Evidence map›Paper›PMID 38644929›Full record

ArticleThe quarterly journal of economics2024

MONITORING FOR WASTE: EVIDENCE FROM MEDICARE AUDITS.

Maggie Shi

Abstract read
In one paragraph

Article in The quarterly journal of economics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Payment integrity in government programs: Takeaways from incorporating the behavioral sciences in US federal evaluations.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Can Machine Learning Target Health Care Fraud? Evidence from Medicare Hospitalizations.Journal of policy analysis and management : [the journal of the Association for Public Policy Analysis and Management] · 2026
    Article
  3. Article
  4. 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

1 author.

Maggie ShiUniversity of Chicago Harris School of Public Policy and NBER.

Funding

NBER Center for Aging and Health Research-Post COVID-19 Experiences Among Adults with ADRDP30AG012810 · NIA · NATIONAL BUREAU OF ECONOMIC RESEARCH · PI DAVID M CUTLER · 1999 to 2026
$24.6M
NBER Training Program in Aging and Health EconomicsT32AG000186 · NIA · NATIONAL BUREAU OF ECONOMIC RESEARCH · PI CUTLER, DAVID M, FINKELSTEIN, AMY N. · 1989 to 2024
$9.6M
The effect of audits on physicians and patients: Evidence from MedicareR36HS027715 · AHRQ · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SHI, MENGDI · 2020 to 2020
$34k
AHRQ HHS R36 HS027715NIA NIH HHS P30 AG012810NIA NIH HHS T32 AG000186
6 · The paper itself

Abstract

This paper examines the tradeoffs of monitoring for wasteful public spending. By penalizing unnecessary spending, monitoring improves the quality of public expenditure and incentivizes firms to invest in compliance technology. I study a large Medicare program that monitored for unnecessary healthcare spending and consider its effect on government savings, provider behavior, and patient health. Every dollar Medicare spent on monitoring generated $24-29 in government savings. The majority of savings stem from the deterrence of future care, rather than reclaimed payments from prior care. I do not find evidence that the health of the marginal patient is harmed, indicating that monitoring primarily deters low-value care. Monitoring does increase provider administrative costs, but these costs are mostly incurred upfront and include investments in technology to assess the medical necessity of care.

Indexed as

H51H83I00I13I18M42M48

Identifiers

PMID38644929
PMCPMC11031258

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.