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Abstract

Background

Many adults are overdue for important screenings and vaccines, but providers have limited resources to address these care gaps. Electronic messaging, including patient portal messaging, can be an effective intervention to increase screening and vaccine adherence. However, there is limited research examining variables influencing intervention efficacy beyond demographic variables.

Objective

This study aims to identify whether patient portal engagement and primary care visits affect the efficacy of patient portal-based screening or vaccine reminders.

Methods

A retrospective analysis of electronic medical record data was used to evaluate the completion of screening mammograms, influenza vaccinations, and fecal immunochemical test (FIT) screenings for approximately 400,000 MyChart patient portal users at a large integrated health system. A logistic regression analysis was performed to calculate odds ratios associated with intervention completion.

Results

When adjusted for age, race, and sex, MyChart engagement is associated with increased odds of completing patient portal interventions for mammograms, flu vaccines, and FIT screenings. When adjusted for age, race, and sex, primary care visits are associated with increased odds of completing flu vaccines and FIT screenings but not mammograms following a patient portal intervention.

Conclusions

Overall patient portal engagement is critical to portal-based preventive health interventions. These interventions are most successful when combined with office-based interventions, but there is a potential in some scenarios that digital interventions can be successful without office-based interventions. This research contributes to the existing literature around screening adherence and patient portals’ impact on health outcomes.

Keywords

Electronic messaging health maintenance patient portal personal health record preventive health

Introduction

Preventive health is critical to improving population health, yet many adults are missing important screenings or vaccines. The World Health Organization (WHO) defines disease prevention as the “population-based and individual-based interventions for primary and secondary (early detection) prevention, aiming to minimize the burden of diseases and associated risk factors.”¹ WHO has developed strategies for intervention programs to increase immunizations² and preventive screenings³ and has led related initiatives like the Cervical Cancer Elimination Initiative, which targets vaccinating 90% of girls with the human papillomavirus (HPV) vaccine and screening 70% of women for cervical cancer by 2030.⁴ Despite progress toward preventive health initiatives, many barriers remain, including limited health literacy, distrust in vaccines and access to care, including the health system's availability of human, physical and financial resources.^2,3 As a result, many care gaps persist. In the United States, 24.1% of women 50–74 did not have a mammogram within the past 2 years; 27.6% of women 21–65 were not up to date with cervical cancer screening; and 28.2% of adults 50–75 had not received colorectal cancer screening in line with recommended guidelines.⁵ Only half (47.2%) of individuals 6 months and older reported being vaccinated against seasonal influenza in 2023–2024.⁶

Background

Primary care providers face increasing burdens that limit their ability to address their patients’ preventive care needs.⁷ As healthcare providers encounter increasing panel sizes and growing administrative burdens, there is growing interest in digital technologies to alleviate physician workload. Patient portals present an opportunity for healthcare providers to engage their patients in important health maintenance tasks. In particular, electronic messaging functionality within patient portals can be a cost-effective alternative to other outreach methods like mailings and can also save clinicians time through pairing messages with the bulk ordering of laboratory requisitions.⁸ Messages can also be paired with self-scheduling functionality, which is an efficient and cost-effective method of increasing cancer screening rates.⁹ Patient portal messaging has been shown to be an effective intervention to encourage patients to complete screenings for breast cancer,¹⁰ cervical cancer,¹¹ colorectal cancer,^12–14 lung cancer,¹⁵ hepatitis C,^16,17 as well as vaccinations for HPV¹⁸ and seasonal influenza.^19–22 However, there is limited literature explaining how certain indicators of engagement—specifically digital engagement or primary care visits—affect the efficacy of portal-based interventions.

Previous research has shown that engaged patients are more likely to participate in preventive health behaviors²³ and that patients who regularly visit a primary care provider are more likely to complete vaccinations, colonoscopies, and mammograms.^24–26 A possible explanation for this lies in the transtheoretical model of change (TTM). The TTM suggests health behavior change involves progressing through a series of stages—precontemplation, contemplation, preparation, action, and maintenance—with behavior change occurring in the action and maintenance stages.²⁷ Patients who engage frequently with their patient portal or primary care provider are already taking action to maintain their health, which may be indicative of the action or maintenance stages. Therefore, completing a recommended screening or immunization may be considered a behavior consistent with their current stage.

Kindratt et al.¹¹ found that patients who electronically messaged their provider had a statistically significant higher odds ratio of completing a mammogram (OR 1.32, 95% CI 1.20–1.44), cervical cancer screening (Pap test) (OR 1.11, 95% CI 1.02–1.20), and colon cancer screening (OR 1.55, 95% CI 1.42–1.69). Hahn et al.¹⁴ examined whether past patient portal utilization was associated with improved outcomes of a colorectal cancer screening intervention. Patients were sent an email inviting them to request a fecal immunochemistry test (FIT) screening through a button in the patient portal. This study used the number of logins over the previous year as a proxy for patient portal utilization and part of a broader view of overall patient engagement. Results showed that 90% of patients who requested a FIT screening had logged into their patient portal in the previous year, compared to 67% of patients who did not request the screening. Therefore, it is hypothesized:

H1: Patient portal utilization is positively associated with screening and vaccine adherence through patient portal interventions.

Previous studies have controlled for primary care visits, however, few have reported how visits influence intervention outcomes. Preventive health is often discussed at primary care visits, and therefore, in-office interventions may reinforce any portal interventions or vice versa. It is important to understand if portal messaging can be effective as a standalone intervention or if it is more effective when accompanied with in-office interventions because face-to-face interventions require continued staff engagement and resource investment.¹⁷ Hahn et al.¹⁴ found that 90.2% of patients requesting a FIT screening had an outpatient visit in the previous year, compared to 82.2% of the patients who did not request the screening. Halket et al.¹⁶ found that upcoming primary care visits were associated with higher completion of hepatitis C screenings. In the cohort of patients without an upcoming appointment, 18% in the control group completed the screening compared to 26% who received a portal message (p < 0.01). In the cohort with a scheduled visit, 34% in the control group completed the screening compared to 58% in the intervention group (p < 0.01). Therefore, it is hypothesized:

H2: Primary care visits are positively associated with screening and vaccine adherence through patient portal interventions.

This study aims to evaluate the relationship between patient portal engagement, primary care visits, and the completion of screenings or vaccines following a patient portal intervention.

Methods

A retrospective analysis of electronic medical record (EMR) data from a large integrated health system was used to identify the relationship between digital engagement, primary care visits, and patient portal interventions. This method was selected to provide a large sample size over an extended period and thus mitigate any short-term factors that might influence screening activity. The study focused on breast cancer screening, colon cancer screening, and influenza vaccination because of the large population eligible for these health maintenance topics and the ability to generalize findings. In the United States, mammograms are recommended for women 40–74²⁸; colorectal cancer screenings are recommended for adults 45–75²⁹; and seasonal influenza vaccines are recommended for everyone 6 months and older.³⁰ Breast cancer screening and influenza vaccines were also selected because patients can self-schedule necessary appointments, and the wait time for these appointments is such that completion can be reasonably attributed to the message intervention. Likewise, completion of a FIT screening did not require an appointment, and therefore, appointment wait times would not impact the ability to attribute screening completion to the intervention.

Data was extracted from the EMR using Epic SlicerDicer, a self-service data exploration tool that allows clinicians to query large patient populations.³¹ Study selection criteria and the data exported are described in Figure 1. Patients were included in the study who (1) had a primary care provider (2) had an active MyChart patient portal account, and (3) received a MyChart message inviting them to complete a breast cancer screening, seasonal influenza vaccine, or FIT screening between 1 May 2023 and 30 November 2024. Data exported from the EMR included patient demographic variables and the dates patients received the intervention. The primary outcome was a binary indicator of screening or vaccine completion, defined as completion within 2 months after the message was sent. This threshold was developed based on previous literature³² as well as feedback from primary care physicians and consideration of wait times for mammogram and flu vaccine appointments. Other variables included a binary indicator of primary care visits. Patients were considered to have a visit if they completed a primary care visit in the 2 months before or after the outreach. Visits were excluded if they occurred within this time frame but after the completion of the screening or vaccine.

Figure 1.

Study inclusion criteria and data extraction process.

Patient portal utilization was operationalized as the MyChart Digital Engagement Summary metric developed by Epic. Each MyChart user is given a score ranging from 0 to 100 based on five components:

Percentage of messages sent to patients that were viewed in the previous 30 days (30% of total score).

Percentage of test results available to patients that were viewed in the previous 180 days (30% of total score).

Percentage of appointments scheduled online in the previous 30 days (15% of total score).

Percentage of appointments where eCheck-in was available and used in the previous 30 days (15% of total score).

Number of logins to MyChart in the previous 180 days (20% for each login, maximum of 5) (10% of total score).

The data exported from the EMR included each patient's MyChart Digital Engagement Summary score and the individual component scores the day before the message was sent.

SlicerDicer queries were exported to Microsoft Excel files and merged using Python. Detailed methodology including the Python script is provided in Supplemental Appendix A.

Statistical analysis

Statistical analysis was performed using SPSS (version 28). For each health maintenance topic, a point-biserial correlation analysis was performed to determine the relationship between the MyChart Digital Engagement Summary and primary care visit variables. Logistic regression analysis was performed to calculate odds ratios for each of the variables independently. Adjusted odds ratios were then calculated by entering both variables into the model along with demographic variables (age, race, and sex).

Results

The demographic characteristics of the population are described in Table 1. The study included 21,729 patients who received a mammogram message, 324,687 patients who received an influenza vaccine message, and 54,226 patients who received a FIT screening message. The three populations were similar across demographic categories—patients were predominately White and non-Hispanic with commercial insurance. Key differences between the populations included age and sex. Flu vaccine messaging included all ages, and mammogram messaging primarily reached female patients.

Table 1.

Patient demographics.

	Mammogram Total (%)	Influenza Total (%)	FIT Total (%)
Race
White	19,098 (87.9%)	265,771 (81.9%)	47,293 (87.2%)
Black or African American	839 (3.9%)	19,978 (6.2%)	2253 (4.2%)
Asian	300 (1.4%)	4646 (1.4%)	780 (1.4%)
Other	1155 (5.3%)	26,101 (8%)	2735 (5%)
Unknown or not reported	337 (1.5%)	8191 (2.5%)	1165 (2.1%)
Ethnicity
Not Hispanic or Latino	20,144 (92.7%)	285,292 (87.9%)	50,041 (92.3%)
Hispanic or Latino	1198 (5.5%)	30,518 (9.4%)	2797 (5.2%)
Unknown or not reported	387 (1.8%)	8877 (2.7%)	1388 (0.0%)
Sex
Female	21,722 (100%)	179,651 (55.3%)	30,409 (56.1%)
Male	6 (0.0%)	144,941 (44.6%)	23,813 (43.9%)
Unknown or not reported	1 (0.0%)	95 (0.0%)	4 (0.0%)
Age
0–17	0 (0%)	74,948 (23.1%)	0 (0%)
18–34	0 (0%)	68,370 (21.1%)	0 (0%)
35–49	6201 (28.5%)	66,872 (20.6%)	10,691 (19.7%)
50–64	9688 (44.6%)	70,541 (21.7%)	29,056 (53.6%)
65+	5840 (26.9%)	43,956 (13.5%)	14,479 (26.7%)
Insurance type
Commercial	12,072 (55.6%)	183,333 (56.5%)	28,906 (53.3%)
Medicare	5375 (24.7%)	40,530 (12.5%)	13,036 (24.0%)
Medicaid	1994 (9.2%)	62,808 (19.3%)	5129 (9.5%)
Other	2288 (10.5%)	38,016 (11.7%)	7155 (13.2%)
Total	21,729	324,687	54,226

FIT: fecal immunochemical test.

Primary care visits and digital engagement are summarized in Table 2. Fewer than half of patients had a primary care visit around the time of the patient portal message: 48.0% of mammogram patients, 36.6% of influenza vaccine patients, and 47.1% of FIT screening patients. Overall digital engagement was consistent across the three populations: The mean MyChart Digital Engagement Summary was 32.2 (SD 23.4) for mammogram patients, 27.6 (SD 23.6) for influenza vaccine patients, and 29.4 (SD 23.6) for FIT screening patients. Feature utilization was also similar across the three cohorts. Mean login scores were between 82.3 and 89.2, suggesting patients logged into MyChart on average between 4 and 5 times during the previous 180 days. Among the four features of MyChart—messaging, test results, online scheduling, and eCheck-in—viewing test results was the most used feature. Mean scores ranged from 40.3 (SD 46.7) among influenza vaccine patients to 51.1 (SD 46.8) among mammograms, indicating patients on average viewed between 40.3 and 51.1 of available test results in their portal.

Table 2.

Primary care engagement and MyChart digital engagement summary descriptive statistics.

	Mammogram	Influenza	FIT
Patients with primary care visit (%)	10,429 (48.0%)	118,881 (36.6%)	25,563 (47.1%)
Mean MyChart Digital Engagement Summary score (SD)	32.2 (23.4)	27.6 (23.6)	29.4 (23.6)
Mean Messages score (SD)	17.0 (36.8)	14.4 (34.3)	14.9 (34.7)
Mean Test results score (SD)	51.1 (46.8)	40.3 (46.7)	46.6 (46.7)
Mean Online scheduling score (SD)	8.7 (25.0)	8.4 (24.9)	7.8 (23.4)
Mean eCheck-in score (SD)	10.1 (25.8)	9.6 (27.3)	10.5 (27.1)
Mean Logins score (SD)	89.2 (26.9)	85.3 (32.0)	82.3 (33.8)

FIT: fecal immunochemical test.

Results of the point-biserial correlation analysis found a small positive correlation between MyChart Digital Engagement Summary and primary care visits based on the guidelines of Cohen.³³ The correlation was r = 0.259 (p = 0.000) for mammogram patients, r = 0.286 (p = 0.000) for influenza vaccine patients, and r = 0.266 (p = 0.000) for FIT screening patients.

Hypothesis 1

The results of the logistic regression analysis are summarized in Table 3. Among patients overdue for a mammogram, regression analysis showed that MyChart engagement was associated with increased odds of completing a mammogram (OR 1.008, 95% CI 1.007–1.010) after a MyChart message. The same was true when adjusted for recent primary care visits, age, sex, and race (OR 1.008, 95% CI 1.006–1.009). Among patients due for a seasonal influenza vaccine, MyChart engagement was associated with increased odds of vaccination independently (OR 1.019, 95% CI 1.019–1.019) and when adjusted for recent primary care visits, age, sex and race (OR 1.013, 95% CI 1.013–1.014). Among patients due for FIT screening, MyChart engagement was associated with increased odds of screening completion independently (OR 1.017, 95% CI 1.016–1.019) and when adjusted for recent primary care visits, age, sex and race (OR 1.015, 95% CI 1.013–1.016). Therefore, patient portal utilization is positively associated with completing mammograms, FIT screenings, and influenza vaccinations.

Table 3.

Unadjusted and adjusted odds ratio.

	Unadjusted odds ratio^a [95% CI]	Adjusted odds ratio^b [95% CI]
Mammogram
MyChart Digital Engagement Summary	1.008 [1.007–1.010]	1.008 [1.006–1.009]
Primary care visit	1.223 [1.145–1.306]	1.038 [0.968–1.113]
Influenza vaccine
MyChart Digital Engagement Summary	1.019 [1.019–1.019]	1.013 [1.013–1.014]
Primary care visit	2.221 [2.179–2.264]	1.548 [1.516–1.580]
FIT screening
MyChart Digital Engagement Summary	1.017 [1.016–1.019]	1.015 [1.013–1.016]
Primary care visit	1.931 [1.781–2.094]	1.537 [1.411–1.674]

Model includes a single independent variable (either MyChart Digital Engagement Summary or Primary care visit).

Model includes MyChart Digital Engagement Summary, Primary care visit, age, race, and sex.

Hypothesis 2

Regression analysis indicated primary care visits were associated with increased odds of completing a mammogram (OR 1.223, 95% CI 1.145–1.306). However, when adjusted for MyChart engagement, age, race, and sex, the primary care visit variable was no longer statistically significant (OR 1.038, 95% CI 0.968–1.113). Among patients due for a seasonal influenza vaccine, recent primary care visits were associated with increased odds of completing the vaccine independently (OR 2.221, 95% CI 2.179–2.264) and when adjusted for MyChart engagement, age, race, and sex (OR 1.548, 95% CI 1.516–1.580). Among patients due for FIT screening, recent primary care visits were associated with increased odds of completing the intervention independently (OR 1.931, 95% CI 1.781–2.094) and when adjusted for MyChart engagement, age, race, and sex (OR 1.537, 95% CI 1.411–1.674). Therefore, primary care visits are positively associated with completing FIT screenings and influenza vaccination.

Discussion

The results showed a higher digital engagement score is associated with increased odds of completing a screening or vaccine. Though statistically significant, digital engagement exhibited a very small effect size on the outcome based on the guidelines of Chen et al.³⁴ In each of the three screenings or vaccines, the odds ratio was between 1.08 and 1.15 when adjusting for age, race, sex, and recent primary care visits. Therefore, Hypothesis 1 is confirmed.

Primary care visits had a greater influence on odds ratios than digital engagement in two of the three health maintenance topics studied—FIT screenings and flu vaccines. Among the three health maintenance topics, the relationship between primary care visits and intervention completion was strongest among patients due for a flu vaccine (OR 1.548, 95% CI 1.516–1.580). This can be explained by the fact that flu vaccines can be administered at a primary care visit. Mammogram completions had the weakest relationship with primary care visits, and this relationship was not statistically significant when adjusted for age, race, sex, and MyChart engagement. One possible explanation is that breast cancer awareness messaging has become so prevalent that office-based clinician endorsement is not needed as strongly to encourage patients to complete the screening. Therefore, Hypothesis 2 is accepted for FIT screening and influenza immunization but rejected for mammograms.

There is no standard definition for patient portal utilization. A review of patient portal utilization metrics found that utilization is operationalized in many ways across the literature, namely through patient use/adoption (e.g. number of logins), frequency (e.g. activities per month), duration (e.g. time spent using the portal), intensity (e.g. number of pages viewed), or super user (e.g. stratifying high utilization groups).³⁵ This study uses a frequency metric that incorporates multiple key features of MyChart. Based on this operationalization of patient portal utilization, a patient's digital engagement can explain the odds of completing a screening or vaccine following a portal message. For example, among patients due for an influenza vaccine, a 1 percent increase in digital engagement score results in a 1.3 percent increase in the odds ratio of completing the screening. Using a hypothetical example, if Patient A logs into MyChart 5 times and schedules 100% of eligible visits online but does nothing else, this patient would have a MyChart Digital Engagement Summary score of 25. Patient B, who logs in 5 times, schedules 100% of appointments online, and views 100% of test results, would have a MyChart engagement score of 55. Thus, Patient B will have 39% greater odds (30 × 1.3) of acting on the influenza patient portal intervention compared to Patient A. These findings are most directly applicable to settings where digital engagement is measured using a frequency metric. However, future research could utilize a different type of metric to examine the transferability of these findings to other settings.

This research suggests that in some scenarios, patient portal interventions may be successful without visit-based interventions, while in other situations, patient portal messaging will work best when complemented by visits. In the case of mammograms, having a primary care visit did not increase the likelihood that patients would complete the screening. In the case of FIT screenings and flu vaccines, both digital engagement and visits influenced whether patients completed the intervention.

Limitations

This study has several limitations. EMR data did not contain several variables that can predict screening or vaccine adherence. For example, increased health literacy is associated with increased screening and vaccination behavior,³⁶ however, health literacy data was not available. Education level³⁷ and income³⁸ can also influence screening adherence but this data was not available.

In addition, it is possible that screening and vaccine completions were under-reported because patients may have completed these health maintenance topics at an external provider and these records were not updated in the EMR.

The study was also limited in that there is variation in how each provider addresses health maintenance topics in a visit. Review of health maintenance topics is a standard part of the rooming process and is often part of the discussion with a provider during routine or preventive visits, but there are variations in the approaches or techniques that clinicians use to motivate patients to complete these.

It is also difficult to control for other factors that may influence patients completing screenings. For example, there are many public health campaigns during Breast Cancer Awareness Month. While the study period looked longitudinally to mitigate some of this effect, these external factors were not accounted for in this research.

Conclusions

This study aimed to evaluate the relationship between patient portal engagement, primary care visits, and the completion of screenings or vaccines following a patient portal intervention. This addresses a gap in the literature as patient portal messaging can be an effective means to close care gaps, yet few studies have investigated this relationship. Using a retrospective analysis of EMR data at a large health system, this study evaluated the completion of mammograms, influenza vaccines, and FIT screenings following a patient portal intervention. Results showed that increased digital engagement and primary care visits are associated with increased screening and vaccination behaviors.

These findings primarily contribute to the digital health and preventive care literature by suggesting additional variables that influence screening and vaccine adherence when interventions are delivered via a patient portal. More broadly, this study adds to the understating of the path by which patient portal utilization can lead to improved health outcomes. Increased engagement with the patient portal contributes to higher screening and vaccine adherence when interventions are delivered via the portal. In addition, this study adds to the knowledge around patient portal utilization metrics. This is one of the first studies to specifically examine Epic's MyChart Digital Engagement Summary and its relation to health outcomes.

Future direction

Future research can incorporate additional variables such as health literacy, education, or income to produce more robust predictive models. In addition, future research can use alternate patient portal utilization metrics, screenings, or vaccines to validate the applicability of the findings to other settings.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251356013 - Supplemental material for Digital engagement and the efficacy of patient portal-based preventive care interventions

Supplemental material, sj-docx-1-dhj-10.1177_20552076251356013 for Digital engagement and the efficacy of patient portal-based preventive care interventions by Marcus A Rauhut in DIGITAL HEALTH

Footnotes

Acknowledgements

The author acknowledges Ted Bell, Holly Lobb, Dr Brian Pollak, MD, John Foley Sherman, PhD, and Josiah Meneghini for conceptual and manuscript feedback.

ORCID iD

Marcus A Rauhut

Ethical considerations

Study activities were determined exempt from review by the WellSpan Health Institutional Review Board (2260838-1).

Consent to participate

Consent to participate was waived by the Institutional Review Board as it was impractical to obtain.

Author contributions

MAR: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - Original Draft.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available as consent to share data publicly was not obtained from the Institutional Review Board.

Supplemental material

Supplemental material for this article is available online.

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