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Record W3200333554 · doi:10.2196/28259

Experiences and Perceptions of Patients and Providers Participating in Remote Titration of Heart Failure Medication Facilitated by Telemonitoring: Qualitative Study

2021· article· en· W3200333554 on OpenAlexaffvenueabout
Veronica Artanian, Patrick Ware, Valeria E. Rac, Heather J. Ross, Emily Seto

Bibliographic record

VenueJMIR Cardio · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsToronto General HospitalTed Rogers Centre for Heart ResearchUniversity Health NetworkPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsRandomized controlled trialMedicineIntervention (counseling)Qualitative researchNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Guideline-directed medical therapy (GDMT), optimized to target doses, improves health outcomes in patients with heart failure. However, GDMT remains underused, with <25% of patients receiving target doses in clinical practice. A randomized controlled trial was conducted at the Peter Munk Cardiac Centre in Toronto to compare a remote GDMT titration intervention with standard in-office titration. This randomized controlled trial found that remote titration increased the proportion of patients who achieved optimal GDMT doses, decreased the time to dose optimization, and reduced the number of essential clinic visits. This paper presents findings from the qualitative component of the mixed methods study, which evaluated the implementation of the remote titration intervention. OBJECTIVE: The objective of the qualitative component is to assess the perceptions and experiences of clinicians and patients with heart failure who participated in the remote titration intervention to identify factors that affected the implementation of the intervention. METHODS: We conducted semistructured interviews with clinicians (n=5) and patients (n=11) who participated in the remote titration intervention. Questions probed the experiences of the participants to identify factors that can serve as barriers and facilitators to its implementation. Conventional content analysis was first used to analyze the interviews and gain direct information based on the participants' unique perspectives. Subsequently, the generated themes were delineated and mapped following a multilevel framework. RESULTS: Patients and clinicians indicated that the intervention was easy to use, integrated well into their routines, and removed practical barriers to titration. Key implementation facilitators from the patients' perspective included the reduction in clinic visits and daily monitoring of their condition, whereas clinicians emphasized the benefits of rapid drug titration and efficient patient management. Key implementation barriers included the resources necessary to support the intervention and lack of physician remuneration. CONCLUSIONS: This study presents results from a real-world implementation assessment of remote titration facilitated by telemonitoring. It is among the first to provide insight into the perception of the remote titration process by clinicians and patients. Our findings indicate that the relative advantages that remote titration presents over standard care strongly appeal to both clinicians and patients. However, to ensure uptake and adherence, it is important to ensure that suitable patients are enrolled and the impact on the physicians' workload is minimized. The implementation of remote titration is now more critical than ever, as it can help provide access to care for patients during times when physical distancing is required. TRIAL REGISTRATION: ClinicalTrials.gov NCT04205513; https://clinicaltrials.gov/ct2/show/NCT04205513. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19705.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.356
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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".

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Citations15
Published2021
Admission routes3
Has abstractyes

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