4.3 Ecosystem-Nested Translational Innovation in Health
Informatics
What is the next pattern of innovation? This book proposes ecosystem-nested
translational innovation. Let us consider the changes in health informatics as an
example of ecosystem-nested translational innovation currently emerging in the
world of healthcare.
Nowadays, EHRs are evolving by loading the EHR platform with various types
of information generated by related technologies. The information that
old-generation EHRs handled was no more than conventional text, numerical values,
and image information. Current EHRs include color animations, three-dimensional
animations, various vital information such as electrocardiograms acquired using
sensors, life logs, narratives of patients, cost information on medical fee, divergence
from Diagnosis Procedure Combination/Per-Diem Payment System (DPC/PDPS)
data, and real-time big data information from patient monitors, which is centrally
managed for each patient and stored in Clinical Big Data Repository (CBDR). In
addition, EHRs are among the most influential ICT tools that intervene in communication between patients and the interprofessional collaboration team as well as
complex communication within the interprofessional collaboration team. As such,
innovation of EHRs is evolving in complex ways with other health information
system innovations.
These data and information are collected by systems provided by independent
vendors. By coordinating the information collected by these vendors and independent vendor systems, the collaboration changes into an ecosystem. This cooperation
will foster ecosystem-nested translational innovation. Thus, pan-enterprise
interprofessional team collaboration within the industry is the basis of ecosystemnested translational innovation.
The environment in which such an EHR operates is used routinely and clinically.
Moreover, it is possible to translate the information stored in CBDR for various other
uses. For example, by combining exploratory matching with machine learning, we
can achieve drug side effects matching, process control related to new drug development in post-genome and drug-discovery eras, and analysis of pharmacogenetic
drug response characteristics for each patient. In addition, cutting-edge information
analysis technologies such as machine learning will be introduced into CBDRs that
can be operated over a long period of time to make effective use of stored big data
patient information. Thus, health informatics of translational EHRs is emerging
(Fig. 7).
14
H. Matsushita
Informatics
What is the next pattern of innovation? This book proposes ecosystem-nested
translational innovation. Let us consider the changes in health informatics as an
example of ecosystem-nested translational innovation currently emerging in the
world of healthcare.
Nowadays, EHRs are evolving by loading the EHR platform with various types
of information generated by related technologies. The information that
old-generation EHRs handled was no more than conventional text, numerical values,
and image information. Current EHRs include color animations, three-dimensional
animations, various vital information such as electrocardiograms acquired using
sensors, life logs, narratives of patients, cost information on medical fee, divergence
from Diagnosis Procedure Combination/Per-Diem Payment System (DPC/PDPS)
data, and real-time big data information from patient monitors, which is centrally
managed for each patient and stored in Clinical Big Data Repository (CBDR). In
addition, EHRs are among the most influential ICT tools that intervene in communication between patients and the interprofessional collaboration team as well as
complex communication within the interprofessional collaboration team. As such,
innovation of EHRs is evolving in complex ways with other health information
system innovations.
These data and information are collected by systems provided by independent
vendors. By coordinating the information collected by these vendors and independent vendor systems, the collaboration changes into an ecosystem. This cooperation
will foster ecosystem-nested translational innovation. Thus, pan-enterprise
interprofessional team collaboration within the industry is the basis of ecosystemnested translational innovation.
The environment in which such an EHR operates is used routinely and clinically.
Moreover, it is possible to translate the information stored in CBDR for various other
uses. For example, by combining exploratory matching with machine learning, we
can achieve drug side effects matching, process control related to new drug development in post-genome and drug-discovery eras, and analysis of pharmacogenetic
drug response characteristics for each patient. In addition, cutting-edge information
analysis technologies such as machine learning will be introduced into CBDRs that
can be operated over a long period of time to make effective use of stored big data
patient information. Thus, health informatics of translational EHRs is emerging
(Fig. 7).
14
H. Matsushita