Decreasing patient length of stay via new flexible exam room allocation policies in ambulatory care clinics

Abstract

To address prolonged lengths of stay (LOS) in ambulatory care clinics, we analyze the impact of implementing flexible and dynamic policies for assigning exam rooms to providers. In contrast to the traditional approaches of assigning specific rooms to each provider or pooling rooms among all practitioners, we characterize the impact of alternate compromise policies that have not been explored in previous studies. Since ambulatory care patients may encounter multiple different providers in a single visit, room allocation can be determined separately for each encounter accordingly. For the first phase of the visit, conducted by the medical assistant, we define a dynamic room allocation policy that adjusts room assignments based on the current state of the clinic. For the second phase of the visit, conducted by physicians, we define a series of room sharing policies which vary based on two dimensions, the number of shared rooms and the number of physicians sharing each room. Using a discrete event simulation model of an outpatient cardiovascular clinic, we analyze the benefits and costs associated with the proposed room allocation policies. Our findings show that it is not necessary to fully share rooms among providers in order to reduce patient LOS and physician idle time. Instead, most of the benefit of pooling can be achieved by implementation of a compromise room allocation approach, limiting the need for significant organizational changes within the clinic. Also, in order to achieve most of the benefits of room allocation policies, it is necessary to increase flexibility in the two dimensions simultaneously. These findings are shown to be consistent in settings with alternate patient scheduling and distinctions between physicians.

Nurses Can Reduce the Patient’s Length of Stay in Emergency Department: An Quality Initiatives

“…CINAHIL, PUBMED, GOOGLE SCHOLAR, 10 abstract only 5 have the recommendation to reduce LOS in an emergency, most of the studies were recommended the same thing mentioned above. One of the studies strongly recommended developing a protocol or Algorithm for emergency LOS and teaching staff according to Algorithm [11].…”

Using lean techniques and discrete-event simulation for performance improvement in an outpatient clinic

“…For instance, previous researchers (Famiglietti et al, 2017;Mustafee et al, 2010) used DES in an outpatient clinic to evaluate and analyze the factors that impact the clinic performance in terms of patient waiting and resource utilization, while another study (Berg et al, 2013) used DES to estimate the effects of noshows on an outpatient clinic in terms of cost. In contrast, implementing DES improvement strategies within outpatient settings is often challenging because of the tremendous amount of data extracted under the conflict of interest between the stakeholders (Eldabi et al, 2007;Matta and Patterson, 2007;Robinson et al, 2012); as a result, improvements with DES tend to be focused on testing suggested new changes by the management in terms of layout, patient flow and resource flow (Demir et al, 2018;Deo et al, 2012;Harper and Gamlin, 2003;Kopach et al, 2007;Vahdat et al, 2018) or it could be focused on performing sensitivity analysis to test the impact of one or a small number of factors on the clinic performance such Lean techniques as the arrival rate, the number of resources hired and sequencing policies (Day et al, 2014;LaGanga and Lawrence, 2007;Lenin et al, 2015;Pan et al, 2015;Rau et al, 2013). In addition, some researchers applied DES to improve the clinical performance based on the effects of appointment scheduling rules and sequencing rules or other soft systems methodology on the clinical performance in terms of patient waiting and resource waiting times (Cayirli et al, 2019(Cayirli et al, , 2006Cayirli and Gunes, 2014;Yang and Cayirli, 2020).…”

A Method for Balancing Provider Schedules in Outpatient Specialty Clinics

“…In other words, constraint set (3) make certain that a provider's clinic schedule will not be segmented during a given day. Constraint set (4) calculates the average number of providers in clinic across the scheduling time frame and constraint set (5) require that the decision variables defined only take on binary values.…”

“…The result is that the traditional approach of assigning clinic resources (e.g., exam rooms) to providers happens less often and provider staffing levels need to be balanced with availability of these shared resources. 5 Finally, as care becomes more interdisciplinary and the use of team-based care models grows, coordination of provider and ancillary staff schedules is an additional necessary consideration in operational planning. Collectively, these characteristics form the challenges in designing modern outpatient care delivery systems and represent the need for decision support approaches that balance these competing operational goals and requirements.…”