PDF(Pubmed)
Abstract：
BACKGROUND: Referring providers are often critiqued for writing poor-quality referrals. This study characterised clinical referral guidelines and forms to understand which data consultant providers require. These data were then used to codesign an evidence-based, high-quality referral form.
METHODS: This study used both observational and quality improvement approaches. Canadian referral guidelines were reviewed and summarised. Referral data fields from 150 randomly selected Ontario referral forms were categorised and counted. The referral guideline summary and referral data were then used by referring providers, consultant providers and administrators to codesign a referral form.
RESULTS: Referral guidelines recommended 42 types of referral data be included in referrals. Referral data were categorised as patient demographics, provider demographics, reason for referral, clinical information and administrative information. The percentage of referral guidelines recommending inclusion of each type of referral data varied from 8% to 77%. Ontario referral forms requested 264 different types of referral data. Digital referral forms requested more referral data types than paper-based referral forms (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01). A codesigned referral form was created across two sessions with 29 and 21 participants in each.
CONCLUSIONS: Referral guidelines lack consistency and specificity, which makes writing high-quality referrals challenging. Digital referral forms tend to request more referral data than paper-based referrals, which creates administrative burdens for referring and consultant providers. We created the first codesigned referral form with referring providers, consultant providers and administrators. We recommend clinical adoption of this form to improve referral quality and minimise administrative burdens.
METHODS: This study used both observational and quality improvement approaches. Canadian referral guidelines were reviewed and summarised. Referral data fields from 150 randomly selected Ontario referral forms were categorised and counted. The referral guideline summary and referral data were then used by referring providers, consultant providers and administrators to codesign a referral form.
RESULTS: Referral guidelines recommended 42 types of referral data be included in referrals. Referral data were categorised as patient demographics, provider demographics, reason for referral, clinical information and administrative information. The percentage of referral guidelines recommending inclusion of each type of referral data varied from 8% to 77%. Ontario referral forms requested 264 different types of referral data. Digital referral forms requested more referral data types than paper-based referral forms (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01). A codesigned referral form was created across two sessions with 29 and 21 participants in each.
CONCLUSIONS: Referral guidelines lack consistency and specificity, which makes writing high-quality referrals challenging. Digital referral forms tend to request more referral data than paper-based referrals, which creates administrative burdens for referring and consultant providers. We created the first codesigned referral form with referring providers, consultant providers and administrators. We recommend clinical adoption of this form to improve referral quality and minimise administrative burdens.
摘要：
背景：推荐提供者经常因撰写低质量的推荐而受到批评。这项研究以临床转诊指南和表格为特征，以了解数据顾问提供商需要哪些。这些数据然后被用来共同设计一个基于证据的，高质量的推荐表格。
方法：本研究采用了观察性和质量改进方法。审查并总结了加拿大转诊指南。对150个随机选择的安大略省转诊表的转诊数据字段进行了分类和计数。然后转诊提供者使用转诊指南摘要和转诊数据，顾问提供者和管理员共同设计推荐表格。
结果：转诊指南建议在转诊中包括42种转诊数据。转诊数据分类为患者人口统计，提供者人口统计，转介的原因，临床信息和行政信息。推荐纳入每种类型转诊数据的转诊指南的百分比从8%到77%不等。安大略省推荐表格要求提供264种不同类型的推荐数据。与纸质转诊表相比，数字转诊表要求更多的转诊数据类型（55.0±10.6vs30.5±8.1；95％CIp<0.01）。在两个会议上创建了共同设计的推荐表格，每个会议有29名和21名参与者。
结论：转诊指南缺乏一致性和特异性，这使得撰写高质量的推荐具有挑战性。数字推荐表格往往比纸质推荐表格要求更多的推荐数据，这给转介和顾问提供者带来了行政负担。我们与转介提供者一起创建了第一个共同设计的转介表格，顾问提供商和管理员。我们建议临床采用此表格，以提高转诊质量并最大程度地减少行政负担。
方法：本研究采用了观察性和质量改进方法。审查并总结了加拿大转诊指南。对150个随机选择的安大略省转诊表的转诊数据字段进行了分类和计数。然后转诊提供者使用转诊指南摘要和转诊数据，顾问提供者和管理员共同设计推荐表格。
结果：转诊指南建议在转诊中包括42种转诊数据。转诊数据分类为患者人口统计，提供者人口统计，转介的原因，临床信息和行政信息。推荐纳入每种类型转诊数据的转诊指南的百分比从8%到77%不等。安大略省推荐表格要求提供264种不同类型的推荐数据。与纸质转诊表相比，数字转诊表要求更多的转诊数据类型（55.0±10.6vs30.5±8.1；95％CIp<0.01）。在两个会议上创建了共同设计的推荐表格，每个会议有29名和21名参与者。
结论：转诊指南缺乏一致性和特异性，这使得撰写高质量的推荐具有挑战性。数字推荐表格往往比纸质推荐表格要求更多的推荐数据，这给转介和顾问提供者带来了行政负担。我们与转介提供者一起创建了第一个共同设计的转介表格，顾问提供商和管理员。我们建议临床采用此表格，以提高转诊质量并最大程度地减少行政负担。
