Suitability of Fast Healthcare Interoperability Resources (FHIR) for Wellness Data

Wellness data generated by patients using smart phones and portable devices can be a key part of Personal Health Record (PHR) data and offers healthcare service providers (healthcare providers) patient health information on a daily basis. Prior research has identified the potential for improved communication between healthcare provider and patient. However the practice of sharing patient generated wellness data has not been widely adopted by the healthcare sector; one of the reasons being the lack of interoperability preventing successful integration of such device generated data into the PHR and Electronic Health Record (EHR) systems. To address the interoperability issue it is important to make sure that wellness data can be supported in healthcare information exchange standards. Fast Healthcare Interoperability Resources (FHIR) is used in the current research study to identify the technical feasibility for patient generated wellness data. FHIR is expected to be the future healthcare information exchange standard in the healthcare industry. \ A conceptual data model of wellness data was developed for evaluation using FHIR standard. The conceptual data model contained blood glucose readings, blood pressure readings and Body Mass Index (BMI) data and could be extended to accept other types of wellness data. The wellness data model was packaged in an official FHIR resource called Observation. The research study proved the flexibility of adding new data elements related to wellness in Observation. It met the requirements in FHIR to include such data elements useful in self-management of chronic diseases. It also had the potential in sharing it with the healthcare provider system.

Introduction to IT Architectures and Implementations in Healthcare Environments Minitrack

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Comparative study of healthcare messaging standards for interoperability in ehealth systems

Advances in the information and communication technology have created the field of "health informatics," which amalgamates healthcare, information technology and business. The use of information systems in healthcare organisations dates back to 1960s, however the use of technology for healthcare records, referred to as Electronic Medical Records (EMR), management has surged since 1990’s (Net-Health, 2017) due to advancements the internet and web technologies. Electronic Medical Records (EMR) and sometimes referred to as Personal Health Record (PHR) contains the patient’s medical history, allergy information, immunisation status, medication, radiology images and other medically related billing information that is relevant. There are a number of benefits for healthcare industry when sharing these data recorded in EMR and PHR systems between medical institutions (AbuKhousa et al., 2012). These benefits include convenience for patients and clinicians, cost-effective healthcare solutions, high quality of care, resolving the resource shortage and collecting a large volume of data for research and educational needs. My Health Record (MyHR) is a major project funded by the Australian government, which aims to have all data relating to health of the Australian population stored in digital format, allowing clinicians to have access to patient data at the point of care. Prior to 2015, MyHR was known as Personally Controlled Electronic Health Record (PCEHR). Though the Australian government took consistent initiatives there is a significant delay (Pearce and Haikerwal, 2010) in implementing eHealth projects and related services. While this delay is caused by many factors, interoperability is identified as the main problem (Benson and Grieve, 2016c) which is resisting this project delivery. To discover the current interoperability challenges in the Australian healthcare industry, this comparative study is conducted on Health Level 7 (HL7) messaging models such as HL7 V2, V3 and FHIR (Fast Healthcare Interoperability Resources). In this study, interoperability, security and privacy are main elements compared. In addition, a case study conducted in the NSW Hospitals to understand the popularity in usage of health messaging standards was utilised to understand the extent of use of messaging standards in healthcare sector. Predominantly, the project used the comparative study method on different HL7 (Health Level Seven) messages and derived the right messaging standard which is suitable to cover the interoperability, security and privacy requirements of electronic health record. The issues related to practical implementations, change over and training requirements for healthcare professionals are also discussed

The Contribution of Ethical Governance of Artificial Intelligence & Machine Learning in Healthcare

With the Internet Age and technology progressively advancing every year, the usage of Artificial Intelligence (AI) along with Machine Learning (ML) algorithms has only increased since its introduction to society. Specifically, in the healthcare field, AI/ML has proven to its end-users how beneficial its assistance has been. However, despite its effectiveness and efficiencies, AI/ML has also been under scrutiny due to its unethical outcomes. As a result of this, two polarizing views are typically debated when discussing AI/ML. One side believes that AI/ML usage should continue regardless of its unsureness, while the other side argues that this technology is too dangerous and should not be utilized at all. Given the fact that AI/ML can provide prompt and fairly accurate results, it is unrealistic to assume that AI/ML usage will end any time soon. Therefore, governance of AI/ML is needed to ensure that these technologies are reliable. Notably, AI governance has been positively reviewed and pushed for by scholars in the field. While AI governance does guarantee a sense of oversight on AI/ML, this form of governance is not sustainable. AI governance primarily focuses on the safety of the technology, with ethical, legal, and social factors serving as elements of AI governance. The safety of AI/ML is only one of the considerations for producing and ensuring ethical AI/ML. Ethical governance of AI/ML, which concentrates on incorporating ethics into all aspects of AI/ML—specifically, narrowing in on the stakeholders involved, will lead to not only a safer product but a more viable one as well. Thus, ethical governance of AI/ML must be advocated for in order to bring more awareness, which would lead to greater research and implementation of this type of governance. Although AI/ML can be used for a multitude of areas, the healthcare industry is slightly more significant, especially since these technologies directly affect the patients’ health. This dissertation explores the contribution of ethical governance of AI/ML in several facets of healthcare. As AI/ML requires big data to provide outcomes, the context of data analytics is discussed. Other areas the dissertation explores are clinical decision-making, end-of-life decisions, and biotechnology. While these topics certainly do not cover the whole healthcare field, the dissertation attempts to include a wide range of AI/ML functions from the beginning of its process (with data analytics) to the future of AI/ML (with biotechnology). With each of these areas of interest, various ethical governance principles are introduced and endorsed for to develop ethical AI/ML. The goal of this dissertation in discussing the contribution of ethical governance of AI/ML in healthcare is to provide a foundational groundwork for more future research of the ethical governance of AI/ML

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field