Evidence map›Paper›PMID 31450082›Full record

SynthesisInternational journal of medical informatics2019

Clinical decision support for therapeutic decision-making in cancer: A systematic review.

Melissa Beauchemin, Meghan T Murray, Lillian Sung, Dawn L Hershman, Chunhua Weng, Rebecca Schnall

Abstract readSystematic Review
In one paragraph

Synthesis in International journal of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A balancing act: the promise and pitfalls of clinical decision support.Proceedings (Baylor University. Medical Center) · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  9. Article
  10. Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making.Health expectations : an international journal of public participation in health care and health policy · 2022
    Article
  11. Article
  12. Article
  13. Review
  14. 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

6 authors.

Melissa BeaucheminSchool of Nursing, Columbia University, New York, NY, 10032, United States; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, United States. Electronic address: mmp2123@cumc.columbia.edu.
Meghan T MurraySchool of Nursing, Columbia University, New York, NY, 10032, United States.
Lillian SungThe Hospital for Sick Children, Toronto, Canada.
Dawn L HershmanHerbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, United States.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
Rebecca SchnallSchool of Nursing, Columbia University, New York, NY, 10032, United States.

Funding

Reducing Health Disparities Through Informatics - Genomics SupplementT32NR007969 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE BAKKEN, Rebecca Schnall · 2002 to 2026
$7.9M
Reaching Communities through the Design of Information Visualizations (ReDIVis) Toolbox to Address COVID-19 Vaccine Hesitancy and Uptake.P30NR016587 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE, SMALDONE, ARLENE M · 2016 to 2022
$4.3M
NINR NIH HHS P30 NR016587NINR NIH HHS T32 NR007969
6 · The paper itself

Abstract

Cancer management, including supportive care, is complex and requires availability and synthesis of published and patient-specific data to make appropriate therapeutic decisions. Clinical decision support (CDS) may be an effective implementation strategy to support complex decision making although it is unclear whether it improves process outcomes, patient outcomes or both in cancer settings. We therefore conducted a systematic review to identify CDS that have been used to support therapeutic decision making in clinical cancer settings. Outcomes of interest included the effect of CDS on the process, such as clinician's decision making and effect on patient outcomes. Ten studies met inclusion criteria, with variability in the study design, setting, and intervention. Of the nine studies that measured process outcomes, five demonstrated significant improvement; and of the six that measured patient outcomes, four demonstrated significant improvement. All included studies utilized CDS that were informed by clinical practice guidelines. In conclusion, CDS to guide cancer therapeutic decision making is an understudied but promising area. Further research is needed.

Indexed as

Decision MakingDecision Support Systems, ClinicalHumansNeoplasmsCancerClinical decision supportDecision making

Identifiers

PMID31450082
PMCPMC7024607

What OpenQuestion holds

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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.