Evidence map›Paper›PMID 29843767›Full record

ArticleBMC medical informatics and decision making2018

Algorithm-based decision support for symptom self-management among adults with Cancer: results of usability testing.

Mary E Cooley, Janet L Abrahm, Donna L Berry, Michael S Rabin, Ilana M Braun, Joanna Paladino, Manan M Nayak, David F Lobach

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.4field-weighted citation impact, top 35% 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

7 citing papers in PubMed, 17 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Mary E CooleyThe Phyllis F. Cantor Center, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA. mary_cooley@dfci.harvard.edu.
Janet L AbrahmDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
Donna L BerryThe Phyllis F. Cantor Center and the Department of Medicine, Dana-Farber Cancer Institute, 450 Brookline Ave, LW-512, Boston, MA, 02115, USA.
Michael S RabinLowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
Ilana M BraunDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
Joanna PaladinoDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
Manan M NayakThe Phyllis F. Cantor Center, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
David F LobachKlesis Healthcare and Department of Family Medicine, Durham, NC, 27705, USA.
Dana-Farber Cancer Institute · USDuke University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is essential that cancer patients understand anticipated symptoms, how to self-manage these symptoms, and when to call their clinicians. However, patients are often ill-prepared to manage symptoms at home. Clinical decision support (CDS) is a potentially innovative way to provide information to patients where and when they need it. The purpose of this project was to design and evaluate a simulated model of an algorithm-based CDS program for self-management of cancer symptoms.

methodsThis study consisted of three phases; development of computable algorithms for self-management of cancer symptoms using a modified ADAPTE process, evaluation of a simulated model of the CDS program, and identification of design objectives and lessons learned from the evaluation of patient-centered CDS. In phase 1, algorithms for pain, constipation and nausea/vomiting were developed by an expert panel. In phase 2, we conducted usability testing of a simulated symptom assessment and management intervention for self-care (SAMI-Self-Care) CDS program involving focus groups, interviews and surveys with cancer patients, their caregivers and clinicians. The Acceptability E-scale measured acceptability of the program. In phase 3, we developed design objectives and identified barriers to uptake of patient-centered CDS based on the data gathered from stakeholders.

resultsIn phase 1, algorithms were reviewed and approved through a consensus meeting and majority vote. In phase 2, 24 patients & caregivers and 13 clinicians participated in the formative evaluation. Iterative changes were made in a simulated SAMI-Self-Care CDS program. Acceptability scores were high among patients, caregivers and clinicians. In phase 3, we formulated CDS design objectives, which included: 1) ensure patient safety, 2) communicate clinical concepts effectively, 3) promote communication with clinicians, 4) support patient activation, and 5) facilitate navigation and use. We identified patient barriers and clinician concerns to using CDS for symptom self-management, which were consistent with the chronic care model, a theoretical framework used to enhance patient-clinician communication and patient self-management.

conclusionPatient safety and tool navigation were critical features of CDS for patient self-management. Insights gleaned from this study may be used to inform the development of CDS resources for symptom self-management in patients with other chronic conditions.

Indexed as

AlgorithmsDecision Support Systems, ClinicalProgram DevelopmentProgram EvaluationAdultAgedFemaleFocus GroupsHumansMaleMiddle AgedNeoplasmsSelf-ManagementYoung AdultCancerPatient engagementPatient self-managementRule-based clinical decision supportSymptom management

Identifiers

PMID29843767
PMCPMC5975425
OpenAlexW2806197765

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.