scholarly journals Identifying patients who access musculoskeletal physical therapy: a retrospective cohort analysis

2020 ◽  
Author(s):  
Jason A Sharpe ◽  
Brook I Martin ◽  
Julie M Fritz ◽  
Michael G Newman ◽  
John Magel ◽  
...  

Abstract Background Musculoskeletal conditions are common and cause high levels of disability and costs. Physical therapy is recommended for many musculoskeletal conditions. Past research suggests that referral rates appear to have increased over time, but the rate of accessing a physical therapist appears unchanged. Objective Our retrospective cohort study describes the rate of physical therapy use after referral for a variety of musculoskeletal diagnoses while comparing users and non-users of physical therapy services after referral. Methods The study sample included patients in the University of Utah Health system who received care from a medical provider for a musculoskeletal condition. We included a comprehensive set of variables available in the electronic data warehouse possibly associated with attending physical therapy. Our primary analysis compared differences in patient factors between physical therapy users and non-users using Poisson regression. Results 15 877 (16%) patients had a referral to physical therapy, and 3812 (24%) of these patients accessed physical therapy after referral. Most of the factors included in the model were associated with physical therapy use except for sex and number of comorbidities. The receiver operating characteristic curve was 0.63 suggesting poor predictability of the model but it is likely related to the heterogeneity of the sample. Conclusions We found that obesity, ethnicity, public insurance and urgent care referrals were associated with poor adherence to physical therapy referral. However, the limited predictive power of our model suggests a need for a deeper examination into factors that influence patients access to a physical therapist.

2006 ◽  
Vol 86 (12) ◽  
pp. 1619-1629 ◽  
Author(s):  
Diane U Jette ◽  
Kerry Ardleigh ◽  
Kellie Chandler ◽  
Lesley McShea

Abstract Background and PurposeOpponents of direct access to physical therapy argue that physical therapists may overlook serious medical conditions. More information is needed to determine the ability of physical therapists to practice safely in direct-access environments. The purpose of this study was to describe the ability of physical therapists to make decisions about the management of patients in a direct-access environment. Subjects. Of a random sample of 1,000 members of the Private Practice Section of the American Physical Therapy Association, 394 participated. Methods. A survey included 12 hypothetical case scenarios. For each case, participants determined whether they would provide intervention without referral, provide intervention and refer, or refer before intervention. The percentage of correct decisions for each group of scenarios was calculated for each participant, and participants were classified as having made correct decisions for 100% of cases or less for each group. Three sets of logistic regressions were completed to determine the characteristics of the participants in relation to the decision category. Results. The average percentages of correct decisions were 87%, 88%, and 79% for musculoskeletal, noncritical medical, and critical medical conditions, respectively. Of all participants, approximately 50% made correct decisions for all cases within each group. The odds of making 100% correct decisions if a physical therapist had an orthopedic specialization were 2.23 (95% confidence interval=1.35–3.71) for musculoskeletal conditions and 1.89 (95% confidence interval=1.14–3.15) for critical medical conditions. Discussion and Conclusion. Physical therapists with an orthopedic specialization were almost twice as likely to make correct decisions for critical medical and musculoskeletal conditions.


2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Maggie E. Horn ◽  
Gerard P. Brennan ◽  
Steven Z. George ◽  
Jeffrey S. Harman ◽  
Mark D. Bishop

2020 ◽  
Vol 72 (2) ◽  
Author(s):  
Silvia Alboresi ◽  
Alice Sghedoni ◽  
Giulia Borelli ◽  
Stefania Costi ◽  
Laura Beccani ◽  
...  

2020 ◽  
Author(s):  
Thomas Tschoellitsch ◽  
Martin Dünser ◽  
Carl Böck ◽  
Karin Schwarzbauer ◽  
Jens Meier

Abstract Objective The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2. Methods In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 1353 unique features was trained to predict the RT-PCR results. Results Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1528 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.90. Conclusion Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.


Author(s):  
Serena Xodo ◽  
Fabiana Cecchini ◽  
Lisa Celante ◽  
Alice Novak ◽  
Emma Rossetti ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document