Citation: Xie, Y.; Gu, D.; Wang, X.;
Yang, X.; Zhao, W.; Khakimova, A.K.;
Liu, H. A Smart Healthcare
Knowledge Service Framework for
Hierarchical Medical Treatment
System. Healthcare 2022, 10, 32.
https://doi.org/10.3390/healthcare
10010032
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 8 November 2021
Accepted: 20 December 2021
Published: 24 December 2021
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Article
A Smart Healthcare Knowledge Service Framework for
Hierarchical Medical Treatment System
Yi Xie
1,2
, Dongxiao Gu
1,
*, Xiaoyu Wang
3
, Xuejie Yang
1,
* , Wang Zhao
1
, Aida K. Khakimova
4
and Hu Liu
1
1
The School of Management, Hefei University of Technology, Hefei 230009, China; yixie928@163.com (Y.X.);
2019110768@mail.hfut.edu.cn (W.Z.); lu18752099127@163.com (H.L.)
2
The School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK
3
The Department of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei 230009, China;
xywang0551@163.com
4
Scientific-Research Center for Physical-Technical Informatics, Russian New University, 105005 Moscow,
Russia; aida_khatif@mail.ru
* Correspondence: gudongxiao@hfut.edu.cn (D.G.); xuejie_y@mail.hfut.edu.cn (X.Y.)
Abstract:
This paper reveals the research hotspots and development directions of case-based rea-
soning in the field of health care, and proposes the framework and key technologies of medical
knowledge service systems based on case-based reasoning (CBR) in the big data environment. The
2124 articles on medical CBR in the Web of Science were visualized and analyzed using a bibliometrics
method, and a CBR-based knowledge service system framework was constructed in the medical
Internet of all people, things and data resources environment. An intelligent construction method
for the clinical medical case base and the gray case knowledge reasoning model were proposed. A
cloud-edge collaboration knowledge service system was developed and applied in a pilot project.
Compared with other diagnostic tools, the system provides case-based explanations for its predicted
results, making it easier for physicians to understand and accept, so that they can make better de-
cisions. The results show that the system has good interpretability, has better acceptance than the
common intelligent decision support system, and strongly supports physician auxiliary diagnosis
and treatment as well as clinical teaching.
Keywords:
smart healthcare management; hierarchical medical treatment system; knowledge service;
case-based reasoning
1. Introduction
According to a report of the World Health Organization in 2019, the world’s popula-
tion aged over 60 years has exceeded 1 billion [
1
]. The number of patients with chronic
diseases, such as cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases,
has increased rapidly, and the number of deaths caused by these diseases accounts for
74% of the world’s deaths [
2
]. According to the census data released by the National
Bureau of Statistics, 260 million people are over 60 in China, accounting for 5.44% of the
total population, and the mortality rate from chronic diseases accounts for 88.5% of the
total mortality rate [
3
,
4
]. With the continuous improvement of people’s living standards,
society’s demand for safe and high-quality medical and health services is growing rapidly.
However, problems such as an insufficient amount of social medical resources, unbalanced
allocation, low levels of primary medical services, and difficulties with the decreasing
quality of medical resources still exist, all of which lead to difficulty in implementing a
national hierarchical medical treatment system in developing countries like China. In
many remote and underdeveloped areas, a big gap exists between the medical resources
available, especially the level of medical skills, compared with developed areas. There-
fore, high-quality medical services are unavailable for many patients. As a result, many
patients choose to go to large hospitals that are far away from their homes, resulting in the
phenomenon of “few patients in small hospitals and many patients in large hospitals”.
Healthcare 2022, 10, 32. https://doi.org/10.3390/healthcare10010032 https://www.mdpi.com/journal/healthcare
