Positive benefits of cooperative group membership: Being part of a networked rapid learning system

Introduction: A rapid learning system (RLS) of health care harnesses data generated from routine patient care to create a virtuous cycle of data collection and analysis for quality improvement and research. The success of such systems depends on understanding patient perspectives regarding the ethical issues that arise from the ongoing implementation of this transformative concept. Methods: An interview guide was designed to evaluate patient perspectives to inform the ethical implementation of an oncology RLS. A purposively selected, diverse sample of 32 patients with cancer was recruited from two institutions to participate in semistructured, in-depth interviews for formal qualitative analysis. Results: The extent to which respondents expressed discomfort with more permissive system features (less formal notification/consent, broader uses/users, inclusion of sensitive data) reflected their trust, which in turn seemed to vary by sociodemographic features. It was also influenced by their familiarity with technology and their attitudes and beliefs regarding privacy and the use of electronic medical records more generally. Distrust of insurers and the pharmaceutical industry led subjects to desire greater oversight and restriction of these potential users of the system. Subjects were most comfortable when doctors were the primary users, engaged patients directly in the notification and consent discussion, and oversaw the system. Conclusion: Those actively developing RLSs should recognize the critical importance of trust and the key role that doctors will need to play in order for such systems to be successful and to ensure that their implementation is ethically palatable to the patients whose data are being included.

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Medial prefrontal cortex is a crucial node of a rapid learning system that retrieves recent and remote memories

Neurobiology of Learning and Memory ◽

10.1016/j.nlm.2013.04.006 ◽

2013 ◽

Vol 103 ◽

pp. 19-25 ◽

Cited By ~ 26

Author(s):

Carolina Gonzalez ◽

Cecilia Kramar ◽

Fernando Garagoli ◽

Janine I. Rossato ◽

Noelia Weisstaub ◽

...

Keyword(s):

Prefrontal Cortex ◽

Medial Prefrontal Cortex ◽

Learning System ◽

Rapid Learning

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‘Rapid-Learning System’ Brings New Knowledge to the Clinic in Real Time

Oncology Times ◽

10.1097/01.cot.0000390561.94006.9f ◽

2010 ◽

Vol 32 (20) ◽

pp. 37-38

Author(s):

Daniel M. Keller

Keyword(s):

Real Time ◽

Learning System ◽

Rapid Learning ◽

New Knowledge

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Qualitative Study of Oncologists’ Views on the CancerLinQ Rapid Learning System

Journal of Oncology Practice ◽

10.1200/jop.2016.016816 ◽

2017 ◽

Vol 13 (3) ◽

pp. e176-e184 ◽

Cited By ~ 10

Author(s):

Rachel M. Mayo ◽

Julie F. Summey ◽

Joel E. Williams ◽

Rebecca A. Spence ◽

Shally Kim ◽

...

Keyword(s):

Clinical Decision Making ◽

Ethical Issues ◽

Health Care Systems ◽

Clinical Decision ◽

Learning System ◽

Patient Identification ◽

Rapid Learning ◽

Ethical Implications ◽

Patient Consent ◽

The Impact

Introduction: ASCO is actively developing CancerLinQ (CLQ), a rapid learning system for oncology care. The purpose of this study was to explore providers’ opinions and concerns related to implementation of CLQ, including ethical issues. Methods: Twenty key informant oncologists were recruited for individual in-depth interviews through ASCO contacts, purposively selected to represent a wide variety of cancer specialties as well as different levels of familiarity with CLQ (familiar v unfamiliar). Qualitative data analysis was completed by a three-member team using an inductive narrative approach. Themes were examined by participants familiar and unfamiliar with CLQ, and quotations exemplifying each theme are provided. Results: Participants’ opinions centered on three main themes: (1) general attitudes regarding learning health care systems, (2) optimal approach to patient consent, and (3) appropriateness of data use. There was clear support for the use of big data in clinical decision making for patients and in research. Unfamiliar participants expressed concerns regarding system protections against patient identification, and both familiar and unfamiliar participants discussed the dilemma of including genetic information. Respondents were in agreement with notifying patients early; however, there was debate over whether patients should opt in or opt out. Overall, there was great concern regarding sharing data with drug companies and insurers. Conclusion: Understanding oncologists’ perspectives regarding the ethical implications of CLQ implementation is critical to its success. More research is needed on the impact of rapid learning systems on providers, patients, health systems, and the ultimate effect on cancer care.

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Rapid-Learning System for Cancer Care

Journal of Clinical Oncology ◽

10.1200/jco.2010.28.5478 ◽

2010 ◽

Vol 28 (27) ◽

pp. 4268-4274 ◽

Cited By ~ 234

Author(s):

Amy P. Abernethy ◽

Lynn M. Etheredge ◽

Patricia A. Ganz ◽

Paul Wallace ◽

Robert R. German ◽

...

Keyword(s):

Health Care ◽

Information Technology ◽

Cancer Care ◽

Health Care Quality ◽

Scientific Discovery ◽

Care Quality ◽

Learning System ◽

Control Measures ◽

Care Needs ◽

Rapid Learning

Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is “rapid-learning health care.” In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled “A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care” in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action.

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A conceptual model for advancing the healthcare transformation with rapid learning system: Vision abstracts

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2018.12.013 ◽

2019 ◽

Vol 171 ◽

pp. 6

Author(s):

Abdurrahman M Jabour

Keyword(s):

Conceptual Model ◽

Learning System ◽

Rapid Learning ◽

Healthcare Transformation

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Strategies to Overcome Clinical, Regulatory, and Financial Challenges in the Implementation of Personalized Medicine

American Society of Clinical Oncology Educational Book ◽

10.14694/edbook_am.2013.33.118 ◽

2013 ◽

pp. 118-125 ◽

Cited By ~ 2

Author(s):

Apostolia M. Tsimberidou ◽

Ulrik Ringborg ◽

Richard L. Schilsky

Keyword(s):

Health Care ◽

Personalized Medicine ◽

Clinical Laboratory ◽

Clinical Care ◽

Learning System ◽

Molecular Networks ◽

Molecular Testing ◽

Targeted Agents ◽

Rapid Learning ◽

Molecular Aberrations

This article highlights major developments over the last decade in personalized medicine in cancer. Emerging data from clinical studies demonstrate that the use of targeted agents in patients with targetable molecular aberrations improves clinical outcomes. Despite a surge of studies, however, significant gaps in knowledge remain, especially in identifying driver molecular aberrations in patients with multiple aberrations, understanding molecular networks that control carcinogenesis and metastasis, and most importantly, discovering effective targeted agents. Implementation of personalized medicine requires continued scientific and technological breakthroughs; standardization of tumor tissue acquisition and molecular testing; changes in oncology practice and regulatory standards for drug and device access and approval; modification of reimbursement policies by health care payers; and innovative ways to collect and analyze electronic patient information that are linked to prospective clinical registries and rapid learning systems. Informatics systems that integrate clinical, laboratory, radiologic, molecular, and economic data will improve clinical care and will provide infrastructure to enable clinical research. The initiative of the EurocanPlatform aims to overcome the challenges of implementing personalized medicine in Europe by sharing patients, biologic materials, and technological resources across borders. The EurocanPlatform establishes a complete translational cancer research program covering the drug development process and strengthening collaborations among academic centers, pharmaceutical companies, regulatory authorities, health technology assessment organizations, and health care systems. The CancerLinQ rapid learning system being developed by ASCO has the potential to revolutionize how all stakeholders in the cancer community assemble and use information obtained from patients treated in real-world settings to guide clinical practice, regulatory decisions, and health care payment policy.

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