This dataset provides information on 184,761 in United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This statistic shows the number of pharmacists in the United States from 2001 to 2016. In 2001, there were 126,450 physical therapists employed in the United States. In 2016, there were 216,920 physical therapists employed.

This dataset provides information on 17,373 in California, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 4,610 in Maryland, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This statistic shows the frequency adults in the U.S. visited or consulted a physical/occupational therapist as of 2018. According to data provided by Ipsos, eight percent of U.S. adults stated they visited or consulted a physical/occupational therapist once a year.

This study aimed to conduct a personality-oriented job analysis to identify non-cognitive factors that may predict successful performance or performance difficulties in doctor of physical therapy (DPT) students. Eleven SMEs were recruited to participate in the study. Nine SMEs participated, including 6 DPT faculty members and 3 recent graduates who had passed the NPTE and were employed as physical therapists. A questionnaire with 22 POJA traits and definitions was developed. The wording of the scales was modified to be appropriate for student admissions rather than job applicants.

United States - Employed full time: Wage and salary workers: Physical therapist assistants and aides occupations: 16 years and over: Women was 42.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Physical therapist assistants and aides occupations: 16 years and over: Women reached a record high of 44.00000 in January of 2009 and a record low of 22.00000 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Physical therapist assistants and aides occupations: 16 years and over: Women - last updated from the United States Federal Reserve on May of 2025.

This dataset provides information on 2,190 in Iowa, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

In order to understand student perceptions of the original advisory process, baseline information was gathered by administering a questionnaire developed by the authors to physical therapy and occupational therapy students at the University of Mississippi Medical Center who had just completed a first full academic year in their respective programs in 2015.

The purpose of this project was to implement a process for learner-driven, formative, prospective, ad-hoc, entrustment assessment in Doctor of Physical Therapy clinical education. Our goals were to develop an innovative entrustment assessment tool, and then explore whether the tool detected (1) differences between learners at different stages of development and (2) differences within learners across the course of a clinical education experience. We also investigated whether there was a relationship between the number of assessments and change in performance.

A quasi-experimental nonrandomized pretest-posttest design was used with a convenience sample of 42 participating student PTs during their third and final year of didactic coursework in the Midwest of the United States. Students participated in two 8-week PBL courses, with concurrent ICE throughout. The ICE stemmed from a departmental-sponsored and faculty-supervised SRFC that provided pro bono physical therapy services 2 afternoons per week to underserved individuals within the community. Clients across the lifespan were treated, including those with neurologic and orthopedic diagnoses. Third-year students were paired with an underclassman and faculty supervisor for all clinical care. Simultaneously, the students completed two 8-week PBL courses with small-group case-based sessions totaling 6 hours per week for 16 weeks. The Self-Assessment of Clinical Reflection and Reasoning (SACRR) survey, with 26 items rated on a 5-point Likert scale, ranging from 5 (strongly agree) to 1 (strongly disagree), is a reliable and valid tool for examining students’ self-reported perceptions of CR development across multiple curricular designs in allied health training, including physical therapy. The pretest-posttest survey analyses included changes in SACRR individual item responses and overall scores (aggregate of all items) between the beginning and the end of the 16-week session.

This dataset provides information on 1,650 in Idaho, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

ABSTRACT This work aims to recollect information about the experience of physical therapists trained in MEP Sport, to know how many treatments they did per week, the adverse effects that might have appeared and the patients and therapists’ satisfaction. A mixed multiple choice survey with the option of choosing one or more alternatives to assess the opinion and experience of physical therapists trained in MEP Sport was carried out. SurveyMonkey was used for data collection. The invitations were sent by email to 1.096 physical therapists of Latin America. The survey was answered by 315 professionals, of whom 165 (56,51%) treat 1 to 5 patients per week. The answers about adverse effects were: I’ve never had adverse effects: 159 answers (56,79%), Hypotensive shock: 55 answers (19,64%), Allergy to metal 15 answers (5,36%). The most common areas/conditions where the MEP is applied are: Patellar tendon (10,77% - 198 answ.), Achilles tendon, (9,58% - 176 answ.), Supraspinatus tendon (9,36% - 172 answ.), Plantar fasciitis/Calcaneal spurs (8,05% - 148 answ.), Trigger points (7,18% - 132 answ.). The professionals’ satisfaction was: Satisfied (51,87%, 152 answ.) and Very Satisfied (40,96%, 120 answ.). Patients’ satisfaction was: Satisfied (61,90%, 182 answ.) and Very satisfied (29,93%, 88 answ.). MEP is applied mainly in tendinopathies and produces satisfactory and very satisfactory results, both for patients and professionals, with low incidence of adverse effects.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Physical therapists occupations: 16 years and over: Women was 1535.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Physical therapists occupations: 16 years and over: Women reached a record high of 1535.00000 in January of 2024 and a record low of 816.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Physical therapists occupations: 16 years and over: Women - last updated from the United States Federal Reserve on May of 2025.

VA analyzes what Veterans are saying about their outpatient experiences (including mental health, primary care, optometry, physical therapy, cardiology, etc.), and levels of outpatient ease, effectiveness, and emotion are anticipated to drive increases in outpatient trust using Veterans Signal survey technology.

This dataset provides information on 1,874 in Nebraska, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Results of the binary logistic regression analysis with simultaneous entry.

Strong partnerships between academic health professions programs and clinical practice settings, termed academic-clinical partnerships, are essential in providing quality clinical training experiences. However, the literature does not operationalize a model by which an academic program may identify priority attributes and evaluate its partnerships. This study aimed to develop a values-based academic-clinical partnership evaluation approach, rooted in methodologies from the field of evaluation and implemented in the context of an academic Doctor of Physical Therapy clinical education program. The authors developed a semi-quantitative evaluation approach incorporating concepts from multi-attribute utility analysis (MAUA) that enabled consistent, values-based partnership evaluation. Data-informed actions led to improved overall partnership effectiveness. Pilot outcomes support the feasibility and desirability of moving toward MAUA as a potential methodological framework. Further research may lead to the development of a standardized process for any academic health profession program to perform a values-based evaluation of their academic-clinical partnerships to guide decision-making.

The Physical Therapy Electronic Medical Record (EMR) and Billing Software market is experiencing robust growth, driven by increasing adoption of electronic health records (EHRs) within healthcare settings and a rising demand for streamlined billing processes. The market's Compound Annual Growth Rate (CAGR) of 5% from 2019-2024 suggests a steady expansion, projected to continue into the forecast period (2025-2033). This growth is fueled by several key factors. Firstly, the increasing volume of patient data and the need for efficient management necessitate the use of EMR systems. Secondly, regulatory mandates pushing for electronic health records adoption across various healthcare settings accelerate market penetration. Thirdly, the integration of billing capabilities within EMR software streamlines revenue cycle management, reducing administrative burdens and improving financial efficiency for physical therapy practices. The market is segmented by application (hospitals and clinics, research institutions) and type (web-based, cloud-based). Web-based solutions offer accessibility, while cloud-based solutions provide scalability and data security benefits. Major players like Allscripts, Epic Systems, and NextGen Healthcare are driving innovation through advanced features such as telehealth integration, patient portals, and robust analytics capabilities. The North American market currently holds a significant share, driven by early adoption and technological advancements. However, growth is anticipated in other regions, including Europe and Asia Pacific, as healthcare systems increasingly prioritize digital transformation. Restraints on market growth may include high initial investment costs for implementing new systems, concerns around data security and privacy, and the need for ongoing staff training. Despite these challenges, the long-term outlook for the Physical Therapy EMR and Billing Software market remains positive, indicating substantial growth potential in the coming years. The continued focus on improving patient care, enhancing operational efficiency, and complying with regulatory guidelines will further propel market expansion. The competitive landscape is characterized by a mix of established players and emerging companies. While larger vendors like Epic Systems and Allscripts offer comprehensive solutions catering to large healthcare systems, smaller companies focus on niche markets and specific functionalities. Strategic partnerships, acquisitions, and the development of innovative solutions are expected to intensify competition in the coming years. The market's future trajectory will depend on the pace of technological advancements, evolving regulatory landscapes, and the ongoing adoption of EMR and billing software across various physical therapy settings globally. The focus on interoperability, improved data analytics, and personalized patient care will be critical factors influencing the market's growth and shaping the strategies of key players.