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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62590
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
MIT Art, Design and Technology University, Pune, Maharashtra, India
Computer Science and Engineering
MIT Art, Design and Technology University, Pune, Maharashtra, India
Computer Science and Engineering
MIT Art, Design and Technology University, Pune, Maharashtra, India
If you are in a government hospital in Maharashtra on a Tuesday afternoon during a festival weekend, you do not need to be a statistician to know there is a problem. The wards spill into corridors. One nurse covers three areas. Parents sit on the floor because there are no chairs. What is not immediately apparent is that most of this is foreseeable. The information to predict those spikes exists in air quality data, government reports, and public event listings. The problem is that this data never reaches those who schedule nurses. That is where we created HealthSurge AI. It is a Long Short-Term Memory (LSTM) network trained on four sources: past admissions, CPCB air pollution, a festival proximity score, and an IDSP outbreak index. An agent layer generates actionable advice via email or SMS in English and Marathi. Evaluated on 730 days of real-world data from three hospitals in Maharashtra, the 48-hour mean absolute percentage error (MAPE) was 8.3 percent. All real surges in the test period were captured before peak, with a precision of 91.6 percent and recall of 94.2 percent.
KEYWORDS
We began this project when an administrator in Pune truthfully told us how staffing decisions were made: experience, she said. HealthSurge AI is a second tool for her — one that links public-domain data to a supervisor's mobile phone in a way that makes timely response possible. After two years of real data from three hospitals in Maharashtra, it achieved a 48-hour MAPE of 8.3 percent and detected every real surge in the test period before it peaked. An operational trial gave early signs that the forecast leads to earlier staff deployment and significantly shorter patient queues. The H1 nursing supervisors said they preferred the recommendations panel to the forecast chart. There is still work to do: more sites, more states, another season of controlled testing. We believe it is a direction worth exploring and hope this work can help those who follow.
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Publication Date
5/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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