Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

Journal: BMC health services research
PMID: 40089758

Abstract

OBJECTIVES: Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fibrosis (IPF). We conducted a cost-effectiveness analysis of diagnostic strategies for IPF.

Authors

  • Christopher J Cadham
    Department of Health Management and Policy, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2013, USA. ccadham@umich.edu.
  • Joshua Reicher
    Department of Radiology, Palo Alto VA Medical Center, Palo Alto, California.
  • Michael Muelly
    Imvaria, Inc, USA.
  • David W Hutton
    School of Public Health, University of Michigan Ann Arbor, MI.

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