Lab Soft News

Healthcare Will Contribute a Sizable Portion of Future Earnings for Apple

In a recent note, I discussed how Apple was sponsoring the MIPACT study at the University of Michigan involving the use of Apple Watches + iPhones and home health monitoring (see: MIPACT Study Sponsored by Apple Provides a Preview of Home Health Monitoring). A recent article predicts that healthcare will be a major source of revenue for Apple in the future (see: Health care is one of Apple's most lucrative opportunities). Below is an excerpt from the article:

Apple's opportunity in health care is so large with the Apple Watch that the company should soon generate tens of billions of dollars a year in annual revenue from wearables and health services. It's a nascent space for Apple and hard to predict how much money the company will make from health services as it potentially dives deeper into monitoring features such as blood glucose and blood pressure....Apple has a few key advantages over its technology rivals, including Alphabet and Microsoft, as it looks to move into the medical market and attack the $3.5 trillion health-care industry....Apple was able to recruit 400,000 people in less than a year for its Apple Watch heart health study with Stanford University, suggesting that people are willing to share their medical information with Apple. Beyond consumer device sales, the company has started to sign deals with health insurers who are willing to pay for some portion of the Apple Watch on behalf of their members. Apple has already inked that type of partnership with Aetna (see: Wearable Health Monitoring Devices: a Means to Lower Insurance Costs?). It's also currently in talks with private Medicare plans, which could mean increased access to the Apple Watch for seniors....The company also has revenue potential in the electronic medical records market. IPhone users will have access to the Apple's health app, which allows customers to pull together medical information from dozens of hospitals and clinics (see: Apple Has Plans to Copy EHR Records to iPhones and Apple Watches). In its current form, the feature is designed for consumers, because it's a huge challenge for people to aggregate their lab reports, immunization records and more. Morgan Stanley said it could turn into a real business if Apple starts pulling together data and selling reports to health systems.

As the article suggests, Apple has launched various projects that will provide the company with broad access to anonymized patient EHR records. Such access is being developed as a result of various research projects and also through its program to download EHR records to iPhones (see: Big Tech Is Knocking on Hospital Doors; It's All About the Data). The value of these records for Apple is that they can be the basis for the development of four types of analytics research: descriptive, diagnostic, predictive, and prescriptive (see: 4 types of data analytics to improve decision-making). Descriptive analytics provides information about what has already happened to a patient, diagnostic analytics provides the root cause of what has happened, predictive analytics predicts what will happen in the future, and prescriptive analytics goes beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes and the interrelated effects of each decision.

Much of the important data contained in EHRs is in the form of natural language which can be understood by natural language processing (NLP). This is the subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages. In particular, it addresses how to program computers to process and analyze large amounts of natural language data (see: Natural language processing). I have posted a number of previous notes about NLP (see, for example: Assessing Drugs Using "Real World Evidence" in Addition to Clinical Trials; AI Allows Computers to "Read" EHR Records and Make Predictions; Amazon Launches New Medical Record Language Processing Service). Apple will require substantial expertise in NLP in order to make some sense of much of the data it is acquiring via access to EHRs but this should not be that much of a challenge for Apple given its earnings and technical reputation.

UnitedHealth Expands Bundled Payments in Its Medicare Advantage Plans

The essence of value-based care is that reimbursement is based on positive outcomes for patients as opposed to fee-for-service in which in which providers select the services delivered, often with insufficient attention to outcomes. Ideally, value-based care results in higher quality care at a lower cost (see: Provider Organizations Beginning to Compete on the Basis of Value-Based Care. One of the key questions regarding value-based care is how to achieve this goal. It now appears that a major "on-ramp" will be Medicare Advantage plans and bundled payments as explained in a recent article (see: UnitedHealth To Expand Bundled Payments In Medicare Advantage). Below is an excerpt from it:

UnitedHealth Group said it will expand to its private Medicare Advantage plans an effort to pay doctors and hospitals for an array of surgeries and procedures by moving to bundled payments, a proliferating value-based care model. UnitedHealth’s UnitedHealthcare is rolling out a new voluntary program that offers doctors and hospitals contracted with its Medicare Advantage plans in more than 30 states “the opportunity to participate in the value-based care payment program” for eight medical procedures including certain hip and knee joint replacements, spinal infusion, heart valve replacement and coronary bypass....The move to bundled payment models in Medicare Advantage by the nation’s largest health insurer is significant given the rapid growth of such insurance. Across the country, more than 22 million seniors are signed up for Medicare Advantage with projections that there will be nearly 40 million or 50% market penetration by the end of 2025.

Medicare Advantage plans contract with the federal government to provide extra benefits and services to seniors, such as disease management and nurse help hotlines, with some even providing vision and dental care and wellness programs....Under many bundled payment models, hospitals where the surgery takes place and doctors involved in bundled payment programs are generally accountable for the quality as well as the costs of care from the start of the surgery through outpatient rehabilitation or related care depending on the procedure. The bundled fee pays the medical care providers involved in the “defined episode of care” rather than the traditional fee-for-service model that pays for each treatment, doctor’s appointment or diagnostic test....Last year, UnitedHealth said its spine and joint program offered in its commercial accounts saved $18,000 per procedure for those employers participating.

Here's a definition of bundled payments (see: What Are Bundled Payments?): 

With bundled payments, the total allowable acute and/or post-acute expenditures (target price) for an episode of care are predetermined. Participant providers share in any losses or savings that result from the difference between this target price and actual costs. Bundled payments, also known as episode payment models (EPMs), require participant providers to assume risk, as they must cover costs that go above the target price for an episode of care including those that arise from complications and hospital readmissions. On the upside, providers share in the savings if they keep costs below the target price while maintaining quality standards.

One of the reasons that hip and knee joint replacements, heart valve replacements, and coronary bypass surgery are important targets for value-based care plans is that these operations are clearly defined, expensive, and it's relatively easy for hospitals to eliminate waste and wring out various costs associated with them. Thus, patients within Medicare Advantage plans who undergo the procedures will theoretically experience better outcomes and receive a broader range of services as part of their recuperation. Additionally, the government should save money and the CEO of UnitedHealth Group will make more money if the company's Medicare Advantage plans are popular.

A New Way to Gain Additional Diagnostic Information from Radiology Images

I had previously thought that I had a good grasp of the term biomarker when I posted a note about it more than thirteen years ago (see: Use of Term Biomarker vs. Analyte). The definition that I used  at that time was: any molecular species found to provide correlation to a particular phenotype or perturbation of a biological system. Unstated at that time was that the molecular species was found in a biologic fluids like blood, serum, or urine. Now comes an article that upsets this definition but that makes a persuasive argument for doing so (see: U-M startup analyzes data from patient imaging files to help cure what ails them). Below is an excerpt from it:

Digital medical image files such as X-rays, MRIs and CT scans fill roughly half of all existing electronic storage around the world. And a University of Michigan startup has figured out how to mine and analyze this Big Data with technology that could make treatment choices incredibly precise for each patient...Applied Morphomics Inc. is a biomarker technology and development company that assesses body factors essential for delivering precision medicine....A patient’s body is their biological medical record and contains a tremendous amount of information that clinicians to date have not been able to comprehend. The [company's] technology extracts thousands of digital biomarkers from a patient’s medical imaging files. From this data, physicians can pinpoint a patient’s condition, the state of their disease and the kind of treatments that might be most helpful....A patient’s body is their biological medical record and contains a tremendous amount of information that clinicians to date have not been able to comprehend.

[The founder] and his team began by closely analyzing millions of digital images from CT and other imaging scans of more than 100,000 patients over an almost 20-year period. From this, they were able to define baseline information about gender, age and fitness....The deep analysis of the variety and often less apparent nuances of our physical structure that have been developed by the Morphomics project is a phenomenally deep and sophisticated benchmarking reference....Muscle mass is important because it’s predictive of overall health. For example, by measuring the amount and quality of a patient’s core muscle, AMI can predict how well someone will recover from a major surgery. If the muscles are small and infiltrated with fat compared to an age- and gender-matched reference population, they can recommend a two-week routine of walking to build up the muscle and improve the patient’s rate of recovery. 

The scientists at Applied Morphomics have developed a technology based on the idea of using the myriad data points in an MRI and CT scans as biomarkers. This makes perfect sense if we redefine the term biomarker as a measurable molecular substance or data points in a living organism. On this basis, the company is extracting new information from images that characterize, for example, a patient's muscle or adipose tissue. A physician would not order an MRI for any reason other than to diagnose a disease but this new technique can provide additional benefits from preexisting images. In this sense, these new measurements remind me of so-called incidentalomas whereby a new diagnosis is discovered in an image not anticipated when the study was ordered (see: Introducing the Incidentaloma; Implications of Whole Genome Scanning). I suspect that the company will be able, in time and using computerized image analysis, to assess the shape and mass of body organs such as the heart or lungs. In so doing, they can determine to what extent they deviate from the normal, taking into consideration the age and sex of the patient. An organ may be hypertrophic or hyperplastic, which is to say larger, smaller, or even misshapen on the basis of a disease. Radiologists make such judgements today but these new tools will enable far earlier assessment of diseases based on newly defined radiology biomarkers. 

Questions about the Interpretation of BRCA Testing by 23andMe

Lab testing for the genes that predispose a person to develop cancer is highly complex, both to preform and interpret. For example and relating to BRCA, I have blogged about some of the pitfalls of such testing (see: BRCA Exchange Available on the Web; Understanding BRCA Variants). Here's a quote from this note discussing some of the complexities that are encountered:

It's not known among some healthcare consumers that there are a host of BRCA variants other than the standard ones. Normal BRCA testing looks for three specific genetic variants that are linked to hereditary cancers. Two of these variants — 185delAG and 5382insC — are in the BRCA1 gene. One variant — called 6174delT — is in the BRCA2 gene. A patient may test positive for a variant other than these common ones and it may not be clear under such circumstances what treatment to offer to the patient.

A recent article in the New York Times warns consumers about the fact that the 23andMe BRCA testing may be misleading (see: Don’t Count on 23andMe to Detect Most Breast Cancer Risks, Study Warns ). Below is an excerpt from the article: 

In 2010, Dr. Pamela Munster mailed her saliva to 23andMe, a relatively new DNA testing company, and later opted in for a BRCA test. As an oncologist, she knew a mutation of this gene would put her at high risk for breast and ovarian cancer. She was relieved by the negative result. Two years later, after she learned she had breast cancer, she took a more complete genetic test from a different lab. This time it was positive. A study of 100,000 people released earlier this month suggested that this experience could be widespread. Nearly 90 percent of participants who carried a BRCA mutation would have been missed by 23andMe’s test, geneticists found. 23andMe’s testing formula for this risk is built around just three genetic variants, most prevalent among Ashkenazi Jews. The new study demonstrated that most people carry other mutations of the gene, something many doctors have long suspected....

Dr. Munster said that 23andMe was “not doing anything actively deceptive.” But she is still concerned that many customers do not grasp the limits of mail-in genetic testing. 23andMe now has more than 10 million customers. Even if only a small percentage take the test, that’s thousands who could be misled. Mary-Claire King, a professor at the University of Washington who discovered the region on the genome that became known as BRCA1, had a more blunt assessment of the Food and Drug Administration’s decision to allow the test....“The F.D.A. should not have permitted this out-of-date approach to be used for medical purposes,” Dr. King said. “Misleading, falsely reassuring results from their incomplete testing can cost women’s lives.”

For me, the fundamental question here is whether consumers "can be trusted" to order lab tests and interpret the results without physician intermediation. I have blogged frequently on this subject (see, for example: Comparison of "Traditional" Lab Testing with Direct Access Testing; An Estimate of the Size of the Direct Access Testing (DAT) Market). The excerpt from the article above focuses on an oncologist who seems to have been lead astray by a negative BRCA test from 23andMe, assuming that she was negative for any BRCA variant. I suspect that Dr. Munster was chosen as the focus of this story to make the point that the interpretation of the BRCA test results is so complex that even an oncologist, ordering the test for herself from 23andMe, could misinterpret the result.

I believe that allowing consumers to order certain routine lab tests for themselves is desirable because I also think that it will result in a general improvement in public health. Some people are too busy or not predisposed to visit a physician frequently. For them, ordering lab tests on the web may improve their health status. The pivotal issue in this discussion, then, is how laboratory tests should be reported to consumers. The most complex tests, particularly those requiring complex interpretation, should not be directly available to consumers. Consumers will generally not be lead astray by the performance of common lab tests but must be provided with reference ranges and instructed to seek the help of a physician for results that fall outside of normal ranges and/or when they have symptoms of a disease.

MIPACT Study Sponsored by Apple Provides a Preview of Home Health Monitoring

I will shortly enroll in the MIPACT study sponsored by Apple and being run at the University of Michigan. The acronym stands for Michigan Predictive Activity & Clinical Trajectories study. The goal of the study is to understand the relationship between biosensors, health information, and health outcomes. Participants are required to own an iPhone and are then provided gratis with an Apple Watch and an Omron wireless blood pressure monitor. These devices are necessary to perform various tasks in the study such as blood pressure measurement with the Omron device. EHR access is part of the program and a blood sample is also obtained for genomic testing. 

For me, this study provides a preview of what a home health monitoring program with digital devices and wearables will look like in the future. Such a program is not for everyone. I think that it will appeal most to those who are actively pursuing wellness and also the "worried well." As part of such a health monitoring regimen, daily measurements such as blood pressure, pulse, EKG, and walking/running distances will be the norm. Added to these in the very near future will be the use of home-based lab testing devices.

For such a home monitoring program, various measurements will be recorded in one's cell phone with a copy in the cloud.  Periodic queries to one's EHR record will also be necessary to take into account current medical conditions. All of the data recorded in this "home" personal health record will be scrutinized constantly by an "AI agent" with alerts when some action needs to be taken. A predictive analytic feature will also be present in the near future to give notice of possible pre-diseases (see: Predisposition to Disease and Pre-Disease on the Health Continuum; The Diagnosis and Treatment of Pre-Diseases: A Look at the Future). 

CMS Offers $1M Reward for AI to Predict Unplanned Hospitalizations

Regarding the use of artificial intelligence (AI) to provide efficiency gains in various healthcare processes, there have been a number of articles suggesting how to launch this new technology. I have discussed AI analytics in multiple blog notes (see, for example: Healthcare AI Market to Surge; Already Commonplace in Radiology; Cerner Offers AI Tool to Combat Physician EHR Burnout; Which Healthcare Jobs Are Safe from Replacement by Automation or Robots?). Hospitals will certainly be pursuing this goal on their own in order to reduce risk and costs. A recent article indicated that CMS was accelerating this process by launching a contest with a $1M reward for AI developers who come up with a way to predict unplanned hospitalizations (see: The Centers for Medicare and Medicaid Services is launching a challenge for AI developers to predict unplanned hospitalizations). Below is an excerpt from the article:

The Centers for Medicare and Medicaid Services (CMS) launched a new challenge that pits artificial intelligence (AI) developers against each other to create algorithms that can best predict unplanned hospitalizations. To attract the cream of the AI-developing crop, the agency plans to dole out a $1 million reward to the winner. The CMS' AI Health Outcomes Challenge comes amid speculation that a lack of healthcare AI regulation could spawn misuse, but recent federal government involvement in AI could help put some concerns at bay. Here's what it means: The US government is making moves to push AI deeper into healthcare....Dubbed The American AI Initiative, the order calls for the HHS, among other agencies, to prioritize funding the tech. But they'll have to use existing funds, as no new funds were allocated to the cause. With a newly restricted budget, it's hard to say if an uptick in healthcare AI investment is in the cards for the HHS. The US Food and Drug Administration (FDA) has shifted its regulatory coverage to encourage the development of AI healthcare products. In an effort to catch up with rapid digital innovation in the healthcare industry, the FDA announced last April that it was working to facilitate the inclusion of AI in digital health tools by looking into how it can apply its pre-certification program for tools based on AI.

I think that this AI contest is an appropriate step for CMS to take. After all, a million dollar reward will look trivial compared to the savings that CMS may gain on the basis of successful predictive algorithms. I do have several concerns about this idea, however. The first is that, obviously, any algorithm used to predict unplanned hospitalizations needs to be extensively tested to endure its validity and safety. Secondly, the current hospital EHR environment is very heterogeneous, sometimes even for hospitals running the same EHR software. Such an environment poses a number of challenges not the least of which is that EHR vendor support for AI predictive analytics tools will slow their emergence. Algorithms that reduce costs and increase patient safety will also be extremely valuable. The intellectual property of the developers of predictive analytics needs to be protected in these early hospital testing stages. 

Interactive Audio as an Emerging, Disruptive Technology in Healthcare

The voice-user interface device from Amazon called Alexa is now HIPAA compliant (see: Amazon Alexa Now HIPAA Compliant; Watch for the Next Steps). This means that we are on the verge of a wide variety of new voice-user interface (VUI) apps relating to healthcare, some of which were detailed in a recent article (see: Voice Technology: A Disruptive Force in Healthcare). Below is an excerpt from the article:

Last month, an Amazon spokesperson told a reporter that the company frequently receives positive feedback from “aging-in-place” customers who use Alexa’s smart-home features as an alternative to going up and down stairs. Amazon’s Echo Show is another product that offers users Tap to Alexa, a screen interface that lets users who are deaf and hard of hearing tap common commands....We are seeing seniors embrace today’s connected devices to stay safely independent. Everything from blood pressure and glucose monitors to motion sensors are making seniors’ homes safer and smarter. Furthermore, voice devices can serve as the central data hub for all the connected devices in a person’s home....[M]ost seniors who live independently will do so in smart homes equipped with passive devices that continuously monitor vital signs and activities of daily living. .... According to Lyft, 3.6 million Americans have transportation issues that prevent them from getting to or from doctors’ appointments, and 25% of lower-income patients have missed or rescheduled their appointments due to lack of transportation. That’s where voice technology can help. ....By engaging patients in their homes – particularly those who make up the most high-risk, complex populations – VUI applications can keep patients out of the doctor’s office or hospital, while still providing strong outcomes.

I am particularly intrigued by the idea of a VUI device like Alexa operating as a health aide for seniors who are increasingly choosing to age-in-place at home (see: ALEXA, WHAT'S MY BLOOD-SUGAR LEVEL?). Each Alexa device helping a senior at home can be enhanced by wearables such as the Apple watch that can also monitor the heart and detect atrial fibrillation or detect slips and falls (see: Apple Collaborates with Janssen on Apple Watch AFib/Stroke Clinical Trial). Such wearables would be programmed to connect with the Alexa device in case the consumer is unaware of a problem or physically unable to issue commands to the device. Apple is experiencing an increasing percentage of its earnings from healthcare which will be an incentive to provide many more products in this area (see: Health care is one of Apple's most lucrative opportunities).

I am also intrigued by the opportunity to use Alexa or other VUI devices to coordinate transportation for the aging consumer to healthcare appointments or other activities. Many seniors are reluctant to give up their cars because transportation is so important for them despite the fact they may be accident prone. Alexa can be used to summon a Lyft or Uber ride. Now available from both Uber and Lyft is a subscription plan with a stipulated number of rides per month for a set fee (see: Uber and Lyft unveiled new subscription plans. But are they really worth the money?). For aging seniors, the cost of such a plan compared to the cost of an owner vehicle per month is less relevant than safety issues, both for the senior and for other drivers on the road (see: Older Drivers). For the children of an aged parent still driving, such subscription plan would also offer a break from their senior chauffeuring duties.

The Continuing Problem of Duplicate EHR Records for the Same Patient

I have previously blogged about the chronic and continuing problem of duplicate EHR records (see: Duplicate Patient Records as a Major and Costly EHR and EMPI Problem). Think of the problem in this way: it's relatively easy for a hospital registrar to create a new patient record but very risky to merge two nominally identical patient records because they may not represent the same person. This reluctance to merge records results in hidden data that may be critical in the care of a particular patient. A recent article dealt with this problem (see: Why Is It So Hard to Match Patients With Their Medical Records?) and below is an excerpt from it:

In the half-century since EHR technology tiptoed into health care, hospitals and doctors have used hundreds of vendors, each of which is constantly updating its technology, thereby creating new opportunities for inconsistencies. Today, on average, 18 percent of patient records within organizations are duplicates....And the match rates between organizations — for example, between a doctor’s office and the hospital — can be extremely low....That means physicians are often unaware of information that patients assume they know: test results, diagnoses, medications, and more....

On its face, patient-matching looks like a simple problem to fix, until you look closely. One root problem is that the health care industry has been consolidating for years, and the pace of mergers is only speeding up. Take the example of Northwell Health. Already the largest health care provider in New York State, Northwell is growing rapidly through acquisitions. Every time the system adds a new hospital, their medical records are integrated — and duplicates proliferate.... Meanwhile, Northwell’s 23 hospitals and more than 700 clinics are creating new duplicate records every day....Rather than try to figure out which of those 50 is the right one, [the registrar] will create a new case, knowing that there’s a way to reconcile it later on....In 2016, Harris Health System in Houston reported it had 2,488 records with the name “Maria Garcia;” of those, 231 shared the same birthdate, suggesting some of them refer to the same individual. ....

Pew did a deep dive into these and other potential solutions. Its finding: None of the ideas are as easy as they look at first glance. All the opportunities we examined to address matching have benefits and drawbacks — and none of the opportunities alone can solve this problem ....[One idea that has been] suggested but never implemented nationwide: data standardization. If all health care organizations collected certain pieces of demographic data in a uniform way, patient-match rates would increase significantly. Just a little bit of standardization could go a long way....[A] unique patient identifier — a single number, possibly linked to an iris scan or other biometric, that distinguishes one [person from another] might be the most logical solution to the problem. .... In the U.S., however, it is essentially against the law for the government to invest in a unique health identifier. 

On the face of it, artificial intelligence could possibly provide a solution for this problem by using algorithms to scrutinize multiple duplicate records and determine which ones represent a single patient and which ones do not. However, a recent article introduced a note of caution into such process. It emphasized that bad data can produce worse models and it's a certainty that many of the duplicate records considered for merger may be replete with incorrect data (see: AI can improve EHRs and analytics, but physician input is a must). Here the relevant quote from this latter article:

...[A]utomation has its perils too, of course. For instance, when developing predictive models, "there is nothing more critical than the data. Bad data (such as from the EMR) can be amplified into worse models. Simply allowing machine learning to run without human intervention presents clear risks. Instead, clinicians should seek a partnership in which the machine predicts (at a demonstrably higher accuracy), and the human explains and decides on action.

Amazon Alexa Now HIPAA Compliant; Watch for the Next Steps

Having Alexa HIPAA-compliant is a necessary first step in deploying the device and its supporting software into all manner of health settings. This goal has now been achieved (see: Amazon Alexa is now HIPAA-compliant). Below is an excerpt from the article announcing this step and whats's next:

Alexa is moving into healthcare. Following a trial of Amazon’s smart speakers in patients’ rooms at Cedars-Sinai, the company this morning announced an invite-only program allowing select developers to create and launch HIPAA-compliant healthcare skills for Alexa. The skills allow consumers to ask the virtual assistant for help with things like booking an appointment, accessing hospital post-discharge instructions, checking on the status of a prescription delivery and more....Amazon itself provides the HIPAA-eligible environment for skill building, while the developers themselves are required to comply with the applicable laws. This is a significant step for Amazon, as it means voice app developers who follow HIPAA guidelines can now create skills for Alexa....Amazon itself is venturing into healthcare alongside Berkshire Hathaway and JP Morgan Chase (see: Three Major Corporations Will Form Healthcare Company for Their Employees), which have together teamed up to take on rising healthcare costs for employees. Amazon last year acquired online pharmacy PillPack for less than $1 billion. And the company’s AWS unit is expanding its HIPAA-compliant capabilities. This included the launch of Amazon Comprehend Medical (see: Amazon Launches New Medical Record Language Processing Service) a machine learning tool that gathers information from things like doctors’ notes and patient health records.

The delivery of healthcare is badly in need of greater automation and it is this automation that can help to bring down the cost of care. For example, automating the simple act of making a physician appointment is ripe for such change. Having a simple conversation with an Alexa device, either in one's home or via a connection with a device in a hospital, could enable such a process. Alexa can also serve to enhance the interactions between a physician and the EHR concerning a patient. Here's an explanation of this process from the Amazon Comprehend Medical web site:

Amazon Comprehend Medical is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. Using Amazon Comprehend Medical, you can quickly and accurately gather information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources like doctors’ notes, clinical trial reports, and patient health records. One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history. 

As one example how to understand the idea of accessing "trapped free-form medical text" in the EHR with NLP, a physician admitting a patient to the hospital could query Alexa: Were there any unexpected complications for this patient during previous hospitalizations? Another example: Given the patient's current drug regimen, are there any possible drug-drug interactions that I need to be aware of? The value of such queries to the EHR are significant in terms of quality gains.

Predatory "Publisher and Conference Organizer" Hit with a $50M Court Judgement

I first became aware of the Omics Group six years ago when I blogged about the fraud that the company was perpretating (see: Welcome to the World of Pseudo-Academia: Pathology-2013). Here's a quote from that note about how the company was offering a set of fake scientific conferences and journals to fleece the unwary:

Here's a link to the twenty different conferences that the Omics Group has planned for....[originally 2013 but the web site has now been updated to the present time]. The only way that I can describe this conference enterprise, putting aside the journals, is comprehensively audacious. Do registrants actually show up at these conferences or is the audience just faculty members?  Aside from some grammatical slips and the misstep into plant pathology for Pathology-2013, the web content does have a semi-authentic feel. The biggest clue, of course, is the grandiosity of it all. It seems to be an example of pseudo-academia writ large.

It appears that the the FTC has finally caught up with the India-based Omics International as described in a recent article (see: ‘Predatory’ Scientific Publisher Is Hit With a $50 Million Judgment). Below is an excerpt from it:

In the world of scientific research, they are pernicious impostors. So-called predatory journals, online publications with official-sounding names, publish virtually anything, even gibberish, that an academic researcher submits — for a fee. Critics have long maintained that these journals are eroding scientific credibility and wasting grant money. But academics must publish research to further their careers, and the number of questionable outlets has exploded. Now the Federal Trade Commission has stepped in, announcing on Wednesday that it has won a $50 million court judgment against Omics International of Hyderabad, India, and its owner, Srinubabu Gedela. Omics publishes hundreds of journals in such areas as medicine, chemistry and engineering. It also organizes conferences. The F.T.C. claimed that Omics violated the agency’s prohibition on deceptive business practices.

In addition to the judgment, a federal judge in Nevada — where Omics has mail drops — ordered the company to cease its deceptive business practices, including failing to disclose fees, misleading authors about the legitimacy of its journals and marketing conferences with star speakers who never agreed to participate....Critics of the journals hailed the decision. “It’s great news,” said Jeffrey Beall, a former scholarly communications librarian at the University of Colorado Denver who has studied questionable scientific journals. “There are hundreds of predatory publishers, but Omics is the evil empire.” Predatory publishers pepper academics with pleas to submit papers or speak at conferences. When they receive papers, the journals often accept and publish them immediately, with a perfunctory review or none at all.

I am pleased that some sort of justice has been achieved regarding the predatory Omics. However, I continue to wonder about the extreme naiveté of anyone who would register for one of their conferences or submit an article to one of their faux journals. By 2015, it claimed over 700 journals although about half of them were defunct (see: OMICS Publishing Group). I can only assume that the multiple victims were very naive or desperate for journal citations for their research. Also, take note: there said to be hundreds of other predatory publishers still in business.