Improving Cancer Diagnosis and Care Patient Access to Oncologic Imaging and Pathology Expertise and Technologies: Proceedings of a Workshop (2018) / Chapter Skim
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Integration and Collaboration of Specialties
Integration and Collaboration of Specialties
Pages 41-55
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From page 41... ...
Rationale for Collaboration and Integration Lawrence Shulman, professor of medicine and deputy director of clinical services at the University of Pennsylvania Abramson Cancer Center, said that greater integration of pathology, radiology, and oncology reports is needed for a more unified interpretation of a patient's disease and for treatment planning. Stewart and Warner agreed, and added that multidisciplinary conferences or tumor boards can be very helpful in facilitating collaboration.
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From page 42... ...
John Cox, medical director of oncology services at Parkland Health and Hospital System and professor of internal medicine at the University of Texas Southwestern Medical Center, said there is a real need for integrated reports in his community health care system, especially when test results come from separate labs in different formats: "From a community delivery system standpoint, integrating those reports into a common diagnostic portfolio is really key and something our current technologies ought to help us solve," said Cox. Challenges to Integration Several workshop participants discussed challenges to integrating the disciplines involved in cancer diagnosis.
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From page 43... ...
"Sometimes, exams need to be repeated, which is wasteful, duplicative, and results in increased radiation dose," Stewart said. Friedberg noted that anatomic pathology is further behind radiology in terms of digitizing and sharing information, but predicted that "once pathology becomes digital like radiology, the overlap between the two will naturally disappear and AI will be very useful." Brink agreed that AI is merging pathology and radiology (see also the section on Computational Oncology and Machine Learning)
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From page 44... ...
Present & contextualize genetic test results genetic tests EHR/ Clinical SMART® on FHIR® systems clinico-genomics apps Diagnostic order app Diagnostic reporter app FHIR® data FHIR® data Sequencing Image: SMART Precision Cancer Medicine App lab (b) Return genetic test results FIGURE 2 Standards-enabled workflow of genomic data.
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From page 45... ...
DIGITizE has been taken up by the FHIR Foundation, an implementation body related to Health Level Seven International.35 Stewart pointed out that insurance coverage issues, such as out-ofpocket costs or restrictions on additional testing, may prevent more comprehensive and integrated radiology and/or pathology reports. For example, Cox said self-referral restrictions can impede genetic testing performed by the same institution that performed the initial pathology testing.
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From page 46... ...
The patient outcome is really what we are going after." Alternative Payment Models to Promote Collaboration and High-Quality Care Hricak noted that major cancer centers can afford to have radiologists integrated in their medical oncology clinics. This set-up enables more collaboration and interaction between radiologists and oncologists, she said, adding, "It's a luxury that is wonderful for patient care, but [one that]
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From page 47... ...
Grubbs reported on the Health Care Payment Learning & Action Network, which the Department of Health and Human Services launched in 2015 to align public and private stakeholders in the transition toward high-quality, value-based payment.36 The Network's first initiative was the development of a framework of the different types of payment models, which include (Health Care Payment Learning & Action Network, 2017) : • Category 1: Fee-for-service, no link to quality and value • Category 2: Fee-for-service, link to quality and value • Category 3: Alternative payment models built on fee-for-service architecture • Category 4: Population-based payment Grubbs also discussed the CMS Oncology Care Model (OCM)
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From page 48... ...
. Grubbs said the goals of ASCO's Patient-Centered Oncology Payment Model include adherence to high-quality, evidence-based clinical pathways; reducing unwarranted variation in oncology care; guiding appropriate survivorship care and monitoring; encouraging participation in clinical trials; and eliminating care disparities and protecting against underutilization (Zon et al., 2017)
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Abernethy noted that in Flatiron Health's datasets, the interpretation of imaging and pathology results in lymphoma vary substantially over time and among clinicians. She said large aggregated datasets with longitudinal information could help mitigate diagnostic ambiguity in lymphoma by understanding which interpretations are most accurate: "We're really starting to try and use the longitudinal understanding of the patient to get back to improved diagnosis." Abernethy stressed that large datasets are only useful if the data are properly collected, curated, and aggregated.
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You need to document back to the source and maintain full provenance," she said. Data completeness and quality also has to be documented, she added, including linking unstructured data from clinician notes to other sources of data, such as reports for genetic testing, PROMs, and administrative claims data.
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"The number of diagnostic tests being interpreted by pathologists continues to bloom," he pointed out, and these professionals are needed to guide the development of AI systems and in the interpretation of their findings. Langlotz provided an overview of different types of AI, including machine learning, neural networks, and deep learning.
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From page 52... ...
He added that computer-aided detection and classification, facilitated by machine learning, could help radiologists identify abnormalities when they are evaluating images outside of their specialty. He noted that none of the labeling techniques used in the development of machine learning algorithms are perfect; however, he said that might not matter for large datasets analyzed by neural networks.
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From page 53... ...
Langlotz noted that deep learning in cancer imaging, integrated with gene expression data, could provide useful input for cancer diagnosis and subsequent care (Korfiatis et al., 2016) : "There may be some signal in these images that we're not detecting with the human eye that may correlate directly with genomic signatures, and that would really change the way we stage cancer." In pathology, Cohen noted that a recent study found that some deep-learning algorithms to detect lymph node metastases in women with breast cancer performed better than a panel of 11 pathologists (Bejnordi et al., 2017)
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From page 54... ...
. Data Sharing and Standardization Several workshop participants discussed the importance of data sharing and interoperability to develop and validate machine learning methodologies for cancer diagnosis and care.
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From page 55... ...
Warner noted that for databases to be interoperable they need to share 42 Examples of FAIR databases that Becich described included Project GENIE, ancerLinQ, C Genomic Data Commons, Health Care Systems Research Collaboratory, Oncology Research Information Exchange Network, and National Patient-Centered Clinical Research Network. See http://www.aacr.org/research/research/pages/aacr-project-genie.aspx, https:// cancerlinq.org, https://flatiron.com, https://gdc.cancer.gov, http://www.rethinkingclinical trials.org, http://oriencancer.org, and http://www.pcornet.org (accessed May 10, 2018)
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