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Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.

Objectives: We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic., Methods: We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2., Results: The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient's conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients., Conclusions: Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients' needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.

Objectives: The MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel. We wanted to assess the system's feasibility and potential effects on patients and care providers in two different clinical domains., Materials and Methods: We describe MobiGuide's architecture, which embodies these objectives. Our novel methodologies include a ubiquitous architecture, encompassing a knowledge elicitation process for parallel coordinated workflows for patients and care providers; the customization of computer-interpretable guidelines (CIGs) by secondary contexts affecting remote management and distributed decision-making; a mechanism for episodic, on demand projection of the relevant portions of CIGs from a centralized, backend decision-support system (DSS), to a local, mobile DSS, which continuously delivers the actual recommendations to the patient; shared decision-making that embodies patient preferences; semantic data integration; and patient and care provider notification services. MobiGuide has been implemented and assessed in a preliminary fashion in two domains: atrial fibrillation (AF), and gestational diabetes Mellitus (GDM). Ten AF patients used the AF MobiGuide system in Italy and 19 GDM patients used the GDM MobiGuide system in Spain. The evaluation of the MobiGuide system focused on patient and care providers' compliance to CIG recommendations and their satisfaction and quality of life., Results: Our evaluation has demonstrated the system's capability for supporting distributed decision-making and its use by patients and clinicians. The results show that compliance of GDM patients to the most important monitoring targets - blood glucose levels (performance of four measurements a day: 0.87±0.11; measurement according to the recommended frequency of every day or twice a week: 0.99±0.03), ketonuria (0.98±0.03), and blood pressure (0.82±0.24) - was high in most GDM patients, while compliance of AF patients to the most important targets was quite high, considering the required ECG measurements (0.65±0.28) and blood-pressure measurements (0.75±1.33). This outcome was viewed by the clinicians as a major potential benefit of the system, and the patients have demonstrated that they are capable of self-monitoring - something that they had not experienced before. In addition, the system caused the clinicians managing the AF patients to change their diagnosis and subsequent treatment for two of the ten AF patients, and caused the clinicians managing the GDM patients to start insulin therapy earlier in two of the 19 patients, based on system's recommendations. Based on the end-of-study questionnaires, the sense of safety that the system has provided to the patients was its greatest asset. Analysis of the patients' quality of life (QoL) questionnaires for the AF patients was inconclusive, because while most patients reported an improvement in their quality of life in the EuroQoL questionnaire, most AF patients reported a deterioration in the AFEQT questionnaire., Discussion: Feasibility and some of the potential benefits of an evidence-based distributed patient-guidance system were demonstrated in both clinical domains. The potential application of MobiGuide to other medical domains is supported by its standards-based patient health record with multiple electronic medical record linking capabilities, generic data insertion methods, generic medical knowledge representation and application methods, and the ability to communicate with a wide range of sensors. Future larger scale evaluations can assess the impact of such a system on clinical outcomes., Conclusion: MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Leveraging the experience of the European project MobiGuide, this paper elaborates on the nurses' role in developing, delivering and evaluating e-health based services. We focus on the home monitoring of atrial fibrillation. Patients enrolled in our study are provided with a smartphone and an ECG sensor, and receive recommendations, reminders and alerts concerning medications and measurements that they should perform through a mobile decision support system that is constantly updated by a backend system. Patients' data are sent to health care personnel that may visualize them, and act accordingly. Nurses play a central role in such setting. After being involved in the design of the caregiver interface, they are responsible for the patients' enrollment phase (which includes patients' training), for the daily checking of incoming data, for the triage of patients' complaints, and for the final phase of the study where patients are interviewed about their experience with the system.

Objectives: This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care., Methods: We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support., Results: We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness., Conclusions: Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

Objective: Information about medications is critical in supporting decision-making during the prescription process and thus in improving the safety and quality of care. In this work, we propose a methodology for the automatic recognition of drug-related entities (active ingredient, interaction effects, etc.) in textual drug descriptions, and their further location in a previously developed domain ontology., Methods and Material: The summary of product characteristics (SPC) represents the basis of information for health professionals on how to use medicines. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. Our approach exploits a combination of machine learning and rule-based methods. It consists of two stages. Initially it learns to classify this information in a structured prediction framework, relying on conditional random fields. The classifier is trained and evaluated using a corpus of about a hundred SPCs. They have been hand-annotated with different semantic labels that have been derived from the domain ontology. At a second stage the extracted entities are added in the domain ontology corresponding concepts as new instances, using a set of rules manually-constructed from the corpus., Results: Our evaluations show that the extraction module exhibits high overall performance, with an average F1-measure of 88% for contraindications and 90% for interactions., Conclusion: SPCs can be exploited to provide structured information for computer-based decision support systems., (Copyright © 2012 Elsevier B.V. All rights reserved.)

MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial Fibrillation (AF). In this paper we describe the process of (1) identifying guideline recommendations that will require patients to take actions (e.g., take measurement, take drug), thus impacting patients' daily-life behavior, (2) eliciting from the medical experts the corresponding set of personalized operationalized advices that are not explicitly written in the guideline (patient-tailored workflow patterns) and (3) delivering this advice to patients. The analysis of the AF guideline has resulted in four types of patient-tailored workflow patterns: therapy-related advisors, measurements advisors, suggestions for dealing with interventions that may require modulating patient therapy, and personalized packages for close monitoring of patients. We will show how these patterns can be generated using information stored in a patient health record that embeds clinical data and data about the patient's personal context and preferences.

This work describes the results of the implementation of a workflow management system integrated into the electronic clinical chart of a Stroke Unit. The workflow logic is based on the rules provided by the SPREAD guidelines for stroke management. In this way, the already existing clinical chart has been transformed into an evidence-based, real-time decision support system, meanwhile maintaining the same look the users were familiar with. Since the final aim of the work was to improve evidence-based behavior and detect possible organizational bottlenecks, non-compliance to the clinical practice guidelines, before and after the system introduction, have been analyzed, as well as the accuracy of the clinical chart compilation, some care process variables, and system usability. Results show that the system enhances the clinical practice without boring users. Moreover, non-compliance analysis gives rise to ideas for further improvement.

Despite the great emphasis that the medical community has dedicated to clinical practice guidelines in the last few decades, it has become clear that it is difficult to adequately distribute texts (paper-based guidelines) and for them to be used by final users. The reasons for this are manifold: the large number of guidelines delivered each year, their necessary updates on the face of new scientific evidence, the text is not always free from ambiguities, etc. When these problems became evident, the medical informatics community hypothesised that more formal electronic versions would increase the probability of the effective diffusion of guidelines and also physicians' compliance to them. This claim was based on a few hypotheses: one is that formal models, such as flowcharts or other graphical metaphors, provide a more friendly and immediate way of reading a guideline, thus improving learning and absorbing of the guideline content (e. g. using formal models as educational/simulation tools); another hypothesis is that formal models allow easier guideline integration with the daily clinical workflow, raising the possibility of building real-time decision support systems, which again should improve physicians' compliance. This paper focuses on the issues that must be taken into account when modelling guidelines to ensure their effective implementation in real-world computerised environments.

Literature results and personal experience show that intrusive modalities of presenting suggestions of computerized clinical practice guidelines are detrimental to the routine use of an information system. This paper describes a solution for smoothly integrating a guideline-based decision support system into an existing computerized clinical chart for patients admitted to a Stroke Unit. Since many years, the healthcare personnel were using a commercial product for the ordinary patients' data management, and they were satisfied with it. Thus, the decision support system has been integrated keeping attention to minimize changes and preserve existing human-computer interaction. Our decision support system is based on workflow technology. The paper illustrates the middleware layer developed to allow communication between the workflow management system and the clinical chart. At the same time, the consequent modification of the graphical users' interface is illustrated.

This paper describes the architecture of the Guide Project, a proposal for innovation of Health Information Systems, putting together medical and organizational issues through the Separation of Concerns paradigm. In particular, we focus on one building block of the architecture: the Guideline Management System handling the whole life cycle of computerized Clinical Practice Guidelines. The communication between the Guideline Management System and the other components of the project architecture is message-based, according to specific contracts that allow an easy integration of the components developed by different parties and, in particular, with legacy systems (i.e. existing electronic patient records). In turn, the Guideline Management System components are organized in a distributed architecture: an editor to formalize guidelines, a repository to store and publish them, an enactment system to implement guidelines instances in a multi-user environment and a reporting system able to completely trace any individual physician's guideline-based decision process. The repository is organized in different levels that can be international, national, regional, down to the specific health care organization, according to the healthcare delivery policy of a country. Different organizations can get Clinical Practice Guidelines from the repository, adapt and introduce them in clinical practice.

This paper describes the architecture of NewGuide, a guide-line management system for handling the whole life cycle of a computerized clinical practice guideline. NewGuide components are organized in a distributed architecture: an editor to formalize guidelines, a repository to store them, an inference engine to implement guidelines instances in a multi-user environment, and a reporting system storing the guidelines logs in order to be able to completely trace any individual physician guideline-based decision process. There is a system "central level" that maintains official versions of the guidelines, and local Healthcare Organizations may download and implement them according to their needs. The architecture has been implemented using the Java 2 Enterprise Edition (J2EE) platform. Simple Object Access Protocol (SOAP) and a set of con-tracts are the key factors for the integration of NewGuide with healthcare legacy systems. They allow maintaining unchanged legacy user interfaces and connecting the system with what-ever electronic patient record. The system functionality will be illustrated in three different contexts: homecare-based pressure ulcer prevention, acute ischemic stroke treatment and heart failure management by general practitioners.

One of the principal challenges in the medical practice is the update of their knowledge. One of the prime roles of the Continuing Medical Education is to train the medical practitioners with the latest advances in health care, specialized to their needs. Online courses and classroom teaching with computer-based representations have become an established mode of delivering medical education. This paper deals with the modularized representation of a medical text concerning clinical practice guidelines. The proposed system takes into consideration the semantics of the Unified Medical Language System and is based upon the marking up and display of the knowledge using the XML and XSLT languages. This modularization of the concepts leads to the determination of the context of a portion or the whole document. Thus, after marking up using our system, the text components can be exchanged, modified or reconstructed, which, in turn, would help to maintain the updates in medical knowledge.

Objectives: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed., Design: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure., Measurements: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support., Results: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions., Conclusion: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

The purpose of this study was to evaluate, through a meta-analysis study, whether the use of computer-based systems reported in the literature improves the metabolic control of diabetic patients. On the retrieved papers, a set of meta-analysis studies were performed: first the difference of HbA1c between cases and controls at follow-up was evaluated (sign test); then the difference between cases and controls in the total variation of HbA1c from the beginning to the end of the trial was considered (method of effect sizes). The latter methodology was reapplied also on three more homogeneous article subgroups. The sign test was performed on 16 papers: in two of them, the HbA1c level was higher in the intervention group than in the control group at follow-up: it is unlikely that this is a random occurrence (p < 0.01). The method of effect sizes was first applied to 13 papers, as in the others some needed data were missing: the results obtained showed a statistically significant amelioration of metabolic control in the intervention group in comparison to the control group (p < 0.01). A progressive reinforcement of this outcome was obtained on the trial subgroups. Our study supports the hypothesis that the use of computer-based systems can be an effective means of improving metabolic control. The differential benefit obtained in the amelioration of HbA1c does not justify, by itself, the applicability of such systems into clinical practice; additional investigations should be carried out to evaluate the enhancement of other clinical and organizational indicators.

Workflow Management Systems integrate domain and organisational knowledge to support business processes. When applied to the medical environment, they can be termed "Careflow Management Systems", and may be used to manage care delivery by enhancing co-operation among healthcare professionals. This paper focuses on care delivery based on clinical practice guidelines. Healthcare organisations are very different from industrial or commercial companies: their main goal is not profit, but maintaining and improving the health of the public. Therefore, outcomes are difficult to measure. Firstly, physicians, while playing a variety of roles, are quite independent decision-makers; secondly, the object of the process, i.e. the patient, may be involved in choosing treatment options, and may be treated by different institutions. For these reasons, the standard functionality of typical Workflow Management Systems must be strongly enhanced in order to cope with healthcare delivery needs. A major issue is accounting for exceptions. In most non-clinical settings this is not a problem because processes are very well defined and can often be easily controlled by some higher authority. As explained above, this does not happen in healthcare organisations. Responsibilities are widely shared, and health care professionals may be non-compliant with guidelines for a variety of reasons. The paper presents a classification of possible exceptions, and shows how the sequence of tasks described by a guideline may be altered, at the implementation level, in order to meet actual user needs, while maintaining guideline intentions as much as possible. A terminology server is also exploited towards this end. This work illustrates a prototype of a Careflow Management System based on an international guideline for ischemic stroke treatment, developed by the American Heart Association.

This paper illustrates the implementation of a computerized guideline for pressure ulcer prevention. In particular, it describes the aspects related to the site-specification of a guideline delivered by the Agency for Health Care Policy Research (AHCPR), to its integration with the electronic patient record, and to its implementation within the clinical routine. The primary goal of the system is both to facilitate nurses assessing the risk of ulcer development, and to manage patients at risk by producing daily prevention work-plans. Concerning this functionality, particular attention has been paid to manage nurse's non-compliance with the guideline suggestions and to collect data for evaluating the guideline impact. Moreover, since it is well known that nurses are often over-loaded, the human computer interaction has been studied in such a way to optimise the time spent for data input. An additional functionality of the system is the novice nurses' education - they can browse a graphical representation of the guideline, asking details about the different tasks, and they can simulate patients to obtain real-time advice. The educational tool is written in Java and it is based on a representation of the guideline as a relational database. A preliminary evaluation of the system has been performed and the results are presented on the management of about 40 patients.