A/R Follow-Up AI Reviewer

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI by connecting them with meaningful annotation and review work and enabling contributors to build a durable AI training portfolio they control.

OpenTrain AI is the hiring organization for this role. You will join a distributed team of specialists who shape how AI systems behave by providing high-quality feedback and evaluations.

Why AI Training Work Matters

AI training (data labeling and human feedback) is the human side of building modern AI. Contributors annotate data, evaluate model outputs, and provide structured feedback that directly improves accuracy and reliability.

This work is flexible, remote, and accessible: it’s a way to apply your domain expertise (here, healthcare revenue cycle) to cutting-edge automation, helping shape systems that handle claims follow-up and payer interactions.

The Role

We are recruiting an A/R Follow-Up AI Reviewer to evaluate AI tools built for healthcare accounts receivable (A/R) follow-up and payer collections. You will review AI-generated claim status follow-up recommendations, payer correspondence drafts, and resolution outputs and provide structured feedback to improve prioritization, accuracy, and revenue recovery.

This is a part-time contractor role: 20+ hours per week, remote within the United States, English required. Pay is $75/hour.

• Employment type: Contractor, Part-time
• Schedule: 20+ hours/week (flexible hours)
• Location: Remote — United States only
• Language: English (fluent written and verbal required)
• Pay: $75 USD per hour

What You'll Do

You will assess AI outputs across commercial, Medicare, Medicaid, and managed care claims. Feedback you provide will be used to train models and improve automated A/R workflows.

• Review AI-generated A/R follow-up recommendations for claim status workflows and payer-specific resolution steps.
• Evaluate claim status inquiry outputs, payer correspondence drafts, and follow-up strategies for effectiveness and compliance.
• Assess prioritization of A/R queues by payer, aging bucket, and dollar value.
• Identify payment discrepancies, payer processing errors, and underpayments within AI-assisted content.
• Provide structured, high-quality feedback and ratings that support RLHF and evaluation datasets.

Requirements

We require demonstrable, expert-level experience in A/R follow-up and payer collections. You will work directly with text-based AI outputs (claim notes, correspondence, status responses) and must be comfortable evaluating technical, operational, and compliance-related items.

• Extensive background in A/R follow-up, payer collections, or revenue cycle operations (expert level).
• Strong knowledge of EDI 276/277 claim status transactions, payer portals, and billing systems.
• Familiarity with Medicare, Medicaid, commercial, and managed care claims processing.
• Excellent written and verbal English communication skills and high attention to detail.
• Ability to evaluate AI-generated payer correspondence and claim resolution outputs for accuracy and prioritization.

Helpful Background (Preferred)

These qualifications are not required but will help you be successful and move faster through training and assignments.

• CRCR, CPC, or CHFP certification.
• Experience with automated A/R follow-up tools, RCM technology platforms, or payer workflow automation.
• Background in hospital or physician group follow-up operations.
• Experience presenting A/R performance, reduction plans, or remediation strategies to leadership.

How the Project Works

You will receive batches of AI-generated text (recommendations, correspondence drafts, resolution notes) to review and rate. Work involves providing structured feedback, selecting evaluation ratings, and occasionally adding corrective examples or comments that will be used to improve models.

Labeling types include evaluation ratings and RLHF-style feedback. All work is text-focused; no image or audio annotation is required. OpenTrain provides onboarding instructions and evaluation guidelines — you are expected to follow those standards and produce reliable, high-quality feedback.

• Data type: Text
• Label types: Evaluation ratings, RLHF
• Tooling: Training and guidelines provided; specific labeling software will be supplied as needed
• Output quality: Accurate, reproducible, and well-documented feedback is essential

Apply If...

This role is a fit if you are an experienced revenue cycle professional who wants flexible, remote work and is skilled at turning domain expertise into clear, structured feedback for AI systems.

• You have hands-on A/R follow-up and payer collections experience.
• You can evaluate complex payer scenarios and explain why an AI output is correct or needs improvement.
• You want to contribute to building better healthcare automation while working as a contractor.