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Record W3162071429 · doi:10.1111/head.14110

Patient experience of telemedicine for headache care during the COVID‐19 pandemic: An American Migraine Foundation survey study

2021· article· en· W3162071429 on OpenAlexaff
Chia‐Chun Chiang, Rashmi B. Halker Singh, Nim Lalvani, Ken Stein, Deborah Henscheid Lorenz, Christine Lay, David W. Dodick, Lawrence C. Newman

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Toronto
FundersUniversity of OxfordNational Institutes of HealthH. Lundbeck A/SUniversity of CambridgeTeva Pharmaceutical IndustriesAllerganEli Lilly and CompanyPatient-Centered Outcomes Research InstitutePfizerAmgenU.S. Department of Defense
KeywordsTelemedicinePandemicMedicineTelehealthMigraineMedical emergencyCoronavirus disease 2019 (COVID-19)Health careDeclarationFamily medicineDiseasePsychiatryInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We sought to investigate the patient experience of telemedicine for headache care during the coronavirus disease 2019 (COVID-19) pandemic. BACKGROUND: The use of telemedicine has rapidly expanded and evolved since the beginning of the COVID-19 pandemic. Telemedicine eliminates the physical and geographic barriers to health care, preserves personal protective equipment, and prevents the spread of COVID-19 by allowing encounters to happen in a socially distanced way. However, few studies have assessed the patient perspective of telemedicine for headache care. METHODS: The American Migraine Foundation (AMF) designed a standardized electronic questionnaire to assess the patient experience of telemedicine for headache care between March and September 2020 to help inform future quality improvement as part of its patient advocacy initiative. The date parameters were identified as the emergence of severe acute respiratory syndrome coronavirus 2 disease and the declaration of a national emergency in the United States. The questionnaire was distributed electronically to more than 100,000 members of the AMF community through social media platforms and the AMF email database. RESULTS: A total of 1172 patients responded to our electronic questionnaire, with 1098 complete responses. The majority, 1081/1153 (93.8%) patients, had a previous headache diagnosis prior to the telemedicine encounter. A total of 648/1127 (57.5%) patients reported that they had used telemedicine for headache care during the study period. Among those who participated in telehealth visits, 553/647 (85.5%) patients used it for follow-up visits; 94/647 (14.5%) patients used it for new patient visits. During the telemedicine encounters, 282/645 (43.7%) patients were evaluated by headache specialists, 222/645 (34.4%) patients by general neurologists, 198/645 (30.7%) patients by primary care providers, 73/645 (11.3%) patients by headache nurse practitioners, and 21/645 (3.2%) patients by headache nurses. Only 47/633 (7.4%) patients received a new headache diagnosis from telemedicine evaluation, whereas the other 586/633 (92.6%) patients did not have a change in their diagnoses. During these visits, a new treatment was prescribed for 358/636 (52.3%) patients, whereas 278/636 (43.7%) patients did not have changes made to their treatment plan. The number (%) of patients who rated the telemedicine headache care experience as "very good," "good," "fair," "poor," and "other" were 396/638 (62.1%), 132/638 (20.7%), 67/638 (10.5%), 23/638 (3.6%), and 20/638 (3.1%), respectively. Detailed reasons for "other" are listed in the manuscript. Most patients, 573/638 (89.8%), indicated that they would prefer to continue to use telemedicine for their headache care, 45/638 (7.1%) patients would not, and 20/638 (3.1%) patients were unsure. CONCLUSIONS: Our study evaluating the patient perspective demonstrated that telemedicine facilitated headache care for many patients during the COVID-19 pandemic, resulting in high patient satisfaction rates, and a desire to continue to use telemedicine for future headache care among those who completed the online survey.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.411
Teacher spread0.326 · 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 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

Citations47
Published2021
Admission routes1
Has abstractyes

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