Practice-based evidence to evidence-based practice: building the National Radiation Oncology Registry

Association between adherence to radiation therapy quality metrics and patient reported outcomes in prostate cancer

BACKGROUND

Prior studies have shown significant variability in the quality of prostate cancer care in the US with questionable associations between quality measures and patient reported outcomes. We evaluated the impact of compliance with nationally recognized radiation therapy (RT) quality measures on patient-reported health-related quality of life (HRQOL) outcomes in the Comparative Effectiveness Analysis of Surgery and Radiation (CEASAR) cohort.

METHODS

CEASAR is a population-based, prospective cohort study of men with localized prostate cancer from which we identified 649 who received primary RT and completed HRQOL surveys for inclusion. Eight quality measures were identified based on national guidelines. We analyzed the impact of compliance with these measures on HRQOL assessed by the 26-item Expanded Prostate Index Composite at pre-specified intervals up to 5 years after treatment. Multivariable analysis was performed controlling for demographic and clinicopathologic features.

RESULTS

Among eligible participants, 566 (87%) patients received external beam radiation therapy and 83 (13%) received brachytherapy. Median age was 69 years (interquartile range: 64-73), 33% had low-, 43% intermediate-, and 23% high-risk disease. 28% received care non-compliant with at least one measure. In multivariable analyses, while some statistically significant associations were identified, there were no clinically significant associations between compliance with evaluated RT quality measures and patient reported urinary irritative, urinary incontinence, bowel, sexual or hormonal function.

CONCLUSIONS

Compliance with RT quality measures was not meaningfully associated with patient-reported outcomes after prostate cancer treatment. Further work is needed to identify patient-centered quality measures of prostate cancer care.

Radiotherapy-specific quality indicators at national level: How to make it happen

PURPOSE /OBJECTIVE

To promote best practice and quality of care, the Belgian College of Physicians for Radiotherapy Centers established a set of radiotherapy specific quality indicators for benchmarking on a national level. This paper describes the development, the collected QIs, the observed trends and the departments' evaluation of this initiative.

MATERIAL AND METHODS

The Donabedian approach was used, focussing on structural, process and outcome QIs. The criteria for QI selection were availability, required for low-threshold regular collection, and applicability to guidelines and good practice. The QIs were collected yearly and individualized reports were sent out to all RT departments. In 2021, a national survey was held to evaluate the ease of data collection and submission, and the perceived importance and validity of the collected QIs.

RESULTS

18 structural QI and 37 process and outcome parameters (n = 25 patients/pathology/department) were collected. The participation rate amounted to 95 % overall. The analysis gave a national overview of RT activity, resources, clinical practice and reported acute toxicities. The individualized reports allowed departments to benchmark their performance. The 2021 survey indicated that the QIs were overall easy to collect, relevant and reliable. The collection of acute recorded toxicities was deemed a weak point due to inter-observer variabilities and lack of follow-up time.

CONCLUSION

QI collection on a national level is a valuable process in steering quality improvement initiatives. The feasibility and relevance was demonstrated with a high level of participation. The national initiative will continue to evolve as a quality monitoring and improvement tool.

Transparency in quality of radiotherapy for breast cancer in the Netherlands: a national registration of radiotherapy-parameters

Background

Radiotherapy (RT) is part of the curative treatment of approximately 70% of breast cancer (BC) patients. Wide practice variation has been reported in RT dose, fractionation and its treatment planning for BC. To decrease this practice variation, it is essential to first gain insight into the current variation in RT treatment between institutes. This paper describes the development of the NABON Breast Cancer Audit-Radiotherapy (NBCA-R), a structural nationwide registry of BC RT data of all BC patients treated with at least surgery and RT.

Methods

A working group consisting of representatives of the BC Platform of the Dutch Radiotherapy Society selected a set of dose volume parameters deemed to be surrogate outcome parameters, both for tumour control and toxicity. Two pilot studies were carried out in six RT institutes. In the first pilot study, data were manually entered into a secured web-based system. In the second pilot study, an automatic Digital Imaging and Communications in Medicine (DICOM) RT upload module was created and tested.

Results

The NBCA-R dataset was created by selecting RT parameters describing given dose, target volumes, coverage and homogeneity, and dose to organs at risk (OAR). Entering the data was made mandatory for all Dutch RT departments. In the first pilot study (N = 1093), quite some variation was already detected. Application of partial breast irradiation varied from 0 to 17% between the 6 institutes and boost to the tumour bed from 26.5 to 70.2%. For patients treated to the left breast or chest wall only, the average mean heart dose (MHD) varied from 0.80 to 1.82 Gy; for patients treated to the breast/chest wall only, the average mean lung dose (MLD) varied from 2.06 to 3.3 Gy. In the second pilot study 6 departments implemented the DICOM-RT upload module in daily practice. Anonymised data will be available for researchers via a FAIR (Findable, Accessible, Interoperable, Reusable) framework.

Conclusions

We have developed a set of RT parameters and implemented registration for all Dutch BC patients. With the use of an automated upload module registration burden will be minimized. Based on the data in the NBCA-R analyses of the practice variation will be done, with the ultimate aim to improve quality of BC RT.

Trial registration Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02043-0.

A Conceptual Model of an Oncology Information System

Introduction

Oncologists are usually faced with a huge amount of diagnostic and therapeutic data in the process of cancer care. However, they do not have access to the integrated data. This research aimed to present a conceptual model of an oncology information system based on the users' requirements.

Methods

This study was conducted in 2019 and composed of two phases. Initially, a questionnaire was designed, and clinical experts (n=34) were asked to identify the most important data elements and functional requirements in an oncology information system. In the second phase, conceptual, structural and behavioral diagrams of the system were drawn based on the results of the first phase. These diagrams were also reviewed and validated by five experts.

Results

Most of the data elements and all functional requirements were found important by the experts. The data elements were related to different phases of cancer care including screening, prevention, diagnosis, treatment, mental care and pain relief, and end-of-life care. Then, conceptual, structural and behavioral diagrams of the system were designed and approved by the experts or revised based on their comments.

Conclusion

The conceptual model and the diagrams presented in the current study can be used for developing an oncology information system. This system will be able to manage patients' cancer data from screening to the end-of-life care. However, the system needs to be designed and implemented in a real healthcare setting to see how it can meet users' requirements.

Oncology Information System: A Qualitative Study of Users' Requirements

Background:

Cancer care is a complex care process and is associated with generating a variety of data during the care process. Therefore, it seems that designing and using information systems is necessary to enhance the accessibility, organization and management of cancer-related data. The aim of this study was to identify users’ requirements of an oncology information system (OIS).

Methods:

This was a qualitative study conducted in 2018. In depth semi-structured interviews were performed with clinicians and non-clinicians in five teaching hospitals to identify users’ requirements. Data were analyzed by using framework analysis.

Results:

The four themes emerged from data analysis included: a) methods of recording cancer data in the hospitals, b) required cancer data in different departments, c) comprehensive cancer care documentation, and d) required functions of an oncology information system.

Conclusion:

According to the results, currently, electronic documentation is less frequently used for cancer patients. Therefore, an extensive effort is needed to identify users’ requirements before designing and implementing an oncology information system. As multidisciplinary teams are involved in cancer care, all potential users and their requirements should be taken into account. Such a system can help to collect and use cancer data effectively.

Contemporary prostate cancer radiation therapy in the United States: Patterns of care and compliance with quality measures

PURPOSE

Quality measures represent the standards of appropriate treatment agreed upon by experts in the field and often supported by data. The extent to which providers in the community adhere to quality measures in radiation therapy (RT) is unknown.

METHODS AND MATERIALS

The Comparative Effectiveness Analysis of Surgery and Radiation study enrolled men with clinically localized prostate cancer in 2011 and 2012. Patients completed surveys and medical records were reviewed. Patients were risk-stratified according to D'Amico classification criteria. Patterns of care and compliance with 8 quality measures as endorsed by national consortia as of 2011 were assessed.

RESULTS

Overall, 926 men underwent definitive RT (69% external beam radiation therapy [EBRT]), 17% brachytherapy (BT), and 14% combined EBRT and BT with considerable variation in radiation techniques across risk groups. Most men who received EBRT had dose-escalated EBRT (>75 Gy; 93%) delivered with conventional fractionation (<2 Gy; 95%), intensity modulated RT (76%), and image guided RT (85%). Most men treated with BT received I125 (77%). Overall, 73% of the men received EBRT that was compliant with the quality measures (dose-escalation, image-guidance, appropriate use of androgen deprivation therapy, and appropriate treatment target) but only 60% of men received BT that was compliant with quality measures (postimplant dosimetry and appropriate dose). African-American men (64%) and other minorities (62%) were less likely than white men (77%) to receive EBRT that was compliant with quality measures.

CONCLUSIONS

Most men who received RT for localized prostate cancer were treated with an appropriately high dose and received image guidance and intensity modulated RT. However, compliance with some nationally recognized quality measures was relatively low and varied by race. There are significant opportunities to improve the delivery of RT and especially for men of a minority race.

Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow

Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow.

An Update From the Pediatric Proton Consortium Registry

Background/objectives

The Pediatric Proton Consortium Registry (PPCR) was established to expedite proton outcomes research in the pediatric population requiring radiotherapy. Here, we introduce the PPCR as a resource to the oncology community and provide an overview of the data available for further study and collaboration.

Design/methods

A multi-institutional registry of integrated clinical, dosimetric, radiographic, and patient-reported data for patients undergoing proton radiation therapy was conceived in May 2010. Massachusetts General Hospital began enrollment in July of 2012. Subsequently, 12 other institutions joined the PPCR and activated patient accrual, with the latest joining in 2017. An optional patient-reported quality of life (QoL) survey is currently implemented at six institutions. Baseline health status, symptoms, medications, neurocognitive status, audiogram findings, and neuroendocrine testing are collected. Treatment details of surgery, chemotherapy, and radiation therapy are documented and radiation plans are archived. Follow-up is collected annually. Data were analyzed 25 September, 2017.

Results

A total of 1,854 patients have consented and enrolled in the PPCR from October 2012 until September 2017. The cohort is 55% male, 70% Caucasian, and comprised of 79% United States residents. Central nervous system (CNS) tumors comprise 61% of the cohort. The most common CNS histologies are as follows: medulloblastoma (n = 276), ependymoma (n = 214), glioma/astrocytoma (n = 195), craniopharyngioma (n = 153), and germ cell tumors (n = 108). The most common non-CNS tumors diagnoses are as follows: rhabdomyosarcoma (n = 191), Ewing sarcoma (n = 105), Hodgkin lymphoma (n = 66), and neuroblastoma (n = 55). The median follow-up is 1.5 years with a range of 0.14 to 4.6 years.

Conclusion

A large prospective population of children irradiated with proton therapy has reached a critical milestone to facilitate long-awaited clinical outcomes research in the modern era. This is an important resource for investigators both in the consortium and for those who wish to access the data for academic research pursuits.

Big Data in radiation therapy: challenges and opportunities

Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume. In radiation oncology, there are many efforts to collect data for research and innovation purposes. Clinical trials are the gold standard when proving any hypothesis that directly affects the patient. Collecting data in registries with strict predefined rules is also a common approach to find answers. A third approach is to develop data stores that can be used by modern machine learning techniques to provide new insights or answer hypotheses. We believe all three approaches have their strengths and weaknesses, but they should all strive to create Findable, Accessible, Interoperable, Reusable (FAIR) data. To learn from these data, we need distributed learning techniques, sending machine learning algorithms to FAIR data stores around the world, learning from trial data, registries and routine clinical data rather than trying to centralize all data. To improve and personalize medicine, rapid learning platforms must be able to process FAIR “Big Data” to evaluate current clinical practice and to guide further innovation.

Development of a model web-based system to support a statewide quality consortium in radiation oncology

PURPOSE

A database in which patient data are compiled allows analytic opportunities for continuous improvements in treatment quality and comparative effectiveness research. We describe the development of a novel, web-based system that supports the collection of complex radiation treatment planning information from centers that use diverse techniques, software, and hardware for radiation oncology care in a statewide quality collaborative, the Michigan Radiation Oncology Quality Consortium (MROQC).

METHODS AND MATERIALS

The MROQC database seeks to enable assessment of physician- and patient-reported outcomes and quality improvement as a function of treatment planning and delivery techniques for breast and lung cancer patients. We created tools to collect anonymized data based on all plans.

RESULTS

The MROQC system representing 24 institutions has been successfully deployed in the state of Michigan. Since 2012, dose-volume histogram and Digital Imaging and Communications in Medicine-radiation therapy plan data and information on simulation, planning, and delivery techniques have been collected. Audits indicated >90% accurate data submission and spurred refinements to data collection methodology.

CONCLUSIONS

This model web-based system captures detailed, high-quality radiation therapy dosimetry data along with patient- and physician-reported outcomes and clinical data for a radiation therapy collaborative quality initiative. The collaborative nature of the project has been integral to its success. Our methodology can be applied to setting up analogous consortiums and databases.

Big data in oncologic imaging

Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle "the whole is greater than the sum of parts." To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?

Historically, basic scientists and clinical researchers have transduced reality into data so that they might explain or predict the world. Because data are fundamental to their craft, these investigators have been on the front lines of the Big Data deluge in recent years. Radiotherapy data are complex and longitudinal data sets are frequently collected to track both tumor and normal tissue response to therapy. As basic, translational and clinical investigators explore with increasingly greater depth the complexity of underlying disease processes and treatment outcomes, larger sample populations are required for research studies and greater quantities of data are being generated. In addition, well-curated research and trial data are being pooled in public data repositories to support large-scale analyses. Thus, the tremendous quantity of information produced in both basic and clinical research in radiation therapy can now be considered as having entered the realm of Big Data.

Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer

PURPOSE

Clinical data that are generated through routine radiation therapy procedures can be leveraged as a source of knowledge to provide evidence-based decision support for future patients. Treatment planning in radiation therapy often relies on trial-and-error iterations, experience, judgment calls and general guidelines. The authors present a knowledge-driven decision support system that assists clinicians by reducing some of the uncertainties associated with treatment planning and provides quantified empirical estimates to help minimize the radiation dose to healthy critical structures surrounding the tumor.

METHODS

A database of retrospective DICOM RT data fuels a decision support engine, which assists clinicians in selecting dose constraints and assessing dose distributions. The first step is to quantify the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to the new patient. The dose profiles of these database cases can help clinicians to estimate an acceptable dose distribution for the new case, based on empirical evidence. Since database diversity is essential for good system performance, an infrastructure for multi-institutional collaboration was also conceptualized in order to pave the way for data sharing of protected health information.

RESULTS

A set of 127 retrospective test cases was collected from a single institution in order to conduct a leave-one-out evaluation of the decision support module. In 72 % of these retrospective test cases, patients with similar tumor anatomy were also found to exhibit similar radiation dose distributions. This demonstrates the system's ability to successfully extract retrospective database cases that can estimate the new patient's dose distribution.

CONCLUSION

The radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.

Technology for Innovation in Radiation Oncology

Radiation therapy is an effective, personalized cancer treatment that has benefited from technological advances associated with the growing ability to identify and target tumors with accuracy and precision. Given that these advances have played a central role in the success of radiation therapy as a major component of comprehensive cancer care, the American Society for Radiation Oncology (ASTRO), the American Association of Physicists in Medicine (AAPM), and the National Cancer Institute (NCI) sponsored a workshop entitled "Technology for Innovation in Radiation Oncology," which took place at the National Institutes of Health (NIH) in Bethesda, Maryland, on June 13 and 14, 2013. The purpose of this workshop was to discuss emerging technology for the field and to recognize areas for greater research investment. Expert clinicians and scientists discussed innovative technology in radiation oncology, in particular as to how these technologies are being developed and translated to clinical practice in the face of current and future challenges and opportunities. Technologies encompassed topics in functional imaging, treatment devices, nanotechnology, and information technology. The technical, quality, and safety performance of these technologies were also considered. A major theme of the workshop was the growing importance of innovation in the domain of process automation and oncology informatics. The technologically advanced nature of radiation therapy treatments predisposes radiation oncology research teams to take on informatics research initiatives. In addition, the discussion on technology development was balanced with a parallel conversation regarding the need for evidence of efficacy and effectiveness. The linkage between the need for evidence and the efforts in informatics research was clearly identified as synergistic.

Inception of a national multidisciplinary registry for stereotactic radiosurgery

Stereotactic radiosurgery (SRS) represents a multidisciplinary approach to the delivery of ionizing high-dose radiation to treat a wide variety of disorders. Much of the radiosurgical literature is based upon retrospective single-center studies along with a few randomized controlled clinical trials. More timely and effective evidence is needed to enhance the consistency and quality of and clinical outcomes achieved with SRS. The authors summarize the creation and implementation of a national SRS registry. The American Association of Neurological Surgeons (AANS) through NeuroPoint Alliance, Inc., started a successful registry effort with its lumbar spine initiative. Following a similar approach, the AANS and NeuroPoint Alliance collaborated with corporate partners and the American Society for Radiation Oncology to devise a data dictionary for an SRS registry. Through administrative and financial support from professional societies and corporate partners, a framework for implementation of the registry was created. Initial plans were devised for a 3-year effort encompassing 30 high-volume SRS centers across the country. Device-specific web-based data-extraction platforms were built by the corporate partners. Data uploaders were then used to port the data to a common repository managed by Quintiles, a national and international health care trials company. Audits of the data for completeness and veracity will be undertaken by Quintiles to ensure data fidelity. Data governance and analysis are overseen by an SRS board comprising equal numbers of representatives from the AANS and NeuroPoint Alliance. Over time, quality outcome assessments and post hoc research can be performed to advance the field of SRS. Stereotactic radiosurgery offers a high-technology approach to treating complex intracranial disorders. Improvements in the consistency and quality of care delivered to patients who undergo SRS should be afforded by the national registry effort that is underway.

Wide Variation in the Diffusion of a New Technology: Practice-Based Trends in Intensity-Modulated Radiation Therapy (IMRT) Use in the State of Michigan, With Implications for IMRT Use Nationally

IMRT use grew significantly across the state of Michigan over time, with four-fold variability among centers, which was related to facility characteristics.

Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets

Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within “Big Data”. Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.

Considerations for observational research using large data sets in radiation oncology

The radiation oncology community has witnessed growing interest in observational research conducted using large-scale data sources such as registries and claims-based data sets. With the growing emphasis on observational analyses in health care, the radiation oncology community must possess a sophisticated understanding of the methodological considerations of such studies in order to evaluate evidence appropriately to guide practice and policy. Because observational research has unique features that distinguish it from clinical trials and other forms of traditional radiation oncology research, the International Journal of Radiation Oncology, Biology, Physics assembled a panel of experts in health services research to provide a concise and well-referenced review, intended to be informative for the lay reader, as well as for scholars who wish to embark on such research without prior experience. This review begins by discussing the types of research questions relevant to radiation oncology that large-scale databases may help illuminate. It then describes major potential data sources for such endeavors, including information regarding access and insights regarding the strengths and limitations of each. Finally, it provides guidance regarding the analytical challenges that observational studies must confront, along with discussion of the techniques that have been developed to help minimize the impact of certain common analytical issues in observational analysis. Features characterizing a well-designed observational study include clearly defined research questions, careful selection of an appropriate data source, consultation with investigators with relevant methodological expertise, inclusion of sensitivity analyses, caution not to overinterpret small but significant differences, and recognition of limitations when trying to evaluate causality. This review concludes that carefully designed and executed studies using observational data that possess these qualities hold substantial promise for advancing our understanding of many unanswered questions of importance to the field of radiation oncology.

Value: a framework for radiation oncology

In the current health care system, high costs without proportional improvements in quality or outcome have prompted widespread calls for change in how we deliver and pay for care. Value-based health care delivery models have been proposed. Multiple impediments exist to achieving value, including misaligned patient and provider incentives, information asymmetries, convoluted and opaque cost structures, and cultural attitudes toward cancer treatment. Radiation oncology as a specialty has recently become a focus of the value discussion. Escalating costs secondary to rapidly evolving technologies, safety breaches, and variable, nonstandardized structures and processes of delivering care have garnered attention. In response, we present a framework for the value discussion in radiation oncology and identify approaches for attaining value, including economic and structural models, process improvements, outcome measurement, and cost assessment.

Delivering High-Quality Cancer Care: The Critical Role of Quality Measurement

In 1999, the Institute of Medicine (IOM) published Ensuring Quality Cancer Care, an influential report that described an ideal cancer care system and issued ten recommendations to address pervasive gaps in the understanding and delivery of quality cancer care. Despite generating much fervor, the report's recommendations-including two recommendations related to quality measurement-remain largely unfulfilled. Amidst continuing concerns regarding increasing costs and questionable quality of care, the IOM charged a new committee with revisiting the 1999 report and with reassessing national cancer care, with a focus on the aging US population. The committee identified high-quality patient-clinician relationships and interactions as central drivers of quality and attributed existing quality gaps, in part, to the nation's inability to measure and improve cancer care delivery in a systematic way. In 2013, the committee published its findings in Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis, which included two recommendations that emphasize coordinated, patient-centered quality measurement and information technology enhancements: Develop a national quality reporting program for cancer care as part of a learning health care system; and,Develop an ethically sound learning health care information technology system for cancer that enables real-time analysis of data from cancer patients in a variety of care settings. These recommendations underscore the need for independent national oversight, public-private collaboration, and substantial funding to create robust, patient-centered quality measurement and learning enterprises to improve the quality, accessibility, and affordability of cancer care in America.