Evidence map›Paper›PMID 31509353›Full record

ArticleCancer medicine2019

Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists.

Donna M McNamara, Stuart L Goldberg, Lisa Latts, Deena M Atieh Graham, Stanley E Waintraub, Andrew D Norden, Cody Landstrom, Andrew L Pecora, John Hervey, Eric V Schultz and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
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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

14 authors.

Donna M McNamaraDivision of Breast Oncology, John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, NJ, USA.
Stuart L GoldbergCota Inc., New York, NY, USA.ORCID 0000-0002-5996-1055
Lisa LattsIBM Watson Health, Cambridge, MA, USA.
Deena M Atieh GrahamDivision of Breast Oncology, John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, NJ, USA.
Stanley E WaintraubDivision of Breast Oncology, John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, NJ, USA.
Andrew D NordenCota Inc., New York, NY, USA.
Cody LandstromCota Inc., New York, NY, USA.
Andrew L PecoraCota Inc., New York, NY, USA.
John HerveyCota Inc., New York, NY, USA.
Eric V SchultzCota Inc., New York, NY, USA.
Ching-Kun WangIBM Watson Health, Cambridge, MA, USA.
Nicholas JungbluthIBM Watson Health, Cambridge, MA, USA.
Phillip M FrancisIBM Watson Health, Cambridge, MA, USA.
Jane L SnowdonIBM Watson Health, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCognitive computing point-of-care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown.

methodsElectronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform.

resultsThe cognitive computing "recommended" option was concordant with selection by breast cancer experts in 78.5% and "for consideration" option was selected in 9.4%, yielding agreements in 87.9%. Fifty-nine percent of non-concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with "recommended," 11.4% "for consideration", and 19.3% "not recommended." Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non-recommended option (P < .01) without WfO/Cota RWE and changed decisions in 39% cases.

conclusionsWatson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non-recommended options in a real world database highlights a need for decision support.

Indexed as

Decision Support Systems, ClinicalAgedAged, 80 and overBreast NeoplasmsClinical CompetenceClinical Decision-MakingElectronic Health RecordsFemaleHumansOncologistsPoint-of-Care SystemsTertiary Care CentersUnited Statesartificial intelligenceelectronic health recordspoint-of-care systemsreal world evidence

Identifiers

PMID31509353
PMCPMC6825991

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.