1 / 9

Going from CER to Patient-Centered Care: Implications of Heterogeneity

Going from CER to Patient-Centered Care: Implications of Heterogeneity. Trial: Is treatment A better than treatment B? Clinician : Is treatment A better than B for this specific patient? Health care system : Is treatment A better than B, and for whom, in which settings?.

karis
Download Presentation

Going from CER to Patient-Centered Care: Implications of Heterogeneity

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Going from CER to Patient-Centered Care: Implications of Heterogeneity Trial: Is treatment A better than treatment B? Clinician: Is treatment A better than B for this specific patient? Health care system: Is treatment A better than B, and for whom, in which settings?

  2. Heterogeneity and Policy • Policies seek to promote use of “best” treatment option. • “Best” treatment for population may not be same as that for individuals. • Most important when variation is: • Common • Leads to big enough differences to change decision making • Treatment choices can’t be adjusted

  3. Audience Response • Is CABG the best option for all patients with diabetes? • How might a health system encourage greater use of CABG in appropriate patients? • Would it be appropriate to discourage CABG in groups where PCI produces equivalent outcomes?

  4. Is CABG the “Best” Choice for Patients with Diabetes? • Need to consider harms and complications • Patient preferences for different outcomes • E.g. short-term risks of CABG • Variation due to quality of surgeon • Applicability of trial evidence

  5. Policies Used To Influence Use of “Best” Treatments • Guidelines • Audit and Feedback • Coverage decisions • Non-coverage • Conditional coverage • Tiered coverage • Quality Measurement • Incentives, Public reporting

  6. Distinguishing Important from Unimportant Heterogeneity • Does it change direction of NET benefit enough to alter decisions? • Is it common? • Is it predictable? • Can it be detected and treatment modified in response to variation in benefits or harms?

  7. Example: SSRIs for Depression • Comparable effectiveness of most agents in depression responsiveness but individual variation • Affect decisions: YES -- Variability in response and side effects • Common: YES • Predictable: NO • Can variable response be monitored? YES

  8. Dealing With Variation in SSRI as Response in Policy • Not possible to identify who will do better on a different agent • Cover only 1-2 SSRIs in formulary? • Recommend starting all patients with a specific SSRI as initial therapy?

  9. Conclusions • Heterogeneity is a real and important phenomenon in research and policy • Examination of pre-specified factors in individual trials, SRs and meta-analysis can detect HTE • Be cautious about post-hoc sub-groups • Policies need to accommodate HTE • But doesn’t mean that complete, unfettered clinician choice is best

More Related