Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework

Abstract

BackgroundAnalytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will benefit patients, care providers, and governments significantly. This work is a novel approach to realize health analytics as services in critical care units in particular.ObjectiveTo design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis.MethodsWe present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and configurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs).ResultsWe demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the final deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids’ NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and 16 CPU cores, respectively. The required amount of storage was estimated as 8.6 TB per year. There will always be 34.9 patients in SickKids NICU on average. Currently, 46% of patients cannot get admitted to SickKids NICU due to lack of resources. By increasing the capacity to 90 beds, all patients can be accommodated. For such a provisioning, Artemis-IC will need 16 TB of storage per year, 55 GB of memory, and 28 CPU cores.ConclusionsOur contributions in this work relate to a cloud architecture for the analysis of physiological data for clinical decisions support for tertiary care use. We demonstrate how to size the equipment needed in the cloud for that architecture based on a very realistic assessment of the patient characteristics and the associated clinical decision support algorithms that would be required to run for those patients. We show the principle of how this could be performed and furthermore that it can be replicated for any critical care setting within a tertiary institution.

Computerized Clinical Decision Support: Contributions from 2015

“…As a proof-of-concept, the approach is illustrated through a clinical scenario of a healthcare team managing a patient with chronic kidney disease. In the domain of big data in healthcare, Khazaei et al [11] presented a cloud-based framework for big data analytics, supporting both real-time and retrospective analyses. The study introduces an architectural paradigm to implement health-analytics-as-a-service, which is currently a central theme in the era of big data in healthcare (e.g., a relevant effort is conducted in the H2020 AEGLE project, http://www.aegle-uhealth.eu/).…”

A Cloud Based Big Data Health-Analytics-as-a-Service Framework to Support Low Resource Setting Neonatal Intensive Care Unit

“…Khazaei et al [8] has proposed a cloud-based Health Analytics as a Service (HAaaS) framework that enabled Artemis to be offered as an Artemis Cloud HAaaS. In that framework, the Data Acquisition component of Artemis can be located at a different location to the remaining Artemis components which are provisioned through cloud services.…”

Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology

“…This notable increase in mortality rate and annual health care expenditure (affected by the increased length of stay) has made sepsis treatment and research a critical domain in medical internet research and medical informatics, resulting in a recent surge in the related literature [21][22][23][24]. Studies have shown that improved and effective methods of early sepsis identification can substantially reduce the severity and epidemiological burden of sepsis in the United States [24][25][26][27][28][29]. In addition, several authors have recommended that identifying the prevalent risk factor(s), followed by an instant diagnosis, can reduce the cost in treatment workflow, and further scale down the mortality rate for patients with sepsis to some extent [26,[30][31][32][33].…”