HealthTech – Blog by Neha Mendon

A Foreword to Artificial Intelligence in Healthcare

Exciting developments are on the horizon for healthcare as leading research organizations and the CIO Agenda reveal a bold vision for digital technology investments! Imagine this: a staggering 91%¹ of organizations are laser-focused on delivering an unparalleled customer experience, while 67% are determined to boost their operating margins. And that’s not all—48% are gearing up to tap into new revenue streams! The future of healthcare is vibrant and full of potential!

AI-based solutions can effectively streamline diagnostic and treatment processes by utilizing large amounts of structured and unstructured medical data across institutions. This support aids physicians at hospitals and health systems in clinical decision-making by providing real-time, data-driven insights, which physicians can modify and implement based on their expertise.

AI in healthcare manifests in various ways, including discovering new connections between genetic codes, powering surgery-assisting robots, automating administrative tasks, and personalizing treatment options. Additionally, AI enhances efficiency and improves customer experience in nonclinical operations. Its ability to quickly analyze vast amounts of information helps hospital and health plan administrators optimize performance, increase productivity, and improve resource utilization, leading to time and cost efficiencies. Moreover, AI-enabled solutions can accelerate and strengthen the insight generation process, allowing organizations to gain a comprehensive understanding necessary for making data-driven decisions. Finally, AI can provide personalized experiences by facilitating conversations with patients through virtual assistants.

Tech-infused tools, or Healthcare Technology, are being integrated into every step of the healthcare experience to address two key challenges: quality and efficiency. Healthcare technology refers to any IT tools or software designed to enhance hospital and administrative productivity, provide new insights into medicines and treatments, and improve the overall quality of care provided.

Modalities of AI in Healthcare

Generative AI has harnessed the capability to create new content based on existing data, simulating human-like creativity through increasingly user-friendly interfaces. This development has significantly expanded the potential for everyday users to utilize generative AI with minimal technical expertise. According to Gartner’s ²Hype Cycle for Emerging Technologies, which visually represents the typical maturity and adoption of technologies over time, Generative AI is currently at an early, yet rapidly evolving, stage in its journey. Generative AI has harnessed the capability to create new content based on existing data, simulating human-like creativity through increasingly user-friendly interfaces. This development has significantly expanded the potential for everyday users to utilize generative AI with minimal technical expertise.

As a sub-field of artificial intelligence (AI), Generative AI has gained immense popularity. While traditional AI systems primarily analyze data and make predictions, generative AI takes it a step further by creating new data from its training information across various modalities, including text, audio, visuals, video, and code. In essence, traditional AI excels at recognizing patterns, while generative AI is adept at creating new ones.

AI is a broad term that encompasses a range of interconnected processes. Some of the most common forms of AI utilized in healthcare include:

Machine Learning (ML): This involves training algorithms with data sets, such as health records, to create models capable of tasks like categorizing information or predicting outcomes.
Deep Learning: A subset of machine learning that uses larger data volumes, longer training times, and more layers of algorithms to produce neural networks capable of performing complex tasks.
Natural Language Processing (NLP): This technology uses machine learning to understand human language, whether spoken or written. In healthcare, NLP helps interpret documentation, notes, reports, and published research.
Robotic Process Automation (RPA): This involves using AI in computer programs to automate administrative and clinical workflows. Many healthcare organizations employ RPA to enhance patient experiences and improve facility operations.

How is AI being leveraged in Healthcare?

AI employs algorithms and machine learning (ML) to analyze and interpret data, deliver personalized experiences, and automate repetitive and costly healthcare operations. These capabilities have the potential to support both operational and clinical staff in decision-making, reduce time spent on administrative tasks, and allow humans to focus on more challenging, engaging, and impactful management and clinical work.

Healthtech Improves Efficiency: Patient waiting times are decreasing, and hospitals are being staffed more efficiently, due to artificial intelligence and predictive analytics. Even surgical procedures and recovery times have been reduced through the use of ultra-precise robots that assist in surgeries, making some procedures less invasive.
Healthtech Promotes Quality Care: Healthcare technology companies are injecting much-needed efficiency by personalizing experiences for individuals. They understand that there is no one-size-fits-all solution to proper care, making customization essential.
Administrative Healthtech: Artificial intelligence assists administrative teams in streamlining patient flow by accurately calculating wait times and predicting peak busy hours for staff scheduling.
Healthtech in Fitness: Fitness has developed hundreds of wearable, apps, and other tools that track workouts, monitor sleep patterns, and ultimately aim to enhance fitness while reducing preventable costs within the healthcare system.

Benefits, Challenges, and Risks

Top priorities in healthcare organizations include increasing process efficiency, enhancing customer offerings, and lowering costs. AI has the potential to create new efficiencies in administrative processes and provide precise and faster diagnoses and treatment plans for patients. This can lead to reduced lengths of stay, fewer subsequent re-admissions, and lower costs. When healthcare leaders were asked about the outcomes they aim to achieve through AI, the top priority cited was more efficient processes. Enhancing existing products and services and lowering costs were seen as secondary priorities. Other surveyed physicians reported that saving time and resources is expected to be the primary benefit of AI.

Top challenges in healthcare organizations include the cost of AI solutions, AI integration problems, and AI implementation and data management. Survey respondents highlighted issues such as poor-quality data, siloed data systems, high initial costs for AI solutions with low return on investment, and difficulties in integrating AI into legacy systems. The top challenges facing organizations include the cost of these systems, the integration of AI into their operations, and related implementation and risk issues. Moreover, when discussing ethical risks, leaders expressed particular concern about the safety of AI-powered systems.

AI algorithms carry inherent risks, such as variability in outputs for patient diagnosis and treatment, data bias, and traditional IT risks like change management. To mitigate these risks, healthcare organizations should verify the integrity and accuracy of their AI algorithms by developing a solid data strategy. Organizations need to conduct internal audits and testing of AI systems and ensure that AI vendors offer unbiased solutions. Understanding the data strategy and processes is crucial for minimizing bias in AI models.

Testing machine learning (ML) or cognitive algorithms differs from traditional test case methods, where actual results can be repeatedly compared to expected outcomes. Organizations should continuously monitor learning algorithms as they adapt to new data over time, ensuring their performance remains within acceptable control limits. This continuous monitoring is essential for maintaining validation and compliance in operational use.

The Future of AI in Healthcare

The future of healthcare will likely be driven by digital transformation, facilitated by radically interoperable data and open, secure platforms. It is essential for organizations to develop an enterprise-wide AI strategy, raise awareness about the upcoming wave of AI healthcare solutions, and establish an enterprise-level AI task force to guide the selection, acquisition, and implementation of AI technologies. Quick wins—those that are easiest to implement and lowest in cost—can demonstrate the value of AI and serve as stepping stones for future successes.

Healthcare organizations often face challenges with siloed, unstructured, and sometimes incomplete or inaccurate data when attempting to make integrated data more accessible. Additionally, patient-level data is sensitive and highly regulated, making it difficult for AI applications to access this information. The lack of access to clean, integrated datasets hampers the ability to train high-performance AI models and deploy them effectively at scale.

AI-enabled solutions can offer immediate benefits to healthcare organizations by reducing costs, assisting in new product development, and enhancing consumer engagement. In the short term, organizations may focus on investing in AI approaches that help achieve cost savings. Examples of such approaches include provider profiling (in supply chain management), detecting and preventing fraud, risk, and abuse, as well as automating healthcare operations.

In the long term, health systems can invest in more transformative AI applications to enhance their competitive positioning, achieve profitable growth, engage consumers, and deliver personalized customer experiences. By adopting a patient-centered approach and involving end users in designing the future patient experience, organizations can make informed choices that lead to positive outcomes for patients, healthcare practitioners, and providers.

Tag: HealthTech

AI Trends in Healthcare