Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

Utilization of existing clinical data for improving patient outcomes poses a number of challenging and complex problems involving lack of data integration, the absence of standardization across inhomogeneous data sources and computationally-demanding and time-consuming exploration of very large datasets. In this paper, we will present a robust semantic data integration, standardization and dimensionality reduction method to tackle and solve these problems. Our approach enables the integration of clinical data from diverse sources by resolving canonical inconsistencies and semantic heterogeneity as required by the National Library of Medicine's Unified Medical Language System (UMLS) to produce standardized medical data. Through a combined application of rule-based semantic networks and machine learning, our approach enables a large reduction in dimensionality of the data and thus allows for fast and efficient application of data mining techniques to large clinical datasets. An example application of the techniques developed in our study is presented for the prediction of bariatric surgery outcomes

Genomics in nursing practice in Australia: a critical realist case study

Genomic research continues to 'change the landscape' of healthcare worldwide (Camak, 2016, p.86). Genomics is beginning to reshape healthcare delivery by changing the way we prevent, diagnose, treat and monitor illness, providing the opportunity to offer more precise and tailored treatments. As genomic developments change healthcare, so too are they changing the nursing profession. This revolution has led to a new responsibility for all nurses to be knowledgeable of genomics and incorporate genomics into nursing practice. Research addressing the integration of genomics into nursing practice in Australia is limited. The aim of this study was to determine how nurses engage with genomics in nursing practice in this country. Case study research was used to achieve the research aim. A case study is 'an empirical inquiry that investigates a contemporary phenomenon (the 'case') in depth and within the real-world context' (Yin, 2014, p. 16). A single holistic case study design drawing on the works of Robert Yin (2014) was conducted. This case study was underpinned by a critical realist philosophy. Critical realism is concerned with the nature and knowability of the social world and social phenomena (Schiller, 2016), making it a suitable framework to guide an exploration of Australian nurses' engagement with genomics. Data were collected via a cross-sectional survey of Australian registered nurses and midwives in 2016, and via semi-structured interviews with registered nurses working in oncology departments within a regional Australian hospital in 2018. Key case findings were generated using thematic analysis, and grouped into three categories: Point of learning (education), Point of reference (professional expectations) and Point of care (clinical practice). These three categories were used as a framework to describe the case, and presented in relation to the key tenets of critical realism - (i) the primacy of ontology, (ii) the stratified character of the realworld (reality) and the search for generative mechanisms, and (iii) the interplay between social structures and human agency (Bhaskar, 1975/2008, 1979/1998, 2011). The case indicated that Australian nurses have limited engagement with genomics at the point of learning, point of reference and point of care. Nurses' inadequacy at each of these points is sequential, meaning that if nurses are not knowledgeable about genomics and are unclear about professional expectations, they cannot be expected to adequately integrate genomics into their practice. The critical realist philosophy underpinning the case led to consideration of the way point of learning, point of reference and point of care form the context for nursing practice. How nurses respond to this context determines the extent to which they are able to transform education, policy and practice. Australian nurses' limited engagement with genomics has consequences for the nurse, the patient and the wider nursing profession. This limited engagement must be addressed. It is recommended that (i) genomics be embedded throughout the nursing curricula with healthcare applications made clear to the learner (point of education), (ii) nursing policy articulates the alignment between the NMBA's Standards for Practice and genomic competencies (point of reference), and (iii) nurses incorporate genomics knowledge and skills into practice (point of care). The 'genomic revolution' (Jenkins et al., 2005, p.98) will require further development of Australia's capacity, capability and infrastructure if these are to support the integration of genomic information and technology into the national health system (Australian Health Ministers' Advisory Council, 2017b). As the largest component of the Australian health workforce, nursing cannot ignore the opportunity before us

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Investigating the role of knowledge management in driving the development of an effective business process architecture

Business Process Architecture (BPA) modelling methods are not dynamic and flexible enough to effectively respond to changes. This may create a barrier that contributes to a lack of knowledge and learning capabilities which can affect the BPA regarding its support for a sustainable competitive advantage in an organisation. New business challenges are driving business enterprises to adopt Knowledge Management (KM) as one means of making a positive difference to their performance and competitiveness. However, shortcomings still remain in utilising knowledge management in business processes where efforts were mostly directed towards the integration of knowledge management with business process management but not including BPAs. The idea of applying KM as a memory to be timely retrieved and updated as needed is no longer sufficient. The resource-based view suggests a number of key factors to be investigated and taken into consideration during the development of knowledge management systems. These key factors are known as Knowledge Management Enablers (KMEs). KMEs are crucial for representing KM and understanding how knowledge is created, shared and disseminated. They are also essential to identify available assets and resources, and to clarify how organisational capabilities are created and utilised.This research is aimed at investigating the role of the knowledge management enablers in the development of an effective process architecture. An effective process architecture needs to be dynamic and supports a sustainable competitive advantage in an organisation. Identifying the KMEs, selecting an appropriate BPA method, aligning these KMEs with this method as well as undertaking a critical evaluation of this alignment are the main objectives set for this research. In order to accomplish the research aim and objectives, a resource-based and semantic-enriched framework, namely the KMEOntoBPA has been designed using KMEs to drive the process of BPA development. Organisational structure, culture, information technology, leadership, knowledge context and business repository have been selected as representatives of the KMEs. The object-based BPA modelling, specifically the semantically enriched Riva BPA (srBPA) method, has been adopted in order to embrace the knowledge resources generated by KMEs and utilise them in the derivation and re-configuration of its constitutional elements. These knowledge resources are employed as business objects. They are considered as Candidate Essential Business Entities (CEBEs) in the Riva method, that characterise or represent a form of business of an organisation. The Design Science Research Methodology (DSRM) is used to guide the research phases with an emphasis on the design and development, demonstration and evaluation of the research framework. The KMEOntoBPA has been demonstrated using sufficient and representative core banking case studies of the Treasury, Deposits and Financing. These case studies have been applied to the DSRM iterations beginning with the Treasury as the 1st case study, followed by the Deposits and the Financing case studies.The results have revealed that KMEs utilisation provides an agile generation of representative CEBEs and their corresponding Riva BPA elements, which reflect the real business in each of the core banking business studies. This research also demonstrated the semantic Riva BPA method as an appropriate object-based method that is well aligned with KMEs in exploiting knowledge resources for the development of a dynamic BPA with reference to robustness and learning capabilities. In addition to these results, the research framework, i.e, the KMEOntoBPA has shown an understanding of the flow of knowledge in the bank and has provided several possible advantages such as the accuracy of service delivery and the improvement of the financial control. It also supports the sources of sustainable competitive advantage (SCA): technical capabilities, core competences and social capital.Finally, a number of significant contributions and artefacts have been attained. For example, there is the aKMEOnt which is the abstract ontology that utilises six KMEs in this research to investigate the effectiveness of using such KMEs in driving the development of the BPA. These contributions along with the research results provide a guide to future research directions such as using the aKMEOnt in the development of different business process modelling and deriving the Enterprise Information Architecture (EIA) and Service Oriented Architecture (SOA)

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Improving Access to Primary Healthcare and Cost Effective Care for Underserved Populations

Abstract Background: Research findings continue to demonstrate populations who lack healthcare insurance have limited or restricted access to primary healthcare. Lack of health insurance has been shown to be the most significant contributing factor to poor quality of care for some of the core measures captured by the Agency for Healthcare Research and Quality. Health insurance coverage is highly correlated with an individual\u27s ability to gain access to health care, from doctor visits to filling prescriptions. Purpose: The purpose of the this Capstone Project was to analyze the potential impact of independent Nurse Practitioner models of care on accessibility and cost-effectiveness in the delivery of primary health care for underserved populations, specifically those lacking health care insurance. Methods: Data findings were accomplished with the completion of an instrument survey tool. The population sample size for the study was N=24 based on statistical application of power analysis. The data was analyzed and presented using descriptive statistical measurement. Findings: The process outcome measurement related to access found that 79% of the participants had same day appointments, 5% were scheduled for the next day, and 16% waited two days to be seen. Demographic findings showed 70% of the participants had healthcare insurance, while 30% did not. The outcome measurement addressing healthcare decision making gave unexpected findings in that 100% of the participants answered that they could afford the cost of today\u27s visit. Conclusions: These findings support the Capstone Project study premise that independent NP models of care create cost-effective healthcare for underserved populations, specifically those without healthcare insurance. The study unexpectedly found independent NP models of care also create cost-effective healthcare for populations having healthcare insurance. The study premise addressing improved access to care could not be fully explored due to lack of benchmarking or comparison against other models of healthcare. Further study is needed to evaluate the impact of independent NP models of care for all consumers of primary healthcare

A Life Cycle Approach to the Development and Validation of an Ontology of the U.S. Common Rule (45 C.F.R. § 46)

Requirements for the protection of human research subjects stem from directly from federal regulation by the Department of Health and Human Services in Title 45 of the Code of Federal Regulations (C.F.R.) part 46. 15 other federal agencies include subpart A of part 46 verbatim in their own body of regulation. Hence 45 C.F.R. part 46 subpart A has come to be called colloquially the ‘Common Rule.’ Overall motivation for this study began as a desire to facilitate the ethical sharing of biospecimen samples from large biospecimen collections by using ontologies. Previous work demonstrated that in general the informed consent process and subsequent decision making about data and specimen release still relies heavily on paper-based informed consent forms and processes. Consequently, well-validated computable models are needed to provide an enhanced foundation for data sharing. This dissertation describes the development and validation of a Common Rule Ontology (CRO), expressed in the OWL-2 Web Ontology Language, and is intended to provide a computable semantic knowledge model for assessing and representing components of the information artifacts of required as part of regulated research under 45 C.F.R. § 46. I examine if the alignment of this ontology with the Basic Formal Ontology and other ontologies from the Open Biomedical Ontology (OBO) Foundry provide a good fit for the regulatory aspects of the Common Rule Ontology. The dissertation also examines and proposes a new method for ongoing evaluation of ontology such as CRO across the ontology development lifecycle and suggest methods to achieve high quality, validated ontologies. While the CRO is not in itself intended to be a complete solution to the data and specimen sharing problems outlined above, it is intended to produce a well-validated computationally grounded framework upon which others can build. This model can be used in future work to build decision support systems to assist Institutional Review Boards (IRBs), regulatory personnel, honest brokers, tissue bank managers, and other individuals in the decision-making process involving biorepository specimen and data sharing