arXiv 31 May 2026 · Econometrics
arXiv:2606.01137 · PDF · DOI · OpenAlex · Extracted main text
Whether investment in digital health technology is associated with differences in hospital productivity is a question of substantial policy relevance, yet interpretation is constrained by challenges in causal identification and prior evidence is mixed. Technical efficiency in NHS acute hospital trusts in England is estimated using Bayesian stochastic frontier analysis. A four-input Cobb--Douglas production function incorporating clinical full-time equivalents, administrative full-time equivalents, non-labour expenditure, and physical capital derived from audited NHS accounts is fitted to 111 acute non-specialist trusts in 2024/25. Digital maturity, measured by the NHS Digital Maturity Assessment, is included in a trust-specific inefficiency equation alongside population deprivation, teaching status, and financial position controls. The composite digital maturity score is estimated to be negatively associated with technical inefficiency (\(\hatγ = -0.612\), 95% credible interval \([-1.289, +0.005]\), \(P(γ< 0) = 0.974\)). Trusts in the highest digital maturity quartile are estimated to operate at 98.0% of their production frontier compared with 93.2% for the lowest quartile. This gap corresponds to approximately £20 million of additional cost-weighted activity per trust at mean output levels, or £1.1 billion in aggregate. Estimates are robust to functional form but are sensitive to the most conservative prior specification. Pillar-level analysis suggests that population health management and care pathway optimisation domains exhibit stronger associations with efficiency than other domains. Catchment deprivation is not estimated to have an independent association with efficiency after controlling for digital maturity.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Battese, George E. and Coelli, Timothy J (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data | 0.737 | 3 | 2 | 100% |
| 2 | Abril-Pla, Oriol and Andreani, Virgile and Carroll, Colin and Dong,… (2023) PyMC: a modern, and comprehensive probabilistic programming framework in Python | 0.511 | 2 | 2 | 50% |
| 3 | NHS England (2025) NHS Oversight Framework: Acute Trust League Table 2024/25 | 0.511 | 2 | 1 | 100% |
| 4 | Bardhan, Indranil R. and Thouin, Mark F (2013) Health information technology and its impact on the quality and cost of healthcare delivery | 0.405 | 1 | 1 | 100% |
| 5 | Castelli, Adriana and Laudicella, Mauro and Street, Andrew and Ward,… (2011) Getting out what we put in: productivity of the English National Health Service | 0.405 | 1 | 1 | 100% |
| 6 | NHS England (2021) What Good Looks Like: Digital Transformation Framework | 0.405 | 1 | 1 | 100% |
| 7 | NHS England (2025) Digital Maturity Assessment 2025 | 0.405 | 1 | 1 | 100% |
| 8 | NHS England (2025) NHS Workforce Statistics, September 2024 | 0.405 | 1 | 1 | 100% |
| 9 | NHS England (2025) National Cost Collection 2024/25 | 0.405 | 1 | 1 | 100% |
| 10 | NHS England (2025) NHS Trust Annual Accounts 2024/25 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.