Surveys show the adoption of AI technologies in business brings better performance results. Automation and cloud-based solutions increase the productivity of the medical staff and help to deal with the workload faster.
According to the Belitsoft software development company expert Dmitry Baraishuk, business intelligence tools contribute to well-informed decisions, as data is the main asset in the healthcare industry.
Key takeaways:
- AI is already changing the medical landscape. 50% of the world’s medical providers will have introduced AI tools into their operation toolkit by 2025.
- Before implementing AI, the medical data should be standardized and put in order. Customized EHRs, supplemented with business intelligence tools, serve as a helpful first step on the way to AI-enforced solutions in healthcare.
Terminology subtleties: narrow AI vs. generative AI vs. general AI
Narrow AI solves a particular task or problem. For example, playing chess with a computer involves interacting with a narrow AI. It knows how to play chess but will not help you with anything else. A list of recommended films on Netflix based on your personal preferences is another example.
In healthcare, narrow AI helps to read images, and scans, and detect diseases and norm alterations. Also doctors and medical experts use narrow AI for creating predictive treatment models, X-ray recognition, and reading MRI scans.
Generative AI (GenAI) performs a broad scope of functions. It is agile and adapts to the request. Most commonly known are ChatGPT and Bard, which generate content such as music, video, and images. In healthcare, GenAI creates responses to patients, develops care plans, and helps with administrative procedures.
General AI (AGI) can recognize emotions and feelings and demonstrate human cognitive functions. It is currently under development. An example of AGI in medicine is the clinical decision support system (CDSS). It is a system that uses patient data to diagnose, suggest treatment plans, track recovery, and assist medical staff with alerts and notifications.
Bonus: Super AI represents the future, in which computers can substitute humans, i.e., perform our duties and make decisions.
Generative AI in medicine: use cases
Applying any AI solution to the internal medical system implies a “human in the loop” approach. It means we are still far from a situation where computers can perform tasks instead of people. Humans need to control and check the processes performed by AI-generated solutions. AI tools assist humans, saving their resources and freeing them for more challenging tasks.
Facilitating clinical healthcare records
Interviewing a patient, taking notes about their health history and symptoms, and then transforming those notes into a standardized medical record takes doctors a long time. With AI-powered apps, medical staff can perform those tasks in minutes. A general practitioner dictates the data to the app while interviewing a patient. The system transforms speech into written records and notifies the doctor of missed fields. Later, the doctor can check the information on the computer, add any other relevant facts by voice or typing, and submit the information to the EHR system. The data can be updated in the system at any time.
Insurance claims
Similarly to the previous point, GenAI assists in insurance plans checking, approving, and denying reimbursement. Insurance authorization and claim processing take, on average, ten days. First, a medical provider develops an approximate treatment strategy.
Then, an insurance specialist checks the coverage plan and asks for any clarifications if needed. The process involves communication via email and manual input. AI-powered solutions and cloud-based EHRs allow for saving time and minimizing human errors and any bias.
Improving customer experience
Patients value not only medical treatment itself but also interaction outside the hospital. Flawless service on the phone or website can help medical providers stay ahead of the competition.
Machine learning technology enables medical providers to examine patients’ experiences on social media and review platforms, their feedback, and complaints. Digesting such data allows AI tools to predict the best ways of customer interaction, understand the appropriate marketing funnels, and create guidelines and scripts for call center agents, chatbots, and website messengers.
Personalized treatment
Today, the customer-centric approach includes the whole interaction with a medical institution, from the experience of making an appointment to the final feedback. Digital technologies help medical providers personalize this interaction and avoid general recommendations that suit all patients.
According to Deloitte, the integration of business intelligence tools and data analytics in healthcare results in a patient-tailored approach. Doctors have access to cloud-based EHRs and make data-driven decisions regarding every case. Patients, in turn, receive a thorough diagnosis and reliable recommendations.
Other AI applications in healthcare
- Radiology: describing images based on patient history and similar cases from the database is the domain of the narrow AI. GenAI goes further and prepares a complete radiology report, taking into account laboratory tests and previous images. The system uses text descriptions with visual annotations.
- Surgery: AI assistants can help find the information in video footage. If, for example, some increase in pressure happened during the operation, the doctor needs to know the reason for it. They can ask a chatbot to find that moment in the recording. Besides, GenAI can find in its knowledge base a theoretical explanation of the case or calculate its frequency.
- Diagnostics: doctors can check blood vessels, their condition, and their proximity with AI-powered endoscopic tools.
- Nursing: AI sensors on the beds of patients notify the staff about their condition and risky health situations.
- Pharmacy: the clinical trials of new drugs take a significant amount of time. AI predictive analytics can boost the process, and evaluate the efficiency of the drugs and possible side effects.
The bottom line on AI Healthcare Systems
The possibilities that generative AI may bring to the healthcare business are blooming. GenAI improves administrative procedures and customer satisfaction. At the same time, the preparation stage is vital. Medical providers should stick to the clear-cut roadmap that includes the following steps:
- assessing the ability of current systems to cope with growing digital demands
- developing a strategy for implementing a cloud-based EHR with BI tools and data analytics
- choosing the right software or transforming the legacy systems
- mentoring the employees
- implementing cybersecurity measures
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