The Development of an EHR-based Measure of Orthopaedic Treatment Success

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Project Summary Despite the high prevalence and treatment costs associated with orthopaedic conditions, there is remarkably little Level I evidence supporting the treatment approaches used in orthopaedics. Treatment decisions for displaced proximal humerus fractures (PHF), the third most common fracture in the elderly, remain challenging and highly varied between physicians. There is a dearth of data to evaluate the effectiveness of orthopaedic treatments used for patients with PHFs. Thus, the status quo for outcome assessment in orthopaedic medicine has been limited to process measures and end points such as survival and surgical complications. Unfortunately, these data points paint an incomplete picture of a patient’s medical experience and whether treatment was successful in achieving the outcome goals prioritized by the patient. Measures of patient-perceived benefits, known as Patient-Reported Outcome Measures (PROMs) are not collected in a standardized manner. Therefore, there is an urgent need to have better evidence on orthopaedic treatment effectiveness to improve the safety and quality of care provided for PHF. Our overall objective for this application is to develop a measure of orthopaedic treatment success from routinely captured electronic health record (EHR) data. Narrative clinical notes captured in EHR systems are generated during each interaction between patients and physicians, thereby producing a record of a patient’s history, physical findings, medical reasoning, and patient care. During orthopaedic encounters and through clinical documentation, orthopaedic surgeons author an evolving patient story of patient response to orthopaedic treatment. Clinical notes document the degree of improvement or relief experienced and reported directly by patients, in addition to scenarios in which symptoms have not been resolved, are lingering, or when subsequent complications have arisen. There has been an increase in research to advance the use of natural language processing (NLP) methods to classify medical concepts found in unstructured clinical notes. Deep learning NLP models have been used for clinical text classification and can be used to identify patients that experience treatment success or failure. The rationale for this project is that the development of a measure of orthopaedic treatment success from routinely captured EHR data will initiate a paradigm shift in how we evaluate the quality of orthopaedic care and enable assessment of whether treatment was successful in achieving the goals prioritized by patients. Once this project is completed and our new approach is adopted, patient outcomes can be determined more easily, more effectively, and with less cost than the gold-standard of PROMs.