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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62590

HealthSurge AI: An Agentic Deep Learning Framework for Predictive Patient Influx Management in Indian Hospitals

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62590

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62590

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.

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Authors

AJ

Aditya Jagtap

Computer Science and Engineering

MIT Art, Design and Technology University, Pune, Maharashtra, India

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SS

Shrikant Shinde

Computer Science and Engineering

MIT Art, Design and Technology University, Pune, Maharashtra, India

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AH

Ayush Holkar

Computer Science and Engineering

MIT Art, Design and Technology University, Pune, Maharashtra, India

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Abstract

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

Key Words: Patients arrival rate hospital management LSTM agentic AI Indian hospitals AQI surge disaster planning

Cite This Article

Aditya Jagtap, Shrikant Shinde, Ayush Holkar, "HealthSurge AI: An Agentic Deep Learning Framework for Predictive Patient Influx Management in Indian Hospitals," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026. https://doi.org/10.55041/IJSREM62590

Full Article/Conclusion

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.

References

[1] World Health Organization, "Health workforce requirements for universal health coverage," WHO Tech. Rep., Geneva, 2016. [2] M. Jain, A. Sharma, and R. Gupta, "Managing mass casualty events at the Kumbh Mela," J. Public Health, vol. 42, no. 3, pp. 612-621, 2020. [3] S. Nair, P. Tripathi, and V. Kumar, "Air quality and emergency room admissions for respiratory illness in Delhi," Indian J. Med. Res., vol. 152, no. 4, pp. 378-387, 2020. [4] World Health Organization, "Global Health Estimates 2020," WHO, Geneva, 2020. [5] S. L. Bernstein et al., "The effect of emergency department crowding on clinically oriented outcomes," Acad. Emerg. Med., vol. 16, no. 1, pp. 1-10, 2009. [6] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997. [7] N. R. Hoot et al., "Forecasting emergency department crowding: A discrete event simulation," Ann. Emerg. Med., vol. 52, no. 2, pp. 116-125, 2008. [8] S. Halachmi et al., "Machine learning-based model for bed management in a general hospital," J. Med. Syst., vol. 42, no. 12, p. 255, 2018. [9] E. Choi et al., "Doctor AI: Predicting clinical events via recurrent neural networks," Proc. MLHC, PMLR vol. 56, pp. 301-318, 2016. [10] Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, "Learning to diagnose with LSTM recurrent neural networks," Proc. ICLR, 2016.

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Publication Date

5/6/2026

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Review Rounds: 2

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