| Issue |
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 3 | |
| Section | Logistic, Healthcare, Materials and Control | |
| DOI | https://doi.org/10.1051/matecconf/201925504002 | |
| Published online | 16 January 2019 | |
Stratification of, albeit Artificial Intelligent (AI) Driven, High-Risk Elderly Outpatients for priority house call visits - a framework to transform healthcare services from reactive to preventive
1 Hanumayamma Innovations and Technologies, Inc., Senior Vice President, Fremont, California, USA 94536
2 CEO, Hanumayamma Innovations and Technologies, Inc.
3 Product Manager, Hanumayamma Innovations and Technologies Private Limited
4 CTO, Hanumayamma Innovations and Technologies Private Limited
* Corresponding author: cvuppalapati@hanuinnotech.com
House calls have nostalgic view and have practiced decades ago when the doctor arrived at the patient's door carrying a big black bag. House calls could prove to be a better way of treating very sick, elderly patients while they can still live at home. One of the greatest benefits is avoidance of Healthcare associated infections. Additionally, house calls save time and energy of immediate care members of and helps seek for ways to have transport for elderly. A house calls doctor, nonetheless, can see only five to seven patients a day. One reason is that a house call visit can take longer than an office visit, even after taking travel time into account. One way of optimizing house call delivery services is to employ AI based system to identify and generate priority list so that the healthcare providers have greater coverage of their needed patients house calls are performed in-time. In this paper, we propose innovative novel idea “AI enabled house calls are best medicine practices for the next generation”. Finally, as part of the paper, we will present Sanjeevani house call service that is been deployed and currently in production
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.