oa Saffara: Intelligent queuing application for improving clinical workflow

This paper examines the impact on patient experience through the creation of a bespoke patient queuing and communication application using in-house developed technologies. Sidra Medicine hospital's outpatient pharmacy was experiencing mismanaged queue lines, dissatisfied patients, and the lack of data necessary to determine the length of time elapsing in obtaining medication. After analyzing patient surveys through the method of sentiment analysis and generation of word clouds, we validated that there was scope for workflow improvement in the pharmacy department. The Center for Medical Innovation, Software, and Technology (CMIST) department was commissioned to develop the software application necessary to deliver efficiency and improvement in response to the lack of a queuing and communication system. The use of an in-house development team to create an application for queuing and communication as opposed to selecting a popular vendor software resulted in many advantages. Some of the main advantages were that the requirements of pharmacy were delivered through rapid customization and in multiple iterations, which were delivered in response to the ever changing customer demand. By using scrum methodology, the team was able to deliver the application called Saffara, for managing queues in the pharmacy and improving patient experience while obtaining medication. The Saffara application, has a unique feature of being integrated to the hospital EMR (Electronic Medical Record) system while ensuring confidentiality, efficiency and time saving. The application integrates with the hospital's EMR to obtain patient information, appointment times and prescribed medication. This integration allowed for the identification of patients' progress and calculation of patients ‘wait times. Patients are automatically notified when their medication is ready for collection, through system generated SMS texts. The application also utilizes a notification display for communication with patients as part of our business continuity procedure. In addition to notifying the patient, the Saffara application also generates detailed analytical reports for each hour and for each patient, which allows us to analyze the bottlenecks in the clinical workflow. We present these technologies to any stakeholders through a web dashboard and detailed web-based reports in our application. The pharmacy stakeholders, i.e., the pharmacy management team utilize the dashboards and quantitative data in the reports to predict staffing levels to deliver optimization in patient medication delivery. In this paper, we present the methods we use to calculate the useful analytics like patient wait times across different stages in the workflow and hourly breakdown of patients being served. We will also discuss how we reduced patient wait times by adding unique features to a queuing application like automation of steps in the pharmacy workflow through generation of patient identifiers and automatic ticket tracking. This paper will also highlight how we are scaling our application from pharmacy to all clinics of the hospital. The goal of the application is to provide a consistent experience for patients in all clinics as well as a consistent way for staff to gather and analyze data for workflow improvement. Our future work is to explore how we can use machine learning to identify the parameters that play a vital role in wait times as well as patient experience. The objective of this paper is to highlight how our technology converges the patient experience and staff workflow enhancements to deliver improvement in a clinical workflow setting.