Systems Modeling of Proliferation Mechanisms in Childhood Acute Lymphoblastic Leukemia

Acute Lymphoblastic Leukemia (ALL) is the most common neoplasm in children, but the mechanisms underlying leukemogenesis are poorly understood, despite the existence of several theories regarding the mechanics of leukemic cell proliferation. However, with the advent of new biological principles, it appears that a systems approach could be used in an effective search of global patterns in biological systems, so as to be able to model the phenomenon of proliferation and gain a better understanding of how cells may progress from a healthy to a diseased state. This chapter reviews the current knowledge on proliferation dynamics, along with a discussion of the several existing theories on leukemogenesis and their comparison with the theories governing general oncogenesis. Furthermore, the authors present some “in-house” experimental data that support the view that it is possible to model leukemic cell proliferation and explain how this has been performed in in vitro experiments.

E-Health Decision Support Systems for the Diagnosis of Dementia Diseases

The increase of incidence and prevalence of dementia diseases makes urgent the clinical community to be supported in the difficult diagnostic process of dementia patients. E-health decision support systems, based on innovative algorithms able to extract information from in vivo neuroimaging studies, can make a quite different way to perform neurological diagnosis and enlarge domains and actors involved in the diagnostic process. A number of image-processing methods that extract potential biomarkers from the in vivo neuroimaging studies have been proposed (e.g. volume segmentation, voxel-based statistical mapping). A number of new shape descriptors have also been developed (e.g. texture-based). Other approaches (e.g. machine learning, pattern recognition) have been proven effective, for both structural and functional data, in making automatic diagnoses. The integration of these sophisticated diagnostic tools into secure, efficient, and wide e-infrastructures is the prerequisite for the real implementation of e-health support services to the clinical and industrial communities managing dementia patients.

Improving Patient Safety with Telemedicine

The patient safety and quality problem in health care are considerable. To err is human, but primum non nocere (first, do no harm) means work against the adverse events and work for good quality. The purpose of the chapter is to explore the potential role for patient safety of a telemedicine network organization with centralization and decentralization taken into consideration. Network organization is of importance for strengthening of professional communities and competence complementation. For the building of strong professional communities, some size can be necessary, and this can be promoted by centralization. In the telemedicine era, a new way of organizing can be network organization, combined with centralization and decentralization. Not to do anything with the significant patient safety and quality problem is fundamentally wrong and morally indefensible. To err is human, to continue to err is diabolic, and to forgive is divine.

An Ontology-Driven Approach to Clinical Evidence Modelling Implementing Clinical Prediction Rules

Diagnostic error is a major threat to patient safety in the context of the primary care setting. Evidence-based medicine has been advocated as one part of a solution. The ability to effectively apply evidence-based medicine implies the use of information systems by providing efficient access to the latest peer-reviewed evidence-based information sources. A fundamental challenge in applying information technology to a diagnostic clinical domain is how to formally represent known clinical knowledge as part of an underlying evidence repository. Clinical prediction rules (CPRs) can provide the basis for a formal representation of knowledge. The TRANSFoRm project defines the architectural components required to deliver a solution by providing an ontology driven clinical evidence service to support provision of diagnostic tools, designed to be maintained and updated from electronic sources of research data, to assist primary care clinicians during the patient consultation through delivery of up to date evidence based diagnostic rules.

Assessment and Confidence Estimates of Newborn Brain Maturity from Sleep EEG

Electroencephalograms (EEGs) recorded from sleeping newborns contain information about their brain maturity. Although these EEGs are very weak and distorted by artifacts, and widely vary during sleep hours as well as between patients, the main maturity-related patterns are recognizable by experts. However, experts are typically incapable of quantitatively providing accurate estimates of confidence in assessments. The most accurate estimates are, in theory, provided with the Bayesian methodology of probabilistic inference which has been practically implemented with Markov Chain Monte Carlo (MCMC) integration over a model parameter space. Typically this technique aims to approximate the integral by sampling areas of interests with high likelihood of the true model. In practice, the likelihood distributions are typically multimodal, and for this reason, the existing MCMC techniques have been shown incapable of providing the proportional sampling of multiple areas of interest. Besides, the lack of prior information increases this problem especially for a large model parameter space, making its detailed exploration impossible within a reasonable time. Specifically, the absence of information about EEG features has been shown affecting the results of the Bayesian assessment of EEG maturity. In this chapter, authors discuss how the posterior information can be used in order to mitigate the problem of disproportional sampling in order to improve the accuracy of assessments. Having analyzed the posterior information, they found that the MCMC integration tends to oversample the areas in which a model parameter space includes EEG features making a weak contribution to the assessment. This observation has motivated the authors to cure the results of MCMC integration, and when they tested the proposed method on the EEG recordings, they found an increase in the accuracy of assessment.

Avoiding Adverse Consequences of E-Health

E-health has been heralded as a possible solution to reducing the major problem of preventable medical error. However, the available evidence is not strong, and there is increasing awareness that implementation of health information technology can result in error of itself. E-health has the potential to alter workflows in unpredictable ways, introduce new types of error, and change the way clinicians communicate and behave. It is necessary to educate designers and clinicians about these problems so that solutions can be created that minimize risk. Given the pace of e-health development, agreement on a broad strategy is needed now to ensure that it helps to improve safety for users of health services. The principles of patient safety should be integrated into e-health solutions so that adverse consequences are avoided.

The Use of Medical Simulation to Improve Patient Safety

In healthcare, medical simulation describes a heterogeneous group of methods that aim to replicate some aspect of clinical care. This chapter discusses the extent to which simulation is being used to explore facets of patient safety from the design of specific devices that are being used in the context of clinical work, to the broader organizational design of systems. The most commonly associated aspect of medical simulation is the education of clinicians in relation to improving patient safety. Few studies have attempted to examine how simulation education can affect patient safety; however, there is an emerging body of evidence that simulation training, particularly with regard to the training of teams, can have a positive effect on patient safety outcomes. The barriers commonly incurred with the implementation of simulation training warrant exploration and discussion so that an informed and strategic approach is adopted to allow the future direction of medical simulation in healthcare to be educationally sound and financially sustainable.

RFID Technologies in the Health Sector

RFID technologies are increasingly acquiring a considerable relevance in the field of patients’ assistance. This contributes to independent living and quality of life for many patients by reducing the need of caretakers and private nursing. In this chapter, the authors present an analysis of the use of RFID technologies for three different purposes: (i) collection and access of all patient records; (ii) tracking the movements of medical equipment; (iii) monitoring the health of patients. Moreover, the authors discuss the privacy implications and existing solutions. Privacy issue represents an obstacle to the acceptance of this technology. Transmitting unprotected signals, compressed in a standard format, through the RFID technology, is associated with the risk that someone might monitor these transmissions, accidentally or intentionally. Patient-monitoring applications require exceptional performance and quality of service to provide accurate, live information to the monitoring side.

Learning to Accept Uncertainty as a Quality of Care Dimension

Uncertainty in clinical decision-making is integral to the pathways chosen while applying available knowledge to a patient’s care process. This chapter explores the ways in which uncertainty can be incorporated into the understanding of better performance approaches, and is thus proposed as an enabling dimension of performance. Tracing the keystone definitions of uncertainty from Hippocrates to Osler, the discussion addresses the dimensions of decision - making appropriateness, its timeliness, the expected and actual value of the care services, and the role of systematic communication between providers of care as well as with patients. The crucial role of Health Information Technology is emphasized, and a unifying model is proposed where the inclusion of uncertainty as a dimension of performance promotes an encompassing evaluation of the quality of health care services.

Risk Perception as a Patient Safety Dimension

Clinical risk management has been shaped by the growing consciousness of the number of errors, incidents, and near-misses that occur in healthcare and their impact on the safety of patients. Instead, patient safety emphasizes performance, team and system orientation, the regulatory framework, and patient centeredness. However, the patient safety movement, dealing either with the person or system approach, is only one aspect of patient safety. Risk perception, as a patient safety dimension, comes into play through personalized self-care. As a result, tailored health communication, that is, any combination of information and behavior change strategies, intended to reach one specific person based on information unique to that person, and derived from an individual assessment, is essential.