A referral letter is a formal document written by a healthcare professional or a medical practitioner to refer a patient to another healthcare provider or specialist for further assessment, diagnosis, or treatment. The referral letter serves as a communication tool between healthcare professionals and helps ensure continuity of care for the patient. The content of a referral letter can vary based on the specific circumstances and the healthcare system in which it is used, but generally, it includes the following information:
Patient Information:
Full name of the patient. Date of birth or age. Gender. Contact information (address, phone number). Referring Healthcare Professional's Information:
Full name and professional title. Contact information (address, phone number, email). Clinic or practice name. Provider's credentials or qualifications. Recipient/Specialist Information:
Full name and professional title of the specialist or healthcare provider to whom the patient is being referred. Contact information (address, phone number, email). Clinic or hospital name. Specialist's credentials or qualifications. Reason for Referral:
Clear and concise explanation of why the patient is being referred. This could include symptoms, diagnostic findings, or the need for specialized care. Relevant medical history to provide context for the referral. Clinical Findings:
Summary of relevant clinical findings or test results that led to the decision to refer the patient. Any diagnostic tests, laboratory results, or imaging reports that support the referral. Treatment to Date:
Overview of any treatments or interventions that have already been provided to the patient. Medications prescribed and their efficacy. Patient's Consent:
Confirmation that the patient has been informed about the referral and has given consent to be seen by the referred healthcare professional. Urgency and Timeline:
Indication of the urgency of the referral, if applicable. Any specific timelines or deadlines for the referral. Follow-Up Information:
Instructions for the specialist on how to communicate findings and treatment plans back to the referring healthcare professional. Whether the referring healthcare professional wants to be involved in ongoing care or just receive consultation. Contact Information for Further Communication:
Contact details for the referring healthcare professional in case the specialist needs additional information or has questions. Referral letters play a crucial role in ensuring that patients receive appropriate and timely care from the right healthcare providers. They facilitate effective communication and collaboration among members of the healthcare team. The content and format of referral letters may vary based on local healthcare practices and specific requirements of healthcare institutions or systems.
Decision support systems (DSSs) for suggesting optimal low back pain treatment (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients' goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment.
The objective of the study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP.
A retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models' accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters.
Data from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results.
This study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application 1).