Abstract
Objective
Digital health apps and patient portals are proposed as part of the drive from ‘analogue to digital’ care for the National Health Service (NHS) 10-Year Plan. Without mitigation strategies, digital inequalities could arise as a result, and more evidence is needed to understand how to mitigate this.
Methods
As part of an equality impact assessment, a retrospective cross-sectional analysis was conducted examining patient portal activation among patients invited to outpatient appointments at two large south-east London Hospital Trusts between 1 May and 1 November 2024.
Results
Of the 503 688 patients invited to attend outpatient clinics during the study period, 52.7% had activated the patient portal. Availability of email contact details was the strongest determinant of onboarding likelihood (OR 10.86). Multivariate logistic regression models showed that the following groups were less likely to activate the patient portal: men (OR 0.84), individuals at the extremes of age (71–80 or 11–20 years), those of mixed or undefined ethnicity (OR 0.58), those of black ethnicity (OR 0.62) and those with the highest degree of socioeconomic deprivation (Index of Multiple Deprivation group 1; OR 0.68).
Conclusion
This large-scale roll-out of a digital health portal provides empirical evidence of factors that drive digital inequalities for patients of two major London NHS Trusts. The observed disparities across demographic and socioeconomic dimensions and simple reliable digital contact mechanisms highlight the risk that digital healthcare initiatives may inadvertently produce new types of inequalities.
Keywords: Electronic Health Records, Health Equity, Patient Involvement
WHAT IS ALREADY KNOWN ON THIS TOPIC
Digital health inequalities exist across healthcare systems, but large-scale evidence from comprehensive secondary care roll-outs examining patterns of inequality across diverse populations has been limited.
WHAT THIS STUDY ADDS
This analysis of nearly 500 000 patients reveals significant and persistent inequalities in patient portal activation across demographic dimensions, with contact details being the strongest predictor. However, this still does not fully explain disparities, suggesting deeper barriers beyond simple technical access.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Healthcare organisations implementing digital-first strategies must develop targeted, equity-focused interventions and continuous monitoring to prevent widening health inequalities as the National Health Service transitions from analogue to digital care delivery.
Introduction
In the UK, the National Health Service of England (NHS England) has recently launched their NHS 10-Year Plan with emphasis on digital-first healthcare approaches.1 2 Other countries are implementing national digital health platforms: Norway’s helsenorge.no provides citizens with comprehensive access to health records, appointment booking and communication with healthcare providers across the entire health system.3 Similarly, Scotland has been developing its ‘digital front door’ approach, aiming to provide a single point of digital access to health and care services.4 These ‘patient portals’ enable individuals to access their medical records and communicate with healthcare providers.
These strategies are laudable but risk escalating the digital divide, as inequality of access to these digital ‘patient portals’ may inadvertently exacerbate existing health disparities if not implemented with equity considerations.5 6 If certain groups experience limited engagement with patient portals, they may face barriers to accessing information and services, potentially widening existing health inequalities. These concerns align with broader evidence suggesting socioeconomic and demographic factors affect health outcomes and the UK government’s commitment to reducing health inequalities.7 8
Our previous work studying the NHS England’s national patient portal, the NHS App, showed geographical differences in uptake and adoption.9 The NHS App acts as a patient portal for NHS primary care services but currently has a limited footprint in secondary and tertiary services.
King’s College Hospital NHS Foundation Trust and Guy’s and St Thomas’ NHS Foundation Trust provide secondary and tertiary health services to a diverse population in London, UK. In 2023, they replaced their electronic health record (EHR) systems and began deployment of a patient portal ‘tethered’ to the new EHR. This study’s objective was to study the demographic and socioeconomic factors related to digital health inequality in secondary and tertiary care as part of the roll-out of a large-scale patient portal.
Methods
Study design and population
A retrospective cross-sectional analysis was conducted examining patient portal activation among patients invited to outpatient appointments at two large London Hospital Trusts between 1 May and 1 November 2024. This timeframe was selected to provide a 6-month snapshot of patient portal activation patterns while ensuring adequate sample size for subgroup analyses. At registration, patient demographic details were obtained from the NHS Spine10 and by speaking to the patient. Opportunities to get patients to activate the patient portal were automatically taken at appointment booking, arrival in clinic and at issue of the electronic summary of attendance. Hyperlinks to patient portal sign-up were sent by email and SMS.
Patient portal implementation
Before launch, a thorough patient-centred approach was taken to ensure the system reflected diverse experiences and preferences. Feedback from a panel of patients, carers and community members led to improvements in language, accessibility and usability. Accessibility standards were independently reviewed and enhancements were implemented. After Go-Live, focus on inclusion and equity was maintained through annual assessments and continuous community engagement. Helpdesks were established to support users and capture real-time feedback, enabling rapid resolution of issues. Outreach sessions were used to increase user confidence, particularly among older, ethnically diverse and digitally excluded groups. Volunteers provided in-person support, while a patient panel informed governance and system improvements.
Two-step verification of identity was required for a patient to access their records in the patient portal. Due to the cost of SMS messages, the host organisations decided to limit two-step authentication to email or an authenticator app. A patient without an email address could activate their patient portal account but would have to use an authenticator app to access their personal details.
Data sources
Data were extracted from the data warehouse, which integrates EHR information from the Epic EHR system implemented across both hospital systems. Data were extracted to ensure extraction of one record per patient. The data were correct at the time of data extraction in March 2025. The extraction was performed by the Hospital Trusts’ data analysis team to ensure appropriate data governance, with all analyses conducted on deidentified data.
Variables and measurements
The primary outcome measure was patient portal activation status, dichotomised as ‘activated’ or ‘not activated’ (combining status categories of pending activation, inactivated, non-standard status, patient declined and activation code generated but disabled).
Key predictor variables included:
-
Demographic characteristics:
Age (analysed both as continuous and categorised into 10-year age bands).
Sex (male/female as recorded in the legal sex field).
Ethnicity (collected using NHS standard ethnic categories, analysed both as high-level groupings and detailed categories).11
First language (English/non-English).
-
Socioeconomic status:
Index of Multiple Deprivation (IMD) decile derived from patient postal code linked to lower super output area data, where 1 represents the most deprived areas and 10 the least deprived.
-
Contact information availability:
Presence of email address in the EHR.
Presence of mobile phone number in the EHR.
Data analysis
Descriptive statistics were calculated for all variables, stratified by patient portal activation status. All variables were analysed as categorical variables, and frequencies and proportions were calculated. Between-group comparisons were conducted using χ2 tests of independence.
The association between predictor variables and patient portal activation was initially assessed through univariate analyses as described above. Subsequently, multivariate logistic regression models were constructed to evaluate independent associations while adjusting for potential confounding. Two primary multivariate models were developed:
A base model including demographic variables (age, sex, ethnicity) and socioeconomic status (IMD decile).
An expanded model additionally incorporating the presence of an email address to evaluate whether contact information availability mediated observed demographic disparities.
Adjusted ORs (aORs) with 95% CIs were calculated for all predictor variables. Forest plots were generated to visually represent these associations, with reference categories established as female sex, white ethnicity, age group 61–70 and IMD decile 10 (least deprived).
All statistical analyses were performed using Python statistical software, with a significance threshold of p<0.05.12
Ethical considerations
This study forms part of a UK public sector statutory duty for equality impact assessment.8 It was conducted in accordance with NHS information governance protocols—all data were extracted and analysed in deidentified form, with results presented as aggregated statistics.
Results
Of the 503 688 patients invited to attend outpatient clinics during the study period, complete data were available for 499 098 (99%). Descriptive statistics showed significant differences in patient portal activation across all analysed variables (as shown in table 1).
Table 1. Patient portal activation among 499 098 patients across two London teaching hospital systems in London, UK, in 2024.
| Independent variables | Patient portal | Total | Test result | |
|---|---|---|---|---|
| Activated (%) | Not activated (%) | |||
| Total | 262 780 (52.7) | 236 318 (47.3) | 499 098 | |
| Sex | ||||
| Female | 156 538 (56.1) | 122 631 (43.9) | 279 169 | Χ2 p<0.001, OR=1.37 (95% CI 1.35 to 1.38) |
| Male | 106 242 (48.3) | 113 687 (51.7) | 219 929 | |
| Age group | ||||
| 0–10 | 6148 (12.9) | 41 643 (87.1) | 47 791 | Χ2 p<0.001 |
| 11–20 | 6784 (16) | 35 677 (84) | 42 461 | |
| 21–30 | 36 230 (67.2) | 17 654 (32.8) | 53 884 | |
| 31–40 | 51 769 (70.6) | 21 575 (29.4) | 73 344 | |
| 41–50 | 41 176 (66.4) | 20 875 (33.6) | 62 051 | |
| 51–60 | 43 505 (62.7) | 25 866 (37.3) | 69 371 | |
| 61–70 | 40 722 (59.3) | 27 962 (40.7) | 68 684 | |
| 71–80 | 25 968 (51.7) | 24 305 (48.3) | 50 273 | |
| 81–90 | 9338 (35.2) | 17 176 (64.8) | 26 514 | |
| 91–100 | 1140 (24.1) | 3585 (75.9) | 4725 | |
| Ethnic group | ||||
| White ethnicities | 128 179 (59.1) | 88 802 (40.9) | 216 981 | Χ2 p<0.001 |
| Mixed or undefined ethnicities | 64 715 (46.9) | 73 351 (53.1) | 138 066 | |
| Black ethnicities | 36 721 (48.3) | 39 345 (51.7) | 76 066 | |
| Asian ethnicities | 15 755 (57.7) | 11 555 (42.3) | 27 310 | |
| Not recorded | 17 410 (42.8) | 23 265 (57.2) | 40 675 | |
| Index of Multiple Deprivation | ||||
| 1 (most deprived) | 4455 (48.7) | 4688 (51.3) | 9143 | Χ2 p<0.001 |
| 2 | 37 644 (49.3) | 38 754 (50.7) | 76 398 | |
| 3 | 46 112 (51.1) | 44 130 (48.9) | 90 242 | |
| 4 | 36 176 (52.2) | 33 091 (47.8) | 69 267 | |
| 5 | 29 347 (53) | 25 974 (47) | 55 321 | |
| 6 | 26 925 (54.2) | 22 765 (45.8) | 49 690 | |
| 7 | 21 636 (54.6) | 17 964 (45.4) | 39 600 | |
| 8 | 20 571 (55.1) | 16 760 (44.9) | 37 331 | |
| 9 | 21 220 (55.3) | 17 122 (44.7) | 38 342 | |
| 10 (least deprived) | 16 890 (56.4) | 13 078 (43.6) | 29 968 | |
| Email address | ||||
| Present | 253 810 (61) | 162 557 (39) | 416 367 | Χ2 p<0.001, OR=12.84 (95% CI 12.55 to 13.13) |
| Absent | 8970 (10.8) | 73 761 (89.2) | 82 731 | |
| Mobile phone | ||||
| Present | 242 028 (53.5) | 210 273 (46.5) | 452 301 | Χ2 p<0.001, OR=1.44 (95% CI 1.42 to 1.47) |
| Absent | 20 752 (44.3) | 26 045 (55.7) | 46 797 | |
| First language | ||||
| English | 239 867 (54.1) | 203 681 (45.9) | 443 548 | Χ2 p<0.001, OR=1.68 (95% CI 1.65 to 1.71) |
| Not English | 22 913 (41.2) | 32 637 (58.8) | 55 550 | |
Demographic characteristics and patient portal activation
Overall, 52.7% of patients invited to attend outpatient clinics had activated the patient portal at the time of analysis. Women showed higher activation rates than men (56.1% vs 48.3%, p<0.001). Age groups showed substantial variation in activation, with the highest rates among those aged 31–40 (70.6%) and lowest among children aged 0–10 (12.9%) and elderly patients aged 91–100 (24.1%). White and Asian ethnic groups demonstrated higher activation rates (59.1% and 57.7%, respectively) compared with black ethnic groups (48.3%).
Socioeconomic factors and patient portal activation
In univariate analyses, all the variables analysed were related to patient portal activation. A clear socioeconomic gradient was observed in activation rates across IMD deciles. Patients from the most deprived areas (IMD decile 1) had lower activation rates (48.7%) compared with those from the least deprived areas (IMD decile 10), with activation rates of 56.4% (p<0.001). This gradient was consistent across intermediate deciles, suggesting a robust association between socioeconomic status and patient portal activation.
Contact information and patient portal activation
An email address was obtained for 416 367 (83%) of patients and a mobile phone number was obtained for 452 301 (91%). In univariate analysis, the OR for the association between email address availability and patient portal activation was high at 12.84 (95% CI 12.55 to 13.13). Only 3% of those who activated the patient portal did not have an email address. The associations with mobile phone (OR 1.44, 95% CI 1.42 to 1.47) and English as a first language (OR 1.68, 95% CI 1.65 to 1.71) were lower.
First language also showed an association with activation rates, with English speakers having higher activation rates than non-English speakers (54.1% vs 41.2%, p<0.001).
Multivariate analysis of patient portal activation
Multivariate logistic regression models were constructed to identify independent predictors of patient portal activation while adjusting for potential confounding factors. Figure 1 shows the aORs for sex, age group, ethnicity and deprivation from multivariate logistic regression.
Figure 1. Multivariate logistic regression showing ORs for adoption of patient portal among 499 098 patients invited to outpatient clinics across two London teaching hospital systems between 26 May 2024 and 26 November 2024. IMD, Index of Multiple Deprivation.
The OR for the reference groups female sex, ‘white’ ethnic group and age group 61–70 is set at 1. When the OR is less than 1, it indicates that the probability of that group having patient portal activated is less than the reference group. If it is greater than 1, it means that the group is more likely to activate the patient portal. Due to large sample sizes, the 95% CIs (the whiskers either side of the dots) are very narrow. When the ORs do not overlap, the difference between groups is statistically significant.
It shows that the following groups were less likely to activate the patient portal: men (OR 0.84, 95% CI 0.83 to 0.85); those in the highest age group (71–80 years, OR 0.67, 95% CI 0.66 to 0.69) and lowest age group (11–20, OR 0.14, 95% CI 0.13 to 0.14); those of mixed or undefined ethnicity (OR 0.58, 95% CI 0.57 to 0.59), black ethnicity (OR 0.62, 95% CI 0.61 to 0.64) or unrecorded ethnicities (OR 0.72, 95% CI 0.7 to 0.74); and those more socioeconomically deprived (eg, IMD group 1) (OR 0.68, 95% CI 0.65 to 0.72).
Figure 2 shows the ORs from the same logistic regression model, with the addition of having had an email address or mobile phone number recorded in the EHR (blue dots).
Figure 2. ORs for adoption of a patient portal before and after adjusting for the presence of an email address and mobile phone number. IMD, Index of Multiple Deprivation.
The presence of an email address was the strongest predictor of patient portal activation (OR 10.86, 95% CI 10.60 to 11.12), with the presence of a mobile phone number less influential (OR 1.24, 95% CI 1.21 to 1.27). These are not shown in the figure.
The figure shows that the availability of an email address and mobile phone number had relatively little impact on the effect of sex and ethnicity on patient portal activation. It resulted in a 30–40% reduction in the OR associated with deprivation and a 10–30% reduction in the association with age. Further multivariate analysis is available in the online supplemental material.
Discussion
This large-scale analysis of patient portal activation reveals persistent inequalities across demographic and socioeconomic groups. Men, individuals at extremes of age (both very young and elderly), black ethnic minorities and those from areas of higher socioeconomic deprivation demonstrated consistently lower rates of patient portal activation. These patterns persisted in multivariate analyses, suggesting independent effects rather than confounding relationships between these factors. The findings align with previous research on digital health inequalities and raise important concerns about potential widening of healthcare disparities in an increasingly digital NHS environment.7
The strong association between email address availability and patient portal activation represents a key finding with practical implications. As mentioned in the Methods section, the Trusts decided to limit the two-step authentication to email or an authenticator app. For those without an email address, this may be a step too far and could be an important barrier to address.
However, it is notable that email address availability did not fully account for the observed demographic disparities shown in figure 2. This suggests that interventions focused solely on collecting email addresses, while necessary, would be insufficient to address the underlying digital inequalities. Interestingly, the distribution of email address availability itself showed similar patterns of inequality across demographic groups, potentially reflecting broader digital literacy and access issues.
The observed ethnic disparities in portal activation warrant particular attention, as they may compound existing inequalities in healthcare access and outcomes. Black ethnic groups consistently showed lower activation rates compared with white and Asian groups, even after adjustment for age, sex and socioeconomic deprivation. These findings echo our prior research on national NHS digital services in primary care, which demonstrated similar socioeconomic gradients in adoption of the NHS App across England.9 Cultural factors, language barriers, trust in digital health systems and varying access to digital devices may all contribute to these disparities and require further investigation.
Age and socioeconomic factors
Age-related differences in activation followed a clear pattern, with particularly low rates at both extremes of the age spectrum. Paediatric patients showed markedly low activation rates, with only 12.9% of 0–10 year-olds and 16% of 11–20 year-olds having activated portals. These low rates are almost certainly attributable to structural barriers around parental access and proxy registration processes. The very young cannot independently activate accounts, making activation dependent on parental digital literacy, engagement with healthcare systems and understanding of portal benefits. Additionally, parents may face technical barriers in navigating proxy access.
At the other extreme, elderly patients demonstrated progressively declining activation rates with advancing age, dropping from 51.7% in the 71–80 age group to just 24.1% among those aged 91–100 years. This pattern likely reflects multiple intersecting factors including reduced digital literacy, physical limitations affecting device use, cognitive changes that may impact technology adoption and potentially greater reliance on family members or caregivers for healthcare management. Some may also have established preferences for traditional communication methods with healthcare providers. The 31–40 years age group showed the highest activation rates (70.6%), representing the ‘digital native’ generation most comfortable with technology adoption.
These substantial disparities across age groups suggest that different approaches are needed to access different segments of the patient population, and omnichannel approaches would require whole health systems to work together.
Socioeconomic gradients in activation, as measured through IMD deciles, persisted even after adjustment for demographic factors. This suggests that material deprivation itself constitutes a barrier to digital health engagement, potentially through mechanisms including limited internet access, device ownership, digital skills and competing priorities. These findings raise concerns that as health systems increasingly prioritise digital pathways, socioeconomically disadvantaged patients may face compounded barriers to accessing care and information, potentially widening rather than narrowing health inequalities.7 13 14
Existing literature on digital health roll-outs
Inequities have been found before in small-scale studies across the mHealth or digital health technologies.15 Such studies have tended to be apps focused on specific health conditions16 17 rather than whole health systems, which are the domain of consultancies and policymakers.18 A study of 5000 patient portal users of a community hospital in Canada found limited functionality of patient portals affected their uptake,19 while a study in UK primary care of 284 000 users showed adoption increased the number of remote consultations.20 These larger scale studies did not explore the inequalities that might result from uneven adoption.
Our study of >500 000 patients is the second largest in a UK population and the only one done comprehensively across secondary or tertiary care in a multihospital system. The only larger scale study reported in the academic literature is our work with the NHS App on 4 million individuals in England during the pandemic.9
Study limitations
This study is inherently descriptive as a cross-sectional study and so is unable to show causation. As a single time point analysis, it cannot comment about activation and ongoing usage. The project team implementing the patient portal across our organisations undertook strenuous efforts to proactively prevent the development of inequalities. Informal feedback from patients suggests that the portal is a big leap forward for patients in understanding their care. Despite this, inequalities have clearly emerged. Longitudinal studies would be needed to understand persistence of digital health use.
Ways to increase adoption and implications
Healthcare organisations implementing patient portals must develop targeted strategies to address these disparities and barriers that have been predominantly about the readiness and scalability of the health systems rolling these out.17 Such approaches might include proactive collection of email addresses during registration and clinical encounters; provision of in-person digital support services in clinical settings; culturally tailored education materials; multilanguage support; and development of alternative access pathways for digitally excluded populations. Almost all of these have been tried with the patient portal implementation studied in this paper. As the NHS progresses from analogue to digital, regular monitoring of portal activation patterns across demographic groups as well as degree of ongoing engagement will be crucial levers to mitigate digital inequalities.
There is an additional risk with the proliferation of digital health tools, websites, apps and portals—multiplicity of digital health apps for different providers and services creating fragmentation of ‘digital front doors’ for patients. It is, after all, not realistic for a patient with multimorbidity and multiple healthcare providers to have a different app for each disease, each clinic or each hospital. Digital market forces may create convergence to a few dominant apps, which has its own disadvantages. There is a need for an omnichannel approach to patient portals in a comprehensive healthcare system like the UK NHS. A promising approach is through cross-integration21 and interoperability as planned with the NHS App22 to prevent oligopolies of ‘walled gardens’.
Ultimately, digital health tools like patient portals offer significant benefits, but realising their promise requires deliberate attention to inclusion and equity in their implementation and will likely need a more continual monitoring approach of a learning health system like real-time analytics-enhanced dashboards rather than classical epidemiological research.
Conclusion
This study provides empirical evidence of significant inequalities in patient portal activation across two major London NHS Hospital Trusts. The observed disparities across demographic and socioeconomic dimensions—particularly affecting men, black ethnic minorities, older adults and those from socioeconomically deprived areas—highlight the risk that digital healthcare initiatives may inadvertently widen existing health inequalities. While email address availability emerged as the strongest predictor of patient portal activation, it did not fully explain the observed disparities, suggesting more complex underlying barriers.
English healthcare systems have been asked to move from ‘analogue-to-digital’.23 24 These findings underscore the importance of implementing targeted, equity-focused strategies to ensure digital health tools promote rather than hinder equitable healthcare access. Healthcare providers should consider developing multifaceted approaches that address both technical barriers to access and deeper issues of digital literacy, trust and engagement across diverse patient populations.25 Regular monitoring of activation patterns across demographic groups should be embedded within institutional equity frameworks to track progress and guide interventions aimed at narrowing the digital divide in healthcare.25
Supplementary material
Acknowledgements
Andrew Wilkinson, Aimee Porter Smith, Pritesh Mistry and Professor Claire Harrison provided comments and review of manuscript drafts. Aimee Porter Smith was listed as a coauthor originally but opted subsequently to be acknowledged only.
Footnotes
Funding: JT received support from UKRI via the National Institutes of Health Research, Medical Research Council and Health Data Research UK, but this study was not in scope of any of this funding.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Data availability free text: Data are not available due to lack of legal basis under GDPR.
Data availability statement
No data are available.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No data are available.