Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Objectives: Clinical decision-support systems (CDSSs) are being recognized as important tools for improving quality of care. In this paper, we review the literature to find trends in CDSSs that were developed over the last few decades and give some indication of future directions in developing successful, usable clinical decisionsupport systems. Methods: We searched PubMed for papers that were published during the past five years with the words Decision Support Systems appearing in the title and used our own knowledge of the field for earlier work. Results: The goals of developers of modern CDSSs are to develop systems that deliver needed information and could be integrated with the healthcare’s organizational dynamics. Such CDSSs form part of knowledge-management activities that healthcare organizations employ in order to excel. During the past few decades, we have witnessed a gradual maturation of knowledge representation formalisms and the needed infrastructure for developing integrated CDSSs, including electronic health record systems (EHR), standard terminologies, and messaging standards for exchange of clinical data. The demand for CDSSs that are effective and that will evolve as circumstances change gave rise to methodologies that guide developers on the construction and evaluation of CDSSs. Conclusion: Although there exist many approaches for representing, managing and delivering clinical knowledge, the design and implementation of good and useful systems that will last and evolve are still active areas of research. The gradual maturation of EHR and infrastructure standards should make it possible for CDSSs implementers to make major contributions to the delivery of healthcare. Haux R, Kulikowski C, editors. IMIA Yearbook of Medical Informatics 2006. Methods Inf Med 2006; 45 Suppl 1: S72-80. <s> BIB001 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> OBJECTIVE ::: The aim of this systematic review is to investigate the effectiveness of using computers to deliver patient self-management programs (PSMPs) to patients with chronic illness in health supported settings. ::: ::: ::: METHODS ::: We included randomized controlled trials (RCTs), where the experimental intervention was compared either with an equivalent 'standard' PSMP delivered by staff, usual care or no intervention and reported data either on clinical or behavioral outcomes. We conducted a narrative synthesis, incorporating a small quantitative analysis to enable comparisons across studies. ::: ::: ::: RESULTS ::: A total of 11 studies met the inclusion criteria. There was insufficient evidence to determine whether computer-based PSMPs were superior to standard programs. However, it appeared that these interventions were effective when compared to no intervention. Interventions incorporating behavior change techniques beyond the provision of information appeared more effective than those that did not. ::: ::: ::: CONCLUSION ::: Evidence from the current review, whilst limited, suggests that computer-based PSMPs, delivered in health-supported settings, show potential for changing health behaviors and improving clinical outcomes in patients with chronic illness. ::: ::: ::: PRACTICE IMPLICATIONS ::: Although the approach shows promise, it is premature to recommend the integration of these interventions into clinical practice. However, more well designed trials are warranted to test their efficacy and cost-benefit. <s> BIB002 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> 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. <s> BIB003 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Background. Despite widespread advocacy for shared decision making (SDM), the empirical evidence regarding its effectiveness to improve patient outcomes has not been systematically reviewed. The purpose of this study was to systematically review the empirical evidence linking patient outcomes and SDM, when the decision-making process has been explicitly measured, and to identify under what measurement perspectives SDM is associated with which types of patient outcomes (affective-cognitive, behavioral, and health). Data Sources. PubMed (through December 2012) and hand search of article bibliographies. Study Selection. Studies were included if they empirically 1) measured SDM in the context of a patient-clinician interaction and 2) evaluated the relationship between SDM and at least 1 patient outcome. Data Extraction. Study results were categorized by SDM measurement perspective (patient-reported, clinician-reported, or observer-rated) and outcome type (affective-cognitive, behavioral, or health). Data Synt... <s> BIB004 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Objective: The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. Method: Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS. Result: In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. Conclusion: CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness. <s> BIB005 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. <s> BIB006 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> 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 fibril-lation (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. <s> BIB007 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Clinical decision support systems (CDSSs) have been proved as an efficient way to improve health care quality. However, the inflexibility in integrating multiple clinical practice guidelines (multi-CPGs), the mass input workload of patient data, and the difficulty in system sharing become barriers of CDSSs implementation. In this paper, we proposed a framework of CDSS for chronic disease based on ontology and service-oriented architecture (SOA) to improve these defects. We used ontology for knowledge base construction on multi-CPGs integration to overcome their differences as well as reduce the input procedure of patient data by ontology reasoning. Furthermore, we built the CDSS on an SOA structure to provide flexibility in system and data sharing, such that patients could get suggestions from the same system for self-management of chronic disease. A typical case was used to validate the CDSS functions and accuracy. Two clients were developed to illustrate the SOA superiority. <s> BIB008 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS. <s> BIB009 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Case-based reasoning and data interpretation is an artificial intelligence approach that capitalizes on past experience to solve current problems and this can be used as a method for practical intelligent systems. Case-based data reasoning is able to provide decision support for experts and clinicians in health systems as well as lifestyle systems. In this project we were focusing on developing a solution for healthy ageing considering daily activities, nutrition as well as cognitive activities. The data analysis of the reasoner followed state of the art guidelines from clinical practice. Guidelines provide a general framework to guide clinicians, and require consequent background knowledge to become operational, which is precisely the kind of information recorded in practice cases; cases complement guidelines very well and helps to interpret them. It is expected that the interest in case-based reasoning systems in the health. <s> BIB010 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Discussion <s> Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task. <s> BIB011
The main goal of this survey was to identify a possible prevalence of data-driven AI methodologies in the development of clinical DSSs, with respect to knowledge-based approaches. In fact, 65% of the papers that  . Focusing on the field of clinical DSSs, the paper in  also clearly states that "black boxes are unacceptable", since a clinical DSS must enable end users to understand all the generated suggestions. Very interestingly, DARPA  proposes the combination of knowledge-based and data-driven methods as a solution to meet future challenges of AI, beginning with explainability. Even though every system should implement its own explanation module, which may vary on the basis of the application domain and of the adopted methodologies and algorithms, we believe that, in general, the synergy between different knowledge types and AI methodologies can in fact represent a promising strategy to deal with transparency and explainability issues. Indeed, various examples of hybrid systems, combining formalized knowledge and learnt knowledge have been retrieved in this survey. This could be a direction to follow, in order to improve DSS competence, flexibility, and, of course, explainability. Therefore, it is also our opinion that the already mentioned theme of knowledge integration will remain a key research direction for the future. As a final consideration, it is worth mentioning that this survey has some limitations. In particular, it has a rather narrow focus, namely AI for DSSs. This choice was motivated by the special topic of the current edition of the IMIA Yearbook (2019). As a consequence, the number of examined works is rather limited, and interesting contributions to the field of DSSs may have been left out. Similarly, some works may have been ignored, if not indexed by PubMed or WoS, or not accessible through our queries. Indeed, we are aware of some interesting contributions that were left out, such as, e.g., the work in BIB006 in the area of deep learning for medical image interpretation, and the work in BIB007 in the area of CIGs. CIGs, in particular, represent a very active research field  , testified in our survey by the works in BIB008 BIB009 BIB010 BIB011 , where AI is typically adopted for domain knowledge representation, and for automated reasoning as well. In order to avoid the exclusion of further papers, we did not concentrate only on journal publications, as it was done in BIB003 , and we did not limit our search to papers focusing on a specific study design, as in BIB004 , or to papers strictly selected on the basis of the quality of the reported study, as in BIB002 . Finally, it is worth noting that this study covers a much more limited time frame (two years), with respect to more comprehensive and elaborated surveys, such as the ones in BIB005 BIB001 .