Predicting the target specialty of referral notes to estimate per-specialty wait times with machine learning.

Background: Currently, in Canada, existing health administrative data and hospital-inputted portal systems are used to measure the wait times to receiving a procedure or therapy after a specialist visit. However, due to missing and inconsistent labelling, estimating the wait time prior to seeing a specialist physician requires costly manual coding to label primary care referral notes.

Methods: In this work, we represent the notes using word-count vectors and develop a logistic regression machine learning model to automatically label the target specialist physician from a primary care referral note.

The receipt of information by family physicians about their patient's emergency department visits: a record linkage study of electronic medical records to health administrative data.

Background: Canadians are known to be frequent users of emergency department (ED) care. However, the exchange of information from ED visits to family physicians (FPs) is not well known. Our objectives were to determine whether Canadian FPs received information about their patient's ED visit and the patient characteristics related to the receipt of ED information.

The quality of diabetes care among cancer survivors: a retrospective cohort study.

Background: As cancer survivorship continues to improve, management of co-morbid diabetes has become an increasingly important determinant of health outcomes for people with cancer. This study aimed to compare indicators of diabetes quality of care between people with diabetes and without a history of cancer.

Methods: We used the Electronic Medical Record Administrative data Linked Database (EMRALD), a database of Ontario primary care EMR charts linked to administrative data, to identify people with diabetes and at least 1 year follow-up.