Entscheidungsanalytische Modellierung in der ökonomischen Evaluation

Becker, Carlos Chávez; Leidl, R.; Stollenwerk, Björn

doi:10.1055/s-0029-1245691

Cited by 7 publications

(4 citation statements)

References 22 publications

(38 reference statements)

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“…In order to use a systematic approach for such evaluations, decision-analytic modeling techniques can be applied. Most common tools are decision trees and Markov modeling (Becker, Leidl, and Stollenwerk 2010). Decision trees help to compare treatment alternatives using probability values at the branches and cost values at the leaves.…”

Section: Related Workmentioning

confidence: 99%

Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation

Djanatliev

German

2013

2013 Winter Simulations Conference (WSC)

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Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

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Section: Related Workmentioning

confidence: 99%

Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation

Djanatliev

German

2013

2013 Winter Simulations Conference (WSC)

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“…Discrete-Event-Simulation (DES) is getting more attention in economic studies as well. Introductions to the field of health economic evaluations are presented by [2,21]. Figure 1 depicts at the top an example decision tree, in order to decide between two alternatives for treatment of patients with symptoms: with or without an additional test.…”

Section: Related Workmentioning

confidence: 99%

Large Scale Healthcare Modeling by Hybrid Simulation Techniques using AnyLogic

Djanatliev¹,

German²

2013

Proceedings of the Sixth International Conference on Simulation Tools and Techniques

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In contrast to already established tools for health technology assessments, Prospective Health Technology Assessment (ProHTA) uses hybrid simulation techniques to make early predictions about the effects of new healthcare innovations. In such studies, it is necessary to consider problems at high abstraction levels, as well as at detailed microscopic levels. According to the diversity of expected output metrics and the lack of medical evidence, the situation can be compared to other large scale and complex simulation problems. In such cases well-known modeling methods are often not sufficient to use. Therefore, advanced modeling and simulation techniques have to be developed and properly applied. This paper describes a methodical and practical approach of hybrid model creation using the simulation tool AnyLogic. We focus on general modeling aspects and on advanced techniques using a Level-Based Architecture that help to develop large scale hybrid simulation models. An implementation of a stroke therapy use-case and its simulation results will be discussed. Finally, some practical ideas for validation will be outlined, as we experienced during the stroke use-case development.

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“…Bei der entscheidungsanalytischen Modellierung in der gesundheitsökonomi-schen Evaluation handelt es sich um einen systematischen Ansatz, der die Ergebnisse unterschiedlicher Quellen in mathematischen Modellen zusammenführt [3,4]. [14], das "UKPDS Outcomes Model" [6], das Cardiff-Modell, sowie das "Sheffield Diabetes Model" [18].…”

Section: Die Rolle Der Entscheidungsanalytischen Modellierung In Der unclassified

Health-economic modelling for diabetes mellitus

2011

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Die gesundheitsökonomische Evaluation gewinnt international sowie inDeutschland zunehmend an Bedeutung. In ihr kommen entscheidungsanalytische Modelle zum Einsatz, die die Evidenz unterschiedlicher Studien miteinander verknüpfen. Für eine chronische Erkrankung wie Diabetes mellitus sind derartige Modelle von hoher Relevanz: Allein für den Typ-2-Diabetes wurden mindestens 20 verschiedene Modelle entwickelt [19]. Entsprechend wurden von der Amerikanischen Diabetes Gesellschaft (ADA) bereits Modellierungsleitlinien erstellt. Viele Diabetesforscher sind jedoch nicht mit der gesundheitsökonomischen Modellierung vertraut. Daher werden ihre Grundprinzipien hier vorgestellt. Die Rolle der entscheidungsanalytischen Modellierung in der gesundheitsöko-nomischen Evaluation Bei der entscheidungsanalytischen Modellierung in der gesundheitsökonomi-schen Evaluation handelt es sich um einen systematischen Ansatz, der die Ergebnisse unterschiedlicher Quellen in mathematischen Modellen zusammenführt [3, 4]. Ziel dieses Ansatzes ist es, die Konsequenzen unterschiedlicher Handlungsalternativen gemäß dem besten Stand der Wissenschaft abzubilden. Die Handlungsalternativen des Modells sollen alle relevanten Alternativen umfassen. Der Zeithorizont des Modells soll so gewählt werden, dass alle Konsequenzen abgebildet werden, die für eine Entscheidung von Bedeutung sind. In der Regel handelt es sich dabei um einen lebenslangen Zeithorizont. > Eine gesundheitsöko-nomische Evaluation sollte alle gesundheitlichen und wirtschaftlichen Folgen einbeziehen Gesundheitsökonomische Evaluationen können -anstelle von Modellen -auch auf klinischen Studien beruhen, in denen alle relevanten Informationen direkt erhoben werden. Allerdings können Studien kaum alle langfristigen Konsequenzen einer chronischen Erkrankung wie Diabetes mellitus direkt erheben. Da eine gesundheitsökonomische Evaluation alle gesundheitlichen und wirtschaftlichen Folgen einbeziehen sollte, ist es in der Praxis kaum möglich, auf eine Modellierung zu verzichten.

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Entscheidungsanalytische Modellierung in der ökonomischen Evaluation

Cited by 7 publications

References 22 publications

Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation

Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation

Large Scale Healthcare Modeling by Hybrid Simulation Techniques using AnyLogic

Health-economic modelling for diabetes mellitus

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