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Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians.

Background:Despite multidisciplinary tumour boards (MTBs), non-compliance with clinical practice guidelines is still observed for breast cancer patients. Computerised clinical decision support systems (CDSSs) may improve the implementation of guidelines, but cases of non-compliance persist.Methods:OncoDoc2, a guideline-based decision support system, has been routinely used to remind MTB physicians of patient-specific recommended care plans. Non-compliant MTB decisions were analysed using a multivariate adjusted logistic regression model.Results:Between 2007 and 2009, 1624 decisions for invasive breast cancers with a global non-compliance rate of 8.3% were analysed. Patient factors associated with non-compliance were age>80 years (odds ratio (OR): 7.7; 95% confidence interval (CI): 3.7–15.7) in pre-surgical decisions; microinvasive tumour (OR: 5.2; 95% CI: 1.5–17.5), surgical discovery of microinvasion in addition to a unique invasive tumour (OR: 4.2; 95% CI: 1.4–12.5), and prior neoadjuvant treatment (OR: 4.2; 95% CI: 1.1–15.1) in decisions with recommendation of re-excision; age<35 years (OR: 4.7; 95% CI: 1.9–11.4), positive hormonal receptors with human epidermal growth factor receptor 2 overexpression (OR: 15.7; 95% CI: 3.1–78.7), and the absence of prior axillary surgery (OR: 17.2; 95% CI: 5.1–58.1) in adjuvant decisions.Conclusion:Residual non-compliance despite the use of OncoDoc2 illustrates the need to question the clinical profiles where evidence is missing. These findings challenge the weaknesses of guideline content rather than the use of CDSSs.

Objectives: To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs). Design: Scoping review. Methods: 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. Results: 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. Conclusions and implications: 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.

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SummaryBackground: Each year, the International Medical Informatics Association Yearbook recognizes significant scientific papers, labelled as "best papers", published the previous year in the subfields of biomedical informatics that correspond to the different section topics of the journal. For each section, about fifteen pre-selected "candidate" best papers are externally peer-reviewed to select the actual best papers. Although based on the available literature, little is known about the pre-selection process. Objective: To move toward an explicit formalization of the candidate best papers selection process to reduce variability in the literature search across sections and over years. Methods: A methodological framework is proposed to build for each section topic specific queries tailored to PubMed and Web of Science citation databases. The two sets of returned papers are merged and reviewed by two independent section editors and citations are tagged as "discarded", "pending", and "kept". A protocolized consolidation step is then jointly conducted to resolve conflicts. A bibliographic software tool, BibReview, was developed to support the whole process. Results: The proposed search strategy was fully applied to the Decision Support section of the 2013 edition of the Yearbook. For this section, 1124 references were returned (689 PubMed-specific, 254 WoS-specific, 181 common to both databases) among which the 15 candidate best papers were selected. Conclusions: The search strategy for determining candidate best papers for an IMIA Yearbook's section is now explicitly specified and allows for reproducibility. However, some aspects of the whole process remain reviewer-dependent, mostly because there is no characterization of a "best paper".

The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guidelinebased decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.(consolidated figures in 2018), breast cancer mortality is declining in France and breast cancer is one of the best prognosis cancers with among the best five and ten-year survival rates (87 % and 76 %, respectively). However, it still remains a therapeutic challenge especially for triple negative breast cancers and HER2+ breast cancers, for which improvements are both possible and necessary [1].Clinical practice guidelines (CPGs) are free-text documents developed by National agencies or academic associations to provide the best recommendations for the management of a set of selected patient profiles. These recommendations are built from published clinical research results and represent the state of the art following evidencebased medicine principles [2,3]. Although studies have shown that implementing oncology CPGs does improve clinical outcomes in both overall and recurrence-free survivals of cancer patients [4][5][6][7][8][9][10][11], there