Stratification of, albeit Mathematical Optimization and Artificial Intelligent (AI) Driven, High-Risk Elderly Outpatients for priority house call visits - A framework to transform healthcare services from reactive to preventive

Publication Date


Document Type

Conference Proceeding

Publication Title

2020 IEEE International Conference on Big Data (Big Data)



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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, in Electronic Health Records (EHR) era, are performed by clinicians sifting through EHR diagnostic or encounter records that exhibit a health condition, an anomaly or a violation of health rule set by the primary physician. 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 of house calls is avoidance of Healthcare associated infections, especially during the Coronavirus (COVID-19) epidemic.Prioritizing patients on to house call list in the shortest amount of time is one of the daunting challenges that many healthcare institutions are facing. The reasons could be growing data volume of healthcare patient cases with combinatorial possibilities of disease conditions intermingling with the COVID-19 pandemics paralyzing a human agent to prepare house call list on a daily basis. The solution is to employ optimization techniques powered by mathematical formulations and derive solution by running solvers to generate priority list of patients so that the healthcare providers have a greater coverage of their needed patients' house calls are performed in-time. In this paper, we propose innovative novel idea "mathematical formulation enabled house calls". Finally, as part of the paper, we will present Sanjeevani house call service that is been deployed and currently in production.


CBC, CPLEX, GLPK, House calls, Mathematical Formulation, Pyomo, Solvers, Z3


Computer Engineering