individual’s way of life so that we can create a society where people can pursue their
happiness until the end of their lives. To realize this situation, the integration of
nursing science and other disciplines is the key.
Evidence-based medicine/nursing and evidence-based practice have been
attracting attention for a long time, but most of the evidence in medicine so far has
been based on data collected for research purposes. The data collected in this way is
“artificial” in a sense and does not necessarily reflect the real-world situation. As a
result, an evidence-practice gap has arisen in which the data obtained from research
cannot be directly applied to a clinical setting or the research results cannot be used
for medical services. Furthermore, in many cases, randomized controlled trials that
can most accurately estimate the effects of interventions cannot be performed due to
ethical or cost issues, making it difficult to accumulate evidence. Therefore, in recent
years, with the progress of information technology and the standardization of
medical information, the utilization of electronic health/medical records
(EHR/EMR) in the medical field has attracted attention. The EHR/EMR contains
the patient’s medical records stored in electronic medical record databases, such as
prescription orders, injection orders, laboratory test orders, medical records compiled by doctors, nurses, and other medical staff, and so forth, and information is
accumulated daily. That is, real-world data is updated daily. In addition, daily
clinical work gathers raw data that is hardly available in research. By analyzing
this, it is possible to create evidence based on actual data rather than data obtained in
an artificial environment. In the field of medicine, various research studies are
underway, including automatic discharge summary generation, the development of
rare adverse event detection algorithms, the prediction of disease prognoses by
machine learning, and the early detection of diseases using artificial intelligence.
However, in the field of nursing, we find that real-world data analysis is not fully
incorporated. Although research using medical fee data and data from various
sensing devices and nursing calls is becoming widespread in the field of nursing,
the utilization of electronic medical record data collected and entered by nurses
themselves has not been sufficiently advanced.
In this chapter, by introducing “Bioengineering Nursing,” which systemizes the
process by which nursing fuses with various disciplines to create new care products,
and showing how care can be transformed using real-world data, we describe how
nursing science can contribute not only to Translational Systems Science (Sanada,
Mori, & Nakagami, 2014), but also innovation in health informatics.
2 Bioengineering Nursing: A New Frontier of Nursing
Science to Create Next-Generation Care
Nursing has achieved great results by using social science research methodologies to
extract and identify health problems as a group. However, there was a situation in
which technical development to solve problems was delayed. On the other hand,
82
G. Nakagami et al.
happiness until the end of their lives. To realize this situation, the integration of
nursing science and other disciplines is the key.
Evidence-based medicine/nursing and evidence-based practice have been
attracting attention for a long time, but most of the evidence in medicine so far has
been based on data collected for research purposes. The data collected in this way is
“artificial” in a sense and does not necessarily reflect the real-world situation. As a
result, an evidence-practice gap has arisen in which the data obtained from research
cannot be directly applied to a clinical setting or the research results cannot be used
for medical services. Furthermore, in many cases, randomized controlled trials that
can most accurately estimate the effects of interventions cannot be performed due to
ethical or cost issues, making it difficult to accumulate evidence. Therefore, in recent
years, with the progress of information technology and the standardization of
medical information, the utilization of electronic health/medical records
(EHR/EMR) in the medical field has attracted attention. The EHR/EMR contains
the patient’s medical records stored in electronic medical record databases, such as
prescription orders, injection orders, laboratory test orders, medical records compiled by doctors, nurses, and other medical staff, and so forth, and information is
accumulated daily. That is, real-world data is updated daily. In addition, daily
clinical work gathers raw data that is hardly available in research. By analyzing
this, it is possible to create evidence based on actual data rather than data obtained in
an artificial environment. In the field of medicine, various research studies are
underway, including automatic discharge summary generation, the development of
rare adverse event detection algorithms, the prediction of disease prognoses by
machine learning, and the early detection of diseases using artificial intelligence.
However, in the field of nursing, we find that real-world data analysis is not fully
incorporated. Although research using medical fee data and data from various
sensing devices and nursing calls is becoming widespread in the field of nursing,
the utilization of electronic medical record data collected and entered by nurses
themselves has not been sufficiently advanced.
In this chapter, by introducing “Bioengineering Nursing,” which systemizes the
process by which nursing fuses with various disciplines to create new care products,
and showing how care can be transformed using real-world data, we describe how
nursing science can contribute not only to Translational Systems Science (Sanada,
Mori, & Nakagami, 2014), but also innovation in health informatics.
2 Bioengineering Nursing: A New Frontier of Nursing
Science to Create Next-Generation Care
Nursing has achieved great results by using social science research methodologies to
extract and identify health problems as a group. However, there was a situation in
which technical development to solve problems was delayed. On the other hand,
82
G. Nakagami et al.