practical cases and theoretical frameworks, and including but not limiting to fields
such as big data, machine learning, interprofessional collaboration, electronic health
records, robotics, telenursing, quality improvement, and safety.
Based on a unique combination of innovative informatics and translational
systems science, this volume provides analytics to help readers rethink the relationship between informatics and innovation particularly in healthcare. Technology in
the area of healthcare is evolving rapidly, probably with new technology emerging
every month. Today, innovation is attracting attention in developed countries, such
as Europe and North American countries and Japan, and in many other countries
worldwide. In these countries, the realization of innovation in the healthcare field is
hailed as a national goal without exception.
For decades, there has been considerable debate about how to effectively and
efficiently create breakthroughs and inventions on the bench, or in a laboratory, and
share them with clinical settings for implementation on patients; this is called the
bench-to-bedside clinical research approach. A bedside-to-bench approach is the
opposite approach, where the needs in clinical settings are quickly and accurately
captured and shared with laboratories to help scientists improve and realize groundbreaking innovation in healthcare. One of the unique parts of this book is that it
proposes a translational view of health informatics and innovation as shown below.
In the earlier days, the translational view was centered on the mutual circulation of
the bedside and bench. However, we need to extend our horizons to patients,
practices, and population, and even to dynamic and systemic interactions between
health policies and management. The government has refined the regulation systems
to evaluate safety and effectiveness while accelerating innovation policies to
enhance the effectiveness of research and development. Newly developed practices
that have been confirmed to be safe and effective will be offered to more patients and
population by being included in healthcare reimbursement systems. Here, the purposive flow and purposeful co-creation of information, amongst diversified disciplines and practices is key to changing information into innovation. By contrast,
hospitals, pharmaceutical companies, medical device manufacturers, clinical trial
service providers, and information and communication service vendors, entrepreneurs, and innovative scientists play diversified roles in innovating these pipelines.
Here too, the importance of information is increasing significantly.
In other words, healthcare translational systems used to focus on bench-tobedside (T1) interactions. The research focused here is translational research. However, as health systems have become more innovation-oriented; translationality has
expanded from patients (T2) to practices (T3) to population (T4). Now, policy and
management, which are becoming more innovation-oriented, are being influential at
T1, T2, T3, and T4. All of these effects are achieved through translating data,
information, knowledge, and wisdom. Therefore, the new era of innovation-oriented
health informatics requires a translational view of health informatics and innovation
as proposed in this book.
vi
Preface
such as big data, machine learning, interprofessional collaboration, electronic health
records, robotics, telenursing, quality improvement, and safety.
Based on a unique combination of innovative informatics and translational
systems science, this volume provides analytics to help readers rethink the relationship between informatics and innovation particularly in healthcare. Technology in
the area of healthcare is evolving rapidly, probably with new technology emerging
every month. Today, innovation is attracting attention in developed countries, such
as Europe and North American countries and Japan, and in many other countries
worldwide. In these countries, the realization of innovation in the healthcare field is
hailed as a national goal without exception.
For decades, there has been considerable debate about how to effectively and
efficiently create breakthroughs and inventions on the bench, or in a laboratory, and
share them with clinical settings for implementation on patients; this is called the
bench-to-bedside clinical research approach. A bedside-to-bench approach is the
opposite approach, where the needs in clinical settings are quickly and accurately
captured and shared with laboratories to help scientists improve and realize groundbreaking innovation in healthcare. One of the unique parts of this book is that it
proposes a translational view of health informatics and innovation as shown below.
In the earlier days, the translational view was centered on the mutual circulation of
the bedside and bench. However, we need to extend our horizons to patients,
practices, and population, and even to dynamic and systemic interactions between
health policies and management. The government has refined the regulation systems
to evaluate safety and effectiveness while accelerating innovation policies to
enhance the effectiveness of research and development. Newly developed practices
that have been confirmed to be safe and effective will be offered to more patients and
population by being included in healthcare reimbursement systems. Here, the purposive flow and purposeful co-creation of information, amongst diversified disciplines and practices is key to changing information into innovation. By contrast,
hospitals, pharmaceutical companies, medical device manufacturers, clinical trial
service providers, and information and communication service vendors, entrepreneurs, and innovative scientists play diversified roles in innovating these pipelines.
Here too, the importance of information is increasing significantly.
In other words, healthcare translational systems used to focus on bench-tobedside (T1) interactions. The research focused here is translational research. However, as health systems have become more innovation-oriented; translationality has
expanded from patients (T2) to practices (T3) to population (T4). Now, policy and
management, which are becoming more innovation-oriented, are being influential at
T1, T2, T3, and T4. All of these effects are achieved through translating data,
information, knowledge, and wisdom. Therefore, the new era of innovation-oriented
health informatics requires a translational view of health informatics and innovation
as proposed in this book.
vi
Preface