BACKGROUND:Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability.OBJECTIVE:This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician's perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow.METHODS:We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings.RESULTS:In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable.CONCLUSIONS:Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

There are inherent limits in classical computation for it to serve as an adequate model of human cognition. In particular, non-commutativity, while ubiquitous in physics and psychology, cannot be sufficiently handled. We propose that we need a new mathematics that is capable of expressing more complex mathematical structures to tackle those hard X-problems in cognitive science. A quantum mind approach is advocated and we hypothesize a way in which quantum computation might be realized in the brain.

Serious games have been used to increase the accuracy and usege of clinical guidelines during routine clinical practice. This document presents the development of a serious game called SIM-GIC, a video game designed to simulate virtual patients and evaluate the decision making of players based on computer-interpretable clinical guidelines. The system is currently being developed with a content focus on antenatal care guidelines, where a number of obstetric guidelines were coded in XML files.

This tutorial highlights opportunities for clinical informatics to introduce new perceptualcognitive models involving figurative-metaphorical reasoning that will more faithfully reflect the flow of clinical practitioners’ reasoning and actions. It is designed for professionals who focus on clinical informatics systems and their foundations in biomedical and health informatics and cognitive science. The level will be introductory. We raise critical questions arising from recent findings in cognitive science and neuroscience that present serious challenges to current paradigms of knowledge representation and understanding in Artificial Intelligence (AI) and statistical thinking, both relying on categorical and classification/generalization assumptions from traditional analytic philosophy, upon which they build. We describe challenging issues of clinical healthcare informatics which incorporate novel scientific approaches on language interpretation and its relationship with human perception, cognition and interaction. These are essential for developing new approaches to clinical and biomedical informatics problems incorporating the “bodies-as-mechanisms” metaphors used in almost all clinical reasoning with the practical heuristics of clinical care in populations. We describe how imaginative computational modeling, including natural language interpretation that introduces models for metaphors, together with figurative language, sketches, and images, are essential to convey concepts and actions in clinical practice. How we build “continents” of biomedical and health knowledge from “oceans” of data will benefit from advances in such figurative reasoning informatics.

The quantum pigeonhole effect (QPE) appears to contradict the classical pigeonhole principle by allowing three quantum particles distributed between two boxes to exhibit no pairwise coincidence. We show that this effect does not signal a breakdown of classical counting, but instead arises from quantum contextuality. By deriving Bell-type inequalities directly from the pigeonhole principle and reformulating the weak-measurement protocol within a bipartite density-operator framework, we demonstrate that the QPE is a form of Bell's theorem without inequalities. The apparent paradox reflects the impossibility of non-contextual eigenvalue assignments rather than a violation of classical combinatorial logic.

Translational Science is receiving increasing attention in order to accelerate the process of developing useful clinical interventions starting with basic biological discoveries. The stages in translation are denoted T0, T1, T2, T3, T4. The four transitions between stages can pose formidable difficulties and are called the four ‘Valleys of Death’. In this paper we suggest that the methodologies of Persuasive Technology and Behavior Change Support Systems can provide a conceptual and theoretical framework for crossing the valley between T2 (Clinical Research) and T3 (Clinical Implementation). We present several studies that provide intriguing evidence for this suggestion.

This poster presents a proposed Clinical Tasks Ontology (PECTO) designed to evaluate effects of technologies on human performance under controlled conditions, such as clinical simulation scenarios (CSS), across multiple clinical domains including prehospital care. In recent years there has been an explosion of technologies, including Information and Communications Technologies (ICTs) that are designed to assist health workers and improve their performance across a spectrum of clinical activities from pre-hospital care to post-surgical care. However, each new technology introduces its own requirements on the health worker and has the potential to either increase or decrease the perceived workload on the health-worker. Since perceived workload can have significant effects on health worker performance [4], it is important to carefully measure work-load changes and relate these to health worker performance measures such as task errors, and procedure compliance. Clinical Simulation Scenarios are often used to perform controlled experiments in which health workers’ performance on clinical conditions, simulated by various means, including Human Patient Simulators , is observed and measured with and without the technology being studied[5]. The Clinical Tasks Ontology (PECTO) was developed, among other applications, to help design such evaluation experiments. A major objective of such studies is to evaluate the performance of health workers as they perform specific clinical tasks. In this context, the PECTO presents a novel approach for task classification and analysis since previous approaches [6]–[8] do not account for sources of workload, and measurement of human performance in terms of errors and protocol compliance. An ontological approach was selected to build the classification system enabling tasks to have multiple properties that can be related to dependent variables. Previous work in task analysis and task classification include an ontological approach to plans and processes[6], some modeling of event evaluation [7], and a clinical task model of care plans[8], as well as comprehensive approaches to model human factors and workload. For the purpose of this research a Clinical Task (task) is defined as an action accomplished by a health care provider for the purpose of solving a clinical case. A case is a clinical situation that includes provider, patient’s conditions, clinical protocol or guideline to de followed and resources available. A case is expected to have a desirable outcome. Clinical Tasks can be determined by protocols. “Clinical protocols are agreed statements about a specific issue, with explicit steps based on clinical guidelines and/or organizational consensus. A protocol is not specific to a named patient”[6]. The PECTO developed here was part of a broader study focused on evaluating use on computerized clinical guidelines by community health workers[10], [11]. PECTO was constructed in Protégé by two clinical experts and is informed by several previous research studies on task analysis and clinical modeling. Each task in the PECTO has 9 possible properties classes, 75 distinct classes and 14 object properties. When applied to 30 pre-hospital cases for community health workers, following 6 clinical protocols, PECTO resulted in 447 identifiable individual tasks. In a study of task performance by Community Health Workers, application of PECTO enabled differentiation between learning and technology effects. Another application of PECTO is the development of a visual representation of case similarity. There are 5 object properties in addition to 9 basic object properties (relationships) between tasks and dimensions, as seen in Figure 2. PECTO object properties. These object properties allow for additional expressivity for particular inferred task, as a critical task (a task that is indispensable or its not execution ends in patient death). Or to establish complexity of tasks accordingly with number of subtask/goal Figure 1 PECTO Fully expanded Figure 2. PECTO object properties A. Extrinsic Evaluation For extrinsic evaluation a total of 982 tasks (individuals) were derived from 30 clinical cases. Each task was assigned at least a leaf class of each of 9 main domains. A total of 200 ontological distinctive task were obtained after applying reasoner. The most frequent task for the study particular data set was “Verify If Pulse is Present”, is it a task present in 20 out of the 30 cases. An ontological representation of such task is shown in Example 1. II. DISCUSSION AND CONCLUSION We developed a Clinical Task Ontology accounting with human performance factors. One limitation is that this first version is based on the clinical domain of pre-hospital care in which Community Health Workers are the primary clinicians. While this domain is very important in the global health context of developing countries, the developmental methodology can be extended to include other clinical domains . An important application, among others, of the PECTO is the ability to create metrics to compare cases from a human performance perspective. Future work include extending type of task model in order to have a reasoner-based classification of task depending on additional properties, instead of simply asserting the task type.

The St. Petersburg Paradox (SPP), where people are willing to pay only a modest amount for a lottery with infinite expected gain, has been a famous showcase of human (ir)rationality. Since inception multiple solutions have been proposed, including the influential expected utility theory. Criticisms remain due to the lack of a priori justification for the utility function. Here we report a new solution to the long-standing paradox, which focuses on the probability weighting component (rather than the value/utility component) in calculating the expected value of the game. We show that a new Additional Transition Time (AT) based measure, motivated by both physics and psychology, can naturally lead to a converging expected value and therefore solve the paradox.

The existence of clinical forums, social networks where patients exchange information about their disease and treatment, web sites specializing in diseases and accessible to the general public language, among many other changes have modified, at least in part, information asymmetry that makes people visit health services. Taking into account these new virtual possibilities, the shortage of health personnel, and other factors referred to, telehealth has every chance in the near future. The purpose of this review is to identify the use of telehealth as a strategy to overcome some of the barriers to access, availability, and timeliness of service for the maternal-fetal binomial. To establish concepts, definitions are taken from biomedical informatics, e-health, telehealth, telemedicine, and tele-education. It is important to evaluate the different models of telehealth care of the maternal-fetal binomial in order to identify lessons learned and success factors necessary for new implementations of models. The technologies applied include mass media, conventional phone, cell phone, text messaging, internet, video conferencing, e-mail, ultrasound, and fetal echocardiography. After evaluating the experiences, advantages included adherence to treatment, increased coverage, and increased capacity for early detection of events, among others. The disadvantages identified involved problems with internet coverage and poor cell phone signal, and resistance to the use of Information and Communications Technology (ICT). With regard to costs, there is no conclusive evidence that telemedicine interventions and telecare are cost-effective compared to traditional health care. However, current evidence suggests that home telehealth has the potential to reduce costs, but its impact from a social perspective remains uncertain pending the availability of higher quality studies. The general use of telemedicine technologies over time (remote fetal monitoring, ultrasound, and teleconsultation) has resulted in a marked reduction in perinatal mortality when compared to an equivalent group of obstetric patients receiving traditional treatment.

One of the ways to develop health simulations is through the use of computers. This paper presents the use of Intelligent Computer-aided Instruction (ICAI) for the development of an interactive simulator for learning Cardio Pulmonary Resucitation (CPR) which incorporates online tutorials, training and evaluation.

Aksentijevic (1) raises issues with our recent report (2) that bear further clarification because they reflect some fundamental confusion about the nature of random sequences. First, it is essential to distinguish statistics for individual elements (i.e., patterns of length one) vs. statistics for patterns consisting of more than one element (i.e., higher order patterns). Aksentijevic’s comments reflect some confusion between these two. The independent Bernoulli process only guarantees that the statistics for patterns of length one are fully random in the usual sense (e.g., independent and identically distributed), where the mean time and waiting time are indeed equal. Once one starts looking at higher order patterns, different statistical structures can, and do, emerge. For example, in a sequence of length three, pattern HH can happen twice but pattern HT cannot. No “assumption of self-correction” is required here. Different waiting times, clustering, or spreading of pattern occurrences are simply consequences intrinsic to the patterns’ composition (3, 4). Although somewhat difficult to comprehend precisely, we hope it is recognized that we did not fudge our random sequences to create structures where none actually exist. These differences are mathematical facts that are readily observed and replicable by anyone.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is ‘the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.’ Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

In July 2004 Anchorage, Alaska started one of the first veterans courts in the United States. That court has now been in continuous operation for over seven years. This Comment briefly describes the steps taken to establish the Alaska Veterans Court and how the court operates. An overview of the characteristics of participants in and graduates from the court is provided, followed by statistics concerning the effect of the court on recidivism. Several potential future areas of study concerning this court are also identified. The Comment concludes by highlighting the importance of the court and by noting that the benefits provided by the court are currently limited by the absence of funding from any source.

Background: As the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from concepts that were mentioned in passing. We focus on the problem of indexing MEDLINE records, a process that is currently performed by highly trained humans at the National Library of Medicine (NLM). NLM indexers are assisted by a system called the Medical Text Indexer (MTI) that suggests candidate indexing terms.Objective: To improve the ability of MTI to select the core terms in MEDLINE abstracts. These core concepts are deemed to be most important and are designated as "major headings" by MEDLINE indexers. We introduce and evaluate a graph-based indexing methodology called MEDRank that generates concept graphs from biomedical text and then ranks the concepts within these graphs to identify the most important ones.Methods: We insert a MEDRank step into the MTI and compare MTI's output with and without MEDRank to the MEDLINE indexers' selected terms for a sample of 11,803 PubMed Central articles. We also tested whether human raters prefer terms generated by the MEDLINE indexers, MTI without MEDRank, and MTI with MEDRank for a sample of 36 PubMed Central articles.Results: MEDRank improved recall of major headings designated by 30% over MTI without MEDRank (0.489 vs. 0.376). Overall recall was only slightly (6.5%) higher (0.490 vs. 0.460) as was F-2 (3%, 0.408 vs. 0.396). However, overall precision was 3.9% lower (0.268 vs. 0.279). Human raters preferred terms generated by MTI with MEDRank over terms generated by MTI without MEDRank (by an average of 1.00 more term per article), and preferred terms generated by MTI with MEDRank and the MEDLINE indexers at the same rate.Conclusions: The addition of MEDRank to MTI significantly improved the retrieval of core concepts in MEDLINE abstracts and more closely matched human expectations compared to MTI without MEDRank. In addition, MEDRank slightly improved overall recall and F-2. (C) 2011 Elsevier Ireland Ltd. All rights reserved.