An analytical comparison of the patient-to-doctor policy and the doctor-to-patient policy in the outpatient clinic

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room, while patients visit for consultation, we call this the Patient-to-Doctor policy. A different approach is the Doctor-to-Patient policy, whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We compare the two policies via a queueing theoretic and a discrete-event simulation approach. We analytically show that the Doctor-to-Patient policy is superior to the Patient-to-Doctor policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. Simulation results indicate that the same applies when the average travel time is lower than the average preparation time. In addition, to calculate the required number of consultation rooms in the Doctor-to-Patient policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation.We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-policy), whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We use a queueing theoretic and a discrete-event simulation approach to provide generic models that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Using Simulation and Six-Sigma Tools in Improving Process Flow in Outpatient Clinics

It is apparent that outpatient clinics are becoming complex and need to be optimized and improved on a daily basis. In this project, we used several methods including discrete event simulation, quality function deployment (QFD), and failure modes and effects analysis (FMEA) to optimize and improve these clinics. We conducted this study at a major suburban outpatient clinic to propose main recommendations which most likely apply to a vast majority of such clinics. Firstly, the simulation-based modeling that we ran assisted us in recognizing optimum staff number which would result in decreasing waiting times that patients usually spend and making the process flow at the facility smoother. Secondly, QFD approach for analyzing outpatient clinic requirement is also proposed and realized through a case study. It is realized that the proposed approach can adjust service quality toward customer requirements effectively. Lastly, the health care failure modes and effects analysis (FMEA) that we implemented as a novel method to discover conditions and active failures and to prioritize these based on the potential severity of risks associated with them

Integration of Simulation and DEA to Determine the Most Efficient Patient Appointment Scheduling Model for a Specific Clinic Setting

This study develops a method to determine the most efficient scheduling model for a specific clinic setting. The appointment scheduling system assigns clinics' timeslots to incoming requests. There are three major scheduling models: centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM). In order to schedule multiple appointments, CSM involves one scheduler, DSM involves all the schedulers of individual clinics and HSM combines CSM and DSM. Clinic settings are different in terms of important factors such as randomness of appointment arrival and proportion of multiple appointments. Scheduling systems operate inefficiently if there is not an appropriate match between scheduling models and clinic settings to provide balance between indicators of efficiency. A procedure is developed to determine the most efficient scheduling model by the integrated contribution of simulation and Data Envelopment Analysis (DEA). A case study serves as a guide to use and as proof for the validity of the developed procedure

Forecasting hospital bed availability using computer simulation and neural networks

The success of hospitals in treating patients and staying in business relies on their efficient use of resources. In particular, the utilization of hospital beds is a critical concern, since over-crowding will result in delays or transfers of patients, and under-utilization will result in lost opportunity to treat patients and generate profit. To this end, hospital decision makers must have reliable forecasts of patient demand and bed availability. The objective of this thesis was to create a general method to forecast the availability of hospital beds in the short term, up to 2 days into the future. Specifically, this thesis employed a computer simulation model of the hospital and a time-dependent neural network to learn from the simulated model and forecast the availability of beds. The computer simulation model was found to be well suited to the task of describing a general hospital system and creating training data for a neural network. The neural network was found to provide accurate performance in predicting bed availability in the short term. The network incorporated the effect of time explicitly to capture the non-stationary behavior of hospital systems. These findings have a number of implications that will be discussed

Simulation analysis of capacity and scheduling methods in the hospital surgical suite

With health-care costs rising and an aging population, the health-care industry is progressively faced with the problem of growing demand and diminishing reimbursements. Hospital administration is often faced with a lack of quantifiable data regarding surgical suite capacity and the impact of adding new surgical procedures. With the inherent variation in surgery due to unique procedures and patients, accurately measuring maximum capacity in the surgical suite through mathematical models is difficult to do without making simplifying assumptions. Several hospitals calculate their operating room (OR) efficiencies by comparing total OR time available to total surgical time used. This metric fails to account for the required non-value added tasks between surgeries and the balance necessary for patients to arrive at the OR as soon as possible without compromising patient satisfaction. Since surgical suites are the financial engine for many hospitals and the decisions made with regard to the surgical suite can significantly impact a hospital’s success, this thesis develops a methodology through simulation to more accurately define current and potential capacity levels within the surgical suite. Additionally, scheduling policies, which schedule patients based on the variability of their surgical time as well as the implementation of flexible ORs capable of servicing multiple operation genres, are examined for individual and interaction effects with regard to surgical suite capacity, patient waiting times, and resource utilization. Through verification and validation, the model is shown to be an effective tool in representing patient flow and testing policies and procedures within the surgical suite. An application to the surgical suite at Chenango Memorial Hospital (Norwich, NY) illustrates the methodology and potential impacts of this research