BUSI-429
Annotated Bibliography
Liberty University
Healthcare Economics and Decision Making
1. German, R. (2013). Prospective healthcare decision-making by combined system
dynamics, discrete-event and agent-based simulation. Retrieved April 12, 2021,
from
https://ieeexplore.ieee.org/abstract/document/6721426
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.
2. Brousselle, A., & Lessard, C. (2011, February 03). Economic evaluation to inform
health care decision-making: Promise, pitfalls and a proposal for an alternative path.
Retrieved April 11, 2021, from
https://www.sciencedirect.com/science/article/pii/S0277953611000505
We document the increasing gap between economic evaluation and health care
decision-making. As the field has expanded, methodologies have become increasingly
sophisticated. Deciphering health economic evaluations constitutes one of the barriers
to its use. New ways have to be found to bring economic evaluation closer to the reality
of decision-making. A combined approach of cost-consequence, budget impact and
marginal analysis could be explored.
3. Taranu, I. (2016). Data Mining in Healthcare: Decision making and precision.
Retrieved
April 11, 2021.
The trend of application of data mining in healthcare today is increased because the
health sector is rich with information and data mining has become a necessity.
, Healthcare organizations generate and collect large volumes of information to a daily
basis. Use of information technology enables automation of data mining and
knowledge that help bring some interesting patterns which means eliminating manual
tasks and easy data extraction directly from electronic records.
4. Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., . . . Ijzerman,
M. (2016, January 08). Multiple criteria decision analysis for health care decision
making-an Introduction: Report 1 of the Ispor Mcda Emerging good practices Task
Force. Retrieved April 11, 2021, from
https://www.sciencedirect.com/science/article/pii/S1098301515051359
Health care decisions are complex and involve confronting trade-offs between
multiple, often conflicting, objectives. Using structured, explicit approaches to
decisions involving multiple criteria can improve the quality of decision making and
a set of techniques, known under the collective heading multiple criteria decision
analysis (MCDA), are useful for this purpose.
5. Jansen, J., Fleurence, R., Devine, B., Itzler, R., Barrett, A., Hawkins, N., . . .
Cappelleri, J. (2011, June 12). Interpreting indirect Treatment comparisons and
NETWORK meta- analysis for Health-Care decision Making: Report of the ISPOR
Task force on Indirect Treatment Comparisons good RESEARCH Practices: Part 1.
Retrieved April 11, 2021,
from https://www.sciencedirect.com/science/article/pii/S1098301511014045
Evidence-based health-care decision making requires comparisons of all relevant
competing interventions. In the absence of randomized, controlled trials involving a
direct comparison of all treatments of interest, indirect treatment comparisons and
network meta-analysis provide useful evidence for judiciously selecting the best
choice(s) of treatment. Mixed treatment comparisons, a special case of network
meta- analysis, combine direct and indirect evidence for particular pairwise
comparisons, thereby synthesizing a greater share of the available evidence than a
traditional meta- analysis.
6. Howick, J., & Search for more articles by this author. (2011, December 01). Exposing
the Vanities-and a QUALIFIED Defense-of Mechanistic reasoning in health care
decision making. Retrieved April 11, 2021, from
https://www.journals.uchicago.edu/doi/abs/10.1086/662561
Philosophers of science have insisted that evidence of underlying mechanisms is
required to support claims about the effects of medical interventions. Yet evidence
about mechanisms does not feature on dominant evidence-based medicine
“hierarchies.” After arguing that only inferences from mechanisms (“mechanistic
reasoning”)—not mechanisms themselves—count as evidence, I argue for a middle
ground. Mechanistic reasoning is not required to establish causation when we have
high-quality controlled studies; moreover, mechanistic reasoning is more problematic
Annotated Bibliography
Liberty University
Healthcare Economics and Decision Making
1. German, R. (2013). Prospective healthcare decision-making by combined system
dynamics, discrete-event and agent-based simulation. Retrieved April 12, 2021,
from
https://ieeexplore.ieee.org/abstract/document/6721426
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.
2. Brousselle, A., & Lessard, C. (2011, February 03). Economic evaluation to inform
health care decision-making: Promise, pitfalls and a proposal for an alternative path.
Retrieved April 11, 2021, from
https://www.sciencedirect.com/science/article/pii/S0277953611000505
We document the increasing gap between economic evaluation and health care
decision-making. As the field has expanded, methodologies have become increasingly
sophisticated. Deciphering health economic evaluations constitutes one of the barriers
to its use. New ways have to be found to bring economic evaluation closer to the reality
of decision-making. A combined approach of cost-consequence, budget impact and
marginal analysis could be explored.
3. Taranu, I. (2016). Data Mining in Healthcare: Decision making and precision.
Retrieved
April 11, 2021.
The trend of application of data mining in healthcare today is increased because the
health sector is rich with information and data mining has become a necessity.
, Healthcare organizations generate and collect large volumes of information to a daily
basis. Use of information technology enables automation of data mining and
knowledge that help bring some interesting patterns which means eliminating manual
tasks and easy data extraction directly from electronic records.
4. Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., . . . Ijzerman,
M. (2016, January 08). Multiple criteria decision analysis for health care decision
making-an Introduction: Report 1 of the Ispor Mcda Emerging good practices Task
Force. Retrieved April 11, 2021, from
https://www.sciencedirect.com/science/article/pii/S1098301515051359
Health care decisions are complex and involve confronting trade-offs between
multiple, often conflicting, objectives. Using structured, explicit approaches to
decisions involving multiple criteria can improve the quality of decision making and
a set of techniques, known under the collective heading multiple criteria decision
analysis (MCDA), are useful for this purpose.
5. Jansen, J., Fleurence, R., Devine, B., Itzler, R., Barrett, A., Hawkins, N., . . .
Cappelleri, J. (2011, June 12). Interpreting indirect Treatment comparisons and
NETWORK meta- analysis for Health-Care decision Making: Report of the ISPOR
Task force on Indirect Treatment Comparisons good RESEARCH Practices: Part 1.
Retrieved April 11, 2021,
from https://www.sciencedirect.com/science/article/pii/S1098301511014045
Evidence-based health-care decision making requires comparisons of all relevant
competing interventions. In the absence of randomized, controlled trials involving a
direct comparison of all treatments of interest, indirect treatment comparisons and
network meta-analysis provide useful evidence for judiciously selecting the best
choice(s) of treatment. Mixed treatment comparisons, a special case of network
meta- analysis, combine direct and indirect evidence for particular pairwise
comparisons, thereby synthesizing a greater share of the available evidence than a
traditional meta- analysis.
6. Howick, J., & Search for more articles by this author. (2011, December 01). Exposing
the Vanities-and a QUALIFIED Defense-of Mechanistic reasoning in health care
decision making. Retrieved April 11, 2021, from
https://www.journals.uchicago.edu/doi/abs/10.1086/662561
Philosophers of science have insisted that evidence of underlying mechanisms is
required to support claims about the effects of medical interventions. Yet evidence
about mechanisms does not feature on dominant evidence-based medicine
“hierarchies.” After arguing that only inferences from mechanisms (“mechanistic
reasoning”)—not mechanisms themselves—count as evidence, I argue for a middle
ground. Mechanistic reasoning is not required to establish causation when we have
high-quality controlled studies; moreover, mechanistic reasoning is more problematic
