Promoting Health Policy Research in Plastic Surgery
Bibliographic record
Abstract
By failing to prepare, you are preparing to fail. —Benjamin Franklin In 1963, scientists developed a vaccine to slow the spread of the measles virus. Although the infection rate in the United States dropped in the following decade, researchers reported that the disease was up to 50 percent more common in states without school vaccination laws.1 Policy—or lack thereof—stood in the way of the public good. Consequently, the Centers for Disease Control and Prevention stepped in to enforce vaccination of school children nationwide. The disease was eventually eradicated, demonstrating the interdependence of research and health policy. When evidence informs policy development, policy can serve its intended purpose and health goals are achieved. In plastic surgery, the direct impact of health care reform is often felt through changes in insurance structures or safety guidelines, rather than protective measures such as immunization. Nevertheless, we must prepare for the future. The response to the coronavirus pandemic demonstrated how unexpected, perhaps unforeseen events can alter our specialty. Moving forward, we need improved health care policy research to convey the consequences of pandemics. If we had this at the outset of the pandemic, legislation would better address ongoing health care issues and impending concerns more effectively. It is vital that plastic surgeons generate rigorous science-based assessments to guide policy development. The Journal strives to be the leader in publishing innovative research discoveries and evidence-based health policy contributions. We are keen to shine light on creative and impactful ideas that promote equitable and quality care for our patients. HOW RESEARCH SHAPES POLICY IN PLASTIC SURGERY The creation of breast reconstruction policy exemplifies how research is embedded in the policymaking process (Fig. 1). In the 1990s, outcomes studies demonstrated the significant psychosocial benefits of postmastectomy breast reconstruction.2,3 This evidence supported advocacy for the Women’s Health and Cancer Rights Act, which mandated insurance coverage for the procedure in 1998. The Act marked a turning point in plastic surgery; it not only validated the importance of reconstructive procedures to enhance quality of life, but also proved that plastic surgeons can be influential in the policymaking sphere. Despite the eventual increase in breast reconstruction rates, studies reported growing racial and ethnic disparities in use of the procedure.4,5 Thus, investigators redirected their efforts to elucidate the underlying causes of inequity among breast cancer patients.Fig. 1.: Research is needed to characterize the problem, inform the development of evidence-based policy, evaluate the policy’s effects, and guide policy revision.Subsequent research revealed that patients who had a documented discussion of breast reconstruction with a provider were more likely to undergo the procedure.6 Thus, New York State required physicians to discuss breast reconstruction with all eligible patients. The Journal recently published two evaluations of this policy. Authors of the first study concluded that the annual increase in the rate of breast reconstruction did not change in response to the law.7 In contrast, the second study demonstrated a substantial increase in reconstruction rates among African Americans and the elderly following legislation, suggesting that the law is effective.8 The policy implications of this discrepancy are concerning and justify further research. How can policymakers act in the best interest of our patients if there is no clear consensus on how to proceed? Translating clinical findings into policy is one challenge, but recognizing the impact of policy in the clinic is another. A 2007 review of outcomes research in plastic surgery indicated that 10 percent of studies showed the direct impact of policy on patient outcomes and fewer than 1 percent of studies were economic analyses.9 Although much has changed in the years since the review, the weak impression of plastic surgery research in the policy arena has revealed itself. For example, accurate indicators of surgical quality are necessary as the United States transitions from a fee-for-service to a value-based payment system. However, existing metrics, such as mortality or readmission rates, are not relevant to most plastic surgery procedures.10 Reimbursements are increasingly tied to value, rather than volume. Consequently, surgeons will face financial penalties if the proper quality indicators are not incorporated into new payment structures. Moreover, patients and our specialty will suffer if we are slow to identify lapses in quality of care in the midst of ongoing health care reform. It is critical that research not only conveys the need for new policies, but also evaluates existing policies to steer the future course of action. THE WAY FORWARD Research drives effective health care and subsequent potential for meaningful reform, yet there is insufficient evidence to communicate the national relevance of plastic surgery to policymakers. To fill this void, investigators have started to leverage their skills in outcomes research to quantify the value of procedures. Economic evaluations are compelling given policymakers’ emphasis on cost reduction. For example, Abbott and colleagues developed a cost improvement map for cleft palate treatment based on patient records at a single institution.11 These data portray how plastic surgery fits into an episode of multidisciplinary care and, perhaps, when combined with patient perspectives, could support the movement to expand coverage for congenital anomalies. Although broader analyses are needed to encourage major policy change, this effort is a step in the right direction. Herein lies the challenge of policy analysis—insufficient data. Although administrative databases provide opportunities for observational research, these sources rarely report the examination findings, provider characteristics, or patient-reported outcomes needed to evaluate unique aspects of plastic surgery. We encourage researchers to apply novel methods to overcome this limitation. For example, a team of hand surgeons recently formed a quality initiative to evaluate how process measures differ across hospital systems.10 The goal is to establish best-practice guidelines for specific aspects of care, such as electrodiagnostic testing for carpal tunnel syndrome or pain control regimens after surgery. Optimizing the delivery of care across practices will limit unnecessary expenditures and enhance the patient experience. It is important to recognize that the aforementioned outcomes—reduced spending and better patient care—appeal to plastic surgeons and policymakers alike. However, translating meaningful evidence into policy is a complicated endeavor. This is in part a result of misaligned perspectives. Although plastic surgeons stand at the intersection of research and patient care, policymakers do not keep up with the scientific literature, nor do they witness how their decisions alter the clinical environment. Nevertheless, policymakers do listen to reason and science-based evidence. Therefore, we as plastic surgeons must focus our efforts to strengthen health policy research. This will clarify the objectives of advocacy and shed light on the impact of policy across subspecialties and practice settings. The goal in the ensuing years is to help plastic surgery establish the proper protocols and platforms to develop meaningful policy research. Specific focus areas include coverage for cleft and congenital deformities, hand surgery, and gender-affirming surgery. There is also an urgent need for policies that protect patient safety by ensuring that nonsurgeons do not perform complex procedures. Furthermore, it is crucial that we explain the science of aging and portray the positive effects of cosmetic surgery not only to enhance longevity, but to improve overall well-being as humans lead longer, more productive lives. We must empower our policymakers to make the right decisions for our aging population, among others. Effective, evidence-based legislation will widen the reach of our services. The intent is not to prove that policies are good or bad—it is to elucidate the effects so that policymakers can use the information for sound decision-making. This will require ingenuity on the part of the plastic surgeon, not only to identify key policy issues, but also to carry out critical assessments and communicate the implications to stakeholders in a way that spurs action. We wish that by promoting evidence-based policy research in the Journal, surgeons can continue to provide patients with safe and equitable care as the health system evolves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".