Seeking American Society of Clinical Oncology-Quality Oncology Practice Initiative (ASCO-QOPI) certification in a northern New England rural health system and cancer care network

In 2006 following several years of preliminary study, the American Society of Clinical Oncology (ASCO) launched the Quality Oncology Practice Initiative (QOPI). This cancer-focused quality initiative evolved considerably over the next decade-and-a-half and is expanding globally. QOPI is undoubtedly the leading standard-bearer for quality cancer care and contemporary medical oncology practice. The program garners attention and respect among federal programs, private insurers, and medical oncology practices across the nation. The MaineHealth Cancer Care Network (MHCCN) has undergone expansive growth since 2017. The network provides cancer care to more than 70% of the cases in Maine in a largely rural health system in Northern New England. In fall 2020, the MHCCN QOPI project leadership, following collaborative discussions with the ASCO-QOPI team, elected to proceed with a health system-cancer network-wide QOPI certification. Key themes emerged over the course of our two-year journey including: (1) Developing a highly interprofessional team committed to the project; (2) Capitalizing on a single electronic medical record for data transmission to CancerLinQ; (3) Prior experience, especially policy development, in other cancer-focused accreditation programs across the network; and (4) Building consensus through quarterly stakeholder meetings and awarding Continuing Medical Education (CME) and American Board of Medical Specialists (ABMS) Maintenance of Certification (MOC) credits to oncologists. All participants demonstrated a genuine spirit to work together to achieve certification. We report our successful journey seeking ASCO-QOPI certification across our network, which to our knowledge is the first-of-its-kind endeavor.

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider

Learning healthcare systems have recently emerged as a strategy to continuously use experiences and outcomes of clinical care for research purposes in precision medicine. Although it is known that learning healthcare transitions in general raise important ethical challenges, the ethical ramifications of such transitions in the specific context of precision medicine have not extensively been discussed. Here, we describe three levers that institutions can pull to advance learning healthcare systems in precision medicine: (1) changing testing of individual variability (such as genes); (2) changing prescription of treatments on the basis of (genomic) test results; and/or (3) changing the handling of data that link variability and treatment to clinical outcomes. Subsequently, we evaluate how patients can be affected if one of these levers are pulled: (1) patients are tested for different or more factors than before the transformation, (2) patients receive different treatments than before the transformation and/or (3) patients’ data obtained through clinical care are used, or used more extensively, for research purposes. Based on an analysis of the aforementioned mechanisms and how these potentially affect patients, we analyze why learning healthcare systems in precision medicine need a different ethical approach and discuss crucial points to consider regarding this approach.

Governance of a Learning Health Care System for Oncology: Patient Recommendations

PURPOSE

The learning health care system (LHS) was designed to enable real-time learning and research by harnessing data generated during patients' clinical encounters. This novel approach begets ethical questions regarding the oversight of users and uses of patient data. Understanding patients' perspectives is vitally important.

MATERIALS AND METHODS

We conducted democratic deliberation sessions focused on CancerLinQ, a real-world LHS. Experts presented educational content, and then small group discussions were held to elicit viewpoints. The deliberations centered around whether policies should permit or deny certain users and uses of secondary data. De-identified transcripts of the discussions were examined by using thematic analysis.

RESULTS

Analysis identified two thematic clusters: expectations and concerns, which seemed to inform LHS governance recommendations. Participants expected to benefit from the LHS through the advancement of medical knowledge, which they hoped would improve treatments and the quality of their care. They were concerned that profit-driven users might manipulate the data in ways that could burden or exploit patients, hinder medical decisions, or compromise patient-provider communication. It was recommended that restricted access, user fees, and penalties should be imposed to prevent users, especially for-profit entities, from misusing data. Another suggestion was that patients should be notified of potential ethical issues and included on diverse, unbiased governing boards.

CONCLUSION

If patients are to trust and support LHS endeavors, their concerns about for-profit users must be addressed. The ethical implementation of such systems should consist of patient representation on governing boards, transparency, and strict oversight of for-profit users.

Patient Preferences Regarding Informed Consent Models for Participation in a Learning Health Care System for Oncology

PURPOSE

The expansion of learning health care systems (LHSs) promises to bolster research and quality improvement endeavors. Stewards of patient data have a duty to respect the preferences of the patients from whom, and for whom, these data are being collected and consolidated.

METHODS

We conducted democratic deliberations with a diverse sample of 217 patients treated at 4 sites to assess views about LHSs, using the example of CancerLinQ, a real-world LHS, to stimulate discussion. In small group discussions, participants deliberated about different policies for how to provide information and to seek consent regarding the inclusion of patient data. These discussions were recorded, transcribed, and de-identified for thematic analysis.

RESULTS

Of participants, 67% were female, 61% were non-Hispanic Whites, and the mean age was 60 years. Patients' opinions about sharing their data illuminated 2 spectra: trust/distrust and individualism/collectivism. Positions on these spectra influenced the weight placed on 3 priorities: promoting societal altruism, ensuring respect for persons, and protecting themselves. In turn, consideration of these priorities seemed to inform preferences regarding patient choices and system transparency. Most advocated for a policy whereby patients would receive notification and have the opportunity to opt out of including their medical records in the LHS. Participants reasoned that such a policy would balance personal protections and societal welfare.

CONCLUSION

System transparency and patient choice are vital if patients are to feel respected and to trust LHS endeavors. Those responsible for LHS implementation should ensure that all patients receive an explanation of their options, together with standardized, understandable, comprehensive materials.

Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data

BACKGROUND

Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.

METHODS

The model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients.

RESULTS

The C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] = 0.715 to 0.775) compared with 0.635 (95% CI = 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P < .001). Our model's predictions were well-calibrated.

CONCLUSIONS

The model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.

The opportunities and shortcomings of using big data and national databases for sarcoma research

The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. Established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. However, the validity of large databases has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. In addition, the utilization of large databases for the study of rare cancers such as sarcoma may be particularly challenging because of the known limitations of administrative data and poor overall data quality. Currently, there are several large national cancer databases, including the Surveillance, Epidemiology, and End Results database, the National Cancer Data Base of the American College of Surgeons and the American Cancer Society, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention. These databases are often used for sarcoma research, but they are limited by their dependence on administrative or billing data, the lack of agreement between chart abstractors on diagnosis codes, and the use of preexisting documented hospital diagnosis codes for tumor registries, which lead to a significant underestimation of sarcomas in large data sets. Current and future initiatives to improve databases and big data applications for sarcoma research include increasing the utilization of sarcoma-specific registries and encouraging national initiatives to expand on real-world, evidence-based data sets.

CancerLinQ: Origins, Implementation, and Future Directions

Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.

Beyond maximum grade: modernising the assessment and reporting of adverse events in haematological malignancies

Tremendous progress in treatment and outcomes has been achieved across the whole range of haematological malignancies in the past two decades. Although cure rates for aggressive malignancies have increased, nowhere has progress been more impactful than in the management of typically incurable forms of haematological cancer. Population-based data have shown that 5-year survival for patients with chronic myelogenous and chronic lymphocytic leukaemia, indolent B-cell lymphomas, and multiple myeloma has improved markedly. This improvement is a result of substantial changes in disease management strategies in these malignancies. Several haematological malignancies are now chronic diseases that are treated with continuously administered therapies that have unique side-effects over time. In this Commission, an international panel of clinicians, clinical investigators, methodologists, regulators, and patient advocates representing a broad range of academic and clinical cancer expertise examine adverse events in haematological malignancies. The issues pertaining to assessment of adverse events examined here are relevant to a range of malignancies and have been, to date, underexplored in the context of haematology. The aim of this Commission is to improve toxicity assessment in clinical trials in haematological malignancies by critically examining the current process of adverse event assessment, highlighting the need to incorporate patient-reported outcomes, addressing issues unique to stem-cell transplantation and survivorship, appraising challenges in regulatory approval, and evaluating toxicity in real-world patients. We have identified a range of priority issues in these areas and defined potential solutions to challenges associated with adverse event assessment in the current treatment landscape of haematological malignancies.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

Improving Quality and Value of Cancer Care for Older Adults

The concepts of quality and value have become ubiquitous in discussions about health care, including cancer care. Despite their prominence, these concepts remain difficult to encapsulate, with multiple definitions and frameworks emerging over the past few decades. Defining quality and value for the care of older adults with cancer can be particularly challenging. Older adults are heterogeneous and often excluded from clinical trials, severely limiting generalizable data for this population. Moreover, many frameworks for quality and value focus on traditional outcomes of survival and toxicity and neglect goals that may be more meaningful for older adults, such as quality of life and functional independence. A history of quality and value standards and an evaluation of some currently available standards and frameworks elucidate the potential gaps in application to older adults with cancer. However, narrowing the focus to processes of care presents several opportunities for improving the care of older adults with cancer now, even while further work is ongoing to evaluate outcomes and efficiency. New models of care, including the patient-centered medical home, as well as new associated bundled payment models, would be advantageous for older adults with cancer, facilitating collaboration, communication, and patient-centeredness and minimizing the fragmentation that impairs the current provision of cancer care. Advances in information technology support the foundation for these models of care; these technologies facilitate communication, increase available data, support shared decision making, and increase access to multidisciplinary specialty care. Further work will be needed to define and to continue to tailor processes of care to achieve relevant outcomes for older patients with cancer to fulfill the promise of quality and value of care for this vulnerable and growing population.