Improving efficiency of a rheumatology outpatient clinic through classification of newly referred patients

Abstract

Objectives: Diagnosing a rheumatological disease in patients newly referred by their general practitioner requires assessment by a rheumatologist and often diagnostic tests. Ideally, these tests are performed prior to the patient's first consultation with the rheumatologist, aiming for quick diagnosis and fewer visits. We retrospectively studied whether a pre-first visit digital patient questionnaire can lead to fewer consultations and faster diagnosis.

Methods: We applied machine learning-based binary classification algorithms to questionnaire data of newly referred patients to classify a patient's diagnostic class (inflammatory vs. non-inflammatory). Afterwards, we quantified the rheumatology clinic's benefit when all patients classified to be non-inflammatory are planned for specific diagnostic tests at their first visit.

Results: Classification for inflammatory vs. non-inflammatory disease could be done with accuracy of 0.771, sensitivity (recall) of 0.809 and precision of 0.833. When non-inflammatory classified patients receive a blood test before first consultation, on average 0.1 in-person consultations and 0.26 teleconsultations per patient are avoided at the cost of having overdiagnostics for 44% of all newly referred patients. If, in addition to a blood test, the first consultation is also preceded by a radiology examination, on average 0.21 in-person consultations and 0.49 teleconsultations per patient are avoided, at the cost of having overdiagnostics (e.g. only the blood test or the combination of the blood test and radiology examination) for 71% of all newly referred patients.

Conclusion: Classification algorithms based on pre-first visit patient questionnaires may shorten the patient journey in a rheumatology outpatient clinic and may therefore improve efficiency.

Keywords: (non)-inflammatory rheumatic disease; binary classification; efficient outpatient clinic scheduling; machine learning; newly referred patients; pre-visit questionnaire.

Figure 1.

Sensitivity analysis. On the horizontal axis, each cost configuration is listed, while on the y-axis, the clinic’s fictional profit is shown for each set of pre-first visit diagnostic tests (which in turn corresponds to a colour). For every combination of cost configuration and pre-first visit diagnostic tests, the best classifier is plotted