Monday
Nov072022

Consumer-Driven Health Plans Fueled Spread of Medical Disinformation

I was moved when I listened to the recent podcast series from the NYTimes, We Were Three[i]. The story was horrifying, touching, and eye-opening at the same time as it revealed some truths about the harm that distrust in the medical system can cause, especially in the midst of a dangerous and deadly pandemic. And, to be honest, it resonated with me due to how it paralleled some issues I have had with close relatives who live in a distant state.

The story is told by the older sister who learns from her brother that her father has died of Covid. It isn’t until her younger brother, who lived with her father, also dies from Covid that she learns of the extent that distrust of doctors and hospitals led her family away from medical care and toward alternative remedies (although “quackery” is a more appropriate term in this case).

Much of the story is heartbreaking—and almost unbelievable, yet as someone who has written about medical disinformation for a long time, not much of the bizarre behavior reported in the story is new to me. Still, it is upsetting to hear how people can act so irrationally and cruelly when they are susceptible to disinformation spread by people who directly or indirectly benefit by purveying snake oil remedies to vulnerable people.

The eye-opening aspect to me was the genesis of the brother’s distrust of the healthcare system. He had dealt with some previous medical care that led to high bills that caused financial distress. So, he and his father, along with others in their community, had grown skeptical of the motives of doctors who led them to expensive treatments without any warning that the costs may not be fully covered by insurance and that just the amount of their co-pays could lead to severe financial problems.

It isn’t difficult to understand how conspiracy theories about the motives of mainstream medicine found fertile ground among populations that faced disastrous consequences from unaffordable medical bills. It only takes a “germ” of truth for conspiracy theories to grow once they are pollinated and spread by social media influencers, as well as community influencers from religious and social groups.

One of the seeds of truth that fed vaccine hesitancy, as described in the We Were Three podcast, was the fact that doctors and pharmacists receive payment for every vaccine they administer. Under normal conditions, I don’t think most people would object to such a reimbursement program, especially if they knew the reimbursement amount for administering a Covid19 vaccine (approximately $40[ii]). However, for people who had been burned previously by co-pays for drugs administered by doctors or hospitals (under the 340B program), it was easy for them to make the logical leap that doctors would steer patients toward treatments, even if not appropriate, if they profited from administering those treatments.

Shortly after listening to the podcast series, there was a Kaiser Health News/NPR Bill of the Month[iii] story about a man who was billed approximately $7,000 for in-office infusions when an alternative in pill form, which he ultimately chose and preferred, cost him $216.

Unintended Consequences

Consumer-driven health plans were envisioned as a means to give patients “skin in the game” in choosing medical treatments by including co-pays for visits and some treatments[iv]. Policymakers believed that healthcare costs were rising, in part, because consumers didn’t have any responsibility for paying for their treatments beyond their insurance premiums and therefore had no incentive to select a lower-cost treatment or test if insurance was going to pay the bill. The flaw in this assumption rests in the fact that consumers don’t know the price they will have to pay in advance and, for the most part, physicians and healthcare practices have rejected the notion that it’s their responsibility to help patients figure out the patient’s cost of treatment.

In the case of policies intended to influence consumer healthcare decisions, the devil is in the details. It may seem sensible to require patients to pay 20% or more for some treatments, but as the KHN story illustrates, requiring patients to pay 20% or more for in-hospital infusions can lead to bills of upwards of $7,000 and can have serious repercussions for the majority of US citizens who cannot afford an unexpected expense of $400.

When hospitals send unpaid bills to collection agencies, it can ruin a person’s future and almost certainly will lead to mistrust of the clinicians who recommended the treatment. Once that trust is breached, can we really be surprised that so many people doubt the intentions of medical professionals and look elsewhere for help?

It would take a much longer article to map out all the ways high and unpredictable medical bills have led to so much distrust in the traditional healthcare system. In this post, my objective is to suggest a link between non-transparent unaffordable medical costs, the rise of distrust in the healthcare system, and increased belief in untrustworthy sources of health information, including sources that espouse conspiracy theories about vaccines. With this link between costs and belief in conspiracy theories on your radar, I can almost guarantee that you’ll start noticing additional stories and news items about the spread of medical misinformation and disinformation that can be traced back to a previous encounter with our non-transparent and often unaffordable medical system.

It is important to acknowledge that there have been policy reforms that attempt to correct for the lack of price transparency (e.g., the Sunshine Act, the No Surprises Act, as well as some attention to the skewed incentives of the 340B program)[v]. Still, we have a long way to go before patients have sufficient pricing information, value information, and ability to choose providers to become smart shoppers.

To underscore my point above about the likelihood of noticing additional stories about the ill-effects of high medical fees and subsequent consumer medical debt, I highly recommend this recent episode of Dan Gorenstein’s Tradeoffs podcast: https://tradeoffs.org/2022/11/03/medical-debt/A Shocking Amount of Misery’: Medical Debt in America[vi]. Dan and his guests offer a succinct and convincing description of the state of medical debt in the US and how it drives so many away from our traditional healthcare system.

 


[i] https://www.nytimes.com/2022/10/11/podcasts/we-were-three.html

[ii] https://www.cms.gov/medicare/covid-19/medicare-covid-19-vaccine-shot-payment

[iii] https://khn.org/news/article/bill-of-the-month-shot-prostate-cancer-drug-testosterone/

[iv] https://www.hfma.org/topics/hfm/2019/november/evolution-of-the-consumer-focus-in-healthcare.html

[v] E.g., https://www.commonwealthfund.org/publications/explainer/2022/sep/federal-340b-drug-pricing-program-what-it-is-why-its-facing-legal-challenges

https://communityoncology.org/featured/examining-hospital-price-transparency-drug-profits-and-the-340b-program-2022/

 [vi] Tradeoffs podcast: https://tradeoffs.org/2022/11/03/medical-debt/A Shocking Amount of Misery’: Medical Debt in America.

 

Thursday
Feb112021

Resources for Managing and Avoiding Misinformation & Disinformation

On Friday, February 12, 2021 at noon ET, I will host the #HITsm tweetchat on the topic of “Detecting and Avoiding Misinformation and Disinformation”. See: https://www.healthcareittoday.com/2021/02/09/detecting-and-avoiding-misinformation-and-disinformation-hitsm-chat-topic/ for more details.

The chat will draw on research I have done over the past year, which was summarized as of last August (2020) in an earlier blog post on Infodemiology and interview with Danny van Leeuwen (@health_hats) on this HealthHats podcast: https://www.health-hats.com/infodemiology-too-much-not-enough/,

Since that time, I have added additional resources that are helpful for training in media literacy and detecting and managing misinformation, which I list below.

I’d like to add a special shoutout to Joan Donovan, Ph.D., Director of the Technology and Social Change (TASC) program at the Shorenstein Center on Media, Politics, and Public Policy at the Harvard Kennedy School. Joan was a speaker at the last in-person event I attended before the pandemic on March 6, 2020: https://www.widscambridge.org/wids-cambridge-2020. The WiDS conference was my introduction to Joan, but since that time, I have followed her research and her too numerous to list articles and media interviews. Follow her on Twitter @BostonJoan and see links below to her research at the Shorenstein Center.  

Below is the list of resources that I highly recommend if you are interested in managing and avoiding the spread of mis- and disinformation. This list supplements the list appended to the Infodemiology blog post.

PEN America, media literacy training: https://pen.org/the-fight-against-disinformation-requires-the-right-tools/

Shorenstein Center on Media, Politics and Public Policy at Harvard Kennedy School: https://shorensteincenter.org/

Technology & Social Change Program (TASC), Joan Donovan, Director:: https://shorensteincenter.org/programs/technology-social-change/

See Definitions section for useful list of terms: https://mediamanipulation.org/definitions

HKS Misinformation Review: https://misinforeview.hks.harvard.edu/ , Harvard Kennedy School.

AFP News Fact Check: Fact Check | (afp.com)

Lib Guide from U. Washington with resources on misinformation and disinformation (including link to course below, “Calling Bullshit”):  https://guides.lib.uw.edu/c.php?g=345925&p=7772376

Calling Bullshit, Carl Bergstrom, U. Washington: https://www.callingbullshit.org/

Good short video on scholarly publishing/consider the source: https://www.youtube.com/watch?v=IZUdd765nA4&list=PLPnZfvKID1Sje5jWxt-4CSZD7bUI4gSPS&index=42

Tuesday
Jan052021

Book Review: The AI-Powered Enterprise

Below is an extended version of a review I posted on Amazon of The AI-Powered Enterprise:: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable, by Seth Earley, CEO of Earley Information Science (EIS). The original review can be found here. Full disclosure, I was given a copy of the book to review.

Not long ago, everyone was exclaiming how data was the “new oil”.  Well, if companies recognize that data are such a valuable commodity, then they need to recognize the importance of investing time and effort in preparing and managing their data/information assets so that AI technology can produce the best-possible value-added outcomes.

In his book, The AI-Powered Enterprise, Seth Earley outlines key principles to apply when developing information products. The information products can include internal collaboration tools, customer-facing ecommerce websites, content marketing programs that deliver personalized content, and other data or content-centric offerings.

The early chapters of the book provide a trove of practical advice on how to create a robust foundation for any knowledge management project. Seth emphasizes the importance of building an ontology that links concepts from information silos across the organization. And he provides case studies with relatable examples from his consulting practice that make (arguably) dry topics like ontology development come to life.  

Later chapters describe how AI can enhance marketing, ecommerce, sales and employee productivity knowledge products.

The maturity models in the final chapter lay out the stages of competency for content optimization, customer information, product information, knowledge management and overall orchestration of the preceding components across the enterprise. These detailed models are valuable tools for planning and governance of any internal or external data or content development project. The inclusion of these maturity models and case studies that put the models in context makes The AI-Powered Enterprise a practical handbook for both executives who are overseeing the digital transformation of their organization and managers responsible for implementing and managing information products.

As someone who has been involved in designing information products and advising publishing companies on digital transformation projects for over 30 years myself, a few of my favorite takeaways include:

  • Never embark on an AI project without a fundamental understanding of the information sources that will be used and how internal and external customers will use the enhanced information.
  • Every data science or content analysis project requires investment in preparing the data sources to determine completeness and consistency. This data preparation steps aren’t sexy and are described by such terms as data scrubbing, data cleansing, data normalization, data wrangling, data harmonization, and quality control. I’ve even seen the term “data janitor” used to describe data preparation tasks.
  • There are technologies that can help with some of these tasks, but I agree with Seth when he warns against letting overzealous salespeople convince you that their AI solution doesn’t require data preparation work.
  • Knowledge management cures information overload.
  • Search is conversation.

AI and Machine Learning (ML) are dynamic fields that are likely to continue to grow exponentially. Seth’s book provides a superb way to play catch-up in the field of knowledge management to put your company in a strong position to benefit from new technologies—and avoid costly missteps.

I highly recommend this book for anybody involved in the development or management of data projects—and that includes digital content management and content marketing programs.

Monday
Nov302020

EPIC Health Research Network: Pandemic-inspired innovation in medical outcomes research

Early announcements of the Epic Health Research Network (EHRN) this past spring didn’t grab my attention the way they should have. But a recent article by John Lynn in HealthcareITToday caught my eye, in large part because of the way he described EHRN as a “near real-time medical journal”.

Epic’s announcement of the EHRN described it as a network for bringing together “healthcare professionals, researchers, and data scientists to publish early data-driven observations” and included the following quote from Judy Faulkner:

“We have a tremendous opportunity to help healthcare professionals and researchers share their discoveries with the world,” said Judy Faulkner, CEO of Epic. “We have been interested in creating this site for years to share new knowledge. With the COVID-19 crisis, the need for fast dissemination of knowledge has become critical.”

On the EHRN.org website, EHRN is further described as follows:

EHRN is a journal for the 21st century, designed for rapid sharing of knowledge with researchers, healthcare professionals, and learners to help solve medical problems.

Electronic health record data collected over decades, spanning millions of patients, could provide clues to help solve medical problems.

EHRN reports are reviewed internally and externally prior to publication. To expedite information sharing, they are published without traditional peer review. It’s important that good data be available sooner, rather than perfect data be available too late—especially in times of public health crisis.

EHRN is where you’ll find our reports. We invite others to contribute as well, from health systems and higher learning institutions to government agencies.

Our goal is for EHRN to light the way for fast, collaborative research.

[EHRN.org/About-Us]

Value of Outcomes Data

Unleashing the value of data stored in EHRs has been at the center of my interest in health IT from the time I first started attending HIMSS conferences over a decade ago. However, in retrospect, I realize that the health IT community had little-to-no experience in conceiving new applications for EHR data or in developing complex data products.

Furthermore, patient privacy concerns and data sharing roadblocks curtailed investment in platforms for sharing research derived from outcomes data. The EHRN doesn’t solve these problems, although it may serve as a catalyst for knocking down some of the obstacles across institutions, especially institutions that use EPIC systems (with some help from the ONC’s Cures Act).

Comparison to Usage of Real World Evidence in Life Sciences Research

The life sciences sector has been quicker to develop platforms for including outcomes data, which they refer to as “real world evidence” (RWE) or “real world data” (RWD). Three notable players in this space, TriNetX, Datavant and Medidata, recently announced a partnership to leverage the technology and data assets of the trio to accelerate advances in clinical trial research. It would behoove medical professionals involved in developing evidence-based decision support tools to study the progress made, and methods used, in clinical trial research.

Future for Medical Journals

Under the scenario where outcomes data research platforms gain momentum and become a primary channel for disseminating research, reputable journal publishers can continue to play an important role in amplifying new research developments and putting new evidence in context. However, traditional publishers need to be aware of the new digital research platforms and consider the impact that research networks like EHRN will have on legacy publishers’ role in the research workflow.

With flat or declining budgets from their traditional library market and a push to adopt open access business models, which constrain publisher revenue, medical journal publishers face a dismal future if they fail to recognize the disruption that is occurring and fail to respond by creating—or partnering to create—innovative methods of adding value to the research workflow and implementation process.

Monday
Nov022020

Econometric Techniques Applied to Orthopaedic Datasets

It’s fun when interests collide. In a recent podcast interview with Davida Dinerman[1], we reviewed how my academic and career experience have led me to where I am today, with an emphasis on developing and disseminating information for decision support in healthcare. We covered a lot of ground in my non-linear career path, but Davida perked up when she heard me mention the term econometrics, which was new to her.

Then, this past weekend, I listened to the Orthopod podcast[2] on the topic of using real-world evidence (RWE), in this case, a Dutch registry that includes over 400,000 orthopaedic patients. Dr. Mohit Bhandari (@orthoevidence), the host, talks with Dr. Rudolf Poolman (@rudolfpoolman), who describes how he was able to question a guideline that calls for an age cut-off for cemented vs. non-cemented hip hemi-arthroplasty, using a research technique borrowed from econometrics.

Both podcasts cover other topics, including empowering patients by including them in research design (Orthopod) and providing access to medical information and data (my interview with Davida), as well as shared decision-making and clinical decision support/guidelines. I encourage you to add both episodes— and series—to your podcast list.

And, I thank Davida for asking me to describe my background, so that I can now point people to the LookLeftforGrowth podcast episode if they want to know how I ended up with my unusual mix of technical, analytic, behavioral economics and market research skills.

I haven’t made direct use of econometrics in my work since I left business school—a long time ago. Nonetheless, my training in econometrics, economics, statistics, mathematics and French(?) have given me a foundation in and perspective on big data and analytic methods that I rely on frequently for envisioning and assessing new research methods in medical and life science research. When listening to the referenced episode of the Orthopod podcast, I felt a sense of satisfaction that my stack of skills[3] has value in today’s big data-enabled, evidence-based medical research environment.

 


[1] https://www.lookleftforgrowth.com/podcast/episode/487d00f0/janice-mccallum-on-the-promise-and-challenges-of-healthcare-data. Relevant discussion occurs between 3 min 15 seconds and 5 min 40 seconds.

[2] https://myorthoevidence.com/Podcast/Show/84? Relevant discussion starts at 14 min 9 seconds.

[3] https://www.theladders.com/career-advice/skill-stacking-instead-of-mastering-one-skill-build-a-skill-set