Assessing Patient Satisfaction and Experience With an Electronic Referral Process
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Our study aimed to identify patients' perception of an eReferral process and e-mail notification system. METHODS: Patients within the Waterloo Wellington Local Health Integration Network who registered their e-mail address with physicians who adopted the eReferral system, and therefore received e-mail notifications of their booked appointment, were invited to complete an online satisfaction survey. This patient experience survey is an ongoing online link embedded within the confirmation e-mail of the booked appointment. The survey is hosted on the eReferral solution platform and has been operational since November 2017. The survey consists of 8 questions with 3 main categories to assess patients' opinion of their experience of the referral process and notification system using a 5-point Likert scale and open-ended questions. RESULTS: A total of 545 patients have completed the patient satisfaction survey within this reporting period with a response rate of 15%. In general, 94% of patients agreed that receiving a confirmation e-mail of their booked appointment had improved their experience with the referral process. The majority (94%) agreed that the eReferral process was easy to follow, and 83% agreed that they were able to get the care they needed within a reasonable time. Compared with their past referral experiences, 80% of patients felt more informed throughout this electronic referral process. Using binominal logistic regression, participants whose preferences were considered had 8.06 times higher odds to exhibit satisfaction with the referral process than those who did not. Patients' qualitative responses identified the eReferral process as being quick, efficient, and resulting in a sense of being in control of their own health care. There are some limitations to the system felt by some of the patients who responded to the open-ended questions of the survey. Patients identified the need to add a complementary structure to the notification design consisting of multiple dates and times with a chance to pick the appointment that suits patients best instead of being restricted to only 1 appointment date. A few patients thought that the heading of the e-mail notification system should be more distinguishable for easier tracking. Furthermore, some patients felt the need to add some notes to the initial e-mail advising patients of the next steps throughout their referral process. CONCLUSION: eReferral has improved patients' experience with the referral process. Our findings in this study would support the solution vendor in its efforts to refine and enhance active communication channels with patients for sustainable health care that meets patients' expectations and needs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".