Payer budget impact of an artificial intelligence in vitro diagnostic to modify diabetic kidney disease progression.
Manasi DatarWilliam BurchenalMichael J DonovanSteven G CocaElaine WangThomas F GossPublished in: Journal of medical economics (2021)
Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.
Keyphrases
- artificial intelligence
- early stage
- systematic review
- end stage renal disease
- machine learning
- big data
- ejection fraction
- healthcare
- deep learning
- newly diagnosed
- chronic kidney disease
- type diabetes
- palliative care
- climate change
- quality improvement
- peritoneal dialysis
- squamous cell carcinoma
- chronic pain
- radiation therapy
- patient reported outcomes