RT Journal Article SR Electronic T1 Methods to describe referral patterns in a Canadian primary care electronic medical record database: modelling multi-level count data JF BMJ Health & Care Informatics FD BMJ Publishing Group Ltd SP 311 OP 316 DO 10.14236/jhi.v24i4.888 VO 24 IS 4 A1 Bridget L. Ryan A1 Joshua Shadd A1 Heather Maddocks A1 Moira Stewart A1 Amardeep Thind A1 Amanda L. Terry YR 2017 UL http://informatics.bmj.com/content/24/4/311.abstract AB Background A referral from a family physician (FP) to a specialist is an inflection point in the patient journey, with potential implications for clinical outcomes and health policy. Primary care electronic medical record (EMR) databases offer opportunities to examine referral patterns. Until recently, software techniques were not available to model these kinds of multi-level count data.Objective To establish methodology for determining referral rates from FPs to medical specialists using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) EMR database.Method Retrospective cohort study, mixed effects and multi-level negative binomial regression modelling with 87,258 eligible patients between 2007 and 2012. Mean referrals compared by patient sex, age, chronic conditions, FP visits, and urban/rural practice location. Proportion of variance in referral rates attributable to the patient and practice levels.Results On average, males had 0.26 and females had 0.31 referrals in a 12-month period. Referrals were significantly higher for females, increased with age, FP visits and the number of chronic conditions (p < 0.0001). Overall, 14% of the variance in referrals could be attributed to the practice level, and 86% to patient level characteristics.Conclusions Both the patient and practice characteristics influenced referral patterns. The methodologic insights gained from this study have relevance to future studies on many research questions that utilise count data, both within primary care and broader health services research. The utility of the CPCSSN database will continue to increase in tandem with data quality improvements, providing a valuable resource to study Canadian referral patterns over time.