## 1 Introduction
Since the system provides the patient with alternative healthcare service sources, it is up to the patient to go to the recommended facility or wait in the queue and risk returning home without getting medical care. Hence a patient's consent will be by virtually in accepting to go to the recommended facility. Since patients are triaged upon arrival (with the most critical served first), we assume that the patients being recommended for transfer are stable enough such that pre-transfer stabilization and preparation are not required. The choice of an appropriate mode of transport is key in this work and is covered in Section 6. The consideration of guardian, patient monitoring during transfer and documentation are beyond the scope of this work. By intelligently re-assigning patients from congested facilities to less congested ones, overcrowding of OPDs can be managed in a timely way. Re-assigning excess patient loads reduces the number of patients that return to their homes without being attended to by physicians and consequently leads to improvement of the quality of the service demanded of medical staff being kept to a minimum. Patient flows in hospitals are usually not continuous and stable; rather, patient flows are complex and may change suddenly [13]. Hospitals experience two kinds of service demands: (1) Event driven demand, i.e., ambulatory arrivals during accidents and natural disasters, and (2) regular demand from the catchment area. However, a lack of real time patient flow information may result in some health facilities straining to satisfy the demand while at the same time facilities in the same vicinity may have minimal demand. While patients may be wary about being transferred to a hospital they know little or nothing about, such transfers may significantly reduce the amount of time a patient must wait to get medical care [14]. Attempts in the literature exist to predict patient traffic, but most of the work focuses on patient flow in the emergency department (ED) [15; 16]. Furthermore, independent attempts have been made to predict and schedule traffic flow [17; 18]. However, to our knowledge, no literature has explored the integration of the two processes using machine learning techniques. In this paper, a deep learning-based patient load prediction model is proposed. The model aides in overcoming the effects of overcrowding in hospital waiting rooms, which results from a mismatch between hospital staffing ratios and the demand for healthcare services. Overcrowding of hospital waiting rooms leads to excessive patient waiting times, incomplete service delivery and unhappy medical staff. Worse, due to the limited patient loads that a health facility can handle, patients may leave the facility before the medical examination is complete. However, it is true that, as one health facility may be struggling with excessive patient load, another facility in the vicinity may have low patient turn out. In this work, we assume the existence of a hospital information management system, which enables timely sharing of excess patient load information among hospitals [19]. The proposed machine learning-based patient load prediction model takes current and historical patient load data as inputs and outputs future predicted patient load. Furthermore, in the case of excess patient loads, we propose to re-assign excess loads to nearby facilities that have a minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care. 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 transport system which can support the timely movement of patients from the bus stop nearest to the source facility to the destination facility. Building on this assumption we propose an Internet of Things (IoT) integrated smart bus system.