Hitachi: Pharmacotherapy Selection System Supports Shared Clinician-Patient Decision-Making in Diabetes Treatment
2018年3月13日 - 1:34PM
JCN Newswire (英)
Hitachi, Ltd. (TSE:6501) and University of Utah Health (U of U
Health), a leading institution in electronic health records and
interoperable clinical information systems research, today
announced the joint development of a decision support system that
allows clinicians and patients to choose from available
pharmaceutical options for treating type-2 diabetes mellitus
(T2DM). The system uses machine-learning methods to predict the
probability that a given medication regimen will achieve targeted
hemoglobin A1c (HbA1c)(1) levels, a common indicator of disease
control for diabetes. The system compares medication regimens
side-by-side, predicting efficacy, risk of side effects, and costs,
in a way that is easy for both clinicians and patients to
understand. The system is integrated with electronic health records
using Health Level 7 (HL7) interoperability standards, making it
seamlessly available to clinicians and allowing for guidance that
is personalized to the individual characteristics of the patient.
By facilitating patient-clinician communication and supporting
shared-decision making, the goal is to make sure that patients are
fully engaged and committed to the treatment plan. Hitachi and U of
U Health will pursue collaborative research aiming for clinical
trials using this system.
In the United States, the number of patients with diabetes has
risen to 23.1 million, and one in four persons aged 65 years or
older is diagnosed with the condition. Unfortunately, about half of
these patients fail to achieve the treatment target (<7% HbA1c)
set by medical guidelines. Further, some patients forego certain
treatments because they cannot afford them due to insufficient
health insurance coverage.
Within the last few years, health care providers have been
emphasizing a shared decision-making approach to medical care -
where patients are involved in deciding their own course of
treatment rather than simply following a physician's or
pharmacist's treatment plan. The new system helps clinicians and
patients to discuss different options available to them. It not
only provides information on the efficacy of each drug but also
takes into consideration other information important to patients,
such as side effects and cost.
The machine learning-based pharmacotherapy outcome prediction and
comparison technology, developed in collaboration between Hitachi
and U of U Health and announced last November(2) was integrated
into a standards-based clinical decision support system developed
at U of U Health (OpenCDS(3)). By employing HL7 FHIR(4), a next
generation standards platform that facilitates interoperability
between electronic health record systems, the new drug selection
support system can be linked to HL7 FHIR-compatible electronic
health records. During development of the new system, the number of
data sets and items were increased, improving the predictive
accuracy of the technology.(5) This system can also be utilized as
a platform to connect electronic health records with various
machine learning based models.
Hitachi and U of U Health will conduct joint research to evaluate
the efficacy of this technology with the ultimate goal of improving
care and outcomes of individuals with diabetes.
A part of the results from this work will be presented at the
American Medical Informatics Association (AMIA) 2018 Informatics
Summit to be held from the 12th to 15th of March 2018 in San
Francisco, U.S.A.
(1) HbA1c (Hemoglobin A1c) value: Laboratory test value that
reflects average blood sugar level for the past three months. It
serves as the main indicator of disease control for diabetes, the
target value for which is decided by the clinician for individual
patients based on age and patient condition.
(2) 6 Nov. 2017 News Release: Hitachi announces pharmacotherapy
outcome prediction technology for drug selection support in type-2
diabetes mellitus
(3) Open Clinical Decision Support (OpenCDS) is a standard-based
open source clinical decision support system developed by the
University of Utah.
(4) FHIR - Fast Healthcare Interoperability Resources is a next
generation standards framework created by HL7 - Health Level Seven
International, a healthcare standards development organization.
(5) Area Under the Curve (AUC) of 0.88. AUC is used in statistical
data analysis as an index of decision and classification accuracy
using a value ranging from 0.5 to 1, with 1 representing the
highest accuracy of 100% probability of correct decision and
classification.
About University of Utah Health
University of Utah Health is the state's only academic health care
system, providing leading-edge and compassionate medicine for a
referral area that encompasses 10% of the U.S. A hub for health
sciences research and education in the region, U of U Health has a
$291 million research enterprise and trains the majority of Utah's
health care professionals at its Schools of Medicine and Dentistry
and Colleges of Nursing, Pharmacy and Health. Staffed by more than
20,000 employees, the system includes 12 community clinics and four
hospitals. For eight straight years, U of U Health has ranked among
the top 10 U.S. academic medical centers in the Vizient Quality and
Accountability Study, including reaching No. 1 in 2010 and
2016.
About Hitachi, Ltd.
Hitachi, Ltd. (TSE:6501), headquartered in Tokyo, Japan, delivers
innovations that answer society's challenges with our talented team
and proven experience in global markets. The company's consolidated
revenues for fiscal 2014 (ended March 31, 2015) totaled 9,761
billion yen ($81.3 billion). Hitachi is focusing more than ever on
the Social Innovation Business, which includes power &
infrastructure systems, information & telecommunication
systems, construction machinery, high functional materials &
components, automotive systems, healthcare and others. For more
information on Hitachi, please visit the company's website at
www.hitachi.com.
Source: Hitachi, Ltd.
Contact:
Hitachi Ltd
Corporate Communications
Tel: +81-3-3258-1111
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