Using an Interactive Voice Response Survey to Assess Patient Satisfaction in Ethiopia: Development and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction surveys can offer crucial information on the quality of care but are rarely conducted in low-income settings. In contrast with in-person exit interviews, phone-based interactive voice response (IVR) surveys may offer benefits including standardization, patient privacy, reduced social desirability bias, and cost and time efficiency. IVR surveys have rarely been tested in low-income settings, particularly for patient satisfaction surveys. OBJECTIVE: In this study, we tested the feasibility of using an IVR system to assess patient satisfaction with primary care services in Addis Ababa, Ethiopia. We described the methodology, response rates, and survey costs and identified factors associated with survey participation, completion, and duration. METHODS: Patients were recruited in person from 18 public and private health facilities in Addis Ababa. Patients' sex, age, education, reasons for seeking care, and mobile phone numbers were collected. The survey included 15 questions that respondents answered using their phone keypad. We used a Heckman probit regression model to identify factors influencing the likelihood of IVR survey participation (picking up and answering at least 1 question) and completion (answering all survey questions) and a Weibull regression model to identify factors influencing the survey completion time. RESULTS: A total of 3403 individuals were approached across 18 health facilities. Nearly all eligible patients approached (2985/3167, 94.3%) had a functioning mobile phone, and 89.9% (2415/2685) of those eligible agreed to be enrolled in the study. Overall, 92.6% (2236/2415) picked up the call, 65.6% (1584/2415) answered at least 1 survey question, and 42.9% (1037/2415) completed the full survey. The average survey completion time was 8.1 (SD 1.7) minutes for 15 Likert-scale questions. We found that those aged 40-49 years and those aged 50+ years were substantially less likely to participate in (odds ratio 0.63, 95% CI 0.53-0.74) and complete the IVR survey (odds ratio 0.77, 95% CI 0.65-0.90) compared to those aged 18-30 years. Higher education levels were also strongly associated with survey participation and completion. In adjusted models, those enrolled in private facilities were less likely to participate and complete the survey compared to those in public health centers. Being male, younger, speaking Amharic, using a private hospital, and being called after 8 PM were associated with a shorter survey duration. The average survey costs were US $7.90 per completed survey. CONCLUSIONS: Our findings reveal that an IVR survey is a feasible, low-cost, and rapid solution to assess patient satisfaction in an urban context in Ethiopia. However, survey implementation must be carefully planned and tailored to local challenges. Governments and health facilities should consider IVR to routinely collect patient satisfaction data to inform quality improvement strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".