The Clinical Laboratory and the Commitment to Quality

The main role that clinical laboratories play in the detection, diagnosis, and treatment of diseases is clearly evident. Clinical laboratories need to sustain a commitment to quality and demonstrate a certifiable level of compliance. Many strategies are used to reduce laboratory errors, including internal QC procedures, external quality assessment programs, implementation of QIs and six-sigma methodology. All strategies should be consistent with the requirements of the international standard for medical laboratory accreditation and suitable for promoting corrective/preventive actions. They must promote total quality and patient safety and be consistent with the definition of a laboratory error. Harmonization process is in progress; however, further efforts must be made. Total quality management must be evaluated periodically. For a patient-centered approach, there is the need to assure that each and every step of the total testing process is correctly performed, that weaknesses are recognized, and that corrective and preventive actions are designed and implemented.

Major Metrics, Concerns, and Assessment Strategy for Mobility Assistive Devices

The ageing of the population is one of the major societal and financial problems. The prevalence of disability increases dramatically by age. The loss of mobility can be devastating to the elderly. Mobility aids are a one-way street to maintain independent mobility. The performance of daily activities is restrained by a series of factors related to the assistive device limitations, or the ones emerged from environmental causes. A literature review reveals minimal tools for assessing mobility assistive devices able to capture users' satisfaction. The chapter presents an assessment methodology in order to investigate assistive mobility devices' limitations, dissatisfaction reasons, and identifies the most appropriate tools to study such limitations and conclude in valid outcomes. One of the valuable characteristics of the study presented in its generalizability since it is not disease oriented. A summary of the results from both the literature review and the real case study on a mixed group of end users are presented in the chapter.

A Model for Determining Process-Wise CTQs for Testing Laboratories

Ensuring the quality of testing laboratory services plays an important role in the field of service sector. Acknowledging the revolution of Six Sigma (SS) in the corporate world and service sector, testing laboratories can also be benefited by the application of the same. SS focuses on process improvement that is a major determinant of customer satisfaction. CTQ (critical to quality) is a quality characteristic of a product or a service that is required to be improved from a customer's point of view. CTQs are the key measurable indicators of a product or process whose performance standards or specification limits must be met in order to satisfy the customer. The aim of this research is to develop a model for establishing CTQ for testing laboratory. The focus is on establishing process-wise CTQ characteristics from the voice of customer taken from direct and indirect customers associated with testing laboratories. The list of established CTQs will be a useful guide for both practitioners and academics willing to evaluate performance of testing laboratories.

Security Improvements for Safer Cross-Border E-Health Services in Europe

Preserving patient safety, patient rights, and safeguarding trust are crucial components for the provision of high-quality medical treatments across borders. This chapter focuses on required technological improvements to address quality challenges through the adoption of generic building blocks (BBs) towards enabling seamless care between European healthcare systems. The authors present important considerations that are relevant to incremental, cross-sectorial advancements for the enhancement of the technology used for the implementation of the directive on the application of patients' rights in cross-border healthcare. These include cross-domain technical BBs to support non-repudiation, capability lookup, dynamic service location, and electronic identification. The authors use cross-border electronic prescription and patient summary, as a case to discuss the use of related international interoperability standards, together with recommendations for future work relevant to the introduction of better quality, trustworthy, cross-border, electronic health services in Europe.

Personalized Nutrition Recommendations in Food Services

The nutritional information on food services could be part of a public health policy against the increasing rate of obesity. The aim of this work is to present the state of art for the nutritional information on food services and the mHealth application usage, worldwide. A particular case study is presented that refers to an Electronic Intelligent System of Personalized Dietary Advice (DISYS) for tablets and smartphones. This application provides nutritional analysis of menu items and personalized suggestions according to the nutritional demands of each customer. The application was characterized as an easy-to-use, comprehensive, and useful tool. Volunteers considered that this application contributes to overall health by enabling the modulation of body weight throughout healthier choices, reduction of calorie intake, and self-monitoring. mHealth applications designed to provide nutritional information seem to be useful for customers as they recommend appropriate nutritional options. They are an effective tool for caterers and nutritionists, who can provide value-added services.

Machine Learning in Healthcare

Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

Digital Transformation Challenges for the Implementation of Quality Electronic Medical Records

The purpose of this work is to analyze the digital transformation challenges related to the implementation of quality electronic medical record systems in Greece, within the wider frame of the European digital single market. The authors explore characteristics of quality, interoperable and secure electronic medical records, and provide an overview of the challenges and factors affecting their adoption, implementation, and operation. Key challenges relate to linking electronic medical records with the workflow, building trust and acceptance by making the best use of champions and key stakeholders, and financing the digital transformation transition and sustainability. The foreseen benefits include better support of medical decisions across all stages of the patient pathway, patients empowered to carry with them clinically significant information, fostering research, and unlocking the full potential of vendors to implement innovative tools to support continuity of care.

Population Health Management, Emerging Technologies, and the Science of the Individual

Population health management (PHM) has been a discipline that studies and facilitates care delivery across a group of individuals or the general population. In the context of PHM, the life science industry has had no motivation to design drugs or devices and even offer treatment of patient management that is only effective for a distinct population segment. The primary outgrowth of the science of individuality, as well as the rising ‘wiki medicine', fully recognizes the uniqueness of the individual. Cloud computing, big data, m-health, and recently, internet of things can offer the resources to deal with numerous shortcomings such as data collection and processing, of the PHM approach, as they facilitate the propagation of the science of individuality.

Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies

The aim of this study is to compare machine learning algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. Four hundred sixteen histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. Fifty percent of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ), and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.

The Science of Individuality and Healthcare Quality

The application of linear models to human systems and healthcare management and quality has improved our understanding of their system structure and function. However, such models often fall short of explaining experimental results or predicting future abnormalities in complex nonlinear systems which help in dissecting and analyzing individual system components. Nonlinear models may better explain how the individual components collectively act and interact to produce a dynamic system in constant flux. They also assist in filling in some of the results that are not adequately explained by linear models. In this context, we should consider the integration of linear and non-linear theories in healthcare quality and management, drawing the initial conditions of chaotic behavior from the standardization of the linear theory, and distinguishing between desirable and undesirable variation relegating statistical process control only to issues of high certainty regarding the outcome.