ArticleJournal of medical systems2026
Measuring Digital Transformation in Chinese Hospitals: Development and Validation of a Digital Maturity Evaluation Framework.
Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Digital transformation is a key priority for modernizing China's public hospitals. However, a standardized and context-specific framework to evaluate their digital maturity remains absent. This study aims to develop and validate a comprehensive, multidimensional evaluation framework tailored to Chinese tertiary public hospitals to support systematic assessment and inform policy decisions. Based on systematic literature review and policy analysis, we constructed a framework comprising Digital Readiness, Technology Application, and Data Management Capability, with 11 subdimensions and 65 indicators. Indicator weights were derived using a two-round Delphi consultation, analytic hierarchy process, and criteria importance through intercriteria dependence method. The framework was applied to 1,361 tertiary public hospitals across 28 mainland provincial-level divisions in China. Digital maturity scores were analyzed using global sensitivity analysis (GSA), k-means clustering, and logistic regression with Firth's penalized likelihood. Digital Readiness received the largest combined weight (34.9%), followed closely by Technology Application (34.7%) and Data Management Capability (30.4%), suggesting that hospital digital maturity reflects a balanced combination of organizational readiness, technology-enabled service application, and data governance. GSA further revealed discrepancies between combined weights and empirical sensitivity rankings, indicating that these approaches captured different aspects of indicator importance. Clustering and regression analyses showed that higher-maturity hospitals had higher values across many indicators, particularly in clinical digital applications and data quality management, whereas data sharing and exchange remained relatively weak across maturity groups. Robustness checks using alternative weighting and clustering methods generally supported the stability of the main findings, while also indicating residual sensitivity to methodological choices. The proposed framework provides a structure- and process-oriented diagnostic tool for assessing digital maturity in Chinese tertiary public hospitals. It can support policy monitoring, institutional benchmarking, and targeted improvement of hospital digital transformation. Future research should update the framework with more recent data and validate maturity scores against healthcare quality, safety, efficiency, patient experience, and equity outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.