Resource allocation for chronic diseases based on a patient willingness survey

Int J Health Plann Manage. 2019 Jul;34(3):926-934. doi: 10.1002/hpm.2864. Epub 2019 Jul 28.

Abstract

In China, patients with chronic diseases have complete freedom to choose the medical institutions at which they are treated, which has resulted in wasted medical resources and increased medical expenses. The purpose of this study is to determine the effective mechanisms to incentivise patients with chronic diseases to obtain referrals from community health centres to tertiary hospitals and estimate the funds that could be saved using various mechanisms. Questionnaire research, expert consultations, and data simulation were applied. We surveyed 1824 outpatients at nine tertiary hospitals in Shanghai, and the results showed that the proportion of patients willing to obtain referrals was 48.4%. By increasing the registration fee, reducing the payment ratio of medical insurance, publicising, and rating the quality of community health centres, up to 51.3%, 50.6%, and 65.41% patients with chronic diseases indicated that they would obtain referrals, respectively. According to the 2015 Shanghai outpatient database, the funding that could be saved through these three mechanisms would be 361.67, 356.73, and 461.14 million yuan, respectively. We conclude that referral of patients with chronic diseases could reduce medical expenses and save medical insurance funds. Nevertheless, no single measure can effectively change patient habits. Comprehensive measures need to be applied to guide patient referral actively.

Keywords: chronic disease; hierarchical medical system; medical resource; referral system; resource allocation efficiency.

MeSH terms

  • China / epidemiology
  • Chronic Disease / economics
  • Chronic Disease / epidemiology
  • Chronic Disease / therapy*
  • Cost Savings / methods
  • Humans
  • Insurance, Health / economics
  • Insurance, Health / statistics & numerical data
  • Patient Preference / psychology*
  • Patient Preference / statistics & numerical data
  • Referral and Consultation / statistics & numerical data
  • Resource Allocation / methods*
  • Surveys and Questionnaires