Patient flow time data of COVID-19 vaccination clinics in 23 sites, United States, April and May 2021

Abstract

Introduction

Public health department (PHD) led COVID-19 vaccination clinics can be a critical component of pandemic response as they facilitate high volume of vaccination. However, few patient-time analyses examining patient throughput at mass vaccination clinics with unique COVID-19 vaccination challenges have been published.

Methods

During April and May of 2021, 521 patients in 23 COVID-19 vaccination sites counties of 6 states were followed to measure the time spent from entry to vaccination. The total time was summarized and tabulated by clinic characteristics. A multivariate linear regression analysis was conducted to evaluate the association between vaccination clinic settings and patient waiting times in the clinic.

Results

The average time a patient spent in the clinic from entry to vaccination was 9 min 5 s (range: 02:00–23:39). Longer patient flow times were observed in clinics with higher numbers of doses administered, 6 or fewer vaccinators, walk-in patients accepted, dedicated services for people with disabilities, and drive-through clinics. The multivariate linear regression showed that longer patient waiting times were significantly associated with the number of vaccine doses administered, dedicated services for people with disabilities, the availability of more than one brand of vaccine, and rurality.

Conclusions

Given the standardized procedures outlined by immunization guidelines, reducing the wait time is critical in lowering the patient flow time by relieving the bottleneck effect in the clinic. Our study suggests enhancing the efficiency of PHD-led vaccination clinics by preparing vaccinators to provide vaccines with proper and timely support such as training or delivering necessary supplies and paperwork to the vaccinators. In addition, patient wait time can be spent answering questions about vaccination or reviewing educational materials on other public health services.

1. Introduction

Public health department (PHD) led vaccination clinics are designed to administer vaccines to large groups of individuals as rapidly and safely as possible. These PHD programs must accurately and efficiently store, distribute, allocate, and administer vaccine and accompanying supplies, while also monitoring vaccine uptake and safety [1]. To achieve the rapid vaccination in a setting of 1) limited supplies of vaccines, 2) medical resources with competing priorities and 3) vaccination priorities by age or high-risk status, large scale vaccination clinics were crucial in expediting an efficient roll-out of the Coronavirus disease 2019 (COVID-19) vaccines [2]. Although PHD-led vaccination clinics are not a novel concept [3], [4], the COVID-19 pandemic presented unique operational challenges to the implementation of such clinics. For example, the mRNA COVID-19 vaccines present logistical challenges related to the ultra-cold chain requirements for distribution and storage and proper preparation before administration. There is also the need for maintaining a physical distance in the clinic for infection control, as well as a need for verifying patient eligibility and vaccination status via immunization information system reporting for safe vaccine administration.

One of the primary goals of PHD-led vaccination clinics is to meet the high demand for vaccination by having a high throughput of patients. Clinic planners draw on a variety of resources to design and run clinics to achieve this goal. In the United States, the Centers for Disease Control and Prevention (CDC) provides guidance on how to plan and prepare for these types of clinics [1]. To adhere to local population needs, clinic preparation decisions are made at the state and local levels with guidance from the CDC [5]. Some PHD-led public health clinics benefited from the innovation and expertise from private-sector partnerships. For example, Washington State partnered with Starbucks, Microsoft, and Costco to enhance operations and build a mock vaccination site to test flow and identify bottlenecks [2].

Limited publications have highlighted the success of vaccination clinic sites [6], [7]. A recent study reviewed previous experiences of PHD-led vaccination centers (primally influenza) to answer questions on how to organize a PHD-led vaccination center during the COVID-19 pandemic, highlighting the most important organizational aspects that should be considered while planning [8]. Although these resources can be beneficial to planners, none of them provide quantitative evidence of specific characteristics across multiple clinics that foster high throughput.

Our study aims to fill this gap by collecting data on the time spent at clinics for 521 patients in 23 COVID-19 vaccination clinics during April and May of 2021, to examine the association between clinic throughput time and the characteristics of clinics. This information will be useful for public health planners and policymakers to consider as they create plans and infrastructure to respond to future pandemics.

2. Methods

2.1. Data collection

Data collection relied on PHD’s willingness and availability of the clinic sites. Twenty-four state and local public health departments that were supporting COVID-19 vaccination clinics were invited to participate in this study based on their interests and willingness. Of these, 17 counties in 6 states (Georgia, Illinois, Michigan, Minnesota, Nevada, and Washington) agreed to participate and support data collection from large scale COVID-19 vaccination clinics run by their respective public health agencies. Data from these clinics were collected during in-person visits to these clinics during April and May of 2021. In some counties, clinics operated in multiple locations or focused on different populations at the same location. We followed individual patients from a distance to record the time spent at each station from the time of entry to post-vaccination observation using a work study app [9]. The application, designed to collect, record and export the time data by each activity for multiple subjects, was loaded onto tablets and used for data collection. Since we did not engage in direct contact with the patients, demographic, socioeconomic or clinical data for the patients were not collected. As the post-vaccination observation time varied depending on a patient’s medical conditions and is beyond the scope of the study, it was not collected, either.

While there was variation in clinic flow across the sampled clinics, all clinics were generally divided into 4 stations: 1) check-in, where the patient was greeted and temperature was taken; 2) documentation, where patient identification was confirmed, vaccination card was provided, and the second dose appointment was scheduled; 3) vaccination, where patients were vaccinated; and 4) post vaccination observation, where patients typically waited for 15 to 30 min after the vaccination and potentially had the opportunity to ask questions regarding the vaccination or second dose appointment. The patient flow time includes time when the patient arrived at the welcome station where staff directed the patients to lines for check-in stations. At each station, the time recording started upon arrival or stopped upon departure. Time spent moving between stations and for waiting to be called was recorded as waiting time. As this study focused on what could be completely controlled by the clinic, the patient flow from outside prior to entering the clinic was not captured. For example, people arriving in large mass outside the clinic in a short time (i.e. bus load) or before the clinic opens will likely increase wait time before entering the clinic. Such is almost uncontrollable and likely not modifiable by the clinic, thus was beyond our scope. Information from clinic managers on clinic operations including clinic hours, number and types of COVID-19 vaccines offered, number of vaccination stations, and number of staff was gathered. We also integrated state and county characteristics such as county population, National Center for Health Statistics (NCHS) urban–rural designation, medically underserved areas, healthcare professional shortage area indicator, states’ COVID-19 vaccination eligible population, and other public health measures [10], [11], [12], [13], [14]. This study was reviewed by the RTI Institutional Review Board and determined not to be human subjects research and had the public health emergency waiver for data collection at the clinics.

2.2. Data analysis

Patient flow time was calculated from check-in to vaccination using the collected time data using STATA (Release 16) [15]. Time at each station and waiting time between stations was included to reflect the flow efficiency of the vaccination clinic. We considered the data as repeated measure of the clinic performance. Thus, mean, median, and range of patient flow time was examined across the clinics. A multivariate linear regression model was conducted against the mean patient waiting time for each clinic to evaluate influential factors controlling the clinic settings such as staffing and patient throughput with the state fixed effects and the nested effects of clinic and county. All statistical data analyses were performed using SAS Enterprise Guide (Ver. 7.1) [16].

3. Results

We visited 23 community-based clinics held in 17 different counties in 6 states. All 23 vaccination clinics were open to anyone aged 16 years and over. Sixteen of the 23 clinics were held in medically underserved areas with limited access to primary care, as designated by Health Resources & Services Administration (HRSA)[14]. Six clinics were held in rural areas. During the site visits, clinic time was measured for 529 patients. Data for this analysis was used from 521 patients, after reviewing the data and notes from the field to exclude outliers with extreme values, possibly due to errors during data collection or missing values due to lost contact with the patients. Clinics on the day of the site visit were open for an average of 6.6 h (range 2 – 8.5) and 931 patients were vaccinated on average each day (range 150 – 2,607) (Table 1 ). The average number of total staff was 61, including 21 medical staff and 15 vaccinators, on average. The number of staff increased with the size of the clinics. Fourteen of the 23 clinics offered only one brand of mRNA COVID-19 vaccine – either Pfizer-BioNTech or Moderna. Clinics offering more than one brand of vaccines deployed 21 more staff members on average, including 10 more medical staff on average. There were 9 appointment-only based clinics, and 14 clinics that accepted walk-ins in addition to appointments. On average, the appointment-only clinics were open 1.3 h longer with 32 more staff to vaccinate more than 1,000 people on average per clinic. Twelve clinics had dedicated accommodations for patients with mobility challenges such as dedicated line for people with disabilities. The clinics with dedicated lines for people with disabilities utilized 8 more staff, on average. Drive-through clinics vaccinated more than 1,000 patients with fewer medical staff (=16), yet with more supporting staff (=44) compared to non-drive-through clinics with 22 medical staff and 39 supporting staff. Eighteen clinics were administering the first vaccine dose, which appeared to need more staff in total (66 vs 43). Eleven clinics used quick-response (QR) codes for appointments and registration. For the 12 clinics that did not use QR codes for appointments, the average operation hours (=7 h) and the number of total staff (=75) were higher than those clinics that used QR codes (6.2 h and 46 total staff members, respectively).

Table 1.

Characteristics of Public Health Department-led COVID-19 Vaccination Clinics in 23 sites, United States, April and May 2021.

Characteristics	Number of Clinics	Average Operating Hours	Average Number of Patients with Appointment	Average Doses Administered	Average Number of All Staff	Average Number of Vaccinators	Average Number of Medical Staff
Overall	23	6.6	941	931	61	15	21
Clinic Hours
< 5 h	6	3.9	376	364	19	7	9
5 ∼ 9 h	17	7.6	1,140	1,132	76	19	25
Clinic size by patient volume on day of visit
Super-Large (>1200)	7	7.6	1,836	1,835	122	32	42
Large (600–1200)	6	7.1	852	818	53	11	13
Medium (350–599)	5	6.0	516	494	24	6	13
Small (<3 5 0)	5	5.2	218	239	22	7	10
Clinic size by number of vaccinators
Large (≥11)	9	7.7	1,653	1,650	109	28	36
Medium (7 ∼ 10)	6	6.0	585	562	41	9	13
Small (1 ∼ 6)	8	5.9	406	400	22	6	10
Different COVID-19 vaccines offered
Yes	9	6.8	918	904	74	20	27
No	14	6.5	955	949	53	13	17
Appointment requirement
Appointment Only	9	7.4	1,122	1,091	80	15	22
Appointment and Walk-ins	14	6.1	824	829	48	15	20
Dedicated lines for people with disabilities
Yes	12	6.9	984	910	64	14	21
No	11	6.3	893	955	57	17	21
Drive-through
Yes	5	6.8	988	1,031	60	13	16
No	18	6.6	928	904	61	16	22
Second-dose only
Yes	5	6.9	1,090	1,057	43	14	19
No	18	6.5	899	896	66	16	21
QR code use for registration
Yes	11	6.2	824	805	46	11	16
No	12	7	1,048	1,047	75	19	25

The mean time for a patient to pass through the clinic, from the welcome station at entry to the completion of vaccination, was 9 min and 5 s (9:05) (Table 2 ). Compared to the average time (12:18) for the small-size clinics (<350 patients per day), larger clinics had lower per patient flow times, particularly the large-size clinics (600–1,200 patients per day). Differences in mean patient flow time compared the small-size clinics were statistically significant. However, the super-large-size clinics (>1,200 patients per day) resulted in higher patient flow times (10 min) than medium or large-size clinics (9:29 and 6:19, respectively). We also found higher and statistically significant average patient flow time for the clinics accepting no-appointment compared to by-appointment (9:50 vs 7:25), the drive-through clinics compared to non-drive-through clinics (12:42 vs 8:40), first-dose clinics (10:18 vs 6:11), and the clinics with dedicated lines or services for people with disabilities (10:05 vs 8:22). The higher average patient flow time was also observed in the clinics offering more than one COVID-19 vaccine brand, and non-QR code clinics, but the differences were not statistically significant.

Table 2.

Descriptive Statistics of Patient Flow time Spent in Clinic at 23 COVID-19 Public Health Department led Vaccination Sites, United States, April and May 2021.

Characteristics	Number of Clinics	Number of Observations	Time from Entrance to Vaccination (in minutes/seconds)
			Mean	Median	Minimum	Maximum
Overall	23	521	09:05	07:22	02:00	29:39
Clinic Hours
< 5 h	6	112	09:47	02:33	02:00	29:17
5 ∼ 9 h (referent)	17	409	08:54	01:49	02:20	29:39
Clinic size by patient volume on day of visit
Super-Large (>1200)	7	162	10:00*	08:17	02:29	29:17
Large (600–1200)	6	166	06:19*	05:13	02:20	27:30
Medium (350–599)	5	110	09:29*	08:10	02:00	29:26
Small (<3 5 0) (Referent)	5	83	12:18	09:27	02:17	29:39
Clinic size by number of vaccinators
Large (≥11)	9	221	08:39*	07:02	02:29	29:17
Medium (7 ∼ 10)	6	146	07:22	05:46	02:17	29:17
Small (1 ∼ 6) (Referent)	8	154	11:20	10:01	02:00	29:39
Different COVID-19 vaccines offered
Yes	9	238	09:29	08:01	02:00	28:21
No (referent)	14	283	08:45	06:34	02:17	29:39
Appointment requirement
Appointment Only	9	163	07:25*	05:11	02:26	28:31
Appointment and Walk-ins (referent)	14	358	09:50	08:09	02:00	29:39
Dedicated lines for people with disabilities
Yes	12	218	10:05*	08:44	02:26	29:17
No (referent)	11	303	08:22	06:40	02:00	29:39
Drive-through
Yes	5	54	12:42*	10:25	02:26	29:17
No (referent)	18	467	08:40	07:11	02:00	29:39
Second-dose only
Yes	5	154	06:11*	04:58	02:20	21:58
No (referent)	18	367	10:18	08:20	02:00	29:39
QR code use for registration
Yes	11	206	08:55	06:54	02:26	29:39
No (referent)	12	315	09:11	07:29	02:00	28:31

*Statistically significant at 5% level.

Factors associated with the patient waiting time in the clinic were evaluated using a multivariate regression model (Table 3 ). The clinic characteristics associated with more waiting time (i.e., positive parameter estimates) were the number of total vaccinations given, the presence of dedicated lines for people with disabilities, and the rural area indicator. Vaccination-related characteristics such as more than one brand of COVID-19 vaccines offered, and clinics where the vaccine was drawn by the vaccinator were associated with longer patient waiting time. The presence of an on-site translator was associated with shorter patient waiting times.

Table 3.

Factors associated with per patient waiting time in the clinics - A linear regression model estimation.

Variables	Estimate	Standard Error	Pr>|t|
Number of Vaccinated Doses administered	0.003	0.001	0.031*
Percentage of Patients Getting their First Dose	0.916	1.603	0.593
Appointment requirement
Appointment Only	3.036	1.244	0.059
Appointment and Walk-ins	Referent
Drive-through Clinic
Yes	−1.124	1.143	0.371
No	Referent
QR code use for registration
Yes	−0.017	0.831	0.985
No	Referent
Dedicated lines for people with disabilities
Yes	1.826	0.701	0.048*
No	Referent
Temperature Check
Yes	−1.930	2.156	0.412
No	Referent
ID Check
Yes	0.536	2.027	0.802
No	Referent
Translators on site
Yes	−7.171	1.928	0.014*
No	Referent
Different COVID-19 vaccines offered
Yes	1.411	0.467	0.029*
No	Referent
Vaccine drawn by vaccinators
Yes	15.341	1.898	0.001*
No	Referent
Number of vaccinators
Large (≥11)	−2.462	2.552	0.379
Medium (7 ∼ 10)	1.028	1.187	0.426
Small (1 ∼ 6)	Referent
Urbanicity**
Rural	4.197	0.557	0.001*
Urban	Referent

*Statistically significant at 5% level; ** National Center for Health Statistics Urban-Rural Indicator was used.

4. Discussion

During the COVID-19 pandemic, PHD-led COVID-19 vaccination clinics served as convenient locations where community residents could easily access a COVID-19 vaccine regardless of health insurance coverage [17]. Patient flow time at vaccination clinics is an important piece of information in evaluating a clinic’s efficiency at achieving the primary goal of vaccinating as many eligible patients as possible with the available resources. However, while there have been a few studies on cost and capacities of PHD-led vaccination clinics for the preparedness exercise operations or 2019 pandemic influenza responses [4], [18], [19], [20], [21], [22], [23], [24], to our best knowledge, there have been few studies to measure the patient-time for vaccination during an ongoing public health emergency.

Before the COVID-19 pandemic, literature on PHD-led vaccination programs and clinics has been reported as a part of preparedness exercises or for the 2009 H1N1 pandemic influenza response. While most reports presented the vaccination doses per hours in PHD-led vaccination settings, to our knowledge, there are no reports on patient flow times based on direct observation [25]. Although the number of doses per hour per vaccinator or clinic is an important piece of information in planning and evaluating PHD-led vaccination clinics, patient flow time and patient waiting times capture another aspect of efficiency. For example, the total number of doses administered depends on the number of patients coming for vaccination during clinic operation hours and numbers of vaccinators and support staff. If the patient turnout were less than expected, then total number of doses per hour per clinic may underestimate the efficiency and capability of the clinics. In this sense, the patient throughput time may capture the actual time spent in the clinics to measure the efficiency of the clinic operations with a certain level of staffing. Furthermore, patient waiting times reflect not only the efficiency of the clinic layouts and staffing but also impacts patient satisfaction while in the clinics [26], [27].

This study documents that a patient spends approximately 9 min on average getting vaccinated at PHD-led vaccination clinics with a large range anywhere from 2 min to 29 min. Clinic size measured by the number of patients was associated with patient flow time in the clinics with the patient flow time decreasing by the patient volume up the point where the clinics serving 1,200 patients. Also, average patient flow times varied significantly by clinic characteristics such as by-appointment requirement for vaccination, dedicated lines or services for people with mobility challenges, or drive-through vaccination. Patient-time was the highest in small clinics with 6 or fewer vaccinators, and lower in clinics with 7 or more vaccinators. Longer patient waiting times were significantly associated with rural areas.

A notable finding is that regression analysis of patient waiting time showed that such variations were most significantly associated with clinic characteristics related to the vaccination station, such as whether the vaccine was drawn by vaccinators, or more than one COVID-19 vaccine was offered. This may imply that in order for patient flow control measures such as QR codes to be effective, vaccination stations could be managed efficiently to reduce the patient waiting times. Indeed, at the time of data collection, most clinics were just opened to the populations eligible for COVID-19 vaccination, which requires more time and staff experience. Over time, the use of QR codes may improve efficiency as more patients become familiar with them. However, vaccination stations must be staffed with medical staff with the appropriate qualifications, trainings and/or supervision. It is critical to identify key vaccine providers and prepare them for efficient implementation of PHD-led vaccination by periodic training and preparedness exercises. Furthermore, we observed many occasions that the drawing of vaccine was conducted in a separate area by other medical personnel in spite of the CDC guidance recommending that the same vaccinator to draw up the vaccine before vaccination [28]. Further studies are warranted to evaluate the impact of such practice on the mass vaccination operations in terms of safety as well as efficiency.

Presence of translators on site was associated with lower waiting time. Through translators, clinics can offer patients necessary help to streamline the vaccination process. While most COVID-19 related information is available in multiple languages [29], [30], staff who are proficient in health communication in languages other than English are critical for patient education, clinic efficiency, and reducing barriers to vaccination.

The majority of COVID-19 vaccination clinics were run by appointments in 30-minute intervals to check vaccine supply and prepare the doses, as well as to enable social distancing. However, the regression analysis found that a prior appointment requirement for vaccination was one factor associated with longer patient waiting time, which may have been caused by simultaneous arrival of patients around their appointment time or patients arriving early. It is not necessarily contradictory that appointment-only clinics had a shorter patient time while the prior appointment is a factor associated with the longer waiting time because other factors might contributed to reducing the overall patient flow time (such as shorter process time, the clinics operations, or fewer patients for vaccination). In addition, other circumstances such as temporary holdup for staff shift change or vaccine lot switches may have caused delays. Because some wait time is generally unavoidable, patients can be otherwise occupied at clinics while they wait. Wait time can be spent answering questions about vaccination or reviewing educational materials on other public health services[27]. To improve vaccination output and to reduce patient waiting time, reviews of staffing needs and further evaluations of resource allocation plans as well as patient flow could be considered[31].

4.1. Limitations

This study is subject to several limitations. First, due to convenience sampling and small sample size, the results may not be generalizable. Second, to minimize contact with patients and maintain privacy, patient characteristics such as disability status or limited English proficiency were not collected for the analysis. As a result, we could not consider any individual effect on longer or shorter patient flow time. Third, COVID-19 vaccination demand had begun to decrease towards the end of the study. Therefore, clinic operations before or after the study period may have differed from what was observed during the study. Fourth, a few clinics we visited were planning to offer the Janssen vaccine, targeting populations that may face barriers to returning for a second dose, such as migrant farm workers, those experiencing homelessness, or those living in remote areas. However, at the time of our site visits, almost all vaccination clinics were only providing the two-dose mRNA vaccines due to the temporary pause of the Janssen COVID-19 vaccine after rare reports of thrombosis with thrombocytopenia syndrome among vaccine recipients [32]. Therefore, our results may not be directly comparable to the time studies conducted with Janssen COVID-19 vaccination clinics or one-dose seasonal or pandemic influenza vaccination sites.

5. Conclusions

In order to vaccinate as many people as possible in a short period of time, efficient flow in vaccination clinics is crucial. Given the standardized procedures outlined by CDC guidelines and state health departments, reducing wait time is critical to lowering the patient flow time by relieving bottleneck effects in the clinic. While this study was conducted with a relatively small sample, it could still provide baseline estimates of patient flow time spent in PHD-led vaccination clinics. This study suggests enhancing the efficiency of the PHD-led vaccination clinics by preparing more vaccinators to provide vaccines with proper and timely support such as training or delivering necessary supplies and paperwork to the vaccinators. When planning mass vaccination clinics, strategic staffing and resource allocation would be crucial to address the needs of the community of focus as well as to build the capacity of public health preparedness.

6. Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ‘Benjamin O Yarnoff and Laurel G Bates reports financial support was provided by Centers for Disease Control and Prevention.’

Data availability

Data will be made available on request.