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Artificial Intelligence for Suicide Prediction

Suicide is a global problem that causes 800,000 deaths per year worldwide. In the United States, suicide rates rose by 25 percent in the past two decades, and suicide now kills 45,000 Americans each year, which is more than auto accidents or homicides.

Traditional methods of predicting suicide, such as questionnaires administered by doctors, are notoriously inaccurate. Hoping to save lives by predicting suicide more accurately, hospitals, governments, and internet companies are developing artificial intelligence (AI) based prediction tools. This essay analyzes the risks these systems pose to safety, privacy, and autonomy, which have been under-explored.

Two parallel tracks of AI-based suicide prediction have emerged.

The first, which I call “medical suicide prediction,” uses AI to analyze patient records. Medical suicide prediction is not yet widely used, aside from one program at the Department of Veterans Affairs (VA). Because medical suicide prediction occurs within the healthcare context, it is subject to federal laws, such as HIPAA, which protects the privacy and security of patient information, and the Federal Common Rule, which protects human research subjects.

My focus here is on the second track of AI-based suicide prediction, which I call “social suicide prediction.” Though essentially unregulated, social suicide prediction uses behavioral data mined from consumers’ digital interactions. The companies involved, which include large internet platforms such as Facebook and Twitter, are not generally subject to HIPAA’s privacy regulations, principles of medical ethics, or rules governing research on human subjects.

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millennial man at home sick with scarf and thermometer talking on the phone

The Millennial Need for Speed in Healthcare

According to a recent Kaiser Family Foundation (KFF) poll, shockingly large swaths of Americans have reported that they don’t have a primary care provider.

The July 2018 report found that 45 percent of 18-29 year olds, as well as 28 and 18 percent of 30-49 and 50-64 year olds, respectively, also lack designated primary care.

Kaiser Health News (KHN) explained that the price transparency, convenience, and speed of alternatives to office-based primary care physician (PCP) visits appear to be some of the preferences driving these patterns. Retail clinics, urgent care centers, and telemedicine websites satisfy many of these preferences, and are therefore appealing alternatives to scheduled appointments with a PCP. For example, extended hours and shorter wait times at increasingly widespread retail clinics have attracted young patients who want to avoid the hassle and wait times involved in scheduling and attending a traditional doctors office.

A 2015 PNC Healthcare survey similarly found that millennials saw their PCP significantly less (61 percent) than baby boomers and seniors (80 and 85 percent, respectively). The study emphasized the effects of technology on millennials’ trends in healthcare acquisition, such as higher utilization of online reviews to shop for doctors (such as Yelp). It also found that millennials are much more likely to prefer retail and acute care clinics, and are more likely to postpone treatment due to high costs than older generations.

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Four Roles for Artificial Intelligence in the Medical System

How will artificial intelligence (AI) change medicine?

AI, powered by “big data” in health, promises to transform medical practice, but specifics remain inchoate.  Reports that AI performs certain tasks at the level of specialists stoke worries that AI will “replace” physicians.  These worries are probably overblown; AI is unlikely to replace many physicians in the foreseeable future.  A more productive set of questions considers how AI and physicians should interact, including how AI can improve the care physicians deliver, how AI can best enable physicians to focus on the patient relationship, and how physicians should review the recommendations and predictions of AI.  Answering those questions requires clarity about the larger function of AI: not just what tasks AI can do or how it can do them, but what role it will play in the context of physicians, other patients, and providers within the overall medical system.

Medical AI can improve care for patients and improve the practice of medicine for providers—as long as its development is supported by an understanding of what role it can and should play.

Four different roles each have the possibility to be transformative for providers and patients: AI can push the frontiers of medicine; it can replicate and democratize medical expertise; it can automate medical drudgery; and it can allocate medical resources.

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Data-driven Medicine Needs a New Profession: Health Information Counseling

By Barbara Prainsack, Alena Buyx, and Amelia Fiske

Have you ever clicked ‘I agree’ to share information about yourself on a health app on your smartphone? Wondered if the results of new therapy reported on a patient community website were accurate? Considered altering a medical device to better meet your own needs, but had doubts about how the changes might affect its function?

While these kinds of decisions are increasingly routine, there is no clear path for getting information on health-related devices, advice on what data to collect, how to evaluate medical information found online, or concerns one might have around data sharing on patient platforms.

It’s not only patients who are facing these questions in the age of big data in medicine. Clinicians are also increasingly confronted with diverse forms of molecular, genetic, lifestyle, and digital data, and often the quality, meaning, and actionability of this data is unclear.

The difficulties of interpreting unstructured data, such as symptom logs recorded on personal devices, add another layer of complexity for clinicians trying to decide which course of action would best meet their duty of beneficence and enable the best possible care for patients.

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Prescription Monitoring Programs: HIPAA, Cybersecurity and Privacy

By Stephen P. Wood

Privacy, especially as it relates to healthcare and protecting sensitive medical information, is an important issue. The Health Insurance Portability and Accountability Act, better know as HIPAA, is a legislative action that helps to safeguard personal medical information. This protection is afforded to individuals by the Privacy Rule, which dictates who can access an individual’s medical records, and the Security Rule, which ensures that electronic medical records are protected.

Access to someone’s healthcare records by a medical provider typically requires a direct health care-related relationship with the patient in question. For example, if you have a regular doctor, that doctor can access your medical records. Similarly, if you call your doctor’s office off-hours, the covering doctor, whom may have no prior relationship with you, may similarly access these records. The same holds true if you go to the emergency department or see a specialist. No provider should be accessing protected information however, without a medical need.

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DNA Donors Must Demand Stronger Privacy Protection

By Mason Marks and Tiffany Li

An earlier version of this article was published in STAT.

The National Institutes of Health wants your DNA, and the DNA of one million other Americans, for an ambitious project called All of Us. Its goal — to “uncover paths toward delivering precision medicine” — is a good one. But until it can safeguard participants’ sensitive genetic information, you should decline the invitation to join unless you fully understand and accept the risks.

DNA databases like All of Us could provide valuable medical breakthroughs such as identifying new disease risk factors and potential drug targets. But these benefits could come with a high price: increased risk to individuals’ genetic data privacy, something that current U.S. laws do not adequately protect. Read More

Facebook Should ‘First Do No Harm’ When Collecting Health Data

By Mason Marks

Following the Cambridge Analytica scandal, it was reported that Facebook planned to partner with medical organizations to obtain health records on thousands of users. The plans were put on hold when news of the scandal broke. But Facebook doesn’t need medical records to derive health data from its users. It can use artificial intelligence tools, such as machine learning, to infer sensitive medical information from its users’ behavior. I call this process mining for emergent medical data (EMD), and companies use it to sort consumers into health-related categories and serve them targeted advertisements. I will explain how mining for EMD is analogous to the process of medical diagnosis performed by physicians, and companies that engage in this activity may be practicing medicine without a license.

Last week, Facebook CEO Mark Zuckerberg testified before Congress about his company’s data collection practices. Many lawmakers that questioned him understood that Facebook collects consumer data and uses it to drive targeted ads. However, few Members of Congress seemed to understand that the value of data often lies not in the information itself, but in the inferences that can be drawn from it. There are numerous examples that illustrate how health information is inferred from the behavior of social media users: Last year Facebook announced its reliance on artificial intelligence to predict which users are at high risk for suicide; a leaked document revealed that Facebook identified teens feeling “anxious” and “hopeless;” and data scientists used Facebook messages and “likes” to predict whether users had substance use disorders. In 2016, researchers analyzed Instagram posts to predict whether users were depressed. In each of these examples, user data was analyzed to sort people into health-related categories.

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Systemic Oversight: a new approach for precision medicine and digital health

By Alessandro Blasimme and Effy Vayena

Imagine a clinical research protocol to test the efficacy of a nutritional regime on the aging trajectory of the participants. Such a study would need to be highly powered and include thousands of people in order to observe a credible effect size. Participants would remain enrolled in the study for many years, maybe decades. Endpoints would include novel measures of healthy aging such as functioning (the capacity to perform certain activities) and the quality of social life. Participants would thus be asked to provide enormous amounts of personal data covering at the same time their health state, their habits and their social activities – most likely with the help of smart appliances, sensor-equipped wearables, mobile phones and electronic records.

In a different scenario a research team aims to develop clinical protocols for cancer treatment according to the unique genomic signature of their tumor. They will need patients, willing to undergo whole genome germline and tumor sequencing right at the moment of diagnosis and be included in a basket trial. Therapy would then be targeted to the specific genetic alterations of each individual in the hope that a combination of targeted drugs would generate better medical outcomes than the current standard of care.

These two scenarios correspond to the prototypical form of, respectively, precision medicine and precision oncology studies. The first is likely to require large (very large) longitudinal cohorts of extensively characterized individuals – like the All of Us Research Program. The second will require sustained sharing of genomic data, information on patients’ clinical history and response to treatment, and possibly a unique repository in which such information would flow to – something akin the NCI’s Genomic Data Common.

This kind of data-intense research, in particular, introduces game changing features: increased uncertainty about foreseeable data uses, expanded temporal span of research activities due to virtually unlimited data lifecycles, and finally, the relational nature of data. This last feature refers both to the fact that, for instance, zip codes contain other types of sensitive information like information about ethnic background (redundant encoding); and to the fact that data about one person contain information about others– as is the case, for instance, with genetic data among family members. Read More

Will the Sun Shine All Over Canada? Making Transparent the Financial Relationships of the Medical Industry (Part 2: Towards Effective Transparency)

By Jean-Christophe Bélisle-Pipon

As detailed in Part 1, Ontario government just enacted the Health Sector Payment Transparency Act, a Canadian first in terms of transparency. The act requires that “transfers of value” (or payments), related to medical products (drugs and medical devices), between a payor and a recipient be reported to the Health Ministry. The Act gives the Ministry unprecedented powers to require, analyze, and publish such data online.

A Transformational Act?

Will this act radically transform the practices and the public knowledge that we have about the financial relations of the medical industry? The effective implementation of the regulations will tell us. However, the fact that Innovative Medicines Canada (formerly known as Rx&D, IMC is the organization representing the interests of the pharmaceutical industry in Canada, like PhRMA in the US) has concerns about the Act is a rather positive sign that this legislation might result into pro-social changes. IMC is invoking both ideological concerns (industry’s interactions with HCPs imply cooperation rather than influence) and logistical concerns (“if the threshold for payments is low, a sales representative could easily lose a receipt and forget to report it”), as well as its  own commitment to limiting undue influences. Read More

Will the Sun Shine All Over Canada? Making the Financial Relationships of the Medical Industry Transparent (Part 1: Theoretical Transparency)

By Jean-Christophe Bélisle-Pipon

While Canada is often viewed positively for its public, comprehensive, universal, and accessible health care system, not all is rosy. Canada often lags behind other countries in terms of pharmaceutical policies. Sometimes, this is advantageous (e.g., delaying the approval of a product to wait for more clinical data or real-world efficiency, so to better assess risk-benefit and determining the maximum selling price), but more often simply a problem: until recently, transparency in Canada was more a buzzword than a strong and assumed government stance.

However, a few days ago in Ontario, the omnibus Strengthening Quality and Accountability for Patients Act received royal assent, thus enacting the Health Sector Payment Transparency Act. This clearly marked the beginning of shedding light on the financial relationships and payments to health care providers and organizations made by the medical industry (pharmaceutical and medical device companies), the explicit goal being to strengthen patient trust in the health care system (including research and education activities) by allowing patients to assess whether their health care providers are subject to influence by industry and to foster more informed choice. While the United States enacted the Physician Payments Sunshine Act (PPSA) in 2010, which requires payment disclosure, this is a first in Canada.

The main provisions of the Act Read More