Master of Science

thesisLack of information is a serious concern for clinicians. Information resources can address this problem, leading to improvements in decision making and patient outcomes. Genomics is an information-rich domain where searching for information can be complex. For example, most physicians agree that pharmacogenomics can be used to improve the quality of care, and there is evidence that many patients harbor actionable pharmacogenomic variation. However, surveys have shown that physicians feel their knowledge of pharmacogenomics to be inadequate. This represents an information need. A natural approach to meet this need is to provide context-aware access to the precise information needed. The Health Level 7 Context-Aware Knowledge Retrieval Standard, a.k.a the Infobutton, offers a modality to deliver context-aware knowledge into electronic health record (EHR) systems. OpenInfobutton is a reference implementation of this standard that offers an open-source instantiation. In this thesis, we aimed to provide insight into pharmacogenomics information needs and an automated mechanism for addressing these needs. Such work can aid the design of tools that support clinical decisions in genomics

Ontologies Applied in Clinical Decision Support System Rules:Systematic Review

BackgroundClinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. ObjectiveOntologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. MethodsThe literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. ResultsCDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. ConclusionsOntologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules

HealthTranslator: automatic annotation of Web documents in order to assist health consumer's searches

A Web é agora uma das principais fontes de informação relativa a saúde. No entanto, consumidores de saúde nem sempre compreendem facilmente a informação obtida, principalmente devido a uma discrepância significante nas terminologias usadas por leigos e especialistas de saúde. Este trabalho apresenta uma ferramenta, disponível como uma extensão para o Google Chrome, que ajuda os utilizadores a ultrapassar as dificuldades com que se deparam ao ler documentos na Web relacionados com saúde. Esta efetua uma anotação automática de conceitos médicos em documentos Web com apresentação de informação adicional ao utilizador, como definição do conceito, relações com conceitos semelhantes ou ligação a recursos externos. As linguagens portuguesa e inglesa serão suportadas. De forma a avaliar a solução desenvolvida, a sua anotação será comparada com um corpus português e inglês. Enquanto o primeiro será manualmente anotado, o último é uma anotação automática efetuada por uma extensão semelhante, designada Medical Translator. Também será feito um estudo de utilizador, de forma a perceber a sua opinião e avaliar a utilidade da ferramenta. É também apresentado o planeamento para a dissertação, de modo a atingir os objetivos definidos.The Web is now one of the main sources of health related information. However, health consumers do not always easily understand the retrieved information, mainly because of a significant gap between terminologies used by laypeople and medical experts. This work presents a tool, available as Google Chrome extension, that helps users to overcome the difficulties they face when reading health Web documents. It provides automatic annotation of medical concepts in Web documents with additional information, such as concept definition, relationships with related concepts or linkage to external resources. Both Portuguese and English languages will be supported. In order to evaluate the developed solution, its' annotation will be compared with Portuguese and an English corpus. While the first one will be manually annotated, the latter is an automatic annotation performed by a similar extension, named Medical Translator. A user study will also be conducted, in order to understand their opinion and evaluate the tool utility. It is also presented the planning for the dissertation, in order to achieve the defined goals

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

An Access Control Model to Facilitate Healthcare Information Access in Context of Team Collaboration

The delivery of healthcare relies on the sharing of patients information among a group of healthcare professionals (so-called multidisciplinary teams (MDTs)). At present, electronic health records (EHRs) are widely utilized system to create, manage and share patient healthcare information among MDTs. While it is necessary to provide healthcare professionals with privileges to access patient health information, providing too many privileges may backfire when healthcare professionals accidentally or intentionally abuse their privileges. Hence, finding a middle ground, where the necessary privileges are provided and malicious usage are avoided, is necessary. This thesis highlights the access control matters in collaborative healthcare domain. Focus is mainly on the collaborative activities that are best accomplished by organized MDTs within or among healthcare organizations with an objective of accomplishing a specific task (patient treatment). Initially, we investigate the importance and challenges of effective MDTs treatment, the sharing of patient healthcare records in healthcare delivery, patient data confidentiality and the need for flexible access of the MDTs corresponding to the requirements to fulfill their duties. Also, we discuss access control requirements in the collaborative environment with respect to EHRs and usage scenario of MDTs collaboration. Additionally, we provide summary of existing access control models along with their pros and cons pertaining to collaborative health systems. Second, we present a detailed description of the proposed access control model. In this model, the MDTs is classified based on Belbin’s team role theory to ensure that privileges are provided to the actual needs of healthcare professionals and to guarantee confidentiality as well as protect the privacy of sensitive patient information. Finally, evaluation indicates that our access control model has a number of advantages including flexibility in terms of permission management, since roles and team roles can be updated without updating privilege for every user. Moreover, the level of fine-grained control of access to patient EHRs that can be authorized to healthcare providers is managed and controlled based on the job required to meet the minimum necessary standard and need-to-know principle. Additionally, the model does not add significant administrative and performance overhead.publishedVersio

Medical Informatics

Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics

Personalized Medicine: the Future of Health Care

BACKGROUND: Most medical treatments have been designed for the “average patients”. As a result of this “one-size-fits-all-approach”, treatments can be very successful for some patients but not for others. The issue is shifting by the new innovation approach in diseases treatment and prevention, precision medicine, which takes into account individual differences in people\u27s genes, environments, and lifestyles. This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants.CONTENT: Researchers have discovered hundreds of genes that harbor variations contributing to human illness, identified genetic variability in patients\u27 responses to different of treatments, and from there begun to target the genes as molecular causes of diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients\u27 responses to targeted therapy.SUMMARY: Personalized medicine seeks to use advances in knowledge about genetic factors and biological mechanisms of disease coupled with unique considerations of an individual\u27s patient care needs to make health care more safe and effective. As a result of these contributions to improvement in the quality of care, personalized medicine represents a key strategy of healthcare reform

Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric