ArticlePharmacoEconomics2019
Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges.
Article in PharmacoEconomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Public hesitancy for AI-based detection of neurodegenerative diseases in France.Scientific reports · 2025Article
- AI revolution in insurance: bridging research and reality.Frontiers in artificial intelligence · 2025Review
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.Bioengineering (Basel, Switzerland) · 2024Review
- The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review.Cureus · 2024Review
- Machine learning in health financing: benefits, risks and regulatory needs.Bulletin of the World Health Organization · 2024Article
- Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study.Healthcare (Basel, Switzerland) · 2023Article
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- Use of big data from health insurance for assessment of cardiovascular outcomes.Frontiers in artificial intelligence · 2023Review
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- Population-scale identification of differential adverse events before and during a pandemic.Nature computational science · 2021Article
- Review
- An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.Frontiers in artificial intelligence · 2021Article
- Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data.Scientific reports · 2020Article
- How can physicians adopt AI-based applications in the United Arab Emirates to improve patient outcomes?Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.
Indexed as
Identifiers
30848452What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.