Information and Communications Technology in U.s. Health Care: Why Is Adoption So Slow and Is Slower Better?
Abstract
Politicians across the political spectrum support greater investment in health care information and communications technology (ICT) and expect it to significantly decrease costs and improve health outcomes. We address three policy questions about adoption of ICT in health care: First, why is there so little adoption? Second, what policies will facilitate and accelerate adoption? Third, what is the best pace for adoption? We first describe the unusual economics of ICT, particularly network externalities, and then determine how those economics interact with and are exacerbated by the unusual economics of health care. High replacement costs and the need for technical compatibility are general barriers to ICT adoption and often result in lock-in to adopted technologies. These effects are compounded in health care because the markets for health care services, health insurance, and labor are interlinked. In addition, the government interacts with all markets in its role as an insurer. Patient heterogeneity further exacerbates these effects. Finally, ICT markets are often characterized by natural monopolies, resulting in little product diversity, an effect ill-suited to patient heterogeneity. The ongoing process for setting technical standards for health care ICT is critical but needs to include all relevant stakeholders, including patient groups. The process must be careful (i.e., slow), flexible, and allow for as much diversity as possible. We find that waiting to adopt ICT is a surprisingly wise policy.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Difficulties in eHealth implementation are international phenomena, with similar problems being widely reported.
“Difficulties in eHealth implementation are international phenomena, with similar problems being widely reported [102-105].”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [102]
The citation lists haven’t been fetched yet
References 49 · Cited by 72
- Authors
- Christensen, M. C., & Remler, D.
- Affiliations
- Novo Nordisk (Denmark) (DK)
- National Bureau of Economic Research (US)
- Venue
- Journal of Health Politics, Policy and Law
- Publisher
- Duke University Press
- Published
- 2009-12-01
- Volume
- 34
- Issue
- 6
- Pages
- 1011-1034
- ISSN
- 0361-6878 (ISSN-L)
- 1527-1927
- OpenAlex
- W2146570676
- Cited by
- 72
- References
- 49
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 6 · 2016: 8 · 2015: 8 · 2014: 9 · 2013: 4 · 2012: 4
- FWCI
- 9.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Health Politics Policy and Law (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Keywords
- ICT adoption, network effects, lock-in, technical compatibility, natural monopoly, health insurance market, patient heterogeneity, technical standards, health policy, health care costs
- MeSH terms
- Communication, Computer Communication Networks, Delivery of Health Care, Diffusion of Innovation, Health Benefit Plans, Employee, Health Personnel, Humans, Information Systems, Insurance Carriers, Politics, Public Policy, Time Factors, United States, Private Sector, Public Sector, Health Care Reform, Systems Integration, Health Insurance Portability and Accountability Act
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review