{"id":4559,"date":"2024-09-11T16:03:31","date_gmt":"2024-09-11T16:03:31","guid":{"rendered":"https:\/\/pwer.com\/?p=4559"},"modified":"2024-09-17T13:25:35","modified_gmt":"2024-09-17T13:25:35","slug":"healthcare-ai-is-not-hype-most-people-are-looking-in-the-wrong-direction","status":"publish","type":"post","link":"https:\/\/pwer.com\/?p=4559","title":{"rendered":"Healthcare AI is Not Hype – Most People Are Looking in the Wrong Direction!"},"content":{"rendered":"

We have been strongly advocating that\u00a0Artificial Intelligence (AI) is going to change healthcare,\u00a0<\/strong>and we follow it with\u00a0\u201cnot in the way most expect.\u201d<\/strong>\u00a0As of the second quarter of 2024, according to\u00a0Modern Healthcare September 10, 2024:<\/em><\/strong><\/p>\n

..of the $60 billion invested by venture capital firms into healthcare AI companies since 2014, more than half has come since 2021 and 70% in the last five years. The increase is due to favorable capital market dynamics, advances in AI and increasing technology needs fueled by the COVID-19 pandemic.<\/p>\n

The problems in healthcare are huge \u2013 $4.7 trillion annual expenditures.\u00a0 That has attracted the capital; however, a huge point to note, most of the money has gone to over 300 clinical care AI start-ups that have received over $11 billion. Healthcare financial focused AI companies have received $6 billion, and patient engagement have received over $3 billion.<\/p>\n

We think AI is too early and not \u201csmart enough\u201d to really work effectively and financially (ROI) in direct healthcare applications. The Forbes article below explains it better than I can.<\/p>\n

This is why we have focused our attention in\u00a0HealthScoreAI<\/u>\u2122\u00a0<\/strong>on \u201cempowering\u201d the consumer with getting access to the 500 Electronic Medical Records in the US alone.\u00a0 Readily obtaining this access has NOT worked as we had expected. We have 3,000 exabytes of healthcare data in the USA that are both stuck in silos and not effectively usable by consumers. We want to change that!\u00a0 This is an incredibly valuable way to use the AI we have available today.\u00a0 We\u2019re not by trying to solve all the world\u2019s problems using Large Language Models (LLMs), or even Small Language Model (SLMs), how about just Intermediate Language Models (ILMs)? More on that later.<\/p>\n

We are highlighting this article, because in our model, we can execute today by gathering longitudinal data to inform consumers, not to treat them or diagnose them, but to be more informed in their conversation with their healthcare provider. We are squarely on the consumers\u2019 side in this situation, not part of the healthcare system.<\/p>\n

In\u00a0\u201c3 Ways to Manage AI Hype in Healthcare,\u201d<\/em><\/strong>\u00a0Spencer Dorn, MD, discusses how the growing expectations surrounding AI\u2019s potential in healthcare have not yet been realized. Despite large investments, the impact of AI remains limited due to technical challenges, regulatory complexities, and skepticism from healthcare professionals. Predictive algorithms struggle to generalize across different settings, and generative AI like GPT-4 is unreliable for critical tasks such as diagnosis and treatment. Additionally, past experiences with digital tools like electronic health records contribute to doubts about AI\u2019s ability to enhance healthcare.<\/p>\n

Dorn recommends three (3) strategies for healthcare organizations navigating AI\u2019s slow adoption. First, experiment safely and evaluate AI solutions rigorously through evidence-based practices. Second, improve care systems by addressing bottlenecks before applying AI to increase efficiency. Third, accept incremental gains and avoid overhyping AI\u2019s potential. Instead, organizations should focus on smaller, practical uses of AI while continuing to optimize non-AI approaches. By maintaining realistic expectations and a patient-centered approach, healthcare providers can leverage AI effectively without being swept up in the hype.<\/p>\n

Healthcare in the US is a problem that is literally getting worse daily, weekly, and monthly.\u00a0 One way to help change the dynamics is by empowering consumers beyond the patient to control and understand their data. We need to work on solutions as more than 11,200 Americans will turn 65 every day \u2014 or over 4.1 million every year \u2014 from 2024 through 2027, according to estimates from the Retirement Income Institute at the Alliance for Lifetime Income. We are living peak Baby-Boomer right now as far as healthcare is concerned.<\/p>\n

-Noel J. Guillama, Chairman<\/p>\n

 <\/p>\n

The link and full article are below-<\/p>\n

https:\/\/www.forbes.com\/sites\/spencerdorn\/2024\/09\/09\/3-ways-to-manage-ai-hype-in-healthcare\/<\/a><\/p>\n

3 Ways To Manage AI Hype In Healthcare \u2013\u00a0<\/strong>Spencer Dorn, MD<\/strong><\/p>\n

Vice Chair & Professor of Medicine, UNC | Balanced healthcare perspectives<\/p>\n

Spencer Dorn is a gastroenterologist who covers healthcare and tech.<\/p>\n

Hospitals, clinical practices, and healthcare systems across the U.S. are struggling. Their workforces are strained and shorthanded. Their operating costs are rising. And demand for their services often exceeds capacity, limiting care access.<\/p>\n

Enter artificial intelligence. In the nearly two years since ChatGPT\u2019s launch thrust AI into the spotlight, investors, tech companies, and healthcare organizations have invested massively in AI, issued countless press releases, and launched innumerable pilots, at times painting breathless visions of AI saving healthcare.<\/p>\n

However, AI\u2019s net impact on healthcare has been limited so far. Are we expecting too much too soon?<\/p>\n

Expectations Versus Reality<\/strong><\/p>\n

Across the broader (non-healthcare) economy, a growing chorus is outlining AI\u2019s bear case as the gap between expectations and reality widens. While many companies now use AI to generate emails, images, and marketing materials, there is no \u201ckiller application\u201d to justify AI\u2019s high costs.<\/p>\n

Compared to other industries, AI may have an even tougher time reshaping healthcare, where the stakes are high, organizations are complex, and regulations are uncertain.<\/p>\n

For one, there are technical challenges. Predictive algorithms do not generalize across settings. For example, hospitals implementing a sepsis algorithm \u201cout of the box\u201d (without training it on local data) experienced many false alarms and undetected sepsis cases. Furthermore, generative AI is too unreliable to apply to high-value tasks, such as performing triage, making diagnoses, and recommending treatments. The challenge is that \u201ca generative AI system like GPT-4 is both smarter than anyone you\u2019ve met and dumber than anyone you\u2019ve met,\u201d explained Microsoft Research President Peter Lee. \u201cWe both assume too much and too little about its potential in health care.\u201d<\/p>\n

Additionally, many physicians, nurses, and healthcare consumers are skeptical of AI, concerned it will jeopardize privacy, exacerbate biases, and tarnish doctor-patient relationships. Based on their experiences with electronic health records\u2014which have failed to meet expectations and contributed to burnout\u2014they find the claim that AI will necessarily improve healthcare no longer credible.<\/p>\n

Finally, implementing AI in the real world is complex, involving many stakeholders, requiring significant resources, and fraught with potential pitfalls. Yet, unlike prior digital initiatives like implementing electronic health records (over $34 billion in meaningful use payments) or temporarily pivoting to virtual care (the COVID-19 pandemic), provider organizations have no major incentives to adopt AI products, which increase costs and force them to change their workflows, usually without directly increasing reimbursement.<\/p>\n

Navigating A Prolonged Transition<\/strong><\/p>\n

None of this is to say that AI is or will be useless in healthcare. Some organizations already use AI solutions for meaningful benefits, such as preventing rehospitalizations and easing doctors\u2019 documentation burden. As AI technology advances, it is poised to improve various aspects of clinical care, operations, and research.<\/p>\n

Still, we must dial back our expectations. History tells us that it will take many years\u2014not months\u2014to build useful AI products, integrate them into workflows, and eventually unlock new, better ways of providing care.<\/p>\n

During this transitional period, healthcare provider organizations should take the following actions to maximize AI\u2019s current and future net benefits.<\/p>\n

    \n
  1. Safely Experiment and Evaluate<\/strong><\/li>\n<\/ol>\n

    They must follow the foundational principles of evidence-based medicine, recognizing that, however exciting the technology is, healthcare is first about people, not products. Organizations like the Coalition for Healthcare AI are developing standards for implementing healthcare AI models and establishing assurance labs to evaluate them. Healthcare providers should pilot solutions for meaningful problems and establish the governance and evaluation necessary to ensure they use effective AI tools safely and fairly.<\/p>\n

      \n
    1. Improve Systems of Care<\/strong><\/li>\n<\/ol>\n

      Healthcare is a complex adaptive system in which multiple, dynamically interacting components determine performance. Organizations implementing AI should follow a holistic systems approach, looking beyond technology to include people, systems, and design.<\/p>\n

      For one, before rushing to automate any healthcare task, they must first ask whether the task is worth doing in the first place. As Peter Drucker taught, \u201cThere is nothing quite so useless as doing with great efficiency something that should not be done at all.\u201d<\/p>\n

      Second, because constraints\u2014points where demand exceeds capacity\u2014determine the pace at which an entire process can work, they must identify and relieve downstream bottlenecks before applying AI to make various processes more efficient. For example, automating patient scheduling will have little impact if doctors\u2019 schedules are already filled. And early identification of patients with sepsis will be useless if nurses and clinicians cannot act on the information.<\/p>\n

        \n
      1. Accept incremental gains<\/strong><\/li>\n<\/ol>\n

        Organizations must resist audacious claims and ground in reality. AI will not magically fix all that ails healthcare. And because large language models cannot reason or truly understand, many healthcare problems may require new hybrid approaches that blend machine learning with more traditional symbolic AI.<\/p>\n

        Still, they can harness today\u2019s AI for modest benefits (e.g., offloading some drudgery and tailoring patient educational and engagement content) while setting themselves up for future success. Importantly, they must not overlook non-AI opportunities to do better. And, most of all, they should reflect on who they are, what they do, and how they can do it better\u2014with or without AI.<\/p>\n","protected":false},"excerpt":{"rendered":"

        We have been strongly advocating that\u00a0Artificial Intelligence (AI) is going to change healthcare,\u00a0and we follow it with\u00a0\u201cnot in the way most expect.\u201d\u00a0As of the second quarter of 2024, according to\u00a0Modern Healthcare September 10, 2024: ..of the $60 billion invested by venture capital firms into healthcare AI companies since 2014, more than half has come since 2021 and 70% in the last five years. The increase is due to favorable capital market dynamics, advances in AI and increasing technology needs fueled by the COVID-19 pandemic. The problems in healthcare are huge […]<\/p>\n","protected":false},"author":6,"featured_media":4560,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4559","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/pwer.com\/wp-content\/uploads\/2024\/09\/Screenshot-2024-09-11-114533.png","_links":{"self":[{"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/posts\/4559","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/pwer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4559"}],"version-history":[{"count":2,"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/posts\/4559\/revisions"}],"predecessor-version":[{"id":4563,"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/posts\/4559\/revisions\/4563"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pwer.com\/index.php?rest_route=\/wp\/v2\/media\/4560"}],"wp:attachment":[{"href":"https:\/\/pwer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4559"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pwer.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4559"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pwer.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}