Evidence map›Paper›PMID 36933748›Full record

ArticleJournal of pain and symptom management2023

Clinical Decision Support Systems for Palliative Care Management: A Scoping Review.

Fabiana Cristina Dos Santos, Urszula A Snigurska, Gail M Keenan, Robert J Lucero, François Modave

Open access · bronzeAbstract readScoping Review
In one paragraph

Article in Journal of pain and symptom management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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  11. Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025
    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

5 authors at 2 institutions in 1 country.

Fabiana Cristina Dos SantosDepartment of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA. Electronic address: fabianadossantos@ufl.edu.
Urszula A SnigurskaDepartment of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA.
Gail M KeenanDepartment of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA.
Robert J LuceroSchool of Nursing (R.J.L.), University of California Los Angeles, Los Angeles, California, USA.
François ModaveDepartment of MD-Anesthesiology (F.M), College of Medicine, University of Florida, Gainesville, Florida, USA.
University of Florida · USUniversity of California, Los Angeles · US

Funding

Tailored Clinical Decision Support Formats Designed to Improve Palliative Care for Cancer and Chronically Ill Patients: A Pre-Clinical TestR01NR018416 · NINR · UNIVERSITY OF FLORIDA · PI DUNN LOPEZ, KAREN, KEENAN, GAIL M · 2019 to 2021
$1.7M
NINR NIH HHS R01 NR018416
6 · The paper itself

Abstract

contextWith the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions.

objectivesTo characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time.

methodsThe CINAHL, Embase, and PubMed databases were searched from inception to September 2022. The review was developed following the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines. Qualified studies were described in tables and assessed the level of evidence.

resultsA total of 284 abstracts were screened, and 12 studies comprised the final sample. The CDSSs selected focused on identifying patients who could benefit from palliative care based on their health status, making referrals to palliative care services, and managing medications and symptom control. Despite the variability of palliative CDSSs, all studies reported that CDSSs assisted clinicians in becoming more informed about palliative care options leading to better decisions and improved patient outcomes. Seven studies explored the impact of CDSSs on end-user adherence. Three studies revealed high adherence to recommendations while four had low adherence. Lack of feature customization and trust in guideline-based in the initial stages of feasibility and usability testing were evident, limiting the usefulness for nurses and other clinicians.

conclusionThis study demonstrated that implementing palliative care CDSSs can assist nurses and other clinicians in improving the quality of care for palliative patients. The studies' different methodological approaches and variations in palliative CDSSs made it challenging to compare and validate the applicability under which CDSSs are effective. Further research utilizing rigorous methods to evaluate the impact of clinical decision support features and guideline-based actions on clinicians' adherence and efficiency is recommended.

Indexed as

Decision Support Systems, ClinicalHospice and Palliative Care NursingHumansPalliative CareReferral and Consultationclinical decision-makingClinical decision supportelectronic health recordnursingpalliative carescoping review

Identifiers

PMID36933748
PMCPMC11162595
OpenAlexW4327560495

What OpenQuestion holds

Textmetadata
LicenceTDM
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.