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Artificial intelligence is shaping how many healthcare operations are conducting business and how they will conduct it moving forward. While AI offers numerous benefits — from patient adherence to operational efficiency — in the highly regulated healthcare industry, founders and business owners need to address some fundamental issues early on and implement best practices before proceeding with full AI adoption.
How will AI help your operation, and are there any downsides or risks?
Before you decide to implement AI, identify where it will help you the most and ensure that any significant investment and changes you make align with your business strategy and long-term plans. Consider how AI will integrate into your existing system and workflow, and identify any potential downsides or risks associated with implementing a new tool.
For example, AI tools can assist clinicians in generating notes after a patient consultation and automating documentation, allowing clinicians to spend more time with patients and see additional patients. That’s good for an organization, but at the same time, there are risks to consider. AI-generated notes can misinterpret speech and medical terminology, as well as miss certain nuances during the consultation.
Clinicians should be trained and aware of the drawbacks of AI so that errors or omissions in the record don’t affect patient safety, coding accuracy and billing compliance. Additionally, AI tools are processing a patient’s protected health information (PHI) and must comply with HIPAA laws.
Related: How AI Is Transforming Healthcare
Will you be developing proprietary AI tools or using existing software?
Some healthcare organizations have a dedicated IT department and may choose to develop a proprietary AI tool, depending on its function. This enables the creation of a tool tailored to an organization’s clinical and operational workflow, providing complete control over how sensitive PHI is handled, processed and stored. Patient data with existing AI tools is generally processed and hosted on the vendor’s servers. This can raise data control concerns.
On the other hand, at the outset, it is more expensive and will take longer to develop a proprietary tool than to launch an off-the-shelf product with vendor support. Additionally, it may take longer to get the necessary regulatory approvals for a proprietary tool.
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