Predictive analytics and “big data” are emerging as important new tools for diagnosing and treating patients. But as data collection becomes more pervasive, and as machine learning and analytical methods become more sophisticated, the companies that traffic in health-related big data will face competitive pressures to make more aggressive claims regarding what their programs can predict. Already, patients, practitioners, and payors are inundated with claims that software programs, “apps,” and other forms of predictive analytics can help solve some of the health care system’s most pressing problems. This article considers the evidence and substantiation that we should require of these claims, focusing on “health” claims, or claims to diagnose, treat, or manage diseases or other medical conditions. The problem is that three very different paradigms might apply, depending on whether we cast predictive analytics as akin to medical products, medical practice, or merely as medical information. Because big data methods are so opaque, its claims may be uniquely difficult to substantiate, requiring a new paradigm. This article offers a new framework that considers intended users and appropriate evidentiary baselines.
Expanding the Use of Provider Orders for Life-Sustaining Treatment for Patients with Advanced Cancer by Thaddeus Mason Pope :: SSRNOctober 19, 2018
Patients with advanced cancer often get more aggressive treatment than they want because too few oncologists elicit their end-of-life treatment preferences. In response to this problem, leading associations, including ASCO and the Institute of Medicine, have called for more advance care planning. Medicare has also recognized the value of advance care planning, expanding reimbursement for the service in 2016.
A key component of advance care planning for some patients is the Provider Orders for Life Sustaining Treatment (POLST) directive, which encourages providers to speak with patients about their end-of-life treatment preferences and creates specific medical orders to be honored during a medical crisis. But POLST remains underutilized by oncologists with their advanced cancer patients. Here, we provide some recommendations on how oncologists should take advantage of POLST to help patients improve their end-of-life care.
Since November 2017, Professor Pope has authored a monthly Law and Ethics in Oncology column for the ASCO Post. This column explores the legal and ethical issues oncologists must be aware of in this era of precision medicine and changing healthcare policy, both to protect patients’ rights and to safeguard against potential legal jeopardy.
The ASCO Post, in partnership with the American Society of Clinical Oncology (ASCO), communicates news of evidence-based multidisciplinary cancer care to a broad audience of 30,000 oncology professionals and ASCO members. Professor Pope authors a monthly Law and Ethics in Oncology column that explores the legal and ethical issues oncologists must be aware of in this era of precision medicine and changing health-care policy, both to protect patients’ rights and to safeguard against potential legal jeopardy.
Uterus transplants provide another treatment for infertility. Some might think that we should embrace such transplants as one more way to assist people to have children. However, in this paper I argue that uterus transplants are not something that we ought to fund, nor something that we should make easy to access. First, I argue that any justification of providing uterus transplants must be based on the value of the experience of gestation, rather than on claims of meeting medical need or promoting normal functioning. Second, I demonstrate that such a justification has limited prospects of success. The value of experiencing gestation is unlikely to be sufficient to justify state funding of uterus transplants and, further, we have reason to refrain from enabling such transplants.
This article examines the possible constructs behind the announcement by Amazon, Berkshire Hathaway, and JPMorgan Chase & Co., that they are jointly building a new healthcare entity for their employees. The article provides context by discussing and comparing the healthcare ambitions of the three largest information technology companies and argues that various forms of hybrid entities will increase their footprint in healthcare data and delivery. The core of the article is a thought experiment about the nature of what the article terms “Prime Health.” That analysis is based initially on observations about Amazon’s existing culture and business model of Amazon. Thereafter the article examines both what Prime Health could and should be, arguing that it will go beyond the pedestrian model of a very large self-funded group insurance plan, will disintermediate traditional healthcare insurers, and attempt to bring consumers and healthcare providers together into some type of online marketplace; an updated, privatized version of managed competition. The final parts of the article deal with the regulatory environment that hybrid healthcare generally and Prime Health in particular will face. The analysis includes federal device and data protection laws, some idiosyncratic state laws, and a brief discussion of the problems inherent in the limited regulation of hybrid healthcare entities.
Health Data and Privacy in the Digital Era by Lawrence O. Gostin, Sam Halabi, Kumanan Wilson :: SSRNSeptember 18, 2018
In 2010, the social networking site Facebook launched a platform allowing private companies to request users’ permission to access personal data. Few users were aware of the platform, which was integrated into Facebook’s terms of service. In 2014, Cambridge Analytica, a UK-based political consulting firm, developed a data-harvesting app. That app prompted Facebook users to provide psychological profiles, including responses such as “I get upset easily” and “I have frequent mood-swings” as part of a “research project.”
The Facebook platform allowed users to share their friends’ data as well, enabling Cambridge Analytica to access tens of millions of personal profiles, identifying voters’ political preferences. The controversy revealed risks to identifiable health data posed by social media and web services companies’ practices. After the Cambridge Analytica controversy, Facebook suspended a project that aimed to link data about users’ medical conditions with information about their social networks.
Individuals often reveal detailed, sensitive health information online. Through wearable devices, social media posts, traceable web searches, and online patient communities, users generate large volumes of health data. Although some individuals participate in online patient forums and wellness information sharing apps under their own names, others participate via pseudonyms, assuming their privacy is preserved. Many users believe their data will be shared only with those they designate.
Existing research on the economic impacts of regulation largely focuses on federal or cross-country regulatory restrictions, but the problem of regulatory accumulation is expected to also occur at the state level. Public choice economics and market process theory offer insight into why regulations alter economic outcomes. Since regulations change the rules of the game and the payoffs that participants receive, looking beyond stated intentions to the way regulations motivate behaviors is critical. Markets are an entrepreneurially driven process characterized by changing conditions, but regulations can inhibit creative destruction and distort incentives. I use the novel State RegData dataset from the QuantGov platform, which analyzes state regulatory texts to provide measures of restriction counts and industry relevance. I estimate the effect of industry-relevant restrictions on business establishments and employment using two econometric models: a multivariate linear regression model with controls and a fixed-effects regression model. I find tentative results that a greater amount of regulation in states is associated with negative percent changes in establishments and employment. My study is a starting point for future investigations of the relationship between regulation and state-level economic outcomes.
Every state has occupational licensing laws or regulations, which require individuals seeking to offer a certain service to the public first to obtain approval from the state. Occupational licensing requirements historically derive from a desire to protect unwitting consumers from bad actors. In recent years, however, the number of licensed professions in the United States has skyrocketed and licensing requirements have become increasingly onerous. When incumbents wield licensing requirements not as a defensive shield to protect consumers but as an offensive sword to exclude new entrants, serious concerns regarding the competitive implications of the licensing schemes arise. Self-interested incumbents have incentives that may differ from consumers, and these self-interested incumbents can—and sometimes do—impose requirements that do not enhance quality, but rather restrict output, increase prices, and hamper innovation. This Paper explores the competitive implications of state occupational licensing regimes. Part I analyzes the historical development and justification for occupational licensing. Part II reviews the empirical evidence regarding the effects of occupational licensing on factors such as quality, price, innovation, and availability. Part III summarizes how antitrust law, and particularly the state action doctrine, treats state board-enacted occupational licensing. Part IV explores the interplay of occupational licensing and antitrust laws in the United States, delving into a particularly striking case at the intersection of occupational licensing and innovation: Teladoc, Inc. v. Texas Medical Board. Part V provides some suggestions for agency engagement in monitoring the effective use of occupational licensing.