Machine learning and big data significantly impact the modern healthcare sector. Especially, COVID-19 has pushed the healthcare and life sciences industries in entirely new directions. Vaccine development is now timed in months rather than years. Practitioners are utilizing telehealth technologies to improve the physician and patient experience. In addition, individuals have adopted a range of devices to take greater ownership and management of their own health. According to several researchers, AI in Healthcare and Pharma can deliver significant benefits in the healthcare sector.
AI is ready to assist health professionals with various duties, including clinical documentation, patient outreach, administrative tasks, and specialized support in medical device automation, image analysis, and patient monitoring. Let’s dive into the article to find the top 9 trends of using AI in Healthcare and Pharma.
Trends of using AI in the healthcare sector
Personalization of Healthcare
AI-based personalized health is a field where we can manage preventive health measures, process data, construct predictive models, and securely share that data with all parties.
AI in healthcare is based on personal data from wearables, trackers, applications, consultations, and medical data and offers new ways to personalize advice. All of this is feasible because of the personal health training and predictive health models, which integrate the data of patients and feed it into AI models. For healthcare professionals and individuals, the outputs of AI-enabled healthcare models must be accessible, explainable, controlled, and trustworthy. However, the development of new AI in healthcare applications should be aided by guaranteed privacy.
Categorization of Applications
Essentially, there are two categories of artificial intelligence applications. The first is the machine learning (ML) category, which analyses structured data, such as genetic data, electrophysiological data (EP), and imaging data. Machine learning (ML) systems in healthcare applications acquire patient individuality or understand the probability of disease impacts.
The natural language processing (NLP) approach is the second category of artificial intelligence application, extracting information from unstructured data, such as health journals or medical observations, to improve structured health check data.
Analyzing Imaging and Reports
Automated analytical systems have started to emerge as a database system that allows for computer-based scanning of medical images and the creation of big data. Artificial intelligence (AI) architectures based on deep learning have been developed and applied to medical imaging, allowing for high-precision diagnosis. Medical imaging must be labeled and standardized for diagnosis.
After pre-processing the data and integrating it into the deep-learning architecture, the final diagnosis reports can be achieved fast and accurately. AI has experience in medical imaging analysis of the lungs, retina, digital pathology, breast, abdomen, nerves, heart, and musculoskeletal system, thanks to its deep-learning architecture.
Claims and Payment Management
Effective claims management using AI now includes initiatives to manage medical expenditures better and improve customer interactions in addition to processing and paying claims.
When it comes to reimbursement and claims processing, hospitals are implementing artificial intelligence (AI) across many systems to outsource and automate repetitive, high-volume activities, reducing human workloads and speeding up the overall revenue cycle. With AI’s precision, hospitals can eliminate the possibility of patient entry or pre-authorization claim errors, as well as unnecessary back-and-forth contacts caused by inaccuracies.
Administrative Processes
Staff in the healthcare industry spends an increasing amount of time on administrative duties, with hardly any awareness of work patterns to guide resource allocation. Scheduling, patient communication, payment collection, survey analysis, post-visit follow-up, and issue triaging for patients seeking care are areas where AI might improve the industry’s administrative procedures.
Today’s best-case scenario is to deploy AI in Healthcare and Pharma to enable personnel to perform more complex duties while focusing on patients in person. As a result, the perfect mix of technology and a human touch can be achieved.
Prediction and Health Policy
AI in Healthcare and Pharma is being used in predictive analytics to look at patient data and predict the possibility of specific diseases and disorders. Research has revealed that AI may detect disorders that are usually difficult to diagnose or identify, such as rare neurodegenerative and hereditary diseases.
Health insurance companies can use artificial intelligence and machine learning to identify patients at risk and cut rising healthcare expenses. The ability of an AI system to build efficient reasoning and intuitively read and analyze trends is critical to its effectiveness in managing a client’s healthcare. Health insurance companies can spend more money on their beneficiaries and less on the processes by utilizing AI to construct a system that can create more accurate risk models and forecast which individuals require specific care.
Electronic Health Records
AI in Healthcare and Pharma is predominantly used in electronic health records to improve data search, extraction, and individualized treatment recommendations. Large amounts of data on patient health are generated due to advances in medical imaging and the expansion of clinical diagnostics and screenings.
AI-powered EHR systems integrate effortlessly and provide a wide range of functionality. Machine learning and Natural Language Processing can assist in documenting patients’ medical experiences, organizing massive EHR data banks for finding essential documents, assessing patient satisfaction, etc.
Diagnosis and Treatment
AI in Healthcare and Pharma is becoming more widely used, particularly in diagnoses and treatment management. AI and machine learning have developed as strong diagnostic tools in recent years. This technology has the potential to enhance healthcare by allowing for more precise diagnostics.
AI can assist in the diagnosis and treatment of diseases and other conditions. It does not replace doctors but supports their decisions and makes recommendations based on analyses of large amounts of healthcare data, such as medical images, symptom data, doctor reports, electronic health records (EHRs), etc.
Drug Development
From assisting scientists in discovering new prospective medicines to forecasting which treatments would fail clinical trials, AI has been successfully employed in different drug discovery and development aspects.
Drug development is a time-consuming, expensive, and slow process that begins with discovering a molecule and ends with the creation of a new molecular entity. Machine learning and AI have the potential to bring about a new era of drug discovery that is both faster and more cost-effective. When only local data is used, artificial intelligence technologies in drug discovery and development can assist researchers in avoiding implicit bias.
Conclusion
Using AI in healthcare and pharma could be highly beneficial. Health sectors in all countries are undergoing significant changes as they use the opportunities provided by communication and information technologies. The primary goals of this transformation process are to improve the quality of care, efficiency, and productivity.
Get in touch with CCom Digital to know how you can leverage the benefits of AI in Healthcare and Pharma. CCom Digital’s experienced and qualified team of experts with diverse industry experience will provide the best solutions and advice for your organization.