Who should use the Generate real-world evidence workflow?
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Science & Healthcare
Streamlined workflow to generate real-world evidence by first reviewing relevant literature, then analyzing real-world clinical data, and finally producing evidence outputs using dedicated RWE tools.
Deliverable outcome
A fully reproducible research package with archived code, data, and protocol.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A fully reproducible research package with archived code, data, and protocol.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use ConcertAI to a clear, feasible research question with documented data source and cohort criteria. Then, you pass the output to Huma.ai to a literature-informed study design protocol with validated code lists and comparator rationale. Then, you pass the output to Hex Magic AI to a clean, analysis-ready dataset with documented variable definitions and quality metrics. Then, you pass the output to ConcertAI to robust effect estimates with documented sensitivity analyses and bias assessment. Then, you pass the output to Huma.ai to a clinically contextualized interpretation with clear limitations and comparison to existing evidence. Then, you pass the output to Lex AI to a publication-ready evidence report with visuals, compliance to reporting guidelines, and dissemination plan. Finally, Dandelion Health is used to a fully reproducible research package with archived code, data, and protocol.
Define research question and feasibility assessment
A clear, feasible research question with documented data source and cohort criteria.
Conduct targeted literature review for study design
A literature-informed study design protocol with validated code lists and comparator rationale.
Extract and transform real-world clinical data
A clean, analysis-ready dataset with documented variable definitions and quality metrics.
Perform statistical analysis and sensitivity checks
Robust effect estimates with documented sensitivity analyses and bias assessment.
Interpret results and contextualize with literature
A clinically contextualized interpretation with clear limitations and comparison to existing evidence.
Produce evidence outputs and disseminate
A publication-ready evidence report with visuals, compliance to reporting guidelines, and dissemination plan.
Archive code, data, and protocol for reproducibility
A fully reproducible research package with archived code, data, and protocol.
Collaborate with clinical stakeholders to formulate a precise, answerable research question (e.g., treatment effectiveness in a specific subpopulation). Then assess feasibility by checking data availability, sample size, and key variables in available real-world data sources (claims, EHR, registries). Document inclusion/exclusion criteria and primary endpoints.
Why ConcertAI: ConcertAI offers cohort discovery and real-world evidence generation, directly supporting feasibility assessment and research question definition with clinical data dictionary capabilities.
Search PubMed, Embase, and grey literature for existing RWE studies on the same topic to identify validated outcome definitions, comparator choices, and known confounders. Extract relevant study designs (e.g., new-user cohort, self-controlled case series) and statistical methods (propensity score matching, instrumental variables).
Why Huma.ai: Huma.ai specializes in systematic literature review and clinical trial synthesis, directly matching the need for targeted literature review and study design.
Query the selected database(s) using SQL or a common data model (e.g., OMOP CDM) to extract patient-level data for the cohort. Apply inclusion/exclusion criteria, map variables to standardized terminologies, and create analytic variables (e.g., time-to-event, baseline comorbidities). Perform data quality checks (missingness, outliers, logical inconsistencies).
Why Hex Magic AI: Hex Magic AI enables natural language to SQL generation and Python data manipulation, directly supporting extraction and transformation of clinical data.
Apply the pre-specified statistical methods (e.g., propensity score matching, Cox regression, or machine learning for confounding adjustment). Conduct sensitivity analyses (e.g., negative control outcomes, different exposure windows, per-protocol analysis) to assess robustness. Document all model diagnostics (balance plots, proportional hazards tests).
Why ConcertAI: ConcertAI supports real-world evidence generation, which includes statistical analysis and sensitivity checks for clinical studies.
Compare findings with the prior literature identified in Step 2, discussing consistency, discrepancies, and potential reasons (e.g., different populations, data sources). Assess clinical significance beyond statistical significance (e.g., number needed to treat, absolute risk differences). Document limitations (e.g., residual confounding, generalizability).
Why Huma.ai: Huma.ai specializes in systematic literature review and real-world evidence analysis, directly supporting interpretation and contextualization with literature.
Create a structured report or manuscript following RWE reporting guidelines (e.g., STROBE, RECORD). Include a graphical abstract, forest plots, and a plain-language summary. Optionally, prepare a regulatory submission dossier or a slide deck for internal stakeholders. Submit to a peer-reviewed journal or post on a preprint server.
Why Lex AI: Lex AI provides AI feedback on drafts, rewriting, and brainstorming, directly supporting the production of evidence outputs like manuscripts and reports.
Package the analytic code, de-identified dataset (if permissible), and study protocol into a reproducible repository (e.g., GitHub, Zenodo). Add a README with instructions to reproduce all results. Optionally, register the study on a public registry (e.g., ClinicalTrials.gov, ENCePP).
Why Dandelion Health: Dandelion Health supports clinical AI model training and algorithm validation, which often involves archiving code and data for reproducibility.
§ Before you start
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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