Code for: Artificial Intelligence in Healthcare? Evidence from online job postings

Principal Investigator(s): View help for Principal Investigator(s) Avi Goldfarb, University of Toronto; Bledi Taska, Burning Glass Technologies

Version: View help for Version V1

Name	File Type	Size	Last Modified
Teodoridis_AIinHealthcare_CodeFile.pdf	application/pdf	82.1 KB	01/08/2020 12:26:PM
Teodoridis_AIinHealthcare_README.pdf	application/pdf	50.8 KB	01/08/2020 12:26:PM

Project Citation:

Goldfarb, Avi, and Taska, Bledi. Code for: Artificial Intelligence in Healthcare? Evidence from online job postings. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-06-29. https://doi.org/10.3886/E117124V1

Project Description

Summary: This paper documents a puzzle. Despite the numerous popular press discussions of artificial intelligence (AI) in healthcare, there has been relatively little adoption. Using data from Burning Glass Technologies on millions of online job postings, we find that AI adoption in healthcare remains substantially lower than in most other industries, and that under 3% of the hospitals in our data posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do observe shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.

Scope of Project

JEL Classification:
      I11 Analysis of Health Care Markets
      J23 Labor Demand
      J24 Human Capital; Skills; Occupational Choice; Labor Productivity
      J31 Wage Level and Structure; Wage Differentials
      O33 Technological Change: Choices and Consequences; Diffusion Processes

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Published Versions

V1 [2020-06-29]

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