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Technology has been an enabler of healthcare for years. From large organizations to small clinics, healthcare providers around the world have leveraged apps, data management platforms, and many other systems as key levers to streamline operations and drive better clinical outcomes. While modernization efforts are still underway, strategic focus is shifting to one major trend: AI.
As healthcare providers face staff shortages and patients continue to demand quality care, AI is emerging as the go-to answer. With access to large data repositories containing patient-specific data, the technology can personalize healthcare delivery, ultimately transforming how healthcare providers diagnose, treat, and interact with patients, moving away from a one-size-fits-all approach.
How will AI personalize care delivery?
While AI has been around for some time, the rapid rise of ChatGPT has pushed its application to the forefront at various levels of the healthcare ecosystem. One of them is diagnosis: AI-driven systems analyze vast amounts of patient data to provide personalized diagnoses and treatment recommendations. Watson Health, a division of IBM focused on applying AI and data analytics to healthcare, is a pioneer in this field. The company's technology analyzes a range of patient parameters, including medical history, genetics, and disease symptoms, to diagnose underlying conditions and provide personalized treatment recommendations.
A study published in the Journal of Clinical Oncology found that Watson Health's oncology-specific AI decision support system achieved a 93% agreement with expert tumor board treatment recommendations. This high level of accuracy demonstrates how AI-enabled personalization can improve diagnostic accuracy and treatment effectiveness, ultimately improving patient outcomes.
AI-driven personalization can help not only detect ongoing issues and recommend treatments, but also preventative care. Essentially, algorithms can analyze a patient's daily metrics, such as SPO2 and BP, and combine that with data related to lifestyle and genetics to predict the likelihood of suffering from a particular health issue, and what steps (think specific lifestyle changes) can be taken to prevent it.
A study published in PLOS One demonstrated that an AI-powered approach could lead to a reduction in recurrent hospitalizations and emergency department visits, not only shifting patients' focus from reactive treatment to proactive prevention, but also saving healthcare provider resources that would normally be spent on extensive follow-up and treatment.
But personalizing prevention, diagnosis and treatment is only one piece of the healthcare puzzle.
AI also plays a key role in personalizing medical prescriptions, where 100% accuracy is essential. Prescribing the wrong medication can lead to side effects and, in extreme cases, death. A 2016 study by Johns Hopkins University School of Medicine found that medical errors, including incorrect prescriptions, are responsible for more than 250,000 deaths annually in the United States alone.
Fortunately, AI-powered tools can address this issue by analyzing factors such as a patient's genetic makeup and medical history to predict how they will respond to certain medications. This allows healthcare providers to customize prescriptions for each patient, significantly reducing the risk of side effects and improving treatment outcomes.
Streamline your administrative processes
While clinical care is at the forefront of AI personalization, the technology is also improving administrative processes in healthcare: tasks like appointment scheduling, billing, and support are simplified, becoming more personalized and efficient.
Zocdoc, an online medical appointment platform, is a prime example of AI helping with administrative tasks. Their platform uses machine learning to ensure appointments are seamlessly integrated into the healthcare provider's schedule, thereby reducing patient wait times and increasing the chances of timely treatment. Additionally, it also allows patients to check their medical insurance coverage and estimate the total cost of treatment by simply taking a photo of their health insurance card. Nabla, another player in this category, uses LLM to generate clinical notes from doctor-patient interactions, eliminating the hassle of manually recording information.
Notably, chatbots and agents powered by generative AI are transforming patient engagement and improving access to healthcare. These chatbots and agents analyze patient data and provide personalized assistance whenever needed, including registration, routing, scheduling, prescription refills, and more. This technology has significantly improved the patient experience, making patients feel more valued and understood.
Overall, the impact of AI-powered personalization on healthcare is significant and growing. The technology is enhancing patient and caregiver interactions, enabling customized prescriptions, and improving preventive care strategies, making healthcare more efficient, effective, and patient-centric. As AI continues to evolve, its potential to revolutionize healthcare will only expand. Future developments may include further personalization of treatment plans and deeper integration of AI into all aspects of healthcare delivery.
But as these advances take shape, it is equally important to address potential challenges associated with AI, particularly the privacy and security of the medical data that AI models use. Organizations using these tools must strive to maintain the human touch in patient care and address potential bias in AI algorithms. Ultimately, it will come down to how we work with the companies developing these tools and the governments that set regulations around their use.
In the long term, the potential benefits of AI-powered personalization in healthcare are enormous, and by adopting this technology responsibly and ethically, the healthcare industry can move toward a future where truly personalized care becomes the norm, leading to better health outcomes for all.
