Academic medical centers: ripe for rapid-learning personalized health care

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients' care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.

Innovation in Regulatory Science Is Meeting Evolution of Clinical Evidence Generation

At the turn of the century, the pharmaceutical industry began a transition toward a focus on oncology, rare diseases, and other areas of high unmet need that required a new, more complex approach to drug development. For many of these disease states and novel approaches to therapy, traditional approaches to clinical trial design fall short, and a number of innovative trial designs have emerged. In light of these changes, regulators across the globe are implementing new programs to provide regular development program support, facilitate accelerated access, use real-world data, and use digital tools to improve patients' lives. Emerging market regulators are also focusing on simplifying their regulatory pathways via regional harmonization schemes with varying levels of ambition. These changes in the external environment imply that biopharma regulatory teams need to adapt and evolve, leveraging digital tools, data, and analytics, and positioning themselves as strategic advisors during development.

How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology

Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health-care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in genomics with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Personalized medicine in Europe: not yet personal enough?

Background

Personalized medicine has the potential to allow patients to receive drugs specific to their individual disease, and to increase the efficiency of the healthcare system. There is currently no comprehensive overview of personalized medicine, and this research aims to provide an overview of the concept and definition of personalized medicine in nine European countries.

Methods

A targeted literature review of selected health databases and grey literature was conducted to collate information regarding the definition, process, use, funding, impact and challenges associated with personalized medicine. In-depth qualitative interviews were carried out with experts with health technology assessment, clinical provisioning, payer, academic, economic and industry experience, and with patient organizations.

Results

We identified a wide range of definitions of personalized medicine, with most studies referring to the use of diagnostics and individual biological information such as genetics and biomarkers. Few studies mentioned patients’ needs, beliefs, behaviour, values, wishes, utilities, environment and circumstances, and there was little evidence in the literature for formal incorporation of patient preferences into the evaluation of new medicines. Most interviewees described approaches to stratification and segmentation of patients based on genetic markers or diagnostics, and few mentioned health-related quality of life.

Conclusions

The published literature on personalized medicine is predominantly focused on patient stratification according to individual biological information. Although these approaches are important, incorporation of environmental factors and patients’ preferences in decision making is also needed. In future, personalized medicine should move from treating diseases to managing patients, taking into account all individual factors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-017-2205-4) contains supplementary material, which is available to authorized users.

Prevalence and characteristics of cancer patients receiving care from single vs. multiple institutions

INTRODUCTION

Patients may receive cancer care from multiple institutions. However, at the population level, such patterns of cancer care are poorly described, complicating clinical research. To determine the population-based prevalence and characteristics of patients seen by multiple institutions, we used operations data from a state-mandated cancer registry.

METHODS AND MATERIALS

59,672 invasive cancers diagnosed in 1/1/2010-12/31/2011 in the Greater Bay Area of northern California were categorized as having been reported to the cancer registry within 365days of diagnosis by: 1) ≥1 institution within an integrated health system (IHS); 2) IHS institution(s) and ≥1 non-IHS institution (e.g., private hospital); 3) 1 non-IHS institution; or 4) ≥2 non-IHS institutions. Multivariable logistic regression was used to characterize patients reported by multiple vs. single institutions.

RESULTS

Overall in this region, 17% of cancers were reported by multiple institutions. Of the 33% reported by an IHS, 8% were also reported by a non-IHS. Of non-IHS patients, 21% were reported by multiple institutions, with 28% for breast and 27% for pancreatic cancer, but 19%% for lung and 18% for prostate cancer. Generally, patients more likely to be seen by multiple institutions were younger or had more severe disease at diagnosis.

CONCLUSIONS

Population-based data show that one in six newly diagnosed cancer patients received care from multiple institutions, and differed from patients seen only at a single institution. Cancer care data from single institutions may be incomplete and possibly biased.

Implementing genomics and pharmacogenomics in the clinic: The National Human Genome Research Institute's genomic medicine portfolio

Increasing knowledge about the influence of genetic variation on human health and growing availability of reliable, cost-effective genetic testing have spurred the implementation of genomic medicine in the clinic. As defined by the National Human Genome Research Institute (NHGRI), genomic medicine uses an individual's genetic information in his or her clinical care, and has begun to be applied effectively in areas such as cancer genomics, pharmacogenomics, and rare and undiagnosed diseases. In 2011 NHGRI published its strategic vision for the future of genomic research, including an ambitious research agenda to facilitate and promote the implementation of genomic medicine. To realize this agenda, NHGRI is consulting and facilitating collaborations with the external research community through a series of "Genomic Medicine Meetings," under the guidance and leadership of the National Advisory Council on Human Genome Research. These meetings have identified and begun to address significant obstacles to implementation, such as lack of evidence of efficacy, limited availability of genomics expertise and testing, lack of standards, and difficulties in integrating genomic results into electronic medical records. The six research and dissemination initiatives comprising NHGRI's genomic research portfolio are designed to speed the evaluation and incorporation, where appropriate, of genomic technologies and findings into routine clinical care. Actual adoption of successful approaches in clinical care will depend upon the willingness, interest, and energy of professional societies, practitioners, patients, and payers to promote their responsible use and share their experiences in doing so.

What do providers, payers and patients need from comparative effectiveness research on diagnostics? The case of HER2/Neu testing in breast cancer

AIMS

Comparing effectiveness of diagnostic tests is one of the highest priorities for comparative effectiveness research (CER) set by the Institute of Medicine. Our study aims to identify what information providers, payers and patients need from CER on diagnostics, and what challenges they encounter implementing comparative information on diagnostic alternatives in practice and policy.

MATERIALS & METHODS

Using qualitative research methods and the example of two alternative protocols for HER2 testing in breast cancer, we conducted interviews with 45 stakeholders: providers (n = 25) from four academic and eight nonacademic institutions, executives (n = 13) from five major US private payers and representatives (n = 7) from two breast cancer patient advocacies.

RESULTS

The need for additional scientific evidence to determine the preferred HER2 protocol was more common for advocates than payers (100 vs 54%; p = 0.0515) and significantly more common for advocates than providers (100 vs 40%; p = 0.0077). The availability of information allowing assessment of the implementation impact from alternative diagnostic protocols on provider institutions may mitigate the need for additional scientific evidence for some providers and payers (24 and 46%, respectively). The cost-effectiveness of alternative protocols from the societal perspective is important to payers and advocates (69 and 71%, respectively) but not to providers (0%; p = 0.0001 and p = 0.0001). The lack of reporting laboratory practices is a more common implementation challenge for payers and advocates (77 and 86%, respectively) than for providers (32%). The absence of any mechanism for patient involvement was recognized as a challenge by payers and advocates (69 and 100%, respectively) but not by providers (0%; p = 0.0001 and p = 0.0001).

CONCLUSION

Comparative implementation research is needed to inform the stakeholders considering diagnostic alternatives. Transparency of laboratory practices is an important factor in enabling implementation of CER on diagnostics in practice and policy. The incongruent views of providers versus patient advocates and payers on involving patients in diagnostic decisions is a concerning challenge to utilizing the results of CER.

Genomic medicine: a decade of successes, challenges, and opportunities

Genomic medicine--an aspirational term 10 years ago--is gaining momentum across the entire clinical continuum from risk assessment in healthy individuals to genome-guided treatment in patients with complex diseases. We review the latest achievements in genome research and their impact on medicine, primarily in the past decade. In most cases, genomic medicine tools remain in the realm of research, but some tools are crossing over into clinical application, where they have the potential to markedly alter the clinical care of patients. In this State of the Art Review, we highlight notable examples including the use of next-generation sequencing in cancer pharmacogenomics, in the diagnosis of rare disorders, and in the tracking of infectious disease outbreaks. We also discuss progress in dissecting the molecular basis of common diseases, the role of the host microbiome, the identification of drug response biomarkers, and the repurposing of drugs. The significant challenges of implementing genomic medicine are examined, along with the innovative solutions being sought. These challenges include the difficulty in establishing clinical validity and utility of tests, how to increase awareness and promote their uptake by clinicians, a changing regulatory and coverage landscape, the need for education, and addressing the ethical aspects of genomics for patients and society. Finally, we consider the future of genomics in medicine and offer a glimpse of the forces shaping genomic medicine, such as fundamental shifts in how we define disease, how medicine is delivered to patients, and how consumers are managing their own health and affecting change.

Comparative effectiveness research in oncology

Although randomized controlled trials represent the gold standard for comparative effective research (CER), a number of additional methods are available when randomized controlled trials are lacking or inconclusive because of the limitations of such trials. In addition to more relevant, efficient, and generalizable trials, there is a need for additional approaches utilizing rigorous methodology while fully recognizing their inherent limitations. CER is an important construct for defining and summarizing evidence on effectiveness and safety and comparing the value of competing strategies so that patients, providers, and policymakers can be offered appropriate recommendations for optimal patient care. Nevertheless, methodological as well as political and social challenges for CER remain. CER requires constant and sophisticated methodological oversight of study design and analysis similar to that required for randomized trials to reduce the potential for bias. At the same time, if appropriately conducted, CER offers an opportunity to identify the most effective and safe approach to patient care. Despite rising and unsustainable increases in health care costs, an even greater challenge to the implementation of CER arises from the social and political environment questioning the very motives and goals of CER. Oncologists and oncology professional societies are uniquely positioned to provide informed clinical and methodological expertise to steer the appropriate application of CER toward critical discussions related to health care costs, cost-effectiveness, and the comparative value of the available options for appropriate care of patients with cancer.

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.

Importance of Health Information Technology, Electronic Health Records, and Continuously Aggregating Data to Comparative Effectiveness Research and Learning Health Care

Rapidly accumulating clinical information can support cancer care and discovery. Future success depends on information management, access, use, and reuse. Electronic health records (EHRs) are highlighted as a critical component of evidence development and implementation, but to fully harness the potential of EHRs, they need to be more than electronic renderings of the traditional paper medical chart. Clinical informatics and structured accessible secure data captured through EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (CER). Use of large linked administrative databases to answer comparative questions is an early version of informatics-enabled CER familiar to oncologists. An updated version of informatics-enabled CER relies on EHR-derived structured data linked with supplemental information to provide patient-level information that can be aggregated and analyzed to support hypothesis generation, comparative assessment, and personalized care. As implementation of EHRs continues to expand, electronic databases containing information collected via EHRs will continuously aggregate; aggregating data enhanced with real-time analytics can provide point-of-care evidence to oncologists, tailored to patient-level characteristics. The system learns when clinical care informs research, and insights derived from research are reinvested in care. Challenges must be overcome, including interoperability, standardization, access, and development of real-time analytics.

Predicting outcomes in radiation oncology--multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Leveraging informatics, mobile health technologies and biobanks to treat each patient right

Personalized medicine is the future of clinical care. Many interpret this to mean that advances in genomic medicine will revolutionize care. However, the reality is much more complex, relying on a combination of health technology, patient engagement, genomics, basic sciences and decision-support tools to move the field forward. There are a number of instances in which providers and researchers have already made significant progress toward the goal, yet the advances often occur in isolation and are not widely acknowledged. With appropriate investment, coordination and vision, the present state can be leveraged to change our approach to a given patient within the next few years. Here we demonstrate how currently available informatics, mobile health technology and biobanking solutions can support personalization of care in the near term, and lead to the development of a learning health system.

Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: a common bond

Despite stunning advances in our understanding of the genetics and the molecular basis for cancer, many patients with cancer are not yet receiving therapy tailored specifically to their tumor biology. The translation of these advances into clinical practice has been hindered, in part, by the lack of evidence for biomarkers supporting the personalized medicine approach. Most stakeholders agree that the translation of biomarkers into clinical care requires evidence of clinical utility. The highest level of evidence comes from randomized controlled clinical trials (RCTs). However, in many instances, there may be no RCTs that are feasible for assessing the clinical utility of potentially valuable genomic biomarkers. In the absence of RCTs, evidence generation will require well-designed cohort studies for comparative effectiveness research (CER) that link detailed clinical information to tumor biology and genomic data. CER also uses systematic reviews, evidence-quality appraisal, and health outcomes research to provide a methodologic framework for assessing biologic patient subgroups. Rapid learning health care (RLHC) is a model in which diverse data are made available, ideally in a robust and real-time fashion, potentially facilitating CER and personalized medicine. Nonetheless, to realize the full potential of personalized care using RLHC requires advances in CER and biostatistics methodology and the development of interoperable informatics systems, which has been recognized by the National Cancer Institute's program for CER and personalized medicine. The integration of CER methodology and genomics linked to RLHC should enhance, expedite, and expand the evidence generation required for fully realizing personalized cancer care.

Personalized nanomedicine advancements for stem cell tracking

Recent technological developments in biomedicine have facilitated the generation of data on the anatomical, physiological and molecular level for individual patients and thus introduces opportunity for therapy to be personalized in an unprecedented fashion. Generation of patient-specific stem cells exemplifies the efforts toward this new approach. Cell-based therapy is a highly promising treatment paradigm; however, due to the lack of consistent and unbiased data about the fate of stem cells in vivo, interpretation of therapeutic effects remains challenging hampering the progress in this field. The advent of nanotechnology with a wide palette of inorganic and organic nanostructures has expanded the arsenal of methods for tracking transplanted stem cells. The diversity of nanomaterials has revolutionized personalized nanomedicine and enables individualized tailoring of stem cell labeling materials for the specific needs of each patient. The successful implementation of stem cell tracking will likely be a significant driving force that will contribute to the further development of nanotheranostics. The purpose of this review is to emphasize the role of cell tracking using currently available nanoparticles.

Creating the future biomedical research workforce

As an NIH task force ponders the future of the U.S. biomedical research workforce, clinical and translational scientists can contribute crucial insights and should share comments by 7 October 2011.