It’s not well known but there’s a lot of people in hospitals who spend a lot of time creating patient registries for quality programs, CMS reporting, clinical trials and lots more. It requires extremely detailed abstraction of patient data from patient records and comparisons with registry demands. Wouldn’t it be clever if an AI system could read the chart and help the people doing that work (usually very expensive nurses) do it quicker? That’s the premise behind Carta Healthcare. Greg Miller and Jared Crapo from Carta demoed the system for me and told me about the market for it–Matthew Holt
Will HHS Enhance or Stall the Promise of Artificial Intelligence for Healthcare?
By STEVEN ZECOLA
In its Strategy for Artificial Intelligence (V.3), the Department of Health and Human Services (“HHS”) acknowledges that: “For too long, our Department has been bogged down by bureaucracy and busy work.” HHS promises that it will accelerate artificial intelligence (“AI”) innovation, including “accelerating drug and biologic approvals at the FDA.”
History shows that well-intended but cumulative regulatory intervention – more so than scientific complexity – is the primary deterrent to rapid technological progress. If AI is subject to the typical pattern of regulatory creep, its potential to accelerate drug discovery and development will be significantly reduced. To avoid this outcome, HHS should develop a plan that is premised on a zero-based regulatory approach. That is, each new technology such as AI should start with a clean slate and only the minimum requirements deemed necessary to show effectiveness and safety should be applied in the approval process for that technology.
The Pace of Innovation
Medical innovation has lagged the pace in the other sectors of the economy. As Dr. Scott Podolsky of Harvard Medical School observed: “Medicine in 2020 is much closer to medicine in 1970 than medicine in 1970 was to medicine in 1920.” Podolsky points to breakthroughs such as antibiotics, antihypertensives, antidepressants, antipsychotics, and steroids that have not been met with same impact as innovations in the later 50 years.
Two explanations have been offered for this phenomenon: 1) the inherent complexity of biological processes; and 2) the regulatory approval process.
As a benchmark for comparison to the following case studies, the development of 4G communications spanned less than a decade, with discussions starting around 2001, technical specifications being released in 2004, and the first commercial networks launching in 2009.
Regulatory Intervention in New Technologies
- The Human Genome (Great Science Leads to Regulatory Paralysis)
The Human Genome Project (HGP) ran from 1990 to 2003, and has been lauded as one of the world’s greatest scientific achievements. The project identified the specific location of genes and DNA, creating a “roadmap” of the human genetic code and facilitating the identification of disease-related genes.
The HGP focused on balancing rapid scientific progress with ethical safeguards. Oversight was primarily managed through internal ethical programs and international data-sharing agreements rather than a single overarching legislative or regulatory body.
Under this structure, the HGP beat its target date by two years. That is to say that the complexity of the problem did not cause any delays, and progress was not impeded by the standard drug-approval bottleneck.
However, once the genetic roadmap was handed off for drug discovery and development, progress slowed dramatically.
Continue reading…The Dimensions of Artificial Intelligence in the Healthcare Industry
By STEVEN ZECOLA
On December 19th, the Department of Health and Human Services (“HHS”) issued a Request for Information seeking to harness artificial intelligence (“AI”) to deflate health care costs and make America healthy again.
As described herein, AI can be used in many dimensions to help lower healthcare costs and improve care. However, to achieve significant breakthroughs with AI, HHS will need to completely revamp the regulatory approach to drug discovery and development.
Dimension #1. Incorporation of AI into Drug Discovery
The biggest benefit to the healthcare industry’s performance from AI is achievable from drug discovery. Accounting for the costs of failures, the average FDA drug approval costs society almost $3 billion and takes decades to reach the market from its inception in the lab.
In contrast, AI identifies potential treatments much faster than traditional methods by processing vast amounts of biological data, uncovering hidden causal relationships, and generating new actionable insights.
AI is particularly promising for complex, multifactorial conditions – such as neurodegenerative diseases, autism spectrum disorders, and multiple chronic illnesses – where conventional reductionist approaches have failed.
In the short-run, HHS should direct its grants toward AI-generated basic research, with a particular emphasis on the hard-to-solve illnesses. At the same time, the FDA should be putting into place a new approval system for AI-initiated programs to enable breakthrough treatments in a compressed timetable.
Dimension #2. Incorporation of AI into the Drug Development Process
Simply relying on AI for drug discovery, while subjecting its advances to the current approval process would undermine the use of the technology.
Rather, improvements from AI can already be had in fulfilling the exhaustive regulatory documentation requirements, which today add up to as much as 30% of the cost of compliance.
Continue reading…Kai Romero, Evidently
Kai Romero is Head of Clinical Success at Evidently. The company is one of many that are using AI to dive into the EMR and extract data to deliver it to clinicians. It works to get really great information from the EMR to various flavors of clinicians in a fast and innovative way. Kai leads me on a detailed exploration of how the technology gets used as a layer over the EMR. And Kai shows me the new version that allows and LLM to deliver immediate answers from the data. This is a demo you really need to see to understand how AI is changing, and improving, that clinical experience. Meanwhile Kai is fascinating. She was an ER doc who became a specialist in hospice. We didn’t get into that too much, but you can tell about her input into Evidently’s design — Matthew Holt
Artificial Intelligence Renders the FDA’s Current Drug Approval Process to be Obsolete
By STEVEN ZECOLA
Artificial intelligence (“AI”) has taken root in the field of drug discovery and development and already has shown signs of running past the traditional model of doing research. Congress should take note of these rapid changes and: 1) direct the Department of Health and Human Services (“HHS”) to phase down the government’s basic research grant program for non-Ai applicants, 2) require HHS to redirect these monies to fund nascent artificial intelligence applications, and 3) require HHS to revamp the roadmap for drug approvals of AI-driven trials to reflect the new capabilities for drug discovery and development.
Background
There are four distinguishing features of the U.S. healthcare industry.
First, the industry’s costs as a percentage of GNP have increased from 8% in 1980 to 17% today, and are expected to exceed 20% by 2030. The federal government subsidizes roughly one-third of these costs. These subsidies are not sustainable as healthcare costs continue to skyrocket, especially in the face of an overall $37 trillion federal deficit.
Second, the industry is regulated under a system that results in an average of 18 years of basic research and 12 years of clinical research for each drug approval. The clinical cost per newly approved drug now exceeds $2 billion. The economics of drug discovery are so unattractive to investors that the federal government and charitable foundations fund virtually all basic research. The federal government does so to the tune of $44 billion per year. When this cost is spread among the 50 or so drug approvals per year, it adds a cost of roughly $880 million to each drug, bringing the total cost to over $3 billion per drug approval. Worse yet, the process is getting slower and more costly each year. As such, drug discoveries under the current research approach will not be a significant contributor to lowering the overall healthcare costs.
Third, the Trump administration has undercut the federal government’s role in healthcare by firing several thousand employees from HHS. Thus, the agency can no longer effectively administer its previously adopted rules and regulations, and therefore, cannot be expected to shepherd drug discovery into lowering healthcare costs.
Fourth, on the positive side, artificial intelligence software combined with the massive and growing computational capacity of supercomputers have shown the potential to dramatically lower the cost of drug discovery and to radically shorten the timeline to identify effective treatments.
Enter Artificial Intelligence (AI) into Drug Discovery
For the past decade, a handful of companies have been exploring advanced automation techniques to improve the many facets of the drug discovery process. Improvements can now be had in fulfilling regulatory documentation requirements, which today add up to as much as 30% of the cost of compliance. More significantly, Ai can be used to accurately create comprehensive clinical documents from raw data with citations and cross-references – and continually update and validate the documentation.
The top Ai drug discovery companies include Insilico Medicine, Atomwise, and Recursion, which leverage Ai to accelerate various stages of drug development, from target identification to clinical trials. Other notable companies are BenevolentAI, Insitro, Owkin, and Schrödinger, alongside technology providers like Nvidia that supply critical Ai infrastructure for the life sciences sector.
Continue reading…AAAA (the four A)
By JACOB REIDER
I haven’t blogged this yet, which kinda surprises me, since I find myself describing it often.
Let’s start with an overview. We can look at health information through the lens of a lifecycle.
The promise of Health Information Technology has been to help us – ideally to achieve optimal health in the people we serve.
The concept @ the beginning of the HITECH act was: “ADOPT, CONNECT, IMPROVE.”
These were the three pillars of the Meaningful Use Incentive programs.
Adopt technology so we can connect systems and therefore improve health.
Simple, yes?
Years later, one can argue that adoption and even connection have (mostly) been accomplished.
But the bridge between measurement and health improvement isn’t one we can easily cross with the current tools available to us.
Why?
Many of the technical solutions, particularly those that promote dashboards, are missing the most crucial piece of the puzzle. They get us close, but then they drop the ball.
And that’s where this “simple”AAAA” model becomes useful.
For data and information to be truly valuable in health care, it needs to complete a full cycle.
It’s not enough to just collect and display. There are four essential steps:
1. Acquire. This is where we gather the raw data & information. EHR entries, device readings, patient-reported outcomes … the gamut of information flowing into our systems. Note that I differentiate between data (transduced representations of the physical world: blood pressure, CBC, the DICOM representation of an MRI, medications actually taken) and information (diagnoses, ideas, symptoms, the problem list, medications prescribed) because data is reliably true and information is possibly true, and possibly inaccurate. We need to weigh these two kinds of inputs properly – as data is a much better input than information. (I’ll resist the temptation to go off on a vector about data being a preferable input for AI models too … perhaps that’s another post.)
2. Aggregate. Once acquired, this data and information needs to be brought together, normalized, and cleaned up. This is about making disparate data sources speak the same language, creating a unified repository so we can ask questions of one dataset rather than tens or hundreds.
3. Analyze. Now we can start to make sense of it. This is where clinical decision support (CDS) begins to take shape, how we can identify trends, flag anomalies, predict risks, and highlight opportunities for intervention. The analytics phase is where most current solutions end. A dashboard, an alert, a report … they all dump advice – like a bowl of spaghetti – into the lap of a human to sort it all out and figure out what to do.
Sure … you can see patterns, understand populations, and identify areas for improvement … All good things. The maturity of health information technology means that aggregation, normalization, and sophisticated analysis are now far more accessible and robust than ever before. We no longer need a dozen specialized point solutions to handle each step; modern platforms can integrate it all. This is good – but not good enough
A dashboard or analytics report, no matter how elegant, is ultimately passive. It shows you the truth, but it doesn’t do anything about it.
Continue reading…Avasure: Tech for helpful watching & remote care in hospitals
Lisbeth Votruba, the Chief Clinical Officer and Dana Peco, the AVP of Clinical Informatics from Avasure came on THCB to explain how their AI enabled surveillance system improves the care team experience in hospitals and health care facilities. Their technology enables remote nurses and clinical staff to monitor patients, and manage their care in a tight virtual nursing relationship with the staff at the facility, and also deliver remote specialty consults. They showed their tools and services which are now present in thousands of facilities and are helping with the nursing shortage. A demo and great discussion about how technology is improving the quality of care and the staff experience–Matthew Holt
What A Digital Health Doc Learned Recertifying His Boards
By JEAN LUC NEPTUNE
I recently got the good news that I passed the board recertification exam for the American Board of Internal Medicine (ABIM). As a bit of background, ABIM is a national physician evaluation organization that certifies physicians practicing internal medicine and its subspecialties (every other specialty has its own board certification body like ABOG for OB/GYNs and ABS for surgeons). Doctors practicing in most clinical environments need to be board-certified to be credentialed and eligible to work. Board certification can be accomplished by taking a test every 10 years or by participating in a continuing education process known as LKA (Longitudinal Knowledge Assessment). I decided to take the big 10-year test rather than pursue the LKA approach. For my fellow ABIM-certified docs out there who are wondering why I did the 10-year vs. the LKA, I’m happy to have a side discussion, but it was largely a career timing issue.
Of note, board certification is different from the USMLE (United States Medical Licensing Examination) which is the first in a series of licensing hurdles that doctors face in medical school and residency, involving 3 separate tests (USMLE Step 1, 2 and 3). After completing the USMLE steps, acquiring a medical license is a separate state-mediated process (I’m active in NY and inactive in PA) and has its own set of requirements that one needs to meet in order to practice in any one state. If you want to be able to prescribe controlled substances (opioids, benzos, stimulants, etc.), you will need a separate license from the DEA (the Drug Enforcement Administration, which is a federal entity). Simply put, you need to complete a lot of training, score highly on many standardized tests, and acquire a bunch of certifications (that cost a lot of money, BTW) to be able to practice medicine in the USofA.
What I learned in preparing for the ABIM recertification exam:
1.) There’s SO MUCH TO KNOW to be a doctor!
To prepare for the exam I used the New England Journal of Medicine (NEJM) review course which included roughly 2,000 detailed case studies that covered all the subspecialty areas of internal medicine. If you figure that each case involves mastery of dozens of pieces of medical knowledge, the exam requires a physician to remember tens of thousands of distinct pieces of information just for one specialty (remember that the medical vocabulary alone consists of tens of thousands of words). In addition, the individual facts mean nothing without a mastery of the basic underlying concepts, models, and frameworks of biology, biochemistry, human anatomy, physiology, pathophysiology, public health, etc. etc. Then there’s all the stuff you need to know for your specific speciality: medications, diagnostic frameworks, treatment guidelines, etc. It’s a lot. There’s a reason it takes the better part of a decade to gain any competency as a physician. So whenever I hear a non-doc saying that they’ve been reading up on XYZ and “I think I know almost as much as my doctor!”, my answer is always “No you don’t. Not at all. Not even a little bit. Stop it.”
2.) There is so much that we DON’T KNOW as doctors!
What was particularly striking to me as I did my review was how often I encountered a case or a presentation where:
- It’s unclear what causes a disease,
- The natural history of the disease is unclear,
- We don’t know how to treat the disease,
- We know how to treat the disease but we don’t how the treatment works,
- We don’t know what treatment is most effective, or
- We don’t know what diagnostic test is best.
- And on, and on, and on…
It’s estimated that there are more than 50,000 (!!) active journals in the field of biomedical sciences publishing more than 3 million (!!!!) articles per year. Despite all this knowledge generation there’s still so much we don’t know about the human body and how it works. I think some people find doctors arrogant, but anyone who really knows doctors and physician culture can tell you that doctors possess a deep sense of humility that comes out of knowing that you actually know very little.
3.) Someday soon the computer doctor will FOR SURE be smarter than the human doctor.
The whole time I was preparing for the test, I kept telling myself that there was nothing I was doing that a sufficiently advanced computer couldn’t accomplish.
Continue reading…Penguin–The Flightless Bird of Health AI
Fawad Butt and Missy Krasner started a new AI company which is building a big platform for both plans and providers in health care. Penguin Ai has a cute name, but is serious about trying to provide an underlying platform that is going enable agents across the enterprise. They are health care only, as opposed to the big LLMs. But does health care need a separate AI company? Are the big LLMs going to give up health? And what about that Epic company? Join us as we discuss how this AI thing is going to be deployed across health care, and how Penguin is going to play. Oh and they raised $30m series A to start getting it done–Matthew Holt
Dr Kaelee Brockway on AI for physical therapy training
Dr Kaelee Brockway is a professor of education and physical therapy who has built a series of AI based “patients” for her PT students to train on. Kaelee is a pioneer in using these tools for training. She showed me the personas that she has built with LLMs that are now being used by her students to figure out how to train their soft skills–a huge part of any training. This a great demo and discussion about how clinical professionals are going to use LLMs in their training and their work–Matthew Holt
