Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.

Impulsando las directrices de la Ley de Calidad del SNS: modelos computacionales de guías de práctica clínica / Promoting directives of the Quality Law of the Spanish National Health System: Computer-interpretable clinical practice guidelines

La Ley de cohesión y calidad del Sistema Nacional de Salud promueve la utilización de nuevas tecnologías para hacer posible la aplicación de la evidencia científica por los profesionales sanitarios. En este sentido, existen herramientas tecnológicas, conocidas como modelos computacionales de guías de práctica clínica (computer-interpretable guidelines), que pueden ayudar a la consecución de este objetivo desde un prisma innovador. Su adopción puede llevarse a cabo de forma iterativa, teniendo un gran potencial inicial como herramientas formativas, de calidad y seguridad del paciente, en la toma de decisiones compartidas y, opcionalmente, podrán ser integradas con la historia clínica electrónica una vez sean validadas de forma rigurosa. En este artículo se presentan los avances de dichas herramientas, se revisan proyectos internacionales y experiencias propias en los que han demostrado su valor, y se ponen de manifiesto las ventajas, riesgos y limitaciones que presentan desde un punto de vista clínico

The Cohesion and Quality Act of the National Health System promotes the use of new technologies to make it possible for health professionals put the scientific evidence into practice. In order to do this, there are technological tools, known as of computer-interpretable guidelines, which can help achieve this goal from an innovation perspective. They can be adopted using an iterative process, having a great initial potential as an educational tool, of quality and safety of the patient, in the decision making and, optionally, can be integrated with the electronic medical history, once they are rigorously validated. This article presents updates on these tools, reviews international projects, and personal experiences in which they have demonstrated their value, and highlights the advantages, risks, and limitations they present from a clinical point of view

[Promoting directives of the Quality Law of the Spanish National Health System: Computer-interpretable clinical practice guidelines]. / Impulsando las directrices de la Ley de Calidad del SNS: modelos computacionales de guías de práctica clínica.

The Cohesion and Quality Act of the National Health System promotes the use of new technologies to make it possible for health professionals put the scientific evidence into practice. In order to do this, there are technological tools, known as of computer-interpretable guidelines, which can help achieve this goal from an innovation perspective. They can be adopted using an iterative process, having a great initial potential as an educational tool, of quality and safety of the patient, in the decision making and, optionally, can be integrated with the electronic medical history, once they are rigorously validated. This article presents updates on these tools, reviews international projects, and personal experiences in which they have demonstrated their value, and highlights the advantages, risks, and limitations they present from a clinical point of view.

Development of a computer-interpretable clinical guideline model for decision support in the differential diagnosis of hyponatremia.

INTRODUCTION: Hyponatremia is the most common type of electrolyte imbalance, occurring when serum sodium is below threshold levels, typically 135mmol/L. Electrolyte balance has been identified as one of the most challenging subjects for medical students, but also as one of the most relevant areas to learn about according to physicians and researchers. We present a computer-interpretable guideline (CIG) model that will be used for medical training to learn how to improve the diagnosis of hyponatremia applying an expert consensus document (ECDs). METHODS: We used the PROForma set of tools to develop the model, using an iterative process involving two knowledge engineers (a computer science Ph.D. and a preventive medicine specialist) and two expert endocrinologists. We also carried out an initial validation of the model and a qualitative post-analysis from the results of a retrospective study (N=65 patients), comparing the consensus diagnosis of two experts with the output of the tool. RESULTS: The model includes over two-hundred "for", "against" and "neutral" arguments that are selectively triggered depending on the input value of more than forty patient-state variables. We share the methodology followed for the development process and the initial validation results, that achieved a high ratio of 61/65 agreements with the consensus diagnosis, having a kappa value of K=0.86 for overall agreement and K=0.80 for first-ranked agreement. CONCLUSION: Hospital care professionals involved in the project showed high expectations of using this tool for training, but the process to follow for a successful diagnosis and application is not trivial, as reported in this manuscript. Secondary benefits of using these tools are associated to improving research knowledge and existing clinical practice guidelines (CPGs) or ECDs. Beyond point-of-care clinical decision support, knowledge-based decision support systems are very attractive as a training tool, to help selected professionals to better understand difficult diseases that are underdiagnosed and/or incorrectly managed.

Assessment of a personalized and distributed patient guidance system.

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.

Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7's Virtual Medical Record standard.

OBJECTIVE: We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support systems. MATERIALS AND METHODS: To achieve seamless communication between the personal health record and heterogeneous data consumers, we used a three-pronged approach. First, the choice of the HL7 vMR as a message model for all components accompanied by the use of medical vocabularies eases their semantic interoperability. Second, the DI follows a service-oriented approach to provide access to system components. Third, an XML database provides the data layer.Results The DI supports requirements of a guideline-based clinical decision support system implemented in two clinical domains and settings, ensuring reliable and secure access, high performance, and simplicity of integration, while complying with standards for the storage and processing of patient information needed for decision support and analytics. This was tested within the framework of a multinational project (www.mobiguide-project.eu) aimed at developing a ubiquitous patient guidance system (PGS). DISCUSSION: The vMR model with its extension mechanism is demonstrated to be effective for data integration and communication within a distributed PGS implemented for two clinical domains across different healthcare settings in two nations.

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks.

OBJECTIVE: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented. MATERIALS AND METHODS: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile. RESULTS: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care. CONCLUSION: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.