ONTOLOGICAL META-ANALYSIS AND SYNTHESIS OF HIPAA

We present ontological meta-analysis and synthesis of HIPAA (Health Insurance Portability and Accountability Act) as a method for reviewing, mapping, and visualizing the research literature in the domain cumulatively, logically, systematically, and systemically. The method will highlight the domain\u27s bright spots which are heavily emphasized, the light spots which are lightly emphasized, the blind spots which have been overlooked, and the blank spots which may never be emphasized. It will highlight the biases and asymmetries in the domain\u27s research; the research can then be realigned to make it stronger and more effective. We present an ontology for HIPAA, map the literature onto the ontology, and highlight its bright, light, and blank/blind spots in an ontological map. We conclude with a discussion of how such a map can be used to realign HIPAA research and practice

Ontology of Strategic Information Systems Planning

Strategically planning and aligning information systems is still one the most challenging IT tasks for organizations. Literature has contributed to describe and analyze the phenomena labeling the process of Strategic Information Systems Planning (SISP) as the one that pursues the alignment of the IS/IT initiatives to achieve business goals. Statistics reveal, however, that those goals are significantly not being achieved, leaving the discussion open to know whether the SISP models, frameworks and methods are correct, complete, applicable, feasible or not. In order to understand and visualize the potential gaps and biases in the SISP literature, the paper introduces an ontology of the SISP process that allows systematically and symmetrically expand study to contribute to maturation of the scientific field as well as to identify the critical omissions within it. Later, the ontological analysis will allow the visualization of bright, light, and blind/blank areas of knowledge documented on SISP

Dynamic Healthcare Connectivity and Collaboration with Multi-Agent Systems

With the growth of international healthcare operations, methods to improve connectivity are sought, along with a reduction in major barriers of electronic connectivity between global trading partners. To address these barriers, a conceptual agent-based framework following a proposed methodology for the analysis and design stages is developed to allow for improved ease of connectivity and interpretability between international trading partners. This framework is comprised of agents and is applied to connectivity between healthcare entities such as payers and providers. While many healthcare entities exchange information electronically, few do so without some form of manual intervention. Information systems may be engaged to further enhance the healthcare industry. Given the increases in costs and international presence, it is vital to make use of electronic systems that improve overall quality and cost of healthcare

Australia's national health programs: An ontological mapping

Australia has a large number of health program initiatives whose comprehensive assessment will help refine and redefine priorities by highlighting areas of emphasis, under-emphasis, and non-emphasis. The objectives of our research are to: (a) systematically map all the programs onto an ontological framework, and (b) systemically analyse their relative emphases at different levels of granularity. We mapped all the health program initiatives onto an ontology with five dimensions, namely: (a) Policy-scope, (b) Policy-focus, (c) Outcomes, (d) Type of care, and (e) Population served. Each dimension is expanded into a taxonomy of its constituent elements. Each combination of elements from the five dimensions is a possible policy initiative component. There are 30,030 possible components encapsulated in the ontology. It includes, for example: (a) National financial policies on accessibility of preventive care for family, and (b) Local-urban regulatory policies on cost of palliative care for individual-aged. Four of the authors mapped all of Australia's health programs and initiatives on to the ontology. Visualizations of the data are used to highlight the relative emphases in the program initiatives. The dominant emphasis of the program initiatives is: [National] [educational, personnel-physician, information] policies on [accessibility, quality] of [preventive, wellness] care for the [community]. However, although (a) information is emphasized technology is not and (b) accessibility and quality are emphasized cost, satisfaction, and quality are not. The ontology and the results of the mapping can help systematically reassess and redirect the relative emphases of the programs and initiatives from a systemic perspective

Big data architecture for pervasive healthcare: a literature review

Pervasive healthcare aims to deliver deinstitutionalised healthcare services to patients anytime and anywhere. Pervasive healthcare involves remote data collection through mobile devices and sensor network which the data is usually in large volume, varied formats and high frequency. The nature of big data such as volume, variety, velocity and veracity, together with its analytical capabilities com-plements the delivery of pervasive healthcare. However, there is limited research in intertwining these two domains. Most research focus mainly on the technical context of big data application in the healthcare sector. Little attention has been paid to a strategic role of big data which impacts the quality of healthcare services provision at the organisational level. Therefore, this paper delivers a conceptual view of big data architecture for pervasive healthcare via an intensive literature review to address the aforementioned research problems. This paper provides three major contributions: 1) identifies the research themes of big data and pervasive healthcare, 2) establishes the relationship between research themes, which later composes the big data architecture for pervasive healthcare, and 3) sheds a light on future research, such as semiosis and sense-making, and enables practitioners to implement big data in the pervasive healthcare through the proposed architecture

Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

Consent forms serve as evidence of permissions granted by patients for clinical procedures. As the recognized value of biospecimens and health data increases, many clinical consent forms also seek permission from patients or their legally authorized representative to reuse residual clinical biospecimens and health data for secondary purposes, such as research. Such permissions are also granted by the government, which regulates how residual clinical biospecimens may be reused with or without consent. There is a need for increasingly capable information systems to facilitate discovery, access, and responsible reuse of residual clinical biospecimens and health data in accordance with these permissions. Semantic web technologies, especially ontologies, hold great promise as infrastructure for scalable, semantically interoperable approaches in healthcare and research. While there are many published ontologies for the biomedical domain, there is not yet ontological representation of the permissions relevant for reuse of residual clinical biospecimens and health data. The Informed Consent Ontology (ICO), originally designed for representing consent in research procedures, may already contain core classes necessary for representing clinical consent processes. However, formal evaluation is needed to make this determination and to extend the ontology to cover the new domain. This dissertation focuses on identifying the necessary information required for facilitating responsible reuse of residual clinical biospecimens and health data, and evaluating its representation within ICO. The questions guiding these studies include: 1. What is the necessary information regarding permissions for facilitating responsible reuse of residual clinical biospecimens and health data? 2. How well does the Informed Consent Ontology represent the identified information regarding permissions and obligations for reuse of residual clinical biospecimens and health data? We performed three sequential studies to answer these questions. First, we conducted a scoping review to identify regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data in the US, the permissions by which reuse of residual clinical biospecimens and health data may occur, and key issues that must be considered when interpreting these regulations and norms. Second, we developed and tested an annotation scheme to identify permissions within clinical consent forms. Lastly, we used these findings as source data for bottom-up modelling and evaluation of ICO for representation of this new domain. We found considerable overlap in classes already in ICO and those necessary for representing permissions to reuse residual clinical biospecimens and health data. However, we also identified more than fifty classes that should be added to or imported into ICO. These efforts provide a foundation for comprehensively representing permissions to reuse residual clinical biospecimens and health data. Such representation fills a critical gap for developing applications which safeguard biospecimen resources and enable querying based on their permissions for use. By modeling information about permissions in an ontology, the heterogeneity of these permissions at a range of levels (e.g., federal regulations, consent forms) can be richly represented using entity-relationship links and embedded rules of inference and inheritance. Furthermore, by developing this content in ICO, missing content will be added to the Open Biological and Biomedical Ontology (OBO) Foundry, enabling use alongside other widely adopted ontologies and providing a valuable resource for biospecimen and information management. These methods may also serve as a model for domain experts to interact with ontology development communities to improve ontologies and address gaps which hinder successful uptake.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162937/1/eliewolf_1.pd

Critical Discourse Analysis as a Review Methodology: An Empirical Example

Research disciplines and subdisciplines are steeped in epistemological beliefs and theoretical assumptions that guide and constrain research. These beliefs and assumptions both enable scientific inquiry and limit scientific progress. Theory and review papers tend to be a means for reproducing ideological assumptions. However, review papers can also challenge ideological assumptions by critically assessing taken-for-granted assumptions. Critical review methods are underdeveloped in the management disciplines. The information systems (IS) discipline must do more to improve the critical examination of its scientific discourse. In this paper, we present a method with guiding principles and steps for systematically conducting critical reviews of IS literature based on Habermasian strains of critical discourse analysis. We provide an empirical example of the method. The empirical example offers a critical review of behavioral information security research with a focus on employees’ security behaviors

Discovering Hidden Signs and Symptoms of Heart Failure in the Electronic Health Record Using the Omaha System

Purpose/Background/Significance: For the past 30 years, heart failure has been in the top 3 readmission diagnoses with patients discharged to community care. This is costly to the healthcare system and negatively impacts the patient’s quality of life. The purpose of this study is to evaluate a community care database to determine if previously under-considered latent variables exist that could provide early detection of heart failure signs and symptoms. Theoretical/Conceptual Framework: The theoretical and conceptual frameworks surrounding this work are the Omaha System and Donabedian’s structure, process, and outcomes theory for healthcare quality improvement supported by Neuman’s Systems Model. The Omaha System was constructed on the combined basis of these theoretical underpinnings by three components: The Problem Classification Scheme, The Intervention Scheme, and The Problem Rating Scale for Outcomes. Methods: This study was a retrospective, descriptive, observational, comparative study using secondary data. Major HF-associated signs and symptoms related to problems of circulation and respiration were queried. Latent Class Analysis (LCA) was used to identify if other significant groupings of signs and symptoms were associated with heart failure signs and symptoms. Findings: Evaluation of the sample for signs and symptoms of HF related to the Omaha System Problems of Respiration and Circulation revealed 4215 individuals. LCA revealed four significant groupings of signs and symptoms related to the problems of Mental health, Cognition, Heart failure and General/Other. Further analysis determined that the HF group had the most interventions and visits yet had the lowest change in Knowledge, Behavior, and Status scores indicating that HF required intensive outpatient care to maintain their status in the community care environment without benefiting from significant final status improvement. Analysis revealed that patients with Cognition group benefited the most from increased visits and interventions. Conclusion: Patients exhibiting signs and symptoms of heart failure may also experience signs and symptoms of Mental health and Cognition changes, which may either contribute to heart failure exacerbation, or be as a result of the heart failure disease process. Further research is needed to examine possible mechanisms that may help defer HF exacerbations

Resilience Culture in the Healthcare Team During COVID-19

Abstract Background: Resilience commonly refers to the ability of an individual or organization to continue to maintain routine, normal, function despite sudden disruptions. Purpose: The purpose of this dissertation research was to provide a deeper understanding of healthcare team resilience. The goal of this research dissertation was to investigate how resilience manifested itself in the healthcare team during the COVID-19 pandemic. AIM 1: What is the concept of resilience in healthcare teams? AIM 2: Identify the barriers and facilitators of healthcare team resilience during the COVID-19 pandemic. AIM 3: Describe how the pandemic influenced healthcare team decision making. Methods: In the first manuscript we performed a comprehensive systematic analysis that delves into the concept of healthcare team resilience in the literature. Based on these results, in the second manuscript the authors utilized an adapted model developed by the research team that frames the healthcare team as a cohesive and aware entity, rather than merely a group of individuals or a subset of personnel within a healthcare system. Finally, the third manuscript uses this adapted model to present research findings from interviews on resilience culture, based on a thematic analysis. Findings: In chapter 2, we found 41 distinct definitions of the concept, with three defining attributes: 1) resilience is triggered by an a priori disruptive event that serves as a catalyst activating the healthcare team\u27s latent potential; 2) this potential leads to the actualization of skills and abilities that enable the team to respond to the disruption in an adaptive manner; 3) the team’s adaptive response enables them to continue executing responsibilities in the face of the disruption. This contributed to AIM 1 by describing the concept of resilience in healthcare teams during COVID-19. The concept analysis brought to light a significant disparity arising from the prevailing literature primarily emphasizing individual resilience as a lens to understand healthcare team resilience, thus potentially obscuring any hidden aspects of resilience within the healthcare team. This discrepancy underscored the necessity to develop a comprehensive model to explore healthcare team resilience during COVID-19 that acknowledges the healthcare team as a singular cognizant entity and not an individual or group of individuals. In chapter 3, we found by integrating knowledge and principles from the domains of resilience engineering, systems engineering, patient safety, and naturalistic decision- making we could create a framework by which AIM 2 and AIM 3 could be addressed. An adapted model was created. The exploration of the barriers and facilitators of resilience and the impact of COVID-19 on the decision-making processes in healthcare teams could be thoroughly explored using the adapted model. A qualitative descriptive study was conducted in 2021 and data were analyzed using reflexive thematic analysis. Chapter 4 presents the findings of this study related to AIM 2 and AIM 3. The study utilized the adapted model as a guide for the interview questions. The author developed the interview questions, which were reviewed and approved by faculty mentors. The author interviewed (N=22) interprofessional healthcare participants who worked during the COVID-19 pandemic. A thematic analysis of the interview data resulted in the identification of five themes related to resilience in the healthcare team during COVID-19: working in a pressure cooker; healthcare team cohesion; applying past lessons to current challenges; knowledge gaps, and altruistic behaviors. The evidence indicates that the pressures form working during COVID-19 and gaps in explicit knowledge, negatively influenced adaptive behaviors to maintain healthcare team resilience. Team cohesion, tacit knowledge and altruistic behaviors positively influenced adaptive behaviors and decision making. Conclusion: This compendium presents the exploration of resilience within healthcare teams amidst the challenges posed by the COVID-19 pandemic. The literature review revealed that the conventional approach to understanding the concept and measuring healthcare team resilience primarily focused on individual resilience. However, this research recognized the need for an adapted model that recognizes the healthcare team as a cohesive and cognizant entity to identify barriers and facilitators of resilience that may be otherwise obscured when solely emphasizing the resilience of individuals, or specific groups. Through a reflexive thematic analysis, several significant findings were identified regarding the impact of the COVID-19 pandemic on the healthcare team: 1) Emotionality played a crucial role in influencing adaptive behaviors, encompassing emotions such as fear, stress, anxiety, and frustration; 2) Drawing upon their tacit knowledge gained from prior experiences, the healthcare team demonstrated the capacity to anticipate and effectively respond to the crisis despite their lack of explicit knowledge, and 3) The solidarity and camaraderie within the healthcare team not only bolstered their overall functionality but also facilitated unified decision-making processes