Using simulation modeling to minimize patient-to-patient contact time while optimizing clinical operations during a pandemic
Authors: Shilpa Ghatnekar, Nurul M. Suhaimi, Jacqueline Griffin, David Rosmarin, Rebecca Yanovsky, Priyank Sharma, F. Clarissa Yang
Journal: Journal of the American Academy of Dermatology (2021)

To the Editor: The COVID-19 pandemic significantly stressed outpatient operations. In March 2020, ambulatory visits declined by nearly 60%, and many practices closed to decrease transmission.1Mehrotra A. Chernew M. Linetsky D. Hatch H. Cutler D. Schneider E.C. The impact of COVID-19 on outpatient visits in 2020: visits remained stable, despite a late surge in cases.https://www.commonwealthfund.org/publications/2021/feb/impact-covid-19-outpatient-visits-2020-visits-stable-despite-late-surgeDate accessed: May 7, 2021Google Scholar As clinics reopened, they had to quickly readjust clinical operations—balancing telemedicine and in-person visits to minimize patient-to-patient contact (PTPC) during in-person visits.2Litchman G.H. Rigel D.S. The immediate impact of COVID-19 on US dermatology practices.J Am Acad Dermatol. 2020; 83: 685-686https://doi.org/10.1016/j.jaad.2020.05.048Abstract Full Text Full Text PDF PubMed Scopus (33) Google Scholar,3Shinkai K. Bruckner A.L. Dermatology and COVID-19.JAMA. 2020; 324: 1133-1134https://doi.org/10.1001/jama.2020.15276Crossref PubMed Scopus (24) Google Scholar By leveraging a previously validated simulation model, we studied how to vary resources to minimize patient exposure risk while maximizing physician utility by maintaining patient volume and access during the COVID-19 pandemic.4Suhaimi N.M. Ghatnekar S. Vahdat V. Griffin J. Rosmarin D. Yang F.C. Operationally balancing resident education and patient waiting time in an outpatient academic setting.J Am Acad Dermatol. February 22, 2021; https://doi.org/10.1016/j.jaad.2021.02.0495Crossref Google Scholar We performed a time study of 570 patients between July 27, 2020, and August 7, 2020, at the Department of Dermatology, Tufts Medical Center, and compared the results with those from our study in 2018.4Suhaimi N.M. Ghatnekar S. Vahdat V. Griffin J. Rosmarin D. Yang F.C. Operationally balancing resident education and patient waiting time in an outpatient academic setting.J Am Acad Dermatol. February 22, 2021; https://doi.org/10.1016/j.jaad.2021.02.0495Crossref Google Scholar Although there were variations in in-person patient and physician volume during the COVID-19 pandemic, no significant difference existed in process flow, rendering the existing simulation model valid for use in this study.4Suhaimi N.M. Ghatnekar S. Vahdat V. Griffin J. Rosmarin D. Yang F.C. Operationally balancing resident education and patient waiting time in an outpatient academic setting.J Am Acad Dermatol. February 22, 2021; https://doi.org/10.1016/j.jaad.2021.02.0495Crossref Google Scholar We evaluated differences in PTPC between before and during the COVID-19 pandemic when baseline resources, defined as 1 medical assistant (MA), 1 resident, and 3 rooms, are assigned (Table I). We measured PTPC based on the percentage of patients who wait in the waiting area with 1 or more patients and their average wait time (AWT).Table IComparison of patient-to-patient contact as measured through percentage of patients who wait in the waiting area with 1 or more patients and average wait time between before and during COVID-19 operations∗Comparison of patient-to-patient contact was performed when 1 medical assistant, 1 resident, and 3 rooms were assigned per attending.†Details of the simulation model parameters for the base-case scenario can be found in the study by Suhaimi et al.4Clinic characteristicsCOVID-19 (July or August 2020)Before COVID-19 (November 2018)In-personPatient volume/week‡Patient volume before no-shows. Decreases in in-person volume during the COVID-19 pandemic were, in part, due to increase in the use of telemedicine services.285508Patients per attending1514.5PPWA, %§PPWA and AWT were calculated only for in-person patients who wait with 1 or more patients in the waiting area.46.967.1AWT in waiting area, min§PPWA and AWT were calculated only for in-person patients who wait with 1 or more patients in the waiting area.29.728.1TelemedicinePatient volume/week1130AWT, Average wait time; PPWA, percentage of patients in the waiting area with 1 or more patients.∗ Comparison of patient-to-patient contact was performed when 1 medical assistant, 1 resident, and 3 rooms were assigned per attending.† Details of the simulation model parameters for the base-case scenario can be found in the study by Suhaimi et al.4Suhaimi N.M. Ghatnekar S. Vahdat V. Griffin J. Rosmarin D. Yang F.C. Operationally balancing resident education and patient waiting time in an outpatient academic setting.J Am Acad Dermatol. February 22, 2021; https://doi.org/10.1016/j.jaad.2021.02.0495Crossref Google Scholar‡ Patient volume before no-shows. Decreases in in-person volume during the COVID-19 pandemic were, in part, due to increase in the use of telemedicine services.§ PPWA and AWT were calculated only for in-person patients who wait with 1 or more patients in the waiting area. Open table in a new tab AWT, Average wait time; PPWA, percentage of patients in the waiting area with 1 or more patients. Despite an average in-person patient volume reduction of 43.9% during the COVID-19 pandemic (due, in part, to some physicians using telemedicine), using baseline resources, the percentage of patients who wait in the waiting area with 1 or more patients and AWT were 46.9% and 29.7 minutes, respectively (Table I). Thus, we simulated 120 scenarios with varying numbers of MAs, residents, rooms, and patients (Table II) with the goal of minimizing PTPC by decreasing AWT to <15 minutes based on the Centers for Disease Control's exposure guidelines.5Managing healthcare operations during COVID-19: planning and staying prepared. Centers for Disease Control.https://www.cdc.gov/coronavirus/2019-ncov/hcp/guidance-hcf.htmlDate accessed: April 24, 2021Google ScholarTable IIEffects of patient volume, number of medical assistants, residents, and rooms per attending on the percentage of patients who wait in the waiting area with 1 or more patients and average wait time as measures of patient-to-patient contact∗Baseline refers to COVID-19 in-person patient volume, as shown in Table I.No. of medical assistantsNo. of residentsPatient volumePPWA†PPWA and AWT were calculated only for in-person patients who wait with one or more patients in the waiting area as measures of patient-to-patient contact.AWT†PPWA and AWT were calculated only for in-person patients who wait with one or more patients in the waiting area as measures of patient-to-patient contact.No. of roomsNo. of rooms234523451−40%25.8%13.4%7.7%4.1%36.524.625.914.0−20%51.6%35.2%27.6%15.5%50.240.734.831.20Baseline80.2%66.8%51.0%43.2%73.159.052.249.4+20%89.0%86.6%81.7%73.3%105.496.280.273.0+40%94.2%90.5%86.9%85.2%152.3130.8120.3115.5−40%19.9%6.7%1.5%1.9%20.911.96.23.6−20%42.6%21.3%14.3%6.9%32.722.522.618.61Baseline67.7%46.9%‡Indicates baseline resourcing scenario.30.5%17.4%43.229.7‡Indicates baseline resourcing scenario.26.720.0+20%83.4%69.6%62.0%53.6%68.951.046.940.7+40%91.1%85.2%78.4%72.1%90.080.967.062.1−40%16.0%3.6%1.7%-18.515.54.92.1−20%39.4%17.8%6.9%3.8%28.019.015.47.02Baseline56.6%35.2%18.2%6.9%35.127.422.011.8+20%80.2%59.6%37.7%27.5%50.035.228.927.0+40%90.3%75.6%65.1%48.7%74.551.541.734.92−40%21.1%11.7%4.6%1.8%25.933.315.611.4−20%54.4%37.8%25.9%14.3%47.833.734.425.60Baseline80.1%69.7%55.8%44.7%75.062.447.141.1+20%88.8%82.3%78.1%70.2%99.789.676.567.4+40%93.2%91.5%87.2%83.8%140.9134.3118102.8−40%17.6%7.8%2.4%0.5%16.020.413.31.6−20%42.1%20.5%12.0%5.4%28.925.920.89.31Baseline63.1%46.5%27.9%24.4%40.729.423.228.9+20%81.4%71.6%52.7%45.1%61.150.838.235.4+40%92.1%83.0%74.3%66.5%84.273.764.153.0−40%13.0%3.8%2.3%0.5%12.513.85.6-−20%27.9%15.0%6.1%2.1%18.615.212.44.82Baseline49.4%28.8%17.5%10.4%26.919.518.612.2+20%72.4%51.1%36.7%26.4%41.629.125.424.2+40%86.7%71.8%57.6%48.0%58.843.933.928.3AWT, Average wait time; PPWA, percentage of patients in waiting area.∗ Baseline refers to COVID-19 in-person patient volume, as shown in Table I.† PPWA and AWT were calculated only for in-person patients who wait with one or more patients in the waiting area as measures of patient-to-patient contact.‡ Indicates baseline resourcing scenario. Open table in a new tab AWT, Average wait time; PPWA, percentage of patients in waiting area. Increasing MAs from 1 to 2 while keeping other volume or resources constant demonstrated the least improvement, whereas increasing the number of rooms and residents had larger effects. Compared with the baseline, the percentage of patients who wait in the waiting area with 1 or more patients only decreased from 46.9% to 46.5% with 1-unit increase of MA but decreased to 35.2% and 30.5% with 1-unit increase in the number of residents and rooms, respectively. Similarly, AWT decreased from 29.7 to 29.4 minutes with the increase of 1 MA but decreased to 27.4 and 26.7 minutes with the increase of 1 resident and room, respectively. Decreasing the patient volume also decreased AWT, but using baseline resources, the patient volume needed to decrease by 40% for AWT to be <15 minutes. Instead, we allocated 2 MAs, 5 rooms, and 2 residents per physician to avoid decreasing patient volume and preserving in-person attending physician utilization (highlighted in Table II). The results in Table II demonstrate the use of a simulation in guiding decision making, even in times of crisis, to understand interdependent operational outcomes. We were able to quickly operationalize and minimize PTPC while preserving attending physician utilization and in-person patient volume. A key limitation in PTPC measurement is that AWT is an approximation of the true PTPC. In the future, we recommend integrating agent-based modeling to assess the actual time patients wait with another patient. Although this study had a single-institutional workflow and fixed resources, this work demonstrates opportunities for dermatology and other outpatient clinics to understand the impacts of varying resource constraints to assist in clinical operational decision making. None disclosed.