The Future of Patient Engagement: Digital Health Trends to Watch

As healthcare shifts from physician-centered to patient-centered care in our post-COVID world, patient engagement solutions become the driving force of this cultural transformation.

It seems like almost all other industry trends emerge in response to the needs for patient-centered care, engendered by this dominant idea.

The reason is – virtually unlimited data.

Oleh Petrivskyy, the Founder at Binariks, a software development company with a focus on custom healthtech solutions, says:

“Prioritizing individual needs and values – either expressed in care decisions in hospitals or personalization features in digital health – fosters higher patient engagement by making patients feel valued and involved.

In turn, engaged patients who actively participate in their own care are more inclined to share comprehensive insights into their preferences, needs, and lifestyles. This information is crucial for healthcare providers to deliver even more personalized treatment, leading to better patient outcomes. And finally, it is also the most valuable data that gives any healthcare business a competitive edge and a fertile ground for innovation.

We witness a circular relationship that can benefit all stakeholders involved: a win-win situation.”

So, let’s explore the most promising industry trends that help improve patient understanding, trust, and involvement.

AI at the Heart of Digital Transformation

The integration of artificial intelligence into healthcare is not just an incremental change; it’s a foundational shift that is already happening. The figures speak for themselves:

  • Projections suggest that AI applications could save the U.S. healthcare economy approximately $150 billion annually by 2026. And it is due to the shift towards a proactive health management model that results in fewer hospitalizations, doctor visits, and reduced treatment needs.
  • Over 75% of healthcare organizations are already actively implementing or planning to execute an AI strategy.

And as for patient engagement and patient-centered care:

  • The global market size of patient engagement solutions was $16.6 billion in 2021 and is expected to grow at a CAGR of 17.4% by 2030, according to a report by Grandview Research.
  • According to research by NTT DATA, patients are now comparing their healthcare experience with that in other industries, with 59% of respondents expecting digital healthcare to offer the convenience and personalization seen in retail.

These brief facts give us a good place to start, once again confirming the rise of AI in healthcare and the market relevance of patient engagement solutions. But how exactly do these concepts work together?

Let’s get straight to real-life examples of how AI is used to increase patient engagement.

  1. Healthcare virtual assistants (chatbots), automating patient interactions for 24/7 support.

Example: Ada Health’s AI-powered chatbot that provides personalized health assessments, increasing patient engagement and care accessibility.

  1. Virtual nursing assistants, freeing up medical staff to focus on more specialized activities (it was found that virtual nursing assistants could reduce nurses’ patient care time by 20%, resulting in $20 billion annual savings in the healthcare industry).

Example: AI-powered ThinkAndor’s virtual nursing module uses generative and transformative AI models to automate more than 40% of nursing workflows, leading to more meaningful interactions between patients and health professionals.

  1. AI-powered patient portals, enhancing patient access to personal health information and care management tools.

Example: MyChart by Epic Systems uses AI to offer personalized health reminders and appointment scheduling.

  1. Risk assessments for preventive care, leveraging AI to predict health risks and recommend preventive measures.

Example: Google Health’s AI models predict patient risk factors, enabling early interventions. Thus, Google Health’s AI for mammograms achieved 94% accuracy, reducing false positives by 5.7% and false negatives by 9.4% compared to radiologists. And when patients learn about a condition early and see effective treatment plans in place, they are also more likely to engage actively in their health care journey.

  1. Healthcare workforce optimization, using AI to optimize staff schedules, reduce burnout, and improve patient care delivery.

Example: iQueue cloud-based solutions employ AI to efficiently manage healthcare staffing, improve revenue, and reduce healthcare delivery costs, enhancing both staff and patient satisfaction. After all, patient experience is often suffered by prolonged delays and wait times that negatively impact patient engagement.

These are just a few real-life use cases for AI-driven solutions changing healthcare. As technology evolves, its role in fostering patient personalizing care will undoubtedly grow, underpinning its critical position in healthcare’s digital transformation journey.

Oleh Petrivskyy says: “In fact, we can easily extrapolate this patient-engagement trend to other industries. Because it’s something really huge, responding simultaneously to the needs of modern healthcare and business in a broad sense.

About 80% of consumers now consider personalization a basic expectation from any product. Just look at such giants as Netflix or Spotify. They disrupted the market business model with AI-powered content recommendations and digital marketing, putting their users’ comfort at the center of their product strategy.

So, I see clearly that personalization and patient centricity are not just a trend but a new norm. Investment in digital solutions fostering patient loyalty will have long-term outcomes.

The question is no longer “Why?” and not even “When?” It is “Why still not?”

Not by AI Alone…

While AI stands out in the digital health revolution, other pivotal trends are also reshaping patient engagement and healthcare delivery. Remote patient monitoring (RPM), connected devices, the Internet of Medical Things (IoMT), telehealth services, and digital therapeutics (DTx) are critical players in this transformation.

These technologies complement AI and synergize, forming a more connected, personalized healthcare ecosystem.

  • AI enhances RPM by analyzing the continuous stream of data collected from patients remotely, identifying trends or anomalies that may indicate a need for intervention. While this synergy is particularly beneficial for early warning alerts in chronic disease management, it can also support post-operative care, elderly care, mental health monitoring, and infectious disease tracking.
  • Wearable devices and AI: Wearables, when combined with AI, can predict health issues before they become severe by analyzing data on physical activities, heart rates, and sleep patterns. Fitbit’s use of AI algorithms to detect irregular heart rhythms showcases how wearables can serve as a first line of preventive healthcare, promoting early medical consultation and intervention.
  • IoMT and AI: IoMT devices, integrated with AI, create a connected healthcare environment where real-time data from various medical devices and applications is analyzed to inform clinical decisions. For example, GE Healthcare’s AI-powered patient monitoring systems analyze data from multiple IoMT devices to optimize patient care pathways and operational efficiency.
  • Telehealth services and AI: AI in telehealth goes beyond chatbots. It includes, among other things, AI-powered patient triage and medical diagnosis. While chatbots provide initial consultations, AI algorithms play a crucial role in triaging patients based on the urgency and complexity of their conditions. Moreover, AI supports medical diagnosis by analyzing medical data and imaging, offering preliminary assessments that guide healthcare providers in making informed decisions.
  • Digital therapeutics (DTx) and AI: DTx applications leverage AI to adapt therapeutic content to the specific needs and progress of the patient. For example, Omada Health’s digital platform for chronic disease management utilizes AI to tailor interventions and feedback, making treatments more effective and engaging for patients.

Together, these digital health trends form a cohesive ecosystem where AI acts as the brain, interpreting vast amounts of data to provide insights and personalize patient care. This integrated approach streamlines healthcare delivery and empowers patients to take a proactive role in managing their health.

Integration Challenge: Crafting Seamless Digital Health Ecosystems

However, integrating all the diverse technologies above poses a significant challenge. Crafting seamless digital health ecosystems that meld AI, RPM, IoMT, and other healthtech trends into a unified patient experience is paramount.

Interoperability stands at the core of this challenge. The adoption of standards like Fast Healthcare Interoperability Resources (FHIR) is crucial for enabling different systems and devices to communicate and work together effectively. This interoperability ensures that patient data is accessible, actionable, and secure across the healthcare continuum.

Besides, investing in advanced interoperability solutions results in 84% better patient safety, 76% higher patient satisfaction, and a 30% decrease in healthcare costs.

The journey towards a fully integrated digital healthcare landscape continues, with the promise of enhanced patient trust and engagement for the better. Yet, achieving a seamless integration that leverages the strengths of each technology requires a concerted effort toward interoperability.

As we explore the integration of these technologies, it’s illuminating to see real-world applications at work. Take, for instance, a project implemented by Binariks. The company leveraged its expertise in FHIR to support a digital health firm in developing a speech-processing platform.

Personalized Care in Action: Case Study

Binariks has a solid experience in FHIR implementation, and many of the company’s case studies in the healthcare sector involve architecture consulting as one of the stages to ensure seamless interoperability.

For example, Binariks worked with a digital health company, focusing on developing a speech-processing platform. This innovative platform was designed to detect physiological changes in the human body by analyzing speech samples collected from digital devices like smartphones and voice-driven digital assistants. Efficient analysis of speech samples was carried out using machine learning algorithms.

This technology was supposed to be used primarily for patient health monitoring, specifically for RPM solutions for Chronic Heart Failure (CHF) patients in the United States healthcare market.

So, the client faced the challenge of expanding their product to the US market while ensuring compliance with the FHIR safety standard, a critical requirement for operating within the US healthcare system. The goal was to make their application more comprehensive and compatible with hospital internal systems, facilitating seamless cooperation.

Binariks’ team developed a patient engagement solution that considers various user flow options, enhancing the user experience and engagement through personalized and user-friendly interfaces.

In addition, they adjusted the solution for implementation in hospitals with various regulations regarding patient PHI and ensured that the solution architecture included integration with FHIR. This was crucial for the ability to scale and further integrate with multiple hospitals.

Generally, this Binariks case study illustrates well how digital health and artificial intelligence technologies work together to enhance patient engagement and highlights the importance of addressing interoperability challenges for future growth.

To Sum Up

As we navigate through the evolving landscape of digital health, the future of patient engagement shines brightly, underscored by the pivotal role of AI and a suite of healthcare technologies, including RPM, IoMT, and DTx.

The tangible examples of AI in action underscore the monumental shift towards a more patient-centered healthcare system. It is not just about technological advancements but a mindset change that places the patient at the very heart of healthcare.

By embracing the outlined trends and investing in them, healthcare providers and innovators are not just responding to the present but shaping the data-driven future – a future where healthcare is not only about treating illness but about fostering wellness, engagement, and a deep, personal connection between patients and their care providers.


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