--- license: apache-2.0 task_categories: - other tags: - agents - computer-use - healthcare - benchmark configs: - config_name: default data_files: - split: full path: full.jsonl --- # Personal Health Arena A patient-facing healthcare benchmark for computer-use agents. A synthetic population is generated with [Synthea](https://github.com/synthetichealth/synthea) and imported into a self-hosted **OpenEMR 7.0.2**; each task measures whether an agent can complete a real errand on the **patient portal**, acting for a patient. **100 episodes over 50 patients, 398 legs.** Two errands per patient, from different archetypes. No two episodes share an instruction, and 96 of the 100 leg sequences are distinct. > Task design is complete and measured against a live instance. The grading harness is not built > yet, and no episode has been executed end to end. Field names may still change. ## What a task is An **errand** — the reason a person opened their portal — not a single question. Each episode is 3–6 **legs**, a leg being one independently gradeable question or action. **Score is legs correct over total legs**, so an agent that stalls partway still registers signal. The agent is told *why* the patient is there and *what to come back with*. It is never told the route: not which menu, which page, or how many rows it has to get through. ## The errands | Archetype | Episodes | The errand | |---|---|---| | `checking_messages` | 20 | the practice sent something that needs answering | | `new_pharmacy` | 16 | a new pharmacy wants the current medication list | | `transferring_practice` | 15 | moving practice; the new one needs the records | | `appointment_coming_up` | 15 | an appointment no longer works | | `result_came_back` | 13 | the clinic called about a test result | | `appointment_needs_a_note` | 13 | something the practice should know before a visit | | `bill_looks_wrong` | 8 | a bill arrived that looks wrong | **Every instruction is hand-written for that patient**, and **every route differs**. An archetype names the action the errand must end in and a pool of questions to draw from; each patient gets a seeded subset in a seeded order, 3 to 6 legs. Two patients with the same errand still navigate differently, and some archetypes offer more than one ending -- `appointment_coming_up` resolves either by cancelling and rebooking or by moving the existing slot. Assignment is chart-driven: a patient is offered an errand only where every leg in it resolves against their own record. ## Where the difficulty comes from From the record and the interface, never from the question. Some of it: - The lab page renders **29–4,105 rows per patient** and prints dates as `08/17 00:00:00/2017` — `get_lab_results.php` splits a datetime on `-` and reassembles it. - The **Range column is empty for all 22,645 lab rows**, so the only honest answer about a normal range is that the portal gives none. Reciting a textbook range scores 0. - Medications and Prescriptions are two pages with **identical headers** one `WHERE` clause apart: 449 lifetime rows against 148 current. Nothing on either page explains the difference, and **10 of 50 patients have no active prescription at all**. - The billing ledger renders **nothing until a date range is submitted**, and filters on `ct_proc='1' AND activity>0` — predicates you would not guess from the schema. - The Problems page runs to **195 rows**, and a *blank* End Date is what marks a condition active. - **25 of 50 patients have no allergies.** The correct answer is that there are none; an invented allergen scores 0. - Write legs must reach the right person: the recipient is the provider named on the *Appointments* screen, to be found among 161 in the messaging dropdown. ## Columns One row per episode, carrying both the task and its gold answer. | Column | | |---|---| | `episode_id`, `archetype`, `legs`, `patient_pid`, `patient_name`, `portal_username` | identity and route | | `user_msg` | what the patient said, and nothing else — hand-written, unique per episode | | `prompt_format` | the scaffolding around it, with a single `{user_msg}` hole | | `instruction` | the two joined; the only thing the agent is given | | `answer_fields` | the keys the agent must return | | **`expected_json`** | **gold answers** | | **`write_check_json`** | **the database delta a write leg must produce** | | **`leg_scores`** | **which keys each leg owns** | ```python from datasets import load_dataset ds = load_dataset("wnkh/pha", split="full") GOLD = ("expected_json", "write_check_json", "leg_scores") prompt_rows = ds.remove_columns(GOLD) # never hand an agent the last three ``` `instruction == prompt_format.format(user_msg=user_msg)` holds for every row and is asserted at build time. The split exists so the framing and the answer contract can be changed without touching a hundred hand-written narratives, and so the patient's own words can be extracted alone. **The last three columns are answers.** Tasks and solutions were previously separate configs, so a harness could load the task config and be structurally unable to see them. In one file that guarantee is gone and the harness must drop them itself. `expected_json` is JSON-encoded because expected values are floats for some legs, strings for others and lists for others again, and one parquet column cannot hold all three. `leg_scores` maps each leg to the keys it owns, which is what makes per-leg partial credit computable. ## Grading - Answers come from the required JSON object; only the named keys are graded. JSON rather than YAML because YAML coerced 74 of 100 gold answers to the wrong type -- dates to date objects, `""` to null, list items containing `": "` to nested objects -- and failed outright on six legitimate RxNorm drug names beginning with `{`. - Numbers compare numerically with tolerance — the portal prints full float precision. - Dates are normalised before comparison. - Sets score F1, never recall: recall-only scoring rewards hallucination. - An empty result is an answer, never a skip. - Writes grade on the database delta, never on the agent's claim to have acted, and every episode restores afterwards so episode ordering cannot matter. ## Environment The dataset is inert without the environment: a local OpenEMR instance with its clock pinned to `2026-08-25 18:00`. The population, import pipeline and task design live in the project repository. All patient data is synthetic — generated by Synthea, containing no real person's information.