Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Abstract

SummaryObjectives: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. Methods: We identify three reasons for the lack of integration. The first is that "Big Data" is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on "cloud-native" platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. Results: A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Conclusion: Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.

Social innovation for life expectancy extension utilizing a platform-centered system used in the Iwaki health promotion project: A protocol paper

“…In many fields, data platforms (including digital platforms) are being constructed, and activities to create innovations have been carried out using them. 30 – 37 However, it is still rare to achieve a collaboration that involved a huge number of fields and areas in the society like IHPP. Many experts and stakeholders come together at IHPP, and it will grow into a global innovation by branching out further to new areas continuously.…”

Big data in health care delivery.

“…Widespread recognition of the potential of data analytics solutions to important socioeconomic and health problems has motivated the development of curated data repositories and analytics frameworks in various application domains, including health care (Gutiérrez-Sacristán et al, 2018;Hripcsak et al, 2015;Kohane, Churchill, & Murphy, 2012;Margolis et al, 2014;Murphy, Castro, & Mandl, 2017;Murphy et al, 2010;Ohno-Machado et al, 2012;Scheufele et al, 2014). These first-generation systems typically collect, curate, and present data in a mediated schema or federate access across databases using conventional web services technologies.…”

“…Informatics for Integrating Biology and the Bedside, i2b2 (Abend, Housman, & Johnson, 2009;Hong et al, 2016;Johnson et al, 2014;McMurry et al, 2013;Murphy, Castro, & Mandl, 2017;Murphy et al, 2007Murphy et al, , 2010Segagni et al, 2011), is an open-source software system for easy access and basic statistical analysis on large EHR systems. The system is designed for tasks such as cohort identification and retrospective data analysis.…”

“…Patient-Centered Informatics Common: Standard Unification of Research Elements (PIC-SURE; Gutiérrez-Sacristán et al, 2018;Murphy et al, 2017) is developing an open-source infrastructure to incorporate multiple heterogeneous patient-level data, including clinical, omics, and environmental data. The core idea of PIC-SURE is to utilize distributed data resources of various types and protocols by a single communication interface to perform queries and computations across different resources.…”

Collaborative Cloud Computing Framework for Health Data with Open Source Technologies

“…PIC-SURE [2,26,39] is an open source software platform to incorporate multiple heterogeneous patient level data including clinical, -omics, and environmental data. The core idea of PIC-SURE is to utilize distributed data resources of various types and protocols such as SciDB, i2b2 [40], and any other data systems by a single communication interface to perform queries and computations across different resources.…”