The role of mobile devices in doctor-patient communication: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction In the last few years, the use of telecommunication and mobile technology has grown significantly. This has led to a notable increase in the utilization of this telecommunication in healthcare, namely phone calls and text messaging (SMS). However, evaluating its global impact on improving healthcare processes and outcomes demands a more comprehensive assessment. In this study, we focused on the role of mobile devices via phone calls and SMS in patient-doctor communication, and aimed to assess its impact on various health outcomes. Methods Major databases, including MEDLINE, EMBASE, PsycINFO, Global Health, and Cochrane CENTRAL, were searched for clinical trials that investigated mobile-device technology in any facet of doctor-patient communication published between 1990 and April 2015. A meta-analysis was performed where appropriate. Results Sixty-two articles met our inclusion criteria. Of those, 23 articles investigated mobile appointment reminder technologies, 19 investigated medication adherence, 20 investigated disease-control interventions, and two investigated test-result reporting. Patients who received an appointment reminder were 10% less likely to miss an appointment (relative risk [RR] = 1.11, 95% confidence interval [CI] 1.08-1.15). Mobile interventions increased medication adherence by 22% (RR = 1.22, 95% CI 1.09-1.36). Ten of 20 studies examining disease control reported statistically significant reductions in clinically meaningful endpoints. The use of mobile-device interventions improved forced expiratory volume in one second and hemoglobin A1c percentage in meta-analyses. Conclusion The use of mobile-device technologies exerted modest improvements in communication and health outcomes. Further research is needed to determine the true effect of these technologies on doctor-patient communication.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".