Why Patient-Reported Outcomes Drive Intelligent Care Delivery
Patient-reported outcomes (PROs) are effective tools to better understand a patient’s health condition, goals, and unique factors related to their care . When implementing an effective patient-centered care strategy it is vital for providers to track PRO scores over time. While clinicians have always been trained to assess clinical measures, such as a patient’s blood pressure, it is a rather new paradigm to also consider PRO scores during hospital encounters. PROs offer an exciting new data stream for care providers and are a reliable metric for reporting symptoms, quality of life, healthcare experience, functional status, and morbidity.
PROs are collected using validated questionnaires directly from the patient. These questionnaires range from general health surveys to diagnosis-specific measures. For example, the Knee Injury and Osteoarthritis Outcome Score (KOOS) measures knee symptoms and is a helpful diagnostic tool for determining when a patient may be ready for a total knee replacement surgery. New PRO scores are constantly being developed and there continues to be rapid innovation in this space. For instance, newer libraries are being introduced such as the Patient-Reported Outcomes Measurement Information System (PROMIS) measures, which are built upon item response theory to minimize patient burden, while simultaneously improving the precision of the scores generated.
In addition to quantifying patient outcomes, PROs have been validated for a huge variety of clinical and operational healthcare scenarios. See a handful of examples below for reference:
PatientIQ is an EHR integrated PRO platform designed to empower clinical teams to practice data-driven medicine. Our technology helps healthcare providers and health systems collect patient-reported outcomes at scale, in a workflow optimized for both the patient and provider experience. Once enabled, PatientIQ’s PRO analytics engine and dashboards are crucial infrastructures to unlock the tremendous potential of PROs for clinical care.
For example, using the machine learning analytics tools from PatientIQ, researchers were able to train a predictive algorithm for identifying patients who will respond best to hip arthroscopic surgery for femoroacetabular impingement syndrome. Patient-reported factors such as anxiety and depression, duration of preoperative symptoms, activities of daily living score, and, surprisingly, receiving preoperative steroid injections were all factors that significantly predicted worse outcomes (2). Leading institutions are now using this PatientIQ algorithm to provide precision medicine tailored to the specific factors of each patient.
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