The Future of Healthcare? A Blueprint for Responsible AI Adoption With Dr. Harvey Castro MD MBA

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TLDR;

• AI has the potential to massively improve healthcare by giving doctors a digital assistant to enhance productivity, free up patient face time, reduce errors, and drive better outcomes, as well as the future of healthcare in general.
• Adoption has moved slowly because of a lack of transparency into AI reasoning, privacy considerations, physician skepticism, and knowledge gaps about true capabilities.
• The keys to responsible implementation are collaborating with doctors when building solutions and starting small to prove value before expanding AI across a health system.

Before we dive in, check out the full episode here:

Meet Harvey - Dr. MD MBA

Dr. Harvey Castro is an entrepreneur and former CEO of a healthcare system, and a leading voice in the healthcare AI industry.

His passion for improving health systems began very early on in his childhood, which fueled his fire to become a successful doctor who was passionate about doing better, doing more, and, most importantly, taking healthcare experience to the next level.

He’s also known for creating the first app (IV Meds) that went viral and immediately impacted emergency care.

From there, Harvey knew the moment ChatGPT came out that it would change medicine and healthcare institutions. He combined healthcare and technology, becoming a leading voice in the AI healthcare field.

Healthcare Future: Envisioning the Potential

Imagine if doctors had a digital assistant with an encyclopedic memory to suggest diagnosis from symptoms, risk factors, and medical history. Maybe even having an aide prepping relevant information before appointments to get physicians up to speed in seconds.

That’s the promise of healthcare AI – giving doctors a helping hand to augment decision-making, productivity, and patient care. The tech offers a profound opportunity to enhance physician expertise if thoughtfully designed alongside clinical environments.

Doctors spend nearly half their day documenting visits rather than caring for the ill. AI could handle the tedious bureaucracy like writing up patient stories and prescription orders so doctors can better focus on healing.

AI can also expand access to patient care through automated triage, reaching more people. Surfacing relevant patient information from volumes of records can enable earlier diagnosis of rare diseases preventing later complications.

So AI gives healthcare workers a specialist assistant to focus on insights and expand attention.

Currently, AI demonstrates proven value in radiology, screening scans for signs of conditions like strokes and pneumonia. Studies show AI can spot anomalies and diagnose some conditions, as well as experienced radiologists, from images alone. This allows more rapid attention to time-sensitive cases rather than waiting for backlogged specialists.

“Suitably applied, diagnostic AI does not replace health professionals but gives them a second set of eyes to validate suspicions and prevent mistakes. Like autopilot aids pilots rather than replaces them, healthcare AI aims to assist doctors, not edge them out.”

However,  it only meaningfully augments care when thoughtfully designed for medical settings – which requires collaborating with doctors directly. It cannot remain delegated fully to technical teams less familiar with healthcare intricacies.

Responsible AI building means bringing users into solution design early and often.

We’ve been going deep down a rabbit hole lately on how AI is impacting healthcare, so we invited Dr. Harvey Castro, MD, MBA to our podcast to discuss this topic. With over 20 years as a physician, entrepreneur, and former CEO of a healthcare system, he is one of the leading voices in healthcare AI right now.

Having authored numerous books on the topic, consistently collaborating with physicians, and integrating advanced Healthcare Management practices, Dr. Castro is at the forefront of healthcare's AI revolution, serving as a Strategic Advisor for ChatGPT & Healthcare.

Future of Health Care Slowed Down by Barriers

Despite the promise, AI adoption lags behind other industries like banking and technology.

Challenges stemming from opacity, privacy rules, clinician doubt, and organizational inertia slow progress. However, strategies focused on constructing physician trust through education, transparency, and collaboration can ease the barriers.

Dr. Harvey Castro noticed from personal experience that many barriers slow down the adoption of AI in the healthcare industry.

Harvey mentions that one of the most important barriers is leadership. Early adopters are eager to put technology to work, but the opposite side might have yet to see the implementation of AI healthcare systems and would rather just retire before that point.

He also points out that there is a difference between hospitals in bigger versus smaller towns and areas, where there’s often a day-and-night difference.

Ankur and Harvey agreed that education is one of the steps to getting through barriers, which should help ease the black box problem of AI.

The Black Box Problem: Healthcare in the Future

‍Healthcare industry leaders balk at trusting AI systems to influence patient outcomes without visibility into the reasoning. A model concluding that a patient has pneumonia means little without seeing what symptom combinations led to that diagnosis.

Transparency and interpretability matter greatly when lives are at stake. Medicine embraces solutions backed by observable biological cause-and-effect relationships, and AI currently falls short.

Model creators can make the logic more understandable through “explanations” showing what patterns and relationships drive conclusions. Graphs tracing how symptoms route to suggested diagnoses also build confidence.

Doctors then assess plausibility based on knowledge and experience. Making mechanisms visible unlocks informed scrutiny, so physicians appropriately integrate AI rather than blindly following opaque outputs.

Some of the biggest companies, like Microsoft, are opening the doors to the use of AI in healthcare, which helps reduce the black box problem.

AI has a chance to use the best of both worlds but there’s still a long way to go until the black box problem is taken care of.

Navigating Privacy Rules of AI Health Systems

‍Stringent healthcare privacy protections also slow adoption when solutions require sharing sensitive patient healthcare data externally.

Hospital legal teams resist platforms ingesting identifiable records into cloud systems, regardless of security guarantees. Like the valsartan contamination crisis jeopardizing patients through tainted pharmaceutical supply chains, healthcare’s do no harm ethos means erring protectively with data.

Enabling self-build options through open standards allows improved health systems to construct solutions internally while controlling access.

Local models tapping hospital data train AI aides without information ever leaving facility firewalls. The right to inspect algorithms and reasoning then remains in practitioners’ hands rather than opaque commercial applications.

Internal development admittedly progresses slower than leveraging external expertise. However, the trust and control tradeoff warrants investment when stewarding sensitive health data.

Integration platforms like Epic’s electronic health records (EHR) embed AI enhancements locally, keeping computation on facility servers so data stays in-house while decision capacity expands.

Ankur shared great advice on navigating privacy and sensitive data:

“Building on your infrastructure with your data never leaving your walls is going to be the most secure way” - Ankur Patel

Overcoming Skepticism of an AI Healthcare System

Like most traditional industries, healthcare leadership leans conservative, prioritizing pledged over unproven innovation. So visibility is pivotal.

Younger tech-savvy doctors pioneer tools like ChatGPT before administration endorsement. They showcase benefits to reluctant colleagues through real-life examples. Top-down mandates lack credibility to sway peers; peer success convinces best.

These pioneers also identify flaws to enhance solutions’ reliability before system-wide rollout. Skepticism gets resolved through demonstration, not decrees.

Correcting knowledge shortcomings around assets and limitations also smooths adoption by setting realistic expectations. No technology revolutionizes through purchase and installation alone.

But AI misconceptions are frequent. Unsure leaders get disillusioned when lofty vendor promises meet underwhelming reality. Doctors and physicians with the details are best at guiding the right setup.

Cross-disciplinary experts fluent in both clinical and technical lexicon bridge the communication gaps between builders and users — a role Dr. Harvey Castro fills in as an ER doctor turned AI commentator. Expanding clinician literacy empowers realizing healthcare AI’s full potential.

It’s going to take younger physicians, thought leaders, and people who are ready to break the “rules” and do things differently to change medicine.

Another way to overcome skepticism is to have a visual process of how AI comes to diagnosis and suggestions, which would remove another barrier among doctors who might not be ready or eager to get help from AI.

Strategies to Responsibly Advance Healthcare AI

Moving healthcare AI from promise to practice means addressing barriers through common sense solutions:

1. Improve model transparency so doctors can validate reasoning.
2. Enable self-build options maintaining data control.
3. Cultivate peer testimonials demonstrating safe value before scaling.
4. Broaden cross-disciplinary understanding bridging domains.

With trust, privacy protection, education, and proof, doctors adopt assistive AI as readily as the stethoscope.

Start Small Before Going Big

To introduce AI responsibly, hospitals target specialty niches to control variables before permitting system-wide deployment. Just as physician fellowships cultivate subfield expertise, AI implementation progresses through focused use cases first.

Perfecting defined applications minimizes risk if limitations surface.

Areas like cardiology and oncology offer concentrated challenges suited for algorithmic aids applied against digitized symptoms and treatment history. Patients further along diagnostic or care continuums produce richer data for training assistants.

Wise health systems first pilot AI in targeted domains to ensure effective augmentation before granting system-level inferences. Focused success builds confidence for system-wide rollout.

Harvey says it’s important to start going back to education, and as we start getting educated, we’ll start learning the good, the bad, and the limitations of this technology — ensuring small but important steps to allowing AI to help the healthcare industry.

The Future of the Healthcare Industry and AI

As earlier automation killed some jobs but elevated work overall, assistive AI aims to enhance medicine through fusion, not replacement. Technologies tackle repetitive tasks, while wisdom handles what computers cannot.

Doctors focused on caring for patients gain thinking partners to expand their reach. Patients receive care personalized to their needs by integrated intelligence linking records, histories, considerations, and actions.

However, realizing this collaborative potential requires recognizing domains. Technologists must respect constraints on acceptable use and risk.

Clinicians doubtful of still-unfamiliar technologies discover aids to ease overburdened vocations. And leaders gain tools to improve appropriate care, experience, and operations in synchrony.

When AI development happens cooperatively across specialties rather than competitively, pragmatically balancing privacy protection with progress, caregivers and technologists better serve patients.

Much as autopilot improved aviation while keeping pilots flying, healthcare AI’s future promises to treat patients with chronic diseases, remote patient monitoring, and improved healthcare delivery.

Hopefully, this will lead to a much more rested healthcare workforce, improved medical devices, and better workforce planning, which could lead to a better care delivery model in general.

The tools emerging at the intersection of computers and clinicians will likely feel as ordinary to future generations as robotic surgery and synthesized imaging do today. But it starts small, with focused pilots chosen by and for the doctors whose workflows integrate the technology.

AI can only shift from promise to practical improvement by proving value specialty by specialty before standardizing systemwide. Carefully and responsibly developed AI algorithms can be helpful tools in healthcare.

“I remember looking at plain films, and then somewhere in my career that was like ancient, and now it’s all digital… now that it’s all structured data, it can be fed into machine learning and the computer can now look for patterns.” — Dr. Harvey Castro

That being said, with technological advancements, the future of healthcare looks very promising.

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Want to learn more about AI in healthcare? Check out this episode on individualized, AI-powered healthcare with Jayodita Sanghvi, Senior Director of Data Science @ Included Health.