Assessment of Qatar’s Health Care Community Call Center Efficacy in Addressing COVID-19 Pandemic Health Care Challenges: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The global COVID-19 pandemic caused by SARS-CoV-2 created many unprecedented challenges for health care organizations worldwide, placing a great deal of strain on the health care systems, especially access to health care services. To address these challenges, Qatar established a centralized digital platform as a community call center, initially offering digital consultations via its hotline (number: 16000) and later expanding to include a COVID-19 vaccination hotline (number: 7077) for mass immunization. OBJECTIVE: This study aims to comprehensively examine the community call center's operations and their significant role during the COVID-19 pandemic. METHODS: Retrospective data were collected from the Health Information and Technology Department of the Primary Health Care Corporation, Qatar, from March 29, 2020, to January 27, 2022. Data analysis for the hotline (number: 16000) focused on telephone and video call volumes, call response rates, abandonment rates, and call classification. In addition, data from the COVID-19 vaccination hotline (number: 7077) were analyzed for call volumes, call response rates, abandonment rates, appointment booking rates, confirmations, rescheduling, and cancellations. RESULTS: The hotline (number: 16000) received a substantial total of 429,212 calls, with 284,849 (66.37%) calls effectively answered. The average number of calls received per day during the study period was 640.61 (SD 470.53), and the average number of calls answered per day was 425.14 (SD 206.64). Notably, of the total 128,468 consultations, video consultations were conducted for 3810 (2.96%). Among the diverse call categories, diabetes mellitus (6284/84,299, 7.45%), prescriptions and medications (4709/84,299, 5.59%), hypertension (3874/84,299, 4.6%), vitamin D-related issues (3770/84,299, 4.47%), upper respiratory tract infections (2690/84,299, 3.19%), and COVID-19-related inquiries (2590/84,299, 3.07%) were most frequently addressed. For the COVID-19 vaccination hotline (number: 7077), an impressive total of 1,512,354 calls were received, with a 58.27% (n=881,305) call response rate. The average number of calls per day during the study period was 3828.74 (SD 2931.94), and the average number of calls answered per day was 2231.15 (SD 1496.02). Appointment booking accounted for 26.37% (265,721/1,007,596), appointment confirmation accounted for 10.24% (103,136/1,007,596), rescheduling accounted for 7.95% (80,124/1,007,596), and cancellations accounted for 1.6% (16,128/1,007,596) of the calls. CONCLUSIONS: The findings of this research highlight the crucial significance of the community call center hotline (number: 16000) and the COVID-19 vaccination hotline (number: 7077) in effectively addressing the multifaceted challenges posed by the global COVID-19 pandemic. In Qatar, the community call center emerged as an indispensable and accessible centralized resource, facilitating streamlined digital consultations and vaccination appointments. The impressive call response rate highlights its operational efficiency, adeptly managing a diverse range of health-related issues. This study emphasizes the critical role of community call centers in health care emergency response, signaling their potential as invaluable assets for future preparedness and effective mitigation strategies during similar public health crises.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".