Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System

Healthcare Informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use. Bioinformatics employ computational tools and techniques to study and analyse large biological databases and to absolutely understand disease and grasp the genetics and proteomics by relating them with healthcare data. The focus is on processing genomic and proteomics data for basic research in biology, but also medicine, drug discovery, and related areas. Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare improvement, reduction in cost and safety of lives. Applications of data analytics in healthcare is as a result of the eruption in data to mine understandings so as to make informed decisions. This paper reviews bioinformatics, Healthcare Informatics and Analytics as an imperative for an improved Healthcare System. It looks at the benefits, the contribution of each of them to improving healthcare system, the overlap among bioinformatics, healthcare Informatics and analytics and finally the future prospects of healthcare informatics and analytic

Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System

Healthcare Informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use. Bioinformatics employ computational tools and techniques to study and analyse large biological databases and to absolutely understand disease and grasp the genetics and proteomics by relating them with healthcare data. The focus is on processing genomic and proteomics data for basic research in biology, but also medicine, drug discovery, and related areas. Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare improvement, reduction in cost and safety of lives. Applications of data analytics in healthcare is as a result of the eruption in data to mine understandings so as to make informed decisions. This paper reviews bioinformatics, Healthcare Informatics and Analytics as an imperative for an improved Healthcare System. It looks at the benefits, the contribution of each of them to improving healthcare system, the overlap among bioinformatics, healthcare Informatics and analytics and finally the future prospects of healthcare informatics and analytics

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

p-medicine: a medical informatics platform for integrated large scale heterogeneous patient data

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare

Primer for Image Informatics in Personalized Medicine

AbstractImage informatics encompasses the concept of extracting and quantifying information contained in image data. Scenes, what an image contains, come from many imager devices such as consumer electronics, medical imaging systems, 3D laser scanners, microscopes, or satellites. There is a marked increase in image informatics applications as there have been simultaneous advances in imaging platforms, data availability due to social media, and big data analytics. An area ready to take advantage of these developments is personalized medicine, the concept where the goal is tailor healthcare to the individual. Patient health data is computationally profiled against a large of pool of feature-rich data from other patients to ideally optimize how a physician chooses care. One of the daunting challenges is how to effectively utilize medical image data in personalized medicine. Reliable data analytics products require as much automation as possible, which is a difficulty for data like histopathology and radiology images because we require highly trained expert physicians to interpret the information. This review targets biomedical scientists interested in getting started on tackling image analytics. We present high level discussions of sample preparation and image acquisition; data formats; storage and databases; image processing; computer vision and machine learning; and visualization and interactive programming. Examples will be covered using existing open-source software tools such as ImageJ, CellProfiler, and IPython Notebook. We discuss how difficult real-world challenges faced by image informatics and personalized medicine are being tackled with open-source biomedical data and software

Visual analytics: tackling data related challenges in healthcare process mining

2018 Conference paper presented at Strathmore University. Thematic area(Health, Healthcare Management and Research Ethics)Data-science approaches such as Visual analytics tend to be process blind whereas process-science approaches such as process mining tend to be model-driven without considering the “evidence” hidden in the data. Use of either approach separately faces limitations in analysis of healthcare data. Visual analytics allows humans to exploit their perceptual and cognitive capabilities in processing data, while process mining represents the data in terms of activities and resources thereby giving a complete process picture. We use a literature survey of research that has deployed either or both Visual analytics and process mining in the healthcare environments to discover strengths that can help solve open problems and challenges in healthcare data when using process mining. We present a visual analytics (data science) approach in solving data challenges in healthcare process mining (process science). Historical data (event logs) obtained from organizational archives are used to generate accurate and evidence-based activity sequences that are manipulated and analysed to answer questions that could not be tackled by process mining. The approach can help hospital management and clinicians among others, audit their business processes in addition to providing important operational information. Other beneficiaries are those organizations interested in forensic information regarding individuals and groups of patients.1.Institute of Computing and Informatics, Technical University of Mombasa; 2.Faculty of information Technology, Strathmore University 3.School of Computing and Information technology, Muranga University of technology; 4.School of Computing and Informatics, Masinde Muliro University of Science and Technolog

Data analytics based positioning of health informatics programs

The Master of Science in Computer Information Systems (CIS) with concentration in Health Informatics (HI) at Metropolitan College (MET), Boston University (BU), is a 40-credit degree program that are delivered in three formats: face-to-face, online, and blended. The MET CIS-HI program is unique because of the population of students it serves, namely those interested in gaining skills in HI technology field, to serve as data analysts and knowledge-based technology drivers in the thriving health care industry. This set of skills is essential for addressing the challenges of Big Data and knowledge-based health care support of the modern health care. The MET CIS-HI program was accredited by the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM) in 2017

3D Analytics: Opportunities and Guidelines for Information Systems Research

Progress in sensor technologies has made three-dimensional (3D) representations of the physical world available at a large scale. Leveraging such 3D representations with analytics has the potential to advance Information Systems (IS) research in several areas. However, this novel data type has rarely been incorporated. To address this shortcoming, this article first presents two showcases of 3D analytics applications together with general modeling guidelines for 3D analytics, in order to support IS researchers in implementing research designs with 3D components. Second, the article presents several promising opportunities for 3D analytics to advance behavioral and design-oriented IS research in several contextual areas, such as healthcare IS, human-computer interaction, mobile commerce, energy informatics and others. Third, we investigate the nature of the benefits resulting from the application of 3D analytics, resulting in a list of common tasks of research projects that 3D analytics can support, regardless of the contextual application area. Based on the given showcases, modeling guidelines, research opportunities and task-related benefits, we encourage IS researchers to start their journey into this largely unexplored third spatial dimension