Codesigned standardised referral form: simplifying the complexity.

Area of Science:

• Healthcare quality improvement
• Medical informatics
• Health services research

Background:

• Referring providers face challenges with inconsistent referral guidelines and forms.
• Existing referral processes often result in poor-quality referrals.
• Understanding consultant needs is crucial for effective referral data collection.

Purpose of the Study:

• To characterize data requirements for clinical referrals based on guidelines and existing forms.
• To codesign an evidence-based, high-quality referral form with key stakeholders.
• To improve the quality and efficiency of the referral process.

Main Methods:

• Review and summarization of Canadian clinical referral guidelines.
• Categorization and counting of referral data fields from 150 Ontario referral forms.
• Collaborative codesign of a new referral form involving referring providers, consultant providers, and administrators.

Main Results:

• Referral guidelines recommended 42 data types, with varying recommendation percentages (8%-77%).
• Ontario referral forms requested 264 distinct data types; digital forms requested significantly more than paper forms.
• A codesigned referral form was successfully developed through multi-stakeholder sessions.

Conclusions:

• Inconsistent referral guidelines hinder the creation of high-quality referrals.
• Digital referral forms can increase administrative burden due to excessive data requests.
• The codesigned referral form offers a solution to enhance referral quality and minimize administrative workload.