HealthTasks Agents are AI-powered institutional intelligence agents designed for healthcare education.
Unlike traditional dashboards or static reports, HealthTasks Agents can reason across live institutional data in real time. Educators and administrators ask natural language questions and instantly generate insights, analytics, reports, visualizations, accreditation evidence, and operational summaries. Agents can also investigate the next step and prepare useful work.
HealthTasks Agents are built to unify fragmented educational data into a single intelligent experience — turning disconnected systems into a connected healthcare education ecosystem. They can act on that understanding: propose clinical placement changes—sites, location groups, schedule tracks, unit capacity, and nearby-site matching—for coordinator approval before anything is saved. They can answer support questions, guide users with in-app links, and prepare compliance-template changes too.
What HealthTasks Agents do
Ask naturally. Get useful work back.
Ask a question in plain language and get an answer, a report, a proposed plan, or a direct path to the right place in the app. No new query language or maze of menus required.
Investigate across your institution
Agents reason across placements, students, preceptors, locations, compliance, curriculum, and connected systems. They separate what is configured from what is currently available, trace relationships, and surface uncertainty instead of inventing facts.
From exploration to approved work
Whether you are digging into trends, matching placements, or preparing operational changes, the agent does the legwork. Educators review the proposed work, approve what is right, and stay accountable for the result.
AI does the work. Educators stay in control.
Start with questions and read-only answers, then let trusted workflows become agent-operated. HealthTasks Agents investigate across your records, explain what they found, and prepare the next step. Every proposed write follows a human-in-the-loop workflow: the agent prepares the work, the educator remains the expert in the loop, and a pre-change snapshot makes approved work reversible.
Investigate
Find records, compare constraints, and show the evidence behind the answer.
Prepare
Turn the request into a clear draft with each proposed change spelled out and a before-change snapshot captured.
You decide
Review as the expert in the loop, approve or dismiss, then roll back from the snapshot if needed.
Operational workflows
Put the agent to work
Find nearby placement matches for unassigned students and preceptors in NUR 412
I found three nearby matches for unassigned students and preceptors across affiliated sites. I checked student compliance, active affiliation agreements, and current unit availability before preparing these changes.
Uncheck any rows you do not want, then approve.
- A. Rivera → Memorial Medical Center (2.4 mi · Med-Surg 3 · 1 open seat)
- J. Patel → Northside Health (5.1 mi · Pediatrics · 1 open seat)
- K. Nguyen → Lakeside Clinic (7.8 mi · Family Practice · preceptor matched)
Increase ICU capacity, add the downtown clinic, and organize both under a new location group
Set the capacity to 10 students for all 10 active units currently configured at the program's Florida locations, including Medical-Surgical A and B, Intensive Care Unit, Emergency Department, Pediatrics, Labor and Delivery, Behavioral Health, Perioperative Services, Telemetry / Step-down, and Outpatient Clinic at West Florida Hospital. The other five active Florida locations do not currently have units, so no unit records were invented for them.
Uncheck any rows you do not want, then approve.
- Update “Medical-Surgical A” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Medical-Surgical B” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Intensive Care Unit” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Emergency Department” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Pediatrics” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Labor and Delivery” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Behavioral Health” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Perioperative Services” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Telemetry / Step-down” at “WEST FLORIDA HOSPITAL” (capacity → 10)
- Update “Outpatient Clinic” at “WEST FLORIDA HOSPITAL” (capacity → 10)
Create a compliance template for the Fall pediatric rotation and show me what will change
I created a draft template for the Fall pediatric rotation using the requirements already configured for your program. Please review the list before assigning it to students.
Uncheck any rows you do not want, then approve.
- Create “Pediatric rotation” compliance template
- Add BLS certification requirement
- Add immunization record requirement
- Add background check requirement
- Add clinical orientation requirement
Help and support
Get answers without digging through the app
where do i go to add more preceptors
Go to Classrooms, open the relevant class, and select the Preceptors tab. From there, you can select existing people or invite new preceptors, including by bulk import, then save with Update Classroom.
healthtasks.ai
For placement-specific scheduling, use Placements → Schedules to review the classroom's schedule tracks and clinical groups.healthtasks.ai
2 sourcesProgram intelligence
Understand what your data is telling you
Map competency performance vs. assessment volume across our curriculum
Competency Performance Map
Look at our skills checkoffs and see how often our instructors overturn the AI grades. Looking for rater variability stats.
AI vs. Instructor Grade Agreement
Based on an analysis of the skills checkoff data, there is a strong baseline of agreement between the automated AI evaluations and your instructors, with adjustments occurring under predictable circumstances.
Below is the detailed breakdown of rater consistency, instructor adjustments, and rater variability based on 286 comparable steps where both an AI score and an instructor score were recorded across the institution's checkoffs.
Key Findings
1. High Overall Agreement
- Agreement Rate: 94.8% (271 out of 286 steps matched perfectly).
- Disagreement Rate: 5.2% (15 out of 286 steps were adjusted by instructors).
This indicates that the AI grading is highly consistent with instructor standards, serving as a reliable pre-grading baseline.
2. Systematic Upward Bias (No Downward Overturns)
- Overturned Upwards (AI 0 → Instructor 1): 100.0% of all disagreements (15 steps).
- Overturned Downwards (AI 1 → Instructor 0): 0.0% of disagreements (0 steps).
When instructors adjust an AI grade, they always grant credit where the AI was conservative. The AI acts as a conservative, strict evaluator, and instructors step in to correct false negatives or extend the benefit of the doubt to students. No instructor has had to downgrade an AI-passed step.
3. High Correlation with AI Confidence Flags
The AI generates a confidence rating (high or low) for its assessments. Instructors are significantly more likely to adjust grades when the AI itself flags a step as “low confidence”:
- High-Confidence AI Steps: 1.7% disagreement rate (only 4 adjustments out of 235 steps).
- Low-Confidence AI Steps: 21.6% disagreement rate (11 adjustments out of 51 steps).
This demonstrates that the AI's confidence flag is highly reliable. Instructors are 12.7 times more likely to adjust a grade when the AI flags a step as low confidence. This indicates that the confidence flag is a prime indicator for when manual review is actually necessary.
Accreditation readiness
Turn findings into evidence
Draft self-study narration for Standard I-A: Faculty and Staff Assessment of Program Outcomes
Standard I-A: Faculty and Staff Assessment of Program Outcomes
The program maintains an ongoing, systematic, and evidence-based process through which faculty and staff assess and evaluate achievement of identified program outcomes. This process is documented in the institution's CNEA Standard 1 Compliance Report, which details the framework, responsibilities, and continuous improvement cycle used during the 2024-2025 academic year.
Systematic Evaluation Plan (SEP)
The program utilizes a centralized SEP to track achievement of two categories of outcomes:
- Student Learning Outcomes (SLOs): Clinical competence, critical thinking, and professional ethics.
- Program Outcomes (POs): Graduation rates, NCLEX-RN pass rates, and employer satisfaction.
Key data points are collected on a defined schedule with assigned responsibility and specified evidence sources, as outlined below:
Evidence-Based Assessment Cycle
Faculty engage in a structured four-stage cycle to ensure continuous improvement:
- Data Collection: Quantitative scores are gathered from standardized testing (HESI/ATI), and qualitative feedback is obtained from the Program Advisory Board.
- Analysis: Faculty meetings are held every second Tuesday to review aggregate student performance against the 80% benchmark for all core competencies.
- Evaluation: Current data are compared against historical trends to identify curriculum gaps.
- Action/Change: The program implements revised teaching strategies or clinical rotations based on identified weaknesses.
Closing the Loop: Recent Program Improvements
The assessment process has directly informed curricular change. During the 2024 mid-year evaluation, data indicated a 12% drop in Pharmacological Math proficiency. In response, the program integrated mandatory dosage calculation modules into Level 1 and Level 2 clinical preparation. End-of-year assessments subsequently showed a 15% increase in math competency scores, restoring performance to the program benchmark.
Faculty and Staff Engagement
The systematic evaluation process involves both faculty and staff through the following structures:
- Annual Retreat: Dedicated to the comprehensive review of the SEP and CNEA standards.
- Curriculum Committee: Conducts monthly reviews of course-level outcomes to ensure alignment with overarching program goals.
- Staff Integration: Admissions and advising staff provide data on retention triggers, which are integrated into the systematic review of student success.
Through these mechanisms, faculty and staff collectively participate in an ongoing, evidence-based evaluation process that directly supports the achievement of identified program outcomes.
One agent across the work that matters
The same natural-language interface can move from a question about performance to a placement proposal to accreditation evidence. Keep the workflows your team already knows, and add agentic help where it removes the most administrative work.
Clinical operations
Prepare placement matches, location changes, unit-capacity updates, and schedule-track organization from one conversation. Human-in-the-loop approval cards make every change inspectable before it reaches your system.
Explore clinical placementsProgram intelligence
Move from a question about performance to evidence, trends, and a clear explanation of what the data supports.
Explore AI Insights & CQICompliance and readiness
Answer support questions, build compliance templates, and turn connected evidence into review-ready work.
Explore self-study drafting