Evidence map›Paper›PMID 37806365›Full record

ArticleAmerican journal of preventive medicine2024

Implementation Mapping for Managing Patients at High Risk for Hereditary Cancer.

Caitlin G Allen, Colleen Donahue, Emma Coen, Kiersten Meeder, Kristin Wallace, Cathy Melvin, Brian Neelon, Kevin Hughes

Open access · greenAbstract read
In one paragraph

Article in American journal of preventive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

8 authors at 1 institution in 1 country.

Caitlin G AllenDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina. Electronic address: allencat@musc.edu.
Colleen DonahueDepartment of Surgery, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Emma CoenDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Kiersten MeederDivision of Oncologic and Endocrine Surgery, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Kristin WallaceDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Cathy MelvinDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Brian NeelonDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Kevin HughesDepartment of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Medical University of South Carolina · US

Funding

Translational Science Laboratory Shared ResourceP30CA138313 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Jian Ouyang · 2009 to 2026
$42.7M
SC Biomedical Informatics & Data Science for Health Impact (SC BIDS4Health)T15LM013977 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Alexander V Alekseyenko, Brian Dean · 2022 to 2026
$1.2M
Improving Family Cancer History Collection using Social Network, Human Centered Design, and Implementation Science ApproachesK00CA253576 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI ALLEN, CAITLIN GLOECKNER · 2020 to 2024
$443k
NCI NIH HHS K00 CA253576NCI NIH HHS P30 CA138313NLM NIH HHS T15 LM013977
6 · The paper itself

Abstract

introductionCurrently, no standard workflow exists for managing patients with pathogenic variants that put them at higher risk for hereditary cancers. Therefore, follow-up care for individuals with pathogenic variants is logistically challenging and results in poor guideline adherence. To address this challenge, authors created clinical management strategies for individuals identified at high risk for hereditary cancers.

methodsAn implementation mapping approach was used to develop and evaluate the establishment of a Hereditary Cancer Clinic at the Medical University of South Carolina throughout in 2022. This approach consisted of 5 steps: conduct a needs assessment, identify objectives, select implementation strategies, produce implementation protocols, and develop an evaluation plan. The needs assessment consisted of qualitative interviews with patients (n=11), specialists (n=9), and members of the implementation team (n=4). Interviews were coded using the Consolidated Framework for Implementation Research to identify barriers and facilitators to establishment of the Hereditary Cancer Clinic. Objectives were identified, and then the team selected implementation strategies and produced implementation protocols to address concerns identified during the needs assessment. Authors conducted a second round of patient interviews to assess patient education materials.

resultsThe research team developed a long-term evaluation plan to guide future assessment of implementation, service, and clinical/patient outcomes.

conclusionsThis approach provides the opportunity for real-time enhancements and impact, with strategies for care specialists, patients, and implementation teams. Findings support ongoing efforts to improve patient management and outcomes while providing an opportunity for long-term evaluation of implementation strategies and guidelines for patients at high risk for hereditary cancers.

Indexed as

Guideline AdherenceNeoplasmsGenetic Predisposition to DiseaseHumansNeeds AssessmentQualitative Research

Identifiers

PMID37806365
PMCPMC10922485
OpenAlexW4387398129

What OpenQuestion holds

Textmetadata
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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.