Reminders, pop-ups, clinical guidelines, documentation templates—while these may feel like second nature, healthcare professionals didn’t always have clinical decision support (CDS) tools to fall back on.
CDS software has become the missing link to inform patient care, comparing personal health information to medical databases for up-to-the-minute guidance. It’s come a long way in its 50-plus-year history, facing a new turning point with the rise of artificial intelligence (AI). Explore the rise of clinical decision support and how your organization can put it to work to overcome care delivery challenges.
A Quick Timeline of Clinical Decision Support
The roots for today’s clinical decision support took hold ages ago, dating all the way back to 1950s research. But back then, it was just an idea, and it would take decades to turn into an actionable concept—let alone evolve into shared data and high technology powered by AI.
Wright and Sittig sum up the evolution of clinical decision support across four phases:
Stand-Alone Decision Support Systems
In its infancy, clinical decision support functioned independently, adding yet another system for clinicians to juggle. Tools such as MYCIN and INTERNIST-I helped by improving diagnoses, suggesting treatments, and even critiquing doctors’ proposed plans.
The problem? They couldn’t connect to hospital systems, requiring time-consuming manual input.
Decision Support Integrated into Clinical Systems
Gradually, CDS found its way into everyday systems. A few early innovations, such as WizOrder and Brigham Integrated Computing System, were built directly into electronic health record (EHR) systems to guide doctors through treatment plans and generate real-time alerts—improving patient outcomes.
There were just a few bumps in the road: Each was custom-built, requiring developers to make updates most hospitals couldn’t afford.
Standards for Sharing Clinical Decision Support Content
Because every system was custom-built, information couldn’t readily be shared across locations. Experts created universal ways of writing medical decision rules to help different systems share content. Arden Syntax—using if/then statements for simple alerts such as checking for drug interactions—gave way to Guideline Interchange Format to handle complex care pathways.
Imitation is the sincerest form of flattery, and the clarity and organization of standardized systems inspired competition. Differing standards with unique terminology complicates sharing all over again.
Service Models for Decision Support
There had to be some way to finally put the pieces together. Developers started dissecting CDS systems to learn more, putting them back together with APIs for any hospital. The revitalized systems "plug into" decision tools—even if they were built separately.
Finally, something that works for clinicians—and with choices, to boot:
- Shareable Active Guideline Environment allows outside tools to interact with an EHR by using a fixed vocabulary and converting data to the same format.
- SEBASTIAN adds an API to the CDS so any connected hospital system can ask questions and get advice in return.
CDS Evolution Over Time
- 1970s: Experimental, academic, and theoretical
Early CDS was time-consuming, poorly integrated, and unregulated—making it difficult to trace mistakes. - 1980s and ‘90s: Implementation in clinical settings
CDS gained traction as integration and real-time support evolved. - 2000s: Quality and efficiency
CDS tools rapidly advanced, making their use more mainstream with a focus on evidence-based medicine and guideline-based care. - 2010s and onward: Surpassing the fourth phase
Modern CDS integrates smartphones, voice recognition, and automation. It’s often administered through electronic medical records and integrated with EHRs.
The Five “Rights”
In practice, the secret to clinical decision support tools is that they meet organizational goals without creating unintended consequences. CDS should be helpful, targeted, and easy to use. The CDS Five Rights framework lays out the basics:
Right Information
Cut through the noise. Clinical decision support data should hit all the right notes, combining concise, evidence-based, and relevant information for users. If it isn’t, clinicians may well ignore it.
Right Person
Does the nurse need it, or does the doctor? CDS data must be directed to the right person to support decision-making; otherwise, it could contribute to alert fatigue.
Right Format
What do current workflows look like? CDS data—alerts, order sets, or passive reminders—has the most impact when it aligns with these preferences. Clear and easy-to-use information is usually ideal.
Right Channel
Do clinicians spend more time in EHRs or portals? Deliver clinical decision support information to the right channel based on the use case. And don’t forget about tech glitches! Implement backups for when systems go down.
Right Time
Clinician needed something yesterday? It might be too late. Deliver CDS data at the right time to support care and decision-making without avoidable frustration.
Challenges from Inadequate Clinical Decision Support
Modern clinical decision support is designed to make life easier for clinicians across the continuum of care, but it will always be a work in progress. Stay aware of the challenges as you optimize your solutions.
Alert Fatigue
How many notifications do you actually pay attention to? Clinicians get so many alerts that it’s impossible to distinguish hard stops from the “noise,” so they eventually tune most of it out.
Inaccurate Information or Errors
Missing just one key piece of information can cause diagnostic errors, leading to treatment delays, unnecessary tests, and even harm or death. If CDS tools have poor interoperability across systems and clinicians don't have up-to-date, complete information, it can be disastrous.
Overreliance on Technology
AI and machine learning are permeating healthcare operations, and the risks are the same for clinicians as the guy on his couch using ChatGPT: inaccuracy. But here, it could cost lives because trusting technology without question may contribute to flawed recommendations. Your best bets? Always double-check the tech, continuously monitor AI efficacy, and stay updated on how to use AI and when to question it.
Benefits of Up-to-Date Clinical Decision Support Tools
Achieve marked improvements in collaboration and care delivery by using clinical decision support data to your—and your patients’—advantage. CDS tools provide several core benefits.
Better Decision-Making
In medicine, choices can mean the difference between life and death. Clinical decision support offers fresh insights and evidence-based recommendations so clinicians can make informed decisions, improving their diagnostic accuracy, treatment plans, and outcomes.
Fewer Errors
No matter your experience or expertise, human errors still occur. But clinical decision support plugs those gaps through real-time alerts and reminders to support patient safety. The right tools flag everything from allergies to drug interactions and incorrect dosages.
Increased Efficiency
The amount of time you spend on paperwork has gotten out of hand. Zap your administrative burden and enhance workflows through automation and data analysis—and get back to patient care.
Satisfied Patients and Providers
Ensure seamless, personalized care that suits both sides. CDS tools make patient information accessible across providers and settings, so patients don’t have to repeat their medical history and clinicians can easily make informed decisions.
Follow the Future of Clinical Decision Support
From stand-alone decision support systems to the edge of AI, clinical decision support has grown exponentially. As AI and machine learning are increasingly baked into clinical workflows, it’s opening access to active decision-making support, transforming workflows, and reducing administrative burdens so clinicians can refocus on care.
Juno EHR is proud to be part of this evolution, offering a fully integrated CDS system to help clinical teams. Our technology is built on a codified system with enhanced security features and architecture and includes alert customization and personalization to enhance adoption.
And it keeps getting better! Juno Health is enhancing CDS by integrating Avo’s AI Scribe into Juno EHR. Scribe, a fully AI-driven innovation, transcribes interactions and recommends next best steps of service for patients.
The combination of Juno EHR + Avo creates a system that enables HIPAA-secure, real-time transcription in more than 50 languages—even with multiple participants—so clinicians can focus on patient care. Reduce documentation time by up to 10 minutes per note.
Discover how Juno EHR can support your organization. Reach out to schedule your complimentary demo and improve clinical operations.
