How AI is transforming the future of healthcare

Artificial intelligence or AI is discreetly sneaking into our lives. Nowadays, it’s quite hard to speak about technology without artificial intelligence, or AI, entering into the discussion. Artificial intelligence presents itself in one form or the other in varying degrees and has begun to impact day-to-day activities. From logging into your social media, web search, digital personal assistants, online shopping, advertising, to using car ride services, all involve artificial intelligence algorithms to improvise user experience.

The adoption of artificial intelligence has gained traction in recent years and has empowered businesses to offer personalized services to every individual as devices learn their preferences. In addition, it is enhancing human capabilities to automate repetitive tasks. AI is making its way into every business process and seems to be everywhere. Health care is no exception.

AI is gaining significant momentum in the healthcare sector and has the potential to alter the field of healthcare. With machine learning (ML) and natural language processing (NLP) capabilities, AI is bringing a paradigm shift to the healthcare sector, aiming to transform various aspects of patient care, its practitioners, and their interaction with patients. AI can transform and outperform in any aspect of the healthcare industry. From cancer and chronic illness to radiology and risk assessment, AI can be leveraged to deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. AI can assist the medical practitioner in the foreseeable future with up-to-date medical information to make better clinical decisions, minimize human error or even replace human judgment in specific functional areas of healthcare.

In this blog, we will discuss how AI-enabled solutions can reshape and impact the various areas of medicine and healthcare to improve patient care and potentially save lives.

Artificial intelligence, often abbreviated as “AI,” refers to a system or machine that mimics human behavior of learning, planning, problem-solving, pattern recognition, and decision-making to perform the task and improve themselves. AI uses technologies like machine learning (ML) and natural language processing (NLP) to automate tasks, interpret events, create their own logic, make decisions, and take actions helping businesses to improve customer service and achieve business objectives.

AI in healthcare refers to the use of deep learning algorithms to interpret, review, analyze the data that is fed by doctors, researchers, and scientists into computers to find solutions to complex medical problems. AI is employed in clinical practices such as developing treatment protocol, diagnosis process, drug development, patient monitoring, tailoring treatments, and more.

As early diagnosis is the key to the ultimate treatment and outcome of a patient’s care, AI has shown promising results in the process of early diagnostics. AI deep-learning algorithm and image interpretation algorithm can compare the patient’s scans with tens of thousands of other scans from healthy patients are employed by clinicians to shave down the time to diagnose illnesses and offer the best treatment to the patients. With these deep learning algorithms, analytics can drill down to the pixel level on the scans and identify nuances that may escape the human eye resulting in better diagnosis.

AI-enabled medical devices aim to improve patient care. Smart devices are critical for monitoring patient condition by uncovering new insights from the data generated by an individual patient and comparing it with the collective experience of many patients. These smart devices help clinicians manage chronic diseases by monitoring patients using sensors, improving scan image quality with AI-driven medical scanning devices and monitoring patient adherence to treatment protocols by integrating AI and IoT.

Electronic health records have played a vital role in the digitalization process of the healthcare industry. But has encountered problems such as endless documentation, cognitive overload, endless documentation, and user burnout. Today, EHR developers are employing artificial intelligence to automate mundane tasks and create more intuitive interfaces to save time and effort in the documentation process. Also, the usage of voice recognition and dictation with natural language processing (NLP) tool is improvising the clinical documentation process. Furthermore, there is a high possibility of using AI and ML for indexing video recordings of a clinical encounter for future information retrieval.

Nowadays, most consumers have access to devices with sensors, and these devices can collect valuable information about their health. From tracking steps, SPO2 to heartbeat, these devices work around the clock and generate large datasets related to an individual’s health on the go.

Collecting all the data supplemented by these devices and analyzing them will offer a unique perspective on an individual’s health. AI can extract actionable insights from these large datasets and help clinicians to monitor individual’s heath. Clinicians can employ this information to offer personalized and accurate treatment for their patients. The data from the wearable devices can have a major impact, as the collection of granular data in a continuous fashion will help clinicians take better care of patients.

Patient Intake and Engagement is a critical part of the healthcare journey where AI can play an important role in improving the process. Recent advances in natural language processing can automate patient screening and care navigation by using conversational interfaces that can enable patients with the ability to share symptoms and questions via text or voice input and receive automated clinical guidance in response. Using AI can improve interaction and engagement with patients, reduce cost, democratize access to healthcare, and provide quality primary care accessible for more people even at remote locations.

Also, Chatbots are advancing at a breathtaking rate with NLP technology can open new opportunities for the creation of advanced conversational AI. Patient communication in the coming years ahead is going to become increasingly automated.

Combining virtual reality with artificial intelligence has led to the creation of simulated realities that can distract patients from their pain and, in some cases, even help with the opioid crisis. Also, leveraging AI with VR can help create a simulated environment that can train physicians using rules-based algorithms, enabling healthcare professionals to get better.

Appointment cancellations and no-shows are often a costlier affair for health providers. RPA can streamline online patient scheduling, minimize human involvement and optimize appointment turnout. RPA can gather and compile a report of appointment requests from multiple platforms along with their preference to optimize appointments that create a positive impact on patients, fewer mistakes, and proper allocation of doctors’ working time.

Whenever a patient interacts with the healthcare provider, there are a number of support processes that take place in the background, from patient check-in, claims processing, verification discovery, prescription orders, invoicing, and more. These processes are usually manual and prone to human error. AI and machine learning can help replace or supplement human interaction to create financial and administrative efficiencies, automate support process tasks, streamline regulatory compliance functions, and more.

Revenue cycle management (RCM) is one of the important and challenging administrative functions for healthcare providers. Revenue cycle management is a set of processes employed by healthcare providers to track and collect payments for their services rendered to patients.

As there are numerous stakeholders involved in governing the reimbursement of payment to the healthcare system like private insurers, government agencies, employers, and more, error and delayed payment are common. AI can help the administrative departments to automate revenue cycle management for better processes of the reimbursement system.

Despite clear progress in AI, we are still in the infancy stage of fully understanding AI and its full potential in healthcare. AI is poised to make disruptive, transformative advances in health care. But the greatest challenge is not whether AI is capable of delivering all that it promises but rather ensuring the adoption of AI-based applications in daily clinical practice. Despite advances in AI, there are some of the concerns that clinical leaders face, such as data privacy concerns, compliance to regulations, stakeholder complexities. To overcome these challenges, AI systems must be standardized, approved by regulators, taught to physicians, and updated from time to time for widespread adoption to occur. AI-powered solutions are bound to play a crucial part in how future healthcare systems function, enhance clinical resources and ensure the best possible patient outcomes. As and when AI applications become widely adopted in medicine, effective and personalized healthcare is achievable, and more and more people will gain access to high-quality, efficient healthcare.

The adoption of AI in the healthcare sector is accelerating at an incredible pace and doesn’t appear to be slowing down any time soon. Suppose you are looking to know how you can effectively use of latest artificial intelligence technology throughout a healthcare organization.