First, I am glad we finally got the posting problem fixed. I do appreciate the comments and criticisms (most of them.)
Second, with respect to Merrill's comments. CMPI is a separate 501 c 3 and we would be happy to send you our 990.
Third, I appreciate Dr. Posen's comments too. I think we are all on common ground...what comparative effectiveness should look like. I would be more than happy to debate/discuss with Merrill you or anyone and provide a forum to do so. Here's our view of comparative effectiveness:
Comparative effectiveness research as currently constructed consists of centralizing coverage decisions for entire groups of people using population-based studies. It looks a single treatment or device in isolation, rather than an integrated focus on personalized, predictive and prospective medicine. Comparative effectiveness only looks at the bottom line of insurers. Tailored treatments rely on combining information about individual differences in genetic and clinical responses to improve wellbeing and measuring outcomes and the value of care to patients, employers and families.
Further, the introduction of the use of “quality adjusted life year†as a bench mark for comparative effectiveness, coverage and reimbursement flows from a method and model of analysis that is similarly outdated and which fails to take into account the value that personalized and targeted therapies provide individuals and their families. In general, the default value of a QALY appears to be $50000 US dollars though that figure has little empirical evidence and was developed to assess the value of dialysis for end stage renal patients in 1979.
CMPI has set up a Patient Centric Health Leadership Forum.
In contrast to the reliance on meta-analyses and large trials that exclude patient variation, we hope to advance the use of individual patient level information from conventional clinical assessments, genomic and biomarker analyses, and, where appropriate, advanced imaging studies.
Such information will be used to increase the adoption of the use of real time updates and refinement of the risk prediction algorithms and health plan strategies that are supported by data-mining techniques, filtered through expert panels at the patient level.
Third, we want to work with policymakers, insurers and government to ensure that value of personalized evidenced, integrated care and targeted medicine is fully articulated at all policy considerations about comparative effectiveness.
Second, with respect to Merrill's comments. CMPI is a separate 501 c 3 and we would be happy to send you our 990.
Third, I appreciate Dr. Posen's comments too. I think we are all on common ground...what comparative effectiveness should look like. I would be more than happy to debate/discuss with Merrill you or anyone and provide a forum to do so. Here's our view of comparative effectiveness:
Comparative effectiveness research as currently constructed consists of centralizing coverage decisions for entire groups of people using population-based studies. It looks a single treatment or device in isolation, rather than an integrated focus on personalized, predictive and prospective medicine. Comparative effectiveness only looks at the bottom line of insurers. Tailored treatments rely on combining information about individual differences in genetic and clinical responses to improve wellbeing and measuring outcomes and the value of care to patients, employers and families.
Further, the introduction of the use of “quality adjusted life year†as a bench mark for comparative effectiveness, coverage and reimbursement flows from a method and model of analysis that is similarly outdated and which fails to take into account the value that personalized and targeted therapies provide individuals and their families. In general, the default value of a QALY appears to be $50000 US dollars though that figure has little empirical evidence and was developed to assess the value of dialysis for end stage renal patients in 1979.
CMPI has set up a Patient Centric Health Leadership Forum.
In contrast to the reliance on meta-analyses and large trials that exclude patient variation, we hope to advance the use of individual patient level information from conventional clinical assessments, genomic and biomarker analyses, and, where appropriate, advanced imaging studies.
Such information will be used to increase the adoption of the use of real time updates and refinement of the risk prediction algorithms and health plan strategies that are supported by data-mining techniques, filtered through expert panels at the patient level.
Third, we want to work with policymakers, insurers and government to ensure that value of personalized evidenced, integrated care and targeted medicine is fully articulated at all policy considerations about comparative effectiveness.