policy and politics
The Ethics of Translating High-Throughput Science into Clinical Practice by Pilar N. Ossorio
B
iomedical research is increasingly data intensive and computational, and “big data science” is migrating into the clinical arena. Teaching hospitals are making substantial investments in DNA sequencing capacity, and some now advertise their use of sequencing to help guide medical treatment.1 Sequencing is but one component of the emerging precision medicine paradigm.2 In addition to genomics, precision medicine will likely include epigenomics, transcriptomics, proteomics, metabolomics, and other data intensive, computationally complex “omic” sciences. Furthermore, any move toward learning health care systems will entail the collection and continuous analysis of large volumes of information about each patient and health care system, as well as analysis across health care systems.3 Unfortunately, ethicists, regulators, and policy-makers have barely begun to explore the ethical, legal, and social issues raised by the variety of analytical and computational approaches in use and under development in biology and medicine. Most scholarship concerning big data bioscience has focused on privacy, a vitally important consideration but not the only one. Among the issues raised by new computational technologies are questions about safety and safety assessment, justice, and how to obtain proper informed consent. These technologies also raise a myriad of regulatory issues that could influence the probability of translating new assays or computational tools to the clinical or public health spheres. 8 HASTIN G S C E N T E R R E P ORT
Institutional review boards (IRBs) and research ethicists need to understand and evaluate the analytical approaches used in big data research. Consider, for instance, the phenomenon of imputation, which involves computationally inferring missing data points. In studies that aim to find associations between genetic markers and particular medical outcomes or other phenotypes (genome-wide association studies), analytical algorithms often impute some genetic markers so that the final analysis can evaluate more markers than were actually measured.4 Imputation of genetic markers is also used to harmonize datasets for meta-analysis. Algorithms impute markers in a genotype by recognizing regions of DNA that are frequently inherited as a contiguous segment. If the algorithm recognizes a few markers that identify a known segment, then it can infer markers that were not measured but are typical of that particular segment. In some respects, imputation is analogous to inferences made by contestants on Wheel of Fortune when they fill in missing letters to complete words and phrases. Imputing genetic markers is useful in research, but the ability to impute can also raise research ethics and research design concerns, such as whether we can honor a person’s “right not to know” information about herself, how we obtain adequate informed consent, and how we assess the risks and benefits of a project. For example, when Jim Watson had his genome sequenced, he wanted most of it made available to researchers without restriction, but he did not want to learn
about his apolipoprotein E alleles, certain of which confer an increased risk of developing Alzheimer’s disease. In deference to Watson’s wishes, researchers masked the APOE-containing portion of his genome when they published the sequence, but shortly afterward other scientists published a method for imputing the markers necessary to predict Watson’s APOE alleles.5 Imputation can also be used to fill in missing information in electronic health records. From a data analyst’s viewpoint, EHRs contain noisy, fragmented data reflecting irregular sampling and human error.6 Suppose an EHR indicated that the patient had frequent skin infections, blurry vision, and fatigue and that a physician had prescribed the patient an alpha-glucosidase inhibitor, but the EHR contained no diagnosis. An algorithm might use the existing information and knowledge from medical literature databases to infer a diagnosis of diabetes. Imputation can be used to fill in missing demographic data as well. At least one commercial firm uses a proprietary algorithm to impute race and ethnicity information based on patient names and census data pertaining to each patient’s geographic location.7 Imputing EHR information raises justice issues. Missing data in health records may be unevenly distributed across patients. For instance, older people might be missing more data than younger ones, and people of a lower socioeconomic status might be missing more data than people of a higher socioeconomic status. When missing data in EHRs are not evenly distributed, can imputation make research more just by appropriately filling in information gaps? Alternatively, might imputation obscure important indicators of inadequate treatment for some patient groups? If the imputation algorithm is proprietary, how will researchers, IRBs, or article reviewers know whether the imputation approach was validated for the population being studied? These are just a few questions we ought to be asking. IRBs and researchers should also understand that software intended to aid people in diagnosing, preventing, September-October 2014
or treating disease is a medical device.8 When such software is used to direct treatment in a clinical trial, investigators must obtain an investigational device exemption from the Food and Drug Administration before the trial can commence, and the software should be validated, fully described, and “locked down,” meaning that changing it will not be possible once the trial has begun. Researchers who aim to produce medical software products for distribution to clinical users should realize that such software must receive FDA clearance or approval: it will need to demonstrate safety and efficacy. Throughout its development lifecycle, it will be subject to design controls and validation requirements arising from the medical device quality systems regulation.9 To improve the chances of designing software that can translate effectively into medical practice, researchers should begin talking with the FDA early in the software development process. Novel programming approaches may challenge the FDA, IRBs, and ethicists. Scientists are developing machine learning programs that aim to use heterogeneous data sources to develop predictive models for diagnosing existing diseases, directing choice of therapies, or predicting people’s future health. For instance, Memorial Sloan Kettering and IBM are attempting to teach a version of IBM’s Watson computer relevant medical literature, molecular and imaging data, and other information that will allow Watson to build models for predicting the best treatment options for individual lung cancer patients.10 Machine learning adds layers of complexity to analytical processes and to the ethical and regulatory issues. In machine learning, the program, rather than the programmer, builds the predictive model. The same initial program, trained on different data sets, might build different predictive models. In some instances, particularly with nonlinear approaches, programmers cannot
September-October 2014
determine what variables a model relies on to make its predictions. IRBs and researchers might then have difficulty determining such a model’s generalizability and the appropriateness of proposed inclusion and exclusion criteria for a clinical trial using the model. Researchers, IRBs (and, ultimately, health care providers) might have difficulty determining when a person’s or population’s risk factors have changed such that the model is no longer applicable. Computational scientists, ethicists, and regulators will need some agreement on how to validate these models and determine when they are robust enough to enter human trials. We will need to develop thoughtful approaches to risk assessment and life cycle monitoring. Machine learning raises numerous other ethical and legal questions. Suppose, for instance, that a machine learning model used patients’ educational levels or racial information in ways we would consider wrongly discriminatory if done by a person. Could we determine what social categories were being used in questionable ways? Could a public or Veterans Affairs hospital have liability for acting unconstitutionally because of decisions guided by such a model? Can programmers design algorithms so that they do not use information inappropriately, and if so, what trade-offs would that involve? Furthermore, it should be obvious that some of the concerns that gave rise to the Genetic Information Nondiscrimination Act are raised by medical prediction software. For instance, should nonmedical sectors of society, such as employers, be permitted to use such software to make business decisions? To address these and other questions, more information scientists, statisticians, and others with computational biology expertise should be included in bioethics advisory groups, IRBs, and regulatory oversight processes.
1. A. Hartocollis, “Cancer Centers Racing to Map Patients’ Genes,” New York Times, New York Edition, April 22, 2013, A1. 2. D. F. Hayes, “OMICS-Based Personalized Oncology: If It Is Worth Doing, It Is Worth Doing Well!,” BMC Medicine 11 (2013): 221-25. 3. M. Smith et al., Best Care at Lower Cost: The Path to Continuously Learning Healthcare in America (Washington, D.C.: National Academies Press, 2012). 4. See, for example, E. Porcu et al., “Genotype Imputation in Genome-wide Association Studies,” in Current Protocols in Human Genetics (John Wiley & Sons, 2013): unit 1.25.11.25.14, at http://onlinelibrary.wiley.com/ book/10.1002/0471142905. 5. D. R. Nyholt, C. E. Yu, and P. M. Visscher, “On Jim Watson’s APOE Status: Genetic Information Is Hard to Hide,” European Journal of Human Genetics 17, no. 2 (2009): 147-50. 6. S. Hoffman and A. Podgurski, “The Use and Misuse of Biomedical Data: Is Bigger Really Better?,” American Journal of Law and Medicine 39, no. 4 (2013): 497-538. 7. D. E. Levy et al., “Underutilization of BRCA1/2 Testing to Guide Breast Cancer Treatment: Black and Hispanic Women Particularly at Risk,” Genetics in Medicine 13, no. 4 (2011): 349-55. 8. C. M. Micheel, S. J. Nass, and G. S. Omenn, eds., Evolution of Translational Omics: Lessons Learned and the Path Forward (Washington, D.C.: National Academies Press, 2012). 9. U. S. Department of Health and Human Services, 21 CFR §820.30(g); S. A. Mailhot, The Challenge of Software (Aspatore, 2014); Food and Drug Administration Center for Devices and Radiologic Health, “General Principles of Software Validation,” 2002, http:// www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm085371.pdf. 10. M. Kris, “Progress and Promise in Therapy of Lung Cancers,” Lung Cancer Management 3, no. 1 (2014): 17-21. Acknowledgment
The author thanks the Morgridge Institute for Research for summer support during her work on this essay. DOI: 10.1002/hast.351 This column appears by arrangement with the American Society for Bioethics and Humanities.
H AS TI N GS C E N TE R RE P O RT
9
The Ethics of Translating High-Throughput Science into Clinical Practice by Pilar N. Ossorio
B
iomedical research is increasingly data intensive and computational, and “big data science” is migrating into the clinical arena. Teaching hospitals are making substantial investments in DNA sequencing capacity, and some now advertise their use of sequencing to help guide medical treatment.1 Sequencing is but one component of the emerging precision medicine paradigm.2 In addition to genomics, precision medicine will likely include epigenomics, transcriptomics, proteomics, metabolomics, and other data intensive, computationally complex “omic” sciences. Furthermore, any move toward learning health care systems will entail the collection and continuous analysis of large volumes of information about each patient and health care system, as well as analysis across health care systems.3 Unfortunately, ethicists, regulators, and policy-makers have barely begun to explore the ethical, legal, and social issues raised by the variety of analytical and computational approaches in use and under development in biology and medicine. Most scholarship concerning big data bioscience has focused on privacy, a vitally important consideration but not the only one. Among the issues raised by new computational technologies are questions about safety and safety assessment, justice, and how to obtain proper informed consent. These technologies also raise a myriad of regulatory issues that could influence the probability of translating new assays or computational tools to the clinical or public health spheres. 8 HASTIN G S C E N T E R R E P ORT
Institutional review boards (IRBs) and research ethicists need to understand and evaluate the analytical approaches used in big data research. Consider, for instance, the phenomenon of imputation, which involves computationally inferring missing data points. In studies that aim to find associations between genetic markers and particular medical outcomes or other phenotypes (genome-wide association studies), analytical algorithms often impute some genetic markers so that the final analysis can evaluate more markers than were actually measured.4 Imputation of genetic markers is also used to harmonize datasets for meta-analysis. Algorithms impute markers in a genotype by recognizing regions of DNA that are frequently inherited as a contiguous segment. If the algorithm recognizes a few markers that identify a known segment, then it can infer markers that were not measured but are typical of that particular segment. In some respects, imputation is analogous to inferences made by contestants on Wheel of Fortune when they fill in missing letters to complete words and phrases. Imputing genetic markers is useful in research, but the ability to impute can also raise research ethics and research design concerns, such as whether we can honor a person’s “right not to know” information about herself, how we obtain adequate informed consent, and how we assess the risks and benefits of a project. For example, when Jim Watson had his genome sequenced, he wanted most of it made available to researchers without restriction, but he did not want to learn
about his apolipoprotein E alleles, certain of which confer an increased risk of developing Alzheimer’s disease. In deference to Watson’s wishes, researchers masked the APOE-containing portion of his genome when they published the sequence, but shortly afterward other scientists published a method for imputing the markers necessary to predict Watson’s APOE alleles.5 Imputation can also be used to fill in missing information in electronic health records. From a data analyst’s viewpoint, EHRs contain noisy, fragmented data reflecting irregular sampling and human error.6 Suppose an EHR indicated that the patient had frequent skin infections, blurry vision, and fatigue and that a physician had prescribed the patient an alpha-glucosidase inhibitor, but the EHR contained no diagnosis. An algorithm might use the existing information and knowledge from medical literature databases to infer a diagnosis of diabetes. Imputation can be used to fill in missing demographic data as well. At least one commercial firm uses a proprietary algorithm to impute race and ethnicity information based on patient names and census data pertaining to each patient’s geographic location.7 Imputing EHR information raises justice issues. Missing data in health records may be unevenly distributed across patients. For instance, older people might be missing more data than younger ones, and people of a lower socioeconomic status might be missing more data than people of a higher socioeconomic status. When missing data in EHRs are not evenly distributed, can imputation make research more just by appropriately filling in information gaps? Alternatively, might imputation obscure important indicators of inadequate treatment for some patient groups? If the imputation algorithm is proprietary, how will researchers, IRBs, or article reviewers know whether the imputation approach was validated for the population being studied? These are just a few questions we ought to be asking. IRBs and researchers should also understand that software intended to aid people in diagnosing, preventing, September-October 2014
or treating disease is a medical device.8 When such software is used to direct treatment in a clinical trial, investigators must obtain an investigational device exemption from the Food and Drug Administration before the trial can commence, and the software should be validated, fully described, and “locked down,” meaning that changing it will not be possible once the trial has begun. Researchers who aim to produce medical software products for distribution to clinical users should realize that such software must receive FDA clearance or approval: it will need to demonstrate safety and efficacy. Throughout its development lifecycle, it will be subject to design controls and validation requirements arising from the medical device quality systems regulation.9 To improve the chances of designing software that can translate effectively into medical practice, researchers should begin talking with the FDA early in the software development process. Novel programming approaches may challenge the FDA, IRBs, and ethicists. Scientists are developing machine learning programs that aim to use heterogeneous data sources to develop predictive models for diagnosing existing diseases, directing choice of therapies, or predicting people’s future health. For instance, Memorial Sloan Kettering and IBM are attempting to teach a version of IBM’s Watson computer relevant medical literature, molecular and imaging data, and other information that will allow Watson to build models for predicting the best treatment options for individual lung cancer patients.10 Machine learning adds layers of complexity to analytical processes and to the ethical and regulatory issues. In machine learning, the program, rather than the programmer, builds the predictive model. The same initial program, trained on different data sets, might build different predictive models. In some instances, particularly with nonlinear approaches, programmers cannot
September-October 2014
determine what variables a model relies on to make its predictions. IRBs and researchers might then have difficulty determining such a model’s generalizability and the appropriateness of proposed inclusion and exclusion criteria for a clinical trial using the model. Researchers, IRBs (and, ultimately, health care providers) might have difficulty determining when a person’s or population’s risk factors have changed such that the model is no longer applicable. Computational scientists, ethicists, and regulators will need some agreement on how to validate these models and determine when they are robust enough to enter human trials. We will need to develop thoughtful approaches to risk assessment and life cycle monitoring. Machine learning raises numerous other ethical and legal questions. Suppose, for instance, that a machine learning model used patients’ educational levels or racial information in ways we would consider wrongly discriminatory if done by a person. Could we determine what social categories were being used in questionable ways? Could a public or Veterans Affairs hospital have liability for acting unconstitutionally because of decisions guided by such a model? Can programmers design algorithms so that they do not use information inappropriately, and if so, what trade-offs would that involve? Furthermore, it should be obvious that some of the concerns that gave rise to the Genetic Information Nondiscrimination Act are raised by medical prediction software. For instance, should nonmedical sectors of society, such as employers, be permitted to use such software to make business decisions? To address these and other questions, more information scientists, statisticians, and others with computational biology expertise should be included in bioethics advisory groups, IRBs, and regulatory oversight processes.
1. A. Hartocollis, “Cancer Centers Racing to Map Patients’ Genes,” New York Times, New York Edition, April 22, 2013, A1. 2. D. F. Hayes, “OMICS-Based Personalized Oncology: If It Is Worth Doing, It Is Worth Doing Well!,” BMC Medicine 11 (2013): 221-25. 3. M. Smith et al., Best Care at Lower Cost: The Path to Continuously Learning Healthcare in America (Washington, D.C.: National Academies Press, 2012). 4. See, for example, E. Porcu et al., “Genotype Imputation in Genome-wide Association Studies,” in Current Protocols in Human Genetics (John Wiley & Sons, 2013): unit 1.25.11.25.14, at http://onlinelibrary.wiley.com/ book/10.1002/0471142905. 5. D. R. Nyholt, C. E. Yu, and P. M. Visscher, “On Jim Watson’s APOE Status: Genetic Information Is Hard to Hide,” European Journal of Human Genetics 17, no. 2 (2009): 147-50. 6. S. Hoffman and A. Podgurski, “The Use and Misuse of Biomedical Data: Is Bigger Really Better?,” American Journal of Law and Medicine 39, no. 4 (2013): 497-538. 7. D. E. Levy et al., “Underutilization of BRCA1/2 Testing to Guide Breast Cancer Treatment: Black and Hispanic Women Particularly at Risk,” Genetics in Medicine 13, no. 4 (2011): 349-55. 8. C. M. Micheel, S. J. Nass, and G. S. Omenn, eds., Evolution of Translational Omics: Lessons Learned and the Path Forward (Washington, D.C.: National Academies Press, 2012). 9. U. S. Department of Health and Human Services, 21 CFR §820.30(g); S. A. Mailhot, The Challenge of Software (Aspatore, 2014); Food and Drug Administration Center for Devices and Radiologic Health, “General Principles of Software Validation,” 2002, http:// www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm085371.pdf. 10. M. Kris, “Progress and Promise in Therapy of Lung Cancers,” Lung Cancer Management 3, no. 1 (2014): 17-21. Acknowledgment
The author thanks the Morgridge Institute for Research for summer support during her work on this essay. DOI: 10.1002/hast.351 This column appears by arrangement with the American Society for Bioethics and Humanities.
H AS TI N GS C E N TE R RE P O RT
9
