Is FIRST/then HIPAA compliant?

The current version is a proof of concept focused on workflow and vision. If FIRST/then were developed into a production platform, HIPAA compliance would be a foundational requirement from day one. The platform would be built on HIPAA-compliant infrastructure with encryption, role-based access, audit logging, secure authentication, and Business Associate Agreements with applicable service providers.

Would AI make treatment decisions?

No.

One of the core principles behind FIRST/then is that AI supports clinicians, it never replaces them. Clinical judgment always belongs to the clinician or BCBA. FIRST/then is designed to organize information, identify patterns, and surface insights, not determine treatment.

How do we know the AI is correct?

AI should be viewed as a clinical assistant, not an authority. It identifies trends and documentation patterns that already exist within the data. Every recommendation or insight should be reviewed and interpreted by the clinician before influencing treatment decisions.

What evidence supports this concept?

FIRST/then is a proof of concept demonstrating a new workflow for clinical documentation. The vision is based on improving the use of information clinicians already collect every day. Any future implementation would require evaluation and validation to measure its impact on documentation quality, efficiency, and clinical decision making.

Would clinicians become dependent on AI?

The goal is the opposite.

FIRST/then is intended to reduce administrative burden so clinicians can spend more time applying their professional expertise. AI supports clinical work, but never replaces critical thinking or professional judgment.

Will this actually save staff time?

That is one of the primary goals.

Rather than documenting, reviewing, graphing, searching through notes, and identifying patterns separately, FIRST/then is designed to combine those processes into a more efficient workflow.

Will staff actually use it?

Successful adoption depends on whether the system genuinely makes clinicians' jobs easier. The vision behind FIRST/then emphasizes reducing duplicate work, minimizing clicks, simplifying documentation, and providing meaningful value beyond compliance.

Can FIRST/then improve staff training?

Potentially, yes.

The same AI technologies supporting documentation could also assist with onboarding, policy education, interactive learning, coaching materials, and knowledge sharing. AI can become a valuable training resource in addition to a clinical documentation tool.

Can it improve documentation quality?

Yes.

Rather than identifying documentation issues after submission, FIRST/then is designed to review notes before completion by identifying missing information, restricted terminology, documentation gaps, and inconsistencies while the clinician is still documenting.

Will this replace our current documentation system?

Not necessarily.

The long-term vision is for FIRST/then to complement existing workflows and maximize the value of documentation clinicians are already completing. Integration strategies would depend on the organization's existing technology infrastructure.

How much would something like this cost?

A financial model has not been developed at this stage because the current focus is validating the clinical vision and workflow. Development costs would depend on project scope, infrastructure requirements, integrations, and implementation strategy.

How difficult would implementation be?

Implementation would ideally occur in phases, allowing clinicians to gradually adopt new workflows without disrupting existing clinical services.

How long would development take?

The timeline would depend on available development resources, project scope, and organizational priorities. The current work focuses on defining the clinical vision and product strategy rather than estimating engineering timelines.

Who owns the data?

The organization.

FIRST/then should function as a clinical tool supporting the organization. Client information should always remain the property of the organization providing services.

Why build FIRST/then?

Every day clinicians collect an incredible amount of valuable clinical information.

After documentation is submitted, most of that information becomes static documentation rather than an active clinical resource.

FIRST/then explores how documentation can become something more by supporting clinicians with meaningful insights derived from information they are already collecting.

Why use AI?

Not because AI is exciting, but because AI is uniquely capable of identifying meaningful patterns across large amounts of structured clinical information.

The goal is to help clinicians recognize information more efficiently while preserving clinical judgment.

What makes FIRST/then different?

Most documentation systems focus on storing information.

FIRST/then focuses on helping clinicians use information.

Is FIRST/then replacing clinicians?

Absolutely not.

The vision is to reduce administrative work so clinicians can spend more time providing care, collaborating with families, and making informed clinical decisions.

Could AI make mistakes?

Yes.

Like any clinical decision support tool, AI can be incorrect or incomplete. Every insight should be reviewed by the clinician before influencing treatment decisions.

What happens if the AI misses something?

Existing clinical safeguards remain in place.

Clinicians continue reviewing documentation just as they do today. FIRST/then is intended to enhance visibility, not replace professional oversight.

Why build something new instead of purchasing existing software?

The idea for FIRST/then did not begin with software.

It began by identifying everyday challenges experienced during clinical work and asking how technology could better support clinicians. The software became the vehicle for solving those problems.

What is the long-term vision?

The goal is not simply to build software.

The goal is to reduce administrative burden, improve documentation quality, support clinical decision making, and ultimately help clinicians spend more time doing what matters most: improving outcomes for children and families.