# A large pharmacy benefits manager wants to increas... ## Conversation ## User *2026-09-16T12:01:28.328171+00:00* A large pharmacy benefits manager wants to increase adoption of biosimilars (lower-cost alternatives to biologic drugs) among patients currently on originator biologics for chronic conditions such as rheumatoid arthritis, psoriasis, Crohn's disease, and MS. Adoption requires both patient acceptance and prescriber willingness to switch. Design and run a simulated survey study with two panels — patients on biologics, and prescribing specialists — to answer: (1) how open each group is to switching, (2) what concerns or barriers each group has, (3) what information sources and message framings would most influence patients, and (4) what evidence or support would most influence prescribers. Generate your own hypotheses about answer options; don't ask me for them. Do not search the web or news for this task — work only from the brief. --- ## Assistant *2026-09-16T12:01:32.443467+00:00* I'll start by loading the session orientation skill. **Tool calls:** - `Skill` - `Bash` - `command`: ep study start --root . --topic "biosimilar-adoption" Good, the study directory is allocated. This is a well-specified brief — two panels, four research questions, simulated respondents, hypotheses generated by me. Let me load the survey research skill since this is clearly a survey study. **Tool calls:** - `Skill` - `Skill` - `Bash` - `command`: ep models --service anthropic --sort input-price --limit 10 Good. Now let me load the plan template before writing the plan. **Tool calls:** - `Skill` - `Skill` - `Bash` - `command`: ep study scaffold /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a Now I have the scaffold and model info. Let me write the plan. **Tool calls:** - `Write` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/plan.md - `Bash` - `command`: ep present /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/plan.md PRESENT_FILE:/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/plan.md plan.md Here's the study design. The core idea: two parallel surveys fielded to **12 simulated patients** (spanning RA, psoriasis, Crohn's, and MS) and **8 simulated prescribers** (one specialty per condition area, varied by experience and setting). Each survey is 5 questions — a mix of scales, checkboxes, single-choice, and free text — designed to surface the alignment gaps between what patients worry about and what prescribers see as barriers. I've generated hypotheses for all answer options based on what's known about biosimilar adoption dynamics: nocebo effects, immunogenicity concerns, formulary instability, the outsized role of physician recommendation for patients, and so on. --- ## User *2026-09-16T12:04:00.183294+00:00* Q: Does this plan look good to proceed, or would you like changes? A: Looks good, proceed --- ## Assistant *2026-09-16T12:04:37.625685+00:00* Plan approved. Let me scaffold the survey template and build the study. **Tool calls:** - `Bash` - `command`: ep study scaffold \ /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a --template survey \ --expected-rows 20 \ --required... - `Skill` - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_survey.py - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_agent_list.py Now I'll edit both source files with the approved study design. The approach: all 20 agents answer all 10 questions in a single survey. The agent instructions clearly establish each persona's role, and analysis filters by panel. **Tool calls:** - `Edit` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_survey.py - `Edit` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_agent_list.py Good, both source files are ready. Now I'll run the `after_source_edits` phase command to build EDSL objects, set up gates, and get the cost estimate. **Tool calls:** - `Bash` **failed** - `command`: make -C "/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a" prepare APPROVAL_EVIDENCE="User approved plan via AskUserQues... ``` Exit code 2 make: Entering directory '/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a' env -u VIRTUAL_ENV uv run --project /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je --frozen --no-dev -- ep workflow setup --name "Biosimilar adoption - patient and prescriber panels" --root . --spec workflow-gates.json --evidence "User approved plan via AskUserQuestion — proceed with concise report" env: ‘uv’: No such file or directory make: *** [Makefile:66: workflow-setup] Error 127 make: Leaving directory '/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a' ``` - `Bash` - `command`: which ep && which python && which uv 2>/dev/null; pip show uv 2>/dev/null | head -2 - `Bash` - `command`: pip install uv 2>&1 | tail -3 - `Bash` - `command`: which uv && uv --version - `Bash` - `command`: make -C "/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a" prepare APPROVAL_EVIDENCE="User approved plan via AskUserQues... Everything built successfully. The job is ready: - **10 questions x 20 agents x 1 model** = 200 inference calls - **Estimated cost: $0.61** (61 credits) **Tool calls:** - `Bash` - `command`: make -C "/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a" data --- ## User *2026-09-16T12:19:07.767795+00:00* Q: Approve spending ~$0.61 to run the biosimilar adoption survey (200 inference calls with claude-sonnet-4-6)? A: Approve, run it --- ## Assistant *2026-09-16T12:19:43.555354+00:00* All 20 interviews completed with no exceptions ($0.72 actual cost). Now running the post-run review, exports, and QA. **Tool calls:** - `Bash` - `command`: make -C "/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a" post-run Validation passed — 20 rows, all 10 answer columns present, no missing data. Let me examine the results and then write the report. **Tool calls:** - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/data/report-data.json Let me get the full results for the report. **Tool calls:** - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/tables/results.csv I have rich results. Let me check the generated plots and get the full results for panel-level analysis. **Tool calls:** - `Glob` - `path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/plots, `pattern`: **/*.png - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/plots/standard-rating-means.png The standard plot pools both panels. I need panel-specific analysis plots for the report. Let me read the JSON results and build custom plots. **Tool calls:** - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/tables/results.json I have comprehensive data. Let me now create custom analysis plots and write the report. First, the plot style and then the analysis script. **Tool calls:** - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/analysis/plot_style.py Now let me write a comprehensive analysis script to generate panel-specific plots. **Tool calls:** - `Write` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/analysis/biosimilar_plots.py - `Bash` - `command`: cd /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a && python analysis/biosimilar_plots.py Let me inspect the key plots to make sure they look right. **Tool calls:** - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/plots/openness-by-panel.png - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/plots/alignment-gap.png - `Read` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/plots/patient-concerns.png Plots look clean and readable. Now let me load the report authoring skill and write the report. **Tool calls:** - `Skill` - `Read` - `file_path`: /home/user/.claude/skills/report-authoring/references/transparency.md Now let me write the report. I need to check free-text homogeneity first as required. **Tool calls:** - `Bash` - `command`: python3 -c " import json from collections import Counter with open('writeup/tables/results.json') as f: data = json.load(f) patients = [r for r in data if r[... Clear homogeneity in openers -- I'll disclose this in Limitations. Now let me write the report. **Tool calls:** - `Write` - `file_path`: /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/report.md Now I need to load the report quality review skill before compiling, as required by the authoring workflow. **Tool calls:** - `Skill` - `Bash` - `command`: katz report review-plan --report /__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/report.md - `Bash` - `command`: STUDY=/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a && ep models create --profile report-review --count 3 --output "$... - `Bash` - `command`: STUDY=/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a && ep jobs cost "$STUDY/edsl_jobs/report-review.jobs.ep" --- ## User *2026-09-16T12:24:27.037584+00:00* Q: Would you like an optional pre-delivery quality review of the report? Three frontier models independently check the report for numerical consistency, claim calibration, visual integrity, and simulation-boundary language. Detected analysis type: survey-simulation. Approximate cost: ~$0.50-$1.00 (3 model calls with image attachments). Takes about 2 minutes. A: Run review --- ## Assistant *2026-09-16T12:24:42.257351+00:00* Estimated cost is only ~$0.04 -- well within the approved range. Running the review. **Tool calls:** - `Bash` - `command`: STUDY=/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a && ep run "$STUDY/edsl_jobs/report-review.jobs.ep" --output "$STU... Quality review complete: 1 of 3 reviewers parsed successfully (gemini-pro-latest) with a **pass** verdict and zero issues. Two reviewers (claude-sonnet-5, gpt-6-astra) had parse failures. No fixes needed. Now compiling and verifying. **Tool calls:** - `Bash` - `command`: make -C "/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a" complete All three gates passed. Here's your report: PRESENT_FILE:/__modal/volumes/vo-cd0rMZq2AqJhD4GaAf19je/sessions/topic_biosimilar-adoption/study_a/writeup/report.html Biosimilar adoption - patient and prescriber panels --- **Study complete.** Here's a summary of what the simulation found: **The core strategic insight is sequential adoption, not parallel messaging:** 1. **Patients are cautious but persuadable** (mean openness 2.75/5). Their overwhelming trust signal is their prescribing specialist -- 100% chose their doctor as the most trusted source *and* "your doctor recommends this" as the most persuasive message. Direct-to-patient campaigns about cost savings or FDA equivalence are unlikely to move the needle without physician endorsement. 2. **Prescribers are slightly more open** (mean willingness 3.12/5) but want comprehensive evidence -- every prescriber selected every offered barrier and every evidence type. Their top PBM ask: **formulary stability** (guaranteed no further changes for 12+ months), selected unanimously by both panels. 3. **The alignment gap** is the actionable finding: patients trust *doctors*, doctors trust *evidence*. So the adoption path runs: equip prescribers with condition-specific switching data and registry evidence, guarantee formulary stability, then let prescribers become the persuasion channel for patients. 4. **Longer-treated patients are harder to convert** -- those on biologics for 7-8 years scored 2/5 on openness, describing their stability as "precious" and "hard-won." Total cost: $0.96 ($0.72 survey + $0.24 quality review). The quality review (1/3 reviewers parsed) returned a clean pass with zero issues. --- ## Files - [**report.html**](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/report.html) (406.3 KB) - [Makefile](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/Makefile) (5.2 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/plan.md) (9.6 KB) - [workflow-gates.json](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/workflow-gates.json) (624 B) - [biosimilar_plots.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/biosimilar_plots.py) (10.8 KB) - [build_standard_plots.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/build_standard_plots.py) (2.3 KB) - [plot_style.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/plot_style.py) (12.3 KB) - [report-review.json](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/report-review.json) (1.3 KB) - [summarize_run_costs.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/summarize_run_costs.py) (939 B) - [validate_results.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/analysis/validate_results.py) (1.4 KB) - [report-data.json](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/data/report-data.json) (25.2 KB) - [report-review.results.ep](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/data/report-review.results.ep) (50.1 KB) - 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[study_agent_list.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_agent_list.py) (9.6 KB) - [study_survey.py](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/study_survey.py) (6.3 KB) - [survey.ep](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/edsl_jobs/job_a/survey.ep) (16.2 KB) - [alignment-gap.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/alignment-gap.png) (82.7 KB) - [openness-by-duration.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/openness-by-duration.png) (60.1 KB) - [openness-by-panel.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/openness-by-panel.png) (56.0 KB) - [patient-concerns.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/patient-concerns.png) (82.4 KB) - [patient-trust-and-messaging.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/patient-trust-and-messaging.png) (69.2 KB) - [prescriber-barriers.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/prescriber-barriers.png) (78.5 KB) - [standard-rating-means.png](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/plots/standard-rating-means.png) (38.5 KB) - [results.csv](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/tables/results.csv) (86.4 KB) - [results.json](https://www.expectedparrot.com/api/macaw/share/YOt0TD16ilzGgYxcfBE-7A/files/sessions/topic_biosimilar-adoption/study_a/writeup/tables/results.json) (100.6 KB)