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评估精神疾病转诊请求的接受情况:来自沙特阿拉伯的全国性电子转诊数据。

Assessing Mental Illness Referral Request Acceptance: A Nationwide E-Referral Data From Saudi Arabia.

作者信息

Alharbi Abdullah A, Aljerian Nawfal A, Alghamdi Hani A, Binhotan Meshary S, Alsultan Ali K, Arafat Mohammed S, Aldhabib Abdulrahman, Aloqayli Ahmed I, Alwahbi Eid B, Horner Ronnie D

机构信息

Family and Community Medicine Department, Faculty of Medicine, Jazan University, Jazan City, Kingdom of Saudi Arabia.

Medical Referrals Centre, Ministry of Health, Riyadh, Kingdom of Saudi Arabia.

出版信息

J Multidiscip Healthc. 2025 Feb 11;18:735-745. doi: 10.2147/JMDH.S493246. eCollection 2025.

Abstract

BACKGROUND AND OBJECTIVE

Mental disorders significantly impact quality of life and life expectancy, representing a leading cause of global disease burden. Healthcare systems worldwide face challenges in meeting mental health service demands, particularly due to specialist shortages and geographical barriers. Saudi Arabia has implemented an innovative nationwide electronic referral system (SMARC) as part of its digital health transformation strategy to enhance mental healthcare accessibility. This study examined SMARC's effectiveness in facilitating mental health service access by analyzing patient transfer acceptance rates between healthcare facilities and identifying factors influencing these rates.

METHODS

This retrospective cross-sectional study analyzed 9722 mental health electronic referrals within SMARC from January 2020 to December 2021. Descriptive statistics characterized referral patterns, while bivariate and multivariable logistic regression analyses identified factors associated with referral acceptance, calculating adjusted odds ratios (aORs) and 95% confidence intervals.

RESULTS

The system achieved an overall acceptance rate of 82.5%, with different patterns across age groups and regions. Lower acceptance rates were observed for ages 15-25 years (aOR = 0.84; 95% CI = 0.70-0.99) and 46-65 years (aOR = 0.83; 95% CI = 0.70-0.99) compared to ages 26-35 years. Life-saving referrals showed the highest acceptance (aOR = 2.60; 95% CI = 1.51-4.48), while psychiatrist availability significantly influenced acceptance rates (aOR = 1.36; 95% CI = 1.17-1.58). External referrals were half as likely to be accepted as internal ones (aOR = 0.51; 95% CI = 0.42-0.64).

CONCLUSION

SMARC demonstrates effectiveness in optimizing mental healthcare access through strategic matching of patient needs with available resources. The system's selective acceptance patterns reflect its capability to prioritize care based on clinical urgency and resource availability. These findings provide valuable insights for policymakers to keep enhancing digital health infrastructure and mental healthcare delivery. The SMARC model offers a promising framework for implementing similar digital referral systems globally to improve mental healthcare coordination and accessibility.

摘要

背景与目的

精神障碍对生活质量和预期寿命有重大影响,是全球疾病负担的主要原因之一。全球医疗保健系统在满足心理健康服务需求方面面临挑战,特别是由于专科医生短缺和地理障碍。沙特阿拉伯已实施一项创新的全国性电子转诊系统(SMARC),作为其数字健康转型战略的一部分,以提高心理健康服务的可及性。本研究通过分析医疗机构之间的患者转诊接受率并确定影响这些比率的因素,考察了SMARC在促进心理健康服务可及性方面的有效性。

方法

这项回顾性横断面研究分析了2020年1月至2021年12月SMARC内的9722份心理健康电子转诊记录。描述性统计描述了转诊模式,而双变量和多变量逻辑回归分析确定了与转诊接受相关的因素,计算了调整后的优势比(aOR)和95%置信区间。

结果

该系统的总体接受率为82.5%,不同年龄组和地区呈现不同模式。与26 - 35岁年龄组相比,15 - 25岁(aOR = 0.84;95% CI = 0.70 - 0.99)和46 - 65岁(aOR = 0.83;95% CI = 0.70 - 0.99)年龄组的接受率较低。救命转诊的接受率最高(aOR = 2.60;95% CI = 1.51 - 4.48),而精神科医生的可获得性显著影响接受率(aOR = 1.36;95% CI = 1.17 - 1.58)。外部转诊被接受的可能性只有内部转诊的一半(aOR = 0.51;95% CI = 0.4

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4
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6
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