Timely and appropriate clinical decisions can be lifesaving, and decision support systems could help facilitate this. However, user adoption of clinical decision support systems (CDSS) and their impact on patient care have been disappointing. Contemporary theories in information systems and several evaluation studies have failed to explain or predict the adoption of CDSS. To find out why I conducted a qualitative inquiry using the constructivist grounded theory method. Guided by the theory of planned behaviour, I designed a functional clinical decision support system called DermML. Then, I used it as a stimulus to elicit responses through semi-structured interviews with doctors, a community to which I also belong. Besides the interview data, I also collected demographic data from the participants and anonymous clickstream data from DermML. I found that the clinical community is diverse, and their knowledge needs are varied yet predictable. Using theoretical sampling, constant comparison and iterative conceptualization, I scaled my findings to a substantive theory that explains the difference in practitioners' knowledge needs and predicts adoption based on CDSS type and use context. Having designed DermML myself, the data provided me with design insights that I have articulated as prescriptive design theory. I posit that GT can generate explanatory and predictive theories and prescriptive design theories to guide action. This study eliminates the boundaries between the developers of CDSS, study participants, future users and knowledge mobilization partners. I hope the rich data I collected and the insights I derived help improve the adoption of CDSS and save lives. / Thesis / Doctor of Philosophy (PhD)

Telenursing is a highly specialized area of nursing practice that has evolved in response to the advent of new technologies within the delivery of health care. Telenursing has been defined as “the use of communications and information technology [ICT’s] to deliver health and health care services and information over large and small distances (CRNBC, 2016). Telenurses use health information systems (HIS) in the form of a Clinical Decision Support System (CDSS) to assist callers with their health related concerns on a 24/7 basis. As decision making is an integral part of telenurse practice, particularly because they are using a CDSS while assessing the caller over the phone, it was important to understand the factors that influence the decision making process so as to better support telenurse practice in terms of education as well as other supports. This thesis identified those factors and used Rasmussen’s Decision Ladder as a model in order to provide a framework for telenursing. It was found that there were several factors identified that influenced how telenurses made decisions while using a CDSS. Additionally, the decision ladder was validated as a framework to describe telenurse practice. / Graduate

<p> The constantly changing and dynamic nature of medical knowledge has proven to be challenging for healthcare professionals. Due to reliance on human knowledge the practice of medicine in many cases is subject to errors that endanger patients' health and cause substantial financial loss to both public and governmental health sectors. Computer based clinical guidelines have been developed to help healthcare professionals in practicing medicine. Currently, the decision making steps within most guideline modeling languages are limited to the evaluation of basic logic expressions. On the other hand, data mining analyses aim at building descriptive or predictive mining models that contain valuable knowledge; and researchers in this field have been active to apply data mining techniques on health data. However, this type of knowledge can not be represented using the current guideline specification standards.</p> <p> In this thesis, we focus is on encoding, sharing and finally using the results obtained from a data mining study in the context of clinical care and in particular at the point of care. For this purpose, a knowledge management framework is proposed that addresses the issues of data and knowledge interoperability. Standards are adopted to represent both data and data mining results in an interoperable manner; and then the incorporation of data mining results into guideline-based Clinical Decision Support Systems is elaborated. A prototype tool has been developed as a part of this thesis that serves as the proof of concept which provides an environment for clinical guideline authoring and execution. Finally three real-world clinical case studies are presented.</p> / Thesis / Master of Applied Science (MASc)

Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.

Osteoporosis is a major public health concern, affecting over 200 million people worldwide. There is valid evidence outlining how osteoporosis can be diagnosed and managed, but gaps exist between evidence and practice. Graham’s “Knowledge to Action” (KTA) process for knowledge translation and the Medical Research Council (MRC) framework for complex interventions were used to address these gaps. The first 4 KTA steps were collapsed into 3 phases of the PhD research plan. In PhD Phase 1, a systematic review was conducted to identify tools that facilitate decision making in osteoporosis disease management (DM). Results showed that few DM tools exist, but promising strategies were those that incorporated reminders and education and targeted physicians and patients. PhD Phase 2 used the findings from the systematic review and consultation with clinical and human factors engineering experts to develop a conceptual design of the tool. Multiple components targeted to both physicians and patients at the point of care, and which could be used as a standalone system or modifiable for integration with electronic health record systems were outlined. PhD Phases 3a and 3b were devoted to the assessment of the barriers to knowledge. In Phase 3a, a qualitative study of focus groups was conducted with physicians to identify attitudes and perceived barriers to implementing decision support tools in practice, and to identify the features that should be included in the design. Findings from 4 focus groups combined with aging research, and input from design and information experts were used to transform the conceptual design into a functional prototype. In Phase 3b, each component of the prototype was tested in 3 usability evaluation studies using an iterative, participant-centered approach to assess how well the prototype met end users’ needs. Findings from the usability study informed the final prototype, which is ready for implementation as part of the post PhD plan to fulfill the requirements of the remaining steps of the KTA and MRC frameworks.

Osteoporosis is a major public health concern, affecting over 200 million people worldwide. There is valid evidence outlining how osteoporosis can be diagnosed and managed, but gaps exist between evidence and practice. Graham’s “Knowledge to Action” (KTA) process for knowledge translation and the Medical Research Council (MRC) framework for complex interventions were used to address these gaps. The first 4 KTA steps were collapsed into 3 phases of the PhD research plan. In PhD Phase 1, a systematic review was conducted to identify tools that facilitate decision making in osteoporosis disease management (DM). Results showed that few DM tools exist, but promising strategies were those that incorporated reminders and education and targeted physicians and patients. PhD Phase 2 used the findings from the systematic review and consultation with clinical and human factors engineering experts to develop a conceptual design of the tool. Multiple components targeted to both physicians and patients at the point of care, and which could be used as a standalone system or modifiable for integration with electronic health record systems were outlined. PhD Phases 3a and 3b were devoted to the assessment of the barriers to knowledge. In Phase 3a, a qualitative study of focus groups was conducted with physicians to identify attitudes and perceived barriers to implementing decision support tools in practice, and to identify the features that should be included in the design. Findings from 4 focus groups combined with aging research, and input from design and information experts were used to transform the conceptual design into a functional prototype. In Phase 3b, each component of the prototype was tested in 3 usability evaluation studies using an iterative, participant-centered approach to assess how well the prototype met end users’ needs. Findings from the usability study informed the final prototype, which is ready for implementation as part of the post PhD plan to fulfill the requirements of the remaining steps of the KTA and MRC frameworks.

Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. Gait analysis enables the clinicians to differentiate gait deviations objectively. Diagnostic decision making from gait data only requires high level of medical expertise of neuromusculoskeletal system trained for the purpose. An automated system is expected to decrease this requirement by a &lsquo / transformed knowledge&rsquo / of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: &ldquo / Normal&rdquo / , &ldquo / Mild&rdquo / , &ldquo / Moderate&rdquo / , and &ldquo / Severe&rdquo / . Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.

Clinical guidelines are developed to assist healthcare practitioners to make decisions on a patient&#039 / s medical problems and as such they communicate with external applications to retrieve patient data, to initiate medical actions through clinical workflows and to transmit information to alert/reminder systems. The interoperability problems in the healthcare IT domain for interacting with heterogeneous clinical workflow systems and Electronic Healthcare Record (EHR) Systems prevent wider deployment of clinical guidelines because each deployment requires a tedious custom adaptation phase. In this thesis, we provide machine processable mechanisms that express the semantics of clinical guideline interfaces so that automated processes can be used to access the clinical resources for guideline deployment and execution. For this purpose, we propose a semantically enriched clinical guideline representation formalism by extending one of the computer interpretable guideline representation languages, GuideLine Interchange Format (GLIF). To be able to deploy the semantically extended guidelines to healthcare settings semi-automatically, the underlying application&#039 / s semantics must also be available. We describe how this can be achieved based on two prominent implementation technologies in use in the eHealth domain: Integrating Healthcare Enterprise (IHE) Cross Enterprise Document Sharing Integration Profile (XDS) for discovering and exchanging EHRs and Web service technology for interacting with the clinical workflows and wireless medical sensor devices. Since the deployment and execution architecture should be dynamic, and address the heterogeneity of underlying clinical environment, the deployment and execution is coordinated by a multi-agent system. The system described in this thesis is realized within the scope of the SAPHIRE Project.

The use of clinical decision support (CDS) systems has grown progressively during the past decades. CDS systems are associated with improved patient safety and outcomes, better prescription and diagnosing practices by clinicians and lower healthcare costs. Quality assurance of these systems is critical, given the potentially severe consequences of any errors. Yet, after several decades of research, there is still no consensual or standardized approach to their verification and validation (V&amp;V). This project is a descriptive and exploratory case study aiming to provide a practical description of how Cambio CDS, a market-leading developer of CDS services, conducts its V&amp;V process. Qualitative methods including semi-structured interviews and coding-based textual data analysis were used to elicit the description of the V&amp;V approaches used by the company. The results showed that the company’s V&amp;V methodology is strongly influenced by the company’s model-driven development approach, a strong focus and leveraging of domain knowledge and good testing practices with a focus on automation and test-driven development. A few suggestions for future directions were discussed.

Medical records are rapidly being digitized to electronic medical records. Although Electronic Medical Records (EMRs) improve administration, billing, and logistics, an open research problem remains as to how doctors can leverage EMRs to enhance patient care. This thesis describes a system that analyzes a patient's evolving EMR in context with available biomedical knowledge and the accumulated experience recorded in various text sources including the EMRs of other patients. The aim of the Clinician Decision Support (CDS) Dashboard is to provide interactive, automated, actionable EMR text-mining tools that help improve both the patient and clinical care staff experience. The CDS Dashboard, in a secure network, helps physicians find de-identified electronic medical records similar to their patient's medical record thereby aiding them in diagnosis, treatment, prognosis and outcomes. It is of particular value in cases involving complex disorders, and also allows physicians to explore relevant medical literature, recent research findings, clinical trials and medical cases. A pilot study done with medical students at the Virginia Tech Carilion School of Medicine and Research Institute (VTC) showed that 89% of them found the CDS Dashboard to be useful in aiding patient care for doctors and 81% of them found it useful for aiding medical students pedagogically. Additionally, over 81% of the medical students found the tool user friendly. The CDS Dashboard is constructed using a multidisciplinary approach including: computer science, medicine, biomedical research, and human-machine interfacing. Our multidisciplinary approach combined with the high usability scores obtained from VTC indicated the CDS Dashboard has a high potential value to clinicians and medical students. / Master of Science