We now have the entire human genome defined and “sequenced” and the costs of this clinical sequencing for individuals have decreased dramatically. In medicine today, that knowledge is being used to develop new tests so that health professionals can use the genetic tests to assist in making a diagnosis and deciding on treatment. However as the costs of whole exome and genome testing go down even more, a price point is nearing at which it will be less expensive to do a complete clinical sequencing than to do specific genetic tests. This promise of more genetic information at lower costs creates the possibility that we will have more information that can potentially help the patient. There is also the promise of finding abnormalities that we did not anticipate. This is what the American College of Medical Genetics calls “incidental findings” and is no different than that seen in many areas of medicine today. When a doctor sends a person for a chest x-ray, or a chest CT for a cough, unanticipated and incidental findings may be seen in the bones that are visible on the imaging study as an example. With complete clinical sequencing however the number of secondary and incidental findings may be significantly larger than we see today with other diagnostics.
In recognition of these emerging facts, the Institute of Medicine hosted a 2 day meeting in July 2012 to help understand the economics of adopting whole genome sequence information into health care. That led to a report of the meeting and also to a “viewpoint” published in the Journal of the American Medical Association entitled "The Economics of Genomic Medicine" that described the main agreed upon insights by the participants. In my words, their points were:
- Genomic researchers, health care practitioners, payors, and economists are all speaking different languages and have no comfort with the languages of the other disciplines making any discussion of value and economics difficult.
- There is little to no evidence at this time demonstrating that genomic data favorably affect health outcomes.
- We don’t know how to use and explain this clinically sequenced data to people in any sort of logical, understandable and cost effective manner to produce value. We just don’t know what to do with these genetic facts so health professionals have a hard time educating and counseling their patients on the implications of the clinical sequencing.
- We don’t understand “personal utility” and its role in assessing the value of sequencing. Personal utility describes the meaning and worth any test or intervention brings to an individual from that individual’s perspective rather than from any external metric such as morbidity or mortality or from an expert’s perspective.
To summarize, we don’t know the value that this clinical sequence has for an individual, we don’t know how to talk about it, we don’t know how to measure its value, and the experts don’t completely understand one another. Admittedly that makes discussing the economics difficult at best. It also creates a dilemma for the payors, both private and government, about what should be paid.
In this country, we have a combined pre-paid health care model and a health insurance model to pay for care. Insurance is a way of sharing the financial risk of relatively unusual high cost events while pre-paid health addresses the costs of preventive care and screening which are not at all unusual. Preventive care and screening are encouraged and paid as the value from both a public health and a moral point of view are believed to be worth the cost. The theory is that finding clinical risks early leads to lower costs of treating disease and thus would also be advantageous for financial risk. The economic reality has proved to be more complex however and is dependent on the specific clinical issues and the specific use of the information that screening uncovers. That has led to tremendous debates in the public arena concerning specific tests, such as debates around the use of PSA testing for the risk of prostate cancer and the appropriate age to start screening with mammography for breast cancer. These debates occur as we attempt to find definite answers to questions that can really only provide us with statistical estimates of both clinical and financial risk.
Risk is nothing more than a probability based on data. This is true for both clinical risk and financial risk. We now routinely treat disease risk, as much or even more than we treat disease. We screen for silent illnesses such as hypertension that may not be symptomatic to prevent them from causing problems in the future and try to find illnesses at earlier points in their course to affect cures such as screening for breast cancer and colon cancer. But we don't really know what would have happened in each case had we not screened. We can only estimate the probabilities of both clinical outcomes and financial outcomes.
With clinical sequencing, we have entered the potential for a new world of health risk. Right now, the issue is one of deciding how to report the incidental findings from a clinical sequence. The American College of Medical Genetics has spent the last year developing a policy statement on just this issue with the draft recommendations now being circulated among members of the college. Part of their effort is an attempt to identify clinically relevant incidental test results and then recommending whether or not such findings should even be reported to the patient. But this is just the beginning. As our understanding improves we will quantify new risk probabilities and thus new test "results" based on the clinical sequencing and the number of incidental findings may multiply.
I wonder if some of their efforts to control what is reported are in vain and are an outgrowth of the different language they, as experts in genetic diseases, speak when compared to the patients and the public who see the open dissemination of all available information as a human rights issue and a patient autonomy issue, rather than a test reporting issue. The ability of any health professional or any expert organization such as the ACMG to define what results a patient should see and should not see is likely to be strongly challenged and will probably ultimately be refuted.
This can lead us to a situation in which the multiplied “incidental findings” which are reported can lead to patient demands to find reasons for these findings and to attempt to modify any risks inherent in these results. That can only lead to higher total costs with little potential benefit at this point of knowledge.
In this scenario, the economics become challenging. Who should pay for this type of attempt at risk mitigation when the likelihood of clinical impact will likely be low for some time to come? Will it be decided by the American College of Medical Genetics which will look at clinical utility or by the yardstick of “personal utility”? I am as unsure as the members of the esteemed IOM panel. While the potential for good is exciting, the potential for increased financial and clinical risk from attempting to predict and cure every possible illness is a bit overwhelming. We will need to be very careful as we try to address the excellent points made by the Institute of Medicine Committee.