HealthHive: Sahara AI – driven Symptom Checker and Automated Primary Care | IJCT Volume 12 – Issue 6 | IJCT-V12I6P23
International Journal of Computer Techniques
ISSN 2394-2231
Author
Amreen Perween , Deepika Yadav , Deeksha Vishwakarma , Mr Nilesh Khare , Mrs. Divya Singh , Ms. Devika Singh
Abstract
Timely access to quality primary care remains a global challenge due to workforce shortages, diagnostic delays, and limited infrastructure of care delivery[1]. With an anticipated deficit of nearly 10 million healthcare workers by 2030, a technology-driven method to improve efficiency, access, and patient outcomes will be necessary.
This study presents HealthHive: Sahara, a primary-care platform defined by an AI-assisted combination of structured symptom reporting, intelligent triage assessment, and clinician oversight. This primary-care platform uses probabilistic reasoning, transformer-based models, and explainable AI (XAI) modules to ensure that diagnoses are rational, safe and transparent from key considerations of digital health tools [12].
HealthHive was validated through curated clinical vignettes, anonymised electronic.
health record datasets, and usability assessments of patients and clinicians. The study showed better diagnostic performance based on the accuracy of diagnosis, safety of triage, clinician confidence, and reduced unnecessary referrals compared to existing symptom checkers[8], [22].
Overall, HealthHive demonstrates the advantages that hybrid AI–clinician approaches could provide to indelible primary care delivery with an ethically aware, compliant, and adaptable platform even in low-resource settings.
Keywords
AI-assisted primary care, digital symptom checker, hybrid diagnostic system, explainable AI (XAI), clinician-in-the-loop, triage safety, integration, healthcare accessibility, low-resource settings, machine learning in healthcare.
Conclusion
HealthHive: Sahara makes a case that hybrid AI–clinician systems can greatly improve the accuracy, safety, and trustworthiness of primary care digital symptom checkers [19] .
By leveraging probabilistic reasoning, transformer-based models, and explainable AI (XAI) modules behind clinician oversight, the system meaningfully overcomes limitations of existing digital health tools, such as inconsistent diagnostic accuracy, transparency, and ability to integrate into workflows. Through our evaluations using written clinical vignettes, EHR datasets, and usability studies, we see that HealthHive improves triage reliability, lowers unnecessary referrals, and improves both patient and clinician confidence.
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How to Cite This Paper
Amreen Perween , Deepika Yadav , Deeksha Vishwakarma , Mr Nilesh Khare , Mrs. Divya Singh , Ms. Devika Singh (2025). HealthHive: Sahara AI – driven Symptom Checker and Automated Primary Care. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
