Evidence map›Paper›PMID 38443870›Full record

ArticleBMC medical informatics and decision making2024

A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all.

Hayley Dunnack Yackel, Barbara Halpenny, Janet L Abrahm, Jennifer Ligibel, Andrea Enzinger, David F Lobach, Mary E Cooley

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Trial
  2. 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

7 authors at 3 institutions in 1 country.

Hayley Dunnack YackelHartford HealthCare Cancer Institute, 80 Seymour Street, 06106, Hartford, CT, USA.
Barbara HalpennyDana-Farber Cancer Institute, 450 Brookline Ave, LW-508, 02215, Boston, MA, USA.
Janet L AbrahmDana-Farber Cancer Institute, 450 Brookline Ave, LW-508, 02215, Boston, MA, USA.
Jennifer LigibelDana-Farber Cancer Institute, 450 Brookline Ave, LW-508, 02215, Boston, MA, USA.
Andrea EnzingerDana-Farber Cancer Institute, 450 Brookline Ave, LW-508, 02215, Boston, MA, USA.
David F LobachElimu Informatics, 1709 Julian Court, 94530, El Cerrito, CA, USA.
Mary E CooleyDana-Farber Cancer Institute, 450 Brookline Ave, LW-508, 02215, Boston, MA, USA. mary_cooley@dfci.harvard.edu.ORCID 0000-0001-5353-9542
Dana-Farber Cancer Institute · USElectronics for Imaging (United States) · USHartford Hospital · US

Funding

SBIR PHASE II TOPIC 377: ENABLING CLINICAL DECISION SUPPORT FOR GUIDELINE-BASED CANCER SYMPTOM MANAGEMENT (MOONSHOT)75N91020C00019 · NCI · ELIMU INFORMATICS, INC. · PI LOBACH, DAVID · 2020 to 2020
$1.5M
NCI NIH HHS 75N91020C00019NCI NIH HHS HHSN261201800022C
6 · The paper itself

Abstract

backgroundAdults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care.

methodsThe objectives of this project were to develop and evaluate the usability of two symptom management algorithms (constipation and fatigue) across the trajectory of cancer care in patients with active disease treated in comprehensive or community cancer care settings to surveillance of cancer survivors in primary care practices. A modified ADAPTE process was used to develop algorithms based on national CPGs. Usability testing involved semi-structured interviews with clinicians from varied care settings, including comprehensive and community cancer centers, and primary care. The transcripts were analyzed with MAXQDA using Braun and Clarke's thematic analysis method. A cross tabs analysis was also performed to assess the prevalence of themes and subthemes by cancer care setting.

resultsA total of 17 clinicians (physicians, nurse practitioners, and physician assistants) were interviewed for usability testing. Three main themes emerged: (1) Algorithms as useful, (2) Symptom management differences, and (3) Different target end-users. The cross-tabs analysis demonstrated differences among care trajectories and settings that originated in the Symptom management differences theme. The sub-themes of "Differences between diseases" and "Differences between care trajectories" originated from participants working in a comprehensive cancer center, which tends to be disease-specific locations for patients on active treatment. Meanwhile, participants from primary care identified the sub-theme of "Differences in settings," indicating that symptom management strategies are care setting specific.

conclusionsWhile CDS can help promote evidence-based symptom management, systems providing care recommendations need to be specifically developed to fit patient characteristics and clinical context. Findings suggest that one set of algorithms will not be applicable throughout the entire cancer trajectory. Unique CDS for symptom management will be needed for patients who are cancer survivors being followed in primary care settings.

Indexed as

Cancer SurvivorsNeoplasmsNurse PractitionersAdultAlgorithmsHumansUser-Centered DesignUser-Computer InterfaceCancer-related constipationCancer-related fatigueCancer symptom managementClinical decision supportClinical practice guidelines

Identifiers

PMID38443870
PMCPMC10913367
OpenAlexW4392470488

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.