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Decision-analytic valuation of clinical information systems: application to an alerting system for coronary
1Department of Medicine, UCLA School of Medicine, USA.
Insights
Implementing an alerting system for necessary coronary angiography after myocardial infarction (MI) can improve patient survival. Decision analysis predicts this system
Area of Science:
- Cardiovascular Medicine
- Health Informatics
- Decision Analysis
Background:
- Many patients requiring coronary angiography do not receive it, leading to reduced survival rates.
- Coronary angiography is crucial for managing patients post-myocardial infarction (MI).
Purpose of the Study:
- To predict the survival value of an alerting system for necessary coronary angiography using decision analysis.
- To evaluate the effectiveness of information systems in improving patient outcomes.
Main Methods:
- Utilized data from a published cohort study on angiography use and survival post-MI.
- Calculated the Expected Value of Information (EVI) for alerts recommending angiography.
- Performed sensitivity analysis by relaxing the assumption of complete adherence to alerts.
Main Results:
- A maximally effective alerting system could increase survival by 2.2% over 1-4 years post-MI.
- Approximately 46 individuals would need the system to prevent one death.
- System effectiveness decreases with lower adherence to its recommendations.
Conclusions:
- An alerting system for post-MI angiography offers survival benefits comparable to thrombolytic therapy.
- EVI analysis is a valuable framework for assessing information system effectiveness and feature contributions.
Background:
Many patients who need coronary angiography fail to get it and they have decreased survival as a result. This study demonstrates the use of decision analysis to predict the survival value of an alerting system for necessary angiography.
Methods:
Data on the use of angiography and survival after myocardial infarction (MI) were taken from a published cohort study. The expected value of information (EVI) was calculated for alerts that angiography is necessary. Maximal EVI was estimated by assuming that alert advice is always followed. Sensitivity analysis relaxed that assumption. Hypothetical data were generated to demonstrate EVI analysis for narrower subcohorts.
Results:
A maximally effective alerting system would increase survival in this cohort by 2.2% over 1-4 years after MI. The system would therefore need to be applied to 46 people to prevent one death. Its effectiveness would decrease linearly with decreasing adherence to its advice. Given sufficiently detailed outcome and prevalence data, EVI analysis could also predict the survival value of the system's individual data elements.
Conclusions:
An alerting system that ensures necessary angiography post-MI should have a survival value comparable to the value of t-PA over streptokinase. EVI analysis provides a framework for predicting the overall effectiveness of information systems and for understanding the contribution of individual features to a system's effectiveness.
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