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The COVID-19 pandemic was a crash course in the digitalization of the healthcare industry — one that saw practitioners, clinics, hospitals and patients strategize in real time how telemedicine and automated touchpoints should look and feel.

Two equally important trends emerged that have had significant influence on the delivery of care and overall patient experience.

One: The pandemic exacerbated existing patient experience deficiencies.

According to the Assistant Secretary for Planning and Evaluation (ASPE), challenges such as workforce shortages, maldistribution, burnout, stress and mental health problems, were already significant issues faced by the healthcare workforce in the United States prior to the COVID-19 pandemic. 

But the extreme stressors placed on the workforce during the pandemic have likely exacerbated the situation. In fact, according to the Leapfrog Group's report on adult patient experience at acute care hospitals, in comparison to pre-pandemic times, patients reported being less satisfied with doctor and nurse communication, staff responsiveness, care transition and hospital cleanliness.

Even though the federal COVID-19 public health emergency declaration has ended, the effects of the pandemic on the healthcare system in the U.S. are ongoing. For instance, burned out hospital staff continue to leave the sector in droves, with Forbes estimating that 47% of U.S. healthcare workers plan to leave their positions by 2025.

Two: Patients see value in telemedicine, especially for managing chronic conditions.

A 2021 research paper published in the International Journal of Environmental Research and Public Health notes that with the decline in access to in-person care after the start of the pandemic, telemedicine became an important alternative.

"There is evidence of patients who have been managed by using telemedicine and expressed satisfaction with the services received, demonstrating that telemedicine helped assessing, diagnosing, triaging and treating patients [...]. These experiences include patients with transplanted kidney, diabetes, prenatal care, emergency ophthalmological disorders, couple and family therapies, colorectal surgery, cancer, among others," the research paper remarks.

Taken together, these trends demonstrate how the digitalization of certain aspects of healthcare can meaningfully improve the patient experience.

"Patient experience," as defined by the US Agency for Healthcare Research and Quality (AHRQ), "includes several aspects of healthcare delivery that patients value highly when they seek and receive care, such as getting timely appointments, easy access to information and good communication with health care providers."

The agency takes care to differentiate patient satisfaction from patient experience, noting that while the former speaks to whether patient expectations were met, the latter specifically concerns whether something that "should happen" in a healthcare setting "actually happened" — such as provider communication.

Still, in most patients' minds, the patient experience and patient satisfaction are highly interrelated. According to the Beryl Institute, a U.S. healthcare transformation think tank, "More than 95% of consumers see their healthcare experience as grounded in a focus on their health outcomes and overall well-being."

A positive patient experience is directly tied to access, quality and consistency of care, as well as good experiences with the administrative side of healthcare. Interactions with providers matter, as do processes for appointment booking, billing, receiving test results, organizing pre- and post-op care, getting prescriptions and dosage instructions and so on.

These experiences influence patient outcomes, says AHRQ: "Substantial evidence points to a positive association between various aspects of patient experience, such as good communication between providers and patients, and several important healthcare processes and outcomes. These processes and outcomes include patient adherence to medical advice, better clinical outcomes, improved patient safety practices and lower utilization of unnecessary healthcare services."

Four ways healthcare leaders can embrace digitalization to enhance the patient experience

A healthcare system that uses technology to create more streamlined processes is essential to the patient experience. Here are some key areas where technology can alleviate pressure on the healthcare system and deliver quality care at scale:

It's no wonder that the healthcare cloud computing market is expected to surpass $102.3 billion by 2028, up from $39.1 billion in 2021 according to Facts & Factors.

Cloud technology is an important backbone to patient management systems, which allow patients to access their health records all in one place, from anywhere and at any time.

That portability and access is hugely empowering and can improve patient outcomes as they navigate and advocate for their healthcare. The 360-degree patient visibility also gives providers a greater opportunity to collaborate on care and treat their patients holistically.

Healthcare can borrow tips from other industries on omnichannel support when it comes to effortlessly communicating with patients across a variety of channels — SMS, phone, email, chat — while maintaining continuity throughout the patient journey. Leveraging cloud-based technologies and platforms empowers healthcare organizations to consolidate patient data, streamline communication processes and ensure seamless integration across various channels.

This isn't merely a matter of convenience; it also empowers patients to become more engaged and hands-on in their health. With the ability to interact with healthcare professionals through their preferred communication channels, patients are granted greater flexibility and accessibility. They can receive timely updates, access important health information and engage in real-time conversations with their healthcare providers, all in a manner that suits both patient and provider preferences and needs.

So much of healthcare remains stubbornly rooted in the pre-digital era.

But why play phone tag to book a follow-up appointment when a simple calendar link or chatbot scheduler can suffice? Why fax a specialist referral, when emailing or submitting through a HIPAA-compliant portal is faster and easier to consolidate a patient's paper trail?

Smart automation in healthcare reduces practitioner and administrative burdens while also providing patients with improved service. Streamline the patient experience by automating appointment and test bookings and reminders, routine prescription refills and other easy yet time-consuming tasks.

According to Healthcare Informatics Research, about 80% of medical data remains unstructured and untapped after it is created. Since most hospital information systems cannot handle this type of data, it tends to be ignored, unsaved or abandoned. Natural language processing, or simply "NLP," turns human language into forms of data that a machine can easily process. NLP can be used to extract critical information from the unstructured patient data found in electronic health records, giving providers more context and details on a person's condition and healthcare needs.

As the healthcare industry continues to embrace digitalization to streamline processes and improve patient experience, it is crucial to prioritize data protection and privacy. Healthcare providers are in a position of trust, and it is important that their patients' most sensitive data remains safeguarded. For this reason, it is vital that healthcare leaders verify that their digital tools are compliant with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) to ensure that patients' data is collected, processed and stored securely.

By integrating technology responsibly and securely, healthcare providers can further optimize the care they provide, leveraging the benefits of digitalization while upholding the fundamental principles of patient centeredness. Regardless of the myriad of changes brought forth by the COVID-19 pandemic, trust between healthcare providers still remains the backbone of a positive patient experience.

Uncover how digital tech like the cloud, omnichannel support and automation can meaningfully improve the patient experience in the healthcare industry.

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The recent proliferation of generative AI (GenAI) models has sparked a lot of interest and excitement. These advanced large language models (LLMs) can do many extraordinary things — from crafting email responses to recommending a fitness routine to generating computer code; the list is seemingly endless.

However, these models aren't infallible. In fact, their propensity to sometimes hallucinate — a phenomenon where they provide responses that incorporate fabricated data that appears authentic — could significantly impact user trust. A recent TELUS International survey found that 61% of respondents were concerned about generative AI increasing the spread of inaccurate information online. 

Understanding what hallucinations are, why they happen and how to prevent them is critical to maintain users' trust in GenAI applications.

Introduction to generative AI hallucinations

A hallucination describes a model output that is either nonsensical or outright false. An example is asking a generative AI application for five examples of bicycle models that will fit in the back of your specific make of sport utility vehicle. If only three models exist, the GenAI application may still provide five — two of which are entirely fabricated. While this is a relatively low-stakes example of a hallucination, some could have far greater consequences. 

Recently a New York attorney representing a client's injury claim relied on a conversational chatbot to conduct his legal research. The federal judge overseeing the suit noted that six of the precedents quoted in his brief were bogus. It turns out that not only did the chatbot make them up, it even stipulated they were available in major legal databases.

While guardrails to prevent GenAI hallucinations need to be put in place, the first step is understanding how, or rather why, these inaccuracies happen.

Why do generative AI hallucinations occur?

While leading AI experts aren't entirely sure what causes hallucinations, there are several factors that are often cited as triggers.

First, hallucinations can occur if the training data used to develop the model is insufficient or includes large gaps leading to edge cases that are unfamiliar to the model. For example, in drafting a contract, a generative model that was fine-tuned using contracts from the financial services industry may not have sufficient exposure to terms and concepts in healthcare law to draft contracts for that industry. Most of the time, generative AI's primary goal is to produce an output in response to a prompt. Even if it doesn't "understand" the prompt, it might craft a response based on insufficient training data, thereby leading to a faulty result.

Similarly, some machine learning (ML) models suffer from overfitting, where the output is accurate for its training data, but inaccurate for new data. The goal of ML model training is to generalize from training instances so that the model can correctly operate on instances it hasn't seen before. Overfitting occurs when a model is trained to the point where it effectively memorizes the inputs and appropriate outputs for the training set, leaving it unable to generalize effectively to new data. For example, a model used to approve loan applications may be considered to be 90% accurate in predicting the likelihood of loan default. However, if it has been subject to overfitting, it likely has an accuracy rate closer to 70%. Therefore, applying the model to future loan decisions will result in a decrease in business profits and an increase in dissatisfied customers.

Another model development challenge is ensuring training texts and prompts are properly encoded. LLMs map terms to a set of numbers — a process known as a vector encoding — and these encodings have some key advantages over working with words directly. For example, words with multiple meanings can have one encoding per meaning, reducing the chance of ambiguity. So, the word "bank" would use one vector representation when referring to a financial institution and another when referring to the shore of a river. Vector representations also allow mapping semantic operations — like finding a similar word — into mathematical operations. Problems with encoding and decoding between text and representations can lead to hallucinations.

Best practices to prevent generative AI hallucinations

While generative AI hallucinations are a concerning issue, there are ways to reduce their frequency and intensity. Consider the following best practices.

The most obvious protection from hallucinations is high-quality input data. It's critical that your GenAI model is trained on a diverse and representative dataset that covers a wide range of real-world examples. Using sufficient, unbiased data is key to training an accurate, resilient model.

Another crucial part of the training process is incorporating human feedback, also known as reinforcement learning from human feedback (RLHF). Because AI models lack a nuanced understanding of language, they can produce responses that are out of context or irrelevant. RLHF provides models with regular corrections via feedback from people, so that an understanding of the world that only a human can have is incorporated into the training process.

Transparency in AI ensures that it's possible to understand a model's inner workings and how it makes decisions. The issue is coming to the forefront as LLMs evolve, particularly because more advanced models are harder to understand.

Users and machine learning professionals should be able to evaluate how a model was trained and review for potential weaknesses in the learning process. Transparency also makes it easier to identify and correct errors that can lead to hallucinations.

A malicious user of a generative AI system could experiment and probe a model looking for ways to make it hallucinate. It's necessary to incorporate counter measures to help monitor and control the types of inputs allowed in generative AI systems. These include using adversarial examples in the model's training to improve its ability to respond appropriately and to audit the model's anomaly detection capabilities. A further means is by data sanitization, which involves checking that no malicious inputs are being fed into the model. And finally, security updates and patches can help block external threats to your AI model.

While generative AI hallucinations are a frustrating hurdle that can significantly impact user trust, they aren't insurmountable. By training models using high-quality data, ensuring model transparency and enacting effective quality control, developers can improve their model's resiliency to deliver accurate predictions.

Hallucinations are an obstacle to building user trust in generative AI applications. Learn about the phenomenon, including best practices for prevention.

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