Healthcare is at the crux of its digital transformation journey. This is impacted by the lack of well-trained, technologically advanced healthcare experts and the rising total healthcare cost. This leaves gaps in the healthcare sector, resulting in the need for new information technology-based solutions and processes to address these rising challenges.
Due to the lack of information, healthcare professionals have been using clinical studies, patient results, and the cumulative experience of thousands of clinicians. This necessitates using technology, especially advanced Artificial Intelligence, to eliminate human biases.
Verticals in Healthcare that needs Applied AI
Technology is turning out to be the epicentre of any great innovation. In healthcare, the innovation streak is just beginning. Here, technologists and doctors are working together, intending to combine technology and biology to enhance the medical field. Applied AI in healthcare has effectively relieved medical professionals of repetitive and mundane tasks that have now been automated and streamlined to save valuable time from resources.
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Clinical Trials
Trials have always been a source of vast amounts of crucial data through assessments of new drugs on patients suffering from similar chronic conditions. These procedures are known to cost much money and produce minimal results. Automating Clinical trials will utilize the benefit of AI in efficiently managing time-consuming data monitoring procedures. There have been proven reports in the medical world that mentioned how AI-driven clinical trials can shorten the duration of clinical trials. AI also provides an increasing volume of research materials for their Real-World Data (RWD) requirements.
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Drug Discovery
The advent of AI by biopharma companies has helped to speed up their drug discovery process by eliminating repetitive work and streamlining it. These advanced technologies have ensured that drug discovery, research, and testing are much cheaper, faster, and more effective. For this purpose, we have tech giants and universities dedicating their workforce to research and enhance these programs' success by technically supporting them.
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Healthcare Robotics
Smart Prosthetics is helping patients along with the support of medical personnels. Exoskeleton Robots are driven by software that helps them assist paralyzed patients in becoming self-sufficient. Bionic limbs attached to the muscles and nerves in patients can be covered with bionic skin to look and work as efficiently as an actual organ. All these smart devices need a software solution to help them work effectively. As innovation grows, the overlap between different streams like medicine, prosthetics, and technology has slowly diminished.
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Patient Care
Large hospitals have now leveraged AI-based kiosks or welcome booths to ensure patients can automatically register themselves at hospitals with minimum human interactions. This helps automate trivial tasks that were previously utilizing resources. AI has been pivotal in analyzing and processing patient data to generate actionable insights and diagnostic recommendations. These data insights can help diagnose symptoms before they become full-fledged conditions. AI helped forecast risks and complications by analyzing pre-natal data in pregnant patients.
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Medicine Distribution
Several hospitals now have an integrated system where medicine prescriptions are directly sent to the pharmacy. When the patient reaches the pharmacy, the medicines are approved by the insurance company, packed, and ready for delivery. This saves a lot of time and reduces the hassle of visiting hospitals. To make it more efficient the pharmacies use AI-driven arms and conveyor belts to move the medicine across the pharmacy to the billing counters.
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Treatment Recommendations
Sometimes, when there is a shortage of medicines, AI can use data from other doctors to identify alternative medicines that can be given to the patients. This could involve recommending additional dosage or another brand of the same medicine. This could also give doctors visibility towards the hospital's current medicines. Conversely, this AI intelligence can be applied to the insurance segment, where agents can get approvals on prescriptions with alternative medicine and treatment plans for illnesses other than those prescribed in their guidebook.
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Insurance Underwriting
Automated Risk Assessment using AI can analyze vast amounts of data, including demographics, previous health records, claims history, and genetic information, to create a more accurate risk profile for each patient. This can expedite the underwriting process and ensure more precise pricing policies. Based on the pre-trained data sets, AI was prepared to reject or flag high-risk cases that applied for insurance coverage from our client’s insurance app.
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Chatbots in Telemedicine
AI-powered chatbots can guide patients with routine processes and collect initial information, past performances, and symptom collection, helping free up valuable resources to focus on more complex aspects of telemedicine. Utilizing our conversational voice and chatbot in managing queries has helped improve client satisfaction allowing them to enjoy quick responses.
How has Applied AI been beneficial to CirrusLabs’ Customers?
CirrusLabs works with one of the largest Insurance companies in the USA. We collaborate with them on various projects, such as call center operations. We aim to optimize and enhance the employees' productivity while managing client expectations and staying ahead of client satisfaction.
Applied AI in healthcare has played a major role in Customer Segmentation and Targeting. AI analyzes customer data to segment policyholders based on risk profiles, needs, and buying behaviors to help insurance companies. From call management to streamlining processes at the insurance company, applied AI goes a long way toward processing, segmenting, and generating insights based on these data parameters.
Relationship Managers (RMs) could personalize communication, recommend relevant insurance products, and effectively target high-value customers. Our client has used this information to send user behavioral changes to over 600 RMs that could drive meaningful and timely conversations with their customers. These proactive measures can help the company leverage changing behavioral patterns to generate a better bottom line.
If you’re having trouble streamlining your call center operations using AI in healthcare, it’s time to seek help from our experts. Contact Us Now!