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Background: The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.Methods: A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.Results: Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was - 0.09 (95% CI - 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI - 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI - 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI - 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system.Conclusions: The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.Trial Registration: ClinicalTrials.gov, NCT01830569, Registered 12 April 2013. [ABSTRACT FROM AUTHOR]

Background: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality., Objective: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission., Method: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix., Results: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai., Conclusion: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission., Competing Interests: Conflict of interest: G. Wu has nothing to disclose. Conflict of interest: P. Yang has nothing to disclose. Conflict of interest: Y. Xie has nothing to disclose. Conflict of interest: H.C. Woodruff owns shares in Oncoradiomics, outside the submitted work. Conflict of interest: X. Rao has nothing to disclose. Conflict of interest: J. Guiot has nothing to disclose. Conflict of interest: A-N. Frix has nothing to disclose. Conflict of interest: R. Louis has nothing to disclose. Conflict of interest: M. Moutschen has nothing to disclose. Conflict of interest: Jiawei Li has nothing to disclose. Conflict of interest: Jing Li has nothing to disclose. Conflict of interest: C. Yan has nothing to disclose. Conflict of interest: D. Du has nothing to disclose. Conflict of interest: S. Zhao has nothing to disclose. Conflict of interest: Y. Ding has nothing to disclose. Conflict of interest: B. Liu has nothing to disclose. Conflict of interest: W. Sun has nothing to disclose. Conflict of interest: F. Albarello has nothing to disclose. Conflict of interest: A. D'Abramo has nothing to disclose. Conflict of interest: V. Schininà has nothing to disclose. Conflict of interest: E. Nicastri has nothing to disclose. Conflict of interest: M. Occhipinti reports grants from Menarini Foundation and Novartis, outside the submitted work. Conflict of interest: G. Barisione has nothing to disclose. Conflict of interest: E. Barisione has nothing to disclose. Conflict of interest: I. Halilaj has nothing to disclose. Conflict of interest: P. Lovinfosse has nothing to disclose. Conflict of interest: X. Wang has nothing to disclose. Conflict of interest: J. Wu has nothing to disclose. Conflict of interest: P. Lambin reports, within the submitted work, minority shares in The Medical Cloud Company and, outside the submitted work, grants/sponsored research agreements from Varian medical, Oncoradiomics, ptTheragnostic/DNAmito and, Health Innovation Ventures; he received an advisor/presenter fee and/or reimbursement of travel costs/external grant writing fee and/or in kind manpower contribution from Oncoradiomics, BHV, Varian, Elekta, ptTheragnostic and Convert pharmaceuticals; P. Lambin has shares in the company Oncoradiomics SA, Convert pharmaceuticals SA and is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Oncoradiomics and one issued patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, three non-patented invention (softwares) licensed to ptTheragnostic/DNAmito, Oncoradiomics and Health Innovation Ventures and three non-issues, non licensed patents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889). Support statement: This work was supported from ERC advanced grant (ERC-ADG-2015, number 694812 – Hypoximmuno), European Program H2020 (ImmunoSABR – number 733008, PREDICT – ITN – number 766276, CHAIMELEON – number 952172, EuCanImage – number 952103), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY” – number UM 2017-8295), China Scholarships Council (number 201808210318), and Interreg V-A Euregio Meuse-Rhine (“Euradiomics” – number EMR4). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This work was supported by the Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018-2. Funding information for this article has been deposited with the Crossref Funder Registry., (Copyright ©ERS 2020.)

We consider the risk of adverse drug events caused by antibiotic prescriptions. Antibiotics are the second most common cause of drug related adverse events and one of the most common classes of drugs associated with medical malpractice claims. To cope with this serious issue, physicians rely on guidelines, especially in the context of hospital prescriptions. Unfortunately such guidelines do not offer sufficient support to solve the problem of adverse events. To cope with these issues our work proposes a clinical decision support system based on expert medical knowledge, which combines semantic technologies with multiple criteria decision models. Our model links and assesses the adequacy of each treatment through the toxicity risk of side effects, in order to provide and explain to physicians a sorted list of possible antibiotics. We illustrate our approach through carefully selected case studies in collaboration with the EpiCURA Hospital Center in Belgium.

Background: Antibiotics are overprescribed for non-severe acute infections in children in primary care., Aim: To explore two different interventions that may reduce inappropriate antibiotic prescribing for non-severe acute infections., Design and Setting: A cluster randomised, factorial controlled trial in primary care, in Flanders, Belgium., Method: Family physicians (FPs) enrolled children with non-severe acute infections into this study. The participants were allocated to one of four intervention groups according to whether the FPs performed: (1) a point-of-care C-reactive protein test (POC CRP); (2) a brief intervention to elicit parental concern combined with safety net advice (BISNA); (3) both POC CRP and BISNA; or (4) usual care (UC). Guidance on the interpretation of CRP was not provided. The main outcome was the immediate antibiotic prescribing rate. A mixed logistic regression was performed to analyse the data., Results: In this study 2227 non-severe acute infections in children were registered by 131 FPs. In comparison with UC, POC CRP did not influence antibiotic prescribing, (adjusted odds ratio [AOR] 1.01, 95% confidence interval [CI] = 0.57 to 1.79). BISNA increased antibiotic prescribing (AOR 2.04, 95% CI = 1.19 to 3.50). In combination with POC CRP, this increase disappeared., Conclusion: Systematic POC CRP testing without guidance is not an effective strategy to reduce antibiotic prescribing for non-severe acute infections in children in primary care. Eliciting parental concern and providing a safety net without POC CRP testing conversely increased antibiotic prescribing. FPs possibly need more training in handling parental concern without inappropriately prescribing antibiotics., (© British Journal of General Practice 2018.)

Objectives: Developing and implementing clinical decision support systems (CDSSs) is time-consuming and costly. Therefore, prioritization of the most relevant systems is warranted. The physician's perceived usefulness has been identified as a decisive reason for using CDSSs. The objective of this study was to investigate the physician's perceived usefulness of different types of CDSSs and to identify the user needs and expectations regarding future CDSSs., Methods: Cross-sectional single-centre survey among physicians with a clinical assignment in a university hospital. Physicians were questioned about their current experiences with drug prescribing and the perceived usefulness and desired features of future CDSSs., Results: One hundred and sixty-four physicians completed the survey (52·6%). The majority acknowledged that it is very difficult to take all relevant information into account when prescribing drugs. Drug-drug interaction checking, drug-allergy checking, and dosing guidance were considered as most useful. Automated clinical guidelines and adverse drug event monitoring were considered as least useful. The user-friendliness of the systems, clinical relevance of the alerts, and prevention of alert fatigue were perceived as important aspects for a successful implementation., Conclusions: From the physicians' perspective drug-drug interaction checking, drug-allergy checking, and dosing guidance should receive the highest priority for development and implementation. Because the perceived usefulness has been identified as a decisive reason for using CDSSs, it seems feasible to take into account this prioritization when developing and implementing CDSSs. In order to overcome the physicians' perceived disadvantages, attention should go to the development of user-friendly systems that deliver clinical relevant alerts.

Background: Frail older people admitted to nursing homes are at risk of a range of adverse outcomes, including pressure ulcers. Clinical decision support systems are believed to have the potential to improve care and to change the behaviour of healthcare professionals., Objectives: To determine whether a multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention improves adherence to recommendations for pressure ulcer prevention in nursing homes., Design: Two-armed randomized controlled trial in a nursing home setting in Belgium. The trial consisted of a 16-week implementation intervention between February and June 2010, including one baseline, four intermediate, and one post-testing measurement. Primary outcome was the adherence to guideline-based care recommendations (in terms of allocating adequate pressure ulcer prevention in residents at risk). Secondary outcomes were the change in resident outcomes (pressure ulcer prevalence) and intermediate outcomes (knowledge and attitudes of healthcare professionals)., Setting: Random sample of 11 wards (6 experimental; 5 control) in a convenience sample of 4 nursing homes in Belgium., Participants: In total, 464 nursing home residents and 118 healthcare professionals participated., Methods: The experimental arm was involved in a multi-faceted tailored implementation intervention of a clinical decision support system, including interactive education, reminders, monitoring, feedback and leadership. The control arm received a hard-copy of the pressure ulcer prevention protocol, supported by standardized 30 min group lecture., Results: Patients in the intervention arm were significantly more likely to receive fully adequate pressure ulcer prevention when seated in a chair (F=16.4, P=0.003). No significant improvement was observed on pressure ulcer prevalence and knowledge of the professionals. While baseline attitude scores were comparable between both groups [exp. 74.3% vs. contr. 74.5% (P=0.92)], the mean score after the intervention was 83.5% in the experimental group vs. 72.1% in the control group (F=15.12, P<0.001)., Conclusion: The intervention was only partially successful to improve the primary outcome. Attitudes improved significantly while the knowledge of the healthcare workers remained unsatisfactorily low. Further research should focus on the underlying reasons for these findings., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The Intensive Care Unit is a data intensive environment where large volumes of patient monitoring and observational data are daily generated. Today, there is a lack of an integrated clinical platform for automated decision support and analysis. Despite the potential of electronic records for infection surveillance and antibiotic management, different parts of the clinical data are stored across databases in their own formats with specific parameters, making access to all data a complex and time-consuming challenge. Moreover, the motivation behind physicians' therapy decisions is currently not captured in existing information systems. The COSARA research project offers automated data integration and services for infection control and antibiotic management for Ghent University Hospital. The platform not only gathers and integrates all relevant data, it also presents the information visually at the point of care. In this paper, we describe the design and value of COSARA for clinical treatment and infectious diseases monitoring. On the one hand, this platform can facilitate daily bedside follow-up of infections, antibiotic therapies and clinical decisions for the individual patient, while on the other hand, the platform serves as management view for infection surveillance and care quality improvement within the complete ICU ward. It is shown that COSARA is valuable for registration, real-time presentation and management of infection-related and antibiotics data.

Personalized medicine may be considered an extension of traditional approaches to understanding and treating diseases, but with greater precision. A profile of a patient's genetic variation can guide the selection of drugs or treatment protocols that minimize harmful side effects or ensure a more successful outcome. In this paper we describe a decision support system designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine. Development of clinical decision support based semantic web for personalized patient care is to achieve its potential and improve the quality, safety and efficiency of healthcare.

Background: Evidence-based clinical guidelines on contraceptive use were developed and distributed among all Flemish general practitioners (GPs) in Belgium., Objectives: To evaluate the effectiveness of two strategies for implementing a guideline on oral contraceptives in order to enhance the quality of the first contraception consultation in general practice, by either empowering patients or by introducing a computer decision support system (CDSS)., Method: A random sample of 45 GPs was distributed among three study groups. One group was visited by an 'empowered patient', one group's electronic medical record was supplemented with a CDSS, and one group served as the control group. Simulated patients (SPs) assessed the performance of GPs in daily practice before and after the interventions, using a validated 48-point checklist., Results: The baseline mean score of the 43 GPs was 26.16 (SD = 5.76). The SPs received sufficient information about correct pill usage, but not concerning factors associated with pill failure and drug interaction. After the intervention, the GPs' mean score was 26.39 (SD = 6.86). Only the intervention group with the 'empowered patient' scored significantly better (29.92 [SD = 7.11]). The computer group and control group scored lower (24.36 [SD = 6.60] and 24.82 [SD = 5.65], respectively)., Conclusion: Developing and distributing an evidence-based guideline did not change GPs' behaviour. However, empowering patients to participate more proactively significantly improved GPs' performance during a contraception consultation. A CDSS did not.

The increasing complexity of procedures in the intensive care unit (ICU) requires complex software services, to reduce improper use of antibiotics and inappropriate therapies, and to offer earlier and more accurate detection of infections and antibiotic resistance. We investigated whether web-based software can facilitate the computerization of complex medical processes in the ICU. The COSARA application contains the following modules: Infection overview, Thorax, Microbiology, Antibiotic therapy overview, Admission cause with comorbidity and admission diagnosis, Infection linking and registration, and Feedback. After the implementation and test phase, the COSARA software was installed on a physician's office PC and then on the bedside PCs of the patients. Initial evaluation indicated that the services had been integrated easily into the daily clinical workflow of the medical staff. The use of a service oriented architecture with web service technology for the development of advanced decision support in the ICU offers several advantages over classical software design approaches.

The importance of computer aided decision making is continuously increasing. In the ICU, medical decision support services gather and process medical data of patients and present results and suggestions to the medical staff. The medical decision support services can monitor for example blood pressure, creatinine levels or the usage of antibiotics. If certain levels are crossed, they raise alerts so that the medical staff can take appropriate actions if required. This significantly reduces the amount of data needing to be processed by the medical staff. To handle the large amount of data that is generated by the ICU on a daily basis, a platform for routing and processing this data is necessary. In this paper we propose a platform based on JAIN SLEE and an Enterprise Service Bus. The platform takes care of the routing of the data to the appropriate services and allows to easily deploy and manage services. In this paper, we present the design details and the evaluation results. Furthermore, it is shown that the platform is capable of routing and processing all the events generated by the ICU within strict time constraints.

Objective: The differential diagnosis between viral meningitis and bacterial meningitis is often very difficult. The results of peripheral blood and spinal fluid analysis are not 100% accurate. We tried to find a useful 'bedside' decision-making tool, based on laboratory results readily available at the emergency department., Methods: Retrospective study design. Analysis of a consecutive series of all children (age 0-15 years) admitted to the paediatric ward because of a viral or bacterial meningitis, in the period from 1997 to September 2005., Results: Seventy-one children with viral and 21 with bacterial meningitis were included. Bacterial meningitis occurred at much younger ages than viral meningitis. The paediatrician decided to administer antibiotics in 41 of 71 children with viral meningitis and in all children with bacterial meningitis. We developed a 'bacterial meningitis score' based on C-reactive protein in peripheral blood, as well as glucose and protein in cerebrospinal fluid. Using this score, we could distinguish 54 of 71 patients with viral meningitis from the group with bacterial meningitis. When the dispensing of antibiotics was based on this score, only 16 patients with viral meningitis would receive antibiotics., Conclusion: We present a bedside bacterial meningitis score. Using this bacterial meningitis score as a decision-making tool, we would be able to avoid antibiotics in a large number of children with viral meningitis. As this gives a 100% success rate, thus guaranteeing that bacterial meningitis patients would receive the proper therapy, our bacterial meningitis score could be an accurate decision-support tool.