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Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions

OBJECTIVES: Chronic pain affects 50 million Americans and is often treated with non-pharmacologic approaches like physical therapy. Developing a no-show prediction model for individuals seeking physical therapy care for musculoskeletal conditions has several benefits including enhancement of workfor...

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Autores principales: Bhavsar, Nrupen A., Doerfler, Shannon M., Giczewska, Anna, Alhanti, Brooke, Lutz, Adam, Thigpen, Charles A., George, Steven Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162651/
https://www.ncbi.nlm.nih.gov/pubmed/34048440
http://dx.doi.org/10.1371/journal.pone.0251336
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author Bhavsar, Nrupen A.
Doerfler, Shannon M.
Giczewska, Anna
Alhanti, Brooke
Lutz, Adam
Thigpen, Charles A.
George, Steven Z.
author_facet Bhavsar, Nrupen A.
Doerfler, Shannon M.
Giczewska, Anna
Alhanti, Brooke
Lutz, Adam
Thigpen, Charles A.
George, Steven Z.
author_sort Bhavsar, Nrupen A.
collection PubMed
description OBJECTIVES: Chronic pain affects 50 million Americans and is often treated with non-pharmacologic approaches like physical therapy. Developing a no-show prediction model for individuals seeking physical therapy care for musculoskeletal conditions has several benefits including enhancement of workforce efficiency without growing the existing provider pool, delivering guideline adherent care, and identifying those that may benefit from telehealth. The objective of this paper was to quantify the national prevalence of no-shows for patients seeking physical therapy care and to identify individual and organizational factors predicting whether a patient will be a no-show when seeking physical therapy care. DESIGN: Retrospective cohort study. SETTING: Commercial provider of physical therapy within the United States with 828 clinics across 26 states. PARTICIPANTS: Adolescent and adult patients (age cutoffs: 14–117 years) seeking non-pharmacological treatment for musculoskeletal conditions from January 1, 2016, to December 31, 2017 (n = 542,685). Exclusion criteria were a primary complaint not considered an MSK condition or improbable values for height, weight, or body mass index values. The study included 444,995 individuals. PRIMARY AND SECONDARY OUTCOME MEASURES: Prevalence of no-shows for musculoskeletal conditions and predictors of patient no-show. RESULTS: In our population, 73% missed at least 1 appointment for a given physical therapy care episode. Our model had moderate discrimination for no-shows (c-statistic:0.72, all appointments; 0.73, first 7 appointments) and was well calibrated, with predicted and observed no-shows in good agreement. Variables predicting higher no-show rates included insurance type; smoking-status; higher BMI; and more prior cancellations, time between visit and scheduling date, and between current and previous visit. CONCLUSIONS: The high prevalence of no-shows when seeking care for musculoskeletal conditions from physical therapists highlights an inefficiency that, unaddressed, could limit delivery of guideline-adherent care that advocates for earlier use of non-pharmacological treatments for musculoskeletal conditions and result in missed opportunities for using telehealth to deliver physical therapy.
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spelling pubmed-81626512021-06-10 Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions Bhavsar, Nrupen A. Doerfler, Shannon M. Giczewska, Anna Alhanti, Brooke Lutz, Adam Thigpen, Charles A. George, Steven Z. PLoS One Research Article OBJECTIVES: Chronic pain affects 50 million Americans and is often treated with non-pharmacologic approaches like physical therapy. Developing a no-show prediction model for individuals seeking physical therapy care for musculoskeletal conditions has several benefits including enhancement of workforce efficiency without growing the existing provider pool, delivering guideline adherent care, and identifying those that may benefit from telehealth. The objective of this paper was to quantify the national prevalence of no-shows for patients seeking physical therapy care and to identify individual and organizational factors predicting whether a patient will be a no-show when seeking physical therapy care. DESIGN: Retrospective cohort study. SETTING: Commercial provider of physical therapy within the United States with 828 clinics across 26 states. PARTICIPANTS: Adolescent and adult patients (age cutoffs: 14–117 years) seeking non-pharmacological treatment for musculoskeletal conditions from January 1, 2016, to December 31, 2017 (n = 542,685). Exclusion criteria were a primary complaint not considered an MSK condition or improbable values for height, weight, or body mass index values. The study included 444,995 individuals. PRIMARY AND SECONDARY OUTCOME MEASURES: Prevalence of no-shows for musculoskeletal conditions and predictors of patient no-show. RESULTS: In our population, 73% missed at least 1 appointment for a given physical therapy care episode. Our model had moderate discrimination for no-shows (c-statistic:0.72, all appointments; 0.73, first 7 appointments) and was well calibrated, with predicted and observed no-shows in good agreement. Variables predicting higher no-show rates included insurance type; smoking-status; higher BMI; and more prior cancellations, time between visit and scheduling date, and between current and previous visit. CONCLUSIONS: The high prevalence of no-shows when seeking care for musculoskeletal conditions from physical therapists highlights an inefficiency that, unaddressed, could limit delivery of guideline-adherent care that advocates for earlier use of non-pharmacological treatments for musculoskeletal conditions and result in missed opportunities for using telehealth to deliver physical therapy. Public Library of Science 2021-05-28 /pmc/articles/PMC8162651/ /pubmed/34048440 http://dx.doi.org/10.1371/journal.pone.0251336 Text en © 2021 Bhavsar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bhavsar, Nrupen A.
Doerfler, Shannon M.
Giczewska, Anna
Alhanti, Brooke
Lutz, Adam
Thigpen, Charles A.
George, Steven Z.
Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title_full Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title_fullStr Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title_full_unstemmed Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title_short Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
title_sort prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162651/
https://www.ncbi.nlm.nih.gov/pubmed/34048440
http://dx.doi.org/10.1371/journal.pone.0251336
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