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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.
Do you have the infrastructure to implement AI tools?
Any time you implement a new system, it takes time. You must ensure that your infrastructure, including computer power, storage capacity and network system, can handle the AI tools. You may need to upgrade your system, including developing a robust cloud strategy to scale for AI, as you require secure, large-scale data management infrastructure.
Related: Your AI Initiatives Will Fail If You Don’t Address This Crucial Component First
Do you have the people to stay current on evolving AI legal and regulatory changes?
You need the right personnel in your operation to monitor and manage your AI tools, ensuring they are used appropriately and in compliance with regulations. Regulations regarding AI are currently fragmented, but new state laws are beginning to take effect, and healthcare organizations and their stakeholders must be aware of those.
For example, in 2025, California passed new laws requiring patient disclosures for the use of generative AI in written or verbal communications. AI developers must disclose information about their training data, and professional boards can investigate deceptive marketing practices. In Texas, effective September 2025, laws mandate patient disclosure for the use of AI in diagnosis or treatment. Healthcare practitioners must also review and assume responsibility for any AI-generated patient records.
In August 2025, Illinois passed legislation banning the use of AI for mental health without the oversight of licensed clinicians. In June of this year, Nevada passed laws prohibiting organizations from claiming that their AI systems are capable of providing professional mental or behavioral healthcare and from using these systems as a substitute for professional mental health or behavioral healthcare. Additional states are considering similar actions.
Related: How to Implement Ethical AI Practices in Your Company
How will you ensure AI tools are vigorously tested?
To ensure patient safety, maintain data integrity and comply with ethical and legal standards, you must ensure that the AI tools you use in your operation are vigorously and consistently tested. Set up an AI governance committee to define goals and determine which AI solutions are right for your organization. Best practices include vetting third-party vendors; assessing AI tools for bias and how to mitigate and prevent it; creating a robust testing framework that includes pre-implementation testing and integration analysis with your existing systems, such as your electronic health records (EHRs); addressing data privacy and security; and continued performance monitoring after the tools are launched. Be sure to track and report any negative patient outcomes involving the use of the AI tool.
Regular audits and reviews of your AI solutions will help ensure compliance and adherence to internal practices.
Artificial intelligence is transforming clinical support, patient engagement and operational efficiency in the healthcare industry. Ethical use and strong governance, including data security and transparency, are critical to the success of AI use. AI should complement, rather than replace, human decision-making in critical areas such as patient monitoring, clinical diagnosis and staffing optimization. By incorporating AI into clinical and operational workflows, healthcare organizations can boost decision-making efficiency while maintaining human oversight.
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.
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