Voice Data and AI Can Revolutionize Healthcare

AI applications in Healthcare

AI assistants using the power of conversational dataset have the potential to transform healthcare delivery in several key areas. 

1. Improved Omnichannel Support

Merging conversational records with AI solutions can help provide omnichannel support the right way.  

Patients no longer expect to get their queries answered over the phone only. With solutions like Microsoft 365 offering superior Business chat solutions and tons of other channels for communication in easy reach, it’s important for healthcare providers to answer patients’ queries with an omnichannel strategy.  

Implementing AI in patient care can make it easier to craft, implement, and refine an omnichannel customer support solution. For example, in case of an emergency call of a patient in need of immediate support, AI trained on voice data can understand the sentiments of the caller and sense their need for emergency care to provide treatment steps that are fit for the caller’s needs.  

Such an elaborate omnichannel strategy can also help patients get answers in emergency situations.  

2. Realtime Call Translation

It is estimated that around 22% of the U.S. population speak a language other than English. As a healthcare company, if you don’t provide multilingual support, it means you are not providing support to nearly 1 out of 5 people! 

Using voice data with AI can help healthcare companies overcome the language barrier. For example, Microsoft Teams offers live translation features that can enable healthcare providers to serve more people.   

Azure AI Speech is another example of an end-to-end translated voice communication approach. By using solutions like Azure AI, you can answer the queries of patients in real-time regardless of the language they speak. Before the universal and easy availability of LLMs, it was near to impossible to run a truly multilingual health support that transcended borders.  

Dataset of calls can be used to finetune LLMs and ensure that AI can understand vernacular and compare it with existing healthcare data. The superb data processing and compression capabilities of AI will ensure that they get reliable answers in their mother tongue.  

3. Nuanced Patient Care

According to NIH, physicians on average spend 62% of time per patient exploring the EHRs. Voice data trained AI solutions compliant with HIPAA can help physicians save time spent on reviewing EHRs and enable them to propose personalized care to patients.  

By using AI for patient care, healthcare providers can create and send tailored strategies on a mass scale. For example, AI can log into the EHRs of two patients with the same medical condition and generate personalized recovery roadmaps, considering individual factors. 

Tools like Microsoft Copilot’s Conversation Summary feature can help primary care providers like nurses to easily grasp a patient’s needs and provide personalized care right away. Additionally, using Copilot in Excel can help with Action Item Tracking to ensure that healthcare tasks are assigned, tracked, and managed timely. 

The ability of LLMs to generate meaningful results from a base prompt i.e. symptoms and medical history in this case can ensure that patients can get a truly powerful telehealth experience. 

4. Enhanced Productivity

Implementing solutions like Microsoft copilot for healthcare can make it way easier for customer support teams to track KPIs and develop future-proof strategies. As AI trained on conversational data can automate the task of visualizing data, decision-makers can have more time to focus on developing novel customer support solutions.  

As phone calls contribute to around 88% of healthcare appointments, the voice corpus collected from customer interactions can be used to gather conversational data. Here is how conversational data can be harnessed to provide better customer support and ultimate boost productivity:

• • • Find “intent data” to know the reasons for calls en masse.  
    • Gather “sentiment data” to get reliable, actionable CSAT insights. 
    • Collect “entity data” to automate conversational processes.  

Relying on voice data can help you with conversational analytics and conversational automation. These datasets can in turn provide better tracking and enable decision makers in the healthcare industry to make the right decisions. For example, by checking the insights of AI trained on speech dataset, decision makers can ensure better coordination among internal teams to provide seamless customer support.  

5. Seamless Call Routing

Voice data is more reliable as it provides nuanced information like mood and tone of conversation along with factual points. By fusing AI with such a dataset, healthcare providers can manage 24/7 customer support that can be managed even better with intelligent call routing.  

Connecting a caller with the right source of help is not an easy feat. But with the help of AI, healthcare companies can use the power of AI to make learned decisions about the nature of the call and how to tackle it the right way.  

AI solutions can also help with the backend management of available customer support resources. For example, using AI can help identify instantly whether a patient needs to get routed to an expert or if they can feel satisfied by handling their specific query. These applications explain why companies such as Deloitte have dubbed the healthcare industry as one of the prime beneficiaries of AI.

6. Sophisticated Data Protection

Personal Health Information (PHI) and Personally Identifiable Information (PII) are data points included in EHRs that must be stored and used carefully.  

Improper handling of EHRs can have legal consequences and make it difficult for you to win the trust of your customers. You can ensure the safety of sensitive patients’ data by relying on AI models. Using AI can not only protect data but also keep bad actors at bay with superior fraud prevention capabilities.  

Developing an interconnected AI workflow can help identify patients and provide personalized support without compromising their privacy in the long run.  

For example, using Copilot with Microsoft Purview (formerly Microsoft DLP), can identify PHI and PII within voice data. Using Copilot to scan speech data and other communication data can help with assigning Sensitivity Labels to safeguard sensitive information. 

As Copilot can find information from calls, messages, emails, and videos, it can guarantee PHI/PII redaction and keep patients’ data safe.  

Examples of AI Usage in Healthcare 

Several healthcare providers have integrated AI into their organizational workflow to streamline customer support. For example, Babylon Health and Mayo Clinic have LLM-powered chatbots to perform symptom analysis and provide quick, reliable support. 

On the other hand, ResApp Health has launched a mobile app that utilizes AI to analyze cough sounds and diagnose respiratory conditions. NeuroLex Diagnostics has also employed LLMs to detect neurological conditions through speech analysis.  

These examples show how integrating AI in the healthcare industry can help solve some of the biggest healthcare industry challenges.   

Conclusion 

Voice data can be streamlined and turned into a data goldmine with the help of AI models. Healthcare companies can easily rely on AI to embrace and provide next-gen patient care solutions that harness the power of LLMs and Extended Reality. Ensure you choose AI models with stronger algorithms and better datasets to provide matchless customer support to your patients all over the globe unlike ever before! 

If you’re interested in training AI with conversational data, you can start by migrating to Microsoft Voice to provide unique support to patients.