Introduction: The collection of patient-reported outcomes (PROs) in routine clinical practice provides opportunities to "feed-forward" the patient's perspective to his/her clinical team to inform planning and management. This data can also be aggregated to "feedback" population-level analytics that can inform treatment decision-making, predictive modeling, population-based care, and system-level quality improvement efforts.
Methods aiding interpretation and acting on results: Three case studies demonstrate a number of system-level features which aid effective PRO interpretation: (1) feed-forward and feedback information flows; (2) score interpretation aids; (3) cascading measurement; (4) registry-enabled learning health care systems; and (5) the maturational development of information systems.
Discussion: The case studies describe the developmental span of feed-forward PRO programs-from simple to mature applications. The Concord Hospital (CH) Multiple Sclerosis Neurobehavioral Clinic exemplifies a simple application in which PRO data are used before and during clinic visits by patients and clinicians to inform care. The Dartmouth-Hitchcock (D-H) Spine Center exemplifies a mature program which utilizes population-level analytics to provide decision support by predicting outcomes for different treatment options. The Swedish Rheumatology Quality (SRQ) Registry epitomizes an exceptional application which has spread to multiple systems across an entire country.
Key points: Feed-forward and feedback PRO information systems can better inform, involve, and support clinicians, patients and families, and allow health systems to monitor and improve system performance and population health outcomes. Ideal systems have the capability for multilevel analyses at patient, system, and population levels, and an information technology infrastructure that is linked to associated workflows and a supportive practice culture. As systems mature, they progress beyond the ability to describe and inform towards higher level capabilities including prediction and decision support. Finally, there is additional promise for the integration of patient-reported information that is adjusted (or weighted) by preferences and values to guide shared decision-making and inform individualized precision health care in the future.