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In-Home AI Care: How Care Daily Is Transforming Healthcare

Advances in Healthcare AI Bring Personalized, Proactive Care Closer to Reality

Rahul Neel Mani (@rneelmani) • March 4, 2024

Home-based healthcare is progressively gaining popularity, facilitating access to affordable personal care at scale. Healthcare providers lack the requisite technologies despite the significant advancements in AI technology across various industries. However, the industry shows immense potential for growth in terms of AI adoption.

Care Daily, an AI SaaS enterprise, has developed the first scientifically validated in-home AI caregiver service. Arti, Care Daily's open-source AI platform, uses general-purpose AI technology for homes and buildings to detect falls in real time, uncover health problems, and facilitate collaboration between families and healthcare professionals to efficiently provide care. The company enables traditional healthcare organizations to deliver home-based healthcare, and its platform integrates well with health and age tech products, and provides an AI marketplace that fosters the development and deployment of personalized AI services. Arti is integrated with private ambient sensors to understand lifestyle patterns and proactively identify health issues.

"Arti engages with families, provides education, coordinates care and even acts as a security guard, seamlessly integrating into homes and enhancing the caregiving experience," said Dibyendu Roy Chowdhury, data scientist, Healthcare AI, Care Daily.

Care With an AI-Driven Approach

Healthcare companies rely on remote patient monitoring, or RPM, devices - including blood pressure monitors, pulse oximeters and others - to collect data; however, these devices have limitations in detecting critical issues, including falls. As the industry shifts toward value-based care models, Care Daily's fully integrated AI solution offers a distinct advantage. It involves engaging with patients' families, integrating ambient sensors and wearables, and leveraging predictive services to continuously monitor patient health.

"With AI, we aim to improve patient outcomes while reducing the cost. We offer a scientifically validated AI caregiver solution that significantly enhances the well-being of family caregivers, showcasing outcomes comparable to pharmaceutical interventions," Roy Chowdhury said.

AI-Enabled Healthcare Getting Mainstream

Generative AI has shown the potential to bring a transformative shift in the healthcare sector. Impactful AI solutions, however, are still scarce despite significant investments in AI-enabled digital health startups.

"Despite obstacles, AI's potential in healthcare is promising. With willingness among healthcare providers to adopt AI/ML technologies, the adoption will only go higher. However, widespread integration of AI-enabled solutions in healthcare requires further time and scalability," Roy Chowdhury said.

Among the various AI use cases in healthcare that have emerged in the last few years, Roy Chowdhury considers the following to be particularly pervasive:

  • Early Disease Detection: Disease identification at earlier stages by analyzing complex data patterns and identifying risk factors, leading to quicker interventions and improved patient outcomes.

  • Accelerated Drug Development: Analysis of chemical structures and biological data and simulation of drug interactions, and potentially reducing the time and cost required to bring new drugs to market.

  • Infectious Disease Intelligence: Insights for studying the large-scale impact of infectious diseases, which includes modeling new pandemics or analyzing patterns of disease, aiding in prevention and mitigation efforts.

  • Personalized Care: Designing customized medication and treatment plans based on unique genetic makeup, lifestyle, and health data to improve treatment efficacy and patient satisfaction.

  • Clinical Trial Optimization: Streamlining the design and execution of clinical trials by identifying ideal trial sites and principal investigators, optimizing resources and ensuring more efficient and cost-effective trials.

  • Real-Time RPM: Monitoring patients' health using AI-enabled wearable devices and smartwatches and providing real-time data to healthcare providers for timely interventions and predictive analytics.

"Many of these use cases have demonstrated AI's potential in optimizing healthcare processes, enhance accuracy, and improve patient outcomes, ultimately allowing physicians to allocate more time to patient care and treatment planning," Roy Chowdhury said.

Healthcare-Specific LLMs Hold Promise

Since the introduction of LLMs, a considerable amount of effort has been invested in the development of healthcare-specific LLMs. These LLMs have immense potential in reshaping patient care, medical research and education.

"These LLMs facilitate affirmative communication between patients and healthcare professionals. The production and summary of scientific content can assist scientists and clinicians in handling vast datasets," Roy Chowdhury said. "Physicians are utilizing AI-powered chatbots like First Derm and Pahola for patient assessment and guidance in specific conditions."

Google in December 2023 introduced MedLM, a collection of foundational models designed for the healthcare sector. MedLM helps in managing certain complex tasks and answers medical questions. It is capable of generating insights from unstructured data and summarizing medical information. Google Cloud and HCA Healthcare, a leading healthcare provider in the U.S., recently collaborated to improve the workflow of certain tasks in the healthcare sector using generative AI technology. This joint effort aimed to help doctors and nurses with time-consuming tasks, including clinical documentation, so they can focus on patient care.

"BenchSci integrates MedLM to accelerate drug development and enhance pre-clinical research quality. Similarly, Epic's collaboration with Microsoft leverages LLMs to translate clinician-patient conversations into electronic health records using GPT-4," Roy Chowdhury said.

Several challenges such as misinformation risks, data privacy concerns and the need for high-quality training datasets continue to persist and need to be thoroughly examined before adoption at scale. Ethical issues and the lack of publicly available training datasets and source code need to be addressed, Roy Chowdhury said. "It necessitates the need for a deeper understanding of underlying LLM data to ensure their efficacy and reliability in healthcare applications,” he said.

The transformative impact of AI in the healthcare sector is evident with the emergence of generative AI and healthcare-specific fundamental models. Through industry-technology collaboration and by ensuring adherence to ethical considerations, healthcare companies can harness AI's potential to enhance care delivery and improve outcomes.

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