中国全科医学 ›› 2022, Vol. 25 ›› Issue (07): 893-898.DOI: 10.12114/j.issn.1007-9572.2021.00.296

• 新进展 • 上一篇    

慢性病患者就医延迟评估工具及影响因素的研究进展

邹浩, 姜东旭, 张琳琳*   

  1. 150081 黑龙江省哈尔滨市,哈尔滨医科大学护理学院
  • 收稿日期:2021-06-23 修回日期:2021-07-30 出版日期:2022-03-05 发布日期:2022-03-02
  • 通讯作者: 张琳琳
  • 基金资助:
    黑龙江省自然科学基金资助项目(LH2019G011)

Recent Advances in Evaluation Tools and Associated Factors for Patient Delay in Chronic Disease Patients

ZOU HaoJIANG DongxuZHANG Linlin*   

  1. School of NursingHarbin Medical UniversityHarbin 150081China

    *Corresponding authorZHANG LinlinProfessorMaster supervisorE-mailzhanglinlin1971@163.com

  • Received:2021-06-23 Revised:2021-07-30 Published:2022-03-05 Online:2022-03-02

摘要: 就医延迟会导致慢性病并发症发生风险增加、治疗效果下降和慢性病患者生存质量降低。早期识别就医延迟高风险人群并采取针对危险因素的干预策略,对于改善慢性病患者就医延迟现状具有重要意义。本文在文献回顾的基础上,对包括就医障碍自评量表(BACE)、就医决策障碍感知量表(PBHSD)、脑卒中院前延迟意向测评量表(SPDBI)、糖尿病诊治延误认知行为意向量表(DMDBIS)和FISCHER就医态度量表(ATMHSS)在内的慢性病患者就医延迟评估工具进行系统总结,并以可控和不可控因素为分层依据,从社会人口学、疾病特征、心理因素和认知因素角度出发,对影响慢性病患者就医延迟的因素进行归纳和分析,旨在为就医延迟评估和干预方案的构建提供参考与依据。本文发现,目前,慢性病患者就医延迟评估工具的普适性和临床使用率偏低,且较少研究探讨其在预测慢性病患者就医延迟中的效能和最佳阈值。对疾病认识不足、经济状况差和社会支持水平低是影响慢性病患者就医延迟的重要因素。

关键词: 慢性病, 就医延迟, 量表, 影响因素, 综述

Abstract:

Patient delay will lead to increased risk of complications, reduced treatment effectiveness and lowered quality of life in chronic disease patients. Early identifying individuals with chronic diseases at high risk of patient delay, and timely delivering targeted interventions to them may greatly improve current status of patient delay in this population. Based on a literature review, we systematically summarized several major evaluation tools (including Barriers to Access to Care Evaluation scale, Perceived Barriers to Health Care-seeking Decision, Stroke Pre-Hospital Delay Behavior Intention scale, Diabetes Mellitus Diagnosis and Treatment Delayed Cognitive Behavioral Intention Scale, and Attitudes toward Medical Help-seeking Scale developed by Fisher et al.) for patient delay in chronic disease patients, and analyzed controllable (mental and cognitive) and uncontrollable (sociodemographic and disease-specific) factors associated with patient delay, offering evidence for the assessment of patient delay and development of relevant interventions. We found that the applicability and clinical application rate of these tools are low, and their predictive efficacy and threshold have been rarely studied, and patient delay may be significantly associated with patients' insufficient knowledge of the disease, low economic level and low social support.

Key words: Chronic disease, Patient delay, Scale, Influencing factors, Review

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