TY - JOUR
T1 - Decision support tools in low back pain
AU - Coupé, Veerle M.H.
AU - van Hooff, Miranda L.
AU - de Kleuver, Marinus
AU - Steyerberg, Ewout W.
AU - Ostelo, Raymond W.J.G.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).
AB - Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).
KW - Clinical prediction rules
KW - Decision support tool
KW - Low back pain
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U2 - https://doi.org/10.1016/j.berh.2017.07.002
DO - https://doi.org/10.1016/j.berh.2017.07.002
M3 - Article
C2 - 29103551
SN - 1521-6942
VL - 30
SP - 1084
EP - 1097
JO - Best Practice and Research: Clinical Rheumatology
JF - Best Practice and Research: Clinical Rheumatology
IS - 6
ER -