Digital Capability, Open-Source Use, and Interoperability Standards Within the National Health Service in England: Survey of Health Care Trusts
Bibliographic record
Abstract
Background: In 2016, the National Health Service (NHS) England sought to drive digital transformation within select NHS trusts through the Global Digital Exemplar (GDE) program. While the program did advance the NHS's integration with digital technologies, disparities in digital maturity persisted between GDE-funded and nonfunded NHS trusts. The Department of Health and Social Care (DHSC) launched a data strategy in 2022 that aimed to develop the appropriate technical infrastructure and data architecture to enable more effective and efficient use of its data. Given the diversity in digital capabilities, open-source adoption, and interoperability standards within NHS services, official guidance has continued to struggle to provide effective unification. Data about capabilities and technologies from application development teams in the NHS trusts, crucial for advancing these areas, remains insufficient. Objective: This study aimed to further document the capabilities and technologies used in the NHS to develop digital capacity, comparing those with standard funding against those with additional GDE funding. This comparative analysis provides a foundational understanding for evaluating current practices and identifying potential areas for improvement in the NHS digital transformation efforts. Methods: This study was conducted using Freedom of Information (FOI) requests and systematic website searches. The Freedom of Information Act (FOIA) allows individuals to request information held by public authorities. This process supports transparency and accountability by ensuring public access to government data. Data were compiled from two sources: (1) FOI requests submitted to NHS trusts between July 2020 and December 2020, and (2) systematic website searches for technology conducted between August 2020 and July 2021. A series of chi-square tests was conducted to validate and strengthen the robustness of the FOI questions. Results: A total of 191 (84.5%) of the then 226 NHS trusts completed the FOI request, and 161 of the 191 (84%) had software and app development, website, or innovation teams. A total of 112 (69.6%) teams developed front-facing service user websites and apps. Out of 191, 150 (93.2%) worked with clinical staff to formulate innovative ideas, 55 (34.2%) carried out developments for other trusts and external entities, 35 (21.7%) had attempted to secure an innovation grant, and 138 (86%) disclosed the technologies they use. A total of 25 (15.5%) said they always used open-source technology, and 24 (17%) disclosed technologies associated with interoperability standards in their responses. Conclusions: The NHS must adopt a cohesive strategy and refine policies to ensure the success of its digital, open-source technology and interoperability standards initiatives. Five recommendations toward greater organizational interoperability are made by the authors. Future research should examine digital innovation across NHS trusts, focusing on barriers such as limited resources, organizational culture, and technical expertise. Identifying these challenges is essential for developing strategies to reduce disparities and promote equal progress.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".