Evidence map›Paper›PMID 35455673›Full record

ArticleJournal of personalized medicine2022

Analyzing Precision Medicine Utilization with Real-World Data: A Scoping Review.

Michael P Douglas, Anika Kumar

Open access · goldAbstract readScoping Review
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Drug Safety and Relevant Issues in the Real-World.Pharmaceuticals (Basel, Switzerland) · 2023
    Article
  4. Article
  5. Article
  6. Review
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

2 authors at 1 institution in 1 country.

Michael P DouglasCenter for Translational and Policy Research on Precision Medicine (TRANSPERS), Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0001-8517-6678
Anika KumarSchool of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.
University of California, San Francisco · US

Funding

Sharing Aggregate Genomics Research Findings with Diverse Participants: A Pilot Study of an Innovative Design StrategyU01HG009599 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KOENIG, BARBARA A., KWOK, PUI-YAN · 2017 to 2021
$16.3M
BUILDING THE EVIDENCE BASE FOR APPROPRIATE AND EFFICIENT IMPLEMENTATION OF EMERGING GENOMIC TESTS FOR DISEASE MANAGEMENT AND SCREENINGR01HG011792 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PHILLIPS, KATHRYN A · 2021 to 2025
$4.6M
Coverage, Price, and Reimbursement for Multigene Tests for Cancer and Related ConditionsR01CA221870 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PHILLIPS, KATHRYN A · 2018 to 2020
$2.2M
Illumina (United States) NoneNCI NIH HHS R01CA221870NHGRI NIH HHS R01HG011792NHGRI NIH HHS U01HG009599
6 · The paper itself

Abstract

Precision medicine (PM), specifically genetic-based testing, is currently used in over 140,000 individual tests to inform the clinical management of disease. Though several databases (e.g., the NIH Genetic Testing Registry) demonstrate the availability of these sequencing-based tests, we do not currently understand the extent to which these tests are used. There exists a need to synthesize the body of real-world data (RWD) describing the use of sequencing-based tests to inform their appropriate use. To accomplish this, we performed a scoping review to examine what RWD sources have been used in studies of PM utilization between January 2015 and August 2021 to characterize the use of genome sequencing (GS), exome sequencing (ES), tumor sequencing (TS), next-generation sequencing-based panels (NGS), gene expression profiling (GEP), and pharmacogenomics (PGx) panels. We abstracted variables describing the use of these types of tests and performed a descriptive statistical analysis. We identified 440 articles in our search and included 72 articles in our study. Publications based on registry databases were the most common, followed by studies based on private insurer administrative claims. Slightly more than one-third (38%) used integrated datasets. Two thirds (67%) of the studies focused on the use of tests for oncological clinical applications. We summarize the RWD sources used in peer-reviewed literature on the use of PM. Our findings will help improve future study design by encouraging the use of centralized databases and registries to track the implementation and use of PM.

Indexed as

precision medicinereal-world dataregistryscoping reviewutilization

Identifiers

PMID35455673
PMCPMC9025578
OpenAlexW4224325815

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.