Fractional AI Product Manager · Nashville, TN

Hire an AI product manager in Nashville.

SVPs of Product and VPs of Digital Health at Nashville healthcare IT companies face a spec problem. The AI feature sounds right in a demo. Clinical informaticists at the health system want to evaluate it. Engineering needs a definition of done. None of those groups are reading the same document, because the document does not exist at the required level of specificity.

HealthStream, Parallon, Envision Healthcare, and the vendors selling AI tools into HCA Healthcare and Vanderbilt Health need product specs that satisfy engineering, clinical review, and revenue cycle operations simultaneously. That is a specific skill. A fractional AI PM writes that spec.

Fixed-scope engagement, priced to the work. Scope is agreed before work starts.

How we work

Tell us about your healthcare IT AI product challenge.

Who this is for.

This engagement fits two kinds of Nashville healthcare IT teams. The first is an internal product team at a health system vendor (HealthStream, Parallon, or a smaller vendor in the HCA ecosystem) building AI features for clinical or revenue cycle users. The team has engineering capacity. What it is missing is a PM who understands how AI features are evaluated by clinical informaticists and revenue cycle directors.

The second is a Series A or Series B healthcare AI company preparing for a pilot with a Nashville health system. The sales team has a term sheet. The pilot requires a spec. The founders have not written a spec at the level of specificity the health system procurement team will ask for.

In both cases, the blocker is the same: the product spec does not yet define the AI task at the level required for clinical review. A spec that says 'the feature helps with coding' will not pass clinical informaticist review. A spec that says 'the feature suggests a ranked list of ICD-10-CM codes for inpatient diagnoses in the neurology category, with the confidence score displayed when above 0.75, and with full override capability logged to the audit trail' will.

This engagement produces that spec. It also defines the success metric, establishes the baseline from your claims data, and prepares the clinical review package.

Revenue cycle AI: what the spec must define.

Revenue cycle AI features fail clinical evaluation when the spec does not address four specific questions. Here is what each one requires.

Exact task scope

Name the specific coding task or denial type. 'AI-assisted coding' is not a task scope. 'ICD-10-CM code suggestion for outpatient evaluation and management visits' is. The narrower the scope, the clearer the evaluation criteria.

Success metric with a measured baseline

The spec states the primary metric, the current baseline from your claims data, and the target. Without a baseline pulled from actual claims, the target number is a guess.

Confidence threshold for display

The spec defines at what confidence score an AI suggestion is shown to the coder or clinician. Showing every suggestion regardless of confidence creates noise. The threshold is a product decision, not an engineering default.

Override and audit trail

Every AI output must be overridable. Every override must be logged: user ID, timestamp, original AI output, and the override value. Revenue cycle audits require this. It is not optional.

What the engagement produces.

Three deliverables. Each one is a document your engineering team, clinical review team, and health system buyer can work from.

Revenue cycle AI product spec

Task scope, success metric, baseline measurement, confidence threshold, override mechanism, audit trail specification, and failure mode handling. Written at a level of specificity that passes clinical informaticist review.

Clinical review package

Sample AI outputs with clinical plausibility notes, terminology alignment check against current ICD/CPT codes, and a structured review template the informaticist team fills out. Ready to hand to the health system evaluation team.

Baseline measurement plan

The data pull specification for establishing the denial rate or coding accuracy baseline from your claims data. Defines the 90-day lookback window, the payer and claim type segmentation, and the measurement methodology.

How we work.

1
Discovery call (1 hour)
We map the AI feature: what it is supposed to do, who the clinical users are, which payers the revenue cycle feature touches, and what the current evaluation blockers are. Scope and pricing are agreed at the end of this call.
2
Spec and baseline sprint (weeks 1–4)
We interview revenue cycle operations, engineering, and the clinical informatics contact. We write the spec and the baseline measurement plan. We send a draft for review.
3
Clinical review prep (weeks 5–8)
We produce the clinical review package, run the terminology alignment check, and prepare the structured review template. The package is ready to hand to the health system evaluation team before the engagement closes.

Questions from Nashville healthcare IT teams.

What does a revenue cycle AI product spec include that a regular feature spec does not?: A revenue cycle AI spec has four sections a standard feature spec omits. First, task scope definition: the spec names the exact coding task (ICD-10-CM coding for a defined set of diagnosis categories, for example) or the specific denial type (medical necessity denials for inpatient stays). Vague scope like 'help with coding' is not a spec. Second, success metric with baseline: the spec defines the primary metric (denial reduction rate, coding accuracy on a labeled hold-out set), states the current baseline measured from your actual claims data, and sets the target. Third, informaticist review criteria: what a clinical informaticist will evaluate the feature against before clinical go-live, including clinical plausibility checks and terminology alignment with ICD or CPT coding conventions. Fourth, override and audit trail: every AI output must be overridable, and every override must be logged with the user ID, timestamp, original AI output, and the overriding value. Revenue cycle audits go back years.
How do you define success metrics for a denial reduction AI feature, what data establishes a baseline?: The baseline for a denial reduction feature comes from your existing claims data, specifically the denial rate for the claim types the AI feature will touch. You need 90 days of claims data minimum, segmented by payer, denial reason code, and claim type. The AI PM works with your revenue cycle operations team to pull this data before the spec is written. Without a baseline, you cannot define a success threshold. A target of 'reduce denials' is not measurable. A target of 'reduce medical necessity denials for commercial payers from 7.2% to 5.0% within 90 days of go-live' is measurable and gives engineering a definition of done. The spec also defines the measurement window and the payer mix the measurement covers, because mixing Medicare and commercial payer denial rates in one number masks the feature's actual impact.
What does the clinical informaticist review process look like for a new AI product spec?: At health systems like HCA Healthcare or Vanderbilt Health (or vendors selling into these systems), the clinical informaticist review is a structured checkpoint, not an informal sign-off. The AI PM prepares a clinical review package: the task scope definition, the AI output format with sample outputs, the override mechanism, the confidence threshold for display, and any ICD or CPT terminology the model uses. The informaticist review evaluates four things: clinical plausibility of sample outputs, terminology alignment (is the model using current ICD-10-CM codes, not deprecated ones), workflow fit (where in the clinician workflow does the AI output appear, and does that placement match how the decision is actually made), and documentation burden (does the AI feature add steps to the documentation process or reduce them). The review package is a written document. A demo is not a substitute for a written package during clinical review.
How do you write AI product specs that work for both the engineering team and health system buyers?: Engineering and health system buyers read different parts of the same spec. The spec has a business requirements layer and a technical requirements layer, and they must be internally consistent. The business layer states what problem the feature solves, what the success metric is, and what the clinical workflow looks like before and after. Health system buyers evaluate this layer in procurement. The technical layer states API contracts, data inputs, confidence threshold for display, latency requirement, and the audit log specification. Engineering builds from this layer. The AI PM writes both layers and keeps them consistent. The most common failure mode is a business requirements section that promises an outcome the technical specification does not actually produce. For example, a business requirement that says 'surfaces the most likely diagnosis code' paired with a technical spec that does not define what 'most likely' means in terms of model output. Health system procurement teams catch this inconsistency, and it stalls deals.

Describe your healthcare AI product challenge.

Tell us which AI feature you are building and where it is stuck in the clinical or revenue cycle evaluation process. We reply within one business day.

Hire an AI product manager in Nashville.

Fixed-scope engagement, priced to the work. Scope is agreed before work starts.

Who this is for.

This engagement produces that spec. It also defines the success metric, establishes the baseline from your claims data, and prepares the clinical review package.

Revenue cycle AI: what the spec must define.

Revenue cycle AI features fail clinical evaluation when the spec does not address four specific questions. Here is what each one requires.

Exact task scope

Success metric with a measured baseline

The spec states the primary metric, the current baseline from your claims data, and the target. Without a baseline pulled from actual claims, the target number is a guess.

Confidence threshold for display

Override and audit trail

Every AI output must be overridable. Every override must be logged: user ID, timestamp, original AI output, and the override value. Revenue cycle audits require this. It is not optional.

What the engagement produces.

Three deliverables. Each one is a document your engineering team, clinical review team, and health system buyer can work from.

Revenue cycle AI product spec

Clinical review package

Baseline measurement plan

How we work.

Discovery call (1 hour)

We map the AI feature: what it is supposed to do, who the clinical users are, which payers the revenue cycle feature touches, and what the current evaluation blockers are. Scope and pricing are agreed at the end of this call.

Spec and baseline sprint (weeks 1–4)

We interview revenue cycle operations, engineering, and the clinical informatics contact. We write the spec and the baseline measurement plan. We send a draft for review.

Clinical review prep (weeks 5–8)

We produce the clinical review package, run the terminology alignment check, and prepare the structured review template. The package is ready to hand to the health system evaluation team before the engagement closes.

Questions from Nashville healthcare IT teams.

What does a revenue cycle AI product spec include that a regular feature spec does not?

A revenue cycle AI spec has four sections a standard feature spec omits. First, task scope definition: the spec names the exact coding task (ICD-10-CM coding for a defined set of diagnosis categories, for example) or the specific denial type (medical necessity denials for inpatient stays). Vague scope like 'help with coding' is not a spec. Second, success metric with baseline: the spec defines the primary metric (denial reduction rate, coding accuracy on a labeled hold-out set), states the current baseline measured from your actual claims data, and sets the target. Third, informaticist review criteria: what a clinical informaticist will evaluate the feature against before clinical go-live, including clinical plausibility checks and terminology alignment with ICD or CPT coding conventions. Fourth, override and audit trail: every AI output must be overridable, and every override must be logged with the user ID, timestamp, original AI output, and the overriding value. Revenue cycle audits go back years.

How do you define success metrics for a denial reduction AI feature, what data establishes a baseline?

The baseline for a denial reduction feature comes from your existing claims data, specifically the denial rate for the claim types the AI feature will touch. You need 90 days of claims data minimum, segmented by payer, denial reason code, and claim type. The AI PM works with your revenue cycle operations team to pull this data before the spec is written. Without a baseline, you cannot define a success threshold. A target of 'reduce denials' is not measurable. A target of 'reduce medical necessity denials for commercial payers from 7.2% to 5.0% within 90 days of go-live' is measurable and gives engineering a definition of done. The spec also defines the measurement window and the payer mix the measurement covers, because mixing Medicare and commercial payer denial rates in one number masks the feature's actual impact.

What does the clinical informaticist review process look like for a new AI product spec?

At health systems like HCA Healthcare or Vanderbilt Health (or vendors selling into these systems), the clinical informaticist review is a structured checkpoint, not an informal sign-off. The AI PM prepares a clinical review package: the task scope definition, the AI output format with sample outputs, the override mechanism, the confidence threshold for display, and any ICD or CPT terminology the model uses. The informaticist review evaluates four things: clinical plausibility of sample outputs, terminology alignment (is the model using current ICD-10-CM codes, not deprecated ones), workflow fit (where in the clinician workflow does the AI output appear, and does that placement match how the decision is actually made), and documentation burden (does the AI feature add steps to the documentation process or reduce them). The review package is a written document. A demo is not a substitute for a written package during clinical review.

How do you write AI product specs that work for both the engineering team and health system buyers?

Engineering and health system buyers read different parts of the same spec. The spec has a business requirements layer and a technical requirements layer, and they must be internally consistent. The business layer states what problem the feature solves, what the success metric is, and what the clinical workflow looks like before and after. Health system buyers evaluate this layer in procurement. The technical layer states API contracts, data inputs, confidence threshold for display, latency requirement, and the audit log specification. Engineering builds from this layer. The AI PM writes both layers and keeps them consistent. The most common failure mode is a business requirements section that promises an outcome the technical specification does not actually produce. For example, a business requirement that says 'surfaces the most likely diagnosis code' paired with a technical spec that does not define what 'most likely' means in terms of model output. Health system procurement teams catch this inconsistency, and it stalls deals.

Describe your healthcare AI product challenge.

Tell us which AI feature you are building and where it is stuck in the clinical or revenue cycle evaluation process. We reply within one business day.