Evidence map›Paper›PMID 41937101›Full record

ArticleCancer medicine2026

A Novel Simulation Framework for Evaluating and Optimizing Oncology Outreach Policies in a Regional Cancer System.

Bruno T Scodari, A James O'Malley, Nirav S Kapadia, Gabriel A Brooks, Ashlee A Korsberg, Erika L Moen

Abstract read
In one paragraph

Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bruno T ScodariDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.ORCID https://orcid.org/0000-0003-3198-0876
A James O'MalleyDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Nirav S KapadiaThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Gabriel A BrooksThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Ashlee A KorsbergDepartment of Biomedical Data Science, 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.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
Improving cancer health equity by targeting physician networksR37CA263936 · NCI · DARTMOUTH COLLEGE · PI MOEN, ERIKA · 2021 to 2025
$2.3M
Dartmouth Training Program in Quantitative Cancer ResearchT32CA260262 · NCI · DARTMOUTH COLLEGE · PI Scott A. Gerber, Diane Ihn Ae Gilbert-Diamond · 2023 to 2026
$789k
NCI NIH HHS P30 CA023108NCI NIH HHS R37 CA263936NCI NIH HHS R37CA263936NCI NIH HHS T32 CA260262NCI NIH HHS T32CA260262NIH HHS
6 · The paper itself

Abstract

backgroundOncology outreach is a common strategy for addressing cancer workforce shortages, where traveling oncologists commute across healthcare settings to extend specialized care. However, methods for evaluating and optimizing outreach strategies are limited.

methodsWe developed a simulation framework for evaluating and optimizing potential outreach strategies and applied it to two test cases within Dartmouth Health's hub-and-spoke cancer system. Our approach involved sampling incident patients and visit capacity across oncologist/site pairs, assigning patients to oncologist/site pairs, and estimating the total patient travel time incurred across two groups: (1) where a portion of patient visits is shifted from the hub facility to a satellite, and (2) where the original distribution of patient visits is retained. After 5000 simulations, the mean difference in patient travel time among groups was computed to estimate patient travel time savings. We identified the optimal number of new patient visits to reallocate from the hub to satellite that maximized patient travel time savings.

resultsWe identified satellites in St. Johnsbury, VT and Manchester, NH as candidates for increased medical and radiation oncology outreach, respectively. At St. Johnsbury, we found that reallocating 45% of new medical oncology visits resulted in 140 (95% CI: 97-174) total hours saved. For patients whose care was shifted, this corresponded to an average of 1.07 (95% CI: 0.75-1.34) hours saved per visit. At Manchester, we found that reallocating 50% of new radiation oncology visits resulted in 55 (95% CI: 34-71) total hours saved. For patients whose care was shifted, this corresponded to an average of 1.13 (95% CI: 0.70-1.44) hours saved per visit.

conclusionSimulations offer a valuable means to assess the effectiveness and uncertainty of potential outreach strategies. Such approaches are well positioned to provide administrative decision support for health systems and can be leveraged to inform directional investments in outreach policies.

Indexed as

Medical OncologyNeoplasmsComputer SimulationHumans

Identifiers

PMID41937101
PMCPMC13051838

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