Deep Research and Real World Evidence papers

Interesting discussion below. Do you have any ideas?

Deep Research is a powerful new tool designed to autonomously perform multi‐step research tasks by gathering, analyzing, and synthesizing information (including from uploaded files) into detailed, cited reports. In the context of healthcare data analysis and creating Real-World Evidence (RWE) papers, here are some thoughts on its potential and limitations:

Strengths for RWE Paper Generation

• Comprehensive Data Synthesis:

Deep Research can ingest spreadsheet files (CSV, XLSX) containing clinical or claims data and integrate this information with web-sourced evidence. This means it can potentially help generate the background, contextual insights, and even preliminary data analysis needed for an RWE paper by producing a detailed report with citations.

• Automation of Complex Tasks:

Because it’s designed for multi-step research that might take between 5 and 30 minutes per query, Deep Research can automate the tedious parts of gathering and summarizing data. This can be particularly useful when assembling initial drafts, identifying trends, and producing evidence-based summaries—essential ingredients for RWE studies.

Limitations and Considerations

• Turnaround Time:

Deep Research is not optimized for rapid back-and-forth conversation. Since it takes minutes to complete a research task, if your workflow demands iterative refinements or interactive exploration (which is common when finalizing a paper), the longer wait times may interrupt the creative process. In such cases, you might find a more interactive mode (like using GPT-4o with the Code Interpreter for on-the-fly analysis) to be more flexible.

• Interactivity and Iteration:

RWE papers often require multiple rounds of hypothesis testing, data refinement, and detailed discussion of methodology. Deep Research tends to generate a comprehensive report as a single output. While this is excellent for an initial draft, it might be less adaptable if you need to quickly tweak parameters, explore alternative analyses, or update specific sections based on new inputs. You’d likely need to run several separate deep research queries and then manually integrate their outputs.

• File Size and Token Limits:

When uploading large healthcare datasets, remember that each file should ideally remain under roughly 50 MB (or 2 million tokens when processed as text) to ensure smooth processing. For RWE papers where granular data is critical, you might have to partition your datasets into smaller, manageable chunks. This additional pre-processing step can add overhead to your workflow.

Overall Suitability

Deep Research is well suited for producing an in-depth, initial draft of an RWE paper by:

• Automating literature searches and data synthesis to provide a strong foundation.

• Providing documented, cited outputs that can serve as the backbone of your research narrative.

However, if your process relies on rapid iteration and continuous interactive dialogue to refine your paper, you might find its longer processing times and one-shot report style somewhat limiting. In practice, Deep Research may best serve as a high-powered assistant for the heavy lifting of initial evidence gathering and synthesis, with subsequent interactive refinement done using other tools or manual editing.

In summary, for RWE paper creation, Deep Research can be a valuable asset—especially for producing a comprehensive first draft—but its longer turnaround and less interactive nature mean you might still need to supplement it with additional tools or human oversight during the finalization process.