Tag: technology

Given rapid advances in AI, it’s interesting to think about what they mean for US healthcare. Healthcare is a combination of (1) compute tasks (some version of “given their symptoms, how can we help this patient”), (2) simple interventions (“take drug X once a day”), (3) basic patient care in a hospital or other care setting – take vitals, start a line, take blood sample, provide food, and (4) highly specialized procedures e.g. trauma surgery or image guided cardiovascular procedures.

1. Compute/diagnostics/monitoring tasks All text/image/audio compute tasks are ripe for automation and there will be increasing pressure to automate them (higher hospital profits, faster, potential liability for failure to use best methods/tools, improved patient outcomes). Lobbying by professional societies will slow the rate of adoption, but that’s a loosing battle and hopefully the affected fields will rethink themselves rather than focus on delaying the inevitable.

2. Simple interventions can be scaled/automated by connecting an online pharmacy with the triage or diagnostic AI. This trend is already underway, with a human doctor in the loop largely for legal/regulatory reasons. Expect lobbying for transition to a fully-automated integration of #1 and #2 based on super-human performance of the triage/diagnostic AI – if the computer-only system is better than the one with the human doctor in the loop, presumably the FDA and other regulators will cave in at some point (although this may take many years).

3. Basic patient care is here to stay in some form – it consists of many different tasks that can be hard to automate, despite e.g. Japan’s long standing robotics R&D for supporting their aging population through care robots. The main trend will be to replace tasks requiring specialized skills (such as currently provided by registered nurses (RNs) and Physician Assistants (PAs)) with tasks that can be performed by lower paid staff (such as CNAs, certified nurse aides). For example, CNAs do not normally draw blood but hospital procedural changes could normalize that, after additional training for the CNAs. Per #1, any monitoring/diagnostic tasks currently provided by RNs and PAs will be increasingly automated, based on cost/speed/scaling/liability. In parallel, medtech tools and devices that radically simplify existing patient care procedures – such as placing IV lines, taking vitals, or drawing blood – will be championed by hospital CFOs.

4. Highly specialized procedures Upon first thought, it’s hard to imagine things like heart and brain surgery being massively impacted by AI and automation. Sure, minimally invasive procedures are growing and surgeons now frequently use robots, but the basics seem solid (highly trained humans use tools to help patients). The real threat to surgeons (and opportunity for medtech investors and innovators) are tools that allow complex procedures to be completely avoided or replaced by simple procedures. A great example is the replacement of amniocentesis or CVS by non-invasive prenatal testing (NIPT). Rather than first needing to manually collect and biopsy placental cells with a long needle or catheter, equivalent genetic information can be obtained through a simple blood draw and subsequent characterization of the circulating fetal DNA. NIPT is a win for (almost) everyone, since it reduces miscarriage risk to the mom and replaces a highly specialized procedure (done by an experienced doctor with ultrasound guidance) with a vastly simpler procedure (a basic blood draw) that can be performed by a phlebotomy technician. Presumably, startups focusing on down-skilling procedures/interventions currently requiring highly trained doctors, to services that can be performed by aides or technicians, will receive much investment.

TLDR

Trends and opportunities to look out for:

1. AI decision support tech that reduces costs and improves patient outcomes. AI decision support is the precursor to replacing humans due to the need to first collect human vs. computer performance data for regulatory filings, scientific publications, and marketing materials; decision support tools are a natural entry point and necessary stepping stone to full automation.
2. Medtech tools/devices that allow basic patient care to be primarily provided by aides and technicians rather than RNs and PAs, since monitoring/diagnostics will be increasingly provided by computers rather than humans.
3. Medtech tools/devices that dramatically down-skill (or bypass) the need for procedures/interventions currently provided by highly trained professionals. For example, tests using circulating tumor DNA reduce the need for tumor biopsies and redirect payments from doctors and hospitals to genomics/diagnostics tech companies.

Based on a slew of papers over the last few years, as summarized in a Stanford seminar by Bram van Ginneken (“Why AI Should Replace Radiologists”, Nov. 15, 2023), AIs now consistently outperform the best human radiologists across most image/diagnostic tasks. His hope is that human radiologists will lead a transition to AI-based radiology, to improve health outcomes and accessibility while reducing costs. The writing is on the wall and some radiology residents at Stanford are dropping out to start AI-enabled radiology companies, presumably reflecting their agreement with van Ginneken. However, the US healthcare system is a complex for-profit system, unlike European outcomes-focused health systems, and it’s interesting to wonder what the endgame for human radiology will look like in the US. Let’s consider some of the stakeholders:

1/ Patients and Patient Advocacy Groups

Patients rightfully assume they are getting the best care. It will be hard for patients to understand why their images are being read by humans, despite clear scientific evidence that replacing humans with computers would benefit their health, such as by reducing false positives, reducing wait times, and reducing false negatives. At some point, national advocacy groups like the National Breast Cancer Foundation and the National Breast Cancer Coalition will start to ask hard questions about patient benefit and which methods – people or computers – should be used to screen mammograms, just to give one example.

2/ Malpractice Insurance and Trial Lawyers

For an insurer, it’s presumably hard to justify providing reasonably-priced malpractice coverage when a field persists, against scientific evidence, in using antiquated procedures, such as human-based radiology. This is not yet an issue because doctors in the US can only be sued for failing to provide the ‘standard of care’, which is still based on humans. So, as strange as this sounds for a patient, from a liability perspective, it doesn’t matter that there are better technologies out there, since (legally speaking) radiologists do not promise to provide the best care; rather they promise to (and are held accountable for) providing the ‘standard of care’. However, at some point, a smart trial lawyer will connect the dots, see an opportunity, and work with national advocacy groups and affected patients to drive change.

3/ Human Radiologists

Just to be clear, the radiologists I know are awesome people and doctors – sharp, dedicated, passionate, and wanting the best for their patients. What does AI do to their jobs and their job satisfaction? The key issue is liability – imagine the hospital introduces an AI radiology assistant to provide decision support and imagine further that the AI assistant has been demonstrated to outperform even the best humans. Currently, a human doctor must review computer generated findings/suggestions, and can then either (1) accept the computer’s suggestion and sign the note, or (2) disagree with the AI and manually enter an alternative (which, on average, will be worse than what the computer concluded). Very soon, choosing to disagree with a computer known to outperform humans will prompt a call by the hospital’s office of risk management, who are trying to protect the hospital from lawsuits. So then, playing this forward, the human radiologist can either: (1) agree with the computer and click “concur and sign”, or (2) disagree with the computer, write a 4 page memo to risk management to justify the ‘deviation’, and hope they were right. On average, the human radiologist will be wrong, so that strategy is a losing one for all stakeholders (doctors, patients, risk management lawyers, hospital CFO, insurance companies). Rather, the optimal long-term strategy will be to always agree with the quantifiably better computer. At that point, the human radiologist will wonder if the (minimally) 8 years of training, the residency, and the nights were worth it, if their job consists of clicking “concur and sign” 38+ times an hour while sitting at their PACS station.

4/ Tech enabled healthcare competitors

Technology companies with long term strategic interest in healthcare and extensive capabilities in AI, computer vision, and healthcare backends, most notably Amazon, have no need to protect traditional workflows or professions. Presumably, they will seek to optimize overall profit. This is an “all upside” scenario for them – they can offer a better product at lower cost to tens of millions of patients. Certainly, they are regulatory and political barriers to replacing humans across radiology, but these barriers will gradually fall in the face of tech industry lobbying, patient advocates demanding the best care (not just the ‘standard of care’), the difficulty of convincing medical students to chose radiology, and the (increasing) cost of malpractice insurance for radiology.

Outlook

Based on the confluence of the above, my hope is that US radiology will embrace AI not as an existential threat, but as the foundation of modern, reliable, and scalable healthcare. Who are the dedicated, passionate, and smart doctors with excellent quantitative and computer skills who will help to build a healthcare system that provides better care at lower cost? If human radiologists accept that challenge, their profession is secure and they will continue to be at the center of figuring out what’s wrong with people and helping them for a long time to come.