Insights Into Patient Portal Engagement Leveraging Observational Electronic Health Data

ABSTRACT

Objective: To understand patient portal engagement stratified by patient characteristics among adults 50 years and older with at least 1 common chronic medical condition using electronic health records data.

Study Design: This exploratory study retrospectively analyzed categorical and numerical data for a patient cohort receiving any kind of care in the M Health Fairview system.

Methods: Data were retrieved from the Epic Clarity database for patients 50 years and older with 1 or more chronic illnesses during the study period and mapped to International Classification of Diseases codes. Portal activation and usage and health care encounters were stratified by patient characteristics. We performed descriptive analysis, Spearman correlation, and multivariable regression.

Results: Of 250,345 adults 50 years and older with at least 1 chronic condition, 61% of them activated the portal and 54% logged at least 1 session between 2011 and 2024. Enrollment disparities were observed by age, race, language, education, and certain chronic conditions. Lower usage was noted among adults 65 years and older (42%), Black patients (40%), non-English speakers (Hmong [38%], Somali [21%], Spanish [28%]), those with less than a college degree (no diploma [53%], General Educational Development/high school diploma [76%]), and patients with certain conditions. Patients with diabetes, neoplasms, ischemic heart disease, and hypertension showed greater engagement, and those with heart failure or chronic obstructive pulmonary disease had lower engagement. Higher portal use was correlated with a higher number of completed encounters but less so with no-shows. Odds of portal use were lower for patients who were 65 years and older, men, non-White, and non-English speakers. Those with neoplasms, heart disease, and hypertension had highest odds of portal usage. Proxy usage was minimal.

Conclusions: Disparities in patient portal use among adults with chronic conditions varied by patient characteristics. Precisely targeted strategies toward suboptimal users of patient portals could enhance their adoption and sustained use.

Am J Manag Care. 2026;31(1):In Press

Takeaway Points

• This analysis of adults 50 years and older with chronic conditions showed lower portal usage among older, non–English-speaking, and Black patients, underscoring digital health equity gaps.
• Among 250,345 adults included in the analysis, 61% activated their MyChart account and 54% logged at least 1 session.
• Portal engagement was significantly lower among patients 65 years and older, Black patients, and non-English speakers (Somali, Spanish, Hmong) compared with younger, White, and English-speaking patients.
• Patients with diabetes, neoplasms, hypertension, and ischemic heart disease had higher odds of portal use, and those with heart failure or chronic obstructive pulmonary disease had lower engagement.
• Findings highlight persistent digital inequities and the need for multilingual, literacy-appropriate strategies to expand equitable access.

With the continued advancement and widespread use of health technologies, portal use among patients to access health records has become an integral part of optimal health care delivery. Evidence indicates that patients who manage their own health experience a wide range of advantages.1-3 This is particularly true for patients who need close and long-term health monitoring, such as patients with chronic medical diseases.4-7 Some advantages include better communication and doctor-patient relationships, patient empowerment, easy access to health information, minimized need for phone calls and visits, and more streamlined administrative tasks.1-3,8 Although numerous studies have examined general barriers to portal adoption and use, few have leveraged long-term electronic health record (EHR) data to explore specific usage patterns among older adults with chronic conditions across multiple sociodemographic factors. This gap limits our understanding of how disparities unfold in real-world health systems.3,9-11

The barriers to portal adoption and sustained use may arise from various factors. These include (1) patient-related sociodemographic factors,1,4,12 most often seen among people who lag behind in digital health literacy, such as older patients and underserved communities; (2) tool-related barriers resulting in suboptimal experience with information, interaction, and interface3,13; and (3) operational barriers, including lack of encouragement from providers or delay in responses from the care team.14

A recent national survey indicated that the overall proportion of older Americans (approximately 78% of those aged 50-80 years) using at least 1 portal is growing15,16; however, a gap exists in portal usage across sociodemographic characteristics. Prior studies have also shown that social determinants of health, such as income, education, and digital literacy,3,15,17-20 influence portal adoption. Identifying the subgroups of older adults with chronic conditions who lag behind in portal use is an essential step toward equitable health care delivery.3 Given these persistent gaps, we centered our analysis on understanding how portal engagement varies among older adults with chronic conditions.3

Chronic medical conditions remain the predominant causes of mortality and morbidity in the US,21 placing a burden on health care systems and patients. Both systems and patients stand to benefit when portals are integrated into chronic conditions management.4 The swift growth of digital health technologies during the COVID-19 pandemic led to a significant rise in portal usage for specific tasks.11 However, notable sociodemographic disparities continue in access and usage.14,22 The increase in technology use has exacerbated the digital divide among vulnerable populations, benefiting those with access and familiarity with technology.17,23

Ideally, health systems are basing important care delivery decisions, particularly with respect to marginalized communities, on evidence-based practices. Secondary observational data analysis has the potential to provide valuable insights into portal utilization and its effects on health outcomes.24 This approach has been found to be an effective and efficient way to gain insight into patient populations despite resource constraints such as time, workforce, and cost.20,25-27 However, use of secondary data comes with its own challenges.27

The objective of this study was to examine patient portal engagement among adults 50 years and older with at least 1 chronic condition by using 13 years of EHR data from the M Health Fairview system. Specifically, we assessed the associations between sociodemographic factors (age, sex, race/ethnicity, language, and education) and chronic diseases, portal activation, and portal use. We also explored the relationship between health care encounter patterns and portal engagement. Through this analysis, we aimed to identify populations with lower engagement and inform strategies to advance equitable digital health participation within large integrated health systems.9

METHODS

Study Design

This was a retrospective observational cohort study. The University of Minnesota Institutional Review Board (IRB) reviewed the study and determined it to be non–human subjects research (IRB STUDY00025031).

Data Source

We extracted data from the M Health Fairview (MHFV) health system, a collaborative health care organization comprising the University of Minnesota, Fairview Health Services, and University of Minnesota Physicians. Relevant data from January 2011 to January 2024 were identified and retrieved from Epic Clarity, a relational database replicating much of the data from Chronicles, Epic’s operational EHR system. Epic Clarity is optimized for reporting and analysis, enabling complex queries and comprehensive report generation. It is typically updated once daily from Chronicles, making it suitable for day-after reporting rather than real-time data analysis. The analytic data set (N = 250,345) was generated by the MHFV data extraction team based on predefined criteria: patients 50 years and older with at least 1 chronic condition and complete demographic information, extracted from Epic Clarity (2011-2024).

Study Population

The study population included patients who turned 50 years old at the beginning of the study period (January 2011) with or without having an Epic patient portal (MyChart) account. We included patients having at least 1 common chronic medical condition (Table 1), defined using International Classification of Diseases codes referenced on the CMS website (eAppendix A [eAppendices available at ajmc.com]). Because the study period spanned both the International Classification of Diseases, Ninth Revision and the International Statistical Classification of Diseases, Tenth Revision, we included both sets of codes. We considered the patient as belonging to the cohort when they had a diagnosis code as their principal diagnosis for 1 or more of the following chronic medical conditions: diabetes (type 1 or 2), hypertension, ischemic heart disease, congestive heart failure, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and neoplasm. We included the earliest date time stamp for the first diagnosis of the first primary chronic disease. For example, if a patient was diagnosed with hypertension in 2012 and then diabetes in 2019, we considered that patient’s pattern of utilization since 2012.

Study Variables and Measures

Patient demographics and chronic health conditions. We extracted self-reported demographic characteristics such as age, sex, race/ethnicity, education, and presence of certain chronic conditions. We categorized various variables as follows: age as 50 to 64 years or 65 years and older; sex as female or male; race as Black, White, or other/unknown; and ethnicity as Hispanic or Latino or not Hispanic or Latino, with an option to choose not to answer. We categorized educational attainment as no diploma, General Educational Development/high school diploma, some college but no degree, associate degree in technical/vocational training, bachelor’s degree, or advanced degree. The primary language was categorized by the most commonly spoken languages in the sample: English, Hmong, Somali, Spanish, or others/unknown.

Portal usage–related attributes. Portal usage–related activities such as MyChart activation status, activation methods, encounter types, appointment statuses, adoption method, and message types were also extracted to understand user engagement and behavior. Activation was defined as the creation of a unique MyChart account associated with a patient’s record. Active use (≥ 1 session) indicated at least 1 logged-in portal session during the study period. Encounter types included ambulatory/telehealth, inpatient, telephone, or other, as classified by Epic. Appointment status categories included scheduled, completed, canceled, or no-show, and message types consisted of medical advice, questionnaires, reminders, or system alerts. Definitions of all portal-usage variables are expanded upon in eAppendix B.

Data Analysis

Descriptive analyses (counts and percentages for categorical variables; means, SDs, medians, and ranges for continuous variables) summarized portal usage and were broken down by patient demographics and various chronic conditions. Participants with missing values on any of the variables included in the analysis were excluded. This approach assumes that the data were missing completely at random. Spearman correlation analysis explores potential associations between visit types and portal usage. A multivariable logistic regression model was used to estimate the adjusted associations between patient characteristics and portal use (≥1 session). Education and ethnicity were excluded from the regression model due to high levels of missing data. We conducted a statistical analysis using SAS 9.4 (SAS Institute Inc).

RESULTS

Patient Characteristics

Our study cohort (N = 250,345) had a mean (SD) age of 65.0 (10.7) years (range, 50-109 years), with 54% being aged 50 to 64 years and 46% being 65 years and older; 52% were women. Most participants identified as White (87%), with Black participants making up 5% and other/unknown races 8%. The majority (81%) identified as not Hispanic or Latino. English was the predominant language (95%). Education data indicated that a large portion (> 76%) had at least some college. We observed a high prevalence of hypertension (65%) and neoplasms (51%) among chronic conditions. Population characteristics are summarized in Table 1.

Portal Activation Rates and Sustained Use

Overall, 61% of participants activated their MyChart account, with 54% having at least 1 session. Among those aged 50 to 64 years, 71% activated their account, and 64% of them had at least 1 session. Among those 65 years and older, 49% activated their account, with 42% having at least 1 session. Women had a slightly higher activation rate than men (62% vs 59%), with 56% of women and 52% of men having at least 1 session. Black participants had lower rates of activation and of having at least 1 session, at 43% and 40%, respectively, compared with White participants, at 62% and 55%. English speakers had the highest activation and session rates, at 62% and 55%. Participants holding a bachelor’s degree or higher had the highest engagement. Among those with chronic conditions, participants with neoplasms had the highest activation at 73% and session rate at 66%, whereas those with heart failure had the lowest, at 49% and 41%, respectively. We also found very low officially registered proxy rates at 0.4%. A snapshot of portal usage adoption rates and rates of having at least 1 session is shown in the Figure, with additional details provided in Table 1.

Session Duration

The mean (SD) active session duration per patient was 4.8 (5.5) minutes. Patients who spoke Somali, Spanish, and Hmong had lower mean active durations (2.8 minutes, 3.3 minutes, and 3.5 minutes, respectively) than those who spoke English (4.8 minutes), as shown in eAppendix Table 1. Also, patients with no diploma had the shortest mean duration (3.2 minutes) of the education groups.

Correlations Between Portal Use and Health Care Visits

Further analysis showed a mild to moderate positive correlation between the portal usage and encounter types/statuses (Table 2). More active health care usage was correlated with MyChart activation, particularly with respect to having had office visits (ρ = 0.47), telephone visits (ρ = 0.40), and orders only (ρ = 0.41). Similarly, completed visits (ρ = 0.46) and canceled visits (ρ = 0.40) also demonstrated a moderate positive correlation. Conversely, no-show visits (ρ = 0.11) exhibited a weak positive correlation. Additional results around message types and adoption method are provided in eAppendix Tables 2 and 3, respectively.

Adjusted Associations Between Patient Characteristics and Portal Use

In multivariable logistic regression modeling, portal use (≥ 1 session) was associated with several independent variables (Table 3). Patients 65 years and older had approximately half the odds of portal use compared with those aged 50 to 64 years (adjusted OR [AOR], 0.46; 95% CI, 0.45-0.46). Women had slightly higher odds of use than men (AOR, 1.17; 95% CI, 1.15-1.19). Black patients and those with non-English preferred languages, including Somali (0.34), Spanish (0.33), and Hmong (0.62), had lower odds of use compared with White and English-speaking patients. Among the chronic conditions, higher odds of use were observed for patients with neoplasms (2.52), ischemic heart disease (1.30), and hypertension (1.33), and lower odds were seen for those with heart failure (0.75) and COPD (0.61). Education and ethnicity were excluded from the model due to high missingness.

DISCUSSION

In this study, we leveraged EHR data to examine MyChart portal utilization patterns among adults 50 years and older with at least 1 chronic condition, focusing particularly on demographic factors hypothesized to influence portal usage. We identified disparities in portal use among adults with chronic conditions stratified by various patient characteristics such as age, race/ethnicity, and education. Greater health care utilization was also associated with higher portal activation, highlighting distinct patterns of digital engagement within this population.

Overall, portal usage was high among patients with chronic conditions, at 61%, with 54% logging at least 1 session. We found lower utilization among older adults (aged ≥ 65 years), Black patients, and non-English speakers (Somali, Spanish, Hmong), as well as those with lower educational attainment. Suboptimal portal usage was also observed among patients with COPD, heart failure, and cerebrovascular disease compared with other chronic conditions.

Non-English speakers and individuals with lower educational attainment spent less time per portal session, suggesting barriers to sustained engagement. Higher levels of health care utilization, particularly office, telephone, and order encounters, were moderately associated with portal activation and use, reflecting that patients who were more engaged in care were also more likely to use the portal. Notably, patients with chronic illnesses such as neoplasms, diabetes, or cardiovascular conditions showed higher portal usage overall, consistent with prior studies suggesting that a higher disease burden is linked to increased digital engagement among patients managing multiple chronic conditions.7,8 In contrast, those with congestive heart failure, cerebrovascular disease, and COPD demonstrated comparatively lower engagement, underscoring opportunities for tailored outreach and support for these groups.

In adjusted models, disparities in portal engagement persisted after accounting for other demographic and clinical factors. Older adults, Black patients, and non-English speakers continued to show lower odds of portal use compared with younger, White, and English-speaking patients, consistent with prior research documenting persistent digital divides in health technology use.17,22 Conversely, patients with neoplasms, ischemic heart disease, and hypertension remained more likely to engage with the portal, possibly reflecting higher ongoing care needs. These findings underscore that structural and access-related barriers continue to shape portal engagement, even within an integrated health system.

Proxy account use was low, suggesting limited awareness of this functionality among patients. Targeted education on creating and using proxy accounts may improve engagement for older or dependent patients who rely on caregivers.

Patients with greater portal activation also had more completed visits and fewer no-shows, indicating a parallel pattern between portal activity and clinical engagement (eAppendix Figures 1 and 2). This association aligns with prior studies, which show that active portal users tend to maintain higher visit completion rates and overall continuity of care.9,28 However, this relationship reflects association rather than causation, as patients with greater health care utilization may also be more likely to activate and use the portal. Such patterns reflect that individuals who are already more engaged in their care are also more likely to adopt and consistently use digital tools.

The observed disparities in portal use have important implications for digital health equity. As patient portals become central to chronic disease management, unequal access and utilization risk widening existing gaps in care.14,17,21,28 Populations facing barriers such as limited digital literacy, language differences, or socioeconomic constraints may remain underrepresented in digital engagement.3,12

Health systems can mitigate these inequities through targeted outreach and inclusive design, such as multilingual interfaces, simplified enrollment workflows, and personalized training or assistance.4-6 These strategies empower patients with varying levels of comfort with technology to use portals effectively and consistently. By adopting equitable digital health practices, health care organizations can ensure that patient portals serve as tools for connection rather than exclusion, supporting more balanced participation in patient-centered care.1,10,13,14

To achieve this balanced participation and strengthen engagement, organizations can deploy digital health navigators, embedded chatbots, or voice-activated assistants to help patients and caregivers locate functions, complete common tasks, and receive real-time guidance within the portal. This support is especially crucial for individuals challenged by the frequent interface updates common to platforms like MyChart. Prior studies show that older adults and underserved groups particularly benefit from clearer digital navigation.18,23,29,30

Limitations

This study’s retrospective design and reliance on data initially collected for clinical and administrative purposes present several limitations. First, we focused primarily on descriptive statistics in this study, as the very large sample size rendered statistical tests significant even for small differences that may not be practically meaningful. Additionally, the heterogeneity of the selected sample, arising from the implementation dates of MyChart and the secondary nature of the data, could have resulted in some inconsistencies in the data. Second, the historical data collection likely involved variations in definitions and recording practices over time, introducing heterogeneity that could impact the accuracy of usage pattern analysis. This was somewhat mitigated by the purposeful revision and improvement of patient demographic data collected in 2022 and subsequent years. Education and ethnicity were excluded from the regression analysis due to substantial missingness in the EHR, which may limit the interpretation of disparities along these dimensions. We plan to conduct a more granular-level analysis in the next phase, examining various time periods, including pre– and post–COVID-19 comparisons. Third, the focus on MHFV limits the generalizability of findings to other health care contexts, and the inclusion of single-interaction users introduced noise. Finally, the available data lacked the granularity to analyze the potential impact of digital redlining on portal usage in areas with limited internet access, as we were unable to link patients to their zip code addresses. These limitations highlight the need for future prospective studies with standardized data collection across broader populations.

Future Work

Our analysis showed lower portal use among patients 65 years and older compared with those aged 50 to 64 years. Future studies should conduct direct comparative analyses between these age groups while accounting for clinical and sociodemographic confounders. Such work could further clarify age-related differences in digital engagement and guide targeted interventions.

CONCLUSIONS

This study revealed that, among patients with chronic conditions, significant disparities in portal use remain by race, language, and educational status. These inequities underline the need for targeted digital literacy and access initiatives to ensure all patients benefit from digital health tools. Health systems should adopt culturally responsive, community-based digital interventions to promote equitable engagement. Ensuring inclusive access to patient portals and similar technologies is not only a technical challenge but also a moral imperative for advancing health equity.

Acknowledgments

The authors thank Molly Diethelm for her assistance as the point of contact with the IRB. They also acknowledge Richard Lodahl and Elizabeth Lindemann for their support with data access and coordination. Their contributions were invaluable to the completion of this study.

Author Affiliations: Department of Learning, Informatics, Management, and Ethics, Karolinska Institute (MMA), Stockholm, Sweden; Fairview Health Services (MMA, ZH), Minneapolis, MN; Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota (SL), Minneapolis, MN; Department of Family Medicine and Community Health (MA, PA), Department of Surgery (GBM, RR), and Center of Learning Health System Sciences (GBM, RR), University of Minnesota Medical School, Minneapolis, MN; Institute for Health Informatics, University of Minnesota Office of Academic Clinical Affairs (GBM, RR), Minneapolis, MN.

Source of Funding: This research was supported by the Agency for Healthcare Research and Quality/Patient-Centered Outcomes Research Institute K12-P30 Learning Health System Embedded Scientist Training and Research Program (P30HS029744).

Author Disclosures: Mr Ali and Mr Henderson are employed by M Health Fairview, a collaboration between the University of Minnesota Medical School, University of Minnesota Physicians, and Fairview Health Services. Dr Melton is a board member of HL7 International and the American Medical Informatics Association; has served as a consultant or adviser for Washington University in St Louis, The University of Utah, The Ohio State University, Geisinger Health, and American Medical Association; and has received grants from the Agency for Healthcare Research and Quality, Patient-Centered Outcomes Research Institute, National Institutes of Health, Advanced Research Projects Agency for Health, FDA, and state of Minnesota. Dr Rizvi is a University of Minnesota employee and M Health Fairview is one of Epic’s customers. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (PA, MA, RR); acquisition of data (ZH, GBM, RR); analysis and interpretation of data (MMA, SL, ZH, RR); drafting of the manuscript (MMA, SL, ZH, PA, MA, GBM, RR); critical revision of the manuscript for important intellectual content (MMA, SL, PA, MA, GBM, RR); statistical analysis (SL); provision of patients or study materials (MMA, RR); obtaining funding (RR); administrative, technical, or logistic support (MMA, GBM, RR); and supervision (RR).

Address Correspondence to: Rubina Rizvi, MD, PhD, University of Minnesota Medical School, 516 Delaware St, Phillips Wangensteen Building, Minneapolis, MN 55455. Email: rizvi007@umn.edu.

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