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AbstractBackground Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown.Objectives We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity.Methods Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician’s characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50–75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression.Results In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%–88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians’ characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56–1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20–1.49), working in primary care (OR = 1.14, 95% CI 1.02–1.27), mentoring students (OR = 1.12, 95% CI 1.04–1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67–0.71).Conclusion Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.

Abstract Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

The purpose of this article is to discuss how disruptive innovation and technologies, such as artificial intelligence (AI) and clinical decision support systems (CDSS), are used in healthcare practice, the impact of use, and implications for healthcare staff and leaders associated with the protection of sensitive patient data. Additionally, this article will address the implications associated with using disruptive technologies in practice and the need to safeguard health information. The rising use of AI and CDSS, the need for cohesive management of data, and the use of analytic software have been identified as one of the major issues facing healthcare leaders today. In this brief, a summary of the issues and the implications for future healthcare leaders will be discussed. [ABSTRACT FROM AUTHOR]

Background: Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown., Objectives: We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity., Methods: Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician's characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50-75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression., Results: In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%-88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians' characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56-1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20-1.49), working in primary care (OR = 1.14, 95% CI 1.02-1.27), mentoring students (OR = 1.12, 95% CI 1.04-1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67-0.71)., Conclusion: Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.

Previous research has described physicians' reluctance to use computerized diagnostic aids (CDAs) but has never experimentally examined the effects of not consulting an aid that was readily available. Experiment 1. Participants read about a diagnosis made either by a physician or an auto mechanic (to control for perceived expertise). Half read that a CDA was available but never actually consulted; no mention of a CDA was made for the remaining half. For the physician, failure to consult the CDA had no significant effect on competence ratings for either the positive or negative outcome. For the auto mechanic, failure to consult the CDA actually increased competence ratings following a negative but not a positive outcome. Negligence judgments were greater for the mechanic than for the physician overall. Experiment 2. Using only a negative outcome, we included 2 different reasons for not consulting the aid and provided accuracy information highlighting the superiority of the CDA over the physician. In neither condition was the physician rated lower than when no aid was mentioned. Ratings were lower when the physician did not trust the CDA and, surprisingly, higher when the physician believed he or she already knew what the CDA would say. Finally, consistent with our previous research, ratings were also high when the physician consulted and then followed the advice of a CDA and low when the CDA was consulted but ignored. Individual differences in numeracy did not qualify these results. Implications for the literature on algorithm aversion and clinical practice are discussed. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care. [ABSTRACT FROM AUTHOR]

Abstract Background Drug-drug interactions (DDIs) can cause patient harm. Between 46 and 90% of patients admitted to the Intensive Care Unit (ICU) are exposed to potential DDIs (pDDIs). This rate is twice as high as patients on general wards. Clinical decision support systems (CDSSs) have shown their potential to prevent pDDIs. However, the literature shows that there is considerable room for improvement of CDSSs, in particular by increasing the clinical relevance of the pDDI alerts they generate and thereby reducing alert fatigue. However, consensus on which pDDIs are clinically relevant in the ICU setting is lacking. The primary aim of this study is to evaluate the effect of alerts based on only clinically relevant interactions for the ICU setting on the prevention of pDDIs among Dutch ICUs. Methods To define the clinically relevant pDDIs, we will follow a rigorous two-step Delphi procedure in which a national expert panel will assess which pDDIs are perceived clinically relevant for the Dutch ICU setting. The intervention is the CDSS that generates alerts based on the clinically relevant pDDIs. The intervention will be evaluated in a stepped-wedge trial. A total of 12 Dutch adult ICUs using the same patient data management system, in which the CDSS will operate, were invited to participate in the trial. Of the 12 ICUs, 9 agreed to participate and will be enrolled in the trial. Our primary outcome measure is the incidence of clinically relevant pDDIs per 1000 medication administrations. Discussion This study will identify pDDIs relevant for the ICU setting. It will also enhance our understanding of the effectiveness of alerts confined to clinically relevant pDDIs. Both of these contributions can facilitate the successful implementation of CDSSs in the ICU and in other domains as well. Trial registration Nederlands Trial register Identifier: NL6762. Registered November 26, 2018.

Abstract Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with “attractive” visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.

To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs). Scoping review. Search of relevant articles published in the English language between January 1, 2000, and February 29, 2020, in the Medline database. The quality of the selected studies was assessed according to PRISMA guidelines and the Mixed Method Appraisal Tool. From 1828 articles retrieved, 24 studies were selected for review, among which only 6 were randomized controlled trials. Although clinical outcomes are seldom studied, some studies show that CDSSs have the potential to decrease pressure ulcer incidence and malnutrition prevalence. Improvement of process outcomes such as increased compliance with practice guidelines, better documentation of nursing assessment, improved teamwork and communication, and cost saving, also are reported. Overall, the use of CDSSs in NHs may be effective to improve patient clinical outcomes and health care delivery; however, most of the retrieved studies were observational studies, which significantly weakens the evidence. High-quality studies are needed to investigate CDSS effects and limitations in NHs. [ABSTRACT FROM AUTHOR]

Abstract Background Computerized decision support systems (CDSSs) are computer programs that provide doctors with person-specific, actionable recommendations, or management options that are intelligently filtered or presented at appropriate times to enhance health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. Methods/Design The Computerized DEcision Support in ONCOlogy (ONCO-CODES) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio. The trial is designed to evaluate the effectiveness on clinical practice and quality of care of a multi-specialty collection of patient-specific reminders generated by a CDSS in the IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) hospital. We hypothesize that the intervention can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to cancer patients. The primary outcome is the rate at which the issues reported by the reminders are resolved, aggregating specialty and primary care reminders. We will include all the patients admitted to hospital services. All analyses will follow the intention-to-treat principle. Discussion The results of our study will contribute to the current understanding of the effectiveness of CDSSs in cancer hospitals, thereby informing healthcare policy about the potential role of CDSS use. Furthermore, the study will inform whether CDSS may facilitate the integration of primary care in cancer settings, known to be usually limited. The increasing use of and familiarity with advanced technology among new generations of physicians may support integrated approaches to be tested in pragmatic studies determining the optimal interface between primary and oncology care. Trial registration ClinicalTrials.gov, NCT02645357

Background: There is a gap between the abilities and the everyday applications of Computerized Decision Support Systems (CDSSs). This gap is further exacerbated by the different ‘worlds’ between the software designers and the clinician end-users. Software programmers often lack clinical experience whereas practicing physicians lack skills in design and engineering. Objective: Our primary objective was to evaluate the performance of Metabolic Irregularities Narrowing down Device (MIND) intelligent medical calculator and differential diagnosis software through end-user surveys and discuss the roles of CDSS in the inpatient setting. Setting: A tertiary care, teaching community hospital. Study participants: Thirty-one responders answered the survey. Responders consisted of medical students, 24%; attending physicians, 16%, and residents, 60%. Results: About 62.5% of the responders reported that MIND has the ability to potentially improve the quality of care, 20.8% were sure that MIND improves the quality of care, and only 4.2% of the responders felt that it does not improve the quality of care. Ninety-six percent of the responders felt that MIND definitely serves or has the potential to serve as a useful tool for medical students, and only 4% of the responders felt otherwise. Thirty-five percent of the responders rated the differential diagnosis list as excellent, 56% as good, 4% as fair, and 4% as poor. Discussion: MIND is a suggesting, interpreting, alerting, and diagnosing CDSS with good performance and end-user satisfaction. In the era of the electronic medical record, the ongoing development of efficient CDSS platforms should be carefully considered by practicing physicians and institutions.

Background: Computerized decision support systems (CDSSs) are computer programs that provide doctors with person-specific, actionable recommendations, or management options that are intelligently filtered or presented at appropriate times to enhance health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. Methods Design: The Computerized DEcision Support in ONCOlogy (ONCO CODES) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio. The trial is designed to evaluate the effectiveness on clinical practice and quality of care of a multi-specialty collection of patient-specific reminders generated by a CDSS in the IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) hospital. We hypothesize that the intervention can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to cancer patients. The primary outcome is the rate at which the issues reported by the reminders are resolved, aggregating specialty and primary care reminders. We will include all the patients admitted to hospital services. All analyses will follow the intention to treat principle. Discussion: The results of our study will contribute to the current understanding of the effectiveness of CDSSs in cancer hospitals, thereby informing healthcare policy about the potential role of CDSS use. Furthermore, the study will inform whether CDSS may facilitate the integration of primary care in cancer settings, known to be usually limited. The increasing use of and familiarity with advanced technology among new generations of physicians may support integrated approaches to be tested in pragmatic studies determining the optimal interface between primary and oncology care. [ABSTRACT FROM AUTHOR]

Background: Computerized decision support systems (CDSSs) are information technology-based software that provide health professionals with actionable, patient-specific recommendations or guidelines for disease diagnosis, treatment, and management at the point-of-care. These messages are intelligently filtered to enhance the health and clinical care of patients. CDSSs may be integrated with patient electronic health records (EHRs) and evidence-based knowledge.Methods/design: We designed a pragmatic randomized controlled trial to evaluate the effectiveness of patient-specific, evidence-based reminders generated at the point-of-care by a multi-specialty decision support system on clinical practice and the quality of care. We will include all the patients admitted to the internal medicine department of one large general hospital. The primary outcome is the rate at which medical problems, which are detected by the decision support software and reported through the reminders, are resolved (i.e., resolution rates). Secondary outcomes are resolution rates for reminders specific to venous thromboembolism (VTE) prevention, in-hospital all causes and VTE-related mortality, and the length of hospital stay during the study period.Discussion: The adoption of CDSSs is likely to increase across healthcare systems due to growing concerns about the quality of medical care and discrepancy between real and ideal practice, continuous demands for a meaningful use of health information technology, and the increasing use of and familiarity with advanced technology among new generations of physicians. The results of our study will contribute to the current understanding of the effectiveness of CDSSs in primary care and hospital settings, thereby informing future research and healthcare policy questions related to the feasibility and value of CDSS use in healthcare systems. This trial is seconded by a specialty trial randomizing patients in an oncology setting (ONCO-CODES).Trial Registration: ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT02577198?term=NCT02577198&rank=1. [ABSTRACT FROM AUTHOR]

Background: There is a gap between the abilities and the everyday applications of Computerized Decision Support Systems (CDSSs). This gap is further exacerbated by the different 'worlds' between the software designers and the clinician end-users. Software programmers often lack clinical experience whereas practicing physicians lack skills in design and engineering. Objective: Our primary objective was to evaluate the performance of Metabolic Irregularities Narrowing down Device (MIND) intelligent medical calculator and differential diagnosis software through end-user surveys and discuss the roles of CDSS in the inpatient setting. Setting: A tertiary care, teaching community hospital. Study participants: Thirty-one responders answered the survey. Responders consisted of medical students, 24%; attending physicians, 16%, and residents, 60%. Results: About 62.5% of the responders reported that MIND has the ability to potentially improve the quality of care, 20.8% were sure that MIND improves the quality of care, and only 4.2% of the responders felt that it does not improve the quality of care. Ninety-six percent of the responders felt that MIND definitely serves or has the potential to serve as a useful tool for medical students, and only 4% of the responders felt otherwise. Thirty-five percent of the responders rated the differential diagnosis list as excellent, 56% as good, 4% as fair, and 4% as poor. Discussion: MIND is a suggesting, interpreting, alerting, and diagnosing CDSS with good performance and end-user satisfaction. In the era of the electronic medical record, the ongoing development of efficient CDSS platforms should be carefully considered by practicing physicians and institutions. [ABSTRACT FROM AUTHOR]

What is known and objective Clinical decision support software ( CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. Methods A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Results and discussion Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions ( DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors ( PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). What is new and conclusion Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes. [ABSTRACT FROM AUTHOR]

Information must be collected, evaluated and utilized to support every qualified activity. Medicine, with a written scientific tradition stretching back more than 2,000 years, is no exception. Here, we discuss a number of important items associated with the establishment of a drug information centre run by clinical pharmacologists and information pharmacists, serving a broad demand, mainly among clinical specialists. The working methods include a professional literature search, critical evaluation of the material, writing a structured answer, quality control, feedback to the inquirer and storage in a database which is publicly available. One can foresee even more complex systems wherein a number of active and specialized databases communicate to provide relevant advice and support at the point of care, supplying information on drug recommendations, reimbursement, environmental aspects, antimicrobial resistance, pharmacogenetics and adverse effects, and linked to a list of prescribed drugs for the individual patient. This will be possible in both rich and poor countries through the application of modern and developing information technology. However, research on the best and safest methods of such decision support systems will be needed to ensure that they really do improve the quality of drug prescribing and use. [ABSTRACT FROM AUTHOR]

Medication related Computerized Decision Support System (CDSS) are known to have a positive impact on Adverse Drug Events (ADE) prevention but they face acceptance problems due to over alerting and usability issues. We present here a Human factors approach to the design of these Clinical Decision Support (CDS) functions and to their integration into different Electronic Health Record (EHR) / Computerized Physicians Order Entry (CPOE) systems, so that the resulting CDSS corresponds to the users needs and fits clinical workflows and cognitive processes. We used ethnographic observations completed with semi-structured interviews to analyse existing work situations and work processes. These were then described in detail using the SHEL (Software, Hardware, Environment & Liveware) formalism, which enables a structured description of the work system and provides an appropriate classification of human errors potentially leading to ADEs. We then propose a Unified Modelling Language (UML) model supporting the characterization by the CDSS of the drug monitoring and clinical context of patients at risk of ADE. This model combines the status of the lab test orders on the one hand with the validity and normality of the lab results on the other hand. This makes the system able to catch the context of the monitoring of the drugs through their corresponding lab tests and lab results (e.g. kalemia for potassium) and also part of the context of the clinical status of the patient (actual lab values, but also diseases and other pathologies that are identified as potential causes of the ADE e.g. renal insufficiency and potassium). We show that making the system able to catch the monitoring and clinical contexts opens interesting opportunities for the design of the CDS information content and display mode. Implementing this model would allow the CDSS to take into account the actions already engaged by the healthcare team and to adapt the information delivered to the monitoring and clinical context, thus making the CDSS a partner to the clinicians, nurses and pharmacists. [ABSTRACT FROM AUTHOR]

Objective: Feedback to clinicians on their past performance is often aimed at increasing adherence to guidelines. We investigate how various analytical approaches influence the interpretation of adherence data. The analytical approaches vary in considering the actual or the intended use of the feedback, and whether outcomes are inspected over time. Material and Methods: At base line, a computerized decision support system was employed at the ICU bedside to increase adherence to a mechanical ventilation strategy. We intervened by providing feedback about adherence to the guideline at the daily ward rounds. The outcome measure was the percentage of ventilation time (VT) in excess of the guideline's recommendation. Actual usage of the feedback was logged and data analysis was carried out using two approaches: classical statistics, and statistical process control (SPC) that inspect progress of an outcome over time. Design: Prospective, before/after study. Results: The classical analysis stated that the percentage of ventilation time in excess of the guideline's recommendation decreased significantly due to the feedback (5% reduction, p < 0.001). When SPC analysis of the outcome was applied, the effect was deemed not significant. When the actual delivery of feedback over time was also included it showed that the experiment does not allow for conclusive results. Conclusions: The concluded effect of providing feedback on adherence to a guideline depends on whether the actual usage pattern of the feedback and the inspection of the outcome over time are considered. Future evaluative studies should report on usage patterns and progression of outcomes over time. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: The purpose of the study was to measure the effect of a computerized decision support system (CDSS) on adherence to tidal volume (V T) recommendations. Materials and Methods: We performed a prospective before-after evaluation study on applied V T to examine the impact of a CDSS on adherence to our local protocol in a 30-bed mixed medical-surgical intensive care unit of a university hospital. All intensive care unit patients who were intubated and mechanically ventilated for at least 1 hour were included. Results: A total of 3 663 674 V T records of 696 patients were analyzed. The average volume greater than 6 mL/kg predicted body weight (PBW) and the mean percentage of ventilation time with V T greater than 6 mL/kg PBW decreased after intervention by 6.0% and 3.4%, respectively (not significant). A stronger effect of the decision support intervention was found among patients with longer duration of mechanical ventilation (>24 hours): for these patients, the average V T in exceeding 6 mL/kg PBW and the mean percentage of ventilation time with V T greater than 6 mL/kg PBW decreased after intervention by 18.3% (P = .01) and 9.5% (P = .01), respectively. In this group, the mean percentage of ventilation time with V T records between 8 and 10, between 10 and 12, and greater than 12 mL/kg PBW decreased by 21.8% (P = .006), 21.5% (P = .047), and 24.7% (P = .155), respectively. Conclusions: The use of a CDSS, integrated in a patient data management system, improves implementation of a lower V T mechanical ventilation strategy for patients ventilated for longer than 24 hours. [Copyright &y& Elsevier]

Abstract: In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are: [1.] Greater modularity than other architectures, allowing for work to be distributed. [2.] The potential for creating and sustaining a commercial market for clinical decision support. [3.] Reduced cost and risk of trying new decision support systems because of its ability to easily integrate a variety of decision support services, and to easily remove them, if desired, as well. [4.] Significant freedom for developers of clinical decision support systems to choose the way they represent knowledge and internally implement their system, in comparison to other approaches which constrain such developers to a particular knowledge representation formalism. [5.] Unification of the direction and agenda of decision support research and development with promising near-term efforts to improve interoperability of clinical systems. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN, and SAGE. [Copyright &y& Elsevier]

Purpose: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU., Materials and Methods: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting., Results: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting., Conclusions: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue., Competing Interests: Declaration of Competing Interest All authors declare that they have no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Abstract: Purpose: Our hypothesis was that both styles are effective to decrease tidal volume (VT) but that critiquing comprises the most effective strategy. The purpose of this study is to test this hypothesis by measuring the effect of an active computerized decision support system, in 2 communication styles, consulting and critiquing, on adherence to VT recommendations. Materials and Methods: We developed and implemented an active computerized decision support system (CDSS) working in a consulting style that always shows the preferred VT and in a critiquing style that shows the preferred VT only if VT is above the desired threshold. A prospective, off-on-off-on study evaluated the system''s performance in a mixed medical-surgical intensive care unit of a university hospital. Results: Four thousand seven hundred sixty-four patient-day mechanical ventilation from 757 patients were analyzed. The percentage of ventilation time in excess of 6 and 8 mL/kg predicted body weight decreased significantly after intervening with the consulting style (12% reduction and P < .001; 22% reduction and P < .001) and again increased after stopping the CDSS (11% increase and P < .001; 29% increase and P < .001). With the critiquing CDSS, the percentage of ventilation time in excess of 6 and 8 mL/kg predicted body weight again decreased significantly (6% reduction and P < .001; 15% reduction and P < .001). Conclusions: The use of a CDSS in both communication styles improved the use of lower VTs for ventilated patients. When decision support was not sustained, adherence to low VT fell back to its original value. Interestingly, the consulting style had a slightly larger effect. This may stem from the high frequency of showing reminders in this style and the relatively simple underlying guideline where its display implies the associated action of lowering VT. The consulting style, however, was more interruptive for clinicians, calling upon the need to strike a balance between effect and intrusiveness. [Copyright &y& Elsevier]