Analysing Trends in the Healthcare Industry: Impact of Information Technology and Digitalisation on Healthcare Delivery
Bibliographic record
Abstract
With the increasing integration of digital technologies in medical practice, healthcare providers, patients, and administrators are witnessing the evolution of traditional healthcare models into more dynamic, data-driven systems. Aims: To evaluate the effect of information technology digitization on the delivery of medical services, such as the advantages and disadvantages that come with the use of digital healthcare solutions. Study design: A bibliometric analysis was done between 1973 and 2025 using an extensive dataset of 2848 documents from the Scopus database. Methodology: The publication analyses, citation analyses, co-authorship network, and keyword clusters were assessed using the bibliometric software VOSviewer (v.1.6.19) and Biblioshiny (R v.4.4.2). Results: From the analysis of 1,168 sources spanning 1973 to 2025, the research focus is on health digitalization, where 2,848 documents were used. The dataset indicates an annual growth rate of 7.85%. The substantial scholarly impact is suggested through the mean document age of 6.93 years, which has 24.37 citations, on average, per document. There is a strong international research network demonstrated by an international collaboration of 21.38% and co-authorship of 6.35 authors per document. Publication trends indicate peaks in 2021 with 380 articles, followed by 2024 with 319 articles and 2022 with 293 articles. The documents that were cited the most 10 times focused on mHealth, telemedicine, and acceptance of technology, while the most cited paper was on mHealth adoption among the elders. It was published in 2017 and has 705 citations. Major contributors are the USA, China, and European countries, while the leading institutions in the co-authorship network include the University of Ottawa and Harvard Medical School. Conclusion: The analysis highlights the significant growth in health digitalization research, with strong international collaboration and impactful contributions from leading institutions. mHealth, telemedicine, and technology acceptance dominate, with notable studies on mHealth adoption among elders.
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 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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.095 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".