Digital Transformation in Healthcare Administration: A Systematic Review of the Impact of Telehealth on Administrative and Secretarial Roles
Bibliographic record
Abstract
Background: The rapid growth of telehealth has transformed clinical service delivery, but its deep implications for health care's administrative core have gone largely unexamined. Administrative and secretarial staff are essential in the coordination of virtual care, though their role is being reshaped by changing responsibilities, workflow interruptions, and new skill requirements. Aim: The review aims to conduct a systematic inquiry into how telehealth technologies influence the roles, challenges, and competency needs of administrative and secretarial staff in healthcare. Methods: A systematic search was carried out through PubMed, CINAHL, Scopus, Business Source Complete, and Web of Science for literature within the period of 2013-2025. Included were qualitative, quantitative, and mixed-methods studies that were analyzed using narrative synthesis. Results: With telehealth integration, the transformed administrative role encompasses much more complex digital care coordination, patient technology education, and virtual clinic management than traditional scheduling and filing. The integration of telehealth introduced such issues as new digital literacy demands on administration, fragmentation of workflows, and new responsibilities for patient education. Importantly, the "digital front door" to healthcare placed administrative staff at a critical position in the facilitation of equitable access and simultaneously exposed them to new stressors. Conclusion: The administration of healthcare has been irrevocably altered by telehealth, making the roles more complex but also possibly more rewarding. Strategic investments in training, workflow redesign, and supportive technologies will be required to harness the full potential of administrative professionals in the digital health era.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".