---
title: AI Best Practices
description: Discover how strength training transformed not just physical capabilities, but also leadership skills, resilience, and trust in oneself and others.
---

[Blog ](https://www.mypwh.org/blog)

# [AI Best Practices](https://www.mypwh.org/blog/ai-best-practices)

 Written by [PWH Technology Committee](https://www.mypwh.org/blog/author/pwh-technology-committee) | 7/10/26 10:07 PM

## AI Best Practices for Professional Women in Healthcare

*By: PWH Technology Committee*

## Purpose

To provide practical, responsible, and empowering best practices for using Artificial Intelligence (AI) in healthcare and healthcare-adjacent roles. The goal is to help professional women leverage AI to work smarter, lead confidently, and stay ahead—without compromising ethics, privacy, or credibility.

## Guiding Principles

### 1. **AI Is an Assistant, Not an Authority**

AI accelerates thinking; it does not replace judgment. Final decisions—especially clinical, financial, or people-related—must always be human-led.

*Rule of thumb: If you wouldn’t blindly trust a junior analyst with it, don’t blindly trust AI.*

### 2. **Protect Patient, Employee, and Organizational Data**

Never input: - PHI or PII - Confidential contract or pricing data - Confidential Corporate Sponsor data – Confidential Member data - Internal-only strategic materials - Anything you wouldn’t be comfortable seeing on the front page of the Wall Street Journal. Use de-identified, hypothetical, or generalized data whenever possible.

### 3. **Use AI to Eliminate Low-Value Work**

High-impact use cases include: - Drafting emails, presentations, and talking points - Summarizing articles, policies, or long documents - Brainstorming ideas, frameworks, or meeting agendas - Translating complex information into executive-friendly language. AI should *buy back your time*, not create more work.

### 4. **Maintain Professional Voice and Credibility**

AI-generated content should always be: - Reviewed - Edited - Personalized. Ensure tone, accuracy, and intent reflect *you* and your organization. Authentic leadership cannot be automated.

### 5. **Beware of Bias (Including Polite-Sounding Bias)**

AI systems are trained on historical data—which may reflect: - Gender bias - Racial or socioeconomic bias - Traditional power structures in healthcare. Question outputs. Ask: - Who benefits from this recommendation? - Who might be missing from the picture? - Does this reinforce the status quo—or challenge it productively?

### 6. **Validate Before You Amplify**

Before sharing AI-generated insights: - Fact-check statistics - Confirm sources - Sense-check logic - Pressure-test assumptions AI is confident—even when wrong.

### 7. **Be Transparent, Not Apologetic**

You don’t need to hide AI use—but you also don’t need to over-disclose it. Good framing: - “I used AI to accelerate a first draft.” - “This helped me synthesize multiple perspectives quickly.” Using AI is not cutting corners; it’s modern leadership.

### 8. **Stay Within Organizational and Regulatory Guardrails**

Follow: - PWH Bylaws - Employer AI policies - HIPAA and regulatory requirements - Legal and compliance guidance. If policies don’t exist yet, default to conservative, ethical use—and advocate for clarity.

### 9. **Continuously Upskill**

AI literacy is becoming a leadership competency. Recommended habits: - Experiment with prompts weekly - Share effective use cases with peers - Stay informed on healthcare-specific AI regulations and risks. Confidence comes from competence.

## What AI Is *Not* Appropriate For

- Diagnosing patients
- Making autonomous clinical or organizational decisions
- Final hiring, firing, or disciplinary decisions
- Replacing human empathy, judgment, or accountability
- Consistency at scale
- Repeatable workflows
- Embedded institutional knowledge
- Guardrails for high-risk or high-visibility outputs
- Same type of deliverable
- Same structure
- Same inputs
- Same standards
- Quarterly Business Review slide drafting
- Contract review summaries
- RFP response frameworks
- Executive briefing templates
- Policy interpretation assistant
- Your org’s language
- Your methodology
- Your service lines
- Your pricing models
- Your compliance standards
- Regulatory language matters
- Legal/compliance positioning matters
- Financial assumptions must be standardized
- Brand voice must stay consistent
- Controlling tone
- Defining exclusions
- Setting clear boundaries
- Providing approved frameworks
- Use it weekly
- Produce standardized outputs
- Benefit from speed + alignment
- The use case is vague (“help with strategy”)
- The task changes every time
- You don’t have standardized source material
- You don’t know what “good output” looks like
- It’s a one-time project
- It requires constant manual oversight
- Required inputs
- Optional inputs
- Format standards
- Data assumptions
- Client name
- Spend category focus
- Savings performance YTD
- Engagement score
- Identified pipeline opportunities
- Length
- Tone
- Structure
- Formatting
- Required sections
- “Do not include” constraints
- Executive tone
- Bullet-based slides
- No emojis
- No speculative financial claims
- Clear call-to-action at end
- Best past examples
- Templates
- Approved decks
- Policy documents
- Messaging frameworks
- What it should NOT do
- What assumptions it cannot make
- Where it must flag uncertainty
- Where human review is required
- Incomplete data
- Conflicting data
- Edge-case scenarios
- Worst-case compliance scenarios
- Version updates
- Improvement logs
- Feedback loops
- Clear owner
- Onboarding accelerators
- Knowledge retention engines
- Methodology enforcers
- Institutional memory systems
- Change management tools
- Is the work repeatable?
- Is there a clear “gold standard” output?
- Will multiple people use it?
- Does it reduce risk or save meaningful time?
- Do we have high-quality source material?
- Standardization
- Risk reduction
- Speed with alignment
- Scaling expertise without scaling headcount

## When creating a Custom GPT is Beneficial 

A custom GPT should be built when you need:

- Consistency at scale
- Repeatable workflows
- Embedded institutional knowledge
- Guardrails for high-risk or high-visibility outputs

It should **not** be built just because “AI is cool” or because a team wants a novelty tool. If it doesn’t save time, reduce risk, or increase quality in a measurable way — don’t build it.

# Part 1: The Best Scenario to Create a Custom GPT

### ✅ Ideal Use Case Characteristics

Create a custom GPT when the work:

### 1. Is Repetitive and Pattern-Based

- Same type of deliverable
- Same structure
- Same inputs
- Same standards

**Examples: **

- Quarterly Business Review slide drafting
- Contract review summaries
- RFP response frameworks
- Executive briefing templates
- Policy interpretation assistant

If you’re rewriting the same instructions to ChatGPT over and over — that’s your signal.

### 2. Requires Institutional Context

If the GPT needs:

- Your org’s language
- Your methodology
- Your service lines
- Your pricing models
- Your compliance standards

That’s custom GPT territory. Generic AI doesn’t know your playbook. Custom GPT = embeds your playbook.

### 3. Needs Guardrails

Create a custom GPT when:

- Regulatory language matters
- Legal/compliance positioning matters
- Financial assumptions must be standardized
- Brand voice must stay consistent

A custom GPT reduces risk by:

- Controlling tone
- Defining exclusions
- Setting clear boundaries
- Providing approved frameworks

Think: “AI with bumpers.”

### 4. Must Be Used by Multiple People

If only one person needs it occasionally → not worth it. If 10+ team members will:

- Use it weekly
- Produce standardized outputs
- Benefit from speed + alignment

Now you’re building leverage.

### 5. Has Clear ROI

Ask this blunt question: Will this save at least 5+ hours per month per user? If not, skip it.

# Part 2: When NOT to Create a Custom GPT

Do NOT build one if:

- The use case is vague (“help with strategy”)
- The task changes every time
- You don’t have standardized source material
- You don’t know what “good output” looks like
- It’s a one-time project
- It requires constant manual oversight

If the process is chaotic, AI will only automate the chaos. Fix the process first.

# Part 3: How to Create a High-Performing Custom GPT

## Step 1: Define the Job

Be painfully specific.

Instead of: “Helps with QBR prep.”

Define: “Drafts a 10–15 slide executive-level QBR narrative aligned to Vizient’s spend management framework, using structured financial and engagement data inputs.”

Clarity = quality.

## Step 2: Identify Inputs

Document:

- Required inputs
- Optional inputs
- Format standards
- Data assumptions

Examples:

- Client name
- Spend category focus
- Savings performance YTD
- Engagement score
- Identified pipeline opportunities

Garbage in = garbage out.

## Step 3: Define Output Format

Never leave output ambiguous. Specify: 

- Length
- Tone
- Structure
- Formatting
- Required sections
- “Do not include” constraints

Examples:

- Executive tone
- Bullet-based slides
- No emojis
- No speculative financial claims
- Clear call-to-action at end

Custom GPTs perform best with tight constraints.

## Step 4: Load High-Quality Reference Material

This is where most people underinvest. Upload:

- Best past examples
- Templates
- Approved decks
- Policy documents
- Messaging frameworks

Bad source material = scaled mediocrity.

## Step 5: Build Guardrails

Explicitly state:

- What it should NOT do
- What assumptions it cannot make
- Where it must flag uncertainty
- Where human review is required

Example: If financial data is missing, prompt user instead of estimating.

Build skepticism into the GPT.

## Step 6: Test Like a Skeptic

Pressure test with:

- Incomplete data
- Conflicting data
- Edge-case scenarios
- Worst-case compliance scenarios

If it survives that, it’s production-ready.

## Step 7: Version Control

Treat your GPT like a product:

- Version updates
- Improvement logs
- Feedback loops
- Clear owner

If no one owns it, it decays.

# Part 4: Advanced Strategy — Think Bigger

Here’s where it gets interesting. Custom GPTs are not just productivity tools. They can become:

- Onboarding accelerators
- Knowledge retention engines
- Methodology enforcers
- Institutional memory systems
- Change management tools

Instead of asking: “What task should AI help with?”

Ask: “What capability do we need to scale without hiring 5 more people?”

That’s executive-level thinking.

# Final Litmus Test

Before building a custom GPT, ask:

- Is the work repeatable?
- Is there a clear “gold standard” output?
- Will multiple people use it?
- Does it reduce risk or save meaningful time?
- Do we have high-quality source material?

If you can’t confidently answer yes to at least four — pause.

# Bottom Line

Custom GPTs are not about automation.

They’re about:

- Standardization
- Risk reduction
- Speed with alignment
- Scaling expertise without scaling headcount

Build them intentionally. Govern them seriously. Measure their impact. And if it doesn’t move the needle — don’t build it.

## Final Thought

AI won’t replace women in healthcare—but women who understand AI will replace those who don’t.

Use it wisely. Use it boldly. And always stay human.

Use the [Best Practices Checklist](https://www.mypwh.org/hubfs/AI%20Best%20Practices%20Checklist.pdf) to put this guidance into action. 

*Prepared for: Professional Women in Healthcare*

 

  

 

[View full post](https://www.mypwh.org/blog/ai-best-practices)

```json
{
  "@context" : "http://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "PWH Technology Committee"
  },
  "dateModified" : "2026-07-10T22:07:53.864Z",
  "datePublished" : "2026-07-10T22:07:53Z",
  "headline" : "AI Best Practices",
  "image" : {
    "@type" : "ImageObject",
    "height" : 628,
    "url" : "https://14565178.fs1.hubspotusercontent-na1.net/hubfs/14565178/June26-AI%20Best%20Practices%20Blog-100%25opacity%281200x628%29.png",
    "width" : 1200
  },
  "mainEntityOfPage" : "https://www.mypwh.org/blog/ai-best-practices",
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "height" : 60,
      "url" : "/hs/hsstatic/content_shared_assets/static-1.4092/img/default-amp-logo.png",
      "width" : 60
    },
    "name" : "Blog"
  }
}
```