AI Facilitating Remote Patient Monitoring
The healthcare sector is experiencing a remarkable transformation with the integration of artificial intelligence (AI) in remote patient monitoring (RPM). AI technologies are driving efficiency, accessibility, efficiency, and better patient outcomes, revolutionising the way healthcare is delivered. According to a report by Grand View Research, the market for AI in healthcare is poised to be worth around $188 billion by 2030, up from $11 billion in 2021. Financial Express, in a recent article, sheds light on how AI is changing remote patient care.
RPM is the process of collecting and transmitting patient health data to providers via connected devices. An AI-driven RPM solution does the same, but can significantly enhance diagnostic accuracy and efficacy as well. AI algorithms can interpret massive amounts of medical data in seconds, including test results, logged symptoms, and medical history.
By helping medical professionals make error-free and data-driven decisions, the technology enables efficient and fast treatment, lowering the risk of complications and enhancing patient outcomes. According to a study by Frost & Sullivan, AI has the potential to enhance patient outcomes by 30% to 40% while reducing treatment costs by up to 50%.
RPM powered by AI has also proven to be highly beneficial in managing chronic diseases such as diabetes, hypertension, and heart conditions. Continuous monitoring of symptoms, therapeutic adherence, and lifestyle factors have enabled personalised interventions and better disease management.
Wearable devices help clinical trial sponsors gather patient data continuously, but the large amount of data collected can be overwhelming for study teams. The advantage of using AI is that health data gathered by wearables can be analysed using advanced algorithms. These can help identify patterns, predict health trends, and anticipate potential complications. Thus, healthcare providers can intervene on time and proactively prevent imminent health complications.
Clinical trial teams rely on decentralised clinical trial (DCT) platforms to monitor their patients remotely. Through these platforms, patient data is seamlessly transmitted and visualised in real-time dashboards. Next-generation DCT platforms, for example, ObvioGo by ObvioHealth, include AI-assisted technologies that ensure delivering robust clinical outcomes. An AI-enabled DCT system helps surface data and pinpoint key findings. Essentially, AI algorithms serve as vigilant assistants, promptly flagging data anomalies and drawing the study team’s attention to data-related concerns.
The article underscored that AI-powered chatbots are another key player in revolutionising RPM. Chatbots and virtual assistants offer patients or clinical trial participants personalised support, answer medical questions, and send medication reminders. These, in turn, enhance patient engagement and adherence to treatment plans. However, concerns have emerged among patients about the disclosure of confidential information, discussing intricate health issues, and facing usability challenges while using healthcare chatbots.
Despite that, the integration of AI in remote patient monitoring for decentralised clinical trials has significant implications for the future of clinical research. By leveraging AI’s data analysis capabilities, study teams can optimise their workflow, streamline data management processes, and ensure the highest standards of data quality.
In remote patient monitoring in clinical trials, ensuring the participants are adherent to the trial protocol is critical. However, approximately 40% of patients become nonadherent to investigational medical products (IMP) after 150 days in a clinical trial. Prevalence of study non-adherence can affect efficacy (skipping doses) and introduce data variability, leading to inflated trial costs. By notifying participants of pending tasks, an AI-enabled system programmed within the workflow can shift the paradigm to much lower non-compliance rates.
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COMTEX_437118793/2764/2023-07-21T04:54:10