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Record W4412955654 · doi:10.2196/69747

Providing Mobile Patient Access to Their Electronic Secondary Care Patient Record in Adults With Cystic Fibrosis: Results of a Prospective, Parallel, Randomized Open-Pilot Quantitative Study

2025· article· en· W4412955654 on OpenAlexvenueno aff
H. Chadwick, Akhil Sawant, H. White, Lindsey Gillgrass, Giulia Spoletini, I. Clifton, C. Etherington, D. Peckham

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintMedicineOpen labelCystic fibrosisPilot trialElectronic health recordRandomized controlled trialSurgeryWorld Wide WebComputer scienceHealth careInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Leeds regional adult and pediatric cystic fibrosis (CF) services introduced a modified primary care electronic health care record (EHR) in 2007. This resulted in a dramatic improvement in efficiency while providing the benefits of primary care developments, including full Patient Access to their records. OBJECTIVE: This study aims to evaluate the feasibility, benefits, usability, and acceptability to patients of providing secure access to linked secondary care in CF. METHODS: A prospective, parallel, randomized, open, pilot study with an intervention (EHR access; I) and a control group (no EHR access; C). People with CF were recruited on a consecutive basis, from outpatient clinics or as inpatients on the regional Leeds adult CF unit. At baseline and 6 months, paper-based self-report questionnaires were completed by participants to assess having access to EHR on psychological impact, patient satisfaction, quality of life (QoL), patient and physician relationships, and patterns and rates of adherence to treatment. Perceptions and engagement with Patient Access and computer literacy were also assessed. Once completed, participants were randomized into either the intervention or control group (1:1), with those in the intervention group given instructions about how to gain access and the functions of Patient Access by the research team. RESULTS: A total of 91 people with CF completed the 6-month study (intervention n=45; median age 27.5, IQR 12.0 years; 22 male participants; control group n=46; median age 27.0, IQR 15.0 years; 29 male participants). Median number of logins was 9 (range 1-205). There was no effect of Patient Access on levels of anxiety (Generalized Anxiety Disorder-7; I=3.0, C=5.0), all symptom QoL scales and seven QoL domains (Cystic Fibrosis Questionnaire-Revised; respiratory I=63.89, C=55.56; weight I=100.00, C=66.67; digestion I=88.89, C=88.89; physical I=60.42, C=50.00; vitality I=54.17, C=41.67; emotional I=86.67, C=66.67; role I=75.0, C=75.0; body image I=77.78, C=66.67; eating disturbances I=88.89, C=100.0; treatment burden I=55.56, C=55.56), levels of depression (Patient Health Questionnaire-9; I=3.0, C=7.0), confidence in managing health care (Patient Activation Measure-13; I=66.67, C=60.63), level of trust in health care professionals (patient and provider perceived efficacy in patient-physician interaction; I=49.0, C=47.0), and computer literacy. Patient Access scored 86% for satisfaction, 82% for ease of use, and 80% for usefulness (Perceived Health Web Site Usability Questionnaire). Of those who had EHR access, 41 of 42 (98%) participants agreed that access to EHR should continue. CONCLUSIONS: This pilot study suggests that providing access to EHR in adults with CF does not appear to have a negative effect (increase levels of anxiety or decrease QoL), and uptake by patients has been very positive. Prospective studies are needed to investigate the long-term effect on objective health outcomes and how we can improve the functionality of such apps from the patient perspective. TRIAL REGISTRATION: ClinicalTrials.gov NCT06122025; https://clinicaltrials.gov/study/NCT06122025.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.404
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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