Evidence map›Paper›PMID 26115441›Full record

ArticlePloS one2015

Needs Assessment for Research Use of High-Throughput Sequencing at a Large Academic Medical Center.

Albert Geskin, Elizabeth Legowski, Anish Chakka, Uma R Chandran, M Michael Barmada, William A LaFramboise, Jeremy Berg, Rebecca S Jacobson

Abstract read
In one paragraph

Article in PloS one, 2015. 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. Article
  2. Article
  3. Review
  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

8 authors.

Albert GeskinDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
Elizabeth LegowskiDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
Anish ChakkaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America.
Uma R ChandranDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America.
M Michael BarmadaInstitute for Personalized Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America; Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.
William A LaFramboiseDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America.
Jeremy BergInstitute for Personalized Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
Rebecca S JacobsonDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, United States of America; Institute for Personalized Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jessie Nedrow · 1988 to 2026
$158.0M
NCI NIH HHS P30 CA047904NCI NIH HHS P30CA047904
6 · The paper itself

Abstract

Next Generation Sequencing (NGS) methods are driving profound changes in biomedical research, with a growing impact on patient care. Many academic medical centers are evaluating potential models to prepare for the rapid increase in NGS information needs. This study sought to investigate (1) how and where sequencing data is generated and analyzed, (2) research objectives and goals for NGS, (3) workforce capacity and unmet needs, (4) storage capacity and unmet needs, (5) available and anticipated funding resources, and (6) future challenges. As a precursor to informed decision making at our institution, we undertook a systematic needs assessment of investigators using survey methods. We recruited 331 investigators from over 60 departments and divisions at the University of Pittsburgh Schools of Health Sciences and had 140 respondents, or a 42% response rate. Results suggest that both sequencing and analysis bottlenecks currently exist. Significant educational needs were identified, including both investigator-focused needs, such as selection of NGS methods suitable for specific research objectives, and program-focused needs, such as support for training an analytic workforce. The absence of centralized infrastructure was identified as an important institutional gap. Key principles for organizations managing this change were formulated based on the survey responses. This needs assessment provides an in-depth case study which may be useful to other academic medical centers as they identify and plan for future needs.

Indexed as

Academic Medical CentersBiomedical ResearchNeeds AssessmentCooperative BehaviorCosts and Cost AnalysisHigh-Throughput Nucleotide SequencingHumansKnowledgeResearch PersonnelSurveys and QuestionnairesWorkforce

Identifiers

PMID26115441
PMCPMC4483235

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.