Informatics Needs and Challenges in Cancer Research Workshop Summary (2012) / Chapter Skim
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5 Potential Pathways and Models for Moving Forward
5 Potential Pathways and Models for Moving Forward
Pages 63-90
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From page 63... ...
· An integrated knowledge ecosystem that supports moving data from discovery to actionable intelligence could drive better decision making and a learning health care enterprise. · The overarching biomedical informatics challenges are sys tems issues; scale, standards, and sharing; software, storage, and security; sustainability; and social issues (changing mind sets and behaviors)
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From page 64... ...
With these needs and concerns in mind, panelists discussed a variety of approaches for moving the field of cancer informatics forward. PUBLIC DATA-DRIVEN SYSTEMS AND PERSONALIZED MEDICINE Atul Butte, chief of the Division of Systems Medicine at Stanford University and Lucile Packard Children's Hospital, shared examples of how public data can drive science and enable personalized medicine.
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From page 65... ...
. Integrative Genomics to Identify Novel Targets The first example Butte described involves using integrative genomics on public data to find causal factors for complex diseases, factors that can be targets for new drugs.
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From page 66... ...
Butte highlighted the fact that colon cancer and colon polyps clustered together based on molecular profiles (as would be expected since certain polyps are associated with cancer) ; however, cervical cancer was most similar to type 1 autoimmune polyglandular syndrome.
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From page 67... ...
Again, Butte stressed, this entire work was done using publicly available data, and he urged investment in building these types of repositories, keeping them updated, and facilitating access to them. In conclusion, Butte said, bioinformatics is more than just building tools.
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From page 68... ...
Technology convergence and the creation of multidisciplinary datasets are handicapped by single-specialty silos. There are more participants, locations, and distributed data, but there is a pervasive lack of interoperable exchange formats and standards for data annotation, analysis, and curation.
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From page 69... ...
Having reviewed the gaps and challenges, Poste asked if we are still building systems and infrastructure that merely support the collection of data or are working toward an integrated knowledge ecosystem that supports moving the data from discovery to actionable intelligence that can drive a learning health care enterprise. Importance of Having the "Right" Data in the System Poste stressed the importance of pre-analytical variables such as rigorous selection of specimen donors, standardized specimen collection, and annotated health records.
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From page 70... ...
In summary, Poste said that a huge amount of data being put into the public domain is not accurate. Moving forward, Poste listed the need for controlled vocabularies and ontologies; minimal information checklists and open source repositories; algorithms and source code for analytic tools; exchange formats and semantic interoperability; and cross-domain harmonization, integration, migration, and sharing.
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From page 71... ...
A comprehensive clinical data integration system would include, for example, current and planned clinical trials, observational data from the provider as well as patient-reported information, SEER data, mobile health or remote sensor data, and payer datasets. The final reckoning for actionable data is regulatory science, Poste said.
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From page 72... ...
Moving from Silos to Systems Moving forward begins with changing minds and changing behaviors to transition from informational silos to integrated systems. Technology is only the enabler, Poste said.
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From page 73... ...
; and accountability and responsibility, providing improved return on investment of public and private funding and addressing urgent societal and economic imperatives. BIG DATA AND DISRUPTIVE INNOVATION: MODELS FOR DEMOCRATIZING CANCER RESEARCH AND CARE More so than in any other area of health care, cancer research and cancer care are especially overloaded by data, said Jason Hwang, executive director of Health Care at the Innosight Institute.
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From page 74... ...
can be very informative, he said. Learning from Users of Big Data in Diverse Non-Health Venues The idea of using big data to make better decisions is not new and predates computing by a number of years, Hwang said.
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that were processed and incorporated into Amazon's decision making are often thrown away by other companies, Hwang noted. Another big data user, Google, developed its search engine in a way that was very different from anybody else, Hwang said.
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From page 76... ...
This opens up the door to other opportunities, Hwang said, such as allowing consumers to see the criteria on which their creditworthiness is based and to optimize their credit score in order to get the best loans possible. Disruptive Innovation In the ability to use big data there is opportunity to create new economies.
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From page 77... ...
Big data can help to facilitate the decentralization of our highly centralized and expensive health care structure and help to emphasize prevention and wellness. Hwang listed some of the many different types of dataenabled business models that could help to achieve decentralized health care, including telehealth and e-visits, automated kiosks, home monitoring, wireless health devices, retail and worksite clinics, and others.
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From page 78... ...
When software can manage some of the tasks that used to be reserved only for professionals, it frees up the professionals so that they can spend far more time talking to their clients and focusing on higher-value work, where their expertise is really needed. In summary, Hwang predicted that big data will transform cancer care and research first, because the data deluge in the field outpaces anything else in health care.
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From page 79... ...
However, current health IT systems do not have the capability to support this. Trustworthy data Leverage workflow from EHRs Employ analytics to Trustworthy data Translate guide- driven informatics measure results to measure protocol Longitudinal lines and empirical processes to drive and teach people, with analytics to biobank data results into specific to point of care with activate patients, track outcomes or process steps decision support Imaging and transform care deviations analytics -Basic/Translational Research -Clinical Research/Trials -Comparative Effectiveness Research -Deep Analytics/Informatics ...but today's health care IT systems fall short of enabling this vision FIGURE 5-3 Learning health care paradigm supported by robust, interoperable informatics.
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From page 80... ...
In closing, Joshi mentioned one example of the regional initiatives that are gearing up to enable collaboration across the health care ecosystem: the Partnership to Advance Clinical electronic Research (PACeR) initiative, which is a publicprivate partnership between New York State hospitals and life sciences companies.
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the technology infrastructure for care coordination exists, (2) there is a strong business case for care coordination and patient engagement, and (3)
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Functions include staging, problem lists, protocols, treatment planning, review and release, pharmacy verification, electronic medication administration record (eMAR) , flow sheets, and reporting.
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Epic is also working on incorporating genomics and adding the ability to document the care a patient received before entering this system, for a complete oncological history. Butler noted several challenges in moving forward, including encouraging physicians to see the value of using an EHR (he said physicians are very concerned that using an EHR is going to slow them down)
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Data networks allow for the discovery of associations between specific molecular profiles and clinical information from individual patients, leading to new knowledge that can be translated into more personalized cancer care. Hospitals and health care networks FIGURE 5-4 Designing a new federated research and health care network model.
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From page 85... ...
Dalton noted that in its early stages, the database was more of a repository than a warehouse, with some of the queries taking weeks, if not months, and Moffitt sought expert assistance from Oracle, TransMed, and Deloitte. Through this strategic partnership, an integrated health research information platform was developed that creates the real-time relationships and associations from disparate data sources that are needed to create new knowledge for improved patient treatments, outcomes, and prevention.
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From page 86... ...
Proposed Federated Data Model It is one thing to be able to do health research information exchange at a single institution or within a defined consortium, but it is quite another, Dalton said, to manage this on a national scale. He proposed a national health and research information exchange, incorporating regional "hub and spoke" platforms, with cancer centers as the hub and their individual colleagues within the community contributing data and having access to those data.
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Bradford Hesse, chief of the NCI Health Communication and Informatics Research Branch, added that a significant portion of traffic to government health websites such as NCI or the Centers for Disease Control and Prevention (CDC) involves patients.
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Mining Data to Assess the Quality of Cancer Care Allen Lichter, chief executive officer of the American Society of Clinical Oncology (ASCO) , noted that ASCO is interested in informatics from a quality-of-care perspective.
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From page 89... ...
In funding these researchers, Levy said that review committees need to be receptive to the potential of informatics and the value of observational data. If Data Are Available, Users Will Come Levy referred to how Butte and others have made use of publicly available data repositories of cancer information.
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From page 90... ...
2011. Discovery and preclinical validation of drug indications using compendia of public gene expression data.
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