1,407 research outputs found
Appointment scheduling model in healthcare using clustering algorithms
In this study we provided a scheduling procedure which is combination of
machine learning and mathematical programming. Outpatients who request for
appointment in healthcare facilities have different priorities. Determining the
priority of outpatients and allocating the capacity based on the priority
classes are important concepts that have to be considered in scheduling of
outpatients. Two stages are defined for scheduling an incoming patient. In the
first stage, We applied and compared different clustering methods such as
k-mean clustering and agglomerative hierarchical clustering methods to classify
outpatients into priority classes and suggested the best pattern to cluster the
outpatients. In the second stage, we modeled the scheduling problem as a Markov
Decision Process (MDP) problem that aims to decrease waiting time of higher
priority outpatients. Due to the curse of dimensionality, we used fluid
approximation method to estimate the optimal solution of the MDP. We applied
our methodology on a dataset of Shaheed Rajaei Medical and Research Center in
Iran, and we showed how our models work in prioritizing and scheduling of
outpatients
Reallocating resources to focused factories: a case study in chemotherapy
This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions
Reallocating resources to focused factories: a case study in chemotherapy
This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions
Preparation of chemotherapy drugs: planning policy for reduced waiting times
This study investigates the impact of pharmacy policies on patient waiting time in the Chemotherapy Day Unit of the Netherland Cancer Institute - Antoni van Leeuwenhoek hospital (NKI-AVL). The project evaluated whether a reduction in waiting time resulting from medication orders being prepared in advance of patient appointments was justified, given that medications prepared in advance risked being wasted if patients arrived too sick for treatment. Within this context, we derive explicit expressions to approximate patient waiting times and wastage costs allowing management to see the tradeoff between these two metrics for different policies. Using a case study and a simulation model, the approximations are evaluated. The explicit expressions allow the analysis to be easily repeated when medication costs change or when new medications/protocols are introduced. In the same vein, other hospitals with different patient case mixes can easily complete the analysis in their setting. Finally, the outcome from this study resulted in a new policy at the cancer centre which is expected to decrease the waiting time by half while only increasing pharmacy’s costs by 1-2%
Automated Diagnosis of Clinic Workflows
Outpatient clinics often run behind schedule due to patients who arrive late
or appointments that run longer than expected. We sought to develop a
generalizable method that would allow healthcare providers to diagnose problems
in workflow that disrupt the schedule on any given provider clinic day. We use
a constraint optimization problem to identify the least number of appointment
modifications that make the rest of the schedule run on-time. We apply this
method to an outpatient clinic at Vanderbilt. For patient seen in this clinic
between March 27, 2017 and April 21, 2017, long cycle times tended to affect
the overall schedule more than late patients. Results from this workflow
diagnosis method could be used to inform interventions to help clinics run
smoothly, thus decreasing patient wait times and increasing provider
utilization
Organizing Multidisciplinary Care for Children with Neuromuscular Diseases
The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the `Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. These patients require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they visited on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This article describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients to invite for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation to study to estimate the capacity of the CMCA, expressed in the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios
Planning oncologists of ambulatory care units
International audienceThis paper addresses the problem of determining the work schedule, called medical planning, of oncologists for chemotherapy of oncology patients at ambulatory care units. A mixed integer programming (MIP) model is proposed for medical planning in order to best balance bed capacity requirements under capacity constraints of key resources such as beds and oncologists. The most salient feature of the MIP model is the explicit modeling of specific features of chemotherapy such as treatment protocols. The medical planning problem is proved to be NP-complete. A three-stage approach is proposed for determining good medical planning in reasonable computational time. From numerical experiments based on field data, the three-stage approach takes less than 10 min and always outperforms the direct application of MIP solvers with 10 h CPU time. Compared with the current planning, the three-stage approach reduces the peak daily bed capacity requirement by 20 h to 45 h while the maximum theoretical daily bed capacity is 162 h
Chemotherapy planning and multi-appointment scheduling: formulations, heuristics and bounds
The number of new cancer cases is expected to increase by about 50% in the
next 20 years, and the need for chemotherapy treatments will increase
accordingly. Chemotherapy treatments are usually performed in outpatient cancer
centers where patients affected by different types of tumors are treated. The
treatment delivery must be carefully planned to optimize the use of limited
resources, such as drugs, medical and nursing staff, consultation and exam
rooms, and chairs and beds for the drug infusion. Planning and scheduling
chemotherapy treatments involve different problems at different decision
levels. In this work, we focus on the patient chemotherapy multi-appointment
planning and scheduling problem at an operational level, namely the problem of
determining the day and starting time of the oncologist visit and drug infusion
for a set of patients to be scheduled along a short-term planning horizon. We
use a per-pathology paradigm, where the days of the week in which patients can
be treated, depending on their pathology, are known. We consider different
metrics and formulate the problem as a multi-objective optimization problem
tackled by sequentially solving three problems in a lexicographic
multi-objective fashion. The ultimate aim is to minimize the patient's
discomfort. The problems turn out to be computationally challenging, thus we
propose bounds and ad-hoc approaches, exploiting alternative problem
formulations, decomposition, and -opt search. The approaches are tested on
real data from an Italian outpatient cancer center and outperform
state-of-the-art solvers.Comment: 28 pages, 3 figure
A stochastic programming approach for chemotherapy appointment scheduling
Chemotherapy appointment scheduling is a challenging problem due to the
uncertainty in pre-medication and infusion durations. In this paper, we
formulate a two-stage stochastic mixed integer programming model for the
chemotherapy appointment scheduling problem under limited availability and
number of nurses and infusion chairs. The objective is to minimize the expected
weighted sum of nurse overtime, chair idle time, and patient waiting time. The
computational burden to solve real-life instances of this problem to optimality
is significantly high, even in the deterministic case. To overcome this burden,
we incorporate valid bounds and symmetry breaking constraints. Progressive
hedging algorithm is implemented in order to solve the improved formulation
heuristically. We enhance the algorithm through a penalty update method, cycle
detection and variable fixing mechanisms, and a linear approximation of the
objective function. Using numerical experiments based on real data from a major
oncology hospital, we compare our solution approach with several scheduling
heuristics from the relevant literature, generate managerial insights related
to the impact of the number of nurses and chairs on appointment schedules, and
estimate the value of stochastic solution to assess the significance of
considering uncertainty
Effective optimisation of the patient circuits of an oncology day hospital: Mathematical programming models and case study
In this paper, we first use the information we have on the patients of an oncology day
hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this,
we propose a deterministic mathematical programming model in such a way that we minimise the
duration of the waiting room stays of the total set of patients and taking into account the restrictions
of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach.
This model will explicitly consider the existing uncertainty in terms of the different times involved in
the circuit, and this model also allows the reorganisation of the schedules of medical appointments
with oncologists. The models are complemented by a tool that solves the problem of assigning nurses
to patients. The work is motivated by the particular characteristics of a real hospital and the models
are used and compared with data from this case.This research has been funded by the ERDF, the Government of Spain/AEI [grant MTM2017-
87197-C3-3-P] and the Xunta de Galicia [Grupos de Referencia Competitiva ED431C2017/38, and ED431C
2021/24].S
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