Evidence map›Paper›PMID 40240567›Full record

ArticleJournal of medical systems2025

The AI Efficiency Paradox: Reclaiming Quality Patient Care in an Era of Optimization.

Julian Michael Burwell

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Julian Michael BurwellDepartment of Medical Education, Geisinger Commonwealth School of Medicine, Scranton, PA, USA. jburwell1@som.geisinger.edu.ORCID https://orcid.org/0009-0006-1059-5134

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We examine how artificial intelligence (AI) integration in healthcare may create an "efficiency paradox" where technologies designed to reduce workload can instead generate new layers of inefficiency. We argue that AI implementation strategies prioritizing efficiency metrics over meaningful patient interactions risk undermining care quality. A framework is proposed for evaluating AI adoption that balances technological optimization with perseveration of the physician-patient relationship.

Indexed as

Artificial IntelligenceEfficiency, OrganizationalQuality of Health CareHumansPhysician-Patient RelationsAIEfficiencyHealthcareHigh-value carePhysician-patient relationship

Identifiers

What OpenQuestion holds

Textmetadata
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.