Development, simplification, and prospective methodological exploration of a Chinese endoscopy patient experience scale and a decision support system - Scorecard - MDSpire
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Creation, refinement, and future methodological investigation of a patient experience scale for endoscopy in China along with a decision support tool

  • By

  • Chengcheng Gao

  • Xicheng Chen

  • Yan Wang

  • Weijia Dou

  • Qiongjie Shao

  • Rui Zhang

  • Ying Liang

  • Nan Li

  • Fang Hu

  • Yang Zhao

  • Zhenxiong Liu

  • Lei Shang

  • August 22, 2026

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Clinical Scorecard: Creation, refinement, and future methodological investigation of a patient experience scale for endoscopy in China along with a decision support tool

At a Glance

CategoryDetail
ConditionGastrointestinal Endoscopy
Key MechanismsPatient-reported experience measures (PREMs) to assess and improve patient experience during endoscopic procedures.
Target PopulationPatients undergoing gastrointestinal endoscopy in China.
Care SettingTertiary hospitals in Xi’an, China.

Key Highlights

  • Development of the 54-item Endoscopy Patient Experience Scale (EPES) tailored for Chinese patients.
  • Introduction of the Simplified Endoscopy Patient Experience Scale (S-EPES) using multi-objective optimization.
  • Creation of the Endoscopy Patient Experience Decision Support System (EPE-DSS) to assist clinical decision-making.

Guideline-Based Recommendations

Diagnosis

  • Utilization of patient-reported experience measures to identify areas for improvement in endoscopy services.

Management

  • Implementation of the EPES and S-EPES for assessing patient experiences.

Monitoring & Follow-up

  • Regular evaluation of patient feedback to enhance endoscopic care quality.

Risks

  • Limitations in sample representativeness and potential over-engineering of assessment tools.

Patient & Prescribing Data

Patients scheduled for or having undergone colonoscopy or gastroscopy.

Incorporation of patient experiences into clinical decision-making processes.

Clinical Best Practices

  • Adapting existing instruments for local contexts to improve generalizability.
  • Using data-driven methods for scale simplification and validation.

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