critical care departments generates around 1200 data points per day. As rightfully
mentioned by Professor Matsushita in his introduction, since 2000, hospitals have
gradually adopted Electronic Medical Records (EMR) or Electronic Health Records
(EHR) solutions to address this need of gathering, managing, and sharing patients
data in the acute space. Earlier, since 1990, hospitals, private clinics, and radiology
centers have been switching from analogical way of working (e.g., printing films of
radiology imaging) to digitalizing, storing, and sharing digital images led by the
growing usage of advanced radiology imaging devices such as computed tomography (CT) and magnetic resonance imaging (MRI) for the past two decades. These
radiology-oriented informatics solutions are named PACS (picture archiving communication systems), usually associated with advanced visualization software tools.
Our empirical investigation focuses on two information technology solutions
widely used in hospitals, respectively, Radiology informatics and EMRs, as well
as their specific innovation patterns: (1) patient-centric (2) resulting from a
co-creation process between technology and clinical users.
2 Discussion
2.1 Medical Technology, Innovative Practice, and Research
Innovation in clinical practice, involving medical technology, requires some further
exploration, for instance, by distinguishing innovation in medical practice from
research (Eaton & Kennedy, 2007). This implies to identify the boundary between,
on the one hand, biomedical and behavioral research, and on the other hand accepted
and routine practice of medicine. A patient is the client of the physician and should
be able to assume that the physician is acting solely in the client’s best interests. The
resulting duty of care means that, even though ultimate decision-making rests with
the patient, he or she should feel safe in delegating some decisions to the physician.
A research subject, in contrast, is a person experimented on and observed by the
physician. The research physician has an interest in the subject’s welfare but may
have even a greater interest in collecting data to serve a larger community of future
patients. This aspect is fueling the growing interest of accessing large databases of
patients. We will come back to this point when considering the latest advances in
radiology informatics and EMRs concerning artificial intelligence (AI) at large.
Innovation in medical technology, as distinct as medical research, involves the
complexity of the physician–patient interaction (Eaton & Kennedy, 2007). In cases
where several treatments or clinical procedures co-exit, some may be therapy and
some may be more like research.
We then aim to consider a broader approach by defining the concept of innovative
practice. In addition to being a new modality, an innovation in medical technology
can be an existing modality used in a new way, in a new dose, or in combination with
other new or existing established information technology (IT) solutions. Such
evidence criterion deals with a dual variability in medical practice. Variability
Health Informatics and Co-Innovation: Connecting Patients, Clinical Practice,. . .
103
mentioned by Professor Matsushita in his introduction, since 2000, hospitals have
gradually adopted Electronic Medical Records (EMR) or Electronic Health Records
(EHR) solutions to address this need of gathering, managing, and sharing patients
data in the acute space. Earlier, since 1990, hospitals, private clinics, and radiology
centers have been switching from analogical way of working (e.g., printing films of
radiology imaging) to digitalizing, storing, and sharing digital images led by the
growing usage of advanced radiology imaging devices such as computed tomography (CT) and magnetic resonance imaging (MRI) for the past two decades. These
radiology-oriented informatics solutions are named PACS (picture archiving communication systems), usually associated with advanced visualization software tools.
Our empirical investigation focuses on two information technology solutions
widely used in hospitals, respectively, Radiology informatics and EMRs, as well
as their specific innovation patterns: (1) patient-centric (2) resulting from a
co-creation process between technology and clinical users.
2 Discussion
2.1 Medical Technology, Innovative Practice, and Research
Innovation in clinical practice, involving medical technology, requires some further
exploration, for instance, by distinguishing innovation in medical practice from
research (Eaton & Kennedy, 2007). This implies to identify the boundary between,
on the one hand, biomedical and behavioral research, and on the other hand accepted
and routine practice of medicine. A patient is the client of the physician and should
be able to assume that the physician is acting solely in the client’s best interests. The
resulting duty of care means that, even though ultimate decision-making rests with
the patient, he or she should feel safe in delegating some decisions to the physician.
A research subject, in contrast, is a person experimented on and observed by the
physician. The research physician has an interest in the subject’s welfare but may
have even a greater interest in collecting data to serve a larger community of future
patients. This aspect is fueling the growing interest of accessing large databases of
patients. We will come back to this point when considering the latest advances in
radiology informatics and EMRs concerning artificial intelligence (AI) at large.
Innovation in medical technology, as distinct as medical research, involves the
complexity of the physician–patient interaction (Eaton & Kennedy, 2007). In cases
where several treatments or clinical procedures co-exit, some may be therapy and
some may be more like research.
We then aim to consider a broader approach by defining the concept of innovative
practice. In addition to being a new modality, an innovation in medical technology
can be an existing modality used in a new way, in a new dose, or in combination with
other new or existing established information technology (IT) solutions. Such
evidence criterion deals with a dual variability in medical practice. Variability
Health Informatics and Co-Innovation: Connecting Patients, Clinical Practice,. . .
103