Impact of Electronic Health Services on Patient Satisfaction in Primary Care: A Systematic Review
Bibliographic record
Abstract
Electronic health services (EHS) integrate telecommunications, electronic patient data, and computerized medical knowledge. The growing implementation of EHS in primary care underscores the necessity to comprehend its effect on patient satisfaction and highlight areas for improvement. This systematic review study aims to evaluate the impact of EHS on patient experiences in a primary care setting. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A series of searches was conducted until November 2024 in the following databases: PubMed, Science Direct, and the Cochrane Library. Two independent reviewers extracted the data from eligible studies using a standardized extraction sheet. The inclusion criteria encompassed randomized controlled trials (RCTs), observational studies involving adult patients who employed EHS interventions, including electronic health records (EHRs), telemedicine, patient portals, or online appointment systems. The Cochrane Risk-of-Bias tool and Newcastle-Ottawa Scale have been used to assess the risk of bias of included studies. Ten studies involving participants ranging from 52 to 203,903 were included. It was seen that increased provider focus on EHR use, including prolonged silence and gaze at the screen, negatively influenced patient-centered communication and involvement. Nonetheless, in a variety of contexts, the use of EMRs enhanced patient satisfaction with clinical consultations, services, and overall healthcare experiences. Effective prescription and referral procedures, improved communication, and reduced wait times were among the improvements. Patient portals and EHS demonstrated increased satisfaction with healthcare quality, particularly among patients with long-term provider relationships. Socioeconomic factors, such as age, education, and income, influenced preferences for communication modes like portals, phone calls, and text messages. This systematic review demonstrates the transformative potential of EHS in enhancing patient satisfaction within primary care. EHR/electronic medical record (EMR) systems were associated with better service efficiency and patient satisfaction, despite challenges in balancing provider interaction with technology use. By improving access, communication, and efficiency, EHS can play a pivotal role in advancing patient-centered care. However, challenges related to provider communication, interoperability, and health equity highlight the need for thoughtful implementation and continuous refinement of these tools.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".