A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition

Goswami, M; Daultani, Y; Paul, SK; Pratap, S

A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition

Goswami, M Daultani, Y Paul, SK Pratap, S

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Annals of Operations Research, 2023, 328, (1), pp. 577-616
Issue Date:: 2023-08

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Field	Value	Language
dc.contributor.author	Goswami, M
dc.contributor.author	Daultani, Y
dc.contributor.author	Paul, SK
dc.contributor.author	Pratap, S
dc.date.accessioned	2024-02-08T21:55:33Z
dc.date.available	2022-08-02
dc.date.available	2024-02-08T21:55:33Z
dc.date.issued	2023-08
dc.identifier.citation	Annals of Operations Research, 2023, 328, (1), pp. 577-616
dc.identifier.issn	0254-5330
dc.identifier.issn	1572-9338
dc.identifier.uri	http://hdl.handle.net/10453/175516
dc.description.abstract	The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases (qualitative and quantitative), in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the “major” as opposed to the “severe” stage of a medical condition, even though the steady-state probability of the “severe” state was less than that of the “major” state.
dc.format	Print-Electronic
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Annals of Operations Research
dc.relation.isbasedon	10.1007/s10479-022-04914-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Operations Research
dc.subject.classification	35 Commerce, management, tourism and services
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition
dc.type	Journal Article
utslib.citation.volume	328
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Management Discipline
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2024-02-08T21:55:30Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	328
utslib.citation.issue	1

Abstract:

The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases (qualitative and quantitative), in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the “major” as opposed to the “severe” stage of a medical condition, even though the steady-state probability of the “severe” state was less than that of the “major” state.

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http://hdl.handle.net/10453/175516