Prescribing Change: Implementing an AI-Driven Smart Pharmacy Framework in Ujjain

Background/Objectives: This study assessed operational, financial and technology-adoption challenges among retail pharmacies in Ujjain, Madhya Pradesh, and used the findings to design a context-appropriate AI-driven smart pharmacy framework. Materials and Methods: A cross-sectional descriptive survey with mixed-methods instrumentation was conducted among 56 retail pharmacies across four geographic zones of Ujjain using a 26-item questionnaire. Quantitative responses were analysed using frequencies, percentages, descriptive statistics and cross-tabulations; open-ended responses were coded thematically. Results: Thirty-seven pharmacies (66.1%) reported no digital billing or inventory system. Thirty-seven (66.1%) experienced stock shortages at least once or twice per month. Twenty-six (46.4%) reported monthly inventory/expiry losses of ₹5,000-₹20,000 and five (8.9%) reported losses above ₹20,000. The manuscript's conservative midpoint calculation estimated collective monthly loss at ₹5.125 lakh. The leading adoption barrier was data-entry time (42.9%), followed by training difficulty (28.6%) and software cost (26.8%). Forty-seven respondents (83.9%) were willing to adopt an AI system immediately or conditionally, while 36 (64.3%) preferred a fixed monthly fee of ₹1,500. Conclusion: The findings identify a substantial usability and affordability gap rather than simple rejection of technology. The proposed RxScan framework integrates smart billing, inventory and expiry management, pharmacist-supervised prescription OCR, ordering and analytics. It remains a proposed design and requires prospective evaluation before effectiveness claims.