Billionaire Electronic Health Records Pioneer Judy Faulkner Warns Of Cambridge Analytica-Type Data Risk

Judy Faulkner, founder of Epic, one of the largest providers of electronic medical health records, warned that a proposed rule on information sharing could create a Cambridge Analytica-type situation, where the data of a patients’ friends and family is shared without their consent. 

A federal rule on digital health-sharing now under review “has good things to it but there are some bad things that have to be fixed, in my opinion,” Faulkner, 76, told NYU Langone Health CEO Robert Grossman at the Forbes Healthcare Summit in New York City on December 5. 

“It’s a bit like Facebook in that—the friends of the users who gave permission to Cambridge Analytica to use the system—the friends’ data got pulled out with the users who had authorized Cambridge Analytica,” she said.

The full Forbes article can be viewed at this link.  

Anthem Will Use Blockchain To Secure Medical Data For Its 40 Million Members In Three Years

Anthem, the second-largest health insurance company in the U.S, has started to use blockchain technology to help patients securely access and share their medical data. The company plans to roll out the feature, which is in pilot testing now, to groups of members in the next few months. All 40 million members will have access to it in the next two to three years, according to company officials.   

“What blockchain potentially gives us the opportunity to do is not worry about those trust issues,” said Anthem CEO Gail Boudreaux at the 8th Annual Forbes Healthcare Summit in New York last week. “We have an opportunity now to share data that people can make their own decisions on.

The full Forbes article can be viewed at this link.  

Here’s How Health Data Can Help Stem the Opioid Crisis

The number of people losing their lives each day to prescription or illicit opioid-related overdoses is staggering. According to the Centers for Disease Control and Prevention, more than 47,000 Americans died in 2017 — 130 fatalities each day — due to opioid overdoses, making it the deadliest year on record. 

You don’t have to be personally touched by the opioid crisis to understand the gravity of the statistics, let alone the immeasurable and lasting impact it is having on society. This can be addressed by better harnessing the power of data to stem this crisis. 

The full Morning Consult article can be viewed at this link.  

Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a Physician Informatician and a Medical Informatics Researcher

Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined “specialized” tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).

The full article can be downloaded below. 

Challenges of developing a digital scribe to reduce clinical documentation burden

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

The full article can be downloaded below.  

Data exchanges based on blockchain in m-Health applications

The most important aspect of handling data in the healthcare industry is its seamless and secure transition across intercepting nodes. Effective elimination of third-party entities and ensuring direct links between patient and healthcare provider can result in the transmission of error-free, unduplicated data. The use of blockchains can open up opportunities to counter the current requirements due to their ability to safely share information across nodes and networks from the access point and secure the safety of transactions. Currently, sharing medical data is observed to be slow, incomplete, insecure, and provider-centric. These shortcomings prevent data interoperability and are a consequence of lack of foundational, structural, and semantic inoperability. By applying the blockchain technologies with appropriate markers, the safety of patient data can be ensured during data transmission. This paper evaluates the potential use of blockchain technology in association with mobile-based healthcare applications.

The full article can be downloaded below.  

Development of the mHealth App Trustworthiness checklist

Mobile health applications (mHealth apps) currently lack a consensus on substantial quality and safety standards. As such, the number of individuals engaging with untrustworthy mHealth apps continues to grow at a steady pace.

The purpose of this study was to investigate end-users’ opinions on the features or actions necessary for trustworthy mHealth apps; and to convey this information to app developers via a succinct but informative checklist: the mHealth app trustworthiness checklist.

The checklist was formulated in three stages: (a) a literature review of studies identified the desirable features of the most prolific mHealth apps (health and fitness apps); (b) four focus group sessions with past or current users of these apps (n ¼ 20); and (c) expert feedback on whether the checklist items are conceivable in a real-life setting (n ¼ 6).

Five major themes emerged from the focus group discussions: informational content, organizational attributes, societal influence, technology-related features, and user control factors. The mHealth app trustworthiness checklist was developed to incorporate these five themes and subsequently modified following expert consultation. In addition to the trustworthiness themes, we identified features that lie between trust and mistrust (limited digital literacy and indifference) as well as 10 features and actions that cause end-users to mistrust mHealth apps.

This study contributes to the evidence base on the attributes of trustworthy mHealth apps. The mHealth app trustworthiness checklist is a useful tool in advancing continued efforts to ensure that health technologies are trustworthy.

The full article can be downloaded below.  

Top 10 Health Care Industry Predictions For The Year 2020

Here is how I see the dialog around our nation’s health care system evolving in 2020.

1. The push to deliver home-based care will continue
2. The balance of power will begin to shift from hospital systems back to physician groups
3. Drug pricing will continue to be a front-page issue; at the same time, pharmaceutical innovation will also dominate headlines
4. Medicare-for-All will quickly morph into “Medicare Advantage-for-All
5. Big Tech and Silicon Valley will continue to play in health care, but they won’t upend the system anytime soon
6. On a related note, big box retailers and other atypical organizations will attempt to enter the health care market with a big splash
7. Amid revelations about data privacy, companies that are transparent and ethical will come out ahead
8. When it comes to social determinants of health, expect more talk than action
9. Mental health conditions and substance abuse disorders will take the main-stage
10. The public will begin to examine the behaviors and practices of “non-profit” health systems

The full Forbes article can be viewed at this link.  

What Fintech Can Do For Healthcare

In most countries, the process of paying for health coverage is not just costly, but complicated, stressful, and time consuming. It also prohibits people from accessing care.

If exorbitant prescription drug prices and out of pocket expenses were not already enough, healthcare consumers must also navigate payment systems known for their obscurity and susceptibility to error. These systems not only overwhelm current users, but also discourages new ones from finding the coverage that is right for them.

The relationship between a healthcare consumer and their healthcare financing should not—and does not—have to be so fraught. As health services becoming increasingly digital, more opportunities open up for companies to stage data driven interventions that can modernize, and hopefully revitalize, our fragmented healthcare networks.

Such is the aim of fintech, or financial technology, that brings new and improved digital financial service models into the healthcare space. Fintech companies are leveraging powerful innovations blockchain, artificial intelligence, and machine learning to eliminate the inefficiencies and knowledge gaps endemic to most healthcare payment plans. With few exceptions, what unites them all is their ability to streamline the flow of information and money between patients and providers—and in doing so, save everyone involved precious time and effort.

The full Forbes article can be viewed at this link.  

Protecting Explainable AI Innovations In Health Care

Health care innovators are developing artificial intelligence algorithms called Explainable AI (XAI) that actually reveal the logic behind their diagnoses. Because their results can be verified, doctors and regulators will be more likely to adopt these algorithms than traditional “black box” AI. However, the transparency that makes these algorithms valuable to practitioners also makes the technology trickier to protect as intellectual property.

The full Forbes article can be viewed at this link.