidney disease. If appropriate, the patient's general practitioner (GP) was consulted and adjustments to treatment were communicated to the patient. The feasibility of POCCT was evaluated by means of questionnaires completed by patients and healthcare professionals (GPs and pharmacists). RESULTS Of 338 potentially eligible patients, 149 (44%) whose renal function was not known were asked, by letter, to participate in the study. Of these individuals, 46 (31%) gave their informed consent and underwent POCCT. Response rates for completing the patient and professional questionnaires were 87% and 100%, respectively. More than half of the patients who underwent POCCT had mild-to-moderate renal impairment. On the basis of information provided by patients and healthcare professionals, POCCT would appear to be feasible in community pharmacies. WHAT IS NEW AND CONCLUSION POCCT improves the management of drug therapy by community pharmacists and is feasible in daily practice.
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Affiliation(s)
- A F J Geerts
- Division of Pharmacoepidemiology and Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht, the Netherlands.
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Identification of drug-related problems by a clinical pharmacist in addition to computerized alerts. Int J Clin Pharm 2013; 35:753-62. [DOI: 10.1007/s11096-013-9798-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2012] [Accepted: 05/17/2013] [Indexed: 10/26/2022]
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Jiang X, Boxwala AA, El-Kareh R, Kim J, Ohno-Machado L. A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. J Am Med Inform Assoc 2012; 19:e137-44. [PMID: 22493049 PMCID: PMC3392846 DOI: 10.1136/amiajnl-2011-000751] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Objective Competing tools are available online to assess the risk of developing certain conditions of interest, such as cardiovascular disease. While predictive models have been developed and validated on data from cohort studies, little attention has been paid to ensure the reliability of such predictions for individuals, which is critical for care decisions. The goal was to develop a patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. Material and methods A data-driven approach was proposed that utilizes individualized confidence intervals (CIs) to select the most ‘appropriate’ model from a pool of candidates to assess the individual patient's clinical condition. The method does not require access to the training dataset. This approach was compared with other strategies: the BEST model (the ideal model, which can only be achieved by access to data or knowledge of which population is most similar to the individual), CROSS model, and RANDOM model selection. Results When evaluated on clinical datasets, the approach significantly outperformed the CROSS model selection strategy in terms of discrimination (p<1e–14) and calibration (p<0.006). The method outperformed the RANDOM model selection strategy in terms of discrimination (p<1e–12), but the improvement did not achieve significance for calibration (p=0.1375). Limitations The CI may not always offer enough information to rank the reliability of predictions, and this evaluation was done using aggregation. If a particular individual is very different from those represented in a training set of existing models, the CI may be somewhat misleading. Conclusion This approach has the potential to offer more reliable predictions than those offered by other heuristics for disease risk estimation of individual patients.
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Affiliation(s)
- Xiaoqian Jiang
- Division of Biomedical Informatics, University of California at San Diego, La Jolla, California 92093-0728, USA.
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