Discover the transformative power of AI in healthcare policy-making and how AI is shaping the future of healthcare, helping health tech custodians make informed decisions and drive ethical advancements in the healthcare industry. Explore the nitty-gritty through this latest read.
Table of contents
The Pivotal Role of AI, NLP, ML in Personalized Healthcare
1. Focus Areas of AI in Healthcare
1.1.2. Patient Engagement and Empowerment
2. AI’s Transformative Guise in Shaping the Future of Healthcare Policies
The Pivotal Role of AI, NLP, ML in Personalized Healthcare
We live in an ecosystem where we desire a personalized experience, from music to web series, and the products and services we purchase are often recommended to us based on the data that is collected by these websites or applications.
This ability lets us understand our needs and wants for a better living experience.
Similarly, in the healthcare industry, we can monitor our health and get personalized treatment with the help of artificial intelligence (AI), Natural language processing (NLP), and machine learning (ML) models and algorithms, which tech and healthcare visionaries refer to as AI in healthcare.
AI in healthcare is a promising collaboration, as it challenges the traditional way patients are treated by doctors and healthcare specialists to bring a futuristic clinical and administrative solution. Using modern-age technology, doctors, researchers, and other healthcare providers improve healthcare delivery in areas like preventive care, disease diagnosis and prediction, treatment plans, as well as care delivery and administrative work.
AI in healthcare is further helping recruiting companies contribute to consumer health swiftly. Nowadays, the increasing use of AI in consumer wearables and other medical devices is providing value in monitoring and identifying early-stage heart diseases. This AI-powered integration of sensors and devices helps healthcare service providers observe and detect life-threatening diseases at an early stage.
Nevertheless, healthcare areas are plentiful. However, this article will focus on how AI has been implemented and what the future of healthcare policies looks like for the industry.
1. Focus Areas of AI in Healthcare
The introduction of AI in healthcare implements modern healthcare systems that are equipped to cure diseases at a rapid pace with greater accuracy, improving the quality of care through technological advancements.
The integral focus areas for artificial intelligence help in making the modern healthcare process and system more patient-centric, further fostering care delivery, strengthening disease surveillance mechanisms, and enhancing the drug discovery process.
Let’s see a few focus areas of AI in healthcare:
1.1. Patient Centricity
The concept of patient-centricity focuses on AI-based prescription medicine, which offers enhanced personal treatment by empowering patients and providing visual care.
1.1.1. Virtual Care
The shortage of medical professionals and personnel in critical healthcare areas has broadened the need to confront the challenge of delivering quality healthcare services. To curb this issue, AI can be a savior by providing telehealth solutions where physicians can make data-driven decisions by getting real-time patient’s health insights and providing clinical decisions to support, thereby enhancing the quality of care.
To monitor the vitals of patient’s in an ambulance, healthcare service providers implement Internet of Medical Things (IoMT) sensors to gain accurate data, which is further shared with physicians and other healthcare staff who can analyze and plan the treatment for the patients accordingly.
1.1.2. Patient Engagement and Empowerment
AI in healthcare has immense potential to improve the healthcare sector for efficiency, as this technology can be used in supply chain management, clinical procedures, and administrative processes. By using AI, healthcare sectors can increase patient retention rates and improve patients’ relationships with healthcare service providers.
Healthcare service providers can develop ML and AI algorithms to forecast the demand for services during peak events, ensuring optimal allocations and management of beds, oxygen cylinders, and medicines based on availability and needs. By automating these processes and implementing communication mechanisms, healthcare providers can improve the quality of services and reduce unnecessary waiting times for patients.
1.2. Care Delivery
Care delivery usually focuses on making AI-driven clinical decisions for disease management for physicians and AI-based diagnostics to assist healthcare staff in making preventive, curative, and palliative care for ailing patients.
1.2.1. Preventive Care
AI-based preventive care represents a dynamic approach for patients in disease prevention. With the help of AI predictive models, physicians, and healthcare staff can evaluate the social factors of healthcare to foresee and detect disease progression. For instance, the demand for ML and AI models accelerated the diagnosis of diseases, such as early diagnosis of cancer, with the help of the thermal image sensing model rather than the traditional self-examination model.
Other areas where AI-based image algorithms can be used are for detecting symptoms of diabetes-related eye problems. The image taken through AI is further processed through the algorithm of a convolutional neural network (CNN), which helps in understanding the difference between regular eye problems and diabetes-related eye problems.
1.2.2. Curative Care
Post-diagnosis treatment planning is a tedious process; however, ML techniques in AI can be implemented in various strategies to optimize the curative care process. AI-based curative care can identify and personalize patient treatments based on their symptoms and understanding of their needs just by analyzing the patient’s data.
AI-led interventions minimize the process of post-diagnosis treatment and create robust planning that can help physicians curate new treatments for ailing patients. Let’s consider an example of cancer patients who usually receive detailed imaging data during their treatment and checkups as a way to train the AI models to analyze the medical conditions and report on a better diagnosis.
1.2.3. Palliative Care
Due to a shortage of staff, resources, and facilities, it is often known that providing palliative care for patients can be a difficult task, especially for terminally ill patients. However, with AI-based analytics, physicians and healthcare staff can identify and classify patients’ needs in palliative care services and streamline electronic health records.
With conversational AI, terminally ill patients can have sensitized discussions that might be difficult in the presence of a human being.
1.3. Disease Surveillance
1.3.1. Active Surveillance
A pervasive AI-enabled system captures the input of “critical patient-level information” from sources such as wearables, cameras, motion sensors, noise sensors, etc. to detect abnormalities in patients’ health. To detect such self-administration errors, a radio wave-enabled wireless sensed technology has been developed when the patient is taking medicines, injections, or an inhaler.
AI-integrated medical applications on smartphones or tablets can provide notifications to physicians to inform them about patient health and medication intake and to ensure timely medical advice during critical situations.
1.3.2. Passive Surveillance
To discover and find more information about new diseases, researchers and health officials can take advantage of AI’s search algorithm, which can validate any authentic open-source data to identify any abnormalities and new diseases at an early stage.
AI-powered diagnostic models can also help in exploring and diagnosing new diseases and increase the accuracy of diagnosis compared to traditional methods of diagnosis, such as the use of mammography to detect breast cancer.
1.4. Research and Development
Research and development help accelerate the process of discovering new drugs by implementing AI-based clinical trials and drug-resistant technologies.
1.4.1. Drug Discovery
The application of AI is necessary to support the healthcare ecosystem, especially in the case of streamlining drug discovery. The AI algorithms have the capability of high accuracy, which reduces the process and time of discovering new and updated drugs. The use of NLP is quite common in drug discovery, as this method involves data mining, which helps in predicting the effectiveness of potential treatments.
1.4.2. Clinical Trials
For accurate clinical research, the most essential step is to select the right protocol for clinical trials. Earlier, the patient selection process was conducted manually, which was a huge expense in the pharmaceutical industry, as the sampling might have been incorrect or the drug faced a higher failure rate due to patients’ ill health. However, with the help of predictive AI models, the patient recruitment process can be simplified and improved by furnishing effective samples that are selected perfectly for the population.
2. AI’s Transformative Guise in Shaping the Future of Healthcare Policies
The advent of AI technology has brought about significant implications for the development of both public and private health policy. AI proves to be a valuable tool for gaining insights by accessing vast datasets and advanced algorithms. As opposed to traditional systems, one of the main advantages of implementing AI in health policy development is the potential to analyze large amounts of data swiftly. Real-time data systems can help identify trends, patterns, and potential areas for improvement. Trust is key when it comes to adopting AI in policymaking. Healthcare professionals, policymakers, patients, and citizens are integral stakeholders in building this framework.
This feedback loop enables policymakers to create AI-driven policies and access vast amounts of data, allowing them to make informed decisions based on evidence rather than conjecture. Policymakers are becoming more aware of the limitations and potential biases in understanding how to harness this technology effectively, while also spending time educating themselves through training programs that necessitate the necessary knowledge and skills to navigate the complexities of integrating AI into policy frameworks.
The future of AI in healthcare holds immense potential for helping shape public and private health policies. While prioritizing education and training initiatives and embracing this technology responsibly, custodians in the health tech industry can unlock the full potential for creating innovative and lasting solutions that address the relentless healthcare challenges.
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