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Record W2971500532 · doi:10.2196/14462

Comparative Effectiveness of a Web-Based Patient Decision Aid for Therapeutic Options for Sickle Cell Disease: Randomized Controlled Trial

2019· article· en· W2971500532 on OpenAlexaboutno aff
Lakshmanan Krishnamurti, Diana Ross, Cynthia Sinha, Traci Leong, Namita Bakshi, Nonita Mittal, Divya Veludhandi, Anh-Phuong Pham, Alankrita Taneja, Kamesh Gupta, Julum Nwanze, Andrea Matthews, Saumya Joshi, Veronica Vazquez-Olivieri, Santhi Arjunan, Ifechi Okonkwo, Ines Lukombo, Peter A. Lane, Nitya Bakshi, George Loewenstein

Bibliographic record

VenueJournal of Medical Internet Research · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstitutePatient-Centered Outcomes Research Institute
KeywordsDecision aidsMedicineRandomized controlled trialDecision support systemPopulationClinical decision support systemHealth careHealth literacyUsabilityAlternative medicineSurgeryComputer scienceData mining

Abstract

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BACKGROUND: Hydroxyurea, chronic blood transfusions, and bone marrow transplantation are efficacious, disease-modifying therapies for sickle cell disease but involve complex risk-benefit trade-offs and decisional dilemma compounded by the lack of comparative studies. A patient decision aid can inform patients about their treatment options, the associated risks and benefits, help them clarify their values, and allow them to participate in medical decision making. OBJECTIVE: The objective of this study was to develop a literacy-sensitive Web-based patient decision aid based on the Ottawa decision support framework, and through a randomized clinical trial estimate the effectiveness of the patient decision aid in improving patient knowledge and their involvement in decision making. METHODS: We conducted population decisional needs assessments in a nationwide sample of patients, caregivers, community advocates, policy makers, and health care providers using qualitative interviews to identify decisional conflict, knowledge and expectations, values, support and resources, decision types, timing, stages and learning, and personal clinical characteristics. Interview transcripts were coded using QSR NVivo 10. Alpha testing of the patient decision aid prototype was done to establish usability and the accuracy of the information it conveyed, and then was followed by iterative cycles of beta testing. We conducted a randomized clinical trial of adults and of caregivers of pediatric patients to evaluate the efficacy of the patient decision aid. RESULTS: In a decisional needs assessment, 223 stakeholders described their preferences, helping to guide the development of the patient decision aid, which then underwent alpha testing by 30 patients and 38 health care providers and iterative cycles of beta testing by 87 stakeholders. In a randomized clinical trial, 120 participants were assigned to either the patient decision aid or standard care (SC) arm. Qualitative interviews revealed high levels of usability, acceptability, and utility of the patient decision aid in education, values clarification, and preparation for decision making. On the acceptability survey, 72% (86/120) of participants rated the patient decision aid as good or excellent. Participants on the patient decision aid arm compared to the SC arm demonstrated a statistically significant improvement in decisional self-efficacy (P=.05) and a reduction in the informed sub-score of decisional conflict (P=.003) at 3 months, with an improvement in preparation for decision making (P<.001) at 6 months. However, there was no improvement in terms of the change in knowledge, the total or other domain scores of decisional conflicts, or decisional self-efficacies at 6 months. The large amount of missing data from survey completion limited our ability to draw conclusions about the effectiveness of the patient decision aid. The patient decision aid met 61 of 62 benchmarks of the international patient decision aid collaboration standards for content, development process, and efficacy. CONCLUSIONS: We have developed a patient decision aid for sickle cell disease with extensive input from stakeholders and in a randomized clinical trial demonstrated its acceptability and utility in education and decision making. We were unable to demonstrate its effectiveness in improving patient knowledge and involvement in decision making. TRIAL REGISTRATION: ClinicalTrials.gov NCT03224429; https://clinicaltrials.gov/ct2/show/NCT03224429 and ClinicalTrials.gov NCT02326597; https://clinicaltrials.gov/ct2/show/NCT02326597.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.404
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2019
Admission routes1
Has abstractyes

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