1. Patient-Centered Care
Precision Medicine
Lecture b
This material (Comp 25 Unit 8) was developed by the University of Alabama at Birmingham, funded by the
Department of Health and Human Services, Office of the National Coordinator for Health Information
Technology under Award Number 90WT0007.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
License. To view a copy of this license, visit http://creativecommons.org.
2. Precision Medicine
Learning Objectives
• Discuss the challenges of implementing
precision medicine in clinical practice
• Discuss the ethical, legal and social issues in
precision medicine
3. Challenges in Practice
• Manpower
– Geneticists and genetics counselors
• Integration into workflow
– Preemptive testing
– Use of genetic counselors
Source: (Pulley et al, 2012)
4. Challenges in Practice 2
• Priorities
– What is actionable?
– How to prioritize?
– Information sources
o FDA
o CPIC
Source (FDA, 2015
Relling and Klein, 2011)
5. Challenges in Practice 3
• Physician Education
– New information not included in most
physician’s training
– Need to understand enough to use genetic
and genomic information to diagnose and
treat
• Patient Education
– Implications for self and family
6. Technical Challenges
• CLIA-certified laboratories
– Required for tests used in clinical care
– Many genomic research laboratories not
CLIA-certified
– Many hospital labs cannot do genomic testing
• Identification of patients from EHR data
– Data may not be complete
– Data may not be in structured form
Source: (Burke et al, 2014)
7. Technical Challenges 2
• Electronic Health Record (EHR) integration
– EHRs may not be able manage large genomic
data
– Need for ancillary systems
– No standards for integrating data into EHRs
• Development of decision support tools
Source: (Starren et al, 2013)
8. Challenges with Clinical Decision
Support
• Similar challenges to other types of CDS
– Osheroff 5 rights for CDS--information,
person, intervention formats, channels, points
in workflow
– Most CDS are not shareable
• Lack of structured data
– Unstructured reports make clinical decision
support challenging
Source: (Osheroff et al, 2012)
9. Phenotype Identification from EHR
Data
• eMERGE activities
• PheKB
– Library of phenotype algorithms
• Implications for research and practice
– Can use EHR data to target individuals for
pre-emptive testing
– More structured data will improve algorithms
Source: (Kirby et al, 2016
Denny, Ritchie, Basford, 2013)
10. Use of Clinical Decision Support
• Traditional CDS
– Alerts
o Notify physicians when genomic information needs
to be included in medication decisions
– Reminders
o Use family history data to remind for genetic
screening
– Order sets
o Pre-emptive testing
11. Other CDS Strategies
• Infobuttons
– Context sensitive links to additional
information
• Facilitated consults
– Surveillance and intervention for high risk
variants
12. Ethical Legal and Social Issues in
Clinical Practice
• Discrimination
o Does NOT address life insurance
– Genetic Information Non-discrimination Act
(GINA)
o Prohibits discrimination in employment and health
insurance
Source: (GINA, 2009)
13. Ethical Legal and Social Issues in
Clinical Practice 2
• Return of Results
– Research studies often do not return results
to individual patients
– NIH Precision Medicine principles emphasize
transparency and openness
– Variants of unknown/uncertain significance
pose difficult decisions
o Meaning may change over time
o Follow-up can be challenging
Source: (PMI, 2015)
14. Individual and Family Implications
• Individual’s genetic testing has implications for
others
• What are the obligations of clinicians and
patients?
15. Ethical Legal and Social Issues in
Research
• Consent for use of genomic data
– Identified or de-identified?
– Changes in common rule
– Increased privacy and security protection
needed
• Privacy and security
– De-identification and re-identification
Source: (OHRP, 2015
Altman et al 2013)
16. Ethical Legal and Social Issues in
Research 2
• Modifying the genome
– Gene therapy
– Germline modifications
o CRISPR Cas9
Source: (Wade 2015)
17. Ethical Legal and Social Issues in
Research 3
• Return of results
– Incidental findings
– Variants of uncertain significance
– Use of non-CLIA certified labs
Source: (Burke et al, 2014)
18. The Future of Precision Medicine
• Increased understanding of meaning of genomic
variation
• More precision in regard to environment and
lifestyle
• More research on the impact of precision
medicine on health and healthcare
• Changes in technology to support precision
medicine
19. The Future of Precision Medicine 2
• Changes in health professions and public
education
• Greater clarity on the ethical and legal issues
• Increased application of knowledge in clinical
practice
21. Precision Medicine
References – lecture b
References
Altman, R. B., Clayton, E. W., Kohane, I. S., Malin, B. A., & Roden, D. M. (2013). Data re-identification:
societal safeguards. Science (New York, N.Y.), 339(6123), 1032–1033. PMCID: PMC3740512;
NIHMSID: NIHMS495908 www.ncbi.nlm.nih.gov
Burke, W., Evans, B. J., & Jarvik, G. P. (2014). RETURN OF RESULTS: ETHICAL AND LEGAL
DISTINCTIONS BETWEEN RESEARCH AND CLINICAL CARE. American Journal of Medical
Genetics. Part C, Seminars in Medical Genetics, 0(1), 105–111. www.ncbi.nlm.nih.gov
Kirby, J. C., Speltz, P., Rasmussen, L. V., Basford, M., Gottesman, O., Peissig, P. L., . . . Denny, J. C.
(2016, March 28). PheKB: a catalog and workflow for creating electronic phenotype algorithms for
transportability. J Am Med Inform Assoc, [Epub ahead of print]. jamia.oxfordjournals.org
Osheroff, J., Teich, J., Levick, D., Saldana, L., Velasco, F., Sittig, D., . . . Jenders, R. (2012). Improving
Outcomes with Clinical Decision Support (Second ed.). HIMSS.
The Precison Medicine Initiative. (2015, July 8). Precision Medicine Initiative: Proposed Privacy and
Trust Principles. Retrieved April 26, 2016, from www.whitehouse.gov
Pulley, J. M., Denny, J. C., Peterson, J. F., Bernard, G. R., Vnencak-Jones, C. L., Ramirez, A. H., …
Roden, D. M. (2012). Operational implementation of prospective genotyping for personalized
medicine: The design of the Vanderbilt PREDICT project. Clinical Pharmacology and Therapeutics,
92(1), 87–95. www.ncbi.nlm.nih.gov
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22. Precision Medicine
References 2 – lecture b
References
Relling, M., & Klein, T. (2011). CPIC: Clinical Pharmacogenetics Implementation Consortium of the
Pharmacogenomics Research Network. Clinical Pharmacology and Therapeutics, 89(3), 464–467.
www.ncbi.nlm.nih.gov
Starren, J., Williams, M. S., & Bottinger, E. P. (2013). Crossing the Omic Chasm: A Time for Omic
Ancillary Systems. JAMA : The Journal of the American Medical Association, 309(12),
10.1001/jama.2013.1579. www.ncbi.nlm.nih.gov
U.S. Department of Health and Human Services. (n.d.). NPRM for Revisions to the Common Rule.
Retrieved April 26, 2016, from HHS.gov Office for Human Research Protections: www.hhs.gov
U.S. Equal Employment Opportunity Commission. (2009, November 21). Genetic Information
Discrimination. Retrieved April 26, 2016, from U.S. Equal Employment Opportunity Commission:
www.eeoc.gov
U.S. Food and Drug Administration. (2015, May 20). Table of Pharmacogenomic Biomarkers in Drug
Labeling. Retrieved April 26, 2016, from U.S. Food and Drug Administration: www.fda.gov
Wade, N. (2015, December 3). Scientists Seek Moratorium on Edits to Human Genome That Could Be
Inherited. Retrieved April 27, 2016, from The New York Times: www.nytimes.com
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23. Person-Centered Care
Precision Medicine
Lecture b
This material was developed by the
University of Alabama at Birmingham,
funded by the Department of Health and
Human Services, Office of the National
Coordinator for Health Information
Technology under Award Number
90WT0007.
23
Hinweis der Redaktion
Welcome to Patient Centered Care, Precision Medicine. This is lecture B. This lecture will discuss the informatics underpinnings, challenges, and legal, ethical, and social issues involved in the practice of precision medicine.
The objectives for this unit, Precision Medicine, are to:
Discuss the challenges of implementing precision medicine in clinical practice, and
Discuss the ethical, legal and social issues in precision medicine.
If precision medicine is to become a new model for clinical care it will require more geneticists and genetic counselors to interpret the data and to educate the clinicians and patients. Vanderbilt’s program makes extensive use of geneticists in their pharmacogenomics program.
However, if we are going to routinely incorporate precision medicine, we must find a way to integrate the use of this information within the clinical workflow. While more genetics professionals will be needed, it is not likely to be feasible to engage them on an ad hoc basis for every decision about testing nor for interpreting every test result when it is available. Pre-emptive genetic testing would identify actionable variants at a time when there is the opportunity for assessment, consultation with geneticists, development of a management plan, and education of patients by a genetic counselor. It would also assure that the results are available when they are more urgently needed. Data have shown that preemptive testing can actually save money.
Another challenge is deciding what kinds of testing will lead to actionable results. Given that much is still unknown, how do we determine what types of tests have strong enough evidence to warrant testing and what types of results, that may be uncovered, warrant action? The FDA has begun to provide guidance related to genomic considerations and the organization known as CPIC (pronounced C-pick), or the Clinical Pharmacogenomics Implementation Consortium also has recommendations for actionable results.
Because genetics has not been a core specialty area for most physicians and because their medical school training may not have included the latest approaches to precision medicine, physician education is a key cornerstone to making precision medicine a reality. While some physicians will want to learn about the details of the underlying molecular biology, at a minimum all physicians will need to learn enough to be comfortable with understanding the test results to use them in diagnosis and treatment.
Patient education is another challenge. While patients may not need to understand the science behind the tests, they do need to understand the implications of the testing for themselves and their families.
In addition to these educational and manpower challenges, there are technical challenges as well.
Most of the research studies that have been exploring using the results of genomic analyses have not been done in laboratories certified to conform to the standards of the Clinical Laboratory Improvement Act, or CLIA (pronounced KLEE-uh). For the results to be used in routine clinical practice, CLIA-certified labs are required. Although most hospital laboratories are CLIA certified for conducting routine lab tests, they may not be equipped to perform the genetic tests required for precision medicine.
If pre-emptive testing is to occur, or even if there are research studies that need to recruit patients, it is important to be able to identify a pool of patients from data in the EHR. If the EHR data are not complete, or if a significant amount of important information is not in structured form, accurate identification can be challenging.
Integrating genomic data into EHRs poses major technical challenges. The actual sequence datasets are very large and EHRs are generally not equipped to handle them. Starren (pronounced Stair-in) et al. suggested that ancillary servers accessible by the EHR house the data, similar to the way Picture Archiving and Communication Systems or PACS (pronounced packs) house image files, but currently there are no agreed upon architectures or standards for dealing with these data within the EHR.
Clinical decision support will be essential to provide support for test ordering and interpretation.
However, there are many challenges in developing appropriate clinical decision support, or CDS (pronounced C-D-S) tools. Many of these challenges are similar to those encountered when developing CDS for non-genomic use. Osheroff and his colleagues have identified what they call the five rights of CDS, similar to the five rights of medication safety. For appropriate CDS, the right information should be delivered to the right person, in the right manner and at the right time. All of these rights can be hard to achieve in regard to precision medicine, especially when pre-emptive genetic testing is not routinely done..
Another issue related to developing appropriate clinical decision support is that most genomic results reporting is not done using structured data. Although PDF files of test reports may be able to be viewable from within the EHR, they cannot be incorporated into clinical decision support algorithms.
The institutions within the eMERGE network have used routinely collected structured data, as well as natural language processing on unstructured data, to develop a library of algorithms of phenotypes using the data in the EHR. The library is called PheKB (pronounced fee-K-B) and can be used by other institutions not just for genomic studies but for comprehensive cohort identification for recruitment for other clinical studies as well. It can also be used by other institutions who are incorporating precision medicine into clinical practice. Institutions can use the algorithms in PheKB to more precisely target individuals who might need pre-emptive genetic testing for instance. It is likely that there will be more structured data in the EHR as a result of other forces and having the structured data should increase the utility of these algorithms.
As we have said, providing clinical decision support will be essential for precision medicine. If genetic tests and their results can be incorporated into the EHR as are other laboratory tests, they can also be incorporated into traditional clinical decision support systems. Some of the most common clinical decision support approaches are alerts, reminders and order sets.
Alerts can be configured to alert physicians who order drugs for which genomic considerations need to be taken into account. As of the end of 2015, the FDA included alerts for over 160 medications, for which the prescription and/or dosing might vary depending on the individual’s genotype.
Reminders for genetic screening based on family history and other characteristics of the individual can also be incorporated into the EHR.
Especially if pre-emptive testing is decided upon, even before an individual needs a particular medication, order sets can be designed that include appropriate genetic testing, to assure that the results will be available should they be needed.
Infobuttons are another type of clinical decision support. Context sensitive links to additional information about genetic tests, their results, or their relationship to disease can be embedded within the EHR and can be available in real-time to provide support to clinicians. Many sites are using facilitated consults which include surveillance for high risk variation and then intervention when appropriate.
One concern that patients and physicians might have with the new focus on precision medicine is whether they will face discrimination based on the results of genomic analyses. The Genetic Information Non-discrimination Act, or GINA (pronounced Jeena) prohibits discrimination in employment and health insurance based on genetic test results, which will relieve some anxiety. However, GINA does not prohibit discrimination in regard to life insurance. As precision medicine becomes more commonplace and genetic testing is more frequent, the ethical and legal landscape in this regard will most likely evolve.
If patients consent to be part of a biobank for research they often agree to not receive the results of their tests. This decision not to return results has given rise to a good deal of debate on the ethics of this practice. In fact, the NIH precision medicine initiative has adopted a set of principles that promotes transparency and openness. But even in clinical practice when tests are done for specific patients for specific purposes, often only the results of known variants are returned and the clinician and patient may not hear about any results that showed what are called variants of uncertain significance. While at a given point in time, it may make sense to withhold results whose meaning is not clear, over time, with more knowledge, the meaning of the results may change and the issue of what obligations testing laboratories or clinicians have for patient follow-up is an important one that will need to be addressed.
Another ethical consideration that will have to be addressed as precision medicine becomes more commonplace is the issue of notifying family members of test results. In some ways, a genetic test is not just a test of an individual, but may have implications for other family members. Are parents obligated to inform their children if they find they have a disease that could affect their children? Some of these issues have been addressed in the genetics and genetics counseling community, but as precision medicine moves into non-specialty medicine, they may have to be addressed by other physicians who are less accustomed to dealing with these challenges.
Research has always treated consent for use of data differently depending if it involves identified, de-identified or anonymous data. A key question is whether a person’s genome is considered identified data. On the one hand, the genome identifies a unique individual, even if there are no obvious other identifiers attached to the data. On the other hand, people viewing the genomic sequence data without any other reference sources would not be able to identify whose sequence they are viewing.
In 2015 the Office of Human Research Protection announced plans to change what is known as the Common Rule that governs Human Subjects Protection in research. The change will require all research that uses genomic data to obtain informed consent, but the consent form will be simpler than traditional research consent forms and it will also allow use beyond a single study. The change in the rule will mean that genomic data are considered to be Protected Health Information, or PHI (pronounced P-H-I) This change is likely to make it easier to recruit subjects for precision medicine research, although it may increase the burden related to keeping the data private and secure.
Assuming that de-identified data of any sort does not pose privacy risks has recently been challenged by research showing that re-identification may be easier than previously thought. This may be part of the reason for the proposed increase in privacy and security protection of genomic data.
Efforts to study the modifications of human genes to date have generally involved efforts at gene therapy, substituting a “good copy” of the gene for the defective one involved in certain diseases. These efforts have generally involved mutations in somatic cells, not germline cells. Recently, a new technique known as Crispr cas9 (pronounced Crisper-cass-9) has enabled more efficient gene editing and more importantly, the editing can be done on germline cells which would permit the changes to be passed on to offspring. A 2015 conference of experts in this area has called for a moratorium on human experimentation with Crispr because of the ethical implications of altering germline cells.
The issue of return of research results poses challenges in several areas. First, while a given study may focus on one particular condition, it is possible in the course of sequencing the individual’s genome that information on other conditions may be discovered. Is the researcher obligated to inform the patient about this information? Second, while variations may be discovered, their significance is not always clear at the time of the discovery but could possibly become more definitive in the future. Again what are the researchers‘ obligations in these areas? These questions get even more complicated because the data for most genomic research studies have not been processed in CLIA (pronounced Klee-uh) certified laboratories, which are required for data used in clinical care.
With the increased funding for, and interest in, precision medicine, we can expect that our knowledge of the meaning of genomic variations and their association with disease will increase exponentially from where we are today.
We can also expect more precise knowledge about, and more quantification of, environmental and lifestyle factors.
The research foci, which to date have mainly been focused on discovery of associations, will add dimensions focused on the impact of those discoveries. Such research will look at the impact of precision medicine on health outcomes, costs of healthcare, and best strategies for implementing precision medicine in healthcare.
We can also expect improvements in technology, both the technologies required for genomics research, as well as the information technologies needed to integrate precision medicine into the healthcare and health information technology environment.
We are already seeing changes in the education of physicians and nurses to incorporate many of the concepts of precision medicine in basic science education, but those changes will increase as our knowledge increases and we will also see changes in clinical education as well. Patients also will need more understanding of the use of precision medicine and it is likely that general education will begin to incorporate some of the concepts that patients need in order to understand this new paradigm of care.
As we gain more knowledge, the ethical and legal issues should become clearer, although new ones will undoubtedly arise. It is also possible, and maybe even likely, that there will be an initial discordance between our technical abilities to be able to do certain things, and the ethical and legal framework to do them in a socially responsible way. And given the pace of science, we can expect a continued evolution of what that legal and ethical framework entails.
Finally, we will see precision medicine become a new paradigm of clinical care. The new knowledge will start to be incorporated into clinical care even before the education and ethical issues have been addressed, but it will cease to be experimental and will become the routine way care is done, once the supporting structures are in place.
This concludes Precision Medicine, lecture b.
In summary, we discussed the challenges to implementing precision medicine and the legal and ethical challenges of precision medicine in clinical care and research. We also discussed likely future directions. Although the timeline is not precise at this point when precision medicine will become fully integrated into clinical practice, it is almost a certainty that the day will come when precision medicine will not be considered a new model of care, but will be THE model for routine clinical care.