arXiv subjects

Najiya Fatma

Publications and source records attributed to Najiya Fatma.

3 recordsLinked to original sources

arXiv2026

Healthcare Facility Assignment Using Real-Time Length-of-Stay Predictions: Queuing-Theoretic and Simulation-driven Machine Learning Approaches

Longer stays at healthcare facilities, driven by uncertain patient load, inefficient patient flow, and lack of real-time information about medical care, pose significant challenges for patients and healthcare providers. Providing patients with estimates of their expected real-time length of stay (RT-LOS), generated as a function of the operational state of the healthcare facility at their anticipated time of arrival (as opposed to estimates of average LOS), can help them make informed decisions regarding which facility to visit within a network. In this study, we develop a healthcare facility assignment (HFA) algorithm that assigns healthcare facilities to patients using RT-LOS predictions at facilities within the network of interest. We describe the generation of RT-LOS predictions via two methodologies: (a) an analytical queuing-theoretic approach, and (b) a hybrid simulation-driven machine learning approach. Because RT-LOS predictors are highly specific to the queuing system in question, we illustrate the development of RT-LOS predictors using both approaches by considering the outpatient experience at primary health centers. Via computational experiments, we compare outcomes from the implementation of the RT-HFA algorithm with both RT-LOS predictors to the case where patients visit the facility of their choice. Computational experiments also indicated that the RT-HFA algorithm substantially reduced patient wait times and LOS at congested facilities and led to more equitable utilization of medical resources at facilities across the network. Finally, we show numerically that the effectiveness of the RT-HFA algorithm in improving outcomes is contingent on the level of compliance with the assignment decision.

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arXiv2021

Outpatient Diversion using Real-Time Length-of-Stay Predictions

In this work, we show how real-time length-of-stay (LOS) predictions can be used to divert outpatients from their assigned facility to other facilities with lesser congestion. We illustrate the implementation of this diversion mechanism for two primary health centers (PHCs), wherein we divert patients from their assigned PHC to the other PHC based on their predicted LOSs in both facilities. We develop a discrete-event simulation model of patient flow operations at these two PHCs in an Indian district and observe significantly longer LOSs at one of the PHCs due to disparities in the patient loads across both PHCs. We first determine the expected LOS of the patient at the point in time at which they are expected to arrive at a PHC using system state information recorded at the current time at the PHC in question. The real-time LOS predictions are generated by estimating patient wait times on a real-time basis at the queueing subsystems within the PHC. We then divert the patient to the appropriate PHC on the basis of the predicted LOS estimates at both PHCs, and show through simulation that the proposed framework leads to more equitable utilization of resources involved in provision of outpatient care.

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arXiv2021

Patient Diversion Across Primary Health Centers Using Real Time Delay Predictors

In the current work, we consider diversion of childbirth patients who arrive seeking emergency admission to public primary health centers (PHCs). PHCs are the first point of contact for an Indian patient with formal medical care, and offer medical care on an outpatient basis, and limited inpatient and childbirth care. In this context, real-time prediction of the wait time of the arriving patient becomes important in order to determine whether the patient must be diverted to another PHC or not. We study this problem using a discrete event simulation that we develop of medical care operations in two PHCs in India. We approximate the labour room service at each PHC as an M/G/1 queueing system and show how the accuracy of real-time delay predictors impacts the extent of the change in operational outcomes at each PHC. We simulate patient diversion using actual delays as well as the delay estimates generated by various delay predictors based on the state of the system such as queue-length, elapsed service time, and observed delay histories. The simulation of the diversion process also incorporates travel time between the PHCs. We also propose a new delay predictor that incorporates information regarding the system state as well as the service time distribution. We compare the operational outcomes at both PHCs without diversion and with diversion using the above delay predictors. We show numerically that more accurate delay predictors lead to more equitable distribution of resources involved in provision of childbirth care across both PHCs.

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