Methodologies for designing healthcare analytics solutions: a literature analysis

© The Author(s) 2019. Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field

Healthcare Analytics: Examining the Diagnosis–treatment Cycle

AbstractResearch has demonstrated that the patients’ diagnosis-treatment cycles often significantly deviate from the standardized clinical pathways. Analyzing these deviations might result in the further enhancement of the quality of care, the promotion of patient safety, an increase in patient satisfaction and an optimization of the use of resources. Understanding pathway behavior and deviations becomes possible because of an increased availability of reliable data, which originates from the hospitals information systems.In this paper we propose a clinical pathway analysis method for extracting valuable medical and organizational information on past diagnosis-treatment cycles that can be attributed to a specific clinical pathway. The method is applied on the clinical pathway processes in a Gynecologic Oncology Department

Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development

Future healthcare leaders require expert knowledge and practical capabilities in the evaluation, selection, application and ongoing oversight of the best types of analytics to create continuous learning healthcare systems. These systems may result in continuously improving the demonstrable quality, safety and efficiency of healthcare organizations. Data is an asset for organizations. However, many companies do not know how to establish analytical road maps for future action. Population Health Intelligence describes a new discipline whose role is to collect, organize, harmonize, analyze, disseminate and act upon the data available to clinicians, health system leaders, the pharmaceutical and biotechnology industry, and healthcare payers. This webinar on Analytics Leadership will demonstrate how to create and implement Clinical & Business Intelligence Plans that transform data into actionable organizational insights. Agenda Introduction Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development Population Health Intelligence Presentation: 53:3

A Federated Filtering Framework for Internet of Medical Things

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (${To{l_F}}$) and the local filtering parameter ($\delta$); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95\% of the communication cost while maintaining reasonable data privacy and low latency.Comment: 6 pages, 6 Figures, accepted for oral presentation in IEEE ICC 2019, Internet of Things, Federated Learning and Perturbation theor

Attentive Dual Embedding for Understanding Medical Concept in Electronic Health Record

Electronic health records contain a wealth of information on a patient’s healthcare over many visits, such as diagnoses, treatments, drugs administered, and so on. The untapped potential of these data in healthcare analytics is vast. However, given that much of medical information is a cause and effect science, new embedding methods are required to ensure the learning representations reflect the comprehensive interplays between medical concepts and their relationships over time. Unlike one-hot encoding, a distributed representation should preserve these complex interactions as high-quality inputs for machine learning-based healthcare analytics tasks. Therefore, we propose a novel attentive dual embedding method called MC2Vec. MC2Vec captures the proximity relationships between medical concepts through a two-step optimization framework that recursively refines the embedding for superior output. The framework comprises a Skip-gram model to generate the initial embedding and an attentive CBOW model to fine-tune the embedding with temporal information gleaned from sequences of patient visits. Experiments with two public datasets demonstrate that MC2Vec’s produces embeddings of higher quality than five state-of-the-art methods