Evidence map›Paper›PMID 38756977›Full record

ArticleHealth affairs scholar2023

Active steps to reduce administrative spending associated with financial transactions in US health care.

Nikhil R Sahni, Pranay Gupta, Michael Peterson, David M Cutler

Abstract read
In one paragraph

Article in Health affairs scholar, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Correct Coding and Navigating Payer Expectations.Advances in health information science and practice · 2025
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nikhil R SahniDepartment of Economics, Harvard University, Cambridge, MA 02138, United States.
Pranay GuptaCenter for US Healthcare Improvement, McKinsey & Company, Boston, MA 02210, United States.
Michael PetersonCenter for US Healthcare Improvement, McKinsey & Company, Boston, MA 02210, United States.
David M CutlerDepartment of Economics, Harvard University, Cambridge, MA 02138, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

US health care administrative spending is approximately $1 trillion annually. A major operational area is the financial transactions ecosystem, which has approximately $200 billion in spending annually. Efficient financial transactions ecosystems from other industries and countries exhibit 2 features: immediate payment assurance and high use of automation throughout the process. The current system has an average transaction cost of $12 to $19 per claim across private payers and providers for more than 9 billion claims per year; each claim on average takes 4 to 6 weeks to process and pay. For simple claims, the transaction cost is $7 to $10 across private payers and providers; for complex claims, $35 to $40. Prior authorization on approximately 5000 codes has an average cost of $40 to $50 per submission for private payers and $20 to $30 for providers. Interventions aligned with a more efficient financial transactions ecosystem could reduce spending by $40 billion to $60 billion; approximately half is at the organizational level (scaling interventions being implemented by leading private payers and providers) and half at the industry level (adopting a centralized automated claims clearinghouse, standardizing medical policies for a subset of prior authorizations, and standardizing physician licensure for a national provider directory).

Indexed as

administrative spendingclaims processinghealth care spendingprior authorization

Identifiers

PMID38756977
PMCPMC10986268

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.