Evidence map›Paper›PMID 24880002›Full record

ArticleJournal of pain and symptom management2015

Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer.

Mary E Cooley, Traci M Blonquist, Paul J Catalano, David F Lobach, Barbara Halpenny, Ruth McCorkle, Ellis B Johns, Ilana M Braun, Michael S Rabin, Fatma Zohra Mataoui and 3 more

Abstract readEvaluation StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pain and symptom management, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Mary E CooleyDana-Farber Cancer Institute, Boston, Massachusetts, USA. Electronic address: mary_cooley@dfci.harvard.edu.
Traci M BlonquistDana-Farber Cancer Institute, Boston, Massachusetts, USA.
Paul J CatalanoDana-Farber Cancer Institute, Boston, Massachusetts, USA.
David F LobachReligent Health, Durham, North Carolina, USA.
Barbara HalpennyDana-Farber Cancer Institute, Boston, Massachusetts, USA.
Ruth McCorkleYale University, New Haven, Connecticut, USA.
Ellis B JohnsVirginia Commonwealth University Shenandoah Valley, Front Royal, Virginia, USA.
Ilana M BraunDana-Farber Cancer Institute, Boston, Massachusetts, USA.
Michael S RabinDana-Farber Cancer Institute, Boston, Massachusetts, USA.
Fatma Zohra MataouiUniversity of Massachusetts, Boston, Massachusetts, USA.
Kathleen FinnBoston Medical Center, Boston, Massachusetts, USA.
Donna L BerryDana-Farber Cancer Institute, Boston, Massachusetts, USA.
Janet L AbrahmDana-Farber Cancer Institute, Boston, Massachusetts, USA.

Funding

A lung cancer symptom assessment and management interventionR01CA125256 · NCI · DANA-FARBER CANCER INST · PI COOLEY, MARY E · 2008 to 2011
$1.9M
NCI NIH HHS 1 K07 CA92696NCI NIH HHS R01 CA125256
6 · The paper itself

Abstract

contextDistressing symptoms interfere with the quality of life in patients with lung cancer. Algorithm-based clinical decision support (CDS) to improve evidence-based management of isolated symptoms seems promising, but no reports yet address multiple symptoms.

objectivesThis study examined the feasibility of CDS for a Symptom Assessment and Management Intervention targeting common symptoms in patients with lung cancer (SAMI-L) in ambulatory oncology. The study objectives were to evaluate completion and delivery rates of the SAMI-L report and clinician adherence to the algorithm-based recommendations.

methodsPatients completed a web-based symptom assessment and SAMI-L created tailored recommendations for symptom management. Completion of assessments and delivery of reports were recorded. Medical record review assessed clinician adherence to recommendations. Feasibility was defined as 75% or higher report completion and delivery rates and 80% or higher clinician adherence to recommendations. Descriptive statistics and generalized estimating equations were used for data analyses.

resultsSymptom assessment completion was 84% (95% CI=81-87%). Delivery of completed reports was 90% (95% CI=86-93%). Depression (36%), pain (30%), and fatigue (18%) occurred most frequently, followed by anxiety (11%) and dyspnea (6%). On average, overall recommendation adherence was 57% (95% CI=52-62%) and was not dependent on the number of recommendations (P=0.45). Adherence was higher for anxiety (66%; 95% CI=55-77%), depression (64%; 95% CI=56-71%), pain (62%; 95% CI=52-72%), and dyspnea (51%; 95% CI=38-64%) than for fatigue (38%; 95% CI=28-47%).

conclusionThe CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.

Indexed as

AlgorithmsDecision Support Systems, ClinicalAdultAgedAged, 80 and overEvidence-Based MedicineFeasibility StudiesFemaleHumansInternetLung NeoplasmsMaleMiddle AgedPalliative CarePractice Patterns, Physicians'Quality of Lifeclinical decision supportclinical practice guidelineslung cancerPalliative caresymptom management

Identifiers

PMID24880002
PMCPMC4621015

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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.