Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems

Abstract

A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

Smart Waiting Room: A Systematic Literature Review and a Proposal

“…Finally, contributions from Africa amounted to four. It is interesting to observe that there is no contribution from South American countries and that only two works are written by authors from different countries: the study of [20] is authored by African and Asian authors, while [21] sees authors from two different European countries.…”

Artificial intelligence

“…Managing patient needs to help them adhere to follow-up appointments, navigating the third-party payer ecosystem, and effectively managing postcare symptoms and medications are activities that can be augmented by AI 13. The ability of intelligent machines to help mitigate adverse events and reduce hospital readmissions and ED visits leads to cost savings, better care quality, and improved patient experience 14…”

“…13 The ability of intelligent machines to help mitigate adverse events and reduce hospital readmissions and ED visits leads to cost savings, better care quality, and improved patient experience. 14 Engaging patients in the self-management of chronic conditions can be complex, burdensome, and time-consuming. Conversational AI uses chatbots that employ natural language processing (NLP) and machine learning to automate conversations with patients.…”

Application of Machine Learning in Hospital Resource Allocation - A Survey

“…By using sensors and IoT, they developed a smart kit. These kits can be attached to the public transports which reply to the messages of patients and passengers for their time and location [8]. The data science team of Cedars-Sinai used a machine learning platform for the prediction of staffs.…”