If you don’t already use AI in your practice, we’d bet you know many who do. But in such a data-sensitive industry, many approach with caution. From decision support to documentation drafting to drug monograph research, AI is here to stay in healthcare. And, when implemented with care, AI can reduce administrative burden and cognitive load, improve accuracy, and give physicians valuable time back with their patients. When implemented hastily, it can introduce data concerns, risks, biases, and an even greater workload.
In this article, we’ll outline ten best practices for doctors, nurse practitioners, and PAs to use AI safely, effectively, and responsibly in their workflows. Here’s how to implement with care, for a more balanced day-to-day practice.
Using AI For Healthcare: 10 Best Practices For Safe, Successful Implementation
1. Establish Goals
Before adopting an AI tool, clinicians should define which problems they’re trying to solve and how to measure success. Is the goal to reduce the time spent charting or summarizing patient appointment notes? To write faster referral letters? Each practice may have different needs and pain points, so it’s crucial to identify yours.
Those looking to work with AI should establish measurable benchmarks, such as reduced administrative to-dos, shorter documentation time, or faster turnaround on patient message responses. Without a defined outcome, AI can add another layer of complexity instead of being a source of relief.
2. Use AI In Existing Workflows
The most effective AI tools are the ones that seamlessly integrate into a clinician’s current workflow. When users have to reformat inputs, switch platforms, or learn entirely new processes, the tools are less likely to be successful long-term.
Look for AI that integrates with other tools healthcare providers already trust. If there are multiple AI tools that work within the same ecosystem, it’s an added bonus.
3. Start With Simple Automation
When implementing a new AI tool, it’s important not to go from zero to one hundred too quickly. Early success with AI comes from simple automations of low-risk, repetitive tasks. Think summarizing clinical notes, drafting referral documents, or generating one-off appointment summaries.
Starting small allows clinicians to build trust with the tech and understand its limitations. As the clinician learns over time, more advanced use cases can be layered in to achieve even greater workflow efficiency.
4. Look For Sources
Transparency is everything in clinical settings. AI outputs should be traced to credible, evidence-based, or peer-reviewed medical sources in the healthcare industry. The AI should be used as a guide rather than a definitive source of truth, and clinicians should still be able to verify the source of all information.
This supports clinical reasoning instead of obscuring it. Tools that provide source links, citations, or clear explanations of conclusions are safer and easier to validate in any practice.
5. Keep Humans In The Loop
Final decisions, document sign-offs, output interpretations, and patient communications should always involve a human reviewer, whether a doctor or another provider.
Keeping the right clinicians in the loop ensures proper accountability and reduces the risk of errors from overrelying on AI outputs. This is particularly important in treatment planning, diagnosis, and patient-facing communication where context and nuance matter.
6. Choose HIPAA-Compliant Tooling
Privacy and data security are non-negotiable in healthcare. AI tooling used in clinical workflows must comply with HIPAA requirements and adhere to strict data-handling protocols.
Clinicians should understand how patient information is processed, stored, and protected. It’s best practice to avoid open, public-facing AI products, especially those that use patient data to train public models. Trustworthy AI platforms are always transparent about their compliance standards and their security practices.
7. Maintain Data Integrity
AI, of course, can only be as reliable as the data it’s sourcing and processing. Outdated, incomplete, and inaccurate inputs can lead to errors in the outputs. Maintaining well-structured, clean, and regularly updated data is a must for meaningful AI use.
Clinicians should stay vigilant about reviewing AI-generated content for relevance and accuracy. It’s important to perform routine output audits to identify any patterns of error or drift over time.
8. Monitor For Bias
Bias in AI can reflect imbalances or gaps in input data used to train models. In a healthcare context, this can have serious consequences, particularly for underserved or marginalized identities.
Clinicians should understand potential bias in AI recommendations and question outputs that do not align with patient context or clinical experience. Equity comes first in patient care, and diverse data sources, human oversight, and ongoing evaluation play a part in mitigating bias.
9. Opt for Customization And Simple Output
AI tools should adapt to fit physician workflows, not the other way around. By offering customizable settings and templates and allowing clinicians to tailor the tools to their specialty, they’ll see more effective, relevant output.
Simplicity also matters. Concise, clear outputs with summaries at the top are easier to review and fold into routine patient care. Overly complex or poorly formatted responses can increase cognitive load and slow physicians down over time.
10. Focus On The Patient
At the end of the day, a big reason to implement medical AI tooling is to improve the patient experience. Tools that distract from patient interaction or add to the administrative workload should be avoided.
When AI is used properly, it frees clinicians up to spend more time connecting with, listening to, and educating patients. Outcomes, safety, and trust should always guide any AI-based decision.
What To Look For In A Medical AI Tool
When evaluating reputable medical AI platforms, clinicians should consider the following:
- Data security: Is the tool HIPAA-compliant and secure?
- Clinical relevance: Does the tool support my workflow and practice?
- Ease of use: Is there a steep learning curve associated with the tool?
- Transparency: Does the tool clearly outline where its information is sourced?
- Accessibility: Is the tool free or within budget?
- Integration: Can the tool be integrated with or used alongside the current digital stack?
Documentation and resources outlining how to use the tools are also a must. The most trustworthy and effective tools evolve alongside regulatory requirements and clinical needs.
Try Doximity For Free Today
Doximity offers secure, clinician-focused AI tooling designed to fit into daily medical workflows with ease. Products like DoxGPT and Doximity Scribe generate outputs based on medical evidence, and these tools, along with Doximity Dialer, are all HIPAA-compliant. Doximity also prides itself on accessible tooling that makes a difference, so the entire platform is always free.
More than 80% of U.S. physicians are already registered members. Getting started is as simple as signing up with your healthcare credentials.
Get started with Doximity today.