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Want to advance your understanding of patient-specific function with reliable real-world data? You might be experiencing some challenges with advancing the existing data technologies to be controllable for information you can validate. You could be frustrated with the overwhelming amount of information available and lack of direction regarding the appropriate measures of patient performance. We have a database of rehab measures that can help you and we are building a repository of rich data sets to help you advance your patient outcomes faster. 

For more than 30 years, we have been named number 1 for a reason: we bring together the highest performing talent into one place. We can help you.

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The Rehabilitation Measures Database currently boasts over 400 measures for benchmarks and outcomes and receives over 11,000 hits a day. RMD was developed to help clinicians and researchers identify reliable and valid instruments used to assess patient outcomes during all phases of rehabilitation.

The database provides evidence-based summaries that include concise descriptions of each instrument¡¯s psychometric properties, instructions for administering and scoring each assessment as well as a representative bibliography with citations linked to PubMed abstracts.

• Whenever possible, we have included a copy of the instrument for the users to download or information about obtaining the instrument.

The RMD is divided into ten clinical content areas within PMR including Stroke, Spinal Cord Injury, Brain Injury, Parkinson¡¯s Disease, Neuromuscular Conditions, Vestibular Disorders, Older Adults and Geriatric Care, Cancer, Musculoskeletal Conditions and Arthritis.

The Rehabilitation Measures Database was created under the leadership of Allen Heinemann, PhD, [along with the following contributors] the Director of the Center for Rehabilitation Outcomes Research, and one of CSTAR's Clinical Outcome Core Leaders. RMD evolved through collaboration between (CROR) at Shirley Ryan Abilitylab and the Department of Medical Social Sciences Informatics group at Northwestern University Feinberg School of Medicine with funding from the National Institute on Disability and Rehabilitation Research, U.S. Department of Education.

Additional Resources to support RMD learning include the following web pages:
Statistical Terms Used  and  Educational Resources and Teaching Modules

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We have developed our own machine learning and data fusion that creates customized population-specific algorithms for physical activity metrics. By using modified smart technology we are able to take the raw sensor data (steps, energy, etc) and improve the reliability and accuracy of data estimates. Our research demonstrated that machine learning models of activity recognition from wearable sensors must be validated on both the specific patient population under investigation and on the assistive device used. 

From our research, we have developed rich content repositories of data and will be sharing our data sets soon.

We are working away, stay tuned as we are building our library of data for you to access. 

C-STAR is funded by the National Institutes of Health (Grant #P2C HD101899), supported by the NICHD and NINDS.

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