This piece is part of a blog symposium featuring commentary from participants in the Center for Health Policy and Law’s annual conference, Promises and Perils of Emerging Health Innovations, held on April 11-12, 2019 at Northeastern University School of Law. The symposium was originally published on the Northeastern University Law Review Online Forum.
By Ignacio N. Cofone
For e-health treatments to be operational, including electronic health records and remote patient monitoring, a significant amount of personal, and often sensitive, patient information is collected and frequently sent electronically to medical professionals and other actors.
These e-health treatments, while helpful to patients, raise privacy concerns related to a reduction in personal privacy and an increased risk of privacy breaches; privacy concerns which are not considered when evaluating their incorporation with treatments. John Carey, Power to the Patient: How Mobile Technology is Transforming Healthcare 14 (Frieda Klotz ed., 2015). Patients are indeed concerned. About half of the respondents in the survey believe that consumers’ wariness about privacy violations will be a major obstacle for the adoption of mobile technology in healthcare. Id. at 14, 16. Over half of the respondents consider privacy risks to be the biggest concern of these technologies’ application to healthcare. Id.
The absence of an objective way of taking these privacy concerns into consideration, when evaluating health policies, underplays the value patients attribute to their privacy. Moreover, unaddressed privacy concerns can lead to the halt of new and potentially advantageous treatments. See generally Ignacio N. Cofone & Adriana Z. Robertson, Privacy Harms, 69 Hastings L.J. 1039 (2018); see also Ignacio Cofone, Nothing to Hide, but Something to Lose, U. Toronto L.J. (forthcoming 2019).
I propose incorporating patients’ privacy considerations into standard health impact evaluations. Privacy concerns should be incorporated into the Quality-Adjusted Life Year framework (QALY). QALY measures the quality and quantity of life improvements per medical treatment. See Joseph S. Pliskin et al., Utility Functions for Life Years and Health Status, 28 Operations Res. 206, 213 (1980); Richard Zeckhauser & Donald Shepard, Where Now for Saving Lives?, 40 Law & Contemp. Probs. 5, 11-15 (1976).
Through such a method, I propose estimating the privacy-cost per treatment, contingent on the type of personal information involved, with similar methodologies used to evaluate QALY in health economics: Visual Analogue Scale, Standard Gamble, and Time-Trade-Off (all of which are techniques to measure individual’s characteristics, quality of life, attitude and/or preferences in a clinical context). These evaluation methods capture the patients’ subjective assessments of harm. Such costs can be used to weigh the QALY factors and then balance them against health benefits.
How Health Economics Measures What Cannot Be Quantified
Comparing treatments is no easy task. It often demands comparing technologies that have different effects on a patient’s health. For instance, one medication may lower cholesterol levels while another may provide pain relief, making them difficult to compare on a single scale.
To rank treatments on a comparable scale, health economics uses QALYs. David O. Meltzer & Peter C. Smith, Theoretical Issues Relevant to the Economic Evaluation of Health Technologies, in 2 Handbook Of Health Economics 433, 439 (Mark V. Pauly et al. eds., 2011); J. Brazier et al., A Review of the Use of Health Status Measures in Economic Evaluation, 3 Health Tech. Assessment 1, 3-4 (1999).
A QALY represents the value of living a year in perfect health, and it is used as a proxy for the quality of one’s life during that year. Paul Dolan, The Measurement of Health Related Quality of Life for Use in Re-Source Allocation Decisions in Health Care, in 1B Handbook of Health Economics 1723-26 (A.J. Culyer & J.P. Newhouse eds., 2000).
The benefit of a given treatment is translated into the QALY gain that the treatment is expected to provide—both in terms of duration and quality of life. In our pain-lowering and cholesterol-lowering medication example, while the cholesterol medication may extend one’s life, the improved quality of life offered by the pain-reduction medication might make this treatment more favorable.
The question that arises is, how one can measure a person’s quality of life? This is where the Visual Analogue Scale, Standard Gamble, and Time Trade-off tools are utilized. Paul Dolan & Matthew Sutton, Mapping Visual Analogue Scale Health State Valuations onto Standard Gamble and Time Trade-off Values, 44 Soc. Sci. Med. 1519, 1519 (1997).
Each method begins with the health state that the researcher must consider. Paul Dolan et al., Valuing Health States: A Comparison of Methods, 15 J. Health Econ. 209, 210 (1996). Rather than naming actual conditions or diseases, health states are often given in terms of several “dimensions” of health and well-being, such as mobility, self-care, ability to perform everyday tasks, pain or discomfort, and mental health. Id. at 215; Dolan & Sutton, supra, at 1521. For example, a person experiencing “Health State A” may experience the following dimensions:
You have poor mobility.
You have some problems washing and dressing yourself.
You have some problems with performing your usual activities.
You experience moderate pain or discomfort.
The Visual Analogue Scale asks individuals to pinpoint visually on a scale from 0 to 100 how much they would value a given health state. David Parkin & Nancy Devlin, Is There a Case for Using Visual Analogue Scale Valuations in Cost-Utility Analysis?, 15 Health Econ. 653, 655 (2006). The average value selected by individuals is then directly translated to QALYs.
In a Standard Gamble (a method for assessing individual preference(s)), patients are asked to compare their current health state against their definition of perfect health. The QALY value is established by the lowest probability of living in perfect health that people consider being high enough to take the treatment that “cures” them from the described health state. Milton C. Weinstein et al., QALYs: The Basics, 12 Value Health S5, S9 (2009).
For example, if patient A is indifferent between living with chronic migraines and taking a treatment that will (1) cure her migraines with a probability of 95% and (2) cause death with a probability of 5%, then the QALY value of the treatment is 0.95. For the patient, the described health state is 95% as acceptable as perfect health.
The Time-Trade-Off method asks respondents to make a choice similar to that rendered under the Standard Gamble method, but by comparing years in perfect health (X) versus years experiencing a health problem (Y), against the ratio among them (X:Y) to determine the QALY value. Id. at S7. To illustrate, if patient A from the example above has no preference between living for 50 more years with migraines and living for 40 more years in perfect health, then the QALY value of the treatment is 0.8. QALY values are used to select a treatment, when there is a choice, among programs.
Placing Privacy in Health Economics
Privacy costs should be embedded within QALYs. The sharing of data with medical professionals, and the probability of patient data becoming public, can be included in the descriptions of the health states on which the Visual Analogue Scale, Standard Gamble, and Time-Trade-Off methods operate.
Imagine that a new medical intervention becomes available for an illness. This intervention improves a patient’s health. However, the intervention also involves continuous external collection of the patient’s sensitive data, such as heart rate and location. To fully assess this treatment would require consideration of the patient’s privacy concerns. A slight alteration of the traditional Visual Analogue Scale, Standard Gamble, and Time-Trade-Off methods can measure such concerns. As before, each of these methods would begin with a description of the health state under inspection In addition to the traditional “dimensions” of health, the health state could also describe the data being collected, which individuals have access to the data, and the risk of a data breach. The resulting health description could look something like this:
Modification of Health State “A” description:
You have poor mobility.
You have some problems washing and dressing yourself.
You have some problems with performing your usual activities.
You experience moderate pain or discomfort.
You are not anxious or depressed.
Data on your heart rate is being collected.
Data on your location is being collected.
Your doctor and other employees of the hospital have access to this data.
There is a 2% risk that the data becomes publicly available.
After presenting this altered version of health state A, its valuation would then continue as it otherwise would under the chosen method. The resulting valuation of this health state would provide an estimate of the utility of being in health state A, which includes the individual’s privacy concerns. Under this model, the QALYs would present a more complete picture of the non-monetary costs of the medical intervention.
Accounting for privacy concerns in this way could also take place in an experimental setting to evaluate current treatments. Two groups could be posed Standard Gamble or Time-Trade-Off questions, with only one of the groups facing a modified health state description such as that specified above. By comparing both groups’ valuation, there would be a resultant indication of the value patients place on losing personal information in exchange for gaining a health benefit. Large disparities between the modified health state and the non-modified health state might indicate that the current ranking of programs does not represent patients’ true preferences and well-beings. Alternatively, if the proposed method were to be followed for a few e-health treatments, its results could be tested against existing QALY evaluations of other health treatments to compare the outcomes of the standard evaluations with those of the proposed method.
Conclusion
As medical treatments have become increasingly invasive and data intensive, concerns have been raised regarding the privacy of patients. I propose the described method to quantify privacy concerns in health law, which is borrowed from health economics. See Ignacio N. Cofone, A Healthy Amount of Privacy: Quantifying Privacy Concerns in Medicine, 65 Clev. St. L. Rev. 1 (2017). This method would provide some indication of the value of personal information in a medical context and include privacy in the overall costs-benefit analysis for e-health programs. I suggest this be accomplished by incorporating privacy concerns into the cost-effectiveness framework that is already established and employed in public health policy. In Part II of this blog post, I explore the policy advantages and doctrinal consequences of this proposal.
This is Part I of a two-part blog post. Many of these ideas are developed in more detail in Ignacio N. Cofone, A Healthy Amount of Privacy: Quantifying Privacy Concerns in Medicine, 65 Cleveland State L. Rev. (2017). I thank the participants of the Northeastern University School of Law 2019 Annual Health Law Conference for their helpful comments and Malaya Powers for her excellent research assistance.
Bio: Ignacio Cofone is an assistant professor of law at McGill University’s Faculty of Law.
Handle: @IgnacioCofone