Objective: Point-of-care testing (POCT) urinalysis might reduce errors in (subjective) reading, registration and communication of test results, and might also improve diagnostic outcome and optimise patient management. Evidence is lacking. In the present study, we have studied the analytical performance of automated urinalysis and visual urinalysis compared with a reference standard in routine general practice. Setting: The study was performed in six general practitioner (GP) group practices in the Netherlands. Automated urinalysis was compared with visual urinalysis in these practices. Reference testing was performed in a primary care laboratory (Saltro, Utrecht, The Netherlands). Primary and secondary outcome measures: Analytical performance of automated and visual urinalysis compared with the reference laboratory method was the primary outcome measure, analysed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and Cohen's kappa coefficient for agreement. Secondary outcome measure was the user-friendliness of the POCT analyser. Results: Automated urinalysis by experienced and routinely trained practice assistants in general practice performs as good as visual urinalysis for nitrite, leucocytes and erythrocytes. Agreement for nitrite is high for automated and visual urinalysis. kappa's are 0.824 and 0.803 (ranked as very good and good, respectively). Agreement with the central laboratory reference standard for automated and visual urinalysis for leucocytes is rather poor (0.256 for POCT and 0.197 for visual, respectively, ranked as fair and poor) kappa's for erythrocytes are higher: 0.517 (automated) and 0.416 (visual), both ranked as moderate. The Urisys 1100 analyser was easy to use and considered to be not prone to flaws. Conclusions: Automated urinalysis performed as good as traditional visual urinalysis on reading of nitrite, leucocytes and erythrocytes in routine general practice. Implementation of automated urinalysis in general practice is justified as automation is expected to reduce human errors in patient identification and transcribing of results.