Word Embedding and Clustering for Patient-Centered Redesign of Appointment Scheduling in Ambulatory Care Settings

Background. A key to a more efficient scheduling systems is to ensure appointments are designed to meet patient's needs and to design and simplify appointment scheduling less prone to error. Electronic Health Records (EHR) consist of valuable information about patient characteristics and their healthcare needs. The aim of this study is to utilize information from structured and unstructured EHR data to redesign appointment scheduling in community health clinics. Methods. We used Global Vectors for Word Representation, a word embedding approach, on free text field "scheduler note" to cluster patients into groups based on similarities of reasons for appointment. We then redesigned an appointment scheduling template with new types and durations based on the clusters. We compared the current appointment scheduling system and our proposed system by predicting and evaluating clinic performance measures such as patient time spent in-clinic and number of additional patients to accommodate. Results. We collected 17,722 encounters of an urban community health clinic in 2014 including 102 unique types recorded in the EHR. Following data processing, word embedding implementation, and clustering, appointment types were grouped into 10 clusters. The proposed scheduling template could open space to see overall an additional 716 patients per year and decrease patient in-clinic time by 3.6 minutes on average (p-value<0.0001). Conclusions. We found word embedding, that is an NLP approach, can be used to extract information from schedulers notes for improving scheduling systems. Unsupervised machine learning approach can be applied to simplify appointment scheduling in CHCs. Patient-centered appointment scheduling can be achieved by simplifying and redesigning appointment types and durations that could improve performance measures, such as increasing availability of time and patient satisfaction.