Article type
Abstract
Background: Clinical practice guidelines (CPGs) often grade recommendations as Strong or Weak. Weak recommendations are used if there is uncertainty that benefits outweigh harms, due to uncertainty in evidence or uncertainty in the balance of benefits and harms across the range of patient preferences. Shared decision making (SDM) means patients should be informed of relevant evidence for inclusion in decision making for preference-sensitive decisions. Conflating certainty of evidence for net benefit and sensitivity to patient preferences does not preserve these distinctively important concepts.
Objectives: We developed a model to convey strength of recommendations for preference-sensitive decisions.
Methods: Healthcare Guidance for Patients Society (Healthcare GPS) includes experts in evidence-based medicine (EBM), CPGs and SDM. We considered the Grading of Recommendations Assessment, Development and Evaluation (GRADE) and International Patient Decision Aids Standards Collaboration (IPDAS) standards and developed (via a consensus-based approach) a model for recommendation phrasing.
Results: An EBM-SDM framework would use a strong recommendation to offer options with an SDM approach for preference-sensitive conditions with a high certainty of net benefit for some patients (and net harm for some patients with different preferences). This seems more appropriate for phrasing for recommendations such as therapies with well-established benefits and harms for patients with cancer. Strong recommendations to perform the action would be made for preference-insensitive conditions. Similar patterns occur for weak recommendations.
Conclusions: Many current clinical guidelines do not incorporate patient preferences or individual perspectives in the development or implementation of recommendations. SDM tools within guidelines (and explicit recognition of which recommendations are best implemented using an SDM approach) can encourage patient engagement and involvement. We offer a solution on how to incorporate SDM within guideline development. Such phrasing formats can be tested in CPGs to assess impact on clinician and patient understanding, patient engagement and SDM.
Objectives: We developed a model to convey strength of recommendations for preference-sensitive decisions.
Methods: Healthcare Guidance for Patients Society (Healthcare GPS) includes experts in evidence-based medicine (EBM), CPGs and SDM. We considered the Grading of Recommendations Assessment, Development and Evaluation (GRADE) and International Patient Decision Aids Standards Collaboration (IPDAS) standards and developed (via a consensus-based approach) a model for recommendation phrasing.
Results: An EBM-SDM framework would use a strong recommendation to offer options with an SDM approach for preference-sensitive conditions with a high certainty of net benefit for some patients (and net harm for some patients with different preferences). This seems more appropriate for phrasing for recommendations such as therapies with well-established benefits and harms for patients with cancer. Strong recommendations to perform the action would be made for preference-insensitive conditions. Similar patterns occur for weak recommendations.
Conclusions: Many current clinical guidelines do not incorporate patient preferences or individual perspectives in the development or implementation of recommendations. SDM tools within guidelines (and explicit recognition of which recommendations are best implemented using an SDM approach) can encourage patient engagement and involvement. We offer a solution on how to incorporate SDM within guideline development. Such phrasing formats can be tested in CPGs to assess impact on clinician and patient understanding, patient engagement and SDM.