Background And Purpose: Outcome measurement has been shown to improve performance in several fields of healthcare. This understanding has driven a growing interest in value-based healthcare, where value is defined as outcomes achieved per money spent. While low back pain (LBP) constitutes an enormous burden of disease, no universal set of metrics has yet been accepted to measure and compare outcomes. Here, we aim to define such a set.
Patients And Methods: An international group of 22 specialists in several disciplines of spine care was assembled to review literature and select LBP outcome metrics through a 6-round modified Delphi process. The scope of the outcome set was degenerative lumbar conditions.
Results: Patient-reported metrics include numerical pain scales, lumbar-related function using the Oswestry disability index, health-related quality of life using the EQ-5D-3L questionnaire, and questions assessing work status and analgesic use. Specific common and serious complications are included. Recommended follow-up intervals include 6, 12, and 24 months after initiating treatment, with optional follow-up at 3 months and 5 years. Metrics for risk stratification are selected based on pre-existing tools.
Interpretation: The outcome measures recommended here are structured around specific etiologies of LBP, span a patient's entire cycle of care, and allow for risk adjustment. Thus, when implemented, this set can be expected to facilitate meaningful comparisons and ultimately provide a continuous feedback loop, enabling ongoing improvements in quality of care. Much work lies ahead in implementation, revision, and validation of this set, but it is an essential first step toward establishing a community of LBP providers focused on maximizing the value of the care we deliver.
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http://dx.doi.org/10.3109/17453674.2015.1036696 | DOI Listing |
Alzheimers Dement
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Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
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Int J Gen Med
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Department of Cardiology, Shaanxi Provincial People's Hospital, Xi'an, People's Republic of China.
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January 2025
Department of Computer Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
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January 2025
Faculty of Education and Arts, Australian Catholic University, Sydney, NSW, 2118, Australia.
Every node in a network is said to be resolved if it can be uniquely identified by a vector of distances to a specific set of nodes. The metric dimension is equivalent to the least possible cardinal number of a resolving set. Conditional resolving sets are obtained by imposing various constraints on resolving set.
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