An Electronic Health Record Alert for Inpatient Coronavirus Disease 2019 Vaccinations Increases Vaccination Ordering and Uncovers Workflow Inefficiencies

Abstract

Background Despite mortality benefits, only 19.9% of U.S. adults are fully vaccinated against the coronavirus disease 2019 (COVID-19). The inpatient setting is an opportune environment to update vaccinations, and inpatient electronic health record (EHR) alerts have been shown to increase vaccination rates.

Objective Our objective was to evaluate whether an EHR alert could increase COVID-19 vaccinations in eligible hospitalized adults by prompting providers to order the vaccine.

Methods This was a quasiexperimental pre–post-interventional design study at an academic and community hospital in the western United States between 1 January, 2021 and 31 October, 2021. Inclusion criteria were unvaccinated hospitalized adults. A soft-stop, interruptive EHR alert prompted providers to order COVID-19 vaccines for those with an expected discharge date within 48 hours and interest in vaccination. The outcome measured was the proportion of all eligible patients for whom vaccines were ordered and administered before and after alert implementation.

Results Vaccine ordering rates increased from 4.0 to 13.0% at the academic hospital (odds ratio [OR]: 4.01, 95% confidence interval [CI]: 3.39–4.74, p < 0.001) and from 7.4 to 11.6% at the community hospital (OR: 1.62, 95% CI: 1.23–2.13, p < 0.001) after alert implementation. Administration increased postalert from 3.6 to 12.7% at the academic hospital (OR: 3.21, 95% CI: 2.70–3.82, p < 0.001) but was unchanged at the community hospital, 6.7 to 6.7% (OR: 0.99, 95% CI: 0.73–1.37, p = 0.994). Further analysis revealed infrequent vaccine availability at the community hospital.

Conclusion Vaccine ordering rates improved at both sites after alert implementation. Vaccine administration rates, however, only improved at the academic hospital, likely due in part to vaccine dispensation inefficiency at the community hospital. This study demonstrates the potential impact of complex workflow patterns on new EHR alert success and provides a rationale for subsequent qualitative workflow analysis with alert implementation.

Keywords

computerized soft-stop - clinical decision support - electronic health records - decision support systems - vaccination - COVID-19 - academic hospital

Background and Significance

The first coronavirus disease 2019 (COVID-19) vaccine, BNT162b2 mRNA, became available in December 2020. Observational studies showed that vaccines were effective in reducing mortality rates, hospitalizations, emergency department visits, and urgent care visits.[1] [2] In the United States, by March 2023, 79.0% of adults had completed the primary series of COVID-19 vaccines, while only 19.9% of adults had received a booster dose.[3]

Many hospitalized patients are at high risk for severe complications from COVID-19 due to predisposing comorbidities.[4] For this reason, hospitalized patients represent a unique demographic that may disproportionately benefit from vaccination. While outpatient vaccination optimization has been investigated,[5] [6] few studies have examined inpatient COVID-19 vaccination rates. Furthermore, a recent point-prevalence study revealed a strong desire among hospitalized patients for COVID-19 vaccination resources.[7] For these reasons, eligible inpatient encounters without successful COVID-19 vaccination represent missed opportunities for vaccination as defined by the World Health Organization.[8] As a comparison, influenza and pneumococcal vaccination rates in the inpatient setting have been shown to be as high as 67.0% on average.[9] [10] Additionally, inpatient vaccinations represent a unique opportunity to provide needed health services to underserved patient populations with resource restrictions.[11] [12] [13] [14]

One solution to increase vaccination rates is to utilize clinical decision support systems (CDSSs), which use pertinent patient information to guide and improve health-related decision-making.[15] [16] A commonly utilized subset of CDSS is the electronic health record (EHR) alert, which has been effective in capturing vaccine-eligible subjects.[17] In a 1-year intervention study in the primary care setting, up-to-date immunization rates at 24 months of age increased from 81.7 to 90.1% after the implementation of EHR prompts.[18] In another study focusing on inpatient preventive care, the use of EHR alerts increased influenza vaccine ordering rates from 1.0 to 51.4%.[19]

To evaluate the impact of EHR-based alerts to increase the number of clinically indicated COVID-19 vaccinations in the inpatient setting, we analyzed an alert prompting providers to administer the COVID-19 vaccine to eligible hospitalized patients in academic and community hospital settings.

Methods

Design

This study used a quasiexperimental pre–post-interventional design. We collected data regarding inpatient COVID-19 vaccination rates spanning a 10-month period (January 1, 2021–October 31, 2021). These data represented pre- and postimplementation of an automated, computerized soft-stop alert which reminded clinicians to order a COVID-19 vaccine for patients meeting “vaccine eligibility and interest” criteria. Eligible patients were defined as hospitalized adult patients not previously vaccinated against COVID-19. The criteria for the COVID-19 vaccine EHR alert included eligible patients who indicated interest in COVID-19 vaccination during the nurse admission workflow, were expected to be discharged within 48 hours, and did not have a COVID-19 vaccine already ordered ([Fig. 1]). For the purposes of this study, COVID-19 vaccines include Moderna, Johnson & Johnson, and Pfizer-BioNTech COVID-19 vaccines. We used a deidentified data set generated via the local EHR for analysis (Epic Systems, Verona, WI, United States). Duplicate subjects were removed from the data set.

Fig. 1 Vaccine administration workflow.

Setting

This case report describes an intervention conducted at an academic hospital (Oregon Health & Science University) and a community hospital (Hillsboro Medical Center) within the same health network in the western United States. The academic hospital comprises an urban 576-bed, level-1 trauma center that provides primary, secondary, and tertiary care to over 60,000 hospitalized and one million ambulatory patients annually. The community hospital is an urban 167-bed hospital which serves a large adjacent rural area. Both hospitals use the same EHR system. Nurses are alerted to vaccine eligibility during admission workflows as a prompt to assess patient interest in COVID-19 vaccination during their hospitalization. The alert was suppressed for patients who had previous records of any COVID-19 vaccination in the local health record or who had external COVID-19 vaccine administrations documented in reconciled health plan summaries. As of April 2020, a query to the Oregon immunization information system (IIS) is generated automatically when a hospital encounter is created. The IIS was available to clinicians for review in the EHR; however, these records were not incorporated directly into the EHR alert logic.

Intervention

The alert was first implemented at the academic hospital and was later adopted for use by the community hospital on May 12, 2021 and June 30, 2021, respectively. When the provider opened the chart of a patient who met alert criteria, a prominent EHR alert triggered, which prompted users to select “Order” or “Do Not Order” before returning to the EHR ([Fig. 2]). If a provider chose not to order a vaccine, they were prompted to choose a nonrequired acknowledgment reason, which included “Defer for [1, 24, or 48] hours,” “Defer to the primary team,” “No, not giving this admission,” or give a free-text response for deferring. Deferring an order was user specific; hence, the alert would still fire for other providers entering the chart.

Fig. 2 Example of the EHR alert as it appeared in an electronic inpatient chart during the study period (Epic Systems Corporation, Verona, WI, United States).

Methods of Measurement

Outcomes

The proportion of eligible patients for whom COVID-19 vaccines were ordered was the primary outcome and served as a surrogate marker for provider intent to vaccinate. Secondary outcomes included vaccines administered before and after the launch of a COVID-19 vaccine EHR alert, the number of alerts fired after alert implementation, and the patterns of user responses to alerts.

Data Analysis

COVID-19 vaccine ordering and administration rates before and after EHR alert implementation were compared using chi-squared analysis. EHR alert data, including vaccine ordering and administration rates among eligible patients, were presented as descriptive statistics. Rates of action taken were compared using the two-sample test of proportions. The action taken percentage of the vaccine alerts was defined as the number of times an action was performed divided by the number of alert firing occurrences. For the purposes of this study, “action taken” was defined as anytime a user chose to place a vaccination order within the alert window.

Post-hoc Workflow Analysis

Discrepancy between vaccine ordering and administration was investigated through nonstructured independent interviews with two pharmacists, during post-hoc analysis.

Results

During the study period, there were 2,566 and 12,084 eligible patients at the community hospital and academic center, respectively, with 1,045 (40.7%) and 7,515 (62.2%) of those patients representing the postalert implementation period. The COVID-19 EHR alert fired 10,914 times in total for 692 patients meeting alert criteria at the academic hospital and 2,236 times in total for 160 alert-eligible patients at the community hospital. These data reflect that the alert was triggered each time a provider opened the patient chart; thus, multiple alerts could be fired for each patient as different providers could open the patient chart multiple times.

In both settings, the COVID-19 vaccine ordering rate among vaccine-eligible patients significantly increased after alert implementation, from 4.0 to 13.0% at the academic hospital (odds ratio [OR]: 4.01, 95% confidence interval [CI]: 3.39–4.74, p < 0.001) and from 7.2 to 11.1% at the community hospital (OR: 1.62, 95% CI: 1.23–2.13, p < 0.001; [Fig. 3]). However, the administration rate only increased at the academic hospital (3.5–10.6%, OR: 3.21, 95% CI: 2.70–3.82, p < 0.001), not at the community hospital (6.7–6.7%, OR: 0.99, 95% CI: 0.73–1.37, p = 0.994). The rate of vaccine administration among patients who had vaccines ordered was much lower at the community hospital (only 60.0%) compared with the academic hospital (79.0%; [Table 1]). The number needed to alert, or required alerts to produce one additional vaccination order over the standard workflow, was 19.3 and 10.9 at the community hospital and academic hospital respectively. The action taken percentage of the COVID-19 vaccine alerts at the academic hospital was modestly, but significantly, higher than at the community hospital (5.5 vs. 4.3%, p = 0.02). When an action (vaccine prescription) was not taken, a provider could indicate one of several predetermined reasons (percentage of all alerts at the academic and community hospital sites): “Defer to primary team” (12.4 and 5.9%), “Defer for 1 hour” (8.4 and 2.5%), “Defer for 24 hours” (6.2 and 4.2%), “Defer for 48 hours” (2.9% &1.7%), or “No, not giving this admission” (3.6 and 3.4%). The remainder of alerts (61 vs. 78%) were dismissed without taking action or providing a reason.

Fig. 3 Vaccine ordering and administration patterns pre- and post-EHR alert launch. (a) Academic hospital. (b) Community hospital. Dashed lines represent the proportion of eligible patients for whom the vaccine was ordered. Solid lines represent the proportion of eligible patients to whom the vaccine was administered. Vertical solid lines represent the start of the intervention, while vertical dotted lines represent the beginning of the Oregon Health Authority (OHA) vaccine eligibility expansion on April 19th, 2021 for reference. The shaded area at the bottom of each panel represents the daily incidence of COVID cases in Oregon as reported by the OHA.[34]

Table 1 Vaccine ordering and administration rates among vaccine eligible patients, statistical analyses

Among eligible patients	Academic center	Community hospital
Ordered (prealert launch)	169/4,569 (4%)	109/1,521 (7.2%)
Ordered (postalert launch)	1,002/7,515 (13%)	116/1,045 (11.1%)
Odds ratio (95% confidence interval)	4.01 (3.39–4.74)	1.62 (1.23–2.13)
p-Value	<0.001	0.005
Administered (prealert launch)	162/4,569 (3.5%)	102/1,521 (6.7%)
Administered (postalert launch)	794/7,515 (10.6%)	70/1,045 (6.7%)
Odds ratio (95% confidence interval)	3.21 (2.70–3.82)	0.99 (0.73–1.37)
p-Value	<0.001	0.994

Despite vaccine ordering improvement at both sites, vaccine administration only increased at the academic hospital. Workflow analysis revealed that the dispensation of the COVID-19 vaccines occurs daily at the academic hospital and weekly at the community hospital due to lower patient volume and stringent vaccine storage requirements at the time of the study.

Discussion

This study demonstrates improved vaccination ordering rates after the implementation of a COVID-19 vaccination EHR alert at an academic and a community hospital. While alert implementation improved vaccination ordering at both locations, administration only increased at the academic hospital.

These increases in vaccine orders were likely partially attributable to increased state-wide vaccine eligibility as Oregon opened its “Phase 2” in April 2021, making individuals 16 years and older vaccine-eligible. Oregon saw its largest number of vaccines administered, however, in late March and early April during Phase 1B, before all adults 16+ were eligible and before either site implemented their EHR alerts.[20] While both hospital settings saw vaccine ordering and administration reflect statewide trends, they also saw increases in ordering immediately after implementing the alerts. Furthermore, prior to EHR alert launch, there was a slight increase in vaccinations at the academic hospital and a decrease at the community hospital. This may, in part, represent higher rates of vaccine hesitancy as the community hospital borders several rural areas, which per MacDonald et al., have shown disproportionate hesitancy rates.[21]

The difference in vaccine dispensation at the two sites was hypothesized to contribute to low administration rates at the community hospital. Our analysis revealed that patients were likely discharged prior to their vaccines being administered. Furthermore, differences in staffing availability to administer vaccines may have also contributed, as has been found previously.[22]

The action taken percentage of the vaccine alerts was 5.5% at the academic hospital and 4.3% at the community hospital. This is similar to CDSS averages of approximately 5.8%, according to recent meta-analysis.[23] Several variables may have affected vaccine ordering, administration, and action taken percentage at both hospitals. First, patients with active infection were included in the study. The reasoning for this was multifactorial. At the time of the study, there were not yet clear guidelines regarding vaccination during or after recent COVID-19 infection.[24] [25] [26] Given this fact and the potential for persistent positive testing after isolation periods, our analysis did not exclude these cases. Institutional policy at the hospitals where this study was conducted allowed patients recently recovered from COVID-19 infection to be vaccinated while hospitalized during the study period. This lack of clear guidance, along with the influx of admissions related to COVID-19 infection, may have affected ordering and administration rates among “vaccine-eligible” patients as well as action taken percentages. Despite this fact, increased vaccine ordering (and administration at the academic hospital) was still observed after alert implementation. Second, the vaccine administration was not yet integrated into nursing discharge workflows. Third, the COVID-19 vaccine followed an “opt-in” strategy, whereas other inpatient vaccinations at these sites followed an “opt-out” workflow. An example of this is influenza vaccination, which employed a standing order protocol (SOP) to vaccinate prior to discharge. In contrast, COVID-19 vaccines did not have a standing order and therefore necessitated a provider order near the time of discharge. Additionally, alert fatigue may have affected outcomes given many users declined providing a reason for not ordering vaccines.[27] Finally, if patients had received one dose of the vaccine prior to admission, they did not meet alert criteria. This was due to a limitation of the EHR alert at the time of the study causing an inability to differentiate between the first and second doses received, as well as appropriate time elapsed since administration of the first dose. This was improved upon in later updates to the alert criteria after the timeframe of this study. Given the early timeframe in the COVID-19 pandemic, this was likely a small subset of the patients eligible for vaccines.

Future work may include discharge workflow optimization, reassessing patient interest criteria, alert analysis using McCoy et al 10-step process to improve CDSS alerts, or statistical process control methods.[28] [29] Modification to account for common reasons that users ignore alerts, such as alert frequency or response type required, may be helpful in improving alert acceptance rates.[30] [31] Several studies have shown improvement in influenza vaccine administration rates among hospitalized patients after the implementation of an SOP.[32] [33] A subsequent intervention may implement an SOP in conjunction with the alert. Future analyses may include patient and provider intent-to-vaccinate assessment and attitudes towards vaccination.

Limitations

The EHR alert and data used in this retrospective study were not designed for this study thus limiting their analysis. For example, clinicians did not consistently document their reasoning for foregoing vaccine ordering. Furthermore, inherent to this type of study is the potential for selection and information biases. Resource limitations in the community hospital may affect the success of interventions. There may have been other contributing factors within each institution affecting administration rates that were unable to be controlled for such as vaccine availability. Given the retrospective nature of the study and the high likelihood of contamination in randomized designs due to alert configuration options, a randomization scheme was not possible. The use of cluster randomization between different health care centers could achieve this goal. The small sample size of the post-hoc workflow analysis was a limitation as well.

Conclusion

In this study, the rate of vaccine orders improved at both sites after alert implementation. Vaccine administration rates, however, only improved at the academic hospital. Inefficiencies within inpatient vaccine administration workflows at the community hospital were uncovered during EHR alert analysis. Small improvements in inpatient vaccination rates have the potential for larger scale impact by way of decreasing the number of missed opportunities to vaccinate patients given the nature of population immunity. This study is unique in its ability to launch the same intervention in two different hospital sites. It also demonstrates the potential impact of complex workflow patterns on new EHR alert success and provides a rationale for subsequent qualitative workflow analysis with the implementation of such alerts.

Clinical Relevance Statement

This case report outlines the potential for EHR alerts to increase COVID-19 vaccination in hospitalized adults. It also describes workflow challenges teams may encounter when launching an inpatient vaccine-related EHR alert.

Multiple Choice Questions

1. True or False: Electronic health record alerts have been shown to increase inpatient vaccination rates by prompting providers to place vaccine orders for patients who met eligibility criteria.

   - Correct Answer: True

2. Question: Analysis of pre- and postimplementation data for vaccine ordering-related EHR alert implementations may uncover which type of inpatient workflow inefficiency?

   - Answer choices: (a) Nursing administration, (b) vaccine supply issues, (c) patient vaccine hesitancy, (d) electronic health record malfunction.

   - Correct Answer: (e) All of the above

3. Question: True or False: Standing order protocols have been shown to increase inpatient influenza vaccine ordering and administration rates.

   - Correct Answer: True

Conflict of Interest

The contents, views, and opinions expressed in this presentation are those of the authors and do not necessarily reflect official policy or positions of the United States Air Force, Defense Health Agency, or Department of Defense. Mention of trade names, commercial products, or organizations does not imply endorsement by the United States government.

Acknowledgments

We would like to thank Dr. Regan Stiegmann and Dr. Cole Zanetti, co-directors of the Digital Health Track at Rocky Vista University, for their support of this project.

Author Contributions

K.C.B contributed in conceptualization, investigation, methodology, and writing. N.S. was involved in investigation, and writing. M.Z. contributed in conceptualization, investigation, supervision, writing. Z.Z. helped in writing. C.U. contributed in conceptualization, writing, supervision. A.C. contributed in writing, and supervision while B.O was in volved in investigation, writing, and supervision.

Protection of Human and Animal Subjects

This study was reviewed by the OHSU Institutional Review Board and deemed exempt (STUDY00026409).

Data Availability

The data involved in this study are available in the article.

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Publication History

Received: 03 July 2023

Accepted: 19 January 2024

Accepted Manuscript online:
22 January 2024

Article published online:
06 March 2024