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Health Care Reform and Translational Medicine

2010· editorial· en· W1997231372 on OpenAlexaboutno aff
Arthur M. Feldman

Bibliographic record

VenueClinical and Translational Science · 2010
Typeeditorial
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTranslational medicineHealth care reformMedicineMEDLINEHealth carePublic healthHealth policyPolitical scienceNursingPathology

Abstract

fetched live from OpenAlex

In March 2010, the Congress of the United States passed the Health Care Reform Act and the accompanying Reconciliation Act and the President signed them into law. The most significant social and fiscal legislation since the passage of civil rights legislation and the creation of Medicare during the Johnson administration, the health care reform legislation will decrease the large burden of uninsured by approximately 35 million and abrogate the ability of private insurance companies to refuse insurance because of preexisting conditions or rescind an individual's insurance after the development of a new medical problem. It is likely that health care experts, political pundits, and the lay public will debate the merits of health care reform over the next decade: will individual states be able to efficiently manage health insurance exchanges; will reform increase or decrease the federal deficit; will patients who don't receive their health care insurance from their employers buy insurance or will they find it more cost effective to simply pay a fine; will safety net hospitals survive without the current levels of federal and state subsidies; will the current number of physicians and hospitals be overwhelmed by 35 million new insured patients; and will the middle class pay a disproportionate share of the total health care costs? Despite these points of disagreement one thing is clear—we will not be able to continue to deliver cost-effective and quality health care unless we are able to decrease overall health care costs. President Obama has reported and many experts agree that the most effective means of lowering health care costs over the next decade will be to eliminate “waste” in the health care system. The “science” that will inform health care reform fits well within the sphere of translational research as it spans the continuum of bench research, clinical research, and population health. As pointed out by Victor Fuchs, Professor of Economics and Health Research and Policy, Emeritus, at Stanford University, there are two possible definitions of waste (1) any intervention that has no possible benefit for the patient or in which the potential risk to the patient outweighs any potential benefit or “medical waste”; or (2) any intervention for which the value of the expected benefit is less than the expected costs or “economic waste.” Medical waste occurs when a physician or caregiver does the wrong thing either because they are uninformed or are delivering care in an unethical manner. Fortunately, the incidence of medical waste is small. By contrast, economic waste is far more common and has been blamed at least in part on a fee-for-service reimbursement model that incentivizes physicians to do more—often without justification—and a patient population that is highly demanding. For example, there are about three times as many magnetic resonance imaging scans performed each year in the US as there are in Canada; however, the increased number of scans does not translate into improved care. In many cases, economic waste does not occur because physicians are incentivized to overuse tests and devices but because there is little or no information that allows them to make sound judgments regarding the choice between differing treatment strategies. Without scientific documentation, it also becomes problematic for regulatory bodies to recommend or mandate one treatment option as opposed to a second and potentially more expensive option. This equipoise is important because newer treatments are often—but not always—more expensive than older therapies. Examples of these cost-related decisions abound; the use of bare-metal stents versus the more expensive drug eluting stents in patients with coronary artery disease; traditional open surgery versus robotic surgery for the treatment of prostate cancer; and the use of expensive new generation antibiotics versus older generic medications for the treatment of community-acquired infections. Similarly, studies have not defnitively assessed the benefits afforded by many screening tests for disease prevention or to improve survival by earlier detection—the controversy regarding the use of mammography to detect early breast cancer being a good example. Research initiatives in “comparative effectiveness” will focus on addressing these important questions and the health care reform legislation provides funding for such studies. Indeed, the Institute of Medicine has provided a list of the 100 top topics for early intervention. It is in this knowledge gap that the clinical and translational sciences can play a significant role. The “science” that will inform health care reform fits well within the sphere of translational research as it spans the continuum of bench research, clinical research, and population health. Indeed, the rapid translation of new discoveries from the bench to the community setting is a fundamental goal of translational medicine. In addition, investigators in Clinical and Translational Science Award (CTSA) centers routinely carry out studies that compare various drugs and devices as well as surgical or interventional approaches while also performing population and community-based studies to assess the effects of new methods of health care delivery on costs and quality of care—the results of which are essential in understanding and eliminating economic waste. To be successful, these studies will need to bring together multidisciplinary teams of academicians and scholars with expertise in biostatistics, clinical trial design, community and population health, bioethics, health care economics, and health care delivery—the very groups that populate T2 and T3 translational science and the nation's CTSA programs. Though not articulated in any of the health care discussions, personalized medicine or pharmacogenomics also has an enormous potential for lowering health care costs by identifying those patients that will benefit from a specific drug, thereby allowing physicians to target drugs to selected populations. Developing the science of health care reform will not be without challenges. First, new biostatical methods must be formulated and validated to insure that studies assessing the comparative effectiveness of two treatment strategies are appropriately powered to provide unequivocal and meaningful results. In addition, methods must be developed to insure that studies that are not blinded can provide useful information. For example, analysis of a study that compares an invasive strategy with a noninvasive therapy has particular statistical challenges if hard endpoints are not utilized. Second, federal and state agencies must recognize the inherent costs in population-based studies. The RAND Health Insurance Experiment that randomized patients at six sites to one of five health insurance plans, followed those patients for over three decades, and serves as a model for the types of studies we will need to carry out to evaluate health care programs and the quality and cost effectiveness of care would cost $1 billion in 2010 dollars—the total amount set aside by the President to fund comparative effectiveness studies. Finally, will regulatory groups recognize that personalized medicine is not just a fad but might in fact be able to substantially lower the cost of medical care? This becomes important in view of the fact that the pharmaceutical industry has been loath to include genotyping in their clinical trials because it is far more cost-effective for a drug to be marketed for the entire population than for a select population. An excellent example of how pharmacogenetics could impact costs is the recent finding that the expensive new agent for treating women with breast cancer, Herceptin, is only beneficial in patients whose tumors overexpress the her2 receptors. Similar cost savings will hopefully come from studies similar to the recent finding that genotype can predict how a patient responds to the cardiovascular agent clopidogrel. Hospitals, health care workers, legislative bodies, pharmaceutical executives, and private insurers will be challenged over the next few years to make health care reform work and to lower costs. Hopefully, the many individuals that will be administratively responsible for the success of health care reform will recognize that the vast experience and talent that is housed in translational science centers can provide a wealth of expertise and information that can be brought to bear on the science of health care reform. This journal will play its role by considering for publication both positive and negative comparative effectiveness studies, providing a format for the communication of innovative new methods in comparative effectiveness evaluations, and communicating to the translational medicine community new opportunities for research funding and potential regulatory concerns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.146
GPT teacher head0.544
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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