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Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions...
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... There are many uncertainties in the clinical pathway, so that a model may need to simulate pathways in a stochastic manner. For example, the stroke clinical pathway in Figure 1 has a changing number ...
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... summing the counts of all patient components of the form • α Finished , we can get an approximation to the cumulative density function (CDF) for the time it takes for an individual Patient place 0 component to per- form its first a action (see Figure 8). In this paper, we use a tool, Grouped PEPA Analyser , developed in [35] to simulate the CPP model. This tool can estimate resource utilisation and passage time based on fluid analysis. In this section, the CPP modelling method is applied to model the stroke clinical pathway. It begins with the parameter settings of experiment. Then detailed description of the CPP model for stroke clinical pathway is shown. By simulating the pathway using the CPP modelling method, we can optimize the resource allocation, estimate the passage time and find the maximum throughput with the current resource distribution. In order to improve stroke service in London, the NHS investigates seven HASUs opened in 2010 including the one in Charing Cross Hospital. To ensure appropriate and consistent measurements, the SINAP database is used. Among the datasets published by NHS [36], the HASU activity data which contains clinical information for HASU in Charing Cross Hospital is shown in Table 1. It can be used to calculate transition probabilities in Figure 1. For example, there are 116 stroke patients, 27 stroke mimic patients and 15 TIA patients. Then in Figure 1, the transition probability for the patient from A&E to A&E referral is 27/(116+15+27) = 17%. Similarly, the transition probability from CT scan to A&E referral is 15/(116+27) = 10%. To build the formal model of stroke clinical pathway, parameters involved in pathway including execution time of each care activity and the number of resources available at each department should be estimated in advance. Some of them can be directly obtained from Table 1, where we can find the number of beds available in HASU is 20 and the median length of stay in HASU is 2 days. Moreover, NHS records the average time from 999 call to arrive hospital to be 62 minutes. As the FAST test is carried out on ambulance, the mean time of FAST test is therefore assumed to be 1 hour. Other parameters such as the number of stroke teams in A&E and CT scanners are not extracted directly from NHS databases, and they are estimated by consulting related documents and stroke experts. In this paper, we assume that there are 3 stroke teams and 3 CT scanners in Charing Cross Hospital. The number of ambulances is set to be unlimited, ignoring the influence of the ambulance in the model. Moreover, the mean lengths of stay in A&E and CT scan departments are assumed to be 0.75 hour and 3 hours, respectively. All the parameters and their value are summarized in Table 2. This paper applies CPP to model the main stream of the pathway where only the patients who can finally go to the HASU are analyzed. Hence the pathway in Figure 1 is simplified as it is shown in Figure 9. The pathway model contains three parts, which are state definition, resource specification and system description. The CPP representation of these three parts is as ...
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... summing the counts of all patient components of the form • α Finished , we can get an approximation to the cumulative density function (CDF) for the time it takes for an individual Patient place 0 component to per- form its first a action (see Figure 8). In this paper, we use a tool, Grouped PEPA Analyser , developed in [35] to simulate the CPP model. This tool can estimate resource utilisation and passage time based on fluid analysis. In this section, the CPP modelling method is applied to model the stroke clinical pathway. It begins with the parameter settings of experiment. Then detailed description of the CPP model for stroke clinical pathway is shown. By simulating the pathway using the CPP modelling method, we can optimize the resource allocation, estimate the passage time and find the maximum throughput with the current resource distribution. In order to improve stroke service in London, the NHS investigates seven HASUs opened in 2010 including the one in Charing Cross Hospital. To ensure appropriate and consistent measurements, the SINAP database is used. Among the datasets published by NHS [36], the HASU activity data which contains clinical information for HASU in Charing Cross Hospital is shown in Table 1. It can be used to calculate transition probabilities in Figure 1. For example, there are 116 stroke patients, 27 stroke mimic patients and 15 TIA patients. Then in Figure 1, the transition probability for the patient from A&E to A&E referral is 27/(116+15+27) = 17%. Similarly, the transition probability from CT scan to A&E referral is 15/(116+27) = 10%. To build the formal model of stroke clinical pathway, parameters involved in pathway including execution time of each care activity and the number of resources available at each department should be estimated in advance. Some of them can be directly obtained from Table 1, where we can find the number of beds available in HASU is 20 and the median length of stay in HASU is 2 days. Moreover, NHS records the average time from 999 call to arrive hospital to be 62 minutes. As the FAST test is carried out on ambulance, the mean time of FAST test is therefore assumed to be 1 hour. Other parameters such as the number of stroke teams in A&E and CT scanners are not extracted directly from NHS databases, and they are estimated by consulting related documents and stroke experts. In this paper, we assume that there are 3 stroke teams and 3 CT scanners in Charing Cross Hospital. The number of ambulances is set to be unlimited, ignoring the influence of the ambulance in the model. Moreover, the mean lengths of stay in A&E and CT scan departments are assumed to be 0.75 hour and 3 hours, respectively. All the parameters and their value are summarized in Table 2. This paper applies CPP to model the main stream of the pathway where only the patients who can finally go to the HASU are analyzed. Hence the pathway in Figure 1 is simplified as it is shown in Figure 9. The pathway model contains three parts, which are state definition, resource specification and system description. The CPP representation of these three parts is as ...
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... stroke clinical pathway investigated in this paper is for illustrative purposes. Stroke is a complex disease requiring a systematic integration of services, e.g. primary care, ambulance services, acute treatment and rehabilitation, post-acute rehabilitation, and often long- term health and care support in the community. Due to its complexity and requirement of various services, the stroke clinical pathway presents a significant challenge to existing generally fragmented health and social care services. Moving from the current fragmented approach to an integrated stroke system is a complex task. As simulation models are useful during the planning stage of complex service re-configuration, modelling methods are in demand to simulate and optimize the stroke clinical pathway. Figure 1 displays the abstraction of a general stroke clinical pathway, obtained from Charing Cross hospital of Imperial College London. A new 999 call from a patient or other hospitals initiates an instance of this pathway. The information flow is summarized as ...
Citations
... One of the interesting pattern-like structures within this area are clinical pathways (CP) that are used to describe all treatment and patient care processes and include all sorts of events for the treatment of a disease. Often, these clinical pathways are compiled manually with substantial expert involvement [8] or with specific monitoring information systems [9]. Still, due to high complexity and uncertainty in disease development, the task of clinical pathway data-driven discovery is often related to unresolved issues. ...
... cluster sets ##2, 5,8) and bad (e.g. cluster sets ##1, 4,7,9) predictive performance. Still, at the same time, there are clusters where predictive power of the surrogate models varies significantly in different quality and performance criteria (e.g. ...
The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm’s quality and performance. The proposed approach was implemented and investigated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was developed to provide a more interpretable prediction of the target algorithm’s performance and quality that can be further used for parameter tuning.
... Another approach represents diabetic therapy in the form of Markov models (Elghazel et al., 2007;Yang et al., 2012;Bennett and Hauser, 2013;Zhang et al., 2015;Mattila et al., 2016). In particular, the Markov decision process (MDP) can be efficiently used to determine an optimal therapy (policy) (Schaefer et al., 2005). ...
... [20][21][22][23][24] Patients have to visit multiple departments for various tests, diagnosis, or treatment. 25,26 Patients are scheduled at registration or at other departments. Patients are unaware of waiting times at following departments in their pathways; as a result, they wait at different departments. ...
Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system.
... Often these clinical pathways are compiled manually with many specialists [7]. Also, the informational systems have been developed to monitor the ongoing processes and compare them with the specified clinical pathways [8]. Still, due to high complexity and uncertainty in disease development, a task of clinical pathway structuring and analysis is often related to unresolved issues. ...
... Metaheuristic algorithms allow solving complex data analysis and decision-making problems. There is an extensive review of how genetics algorithms are applied in the diagnosis, treatment planning, prediction, and management of health care [8]. ...
The paper proposes an approach for surrogate-assisted tuning of knowledge discovery algorithms. The approach is based on the prediction of both the quality and performance of the target algorithm. The prediction is furtherly used as objectives for the optimization and tuning of the algorithm. The approach is investigated using clinical pathways (CP) discovery problem resolved using the evolutionary-based clustering of electronic health records (EHR). Target algorithm and the proposed approach were applied to the discovery of CPs for Acute Coronary Syndrome patients in 3434 EHRs of patients treated in Almazov National Medical Research Center (Saint Petersburg, Russia). The study investigates the possible acquisition of interpretable clusters of typical CPs within a single disease. It shows how the approach could be used to improve complex data-driven analytical knowledge discovery algorithms. The study of the results includes the feature importance of the best surrogate model and discover how the parameters of input data influence the predictions.
... Time spent by each patient in the hospital may vary significantly. 1,2 Patients spend a lot of time in the outpatient clinic (OPC) before seeing a doctor. 3 The delays in care are detrimental to patients, as they may result in adverse outcomes, increase the costs incurred, and reduce patient satisfaction. ...
This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly.
... Investigating Type Mapping (Ma) [1, 4, 5, 7, 22, 29, 33, 37, 42, 47, 48, 54, 56, 57, 70, 71, 81, 83, 87, 88, 90, 91, 94-99, 104, 112, 121, 126, 127, 131, 136, 138, 139, 144, 149, 154, 157, 161, 162, 164, 170, 172, 177-179, 183-185, 192, 195, 197-203, 206, 209-211] Modelling (Mo) [15, 19-21, 23, 24, 30, 31, 36, 39, 43, 50, 55, 58, 64, 67, 76, 79, 82, 86, 89, 102, 103, 115, 117-119, 122, 129, 145-147, 150, 151, 160, 169, 171, 187, 188, 190, 191, 196, 212] Improving (I) [13, 46] Ma & Mo [3, 9, 11, 17, 18, 40, 60, 62, 66, 72-74, 80, 85, 90, 93, 101, 107, 108, 114, 120, 124, 125, 128, 133, 134, 140, 143, 158, 205] Mo & I [8,16,25,32,34,41,45,52,53,68,75,110,111,113,116,141,148,165, 182] All Types [10,12,14,27,35,49,51,100,142,152,153,163,166,167,186,204] Table A12. Outcome focus of the pathway. ...
... Outcome Pathway Mapping [1, 4, 5, 13, 20, 33, 37, 42, 47, 54-56, 58, 62, 70, 71, 81-83, 86-99, 107, 112, 114, 115, 121, 126-128, 136, 138-140, 149, 154, 157, 162, 164, 171, 172, 177-179, 183, 184, 187, 188, 192, 195, 197-202, 206, 210, 211] Time [3, 8, 10, 12, 15, 16, 18, 23, 29-32, 34-36, 41, 46, 50, 52, 57, 62, 64, 67, 68, 72, 74, 75, 80, 85, 94, 100, 108, 110, 118, 129, 133, 138, 141-143, 145-147, 151, 160, 161, 163, 165-167, 169, 182, 185, 190, 204, 205, 212] Resource [3, 7, 10, 14, 15, 17, 25, 27, 29, 31, 32, 35, 36, 46, 52, 64, 75, 76, 100, 113, 118-120, 142, 145, 148, 151-153, 160, 161, 163, 170, 182, 191, 204] Cost [9,11,14,15,19,21,22,40,43,45,53,60,73,79,101,111,117,118,125,133,134,138,145,158,166,167,209] Patient Progression [24, 29, 39, 40, 45, 48, 49, 51, 60, 79, 86, 101-104, 107, 116, 122, 124, 131, 139, 144, 145, 150, 186, 196] Legal [66,203] [3,10,14,31,32,35,36,40,45,46,52,60,62,64,75,79,86,94,100,101,107,133,139,142,151,160,161,163,166,167,182,204 [3, 8, 9, 14, 16, 19, 22, 25, 27, 29, 31, 32, 39, 41, 43, 45, 46, 49, 52, 60, 64, 67, 72, 73, 80, 101-103, 111, 115, 116, 136, 158, 165-167, 170, 182, 190, 191] Tactical [7, 10, 12, 14, 30, 34-36, 68, 74, 76, 79, 86, 108, 113, 117, 141, 142, 145, 147, 148, 161, 163] Operational [15, 17, 18, 31, 53, 57, 75, 90, 110, 113, 118-120, 134, 143, 146, 150, 153, 160, 169, 186, 212] No Decision [1, 4, 5, 11, 13, 20, 21, 23, 24, 33, 37, 40, 42, 47, 48, 50, 51, 54-56, 58, 62, 66, 70, 71, 81-83, 85, 87-89, 91-100, 104, 107, 112, 114, 121, 122, 124-129, 131, 133, 138-140, 144, 149, 151, 152, 154, 157, 162, 164, 171, 172, 177-179, 183-185, 187, 188, 192, 195-206, 209-211] [5, 7, 8, 11, 20, 24, 29, 33, 37, 42, 47-49, 54, 62, 66, 70-72, 74, 76, 81-83, 85-94, 96-99, 104, 107, 108, 112, 121, 124, 126-128, 133, 136, 138, 139, 143, 144, 149, 150, 154, 157, 162, 164, 167, 170, 172, 177-179, 183-185, 192, 195, 197-203, 205, 206, 209-211] Simulation [1, 3, 7, 9, 10, 12, 15-19, 21, 23, 25, 27, 32, 34-36, 39, 41, 43, 45, 46, 50-53, 60, 64, 67, 68, 73, 79, 100, 102, 108, 110, 111, 113-117, 122, 129, 134, 142, 145-148, 151-153, 158, 160, 163, 165, 166, 169, 171, 182, 186, 188, 190, 191, 196, 204] Optimisation and Heuristics [4, 8, 12-14, 18, 22, 30, 31, 54-58, 74-76, 80, 95, 99, 103, 118, 119, 125, 131, 140, 161, 162, 167, 179, 187, 211] Stochastic Modelling [7,11,40,101,120,140,141,151,190,203,211,212] ...
Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.
... There are a number of groups working on the specific problem of automated processing of CGs, using a variety of semantics, formalisms and representations. The Performance Evaluation Process Algebra (PEPA) [23] formalism is combined with coloured stochastic Petri nets to automatically represent and process CGs in [38], which is however focused on the resource allocation problem, and hence also closer to the work in [10]. A further approach [21] presents a similar approach through the introduction of a PEPA specialisation called Clinical Pathway PEPA (CPP). ...
... A further approach [21] presents a similar approach through the introduction of a PEPA specialisation called Clinical Pathway PEPA (CPP). While the context of the present paper is similar to that of [21] and [38], our goals are very different, since we focus on evidence-based guidelines for handling well-understood chronic conditions, which do not provide probabilities, therefore preventing the possibility of a probabilistic analysis. ...
Common chronic conditions are routinely treated following standardised procedures known as clinical guidelines. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.
... An individual molecular event is subject to stochastic time delays as it takes place whenever the event conditions (availability of substrates, desired level of energy, temperature and pressure, etc.) are present, but not according to a predefined order. Stochastic nature of molecular interactions can be represented and analyzed using the Chapman-Kolmogorov equation [1], stochastic differential equations [2], the Gibson-Bruck algorithm [3], stochastic Pi-calculus [4], the Gillespie algorithm [5], stochastic process algebra [6], and stochastic Petri nets (SPNs) [7]. Expressive capabilities of the SPNs coupled with existing powerful analysis tools make SPN modeling approach an indispensable choice in the stochastic analysis of the biological systems. ...
Randomness and uncertainty are two major problems one faces while modeling nonlinear dynamics of molecular systems. Stochastic and fuzzy methods are used to cope with these problems, but there is no consensus among researchers regarding which method should be used when. This is because the areas of applications of these methods are overlapping with differences in opinions. In the present work, we demonstrate how to use stochastic Petri nets with fuzzy parameters to manage random timing of biomolecular events and deal with the uncertainty of reaction rates in biological networks. The approach is demonstrated through a case study of simulation-based prediction of efficient drug combinationsforspinalmuscularatrophy,forwhichweobtainedverypromisingresults. Thefeasibilityoftheapproachis assessed through statistical analysis of deterministic, pure stochastic and fuzzy stochastic simulation results. Statistical analysis reveals that all three models produce significantly different results which, when coupled with the fact that fuzzy stochastic model provides the closest approximation of underlying biological network, successfully coping not only with randomness but also uncertainty, suggests that fuzzy stochastic model is the most appropriate choice for the present case study. The proposed approach can be adapted or extended to other biological networks.
... Randomness in biological systems is usually analyzed in terms of Chapman-Kolmogorov equation [30], Gillespie algorithm [7], Gibson-Bruck algorithm [6], stochastic differential equations [20], stochastic Pi-calculus [23], stochastic process algebra [34] and stochastic Petri nets (SPNs) [9]. There are several reasons why SPNs tend to be more practical compared to other approaches. ...
... The cell cycle is an ordered sequence of events that are classified into four periods: gap period 1 (G1); synthesis (S); gap period 2 (G2); and mitosis (M). Over the last few decades, quantitative modeling approaches have been extensively used to study the cell cycle at different levels and for different living organisms ranging from primitive prokaryotes to complex eukaryotes [11,16,18,22,34]. For detailed information on cell cycle models the readers are referred to the comprehensive review in this area [5]. ...
In this work we use hybrid Petri nets to create a model of the p16-mediated signaling pathway in higher eukaryotes and conduct its stochastic simulation-based validation by wet lab observations available from literature. The validation is conducted in terms of stochastic simulations with respect to the wild-type p16 protein and its mutated form. Our model catches the behavior of the major molecular regulators of the p16-mediated signaling pathway in wild-type cells as well as when DNA damage is detected or replicative senescence occurs. We observe that the stochastic model predicts some characteristics of the underlying pathway more clearly, evidently and perspicuously compared to the deterministic model, enriching the breadth and the quality of the outcome.
... Randomness may arise from the external environment, intrinsic noise or low number of molecules, and may dramatically affect the behavior of these networks [2,3]. To deal with randomness, stochastic modeling methods have been used, e.g., chemical master equations, stochastic differential equations [1], stochastic Pi-calculus [4], stochastic process algebra [5], or stochastic Petri nets (SPNs) [6]. These approaches address the stochastic aspect of biological systems and thus describe their behavior more accurately than deterministic approaches like ordinary or partial differential equations. ...
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. © 2016 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
