Who should use the Provide Clinical Decision Support workflow?
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
A workflow to prepare clinical data, generate real-world evidence, synthesize recommendations using a clinical decision support system, and validate findings with summaries of relevant research papers, ensuring evidence-based decisions.
Journey overview
How this pipeline works
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use athenaOne to a structured, cleaned clinical dataset is ready for evidence generation and decision support. Then, you pass the output to Komodo Health to actionable insights from real-world data are compiled and ready to complement the decision support system. Then, you pass the output to Cognoa (Canvas Dx) to a prioritized list of differential diagnoses and evidence-based recommendations is produced for clinician review. Finally, ScholarAI is used to a set of summarized references and supporting evidence is attached to the final clinical decision report.
A set of summarized references and supporting evidence is attached to the final clinical decision report.
Input: Generate Real-World Evidence
Actionable insights from real-world data are compiled and ready to complement the decision support system.
Collect and clean clinical datasets such as lab results, patient histories, and imaging reports using Inscripta to ensure high-quality inputs for downstream analysis.
Accurate data preparation prevents errors in later stages and establishes a reliable foundation for clinical decision support.
A structured, cleaned clinical dataset is ready for evidence generation and decision support.
Leverage IQVIA to extract and analyze real-world patient data, including claims and electronic health records, to identify patterns and outcomes that inform clinical decisions.
Real-world evidence adds contextual validity to decision support, bridging gaps between clinical trials and actual practice.
Actionable insights from real-world data are compiled and ready to complement the decision support system.
Use Infermedica to analyze patient symptoms, medical history, and evidence inputs, generating ranked differential diagnoses and treatment recommendations for clinicians.
This is the central step where AI-driven reasoning synthesizes all inputs into actionable clinical guidance.
A prioritized list of differential diagnoses and evidence-based recommendations is produced for clinician review.
Use Jenni AI to automatically summarize recent medical literature relevant to the diagnoses or treatments identified, providing concise evidence citations to support the recommendations.
Validating decisions with current research ensures the recommendations are up-to-date and scientifically grounded.
A set of summarized references and supporting evidence is attached to the final clinical decision report.
Start this workflow
Ready to run?
Follow each step in order. Use the top pick for each stage, then compare alternatives.
Begin Step 1Time to first output
30-90 minutes
Includes setup plus initial result generation
Expected spend band
Free to start
You can swap tools by pricing and policy requirements
Delivery outcome
A set of summarized references and supporting evidence is attached to the final clinical decision report.
Use each step output as the input for the next stage
Why this setup
Repeatable process
Structured so any team can repeat this workflow without starting over.
Faster tool selection
Each step recommends the best tool to reduce trial-and-error.
Quick answers to help you decide whether this workflow fits your current goal and team setup.
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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Streamlined workflow to prepare, analyze, visualize, and automate data analysis for decision-ready insights using specialized AI tools.