A Simulation-Based Planning Methodology for Decreasing Patient Waiting Times in Pure Walk-In Clinics

Introduction

Healthcare systems in the U.S. are constantly under pressure to improve the services provided to patients while decreasing its cost. Some of the current challenges include competition, declining reimbursements, and serving an aging chronically ill patient population. Cost reduction plans can result in an inadequate number of resources staffed, congested flow, and increased patient delays. Service convenience combined with minimal waiting times are key performance measures for primary care clinic practices. Multiple studies have shown that waiting time is one of the main differentiators for “best practice” facilities. Therefore, reducing patient waiting periods can offer a competitive advantage when patients have a choice (Baker, 2001; McCarthy, McGee, & O'Boyle, 2000; Rohleder, Lewkonia, Bischak, Duffy, & Hendijani, 2011). Finding the best balance among these multiple conflicting objectives is a very difficult problem to solve.

Traditional primary care clinics are led by physicians with ancillary support staff. These facilities are equipped to handle both acute and chronic medical conditions, and typically have limited hours and require advance appointment booking. In addition, physicians typically take responsibility and are a stable source of care for a large group of people over a long-term period, building a longitudinal relationship with each person over repeated office visits. In contrast, walk-in clinics are standalone physical clinics that do not require patient appointments. Walk-in clinics are outpatient medical units designed to provide acute treatment for low-risk conditions, such as common coughs and colds but are generally not suited for ongoing monitoring or prevention of long-term complications (Cassel, 2012). The emphasis of walk-in care clinics is patient convenience at an affordable cost. Services are less expensive than visiting an emergency room or an urgent care clinic (Chen, Chen, Hu, & Mehrotra, 2017).

The management and operation of walk-in clinics is difficult. Capacity planning is one of the major challenges because of the uncertainty in the patient demand. Since no appointments are provided to patients two possible scenarios can result when planning the staff capacity for the day: 1) patient might end up waiting long periods of time to see a provider and 2) providers experience long idle times. Patient delays can be categorized in two main areas: appointment delays and real time delays (Bard, Shu, Morrice, Poursani, & Leykum, 2016; Savin, 2006). Appointment delays are computed as the number of days between the requested and scheduled appointment dates and are a tangible indicator of lack of access. Real time delays correspond to the wait time from when the patient check-in until the patient is admitted. These delays are often a result of a complex combination of process inefficiencies. While there are several strategies designed to attack some of these factors, real-time delays cannot be avoided due to the uncertainty in the patient behavior and clinical services time. Since no appointments are provided in walk-in clinics, the rest of the paper will focus only on real time delays.

This research is motivated by the growing popularity of walk-in healthcare clinics in the United States. A number of qualitative and quantitative studies have been performed in the area of primary care scheduling (Cayirli & Gunes, 2013; Dzubay & Pérez, 2016; Mocarzel et al., 2013; Sowle et al., 2014; Walker et al., 2015) and open access scheduling (Kopach et al., 2007; LaGanga & Lawrence, 2012; Robinson & Chen, 2010; Reese, Anandhan, Pérez, & Novoa, 2017), but there is still a lack of investigations on how to manage patients in clinics where scheduling is not allowed. To the best of our knowledge, no study has addressed the allocation of resources in clinics admitting only walk-in patients. As such, to bridge this gap, we develop a discrete event simulation approach in this study to find the best way to manage patients and resources under uncertainty in clinics that only admits walk-in patients. Ultimately, the aim is to provide hospital management with a generalized modeling tool which supports decisions concerning with hiring resources or doing operational changes in the system.