Perspectives: How AI and ML can accelerate the growth of telemedicine across the globe

May 31, 2023 | by

TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion area. Today’s Perspectives is from Deepak Singh, a thought leader in AI and telehealth. In his work, he builds AI-powered healthtech and telehealth solutions that can reach from big cities to remote areas of the world. With double master’s degrees in business and information systems, he has 10 years of experience in product development, management, and design ranging from telecom to multimedia and from IT solutions to enterprise healthcare platforms. This article discusses how artificial intelligence (AI) and machine learning (ML) can accelerate the global growth of telemedicine, including a consideration of risks and possible solutions.

Introduction

The ongoing technological advancements have led the way towards greater opportunities for the growth of the global health business, particularly telemedicine through increased connections via the internet, robotics, data analytics, and cloud technology that will further drive innovation over the next ten years. It is obvious that artificial intelligence (AI) usage plays a noteworthy part in the maneuvering and execution of medical technologies when considering the bulky amount of data handling needed by healthcare, the requirement for consistent accuracy in complex procedures, and the rising demand for healthcare services.

Telemedicine is the practice of performing consultations, medical tests and procedures, and remote medical professional collaborations through interactive digital communication. Telemedicine is an open science that is constantly growing as it embraces new technological developments and reacts to and adapts to the shifting social circumstances and health demands. The primary goals of telemedicine are to close the accessibility and communication gaps in four fields: teleconsultation, which is having all kinds of physical and mental health consultations without an in-person visit to a medical facility; teleradiology, which uses information and communication technologies (ICT) to transmit digital radiological images (such as X-ray images) from one place to another; telepathology, which uses ICT to transmit digitized pathological results; and teledermatology, which uses ICT to transmit medical information about skin conditions.

AI has been progressively implied in the field of telemedicine. AI deals with machine learning (ML) that discloses complex connections that are hard to figure out in an equation. In a way that is similar to the human brain and neural networks that encrypt data using an enormous number of interconnected neurons, ML systems can approach difficult problem-solving in the same way that a doctor might do by carefully analyzing the available data and drawing valid judgments.

A growing understanding of artificial intelligence and data analytics can help to broaden its reach and capabilities. Telemedicine’s goal is to boost productivity and organize experience, information, and manpower based on need and urgency and it can be augmented by the use of AI and ML.

Evolving application of AI and ML in Telemedicine

In order to enable clinicians to make more data-driven, immediate decisions that could enhance the patient experience and health outcomes, AI is being employed in telemedicine more and more. The use of AI in healthcare is a potential approach for telemedicine applications in the future.

Al and ML were able to bring about the necessary revolution in so many sectors due to their competence, increased productivity, and flawless execution of tasks. AI is now surpassing the boundaries of being a mere theory and stepping into a practical domain where the need for human supervision for the execution of jobs by machines will be minimized all due to the presence of enormous datasets along with an increment in the processing power of that data. A computer-based algorithm that uses AI has the ability to analyze any form of input data such as ‘training sets’ using pattern recognition which eventually predicts as well as categorize the output, all of that is beyond the scope of human processing or analytical powers that uses traditional statistical approaches. In the field of telemedicine, the adoption of AI and ML still has to go a long way till its vital concepts are understood and applied likewise, nevertheless, the current scenario gives a promising picture where many research projects have applied AI to predict the risk of future disease incidence, decrypting cutting-edge imaging, evaluating patient-reported results, recording value-based metrics, and improving telehealth. The perspective to mechanize tasks and improve data-driven discernments may be comprehended by profoundly improving patient care with obligation, attentiveness, and proficiency in prompting AI.

Drawbacks of artificial intelligence in telemedicine (more…)

Week-end news roundup: Fold Health launches OS ‘stack’; admin task automator Olive cuts 450 workers; 38% of UK data breaches from cyber, internal attacks; hacking 80% of US healthcare breaches; does AI threaten cybersecurity?

July 21, 2022 | by

Startup Fold Health launched this week. It’s developed a suite of modular tools that are interoperable with existing EHRs or platforms to enable them to work better, together. Fold’s main claim is to “move primary care beyond the constraints of a 15-minute visit and provide a revolutionary consumer first experience through micro, automated workflows and campaigns of care.” There is an athenahealth connection, in that the founders were from Praxify, a virtual assistant/patient engagement app bought by athenahealth for $65 million in 2017. It has a $6 million seed investment from athenahealth. FierceHealthcare

On the other side of the funding mountain,  Olive, an AI-enabled data cruncher that automates routine administrative healthcare processes such as revenue cycle management, has pink-slipped 450 employees, about one-third of its staff. In a letter to employees excerpted in Axios, Olive cites ‘missteps’ and ‘lack of focus’. It follows hiring freezes, major staff departures, and overpromising/underdelivering, including not using AI or machine learning for automating tasks, featured in an April Axios investigation. Olive has gone through over $850 million in nine rounds of funding (the last July 2021, Series H–Crunchbase). FierceHealthcare

Cyber attacks with internal breaches account for 38% of UK organizations’ (of all types) data losses in 2022. This is based on the Data Health Check survey of 400 IT decision makers compiled by Data Barracks, a cloud-based business continuity organization. The second and third reasons for data loss are human error and hardware failure. Of those surveyed, over half have experienced a cyber attack, most commonly caused by ransomware. 44% paid the ransom, 34% didn’t and used backups. Their recommendations include frequent backups and keeping track of how many data versions–both will minimize downtime and data loss. Release, full report

By contrast, returning to the US and healthcare, malicious hacking activity accounts for nearly 80% of all breaches. Fortified Health Security’s mid-year report on the state of healthcare cybersecurity, reviewing HHS Office for Civil Rights (OCR) data,  noted that in first half 2022:

  • Healthcare data breaches primarily originated at providers– 72%. The remainder were at business associates at 16% and health plans at 12%.
  • The number of records affected was 138% higher than the first half of 2020 at over 19 million records
  • Breaches were concentrated in relatively few organizations: Seven entities experienced breaches of more than 490,000 records each, in total 6.2 million records or 31% to date.  
  • OCR’s data breach portal recorded 337 healthcare data breaches that each impacted more than 500 individuals, a small decline from 2021’s 368
  • Hacking incidents rose to 80% from 72% in 2021. Unauthorized access/disclosure incidents totaled 15%; loss, theft, or improper disposal accounted for only 5 percent of breaches.
  • AI and ML-enabled security offerings can bolster cyber infrastructure. Organizations should also look at how IT staff shortages impact their planning and security.    HealthITSecurity

Can AI (and machine learning-ML) lessen breaches–or open the door to worse problems, such as algorithmic bias, plus data privacy and security concerns? Vast quantities of data pumped through AI or ML algorithms are harder to secure. If the algorithms are built incorrectly–such as eliminating or underrepresenting certain populations–what comes out will be skewed and possibly misleading. In the Healthcare Strategies podcast, Linda Malek of healthcare law firm Moses & Singer, who chairs their healthcare, privacy, and cybersecurity practice group, discusses the problems. She suggests some best practices around transparency, security, privacy, and accuracy when developing an AI algorithm, including collecting as much data as possible, and as diverse as possible, for accuracy. Additionally, the design should incorporate privacy and security from the start. HealthcareExecIntelligence

Weekend news and deals roundup: Allscripts closes sale of hospital EHRs, closing out CEO; DEA scrutiny of Cerebral’s ADHD telehealth prescribing; more telehealth fraud; Noom lays off; fundings; and why healthcare AI is only ML

May 6, 2022 | by

That was fast. Allscripts closed its $700 million March sale of its hospital and large physician practice EHRs to Constellation Software Inc. through N. Harris Group. The Allscripts EHRs in the transaction are Sunrise, Paragon, Allscripts TouchWorks, Allscripts Opal, and dbMotion. They reported their Q1 results today. According to HISTalk earlier this week, CEO Paul Black will be stepping down, with President Rick Poulton stepping in immediately. Update–this was confirmed on their investor call Thursday and the transition is effective immediately. No reasons given, but there were no effusive farewells.  Healthcare Dive

A damper on telemental health? Online mental health provider Cerebral, which provides talk therapy, audio/video telehealth, and prescriptions for anxiety, depression, insomnia, ADHD, and other conditions, is finding itself under scrutiny. This week, its main mail fulfillment pharmacy partner, Truepill, stopped filling prescriptions for Adderall, Ritalin, Vyvanse, and other controlled Schedule 2 pharmaceuticals. Cerebral is redirecting current patients with these prescriptions to local pharmacies and as of 9 May, will not prescribe them to new ADHD patients.

Based on reports, the Drug Enforcement Agency (DEA) is looking at Cerebral in particular as part of a wider scrutiny of telehealth providers and pharmacies filling telehealth-generated prescriptions due to allegations of overprescribing. It also didn’t help that a former VP of product and engineering plus whistleblower claims in a wrongful dismissal lawsuit that Cerebral execs wanted to prescribe ADHD drugs to 100% of diagnosed patients as a retention strategy. Bloomberg Law. Unfortunately, Insider is paywalled but you may be able to see a report in the Wall Street Journal. Becker’s Hospital Review, FierceHealthcare

Also troubling telehealth is recurrent fraud, waste, and abuse cases involving Medicare and Medicaid. Back in 2020 the National Healthcare Fraud Takedown took down over 80 defendants in telemedicine fraud [TTA 2 Oct 20, 30 Jan 21]. The Eastern District of NY based in Brooklyn has indicted another physician, an orthopedic surgeon, in a $10 million fraud involving durable medical equipment (DME). In exchange for kickbacks from several telemedicine companies, he allegedly prescribed without examination and with only a cursory telephone conversation DME such as orthotic braces. DOJ release

Some fundings and a sale of note–and a big layoff at a well-known digital health leader:

  • Blue Spark Technologies, an RPM company with a patented Class II real-time, disposable, continuous monitoring body temperature patch good for 72 hours, TempTraq, raised a $40 million intellectual property-based debt solution (??) to fund growth led by GT Investment Partners (“Ghost Tree Partners”) with support from Aon plc (NYSE: AON)  Release
  • Specialty EHR Netsmart acquired TheraOffice, a practice management platform for physical therapy and rehabilitation practices which will be added to its existing CareFabric platform. Neither terms nor management transitions were disclosed in the release.
  • ‘White label’ telehealth/virtual health provider Bluestream Health is implementing its systems in Mankato Clinic, with 13 facilities across southern Minnesota. It’s a rarity–physician-owned and led–and in business since 1916. This also fits into a new telehealth trend–providers working with ‘white label’ telehealth companies and not with the Big 5. Release
  • Ubiquitously advertised (in US) weight-loss app Noom is laying off a substantial number of employees–180 coaches plus 315 more employees. Reportedly they are pivoting away from on-demand text chat to scheduled sessions that don’t require so many people. While profitable in 2020 ($400 million) and with Series F funding of over $500 million in 2021, it’s come under criticism that while its pitch heavily features easy behavioral change achieved through cognitive behavioral therapy (CBT), their real core of weight loss is severe calorie restriction. Engadget
  • Element5, an administrative software provider for post-acute facilities, raised a $30 million Series B from Insight Partners. They claim that their software is AI and RPA (robotic process automation) based. Release, Mobihealthnews

And speaking of the AI pitch in healthcare, a VC named Aike Ho explains why she doesn’t invest in healthcare AI companies because there’s no such thing in healthcare–it’s just machine learning. On that, Ms. Ho and your Editor agree. She also makes the point that the market they address is ancillary and not core services, plus they have difficulty clinching the sale because they don’t relate well to achieving or can’t prove at this stage improved clinical outcomes. Ms. Ho’s looooong series of Tweets is succinctly summarized over at HISTalk (scroll down halfway).

IBM Watson Health’s stumble and possible fall

July 21, 2021 | by

This Editor hadn’t thought about or seen news about IBM Watson Health in over a year…and likely, neither did you. Granted, our minds have been Otherwise Engaged, but for the company that was supposed to dominate AI and health analytics, it’s notable that TTA’s last two articles mentioning Watson Health was 25 April 2019, on a report that its Drug Discovery unit was being cut back as the latest in a series of executive cutbacks and lawsuits (MD Anderson on a failed oncology initiative), and 14 Feb 2020 on 3M’s lawsuit on unauthorized use of their software.

The New York Times in an investigative piece (may be paywalled or require signup for limited access), brings us up to date on what is happening at IBM Watson, and it’s not bright for Watson Health. IBM, like so many other companies, badly underestimated the sheer screaming complexity of health data. Their executives believed they could translate the big win on the “Jeopardy!” game show in 2011, based on brute computing power, into mastery of healthcare data and translation into massive predictive models. The CEO at the time called it their ‘moon shot’. Big thinkers such as Clayton Christensen chimed in. IBM managers sang its praises to all in healthcare who would listen. This Editor, on a gig at a major health plan in NJ that was ‘thinking big’ at the time and used IBM consultants extensively, in 2012 was able to bring in speakers from Watson for an internal meeting.

But we haven’t been on the moon since 1972 (though probes have visited Mars). Since the big push in 2011-12, it’s been one stumble after another. According to the Times:

  • The bar was set much too high with oncology. Watson researchers knew early on in their research at the University of North Carolina School of Medicine that their genetic data was filled with gaps, complexity, and messiness. The experience was similar with Memorial Sloan-Kettering Cancer Center. The products growing out of the UNC and MSKCC research, Watson for Genomics and Watson for Oncology, were discontinued last year. These were in addition to the MD Anderson Cancer Center initiative, Oncology Expert Advisor for treatment recommendations, that was kicked to the curb [TTA 22 Feb 2017] after $62 million spent. At the same time, IBM’s CEO was proudly announcing at HIMSS17 that they were betting the company on multiple new initiatives. 
  • Watson Health, formed in 2015, bought leading data analytics companies and then didn’t know what to do with them. TTA noted in August 2018 that Phytel, Explorys, and Truven Health Analytics were acquired as market leaders with significant books of business–and then shrank after being ‘bluewashed’. HISTalk, in its review of the Times article, noted that along with Merge Healthcare, IBM spent $4 billion for these companies. IBM’s difficulties in crunching real doctor and physical data were well known in 2018 with revealing articles in IEEE Spectrum and Der Spiegel. 

Six years later, Watson Health has been drastically pared back and reportedly is up for sale. Smaller, nimbler companies have taken over cloud computing and data analytics with AI and machine learning solutions that broke problems down into manageable chunks and business niches.

What’s recoverable from Watson? Basic, crunchy AI. Watson does natural language processing very well, as well as or better than Amazon, Google, and Microsoft. Watson Assistant is used by payers like Anthem to automate customer inquiries. Hardly a moonshot or even clinical decision support. For business, Watson applications automate basic tasks in ‘dishwashing’ areas such as accounting, payments, technology operations, marketing, and customer service. The bottom line is not good for IBM; both areas bring in a reported $1 billion per year but Watson continues to lose money. 

Weekend ‘Must Read’: Are Big Tech/Big Pharma’s health tech promises nothing but a dangerous fraud?

July 24, 2020 | by

If it sounds too good to be true, it isn’t. And watch your wallet. In 14 words, this summarizes Leeza Osipenko’s theme for this article. It may seem to our Readers that Editor Donna is out there for clicks in the headline, but not really. Dr. Osipenko’s term is ‘snake oil’. It’s a quaint, vintage term for deceptive marketing of completely ineffective remedies, redolent of 19th Century hucksters and ‘The Music Man’. Its real meaning is fraud.

The promise is that Big Data, using Big Analytics, Big Machine Learning, and Big AI, will be a panacea for All That Ails Healthcare. It will save the entire system and the patient money, revolutionize medical decision making, save doctors time, increase accuracy, and in general save us from ourselves. Oh yes, and we do need saving, because our Big Tech and Big Health betters tell us so!

Major points in Dr. Osipenko’s Project Syndicate article, which is not long but provocative. Bonus content is available with a link to a London School of Economics panel discussion podcast (39 min.):

  • Source data is flawed. It’s subject to error, subjective clinical decision-making, lack of structure, standardization, and general GIGO.
  • However, Big Data is sold to health care systems and the general public like none of these potentially dangerous limitations even exist
  • Where are the long-range studies which can objectively compare and test the quality and outcomes of using this data? Nowhere to be found yet. It’s like we are in 1900 with no Pure Food Act, no FDA, or FTC to oversee.
  • It is sold into health systems as beneficial and completely harmless. Have we already forgotten the scandal of Ascension Health, the largest non-profit health system in the US, and Google Health simply proceeding off their BAA as if they had consent to identified data from practices and patients, and HIPAA didn’t exist? 10 million healthcare records were breached and HHS brought it to a screeching halt.
    • Our TTA article of 14 Nov 19 goes into why Google was so overeager to move this project forward, fast, and break a few things like rules.
  • We as individuals have no transparency into these systems. We don’t know what they know about us, or if it is correct. And if it isn’t, how can we correct it?
  • “Algorithmic diagnostic and decision models sometimes return results that doctors themselves do not understand”–great if you are being diagnosed.
  • Big Data demands a high level of math literacy.  Most decision makers are not data geeks. And those of us who work with numbers are often baffled by results and later find the calcs are el wrongo–this Editor speaks from personal experience on simple CMS data sets.
  • In order to be valuable, AI and machine learning demand access to potentially sensitive data. What’s the tradeoff? Where’s the consent?

Implicit in the article is cui bono?

  • Google and its social media rivals want data on us to monetize–in other words, sell stuff to us. Better health and outcomes are just a nice side benefit for them.
  • China. Our Readers may also recall from our April 2019 article that China is building the world’s largest medical database, free of those pesky Western democracy privacy restrictions, and using AI/machine learning to create a massive set of diagnostic tools. They aren’t going to stop at China, and in recent developments around intellectual property theft and programming back doors, will go to great lengths to secure Western data. Tencent and Fosun are playing by Chinese rules.

In conclusion:

At the end of the day, improving health care through big data and AI will likely take much more trial and error than techno-optimists realize. If conducted transparently and publicly, big-data projects can teach us how to create high-quality data sets prospectively, thereby increasing algorithmic solutions’ chances of success. By the same token, the algorithms themselves should be made available at least to regulators and the organizations subscribing to the service, if not to the public.

and

Having been massively overhyped, big-data health-care solutions are being rushed to market in without meaningful regulation, transparency, standardization, accountability, or robust validation practices. Patients deserve health systems and providers that will protect them, rather than using them as mere sources of data for profit-driven experiments.

Hat tip to Steve Hards.

The confusion within TEC/telehealth between machine learning and AI-powered systems

September 25, 2019 | by

Defining AI and machine learning terminology isn’t academic, but can influence your business. In reading a straightforward interview about the CarePredict wearable sensor for behavioral modeling and monitoring in an AI-titled publication, this Editor realized that AI–artificial intelligence–as a descriptor is creeping into all sorts of predictive systems which are actually based on machine learning. As TTA has written about previously [TTA 21 Aug], there are many considerations around AI, including the quality of the data being fed into the system, the control over the systems, and the ability to judge the output. Using the AI term sounds so much more ‘techie’–but it’s not accurate.

Artificial intelligence is defined as the broader application of machines being able to carry out tasks in a ‘smart’ way. Machine learning is tactical. It’s an application that assumes that we give the machine access to data and let the machine ‘learn’ on its own. Neural networks in computer design have made this possible. “Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty.”, as stated in this Forbes article by Bernard Marr.

CarePredict has been incorporating many aspects of machine learning, particularly in its interface with the wrist-worn wearable and its interaction with sensors in a residence. It gathers more over time than older systems like QuietCare (this Editor was marketing head) and with more data, CarePredict does more and progressed beyond the relatively simple algorithms that created baselines in QuietCare. They now claim effective fall detection, patterns of grooming and feeding, and environment. (Disclosure: this Editor did freelance writing for the company in 2017)

In wishing CEO Satish Movva much success, this Editor believes that using AI to describe his system should be used cautiously. It makes it sound more complicated than it is to a primarily non-techie, senior community administrative and clinical audience. Say what you do in plain language, and you won’t go wrong. AI for Healthcare: Interview with Satish Movva, Founder & CEO of CarePredict

 

Are AI’s unknown workings–fed by humans–creating intellectual debt we can’t pay off?

August 21, 2019 | by

Financial debt shifts control—from borrower to lender, and from future to past. Mounting intellectual debt may shift control, too. A world of knowledge without understanding becomes a world without discernible cause and effect, in which we grow dependent on our digital concierges to tell us what to do and when.

Debt theory and AI. This Editor never thought of learning exactly how something works as a kind of intellectual paydown of debt on what Donald Rumsfeld called ‘known unknowns’–we know it works, but not exactly how. It’s true of many drugs (aspirin), some medical treatments (deep brain stimulation for Parkinson’s–and the much-older electroconvulsive therapy for some psychiatric conditions), but rarely with engineering or the fuel pump on your car. 

Artificial intelligence (AI) and machine learning aren’t supposed to be that way. We’re supposed to be able to control the algorithms, make the rules, and understand how it works. Or so we’ve been told. Except, of course, that is not how machine learning and AI work. The crunching of massive data blocks brings about statistical correlation, which is of course a valid method of analysis. But as I learned in political science, statistics, sports, and high school physics, correlation is not causality, nor necessarily correct or predictive. What is missing are reasons why for the answers they provide–and both can be corrupted simply by feeding in bad data without judgment–or intent to defraud.

Bad or flawed data tend to accumulate and feed on itself, to the point where someone checking cannot distinguish where the logic fell off the rails, or to actually validate it. We also ascribe to AI–and to machine learning in its very name–actual learning and self-validation, which is not real. 

There are other dangers, as in image recognition (and this Editor would add, in LIDAR used in self-driving vehicles):

Intellectual debt accrued through machine learning features risks beyond the ones created through old-style trial and error. Because most machine-learning models cannot offer reasons for their ongoing judgments, there is no way to tell when they’ve misfired if one doesn’t already have an independent judgment about the answers they provide.

and

As machines make discovery faster, people may come to see theoreticians as extraneous, superfluous, and hopelessly behind the times. Knowledge about a particular area will be less treasured than expertise in the creation of machine-learning models that produce answers on that subject.

How we fix the balance sheet is not answered here, but certainly outlined well. The Hidden Costs of Automated Thinking (New Yorker)

And how that AI system actually gets those answers might give you pause. Yes, there are thousands of humans, with no special expertise or medical knowledge, being trained to feed the AI Beast all over the world. Data labeling, data annotation, or ‘Ghost Work’ from the book of the same name, is the parlance, includes medical, pornographic, commercial, and grisly crime images. Besides the mind-numbing repetitiveness, there are instances of PTSD related to the images and real concerns about the personal data being shared, stored, and used for medical diagnosis. A.I. Is Learning from Humans. Many Humans. (NY Times)

Comings & goings: The TeleDentists go DTC, gains Reis as CEO; University of Warwick spinoff Augmented Insights debuts (UK); a new CEO leads GrandCare Systems

June 19, 2019 | by

The TeleDentists leap in with a new CEO. A year-old startup, The TeleDentists, has announced it will be going direct-to-consumer with teledentistry consults. This will permit anyone with a dental problem or emergency to consult with a dentist 24/7, schedule a local appointment in 24-48 hours. and even, if required, prescribe a non-narcotic prescription to a local pharmacy. Cost for the DTC service is not yet disclosed. Currently, the Kansas City-based company has provided their dental network services through several telehealth and telemedicine service providers such as Call A Doctor Plus as well as several brick-and-mortar clinic locations.

If dentistry sounds logical for telemedicine, consider that about 2 million people annually in the US use ERs for dental emergencies; 39 percent didn’t visit a dentist last year. Yet teledentistry is just getting started and is unusually underdeveloped, if you except the retail tooth aligners. Several US groups are piloting it to community health and underserved groups, with Philips reportedly considering a trial in Europe (mHealth Intelligence). This Editor notes that on their advisory board is a co-founder of Teladoc.  Release

The TeleDentists’ co-founder, Maria Kunstadter, DDS, last week announced the arrival of a new company CEO, Howard Reis. Mr. Reis started with health tech back in the 1990s with Nynex Science and Technology piloting telemedicine clinical trials at four Boston hospitals, which qualifies him among the most Grizzled Pioneers. He also was business development VP for Teleradiology Specialists and founding partner of The Castleton Group, a LTC telehealth company, and has worked in professional services for Accenture, Telmarc and SAIC/Bellcore. Most recently, he started teleradiology/telehealth firm HealthePractices. Over the past few years, Mr. Reis has also been prominent in the NY metro digital health scene. Congratulations and much success!  

In the UK, the University of Warwick has unveiled a spinoff, Augmented Insights Ltd. AI will be concentrating on machine learning and AI services that analyze long term health and care data, automating the extraction in real time of personalized, predictive and preventative insights from ongoing patient data. It will be headed by Dr. James Amor, whom this Editor met last summer in NYC. Long term plans center on marketing their analytics services to tech providers. Interested parties or potential users may contact Dr. Amor in Leamington Spa at James@augmentedinsights.co.uk |Congratulations to Dr. Amor and his team! 

And in more Grizzled Pioneer news, there’s a new CEO at GrandCare Systems who’s been engaged with the company since nearly their start in 1993 and in its present form in 2005. Laura Mitchell takes the helm as CEO after various positions there including Chief Marketing Officer and several years leading her own healthcare and marketing consulting firm. Nick Mitchell rejoins as chief technology officer and lead software developer. Founders Charlie Hillman remain as an advisor and Gaytha Traynor as COO. Their offices have also moved to the Kreilkamp Building, 215 N Main Street, Suite 130, in downtown West Bend Wisconsin. GrandCare remains a ‘family affair’ as this profile notes. Congratulations–again!

AI and machine learning ‘will transform clinical imaging practice over the next decade’

April 24, 2019 | by

The great challenges in radiology are accuracy of diagnosis and speed. Yet for radiology, machine learning and AI systems are still in early stages. Last August, a National Institutes of Health (NIH)-organized workshop with the Radiological Society of North America (RSNA), the American College of Radiology (ACR) and The Academy for Radiology and Biomedical Imaging Research (The Academy) kickstarted work towards AI. Their goal was to collaborate in machine learning/AI applications for diagnostic medical imaging, identify knowledge gaps, and to roadmap research needs for academic research laboratories, funding agencies, professional societies, and industry.

The report of this roadmap was published in the past few days in Radiology, the RSNA journal. Research priorities in the report included:

  • new image reconstruction methods that efficiently produce images suitable for human interpretation from source data
  • automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting
  • new machine learning methods for clinical imaging data, such as tailored, pre-trained model architectures, and distributed machine learning methods
  • machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence)
  • validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets.

Another aim is to reduce clinically important errors, estimated at 3 to 6 percent of image interpretations by radiologists. Diagnostic errors play a role in up to 10 percent of patient deaths, according to this report.

It is interesting that machine learning, more than AI, is mentioned in the RSNA materials, for instance in stating that “Machine learning algorithms will transform clinical imaging practice over the next decade. Yet, machine learning research is still in its early stages.” Radiology actually pioneered store-and-forward technology, to where radiology interpretation has been farmed out nationally and globally for many years. This countered a decline in US radiologists as a percentage of the physician workforce that started in the late 1990s and continues to today with some positive trends (Radiology 2015). Perhaps this distribution model postponed development of machine learning technologies. Also Healthcare Dive, RSNA press release  

News roundup: Virginia includes RPM in telehealth, Chichester Careline changes, Sensyne AI allies with Oxford, Tunstall partners in Scotland, teledermatology in São Paolo

January 29, 2019 | by

Virginia closes in on including remote patient monitoring in telehealth law. Two bills in the Virginia legislature, House Bill 1970 and Senate Bill 1221, include remote patient monitoring (RPM) within their present telehealth and telemedicine guidelines and payment in state commercial insurance and the commonwealth’s Medicaid program. It is currently moving forward in House and Senate committees with amendments and. RPM is defined as “the delivery of home health services using telecommunications technology to enhance the delivery of home health care, including monitoring of clinical patient data….” Both were filed on 9 January. Virginia was an early adopter of parity payment of telemedicine with in-person visits. The University of Virginia has been a pioneer in telehealth research and is the home for the Mid-Atlantic Telehealth Resource Center. mHealth Intelligence

Chichester Careline switches to PPP Taking Care. Chichester Careline is currently a 24/7 care line services provided by Chichester District Council. Starting 1 March, PPP Taking Care, part of AXA PPP Healthcare, will manage the service. According to the Chichester release, costs will remain the same, technology will be upgraded, and telecare services will be added. Over the past 35 years, Chichester Careline has assisted over 1 million people across Britain. 

Sensyne collaborates with University of Oxford’s Big Data Institute (BDI) on chronic disease. The three-year program will use Sensyne’s artificial intelligence for research on chronic kidney disease and cardiovascular disease. Sensyne analyzes large databases of anonymized patient data in collaboration with NHS Trusts. BDI’s expertise is in population health, clinical informatics and machine learning. Their joint research will concentrate on two major elements within long-term chronic disease to derive new datasets: automating physician notes into a structure which can be analyzed by AI and integrating it into remote patient monitoring.  Release.

Tunstall partners with Digital Health & Care Institute Scotland. The partnership is in the Next Generation Solutions for Healthy Ageing cluster. Digital Health & Care supports the Scottish Government’s TEC Programme and the Digital Telecare Workstream. The program’s goals are to help Scots live longer, healthier lives and also create jobs.  Building Better Healthcare UK

Teledermatology powered by machine learning helps to solve a specialist shortage in São Paolo. Brazil has nationalized healthcare which has nowhere near enough specialists. São is a city with 20 million inhabitants, so large and spread out that when the aircraft crew announces that they are on approach to the airport, it takes two hours to touch the runway. The dermatology waitlist was up to 60,000 patients, each waiting 18 months to see a doctor. The solution: call every patient and instruct them to go to a doctor or nurse to take a picture of the skin condition. The photo is then analyzed and prioritized by an algorithm, with a check by dermatologists, to determine level of treatment. Thirty percent needed to see a dermatologist, only 3 percent needed a biopsy. Accuracy level is about 80 percent, and plans are in progress to scale it to the rest of Brazil. Mobihealthnews.

Comings and goings: CVS-Aetna finalizing, Anthem sued over merger, top changes at IBM Watson Health

November 8, 2018 | by

imageWhat better way to introduce this new feature than with a picture of a Raymond Loewy-designed 1947 Studebaker Starlight Coupe, where wags of the time joked that you couldn’t tell whether it was coming or going?

Is it the turkey or the stuffing? In any case, it will be the place you’ll be going for the Pepto. The CVS-Aetna merger, CVS says, will close by Thanksgiving. This is despite various objections floated by California’s insurance commissioner, New York’s financial services superintendent, and the advocacy group Consumers Union. CEO Larry Merlo is confident that all three can be dealt with rapidly, with thumbs up from 23 of the 28 states needed and is close to getting the remaining five including resolving California and NY. The Q3 earnings call was buoyant, with CVS exceeding their projected overall revenue with $47.3 billion. up 2.4% or $1.1 billion from the same quarter in 2017. The divestiture of Aetna’s Medicare Part D prescription drug plans to WellCare, helpful in speeding the approvals, will not take effect until 2020. Healthcare Dive speculates, as we did, that a merged CVS-Aetna will be expanding MinuteClinics to create urgent care facilities where it makes sense–it is not a big lift. And they will get into this far sooner than Amazon. which will split its ‘second headquarters’ among the warehouses and apartment buildings of Long Island City and the office towers of Crystal City VA.

Whatever happened to the Delaware Chancery Court battle between Anthem and Cigna? Surprisingly, no news from Wilmington, but that didn’t stop Anthem shareholder Henry Bittmann from suing both companies this week in Marion (Indiana) Superior Court. The basis of the suit is Anthem’s willfully going ahead with the attempted merger despite having member plans under the Blue Cross Blue Shield Association meant the merger was doomed to fail, and they intended all along for “Anthem to swallow, and then sideline, Cigna to eliminate a competitor, in violation of the antitrust laws.” On top of this, both companies hated each other. A match made in hell. Cigna has moved on with its money and bought Express Scripts.

IBM Watson Health division head Deborah DiSanzo departs, to no one’s surprise. Healthcare IT News received a confirmation from IBM that Ms. DiSanzo will be joining IBM Cognitive Solutions’ strategy team, though no capacity or title was stated. She was hired from Philips to lead the division through some high profile years, starting her tenure along with the splashy new Cambridge HQ in 2015, but setbacks mounted later as their massive data crunching and compilation was outflanked by machine learning, other AI methodologies, and blockchain. According to an article in STAT+ (subscription needed), they didn’t get the glitches in their patient record language processing software fixed in ‘Project Josephine’, and that was it for her. High profile partner departures in the past year such as MD Anderson Cancer Centers, troubles and lack of growth at acquired companies, topped by the damning IEEE Spectrum and Der Spiegel articles, made it not if, but when. No announcement yet of a successor.

Coffee break reading: a ‘thumbs down’ on IBM Watson Health from IEEE Spectrum and ‘Der Spiegel’

August 9, 2018 | by

In a few short years (2012 to now), IBM Watson Health has gone from being a 9,000 lb Harbinger of the Future to a Flopping Flounder. It was first MD Anderson Cancer Center at the University of Texas last year [TTA 22 Feb 17] kicking Watson to the curb after spending $62 million, then all these machine learning, blockchain, and AI upstarts doing most of what Watson was going to do, but cheaper and faster, which this Editor observed early on [TTA 3 Feb 17]. At the end of May, IBM laid off hundreds of workers primarily at three recently acquired data analytics companies. All came on board as market leaders with significant books of business: Phytel, Explorys, and Truven. Clients have evaporated; Phytel, before the acquisition ranked #1 by KLAS in analytics for its patient communication system, reportedly went from 150 to 80 clients. IBM denies the layoffs were anything but much-needed post-acquisition restructuring and refocusing on high-value segments of the IT market.

IEEE Spectrum rated the causes as corporate mismanagement (mashing Phytel and Explorys; IBM’s ‘bluewashing’ acquired companies; the inept ‘offering management’ product development process; the crushed innovation) plus inroads made by competition (those upstarts again!). What’s unusual is the sourcing from former engineers–IEEE is the trade group for tech and engineering professionals. The former IBM-ers were willing to talk in detail and depth, albeit anonymously. 

Der Spiegel takes the German and clinical perspective of what IBM Watson Health has gone wrong, starting with the well-documented failures of Watson at hospitals in Marburg and Giessen. The CEO of Rhön-Klinikum AG, which owns the university hospital at Marburg, reviewed it in action in February. “The performance was unacceptable — the medical understanding at IBM just wasn’t there.” It stumbled over and past diagnoses even a first-year resident would have considered. The test at Marburg ended before a single patient was treated.

The article also outlines several reasons why, including that Watson, after all this time, still has trouble crunching real doctor and physical data. It does not comprehend physician shorthand and negation language, which this Editor imagines is multiplied in languages other than American English. “Some are even questioning whether Watson is more of a marketing bluff by IBM than a crowning achievement in the world of artificial intelligence.” More scathingly, the Rhön-Klinikum AG CEO: “IBM acted as if it had reinvented medicine from scratch. In reality, they were all gloss and didn’t have a plan. Our experts had to take them by the hand.”

Hardly The Blue Wave of the Future. Perhaps the analogy is Dr. Watson as The Great Oz.

Digital health is not here. Or it is. Or it’s still “the future” and we’re waiting for the ship to come in.

February 23, 2018 | by

[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2016/06/long-windy-road.jpg” thumb_width=”150″ /]Another bit of convergence this week and last is the appearance of several articles, closely together, about digital health a/k/a health tech or ‘Dr. Robot’. It seems like that for every pundit, writer, and guru who believes “We’ve Arrived”, there’s some discouraging study or contra-news saying “We’re Nowhere Near The New Jerusalem”. This Editor’s been on the train since 2006 (making her a Pioneer but not as Grizzled as some), and wonders if we will ever Get There. 

Nearing Arrival is the POV of Naomi Fried’s article in Mobihealthnews giving her readers the keys to unlock digital health. “Digital health will be the dominant form of non-acute care.” It has value in chopping through the thicket of the low clinical impact technologies that dominate the current scene (Research2Guidance counted only 325,000 health apps and 3.6bn downloads in 2017). Where the value lies:

  1. Diagnosis and evaluation–devices that generate analyzable data
  2. Virtual patient care–telehealth and remote patient monitoring
  3. Digiceuticals–digital therapeutics delivered via apps
  4. Medication compliance–apps, sensors, games, ingestibles (e.g. Proteus) 

At the Arrival Platform and changing the timetable is machine learning. Already algorithms have grown into artificial neural networks that mimic animal learning behavior. Though the descriptions seem like trial and error, they are fast cycling through cheap, fast cloud computing. Machine learning already can accurately diagnose skin cancer, lung cancer, seizure risk, and in-hospital events like mortality [TTA 14 Feb]. It’s being debated on how to regulate them which according to Editor Charles Lowe will be quite difficult [TTA 25 Oct 17]. Returning to machine learning, its effect on diagnosis, prognosis, and prediction may be seismic. Grab a coffee for The Training Of Dr. Robot: Data Wave Hits Medical Care (Kaiser Health News). Hat tip to EIC Emeritus Steve Hards.

The (necessary?) bucket of Cold Water comes from KQED Science which looked at two studies and more, and deduced that the Future Wasn’t Here. Yet.:

  1. NPJ Digital Medicine’s 15 Jan meta-analysis of 16 remote patient monitoring (RPM) studies using biosensors (from an initial scan of 777) and found little evidence that RPM improves outcomes. The researchers found that many patients are not yet interested in or willing to share RPM data with their physicians. The fact that only 16 randomized controlled trials (RCTs) made the cut is indicative of the lack of maturity (or priority on research) for RPM. 
  2. In JMIR 18 Jan, a systematic review of 23 systematic reviews of 371 studies found that efficacy of mobile health interventions was limited, but there was moderate quality evidence of improvement in asthma patients, attendance rates, and increased smoking abstinence rates. 

Even a cute tabletop socially assistive robot given to COPD patients that increases inhaler medication adherence by 20 points doesn’t seem to cut hospital readmissions. The iRobot Yujin Robot helping patients manage their condition through medication and exercise adherence lets patients admit that they are feeling unwell so that a clinician could check on them either through text or phone and if needed to see their regular doctor. The University of Auckland researchers recommended improvements to the robot, integration to the healthcare system, and comparisons to other remote monitoring technology. JMIR (18 Feb), Mobihealthnews.

As Dr. Robert Wachter of UCSF put it to the KQED reporter, we’re somewhere on the Gartner Hype Cycle past the Peak of Inflated Expectations. But this uneven picture may actually be progress. Perhaps we are moving somewhere between the Slough (ok, Trough) of Disillusionment and the Slope of Enlightment, which is why it’s so confusing?

Google ‘deep learning’ model more accurately predicts in-hospital mortality, readmissions, length of stay in seven-year study

February 14, 2018 | by

A Google/Stanford/University of California San Francisco/University of Chicago Medicine study has developed a better predictive model for in-hospital admissions using ‘deep learning’ a/k/a machine learning or AI. Using a single data structure and the FHIR standard (Fast Healthcare Interoperability Resources) for each patient’s EHR record, they used de-identified EHR derived data from over 216,000 patients hospitalized for over 24 hours from 2009 to 2016 at UCSF and UCM. Over 47bn data points were utilized.

The researchers then looked at four areas to develop predictive models for mortality, unplanned readmissions (quality of care), length of stay (resource utilization), and diagnoses (understanding of a patient’s problems). The models outperformed traditional predictive models in all cases and because they used a single data structure, are projected to be highly scalable. For instance, the accuracy of the model for mortality was achieved 24-48 hours earlier (page 11). The second part of the study concerned a neural-network attribution system where clinicians can gain transparency into the predictions. Available through Cornell University Library. Abstract. PDF.

The MarketWatch article rhapsodizes about these models and neural networks’ potential for cutting healthcare costs but also illustrates the drawbacks of large-scale machine learning and AI: what’s in the EHR including those troublesome clinical notes (the study used three additional deep neural networks to discern which bits of the clinical data within the notes were relevant), lack of uniformity in the data sets, and most patient data not being static (e.g. temperature). 

And Google will make the chips which will get you there. Google’s Tensor Processing Units (TPUs), developed for its own services like Google Assistant and Translate, as well as powering identification systems for driverless cars, can now be accessed through their own cloud computing services. Kind of like Amazon Web Services, but even more powerful. New York Times

Themes and trends at Aging2.0 OPTIMIZE 2017

November 10, 2017 | by

Aging2.0 OPTIMIZE, in San Francisco on Tuesday and Wednesday 14-15 November, annually attracts the top thinkers and doers in innovation and aging services. It brings together academia, designers, developers, investors, and senior care executives from all over the world to rethink the aging experience in both immediately practical and long-term visionary ways.

Looking at OPTIMIZE’s agenda, there are major themes that are on point for major industry trends.

Reinventing aging with an AI twist

What will aging be like during the next decades of the 21st Century? What must be done to support quality of life, active lives, and more independence? From nursing homes with more home-like environments (Green House Project) to Bill Thomas’ latest project–‘tiny houses’ that support independent living (Minkas)—there are many developments which will affect the perception and reality of aging.

Designers like Yves Béhar of fuseproject are rethinking home design as a continuum that supports all ages and abilities in what they want and need. Beyond physical design, these new homes are powered by artificial intelligence (AI) and machine learning technology that support wellness, engagement, and safety. Advances that are already here include voice-activated devices such as Amazon Alexa, virtual reality (VR), and IoT-enabled remote care (telehealth and telecare).

For attendees at Aging2.0, there will be substantial discussion on AI’s impact and implications, highlighted at Tuesday afternoon’s general session ‘AI-ging Into the Future’ and in Wednesday’s AI/IoT-related breakouts. AI is powering breakthroughs in social robotics and predictive health, the latter using sensor-based ADL and vital signs information for wellness, fall prevention, and dementia care. Some companies part of this conversation are CarePredict, EarlySense, SafelyYou, and Intuition Robotics.

Thriving, not surviving

Thriving in later age, not simply ‘aging in place’ or compensating for the loss of ability, must engage the community, the individual, and providers. There’s new interest in addressing interrelated social factors such as isolation, life purpose, food, healthcare quality, safety, and transportation. Business models and connected living technologies can combine to redesign post-acute care for better recovery, to prevent unnecessary readmissions, and provide more proactive care for chronic diseases as well as support wellness.

In this area, OPTIMIZE has many sessions on cities and localities reorganizing to support older adults in social determinants of health, transportation innovations, and wearables for passive communications between the older person and caregivers/providers. Some organizations and companies contributing to the conversation are grandPad, Village to Village Network, Lyft, and Milken Institute.

Technology and best practices positively affect the bottom line

How can senior housing and communities put innovation into action today? How can developers make it easier for them to adopt innovation? Innovations that ‘activate’ staff and caregivers create a multiplier for a positive effect on care. Successful rollouts create a positive impact on both the operations and financial health of senior living communities.

(more…)

AI good, AI bad (part 2): the Facebook bot dialect scare

August 2, 2017 | by

[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2017/08/ghosty.jpg” thumb_width=”175″ /]Eeek! Scary! Bots develop their own argot. Facebook AI Research (FAIR) tested two chatbots programmed to negotiate. In short order, they developed “their own creepy language”, in the words of the Telegraph, to trade their virtual balls, hats, and books. “Creepy” to FAIR was only a repetitive ‘divergence from English’ since the chatbots weren’t limited to standard English. The lack of restriction enabled them to develop their own argot to quickly negotiate those trades. “Agents will drift off understandable language and invent codewords for themselves,” said Dhruv Batra, visiting research scientist from Georgia Tech at Facebook AI Research. “This isn’t so different from the way communities of humans create shorthands.” like soldiers, stock traders, the slanguage of showbiz mag Variety, or teenagers. Because Facebook’s interest is in AI bot-to-human conversation, FAIR put in the requirement that the chatbots use standard English, which as it turns out is a handful for bots.

The danger in AI-to-AI divergence in language is that humans don’t have a translator for it yet, so we’d never quite understand what they are saying. Batra’s unsettling conclusion: “It’s perfectly possible for a special token to mean a very complicated thought. The reason why humans have this idea of decomposition, breaking ideas into simpler concepts, it’s because we have a limit to cognition.” So this shorthand can look like longhand? FastCompany/Co.Design’s Mark Wilson sees the upside–that software talking their own language to each other could eliminate complex APIs–application program interfaces, which enable different types of software to communicate–by letting the software figure it out. But for humans not being able to dig in and understand it readily? Something to think about as we use more and more AI in healthcare and predictive analytics.