Accenture Digital Health Technology Vision 2018: Five Trends Driving Intelligent Healthcare and the Critical Need for Trust

Today, technology has a profound impact on our lives. The connection between businesses and individuals is also becoming closer.

Healthcare institutions are increasingly adopting intelligent technologies to deliver personalized, efficient, and appropriate care. However, this innovation brings with it significant responsibilities. To ensure that individuals fully reap the benefits of digital health, healthcare providers and health plans must prioritize issues related to trust and accountability.

Against the backdrop of technology’s integration into daily life, Accenture’s report, “2018 Digital Health Technology Vision,” explores five trends that underscore the importance of building trust. These five trends are:Citizen AI, Extended Reality, Data Authenticity, Scalable Collaboration, and the Internet of Intelligent Things. VCBeat (WeChat: vcbeat) has provided a full translation of this report.

These trends fall into two categories: the drivers of smart healthcare and their outcomes. Extended reality, scalable collaboration, and the Internet of Intelligence demonstrate how integration and personalization are helping people in new ways. Meanwhile, citizen AI and data authenticity reveal the impact of technology on our lives and healthcare.

Leaders in the healthcare industry are poised to leverage emerging technologies to forge deeper, more meaningful connections with people. However, in this process, we must make choices with caution, carefully considering how to apply these technologies to ensure they do not cause harm.

Among Healthcare Workers

94% of people believe that treating consumers as partners is crucial to winning their trust.

92% of respondents believe that ensuring the security of consumer data is critical to building trust.

84% of respondents noted that companies are leveraging technology to transform people's lives.

Currently, AI is continuously evolving in the healthcare sector; it is not merely a technological tool but also an integral part of the workforce.

AI can leverage algorithms on smartphones to diagnose diseases, enabling doctors and caregivers to provide remote care for the elderly and help them live independently and safely. AI can also assist healthcare systems in verifying patients’ insurance information through digital means.

Furthermore, AI is also integral to end-to-end healthcare experiences and will continue to evolve. Among respondents, 85% of healthcare professionals believe that AI-driven decisions will directly impact individuals’ daily lives within the next three years.

Meanwhile, the field of AI is also gradually maturing. Much like a child learning to take responsibility for itself, AI development is not simply programmed but evolves continuously through learning. Learning-based AI technologies can create models based on large volumes of training data. They identify patterns through various factors and perform self-validation on test data. The more data AI processes, the more accurate its predictions become.

The development of AI in the healthcare sector is both compelling and concerning, as an erroneous decision could lead to significant potential consequences. Healthcare institutions must recognize these implications and establish AI systems that are responsible, fair, and transparent—yet most have not yet achieved this.

81% of healthcare professionals believe that medical organizations are not yet prepared to address societal and liability issues, which require them to explain AI-driven behaviors and decisions before these problems can be resolved.

When AI develops in the right direction, it can better assist healthcare enterprises. For instance, AI can support healthcare professionals in decision-making across various domains, such as recommending treatment plans and adjudicating medical insurance claims by approving or denying them. This is crucial for the healthcare sector.

Leaders in the healthcare industry must ensure that the data influencing AI is free from any errors. Such errors could cause harm to humans. For instance, if a healthcare institution builds a diagnostic model for heart disease using data exclusively derived from elderly white males, this could lead to erroneous outcomes, such as inaccurate diagnoses for young African American patients.

For this very reason, enterprises must understand which factors in the data influence AI outcomes when deploying AI technologies. They must continuously monitor and correct errors to minimize risks, mistakes, and potential harm.

Decoding AI

Explainable AI

In the medical field, decision-making through interpretability is crucial for trust, safety, and compliance. Considering that AI systems will work alongside humans, healthcare organizations must build and train AI technologies to provide clear and understandable explanations for the results generated by AI systems.

Responsible AI

Healthcare companies using AI must carefully consider issues of liability, as the AI performs tasks on their behalf.

It is crucial to leverage explainable and responsible AI to build trust in the technology. Trust is the sole pathway to fostering acceptance; to unlock the full potential of AI, every healthcare organization must transparently disclose its motivations for data usage and ensure they align with those of consumers. This entails understanding how to convince consumers that enterprises are justified in accessing their information.

Compared with healthcare providers, health plans may face more trust-related challenges to overcome. For example, consumers perceive the medical information shared by health plans as biased. An Accenture survey found that 69% of consumers believe that the information sources provided by health plans are steering them in a more favorable direction.

As health plans begin to use AI to enhance the core administrative capabilities of healthcare workers, such as processing medical claims, this analytical and decision-making process must be transparent so that consumers can understand it—rather than being hidden in a black box. Furthermore, regulators are often involved in reviewing consumer appeals against denied claims and expect a clear, explainable, and definitive answer.

Healthcare institutions may consider issuing a statement on responsible, explainable AI and AI governance to clarify how patient data is used.

In addition to earning consumers’ trust, clinicians must also trust and accept AI. When they use AI to make decisions, they need to believe that the technology is trustworthy and reliable. But how high must the reliability of this technology be to enter the clinical stage? As reliable as medical students, nurses, or physician assistants?

Demonstrating that its results can earn physicians’ trust in AI. Jvion is leveraging a cognitive computing engine built on Eigen to help healthcare organizations identify patient-specific risks for adverse events and recommend personalized interventions to mitigate these risks. By adopting Jvion’s solution, Health First avoided more than 800 days of unnecessary hospitalizations and saved $2 million in medical supplies.

New solutions will enhance the transparency of AI model decision-making in healthcare institutions, thereby providing a basis for analysts such as physicians and regulatory authorities to make informed decisions and mitigate bias. For example, Pegasystems has developed a Customer Decision Hub featuring a “T-Switch” capability, which allows enterprises to set transparency thresholds for AI systems. This offers clients the option to choose between models that are opaque and those that are more transparent and thus interpretable.

Such solutions allow users—whether clinicians, administrators, or consumers—to examine the internal workings of AI models, including the factors influencing decisions. Through the T-switch, AI becomes a consultative partner in decision-making. According to the European Union’s General Data Protection Regulation (GDPR), such capabilities are crucial for compliant AI, as the regulation includes the “right to an explanation” for decisions made by automated systems.

AI is evolving on a daily basis, with 80% of healthcare professionals believing that the pace of adaptation within medical organizations lags behind the rapid development of AI. As AI continues to advance in the healthcare sector, industry leaders must recognize its emerging roles and impacts in this field to better address the ensuing challenges.

Among healthcare workers

73% of respondents plan to establish internal ethical standards related to AI to ensure the responsible operation of their AI systems.

81% of respondents believe that healthcare institutions must explain AI-driven actions and decisions if issues arise, yet they are not prepared to address these societal and liability challenges.

80% of people believe that within the next two years, AI will work alongside humans as their colleagues, collaborators, and trusted advisors.

Researchers at the Icahn School of Medicine at Mount Sinai in New York have a special collaborator within the hospital: an internal AI system affectionately known as “The Critically Ill Patient.”

By analyzing the electronic health records of 700,000 patients, “Severe Patient” has learned to predict risk factors for 78 different diseases—now the system assists physicians in making diagnoses.

Severe patients may not be a single individual, but it is more than just a program. AI systems learn to make autonomous decisions and have evolved from technological tools into human partners, coordinating and collaborating with humans in the workforce and society. As autonomy and capabilities enhance, AI’s influence continues to grow.

Extended reality technology serves as a bridge connecting people, information, and experiences.

Technological advancements have endowed it with greater potential to bridge geographical gaps, address critical challenges in health and healthcare, and transform people’s work and lifestyles.

“Extended Reality” (XR) encompasses virtual reality and augmented reality. It blurs the boundaries between the physical and virtual worlds. XR technology has popularized immersive experiences, overcoming geographical barriers and minimizing relevance gaps. This has significant implications for the healthcare sector.

Imagine elderly patients in rural areas consulting with world-class experts from the comfort of their own homes. Surgical residents perform operations within a virtual environment at home, rather than using physical medical equipment. Nurses attempt intravenous injections for the first time using vein visualization devices. Veterans receive treatment for post-traumatic stress disorder (PTSD) through cognitive therapy in a virtual environment.

Virtual Reality (VR)

VR visually transports users from the real world into a virtual environment, typically viewed through head-mounted displays and navigated using handheld controllers.

Augmented Reality (AR)

AR overlays digital objects (information, graphics, and audio) onto the real world, enabling users to experience the interplay between the digital and physical realms.

Extended Reality (XR)

XR is a spectrum that blurs the boundaries between the physical and virtual worlds. This technology enables users to immerse themselves in virtual environments through visual, auditory, and potentially olfactory and haptic senses. Virtual reality and augmented reality are the two primary types of XR.

Physical activity in healthcare requires rapid access to information. XR connects all information, such as adding digital information during surgical procedures. In fact, 82% of healthcare workers believe that extended reality can eliminate the distance barriers between people, information, and experiences.

XR has immense potential to bridge the distance between employees, patients, and providers. For employees, XR enables the setup, operation, replication, and adjustment of scenarios from any location, providing hands-on experience across various situations. To enhance patient engagement, Tampa General Hospital in Florida, USA, employs virtual reality models that allow neurosurgeons, patients, and their families to visualize the anatomical structures of brain tumors or aneurysms. This approach helps patients better understand their condition and make informed medical decisions, while enabling physicians to develop detailed surgical plans and share these models with other clinicians for educational purposes regarding complex surgical procedures.

For healthcare providers and medical students, XR can bridge the gap between theory and practice. For instance, a world-renowned medical expert can train residents in another country on how to use a new technology. XR will also help enterprises address workforce challenges by enabling them to acquire the talent needed for growth. Through XR, organizations can access on-demand labor, allowing healthcare institutions to tap into specialized expertise across thousands of different skill sets.

With the assistance of XR, patients can overcome geographical barriers when seeking medical care. 82% of healthcare professionals believe that extended reality solutions enable healthcare institutions to deliver services to clients more conveniently. For instance, distance issues may force patients to receive diagnosis and treatment from non-specialists for certain conditions. However, through immersive experiences, XR allows patients to be diagnosed by specialist physicians.

XR helps bridge the gap between consumers and clinicians, a process that also requires the involvement of information providers. It supplies clinicians with more detailed information, removing potential barriers to decision-making. For example, a surgeon can wear XR glasses to view real-time digital content overlaid on the physical world without diverting attention from the patient on the operating table. When information is augmented onto physical actions, physicians can grasp patient data with greater precision, achieving unprecedented outcomes.

XR not only shortens the distance to information access but also uncovers new insights. Emerging XR tools present data in 3D environments, aligning more closely with how humans naturally perceive and imagine scenarios. This paves the way for novel visualization techniques and new discoveries in the healthcare sector.

For example, surgeons in Texas, USA, are using 3D maps and images as a “GPS system” to better navigate complex anatomy, making surgical procedures more precise. Doctors have recently used this technology to perform minimally invasive surgery for sinusitis. The system records the surgical procedure and plan, which can be used by other surgeons to learn these complex surgical techniques. Body VR creates interactive 3D models from traditional 2D medical imaging, such as CT scans and MRI, to provide more intuitive medical visualization. Similarly, researchers at the University of Oxford have created VR models of genetic data to better visualize conditions within living cells.

Consumers can become more closely connected with information through XR, thereby improving their quality of life. Accenture has developed an AI solution called Drishti to help visually impaired individuals enhance how they experience the world around them and improve work efficiency. Via a smartphone, this solution can inform users of the number of people in a room, as well as their age, gender, and even emotions, by analyzing facial expressions. It can also narrate the content of books and documents and identify obstacles such as glass doors to ensure the safety of visually impaired individuals.

The most significant breakthrough brought by XR may be the provision of shared and collaborative healthcare experiences. While clinicians cannot directly experience their patients’ conditions, XR enables them to gain a deeper understanding of these conditions and even foster empathy. For instance, Embodied Labs has developed VR simulations for training in elderly care. The “We Are Alfred” simulation allows young medical students to experience what it is like to be a 74-year-old man with hearing and visual impairments. Meanwhile, “The Beatriz” simulation enables users to experience the various stages of Alzheimer’s disease.

XR enables clinicians to gain insight into patients’ struggles with their conditions and ensures that these patients receive the necessary treatments. For instance, researchers have achieved significant results in using VR therapy to treat post-traumatic stress disorder (PTSD) in veterans, allowing patients to address trigger-induced stress during real-time interactions with therapists.

The University of Southern California’s Institute for Creative Technologies, in collaboration with the U.S. government, developed Bravemind, a virtual reality exposure therapy tool that places veterans with psychological trauma in an environment where they can confront the sources or cues responsible for their trauma. Researchers found that stress symptoms, including depression, decreased by 80% following this treatment.

Extended reality (XR) technology can also benefit pediatric patients. Hospitals are leveraging XR to distract children from painful procedures, such as injections or dressing changes. Toddlers about to undergo intravenous therapy can virtually leave sterile rooms and enter an immersive ocean world. Nicklaus Children’s Hospital in Miami, USA, has developed an immersive VR system to help healthcare professionals learn cardiopulmonary resuscitation (CPR).

XR is driving industry leaders not only to consider what is possible but also to develop new solutions that address the challenges posed by distance—a clear advantage for those adopting XR. 79% of healthcare professionals believe it is important for medical organizations to take the lead in adopting extended reality solutions.

Among healthcare workers

83% of people believe that extended reality will create a new foundation for interaction, communication, and information.

84% of respondents believe it is important for healthcare organizations to leverage extended reality (XR) solutions to bridge the gap in interactions with employees or customers.

72% of respondents believe that extended reality will impact all industries over the next five years.

Cleveland Clinic is transforming its traditional cadaver-based anatomy curriculum into a multi-platform digital experience, enabling medical students worldwide to learn human anatomy concepts in an interactive virtual environment.

This multi-platform digital solution offers anatomical content based on the clinical study syllabus of the clinic’s medical school. The organization is collaborating with Zygote, a company specializing in digital technologies that also provides 360-degree views of 3D human anatomy models. These views can be shared globally via cloud technology and are suitable for collaborative learning. The intellectual property, clinical expertise, and related skills of these two institutions have set a new standard for digital medical education.

The healthcare sector is more reliant on data than ever before.

As AI applications in management and clinical settings become increasingly prevalent, autonomous, data-driven decision-making is also on the rise. However, AI can only function effectively when built upon robust data training. If the authenticity or accuracy of data is not established, healthcare organizations will be unable to leverage AI effectively.

Inaccurate data can lead to erroneous insights and decisions. Twenty-four percent of healthcare professionals reported that healthcare organizations have repeatedly encountered errors in AI applications, such as bot fraud, spoofed sensor or IoT data, and fabricated location data.

In the healthcare sector, these vulnerabilities can have severe consequences, as data underpins medical decision-making, treatment planning, and even the approval or denial of insurance claims. Erroneous data in patients’ electronic health records may expose them to the risk of receiving incorrect diagnosis and treatment.

Incomplete public health data may lead to misidentification of the source of disease outbreaks.

Unfortunately, many health plans and healthcare providers are not yet prepared to protect themselves. Among respondents, 77% of healthcare workers stated that they are unprepared to face the serious consequences if false data infiltrates their data-driven information systems. Meanwhile, they are already feeling the impact of this vulnerability. A nationwide survey revealed that 83% of responding physicians had previously experienced cyberattacks, which is also a significant concern for health plans. To address this challenge, organizations must undertake a dual mission to maximize accuracy and minimize opportunities for data falsification.

As reported in fake news, in a world rife with fabricated data, no one knows where the next threat will emerge—nor what the most devastating breach might look like. Healthcare institutions should safeguard data from the outset by verifying its source, processing procedures, usage environment, and integrity, thereby minimizing potential errors in the future.

84% of healthcare providers and 68% of health plans believe that automated systems will introduce new risks, including fabricated and falsified data. However, among respondents, only 14% of providers and 6% of health plans reported conducting extensive verification of data sources and expressed being “very confident” in the data quality of healthcare organizations.

Healthcare institutions must undergo a verification process to ensure that users can trust the data output. Throughout the entire system, identifying the sources of erroneous results is key to improving data authenticity. Authentic data can reduce noise and interference, enabling enterprises to identify genuine threats. Ultimately, this will help ensure data credibility, thereby supporting critical future decision-making.

To enhance data accuracy, healthcare institutions must improve existing workflows, identify data sources and contexts, and ensure data integrity and security. Current investments in cybersecurity and data science may need to be adjusted to address data accuracy issues, as data quality is crucial for building user trust and facilitating business operations or clinical decision-making. For instance, technologies such as blockchain can be employed to verify data provenance and demonstrate that data has not been tampered with.

Enterprises must establish capabilities for data intelligence analysis. By leveraging technological tools to track data records, usage, and maintenance activities, cybersecurity and risk management systems can establish a baseline for expected data conditions. For example, over the past three years, Aetna has deleted 10 billion Social Security Numbers (SSNs). Instead of using SSNs as unique identifiers and authenticators, the company has adopted continuous behavioral authentication, providing real-time identity verification through behavioral characteristics captured via web and mobile applications. This approach embeds authentication into ongoing electronic interactions. Aetna uses 30–60 features to calculate a risk index, which is fed back in real time to mobile or web applications that determine the level of access granted to consumers.

Among Healthcare Professionals

86% of respondents believe that healthcare organizations are building their most critical systems and strategies on data, yet many have not validated the authenticity of that data.

24% of respondents indicated that healthcare organizations have repeatedly made errors in the use of AI.

89% of people believe that, as healthcare organizations rely on data-driven decision-making, issues related to data integrity will grow exponentially.

Logistics company DHL partnered with Accenture to create a blockchain-based serialization prototype for tracking pharmaceuticals across the supply chain.

The ledger enables enterprises to comply with legal and regulatory requirements while addressing various issues, all while preserving the characteristics of cryptographic security. Pharmaceutical ledgers can be shared with stakeholders, including manufacturers, warehouses, distributors, pharmacies, hospitals, and physicians. Prototype simulations demonstrate that the blockchain can process over 7 billion unique serial numbers and 1,500 transactions per second.

Now, strategic partnerships are more important than ever for the business growth of healthcare institutions.

As boundaries between different industries become increasingly blurred, unexpected collaborations are unfolding in unconventional ways, serving as bridges that connect the world. Leading industries worldwide demonstrate that technology is the foundation of these partnerships.

Technology-driven collaboration accelerates network development and facilitates entry into more ecosystems. However, healthcare legacy systems are not designed to support such rapid expansion. In the near future, these legacy systems will become major obstacles to future growth.

Healthcare organizations must rethink how to make technology-based collaboration work, positioning themselves for sustainable differentiation and growth. Two technologies are poised to address these challenges: microservices and blockchain.

Microservices break down applications into the simplest component functions. Each function is an independent service with its own API.

Blockchain information is replicated through a network of nodes that transmit data and transactions, which are secure, immutable, yet verifiable.

Microservices are not a technology, but an architectural approach. As applications become more modular, the microservices approach enhances their agility and supports rapid integration with numerous new partners. Such collaboration is crucial for enterprises to grow and stand out within an ecosystem. As healthcare institutions expand their scope of cooperation, business transactions between enterprises have become increasingly complex. Blockchain is a distributed ledger system capable of storing groups of transactions. This technology facilitates the creation, scaling, and management of partnerships, enabling partner accountability without the need to establish trust beforehand.

By adopting a microservices architecture and blockchain technology, and storing self-executing smart contracts on the blockchain, healthcare institutions will lay a solid foundation for technology-driven collaboration to support future differentiation and growth. Those who invest in these changes now will redefine how enterprises conduct transactions in the future. 

Microservices enable enterprises to transcend industry boundaries, collaborate with third-party partners, and bring solutions to market more rapidly. In the healthcare sector, microservices allow medical institutions to adapt quickly and easily to evolving customer experience expectations. By leveraging a suite of technologies such as application programming interfaces (APIs), containers, and cloud computing, microservices decompose applications into simple, modular services. Each feature becomes its own independent service, rather than being bundled into a single monolithic application.

The microservices architecture provides a foundation for companies to rapidly and easily establish partnerships, seamlessly integrating services while reducing friction with partners or customers.

U.S. pharmaceutical retailer Walgreens has revamped its “Balance Rewards” program, aiming to expand partnerships through microservices. The APIs created during the transition to microservices are shared with third-party developers, who can integrate Walgreens’ rewards into their own applications, allowing customers to earn points for activities such as running, blood pressure testing, and even smoking cessation. Walgreens stated that establishing these partnerships now takes only hours, rather than the months it previously required. The company currently has more than 275 partners, and its medications are widely utilized.

88% of healthcare professionals expect their organizations to increase the use of microservices in the coming year, yet microservices remain an emerging architectural approach among healthcare providers and health plans. Nevertheless, a small cohort of experts is leading this trend by actively incorporating microservice architectures into their strategic initiatives. Some healthcare enterprises are leveraging third-party integration layers situated atop systems of record to extract information for use by participating systems.

Companies like Sansoro Health are advancing the use of microservices, offering solutions that simplify API integration across multiple EMR platforms. Third-party applications connect to the platform to enable secure, seamless data exchange, thereby rapidly scaling integration efforts and quickly delivering value.

Industry leaders are leveraging API layers to minimize integration complexity across multiple core management systems. Microservices architectures enable them to adapt to new engagement channels, such as smartphones, wearable devices, and voice-controlled speakers. They are developing Alexa skills and mobile applications to facilitate rapid integration among partners, delivering a unified experience for members.

Healthcare institutions can leverage blockchain to manage extensive partner networks, streamlining the collection and coordination of diverse medical and financial data. Failure to share data among providers, hospitals, health plans, and other collaborators would have significant repercussions. Blockchain can connect these highly fragmented data silos, adding a layer of trust through cryptographic proof of data provenance. No single organization can unilaterally control the blockchain, ensuring that all participants have equal access to the information they are authorized to view.

Adopting blockchain technology enables enterprises to build broader networks, collaborate with new partners, or seamlessly enter new ecosystems. By leveraging blockchain-based smart contracts, businesses can establish computable terms for given relationships and automatically transact with partners that meet these conditions. 78% of healthcare professionals believe that smart contracts will significantly transform the way we conduct transactions and make digital trust possible.

These new technologies are still in the early experimental stage in the healthcare industry, lagging behind sectors such as financial services. Nevertheless, healthcare professionals have recognized their potential. When asked, “When do you expect blockchain to be integrated into your systems?” 32% of healthcare providers and 48% of health plans indicated a timeframe of one to two years.

With the establishment of partnerships, future trends are becoming clearer: if microservices and APIs are key to scaling and integrating collaborations, then blockchain is essential for building trust in the authenticity and accuracy of shared content.

Among healthcare workers

88% of respondents anticipate an increase in the volume of data exchanged with ecosystem partners over the next two years.

88% of respondents believe that microservices are critical for scaling and integrating ecosystem partnerships.

91% of respondents believe that blockchain and smart contracts will have a significant impact on their healthcare organizations within the next three years.

Anthem has partnered with Castlight Health to launch Engage, a next-generation health engagement and digital experience platform designed to improve consumers’ health outcomes and healthcare decision-making. The new platform establishes a centralized hub for employers’ wellness programs and integrates all functionalities to deliver personalized experiences for each user. It also leverages a microservices layer to seamlessly connect with third-party health applications and health plan tools, such as telehealth services, virtual care, and fitness tracker-driven wellness programs.

For example, the platform reminds caregivers to contact patients based on their medical or daily living needs, helps patients adhere to physicians’ care plans, flags missed laboratory tests, and provides additional educational resources.

Anthem Vice President Anil Bhatt stated, “Engage operates on a microservices and event-driven architecture, which facilitates the seamless transmission of data and behaviors as needed. Anthem Engage and other internal applications leverage microservices and APIs to create a seamless integration model.”

From ICU wards with automated patient management to self-maintaining equipment, a growing number of healthcare institutions are developing smart environments that incorporate robotics, extended reality, artificial intelligence, and connected devices.

However, the technological infrastructure supporting this new hyperconnected environment has not evolved at the same pace.

Currently, common enterprise infrastructure in the healthcare sector is not designed to support real-time analytics and action, thereby hindering the large-scale development of intelligent solutions. Existing infrastructure is built upon several fundamental assumptions: that sufficient bandwidth is available to support any remote application, that cloud storage and hardware resources are virtually unlimited, and that adequate computing power will continue to be provided. However, the demand for immediate response times—particularly in the healthcare field—is fundamentally at odds with this approach. 

Bandwidth

Hypothesis: AI technology will be the primary method for uncovering major discoveries in the life sciences.

Reality: As businesses become increasingly reliant on data, bandwidth has emerged as a hard constraint, particularly in the pre-5G era.

Storage

Hypothesis: Cloud storage provides unlimited, low-cost storage space.

Reality: Storing data is cheap, but creating it is even cheaper. Autonomous vehicles are expected to generate 3.6 TB of data per hour, or 1 GB per second.

Calculation

Hypothesis: Hardware will continue to become increasingly powerful.

Reality: Shrinking transistors have reached their physical limits. Performance continues to improve, but the way companies achieve it is changing.

Future efforts will require a thorough overhaul of existing infrastructure. To overcome these challenges, healthcare organizations can adopt the following three strategies: embedding intelligent tools everywhere, balancing cloud and edge computing, and leveraging customized hardware. Reexamining enterprise infrastructure presents new opportunities for healthcare institutions, encouraging them to embrace edge computing as a strategic asset for intelligent environments. Embedding business operations into the surrounding world begins with transforming IT systems—building the capability to support intelligent operations anywhere.

To achieve intelligence, healthcare institutions must analyze and act on the data generated. There is no time to wait for connectivity; decisions must be made immediately. This means pushing processing to edge computing. Eighty-two percent of healthcare professionals believe that edge computing architectures will accelerate the development of many technologies.

“Edge” solutions involve processing and storing “nearby” data on devices. For example, Autonomous Healthcare (formerly AreteX Systems) uses machine learning tools installed on medical facility equipment to monitor patients’ vital signs and automatically allocate and adjust intravenous drips for intensive care unit patients.

Edge computing reduces latency and bandwidth requirements, while minimizing the need to send data to the cloud for analysis, thereby enhancing security. Analytical results are generated at the source. The device can act on these analytical outcomes and, based on the findings, decide whether to discard the data or upload it to the cloud. It is essentially Internet of Things (IoT) technology embedded with software that ensures a low failure rate, which is critical for drug development.

Edge computing can enhance the quality of life and independence for older adults. Through smart sensors and wearable technologies designed for health monitoring, it enables both passive sensing and active guidance. For instance, a device similar to Alexa can alert patients to an elevated heart rate detected by their wearables and advise them to sit down and rest. In more severe cases, the technology notifies caregivers or healthcare providers to intervene. This approach grants patients greater freedom while potentially saving their lives.

In the healthcare sector, the boundary between digitalization and the real world continues to blur, necessitating a more robust and flexible foundation: scalable infrastructure. Scalability does not entail dismantling and replacement; rather, it involves optimizing existing infrastructure to enable healthcare enterprises to fully leverage AI, robotics, and other emerging technologies without imposing additional bandwidth burdens.

If the processing power and energy efficiency of edge computing are improved, real-time action will become possible, thereby saving lives. Edge computing enables healthcare institutions to leverage the technologies they desire and require, as they possess the corresponding processing capabilities.

In the next wave of healthcare innovation, edge computing will become a critical component of infrastructure. Enterprises must determine what should be processed and stored at the “edge” and find the optimal balance between the cloud and the “edge.”

Among healthcare workers

85% of respondents believe that generating real-time analytical insights from anticipated future data volumes requires edge computing at the point of data generation.

82% of respondents believe that healthcare institutions need to refocus on custom hardware and hardware accelerators to support real-time analytics and action.

86% of respondents believe that enterprises must balance cloud computing and edge computing to maximize the flexibility of their technical infrastructure and make intelligent technologies ubiquitous. 

Imagine a patient with epilepsy who has been implanted with a device capable of real-time analysis of their brainwaves to monitor for abnormal activity. Within milliseconds of detecting a seizure, the device delivers pulses to halt the episode—requiring no external systems or any input from the patient. In fact, the patient is entirely unaware of what has occurred. The entire process takes place within the device itself.

This is not a hypothetical scenario, but a treatment regimen currently in clinical use. Following the surgical implantation of NeuroPace’s neurostimulation device into the patient’s skull, it automatically monitors and prevents epileptic seizures, reducing seizure frequency by 44% within the first year alone. This real-time management of a serious medical condition exemplifies an intelligent environment: one that leverages the integration of real-time sensing and computation to enable immediate intervention.