Evolution of Healthcare in USA: From Passive Care to Active Patient Engagement

Published on Sep 12, 2023

Updated on Mar 20, 2024

Imagine walking into a bustling hospital several decades ago. You’d probably feel like just another face in the crowd, a number on a chart, waiting for your turn to see a busy doctor. There’s no denying that, back then, healthcare was all about treatment. The personal touch, understanding, and overall patient experience took a back seat. But as the years rolled on, a transformation was brewing. Today’s healthcare paints a very different picture, and this article aims to journey through that evolution, showcasing how healthcare in the U.S. has shifted from passive care to a deeply engaging, patient-centric approach.

What was Passive Care?

At its core, passive care was a one-way street. Patients came in, got treated, and left. Little room existed for understanding their experiences, emotions, or concerns. Here’s a closer look:

It was all “Number” mentality. Patients often felt they were just numbers in a system. Personal stories and individual concerns? They often got lost amidst the rush to move on to the next patient.

Very limited channels for feedback. If you had a suggestion or a concern, where would you go? Back in the day, feedback mechanisms were few and far between. This meant patients had little say in shaping their own care experiences.

However, things started to change gradually until the year 1999 brought with it a jolt. The Institute of Medicine unveiled a report that estimated a staggering 44,000 to 98,000 people die annually in hospitals from preventable medical errors. It was more than a statistic; it was a clear sign that the system needed change.

As we entered the 21st century, a wind of change began to blow through the corridors of hospitals and clinics across the U.S. What sparked this shift?

Digital Information Wave

The internet changed the game. Suddenly, patients weren’t solely relying on doctors for medical information.

A 2013 Pew Research study found that 72% of internet users sought health information online. This was a significant shift, one that empowered patients to ask questions and demand better care.

Think about the last time you visited a coffee shop or booked a hotel. Chances are, you experienced personalized service. Other sectors were setting the bar high for customer experience, and healthcare couldn’t stay behind.

With platforms like online forums, reviews, and patient communities, individual stories and experiences started echoing louder than ever before. A poor hospital review could now reach thousands, urging institutions to listen and adapt.

Transitioning to Active Engagement

With the foundation laid, healthcare began its transformative journey:

If there’s one thing that streamlined healthcare, it’s technology. Electronic Health Records (EHRs) became pivotal. From being a novelty in 2008, the adoption rate for EHRs in U.S. hospitals jumped to an impressive 96% by 2017. It was clear that healthcare was turning a new leaf, one that was digital and efficient.
With the onset of the COVID-19 pandemic, another trend gained momentum – telemedicine. The convenience of consulting a doctor from one’s living room became not just preferred but essential.
A report from the CDC highlighted a 154% surge in telehealth visits during March 2020 compared to the previous year. It’s undeniable; that healthcare was evolving rapidly, focusing more on patient comfort and safety.

As we navigate through this narrative, it’s clear that the push for change in healthcare wasn’t just internal. External factors, technological advancements, and the rise of patient voices played a massive role in redefining the healthcare experience in the U.S.

Pillars of Modern Healthcare Engagement

As the healthcare landscape shifted, certain principles started standing out as beacons of modern patient care:

Tailored to You: Today, healthcare isn’t just about one-size-fits-all solutions.
Genomic Medicine: Imagine treatments crafted based on your unique genetic blueprint. This isn’t sci-fi; it’s happening now. Genomic medicine is revolutionizing how ailments are treated, ensuring that care is personalized and effective.
Feedback Loop: Hospitals today aren’t just places of healing; they’re learning institutions.
Patient Surveys & Feedback Systems: Clinics and hospitals actively seek out feedback, using it as a tool to continuously evolve and better their services.
Wellness Beyond Medicine: The definition of health has expanded. It’s not just about curing ailments but fostering overall well-being.
Mental and Emotional Health: More than ever, there’s an emphasis on addressing mental health concerns and emotional well-being alongside physical health. A holistic approach is at the forefront.

In U.S. healthcare, the emphasis on customer experience has grown significantly, underscoring the vital role tech companies play in developing digital tools to enhance this experience.

A survey conducted shows that 72% of patients would like to have access to a patient portal, and 64% would like to use a mobile app to manage their health.

We have a vivid example of Manipal Hospital’s mHealth app developed by Mantra Labs. It’s a self-service healthcare mobile application that enables users to – book appointments (OPD, Lab tests, home collection), buy health packages, track health improvement reports, and self-check-in to avoid hospital queues.

Looking back, it’s truly remarkable to trace the journey of the U.S. healthcare system. From crowded waiting rooms where patients were mere numbers to a contemporary era where every individual’s health story is heard and valued—it’s a testament to the resilience and adaptability of the sector. As we move forward, with technological advancements on one hand and a commitment to patient-centric care on the other, the future of healthcare in the U.S. is not just bright—it’s luminous.

The progression from past to present-day healthcare showcases the incredible strides made in patient care, all geared towards creating an ecosystem where every patient feels valued, heard, and cared for.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

Evolution of Healthcare in USA: From Passive Care to Active Patient Engagement