Unlocking Digital Health: Inequalities in the adoption of a Patient Portal
Bibliographic record
Abstract
Abstract Objective Digital health apps and patient portals are proposed as part of the drive from ‘analogue to digital’ care for the 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 equalities 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 May 1st and November 1st, 2024. Results 503,688 patients invited to attend outpatients during the study period, 52.7% of patients invited to attend outpatients had activated the patient portal. Availability of email contact details were strongest determinant for likelihood of onboarding (OR 10.86; CI 10.60-11.12). Multivariate logistic regression models showed the following groups were less likely to activate the patient portal. Men (odds ratio 0.84 (CI:0.83–0.85), extremes of age (71-80 years or 11-20 years), those mixed or undefined ethnicity OR 0.58 (CI 0.57– 0.59), black ethnicities OR 0.62 (CI 0.61–0.64) or un-recorded ethnicities OR 0.72 (CI 0.7– 0.74) and those with highest degree of socio-economically deprivation (IMD group 1) OR 0.68 (CI 0.65–0.72). Conclusion This large scale roll-out of a digital health portal provide empirical evidence of factors which 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. What is the paper about? Were there inequalities in activation of the patient portal, MyChart, in the Apollo programme, by demographic characteristics of the patients? Yes. Table 1 Were the observed inequalities attributable to confounding? No. In a multivariate logistic regression, the inequalities persisted across all variables. Figure 1 Could the inequalities be explained by differential access to email and mobile phones across the groups? For several variables, adjusting for the presence of email address and mobile phone number attenuated the strength of the relationship with Patient Portal activation. It did not remove the effect for any variable and the relationship with ethnicity was barely affected. Figure 2
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".