In healthcare today, clinicians are expected to analyze growing volumes of research, patient data, and clinical guidelines within their workflows. Evidence-based medicine, of course, will remain the gold standard, but applying the best available sources to guide clinical decision-making is becoming increasingly time-consuming and difficult.
This is where AI-powered clinical decision support comes in. It uses machine learning to synthesize large volumes of data, so clinicians can use AI as a guide to make informed decisions without replacing clinical judgment. Here’s how it works, how to ensure it’s secure and evidence-based, and how to embed it into preexisting workflows.
What Clinical Decision Support Initially Looked Like
Clinical decision support refers to the use of digital tools to assist clinicians in making decisions before, during, and after patient appointments. These tools provide relevant information, tables, drug data, or treatment recommendations at specific points in the workflow. The best clinical decision support tools derive their answers solely from evidence-based, often peer-reviewed, medical studies. These AI tools should also be closed, meaning they do not use prompts, queries, or patient data to retrain the model.
Early examples of AI use included:
- Quick medication interaction lookup
- Clinical reminders for screenings and preventative care
- Patient chart summaries
As machine learning has ramped up, the use cases for AI clinical decision support have been more involved than ever. This heavier lift streamlines workflows for doctors, nurse practitioners, and PAs, demonstrating that evidence-based AI is the way forward toward more sustainable workflows.
How Evidence-Based Machine Learning Improves Medicine
Machine learning allows clinical decision support software to move beyond defined rules. Unlike automation, which uses straightforward “if this, then that” logic, machine learning can learn from patterns in a healthcare professional's work and adapt as new data becomes available. This simplifies workflows in a few ways:
1. Vast amounts of data are quickly synthesized: Structured labs and vitals, and unstructured clinical notes and appointment transcriptions are organized and optimized for the most efficient use. AI models can also identify trends that are hard to predict manually, potentially helping clinicians recognize early health risks and diagnoses.
2. AI-powered clinical decision support gets personal: Clinicians can tailor their AI tools to their go-to templates, notes, and anything else that pertains to their workflow and specialty. Rather than supporting every clinician in the same way, AI can be as diverse as the healthcare team at any practice.
3. Machine learning adapts and evolves. If new evidence emerges and clinical practices evolve, machine learning tools evolve with it. AI models can be updated automatically to reflect the latest research, helping clinicians stay aligned with current knowledge and best practices.
AI Clinical Decision Support: Common Use Cases Today
AI-powered clinical decision support is being used in various clinical settings. These applications are focused, practical, and designed to reduce administrative work and workflow friction. Today’s common use cases include:
- Live-recording patient visits, and securely transcribing and summarizing key takeaways
- Producing clinician notes in customized templates
- Providing answers and drug data for healthcare prompts and questions
- Drafting medical documentation, like patient communication and referral letters
- Drafting tables to compare drug data and treatment plans
- Outlining early warning signs and prediagnosis information based on prompts and patient history
In every case, the goal is to go beyond automation by supporting evidence-based clinical decision-making in a more impactful way.
Trust, Transparency, and Safety: The Pillars Of Safe Adoption
While AI clinical decision support is effective, no two software systems are built exactly alike. AI use in healthcare should raise important questions, and clinicians should understand how recommendations are generated before they use them to guide their work. When assessing tooling options, consider data quality and storage, bias in training datasets, transparency of AI outputs, and validation in real-world clinical settings.
Safe tooling must be designed with AI concerns in mind. Reputable tooling, beyond using closed, evidence-based systems, must also be HIPAA-compliant. Clear explanations, strong data governance, and clinician transparency are essential for a secure adoption.
Doximity: An Accessible Way To Get Started With Digital Tooling
Doximity is a trusted source for clinicians looking for AI-powered workflow assistance and clinical decision management. With customization options and evidence-based or peer-reviewed answers, Doximity can fit any doctor, nurse practitioner, or PA workflow. Doximity provides the foundation for high-quality AI assistance, while remaining accessible and free for healthcare professionals.
Not all Doximity tools use AI, but some key AI features and functions include:
DoxGPT: This clinical workflow assistant works with prompts and questions, much like a GPT tool, but provides quality, evidence-based answers. DoxGPT includes succinct medical summaries or tables at the top of each output. It’s clear data clinicians can trust.
Doximity Scribe: This AI tool transcribes patient visits and provides a detailed summary of each visit before discarding the original recording. Physicians can use Doximity Scribe summaries to update patient notes, maintain communication, draft referral letters, and more. Doximity Scribe also tailors summaries to custom clinician templates.
Doximity is also easy to implement. Rather than introducing another system that’s complex to learn and manage, users can sign up with their healthcare credentials and use tools like DoxGPT, Scribe, and Doximity Dialer. Soon enough, clinicians find that it becomes a regular part of their workflows. There’s no upkeep, and it remains user-friendly from day one.
The Future of Evidence-Based Medicine Is Accessible
As machine learning technology advances, clinicians will be able to use trusted, evidence-based research as a guide with greater consistency and confidence.
Doximity stays ahead of the curve by providing digital tools with the features clinicians need to reduce administrative burden, while remaining secure and HIPAA-compliant. More than 85% of U.S. physicians are already Doximity members, and signing up is as simple as creating an account.
Get Doximity for free today, and start using AI in a trusted, efficient way at your practice.