Literature DB >> 20585094

Rapid-learning system for cancer care.

Amy P Abernethy1, Lynn M Etheredge, Patricia A Ganz, Paul Wallace, Robert R German, Chalapathy Neti, Peter B Bach, Sharon B Murphy.   

Abstract

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.

Entities:  

Mesh:

Year:  2010        PMID: 20585094      PMCID: PMC2953977          DOI: 10.1200/JCO.2010.28.5478

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  23 in total

1.  Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy.

Authors:  Sean R Tunis; Daniel B Stryer; Carolyn M Clancy
Journal:  JAMA       Date:  2003-09-24       Impact factor: 56.272

2.  Five next steps for a new national program for comparative-effectiveness research.

Authors:  Jordan M VanLare; Patrick H Conway; Harold C Sox
Journal:  N Engl J Med       Date:  2010-02-17       Impact factor: 91.245

3.  A systems approach to patient-centered care.

Authors:  Steven C Bergeson; John D Dean
Journal:  JAMA       Date:  2006-12-20       Impact factor: 56.272

Review 4.  Moving closer to a rapid-learning health care system.

Authors:  Jean R Slutsky
Journal:  Health Aff (Millwood)       Date:  2007-01-26       Impact factor: 6.301

5.  Using health technology assessment to identify research gaps: an unexploited resource for increasing the value of clinical research.

Authors:  N Ann Scott; Carmen Moga; Christa Harstall; Jacques Magnan
Journal:  Healthc Policy       Date:  2008-02

6.  The evidence dilemma in genomic medicine.

Authors:  Muin J Khoury; Al Berg; Ralph Coates; James Evans; Steven M Teutsch; Linda A Bradley
Journal:  Health Aff (Millwood)       Date:  2008 Nov-Dec       Impact factor: 6.301

7.  Electronic patient-reported data capture as a foundation of rapid learning cancer care.

Authors:  Amy P Abernethy; Asif Ahmad; S Yousuf Zafar; Jane L Wheeler; Jennifer Barsky Reese; H Kim Lyerly
Journal:  Med Care       Date:  2010-06       Impact factor: 2.983

8.  Eight rights of safe electronic health record use.

Authors:  Dean F Sittig; Hardeep Singh
Journal:  JAMA       Date:  2009-09-09       Impact factor: 56.272

Review 9.  Pharmacogenetics/genomics and personalized medicine.

Authors:  Wolfgang Sadée; Zunyan Dai
Journal:  Hum Mol Genet       Date:  2005-10-15       Impact factor: 6.150

10.  A map to bad policy--hospital efficiency measures in the Dartmouth Atlas.

Authors:  Peter B Bach
Journal:  N Engl J Med       Date:  2010-02-18       Impact factor: 91.245

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  121 in total

1.  Time to reboot: resetting health care to support tobacco dependency treatment services.

Authors:  Bradford W Hesse
Journal:  Am J Prev Med       Date:  2010-12       Impact factor: 5.043

2.  From Big Data to Knowledge in the Social Sciences.

Authors:  Bradford W Hesse; Richard P Moser; William T Riley
Journal:  Ann Am Acad Pol Soc Sci       Date:  2015-05-01

3.  Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy.

Authors:  Robert B Den; Kasra Yousefi; Edouard J Trabulsi; Firas Abdollah; Voleak Choeurng; Felix Y Feng; Adam P Dicker; Costas D Lallas; Leonard G Gomella; Elai Davicioni; R Jeffrey Karnes
Journal:  J Clin Oncol       Date:  2015-02-09       Impact factor: 44.544

4.  Physician inter-annotator agreement in the Quality Oncology Practice Initiative manual abstraction task.

Authors:  Jeremy L Warner; Peter Anick; Reed E Drews
Journal:  J Oncol Pract       Date:  2013-05       Impact factor: 3.840

Review 5.  Envisioning Watson as a rapid-learning system for oncology.

Authors:  Jennifer L Malin
Journal:  J Oncol Pract       Date:  2013-05       Impact factor: 3.840

Review 6.  ASCO's approach to a learning health care system in oncology.

Authors:  George W Sledge; Clifford A Hudis; Sandra M Swain; Peter M Yu; Joshua T Mann; Robert S Hauser; Allen S Lichter
Journal:  J Oncol Pract       Date:  2013-05       Impact factor: 3.840

7.  Chart review versus an automated bioinformatic approach to assess real-world crizotinib effectiveness in ALK-positive NSCLC.

Authors:  Nam Bui; Solomon Henry; Douglas Wood; Heather A Wakelee; Joel W Neal
Journal:  JCO Clin Cancer Inform       Date:  2017-03-13

Review 8.  Measuring improvement in populations: implementing and evaluating successful change in lung cancer care.

Authors:  Xinhua Yu; Lisa M Klesges; Mathew P Smeltzer; Raymond U Osarogiagbon
Journal:  Transl Lung Cancer Res       Date:  2015-08

Review 9.  Developing Real-world Evidence-Ready Datasets: Time for Clinician Engagement.

Authors:  James M Snyder; Jacob A Pawloski; Laila M Poisson
Journal:  Curr Oncol Rep       Date:  2020-04-16       Impact factor: 5.075

10.  Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Authors:  Jan C Peeken; Tatyana Goldberg; Christoph Knie; Basil Komboz; Michael Bernhofer; Francesco Pasa; Kerstin A Kessel; Pouya D Tafti; Burkhard Rost; Fridtjof Nüsslin; Andreas E Braun; Stephanie E Combs
Journal:  Strahlenther Onkol       Date:  2018-03-20       Impact factor: 3.621

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