The Promising Potential of Generative AI for Healthcare

Generative artificial intelligence (AI) can fundamentally change how people work, automating some activities and freeing employees to focus on other tasks. Generative AI for healthcare can perform steps in essential yet routine processes, giving healthcare providers more time to focus on patient access, care, and outcomes.

Benefits of Generative AI for Healthcare

Healthcare organizations have multiple data streams, often non-integrated, filled with valuable information that practitioners can use to enhance the quality of care and operate more efficiently. Generative AI solutions allow healthcare organizations to create a central repository for their data and base analyses on all relevant information. Applications of generative AI for healthcare include:

  • Streamlining health plan operations and enhancing member services.
  • Breaking down data silos between hospital system departments for deeper insights.
  • Reducing the time it takes to schedule staff necessary for quality care while preventing burnout.

It can also ensure summaries, instructions, notes, and checklists are complete, prompting practitioners to fill in any gaps.
In addition, generative AI can analyze data from internal and external sources to evaluate market performance, competitor activity, and the effectiveness of marketing campaigns.

Keeping Humans in the Loop

Many healthcare organizations are indeed approaching generative AI implementation with caution. While other industries and markets seemingly dive in with slight apprehension, healthcare providers must carefully weigh the potential risks of using the new technology.  The best strategy is to work with a provider with experience in generative AI for healthcare that offers transparency about how their tool arrives at answers or predictions and keeps protected health information (PHI) secure.
Healthcare organizations must also determine how the tool will provide the most value, likely assisting practitioners and administrators, not replacing them. In this way, professionals with healthcare expertise make the final call on decisions vital to operations and patient care while still benefiting from the time savings and contextual insights from generative AI.

Features of the Best Generative AI for Healthcare

Although various generative AI tools are available, healthcare organizations will benefit from solutions developed specifically for the life sciences industry. Features that will provide the most value to healthcare include:

  • Domain Expertise
    A generative AI platform pre-trained for life sciences understands the domain’s data sources and vernacular right out of the box. A domain-specific solution also deploys more quickly, typically within weeks vs. months, necessary to implement a tool without pretraining in life sciences data.
  • Ease of Use
    Healthcare professionals are skilled and knowledgeable in their areas of expertise. However, they may not have proficiency in data science or IT. Generative AI platforms that leverage natural language processing (NLP) allow users to interact conversationally. Users don’t have to build a dashboard for new insights—or have an IT or data team member do it. All they need to do is ask a question and immediately access the insights or information they’re looking for, automatically presented via optimal visualization
  • Scalability
    Healthcare data is increasing rapidly, and it’s vital for organizations to deploy tools that will scale with an increasing number of data sources and data volumes. Furthermore, the tool should perform just as well when analyzing larger data volumes as when performing less complex analyses.
  • Security and Data Governance
    Healthcare organizations must comply with regulations to protect healthcare data and patient privacy. Generative AI for healthcare applications must have built-in security and access control features that strictly limit who can access the platform. Additionally, the tool should give administrators granular control over which data can be used in analyses, down to column, row, and field.
  • Enterprise Features
    An analytics tool for healthcare should be designed to integrate with the systems and business applications that organizations and practitioners use so that healthcare teams can access data-based insights with just a few clicks. It should also be designed to be highly reliable, delivering maximum uptime.

Frequently Asked Questions

How does WhizAI enable users to interact conversationally?
WhizAI’s platform includes a hybrid natural language processing (NLP) engine. It combines deep learning with sophisticated linguistic techniques to understand human language and respond contextually. Pretraining with life sciences data allows users to ask questions as if speaking to a colleague. Additionally, if WhizAI needs more information, it will ask the user to fill in gaps to provide the precise information requested.
Can collaborative teams use WhizAI?
Yes. User can configure their own dashboards in a no-code environment, save insights to their Pinboards, and share them with collaborators. Additionally, WhizAI creates a repository for an organization’s data and updates it when new information is available. The platform has access to the same data for all analyses, which creates consistency of insights across the organization and better team alignment.
Can WhizAI provide alerts proactively?
Yes, WhizAI can continuously monitor data and alert users when numbers fall outside typical parameters.
Exactly how fast is WhizAI?
WhizAI can analyze petabytes of data and provide insights in less than a second. Healthcare team members will have the answers they need 100x faster than with traditional analytics processes.
How can users access WhizAI insights?
WhizAI can be embedded into applications teams use, such as Microsoft Teams, so that they can access insights with just a few clicks. Additionally, team members can use WhizAI on PCs, laptops, tablets, or smartphones, so insights are available wherever they work.


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