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Ann Thorac Surg ; 110(6): 1869-1873, 2020 12.
Article in English | MEDLINE | ID: mdl-32599050

ABSTRACT

BACKGROUND: Delays in care negatively affect patients with potentially resectable thoracic malignant diseases. The Alberta Thoracic Oncology Program established an automatic referral process for patients with chest computed tomographic (CT) scans suggestive of malignant disease. The objective of this study was to determine whether automatic referral was associated with decreased time to referral or differences in the quality of referral information received. METHODS: A single-center retrospective review of patients referred to a Canadian tertiary thoracic surgical center was performed. The time between the CT scan and the date of referral was calculated, and the type of information provided with the referral was tabulated. Automatic and traditional referral groups were compared using the Student t test, the Mann-Whitney U test, and multivariable analysis. RESULTS: A total of 689 patients met inclusion criteria, and 405 of these patients were automatic referrals. Average time to referral was shorter in the automatic referral group (4.7 days vs 23.6 days; P < .001). Only 2 automatic referrals took longer than 30 days, compared with more than 25% of traditional referrals. Automatic referrals were always associated with a shorter time for referral on subgroup analysis of lung nodules, different referring physician types, and patient location. There was no difference between referral types in the number of referral data provided to the center. CONCLUSIONS: Automatic referrals for patients with potential thoracic malignant disease have a significant beneficial impact on delays in care, and this could result in improved outcomes, such as decreased upstaging and improved survival. This was not associated with a decrease in the amount of information provided with the referral. Thus, automatic referrals may streamline patient care without compromising quality.


Subject(s)
Referral and Consultation , Thoracic Neoplasms/diagnostic imaging , Aged , Delayed Diagnosis , Female , Humans , Male , Middle Aged , Patient Selection , Retrospective Studies , Thoracic Neoplasms/pathology , Time Factors , Tomography, X-Ray Computed
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