If it seems that artificial intelligence dominates health care titles, it is for a good reason. AI in health care is on the right track for rapid growth; Research companies on the markets and lesmaches, for example, predicted an extraordinary annual growth rate of more than 49% for the rest of the decade, driven by investments in in -depth learning, remote monitoring and chronic care management. Meanwhile, McKinsey & Company pointed out to additional AI trends in health care, the rapid rise in generative AI to the use of back office, such as complaint processing and insurance verification.
For managers such as Jordan Archer, chief of the Charlotte exploitation, multisepécialty group of the NC Tryon Medical Partners region, AI systems are precious because they facilitate the work that well -trained staff do every day. “They can manage processes more effectively and deliver more for patients. As an organization, we can better perform in value-based care contracts,” he said.
10 ways to redefine health care
Hospitals, health systems and great medical practices explore many ways to integrate AI into clinical and administrative workflows. Some initiatives are on a small scale, using AI tools in their pilot phase or focusing on individual service lines or patient populations. Others cover the company, especially if the functionalities of the AI have been integrated into the management of the practice or the electronic health file systems (DSE) already in place.
Here is an overview of some of the AI trends that should reshape health care in the years to come.
1. Chatbots connecting with patients
As with many other industries, health care has adopted chatbots powered by AI to complete customer service. Based on how a patient answers a chatbot intake questions, the chatbot can determine whether an automated system can manage an investigation – such as planning an appointment, filling an order or payment of an invoice – or if a patient must speak to a person according to his medical symptoms or the complexity of his needs. In addition, chatbots can increase patient engagement efforts by providing recommendations based on evidence or by translating educational resources in a patient’s mother tongue.
2. Food the productivity of clinicians
Throughout the industry, Rock Health survey data have shown that more than 30% of primary care physicians use AI for office support, such as patient visits documentation and post-visiting tickets for patients. Almost 25% said they used AI for information management and clinical decision assistance. Above all, less than 10% of primary care physicians said they did not want to use AI at work. Cleveland Clinic is a health system that has deployed AI in the examination room, with chatbots integrated into the DSE system to help recover information and ambient listening technology capturing notes and creating instructions for prescriptions or laboratory orders.
3. Sort the data
About 80% of health care data is not structured, which means that it does not easily integrate into the database lines and columns. AI systems can analyze unstructured data sources, such as medical images and laboratory reports, much faster than traditional analysis tools. According to the algorithm used, the models can give an overview of a potential diagnosis, identify patients at high risk or offer treatment recommendations. Analysis of data fueled by AI can also Help hospitals prevent unwanted eventslike harmful drug interactions or sepsis infections.
4. Detection of imagery anomalies
Imaging studies cause diagnostic and processing decisions. However, the naked eye analysis can take time and subject to errors – and the arrears are too common. Image recognition tools can interpret studies such as X -rays, electrocardiograms and cat analyzes to identify irregularities and suggest a potential diagnosis. AI systems can also erase studies with normal results so that radiologists and other members of the care team can focus on studies that require meticulous examination.
5. Robots helping surgery
Robot assisted surgery made its debut in the 1980s And, after an initial apprehension among surgeons, technology began to take off in the 2000s. The American College of Surgeons (ACS) noted an increased interest in robot assisted surgery for procedures involving tight, cramped and sometimes inaccessible areas, in particular prostate and urinary tract. The main advantage is a mini-invasive approach that contributes to shorter hospital stays and faster recovery times. Progress of automation should soon let robotic systems perform minor tasks such as sutures, ACS said.
6. Increase physiotherapy
The practices of physiotherapy practice tend to be based on small studies, as it can be difficult to register patients on a single site with the same diagnosis and the same treatment plan. AI can analyze the real world data of PT patients in many contexts; The release of this analysis can provide personalized recommendations for individual patients and enlighten guidelines for specific populations who need PT services. Meanwhile, the integration of virtual reality in PT treatment can provide immediate feedback to patients on the question of whether a movement is carried out correctly and clinicians on how a patient reacts.
7. Complete the exercise plans
Whether created from requests to generative AI models or fitness applications that collect data from portable clothes, personalized exercise programs are ready to help meet the specific patients of patients and fitness objectives. A Recent literature review People noted participating in various strength, stretching and resistance training programs have shown measurable improvement compared to the control group. As with many cases of use of AI, however, the recommendation of experts is to use AI as an additional tool for exercise programs, but “not as a substitute for personalized, progressive and specific prescriptions to health conditions provided by health and fitness professionals”.
8. Rationalization of the income cycle
The automation of various stages of complaints treatment – for example, the application of good medical codes to a patient visit or the filling of demographic information – means “allegations can go out without touching human,” said Archer. For example, Tryon Medical Partners now files approximately 90% of previously previous authorization requests and denial rates are less than 2%. Before automation, the process was very manual, which led to long waiting times on the phone – not to mention the growing frustration of patients. “This allows us to manage our work more efficiently,” he said.
9. Strengthening of the supply chain
Managing the inventory of vaccines, medicines and other supplies with cold storage requirements or expiration dates can be a delicate balancing. Keep too many items in stock and some may go wrong, costing money to the establishment; Keep too much and supplies will run out, a negative impact on patient results. Predictive analysis completes stock management by following historical data and considering external factors to help anticipate demand and avoid waste. Managers can also see which suppliers have been the most reliable to meet shipping times as well as product safety requirements.
10. Optimization of the commercial strategy
In competitive markets, hospitals and health systems need all the help they can obtain to reach more patients in their watershed area or identify mature expansion service lines. The introduction of AI into sales and marketing helps organizations better understand market dynamics, such as reference models and specific medical needs within the population. This information can help influence commercial decisions that stimulate income growth.
Ensure that everyone can benefit from AI
While the industry seeks to take advantage of the advantages of these AI trends in health care, stakeholders have shared concerns that large hospitals and health systems are better ready than smaller practices to make the most significant progress. “We know that we must support both major institutions and small practices, ensuring that there is equitable access to these tools,” said Dre Margaret Lozovatsky, vice-president of digital health innovations at AMA, at the end of last year in an interview With the head of the association of the association, Todd Unger.
The need for support is particularly acute when it comes to effectively using AI tools, according to Dr. Brian Anderson, CEO of the Coalition for Health IA. Community, rural or federal health centers often have no strong AI governance or expertise to monitor the functions of AI systems.
“How do they make an informed decision to buy models that work well? How will they manage and monitor the models? Can they settle the models locally according to their community data?” questioned. “These are really important things that we will have to approach collectively.”
Brian Eastwood is an independent writer of the Boston region who has covered him for health care for over 15 years. He also has experience as a research and content strategist analyst.
