A Foundation for Evidence-Driven Practice A Rapid Learning System for Cancer Care: Workshop Summary (2010) / Chapter Skim
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3 Basic Elements and Building Blocks of a RLHS for Cancer
3 Basic Elements and Building Blocks of a RLHS for Cancer
Pages 13-36
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From page 13... ...
Electronic networks are able to provide information exchange and feedback to providers. Other key elements of the infrastructure of an active and growing RLHS for cancer discussed in this chapter include the integration of information from clinical trials, comparative effectiveness research, evidence-based clinical practice guidelines, quality metrics, and decision support tools.
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From page 14... ...
Most cancer data comes from hospitals where highly trained cancer registrars extract data from the patients' medical record and enter it into the registry's computing software for transfer to central cancer registries.
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From page 15... ...
SEER data have also been linked to Medicare claims data, thus producing a data set of over 3 million cases that contains all people in SEER found to be Medicare-eligible. Another national registry that can provide not only valuable national cancer care data, but also feedback to providers, is the Commission on Cancer (CoC)
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From page 16... ...
However, even the best registries may not be adequate for addressing key health system questions, such as comparative effectiveness or costeffectiveness analyses. For example, the SEER program routinely collects abundant information on cancer patients, but this program does not collect information on how patients are treated after their first course of therapy, nor does it document disease recurrence, resources consumed, provider characteristics, or patient-reported outcomes.
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From page 17... ...
In Ohio, CoC has a pilot project funded by the CDC that will enable it to link its cancer registry data with private payer claims, including those of United Healthcare and Anthem Blue Cross/Blue Shield, along with data from the Ohio Cancer Incidence and Surveillance System registry. The goal of this pilot project, which Dr.
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From page 18... ...
, which collects data on cancer incidence for all the state's counties. GCCR has been linked to Medicare as well as to Medicaid, with Emory University researchers using the latter linked data to evaluate the impact of the Breast and Cervical Cancer Prevention and Treatment Act.1 A new project, "Using Cancer Registry Data and Other Sources to Track Measures of Care in Georgia," sponsored and funded by the Association of Schools of Public Health (ASPH)
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From page 19... ...
The Georgia Cancer Quality Information Exchange's initial focus is on using the benchmarks and goals the IOM recommended for its 52 metrics as the foundation for aggregating near-real-time clinical data from all of the state's CoC-certified cancer care facilities and linked physician practices and public health data from the Georgia Comprehensive Cancer Registry and other sources (Georgia Cancer Coalition, 2009)
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From page 20... ...
0 Consolidated GA Cancer Data Resource Quality of Care Research Applications to Medicaid Medicare Claim s & Breast Colorectal Claims & Enrollment Cancers Enrollment Data Data Georgia Comprehensive Cancer Registry Kaiser State Health Permanente of Benefit Plan Georgia Claims & Privately Insured Enrollment Clinical & Admin Georgia Center for Data Data Cancer Statistics (Emory) Medical GA Hospital Discharge Chart Data Review Data Other Secondary Data Sources: American Hospital Directory, Medicare Physician Identification and Eligibilit y Registry, Area Resource File, Census FIGURE 3-1 Linking Georgia Cancer Registry Data to public and private sources.
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From page 21... ...
. The Georgia Cancer Quality Information Exchange, which intends to have the information technology infrastructure to accept data from all providers regardless of level of automation or technology platform, has engaged six cancer centers around the state as demonstration partners.
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From page 22... ...
Computer grids are networks of computers that are dispersed geographically and work together to carry out various computing tasks involving large amounts of data and complex analyses. Both data storage and analysis are apportioned among the network of computers to accomplish these large complex tasks.
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From page 23... ...
80000 70000 60000 1994 50000 40000 2002 10000 30000 2006 20000 Diagnostic Imaging: 10000 Functional and 0 US CT/day Anatomical 1000 1M proteins Proteomics and other effector molecules 100 Functional Genetics: Facts per Decision Gene expression profiles 10 Structural Genetics: 25K genes e.g., SNPs, haplotypes Human Cognitive 5 Capacity Decisions by Clinical Symptoms 1990 2000 2010 2020 FIGURE 3-2 Challenges to delivery of individualized clinical care are data explosion and cognitive overload, exceeding human cognitive FIGURE 3-2 capacity.
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From page 24... ...
and that there will be federated analytics, meaning that the data are not going to the place where the analytics sit, but the analytics are going to the place where the data sit. An advantage to having a federated architecture is that it preserves local control over data generated at a particular institution, which is critical to address the patient privacy issues dictated by Health Insurance Portability and Accountability Act (HIPAA)
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From page 25... ...
Recognizing the need to share and do large-scale analyses of the abundant data generated in the studies it supports and to connect and support the cancer community at large, NCI decided in 2003 to create caBIG, which is a shareable, interoperable information infrastructure that connects cancer researchers and practitioners (NCI, 2010a)
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From page 26... ...
Stead pointed out later during the panel discussion, is that they save institutions the costs of developing de novo the technology needed to do complex data analyses. "One of the things that drove the creation of caBIG originally was that all the NCI-designated cancer centers were in the process of collecting molecular data in the form of microarrays.
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From page 27... ...
One of caBIG's first enterprise projects, the Athena Breast Cancer Network, is to integrate diverse breast cancer data, including clinical, genomic, and molecular data, collected from 13 different sites encompassing more than 400,000 women within the University of California system, and make them accessible to end users. Grid computing will be used to standardize collection of structured data, integrating clinical and research processes including molecular profiling, starting at the point of care.
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From page 28... ...
In addition, unlike a lot of clinical trials research, CER studies populations representative of clinical practice. CER tends to focus on patient-centered decision making so that tests or treatments are tailored to the specific characteristics of the patient, Dr.
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From page 29... ...
With this additional funding, AHRQ plans to continue to do its evidence synthesis reviews of current research, but also to do evidence generation -- new research with a focus on underrepresented populations. For this research, the agency plans to expand distributed data network models and national patient registries.
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From page 30... ...
The two are synergistic. GUIDELINES AND STANDARDS FOR CARE In an ideal RLHS, evidence collected from point of care, clinical trials, CER, and other studies would be synthesized to create cancer care guidelines specific for various cancers with finer-grained standards specific to patient subtypes.
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From page 31... ...
The most developed database in that regard is the breast cancer database, encompassing 52,000 patients from 17 NCCN institutions and 15 community cancer centers. Each year, NCCN conducts a major analysis of the data it collects and provides participating patient-level feedback to institutions and physicians regarding concordance with the management stipulated by NCCN guidelines.
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From page 32... ...
ASCO has also begun rapid distribution of provisional clinical opinions to inform oncologists of new developments that affect practice (e.g., the importance of testing for KRAS gene mutations in metastatic colorectal cancer patients to predict response to antiepidermal growth factor receptor antibody therapy)
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From page 33... ...
De-identified data are entered into a registry in real time, and as the registry evolves, it may enable direct data transfer from EHRs. QOPI data are also being used for quality improvement in collaborative networks, such as the NCI Community Cancer Centers Program and the Michigan Oncology Quality Consortium, which was created by Michigan Blue Cross/Blue Shield.
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From page 34... ...
Online predicts how various adjuvant treatments are likely to affect the risk of relapse and mortality, enabling oncologists and their breast cancer patients to personalize their decision making on whether to pursue adjuvant therapies. The model was developed by actuarial analysis of the San Antonio breast cancer data base and SEER data, as well as on estimates of the proportional risk reduction observed in individual randomized breast cancer clinical trials and systematic overviews of randomized adjuvant trials.
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From page 35... ...
Dr. Ganz pointed out that there is some representation of all patient subsets in the observational data, such as SEER, that are used in the development of decision support tools but added that, ideally, a "rapid learning health system would collect good prospective data at the bedside that would help inform us about these decisions because we have very limited information.
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