Different Ways in which Generative AI can be Utilized in the Healthcare Sector for Enhancing Customer Journey

Introduction

As per a recent McKinsey report, generative AI in healthcare comes with the potential to bring in $1 trillion improvement in the healthcare industry by automating tedious and error-prone operational tasks. By relying on deep-learning algorithms, generative AI can create breakthroughs for healthcare operations. It can convert unstructured data into clinical notes and perform unsupervised learning, which includes discovering patterns in data and learning to replicate them. Apart from this, there are a plethora of transformative use cases of generative AI in the healthcare sector and this blog will throw light on the different ways Generative AI can come in handy in the healthcare sector, especially for enhancing customer journeys and experiences and improving employees’ productivity.

Helps with Appointment Scheduling

Deploying a generative AI-based appointment scheduling system instead of traditional methods can reduce human errors, high dependence on staff availability, and also the lack of real-time updates to payers/patients. Further, it adds flexibility, reduces waiting time by rapidly identifying and processing requests, improves engagement, lowers no-shows, generates follow-ups and reminder calls, and enhances service fluidity by analyzing and reducing scheduling conflicts. Since this technology utilizes intelligent algorithms and data analytic capabilities, it can streamline the scheduling process while learning in real time based on previous scheduling factors. It can also tackle a high volume of tasks in a limited duration. For instance, WIZ AI’s TalkGPT, a gen-AI-enabled voice bot, can handle one million calls in an hour. Along with improving customer experience and engagement, a recent study by ASA Advance 2022 found that AI-based appointment scheduling improved clinicians’ average engagement scores from 3.3 to 4.2 out of 5.

Provides Personalized Pre-visit and Post-visit Messages and Alerts

Facilitating personalized patient care based on individual needs and preferences is a necessity yet a hurdle in the healthcare sector. This is primarily due to a lack of resources. This challenge lies in providing timely pre-visit and post-visit care messages and alerts to each patient, as it requires extensive data collection and analysis. This can be cumbersome to perform by relying solely on manual assistance. However, the healthcare sector can utilize NLP models for summarizing patient data, along with generative AI, to create reports, understand each patient’s case, and craft targeted messages pre and post-visit while fostering transparency, improving interactions, and ensuring compliance with data-privacy regulations.

Offers Assistance via Medical Chatbots

With misleading medical information spreading on the Internet, it can often be a hurdle for patients to discover factual and accurate knowledge. Therefore, healthcare sectors, being responsible for facilitating the correct information, can embrace generative AI to develop medical chatbots to provide people with summaries of health information, address their concerns, make customized treatment recommendations by understanding and learning from previous interactions, patient’s medical history and reports, medical journals and studies. Further on, healthcare organizations can leverage LLMs to develop personalized educational materials, translate medical information by overcoming language barriers, and do so much more.Besides, reliance on chatbots can reduce costs, improve engagement, offer accurate and personalized information, identify risks, provide the needed care, and alleviate the workload on employees.

Facilitates Virtual Nursing Assistants to Enhance Customer Journeys

With a shortage of nurses and clinicians at large, a virtual nurturing assistant can monitor patients, offer them emotional support, perform routine wellness checks, predict adverse events, and provide medical assistance 24/7. Besides reducing the burden on healthcare professionals, virtual nursing assistants have the potential to save USD 20 billion annually. These virtual nursing assistants, built on large language models, can provide a human-like experience and interact with patients iteratively based on their queries. These virtual nursing assistants can also be incorporated within the patient’s rooms in healthcare centers as the first line of communication to facilitate personalized care and attention.

Aids with Electronic Health Record (EHR) Generation

Introduced in the 1960s, Electronic Health Records (EHR) have revolutionized the healthcare sector. A recent paper published in BMC Nursing found that the manual process of documentation can take up to 25% to 41% of nurses’ time. This quality time can be utilized in facilitating patient care if the healthcare sector relies on generative AI-backed solutions. Notably, a few milestones have been uncovered in this direction, including Microsoft’s plan to embed gen-AI into Epic, one of the leading EHR vendors, and AWS’ HealthScribe, a HIPPA compliant solution for generating clinical notes by analyzing patient and physician interactions. Additionally, natural language can convert EHR into summaries and reports, paving the way for creating personalized prediction models. In short, the reliance on generative AI in documentation processes like EHR generation can streamline administrative activities and improve clinician’s productivity.

Facilitates Personalized Treatment Plans

Personalized treatment plans, created based on each patient’s medical history, records, genetic data, and lifestyle factors, can increase patient outcomes, reduce drug reactions, improve patient satisfaction, and enhance value-based care. Healthcare sectors can offer such treatment plans with the support of generative AI, as they can perform predictive modeling by synthesizing and analyzing a patient’s complete medical data. Since they leverage sophisticated algorithms, AI can also draw from vast databases containing the latest research reports and medical information and facilitate evidence-based treatment plans for each patient. They can further personalize it by examining a patient’s possible response based on their characteristics and previous interactions.

Helps with Decision Making for Clinicians

As per the Clinician for the Future 2023 report, 50% of the 2,607 doctors and nurses surveyed are keen on utilizing generative AI for clinical decision-making in the future. Currently, 11% of clinical decisions rely on generative AI tools. However, 48% of the respondents suppose that in the next two to three years, physicians will make more use of gen-AI in making clinical decisions. Offering such real-time decision support will provide clinicians with more time to focus on patient care. The generative AI-based tools can facilitate these assistances by leveraging vast amounts of medical information, patient data, and generic information. As part of decision making, they can predict risks, identify health problems, and even diagnose health issues. Although patient diagnosis is something new, a recent Baidu research stated that tests on its deep learning algorithm can identify breast cancer metastasis by surpassing humans.

Assists with Telehealth Services

As per the latest Clinicians of the Future 2023 report, 55% of 2600+ doctors and nurses surveyed stated that they would consider telehealth as a main method for performing routine check-ups by 2028. Being a subset of digital health, healthcare professionals can facilitate live consultations via phones, perform remote monitoring, and more. In all these aspects, generative AI will have a critical role to play. Click here to learn more about Generative AI in telehealthcare from our recent blog.

Conducts Dynamic Patient Monitoring, Follow Ups and Feedback Collection and Analysis

Generative AI can help healthcare organizations with dynamic monitoring and interactive follow-ups of patients by calling patients to check on their well-being, symptoms, and any reactions from medications. This can aid with a patient’s rehabilitative treatment. Along with messages and alerts, these types of monitoring can improve patient engagement, keep patients informed of any next steps, and also post checkups if required. Additionally, generative AI can also perform feedback collection and analysis, which can be done through free-flowing conversation questionnaires. Today, the healthcare sector can handle this by integrating talkbots into your system. Explore more on Wiz Talkbots for healthcare and how they can benefit you in improving patient acquisition, engagement, and retention.

Conclusion

The future of AI and generative AI looks promising, especially in the healthcare sector, with its potential to increase employee productivity by 14%, as per a recent study from the National Bureau of Economic Research. Although there are concerns regarding biases, discrimination, privacy, and intellectual property, it can be said, without a doubt, that over time, these concerns will be resolved with the advancements in generative AI technology. As a healthcare sector, it is time for your firm to embrace generative AI in healthcare to enhance your customer’s journey. Learn more on how NeogenixAI can aid you from here!

References

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