Regulations block advancements in health care
Adam Theirer, Mercatus Center, George Mason, 2016, Permissionless Innovation: The Case for Comprehensive Technological Freedom, Kindle edition, page number at end of card
Consider the field of medicine and the potential for digital technology to help revolutionize the medical profession, thus improving patient care. Unfortunately, America’s healthcare sector is already so heavily regulated that many leading technology companies and investors shy away from investing in new innovations in this space. 71 In particular, the FDA’s complex and costly review process for new drugs and devices makes it extremely difficult for new innovators to get their life-enriching medical services to market quickly. 72 “Due to T ‘regulatory uncertainty’ … [and] the complete and utter capriciousness and unpredictability in the FDA review process of new medical products,” notes Dr. Joseph V. Gulfo, “venture capitalists are becoming less inclined to fund very early stage companies.” 73 Indeed, leading venture capitalists avoid investments in advanced medical technology companies because of the costs associated with years of delay and potential long-run disapproval. “If it says ‘FDA approval needed’ in the business plan, I myself scream in fear and run away,” says Tim Chang, managing director at Mayfield Fund, a venture capital firm. Chang has never backed a company that needed to go through the FDA’s review process. 74 Even major tech companies like Google, which could potentially absorb the significant costs associated with FDA review, still don’t want any part of it. “Generally, health is just so heavily regulated. It’s just a painful business to be in,” says Sergey Brin, one of Google’s founders. “I think the regulatory burden in the U.S. is so high that … it would dissuade a lot of entrepreneurs.” 75 Thierer, Adam. Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom (Kindle Locations 447-457). Mercatus Center at George Mason University. Kindle Edition.
Regulations apply to the IoT
Reda Chouffani is vice president of development with Biz Technology Solutions Inc., which provides software design, development and deployment services for the healthcare industry, no date, Can We Expect the Internet of Things in Health Care? e
The U.S. Food and Drug Administration offersguidelines for medical devices, and regulators will likely continue to regulate connected devices used by patients.
Turn — Huge cyber security risks in health care, triggering network access and patient death
Bill Siwicki, August 17, 2016, 5 steps to cybersecurity for Internet of Things Medical Devices, https://www.healthcareitnews.com/news/5-steps-cybersecurity-internet-things-medical-devices
The healthcare industry is plagued with data breaches and other cybersecurity nightmares. At the same time, connected medical devices – components of the so-called Internet of Things – are multiplying, opening more holes in security and creating terrible potential for patient casualties. Without doubt, unsecured medical devices currently are putting hospitals and patients at risk, according to “Healthcare’s IoT Dilemma: Connected Medical Devices,” a new report from Forrester Research analyst Chris Sherman. “You have less control over connected medical devices than any other aspect of your technology environment,” the report said. “Many times, vendors control patch and update cycles, and vulnerabilities persist that require segmentation from your network. Considering that many of these devices are in direct contact with patients, this is a major cause for concern.” Additionally, medical devices are vulnerable to four attack scenarios, the report said. “Threats against medical devices include denial-of-service (DoS), patient data theft, therapy manipulation and asset destruction,” the report said. “Each represents risk to your organization, with DoS currently being the most severe.”
Data load destroys IoT applicability in health care
Jennifer Bresnick, July 20, 2016, Data Overload May Impede Health Care Internet of Things Growth, https://healthitanalytics.com/news/data-overload-may-impede-healthcare-internet-of-things-growth
The Internet of Things (IoT) may simply be too hot for the healthcare industry to handle as organizations continue to struggle with the huge amount of data produced by wearables, sensors, remote monitors, and other medical devices. Forty-two percent of participants in a cross-industry poll conducted by Strategy Analytics said that the Internet of Things produces too much data to analyze efficiently, despite the fact that 56 percent believe big data analytics is driving greater reliance on IoT technologies. “While data analytics emerged as the top reason for an IoT deployment, a significant percentage of companies struggle with how to analyze that data to benefit their business,” said Andrew Brown, Strategy Analytics Executive Director of Enterprise and IoT Research. “The data deluge is problematic. Over 50 percent report that they have too much data to be able to analyze it efficiently. We also found that 44 percent of respondents currently perform some data analytics but admit they could do a better job and 31 percent of organizations do not currently store IoT data at all,” Brown said. Uncertain prospects for financial return on IoT analytics, coupled with worries over the security of device data, are also contributing to respondents’ reluctance to push forward with IoT deployments, added Laura DiDio, Director of IoT Systems Research and Consulting. “Integration with legacy systems (41 percent) and security are the biggest impediment to IoT deployments,” she said. “Only 13 percent of respondents said that IoT will strengthen security and 56 percent said security is their top technical challenge. Another concern is that nearly half of businesses have not completed a detailed cost analysis, which is crucial.” While 80 percent of firms are currently using Internet of Things data in some capacity or are considering using the IoT within the next twelve months, just 25 percent of respondents said they have completed an end-to-end IoT deployment. he IoT is still in a phase of rapid growth, and the underlying technologies of the connected device ecosystem have not yet been fully developed. The healthcare Internet of Things market may be wortha staggering $410 billion by 2022, predicted one recent report, driven by novel technologies like ingestible and disposable sensors. The healthcare industry, with its hearty appetite for predictive analytics, population health management, chronic disease monitoring, and remote care, is among the earliest adopters and top users of IoT technologies. A previous survey from Strategy Analytics, published in July of 2015, showed similar results, identifying healthcare big data analytics and diagnostics as one of the most promising areas of growth for Internet of Things devices, tools, and techniques. “The survey results show that enterprise interest in IoT is high, driven by the need to address and solve pragmatic business issues,” DiDio said at the time. “But it’s equally clear that organizations are still assessing the myriad offerings and specific integration and migration strategies.” They may need to take their time with doing so, as the challenges to a successful IoT deployment are many. The most recent poll found that 28 percent of participants believe it is difficult to capture IoT data reliably, while 25 percent aren’t sure how to collect data that is useful for their strategic goals. Twenty-seven percent admitted that they are unsure what questions to ask, while 18 percent acknowledged that their business processes are likely too rigid to take advantage of this new multifaceted source of information. For the healthcare industry, these results may be particularly problematic. In addition to being subject to extremely tight security regulations governing the privacy of patient data, many healthcare providers do not currently have access to interoperable technologies, such as electronic health records, patient portals, and population health management systems, which can accept data from IoT devices in a meaningful, easy-to-analyze manner. And the obstacles aren’t just technical in nature. Patient and provider behaviors must change if the IoT is to become a meaningful tool for chronic disease management or communication, and vendors must do a better job of offering their wares in a workflow-friendly manner that presents intuitive, usable, curated data to providers at the point of care. While healthcare vendors and providers are slowly acclimating themselves to the new reality of IoT data, and reliance on APIs that allow IoT connections is growing, healthcare organizations may still have a long road to travel before the Internet of Things becomes a routine part of their big data analytics programs.
Turn – data will be used to discriminate against those with health problems
Kelsey Finch, Westin Research Fellow, International Association of Privacy Professionals, 2015, Welcome to the Metropticon: Protecting Privacy in a Hyperconnected Town,” FORDHAM URBAN LAW JOURNAL v. 41, https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=2549&context=ulj,
In urban and nonurban settings, big data analysis exacerbates concerns about unfairness and discrimination.118 It allows for granular distinctions to be made between individual characteristics, preferences, and activities. Reports by the Federal Trade Commission, for example, indicate that data brokers regularly categorize consumers by inferred interests, sorting them in categories like “Dog Owner,” “Winter Activity Enthusiast,” “Expectant Parent,” or “Diabetes Interest,” and into age-, ethnicicty- and income-focused categories like “Urban Mixers” (which includes “a high concentration of Latinos and African Americans with low incomes”) or “Rural Everlasting” (which includes “single men and women over the age of 66 with ‘low educational attainment and low net worths’”).119 Big data analytics can help mask discriminatory intent behind apparently innocuous mirrors and proxies.120 For example, disparate policies based on location can implicate redlining, the act of denying or increasing the cost of services to residents of neighborhoods comprised mostly of minorities.121 Urban preemptive policing schemes are another area where data-driven policies could mask discriminatory agendas.
Turn – economic discrimination against the unhealthy
Scott Pepper, Professor of Law, University of Colorado School of Law, August 2015, Regulating the Internet of Things: First Steps, https://www.texaslrev.com/wp-content/uploads/2015/08/Peppet-93-1.pdf
First, subpart II(A) explores the ways in which the Internet of Things may create new forms of discrimination—including both racial or protected class discrimination and economic discrimination—by revealing so much information about consumers. Computer scientists have long known that the phenomenon of “sensor fusion” dictates that the information from two disconnected sensing devices can, when combined, create greater information than that of either device in isolation.32 Just as two eyes generate depth of field that neither eye alone can perceive, two Internet of Things sensors may reveal unexpected inferences. For example, a fitness monitor’s separate measurements of heart rate and respiration can in combination reveal not only a user’s exercise routine, but also cocaine, heroin, tobacco, and alcohol use, each of which produces unique biometric signatures.33 Sensor fusion means that on the Internet of Things, “every thing may reveal everything.” By this I mean that each type of consumer sensor (e.g., personal health monitor, automobile black box, or smart grid meter) can be used for many purposes beyond that particular sensor’s original use or context, particularly in combination with data from other Internet of Things devices. Soon we may discover that we can infer whether you are a good credit risk or likely to be a good employee from driving data, fitness data, home energy use, or your smartphone’s sensor data. This makes each Internet of Things device—however seemingly small or inconsequential—important as a policy matter, because any device’s data may be used in far-removed contexts to make decisions about insurance, employment, credit, housing, or other sensitive economic issues. Most troubling, this creates the possibility of new forms of racial, gender, or other discrimination against those in protected classes if Internet of Things data can be used as hidden proxies for such characteristics. In addition, such data may lead to new forms of economic discrimination as lenders, employers, insurers, and other economic actors use Internet of Things data to sort and treat differently unwary consumers. Subpart II(A) explores the problem of discrimination created by the Internet of Things, and the ways in which both traditional discrimination law and privacy statutes, such as the Fair Credit Reporting Act (FCRA),34 are currently unprepared to address these new challenges
Turn – health data can be used to infer other characteristics that can be used to discriminate in ways that laws can’t control
Scott Pepper, Professor of Law, University of Colorado School of Law, August 2015, Regulating the Internet of Things: First Steps, https://www.texaslrev.com/wp-content/uploads/2015/08/Peppet-93-1.pdf
The Legal Problem: Antidiscrimination and Credit Reporting Law Is Unprepared.—There are two main legal implications of the possibility that everything may begin to reveal everything. First, will the Internet of Things lead to new forms of discrimination against protected classes, such as race? Second, will the Internet of Things lead to troubling forms of economic discrimination or sorting? a. Racial & Other Protected Class Discrimination.—If the Internet of Things creates many new data sources from which unexpected inferences can be drawn, and if those inferences are used by economic actors to make decisions, one can immediately see the possibility of seemingly innocuous data being used as a surrogate for racial or other forms of illegal discrimination. One might not know a credit applicant’s race, but one might be able to guess that race based on where and how a person drives, where and how that person lives, or a variety of other habits, behaviors, and characteristics revealed by analysis of data from a myriad of Internet of Things devices. Similarly, it would not be surprising if various sensor devices—a Fitbit, heart-rate tracker, or driving sensor, for example—could easily discern a user’s age, gender, or disabilities. If sensor fusion leads to a world in which “everything reveals everything,” then many different types of devices may reveal sensitive personal characteristics. As a result, the Internet of Things may make possible new forms of obnoxious discrimination. This is a novel problem and one that legal scholars are just beginning to recognize.241 I am not convinced that the most blatant and obnoxious forms of animus-based discrimination are likely to turn to Internet of Things data— if a decision maker wants to discriminate based on race, age, or gender, they likely can do so without the aid of such Internet of Things informational proxies. Nevertheless, the problem is worth considering because traditional antidiscrimination law is in some ways unprepared for these new forms of data. Racial and other forms of discrimination are obviously illegal under Title VII.242 Title I of the Americans with Disabilities Act (ADA) forbids discrimination against those with disabilities,243 and the Genetic Information Nondiscrimination Act (GINA) bars discrimination based on genetic inheritance.244 These traditional antidiscrimination laws leave room, however, for new forms of discrimination based on Internet of Things data. For example, nothing prevents discrimination based on a potential employee’s health status, so long as the employee does not suffer from what the ADA would consider a disability.245 Similarly, antidiscrimination law does not prevent economic sorting based on our personalities, habits, and character traits.246 Employers are free not to hire those with personality traits they don’t like; insurers are free to avoid insuring—or charge more to—those with risk preferences they find too expensive to insure; lenders are free to differentiate between borrowers with traits that suggest trustworthiness versus questionable character.247 As analysis reveals more and more correlations between Internet of Things data, however, this exception or loophole in antidiscrimination law may collapse under its own weight. A decision at least facially based on conduct—such as not to hire a particular employee because of her lack of exercise discipline—may systematically bias an employer against a certain group if that group does not or cannot engage in that conduct as much as others. Moreover, seemingly voluntary “conduct” may shade into an immutable trait depending on our understanding of genetic predisposition. Nicotine addiction and obesity, for example, may be less voluntary than biologically determined.248 The level of detail provided by Internet of Things data will allow such fine-grained differentiation that it may easily begin to resemble illegal forms of discrimination. Currently, traditional antidiscrimination law has not yet considered these problems.