Evidence map›Paper›PMID 40165421›Full record

ArticleStatistical methods in medical research2025

Methodology for supervised optimization of the construction of physician shared-patient networks.

A James O'Malley, Yifan Zhao, Carly Bobak, Chuanling Qin, Erika L Moen, Daniel N Rockmore

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Article in Statistical methods in medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

A James O'MalleyDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.ORCID 0000-0001-8389-6217
Yifan ZhaoProgram in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Carly BobakDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Chuanling QinProgram in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Erika L MoenDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Daniel N RockmoreDepartment of Mathematics, Dartmouth College, Hanover, NH, USA.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
UNDERSTANDING AND IMPROVING EPISODE-BASED HOSPITAL CAREP01AG019783 · NIA · DARTMOUTH COLLEGE · PI Leila S. Agha · 2001 to 2026
$52.9M
NCI NIH HHS P30 CA023108NIA NIH HHS P01 AG019783
6 · The paper itself

Abstract

There is growing use of shared-patient physician networks in health services research and practice, but minimal study of the consequences of decisions made in constructing them. To address this gap, we surveyed physician employees of a National Physician Organization (NPO) on their peer physician relationships. Using the physicians' survey nominations as ground truths, we evaluated the diagnostic accuracy of shared-patient edge-weights and the optimal construction of physician networks from sequences of patient-physician encounters. To further improve diagnostic accuracy, we optimized network construction with respect to the within-dyad difference and summation of edge-strength (two orthogonal measures), optimally combining them to form a final edge-weight. To achieve these goals, we develop statistical procedures to quantify the extent that directionality and other features of referral paths yield edge-weights with improved diagnostic properties. We also develop network models of the survey nominations incorporating directed (edge) and undirected (dyadic) shared-patient network measures as edge and dyad attributes to demonstrate that the measurement of the network as a whole is improved. Finally, we estimate the association of the physicians' centrality in the NPO shared-patient network (a sociocentric feature that cannot be evaluated for the partially-measured survey-based network) with their beliefs regarding physician peer-influence.

Indexed as

Physician-Patient RelationsPhysiciansHealth Services ResearchHumansModels, StatisticalBipartite networkdiagnostic accuracydirectional informationoptimal unipartite projectionphysician beliefsshared-patient physician network

Identifiers

PMID40165421
PMCPMC12270385

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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.