Optimization of Diagnosis and Treatment of Chronic Diseases Based on Association Analysis Under the Background of Regional Integration

  • Kaijian XiaEmail author
  • Xiaowei Zhong
  • Li Zhang
  • Jianqiang Wang
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care


In order to improve medical quality, shorten hospital stays, and reduce redundant treatment, an optimization of diagnosis and treatment of chronic diseases based on association analysis under the background of regional integration in the paper was proposed, which was to expand the scope of application of the clinical pathway standard diagnosis and treatment program in the context of regional medical integration and mass medical data, so that it had a larger group of patients within the region. In the context of regional medical integration, owing to the types of medical data were diverse, the preprocessing requirements and process specifications for diagnosis and treatment data were firstly proposed. At the stage of diagnosis and treatment unit optimization, the correlation between clinical behaviors was analyzed by using association rules of the FP-growth and Apriori algorithm. Through the optimization and combination of diagnosis and treatment units, the optimized clinical pathway was finally achieved. Experiments showed that after the optimization strategy by the paper proposed, the clinical path diagnosis and treatment achieved obvious improvement in medical quality under the condition that the medical cost was basically flat.


Diagnosis and treatment Chronic diseases Regional medical integration FP-growth algorithm Medical quality 



This paper was funded by the Jiangsu Committee of Health (No. H2018071).

Compliance with Ethical Standards

Conflict of Interest

We declare that we have no conflict of interest. The paper does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Kaijian Xia
    • 1
    Email author
  • Xiaowei Zhong
    • 2
  • Li Zhang
    • 1
  • Jianqiang Wang
    • 1
  1. 1.Changshu No.1 People’s HospitalJiangsuChina
  2. 2.Changshu City Health and Family Planning CommissionJiangsuChina

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