Group E: Clinical EffectivenessThere are four questions in this group.
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What strategies optimize adoption and sustainability of guideline concordant cancer treatments in community settings?
Background: Advances in establishing best cancer treatments are painstakingly developed through a well-established and rigorous clinical trials process. Getting these treatments to be widely used and well accepted by the entire community is a complicated process that can be inhibited by many social, economic, and cultural barriers. Some of the many barriers to rapid dissemination include: patients who participate in clinical trials are often younger and much healthier than the most common cancer patients seen in community practice, the site of the trial (e.g., frequently only at academic medical centers or comprehensive cancer centers), or lack of engagement of community practice in the design of the trial (e.g., requires equipment not typically available in office-based practice). However, little research has been conducted on innovative approaches to improve the uptake of guideline related cancer care, particularly in the area of cancer treatment. In cancer screening and in other fields a number of approaches less commonly used in medical practice, such as behavioral economics, systems and industrial engineering, the use of social media, and real-time data sharing across multidisciplinary teams working on common problems, has led to innovative approaches that hasten adoption and may improve sustainability of recommended care. This Provocative Question seeks to understand which methods for adoption of new treatment strategies work most effectively and determine how these methods can be employed more widely.
Feasibility: Investigators are beginning to explore the use of these innovations that have been successful at implementing and sustaining change in non-medical arenas. Some clinicians, health services researchers, and health information innovators have been piloting some of these approaches in medical care.
Implications of success: Exploration of how these diverse approaches to interventions can improve guidelines concordant cancer care could have substantial influence at a time when the US health care system is evolving rapidly under the affordable care act. These innovations could lead to much more rapid uptake of research results, resulting in higher quality, more effective care for less cost.
What care delivery models can be developed to transition cancer patients effectively from active therapy to end of life care?
Background: National research has documented that cancer treatment costs are especially high and remain focused on tumor remission with limited attention to palliation or quality of life in the end-of-life phase for patients with advanced stage or recurrent cancer. Evidence for treatment effectiveness is also not definitive in this domain and yet research suggests that patients and their providers often believe that treatment in this phase will “cure” their cancer. However, recent research has suggested that alternative approaches to clinical care that include shared decision making and early communication about the goals of clinical care during all of the phases of care rather than delaying until the end of life may more effectively help patients and their clinicians to transition from active therapy to end of life care. Use of multidisciplinary teams of clinicians, interactive patient reported outcomes information systems and other approaches may help guide the selection of care that is more effective and appropriate to the phase of a cancer patient’s course.
Feasibility: Recent IOM and other reports have characterized the necessary components of cancer care planning and many cancer-related professional societies have begun discussion on how to incorporate such care planning into practice. Innovations in shared decision making, decision analysis, team science and advances in communication and interactive technologies should greatly advance this field of research.
Implications of success: Research on how to effectively transition providers, patients, and their care takers from active treatment to end of life care will enhance the quality of life of cancer patients, ensure that care during this period of life is appropriate to patients needs and will reduce the cost and adverse effects of active cancer treatment that is unlikely to benefit patients in this phase of their life. Furthermore, it is likely to advance the use of palliative and symptom management care earlier in a patient’s cancer treatment, which may have further benefit.
What methods and approaches induce physicians and health systems to abandon ineffective interventions or discourage adoption of unproven interventions?
Background: It is common for community hospitals and community cancer centers to acquire high cost technology or other cutting edge therapies as a way of differentiating their services in local markets. Examples include cyber knife and robotic surgery as well as a variety of new radiation services such as PET scans. Often these technologies require specialized clinicians experienced in their use. Once these technologies are purchased, there is considerable pressure to maximize their use for clinical applications, including cancer diagnosis and treatment, to recover investment capital. However, a number of these technologies are unproven as they have not been studied to evaluate whether their use improves patients’ outcomes. Furthermore, little is known about the influence of introduction of these technologies on cancer care costs and outcomes, including their comparative effectiveness with more established, and often lower cost, treatment alternatives. Innovations in comparative effectiveness, decision analysis and other approaches can quantify both the short and long terms costs and benefits of premature adoption of such technologies.
Feasibility: The expansion of comparative effectiveness research, decision analysis, and other modeling approaches has been used extensively in the field of cancer screening. These approaches have been minimally applied to new technologies in the area of cancer treatment but could be readily adapted to cancer treatment.
Implications of success: Innovative approaches to providing such information to physicians, health care systems and health care administrators may help guide better quality decisions and improve patient care and outcomes.
What are the best methods to identify and stratify subgroups of patients with particular co-morbidities who will benefit from defined cancer therapies?
Background: Existing clinical trial results are limited to individuals who have few if any co-morbidities. Yet, most cancer patients in the US are over age 65 and often have comorbid conditions that may complicate decisions related to optimal cancer therapies for these patients. In addition, many patients who would be candidates for new therapies may have complex comorbid disease. What are the circumstances in which comorbidities may be a problem with specific therapies and what should clinicians do in assessing the appropriateness of a therapy for patients whose clinical profile do not match trial eligibility criteria?
Feasibility: The existence of large, integrated clinical databases of cancer patients with data on their comorbid conditions, genetic, laboratory and pharmaceutical measures for assessing severity of disease combined with complex statistical and other mathematical modeling approaches enables multi-disciplinary teams of investigators to develop prediction models of anticipated outcomes given diverse clinical and treatment scenarios. These resources allow the validation of predicted models against actual outcomes in patients and the ability to assess if this approach can be used for multiple different cancers and patients with diverse combinations of comorbid disease.
Implications of success: Successful approaches that have been validated in selected patient populations with specific comorbid disease and end organ effects could be applied more broadly to a wide range of cancer patients. The ability to tailor treatment in this way should allow patients with complex medical conditions to maximally benefit from advances in cancer therapy and to avoid adverse complications of that therapy.