MétaCan
Menu
Back to cohort
Record W4381549518 · doi:10.2196/46528

Optimizing Patient-Reported Outcome Collection and Documentation in Medical Music Therapy: Process-Improvement Study

2023· article· en· W4381549518 on OpenAlexvenueno aff
Samuel N. Rodgers‐Melnick, Seneca Block, Rachael L. Rivard, Jeffery A. Dusek

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationData collectionPsychological interventionMedicineOutcome (game theory)MEDLINEProcess (computing)Medical emergencyMedical physicsComputer scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: To measure the effectiveness of nonpharmacologic interventions delivered during clinical care, investigators need to ensure robust and routine data collection without disrupting individualized patient care or adding unnecessary documentation burden. OBJECTIVE: A process-improvement study was undertaken to improve documentation consistency and increase the capture of patient-reported outcomes (PROs; ie, stress, pain, anxiety, and coping) within a medical music therapy (MT) team. METHODS: We used 2 Plan-Do-Study-Act (PDSA) cycles to improve documentation processes among an MT team (13.3 clinical full-time equivalent staff). Trainings focused on providing skills and resources for optimizing pre- and postsession PRO collection, specific guidelines for entering session data in the electronic health record, and opportunities for the team to provide feedback. Two comparisons of therapists' PRO collection rates were conducted: (1) between the 6 months before PDSA Cycle 1 (T0) and PDSA Cycle 1 (T1), and (2) between T1 and PDSA Cycle 2 (T2). RESULTS: Music therapists' rates of capturing any PRO within MT sessions increased significantly (P<.001) from T0 to T1 and from T1 to T2 for all domains, including stress (4/2758, 0.1% at T0; 1012/2786, 36.3% at T1; and 393/775, 50.7% at T2), pain (820/2758, 29.7% at T0; 1444/2786, 51.8% at T1; and 476/775, 61.4% at T2), anxiety (499/2758, 18.1% at T0; 950/2786, 34.1% at T1; and 400/775, 51.6% at T2), and coping (0/2758, 0% at T0; 571/2786, 20.5% at T1; and 319/775, 41.2% at T2). Music therapists' feedback and findings from a retrospective analysis were used to create an improved electronic health record documentation template. CONCLUSIONS: Rates of PRO data collection improved within the medical MT team. Although the process improvement in this study was applied to a nonpharmacologic MT intervention, the principles are applicable to numerous inpatient clinical providers. As hospitals continue to implement nonpharmacologic therapies in response to the Joint Commission's recommendations, routine PRO collection will provide future researchers with the ability to evaluate the impact of these therapies on pain relief and opioid use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.433
Teacher spread0.323 · 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.

Study designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Human FactorsSame topicMusic Therapy and HealthFrench-language works237,207