In today’s world, many professionals are turning to advanced technology for help in a variety of fields. One option that has become increasingly popular is using AI models like ChatGPT to assist in tasks like reading and interpreting legal documents. You may consider using ChatGPT or another Large Language Model (LLM) to DIY your physician contract evaluation. While this may seem like a convenient and cost-effective alternative to hiring a legal professional, it is essential to understand that ChatGPT is an inadequate substitute for the expertise and insight provided by a qualified attorney specializing in physician contract negotiation.
ChatGPT can process language and generate contract explanations, but it does not practice law and lacks the context, experience, and strategy that physician contract negotiation requires. Legal guidance is not just about reading clauses. It’s about interpreting intent, anticipating consequences based on prior experience, and advocating effectively based on your goals, the market, and the deal structure.
1. Lack of Domain-Specific Context
ChatGPT was trained on a wide range of general data, not specialty-specific physician employment law or real-world physician negotiation experience. So while it may understand contract language in theory, it lacks context and experience on:
- Regional legal nuances (like state-specific compensation expectations)
- Market norms (e.g., typical wRVU rates, volume expectations by specialty, what is a normal call requirement)
- Practice setting differences (e.g., it may propose similar critiques for hospital-employed, academics, and pre-ownership private practice… but we know there are very important differences here)
- Strategy (e.g., what terms are realistically negotiable vs. outside of normal negotiation points, or how hard this particular physician can and should push)
- What’s Missing? (e.g., it might simply read what it’s prompted to read, and may not know what should be there but is missing. Call schedule absent from a hospital-employed ortho contract?)
ChatGPT lacks access to proprietary compensation databases (like MGMA) or real contracts unless users provide them. It has no idea what negotiation requests have worked in the past.
2. Weak Judgment or Leverage Analysis
Evaluating a contract means understanding power dynamics and leverage. For example:
- Is this a PE-owned group desperate to recruit?
- Does the physician have a competing offer?
- Is the specialty in high demand?
- Is the physician a standout candidate?
- Does the employer have multiple candidates?
- Does the physician have time to walk away?
Unless you spell out those facts, ChatGPT won’t know them. Most physicians negotiating their first contracts don’t have the background to prompt it effectively. Without these, it can’t tailor effective asks. Knowing what to ask it and how to prompt it correctly is a HUGE aspect of using it effectively, and you can’t really do this without knowing about physician contracts.
3. Risk-Adverse Default Responses
To avoid giving risky or aggressive advice, ChatGPT defaults to cautious, generic suggestions. This can make it sound like it’s hedging or parroting HR, which frustrates users seeking real strategic input. For example:
- It often suggests that noncompetes are bad and recommends negotiating them in every case. This is often not helpful because it is unable to judge how this particular noncompete may impact your personal exit strategy, and which negotiation points may actually help. It also has no idea what noncompete restrictions are normal for any specialty, practice setting, and geographic region.
- It asks that every malpractice insurance policy be occurrence-based, instead of understanding that claims-made plus perpetual tail coverage is essentially the same thing.
4. It Doesn’t “Practice” Negotiation
The practice of law and negotiation is more than just an algorithm. Similar to physicians, many nuanced issues require a certain level of emotional quotient (EQ) that LLMs are just not able to provide. Real-world negotiation involves timing, messaging, prioritization, and reading between the lines. LLMs fall short on the EQ piece necessary to effectively evaluate and negotiation contracts.
How you ask is often as important as what you ask for. ChatGPT can spit out a list of demands, but a lawyer helps you decide when to push, when to pause, and how to read employer reactions. Tone, timing, and sequencing matter. Some of our most impactful advice might be when to stop negotiating, but ChatGPT won’t help you with this.
ChatGPT doesn’t negotiate; you do. It can suggest ideas, but it doesn’t feel out the other side, adjust tone in real time, or understand institutional relationships. How you frame an issue in negotiation is vital to the ultimate outcome, but “ChatGPT” speak often lacks personal nuance and relatability.
5. Prompting Issues (Garbage-In, Garbage-Out)
Vague prompts like ‘help me negotiate my contract’ produce vague answers. Incorrect prompts likely won’t be corrected by the LLM, so you can lead it down the wrong path. If you don’t know exactly what to ask it and understand what issues should be important to you, then the responses will not be helpful. ChatGPT doesn’t know if you’re caring for aging parents, planning to buy a house in Year 3, or hoping to sub-specialize later, and it can’t adjust risk tradeoffs accordingly. ChatGPT often ignores exit planning. It might miss whether final pay is delayed until all collections clear, or if you lose a year’s worth of bonus eligibility just by leaving before a certain date.
6. Inability to Read the Entire Agreement or Spot Interplay Between Clauses
This is what ChatGPT told me about itself:
“ChatGPT can’t synthesize a 20+ page contract holistically. It lacks the ability to track how language in Section 5 undermines protections in Section 1.2 or cross-check whether compensation terms conflict with a buried appendix.”
Ouch! Physician contracts aren’t standalone clauses; they’re systems of interdependent terms. Contracts often include cross-references or “gotchas” that only become clear when clauses are read together. Responsible lawyers know that doing any “one issue” or “one section” evaluation can be a big problem! ChatGPT can’t track these interactions. Good lawyers spot these domino effects and adjust accordingly.
Example
Here is an example of the analysis provided by ChatGPT from a recent client before our meeting, and my discussion points about why those proposals were good or bad.
- The proposed base salary guarantee of $300,000 is significantly below market for a production-based compensation model; we recommend increasing it to the $350,000 range to align with competitive offers.
This was a pre-ownership track opportunity. In many of these, we expect below-market averages for the first couple of years, but ChatGPT does not know this! Also, most physician compensation models are more appropriately evaluated using volume-based metrics, i.e., how much compensation for how much volume. ChatGPT does not know about this and has no access to data or experience based on specialty, practice setting, and geographic region.
- The medical loan repayment amount of $30,000 is also low compared to typical Nevada levels. Consider requesting an increase to $60,000–$75,000.
Frankly, $30K for a pre-ownership track deal in this specialty was not bad based on our prior experience. Also, this physician was likely to make $1.5M+ during the first five years after training, so negotiating on this point often is not a top priority, particularly in this practice setting. I have no idea how ChatGPT determined that a particular amount of money was low for a particular specialty in a particular state. It’s possible that the prompt told it so, but how did the prompter know that?
- The inclusion of an arbitration clause is not appropriate. Consider having it removed and replaced with a more balanced litigation clause.
While this may have actually been good advice, ChatGPT doesn’t know that employers rarely negotiate arbitration clauses. We rarely ask for a change here, and our experience is that it’s typically a low-level ask. Physicians should often focus on more impactful terms instead.
- Regarding the loan forgiveness terms, the current structure makes the physician fully repay the loan if they leave before 48 months, even if they partially fulfill the term. We recommend capping repayment based on time served, such as forgiving 25% per completed year.
This was reasonable. Asking for a tiered sunset clause on the medical loan repayment was not bad advice. However, this maybe should have been adjusted to asking for a 2-year clawback because that was the expected timeline to an offer of ownership, and it might be a better strategy to make it as cheap as possible to walk away at the 2-year mark.
- The agreement makes the physician pay the full cost of tail malpractice insurance.. Consider having the employer cover all or a portion of the tail cost if the contract extends beyond Year 2.
In this specialty, it was quite common for independent physician-owned private practices to require the physician to pay for malpractice tail coverage. While not a bad request, this provided no context for how this request should rank against other considerations.
- Finally, because loan forgiveness is treated as taxable income, it’s reasonable to request either a tax planning stipend or a gross-up clause to mitigate that burden.
We rarely see gross-up clauses on one-off sweeteners. While not a bad idea here, it is not a normal market request.
Summary
ChatGPT can be useful when directed well, but it’s not a substitute when you’re considering a nuanced and personal legal analysis and negotiation strategy. I have used a couple of publicly available physician contracts as examples, and the responses were impersonal and unhelpful. The draft negotiation requests were super soft, more like ‘begging to be heard’ instead of directing the employer to specific proposed language to be inserted or deleted from contracts. However, as I used my knowledge and experience to improve the query, the answers became a bit more helpful. I know what to ask about and look for because I have worked on over a thousand physician contract negotiations… but even then, it was not able to produce accurate results.
I know ChatGPT is exciting. AI and LLM models are having a massive impact on how we work, learn, and live. In fact, I used ChatGPT to help improve this blog! However, if you don’t know how to prompt it safely and check its work correctly, using it becomes very risky!! You are evaluating a 7-figure deal that will have a massive impact on your career and life, and ChatGPT is not what you need right now!
An experienced lawyer learns about your specific goals, whether you’re optimizing for flexibility, income, ownership, or location. Your negotiation strategy might look entirely different depending on your career goals and life plans. The value of a thoughtful guide at this point in your career, a real-life person who learns about you and helps you with a personally tailored approach to your physician contract evaluation and negotiation, is indispensable.
Lagniappe
I asked ChatGPT to explain what aspects of this blog were most confusing. One of the proposals was to include an example of MGMA benchmarks for a particular specialty. MGMA usage limitations do not allow me to do this… listening to ChatGPT here could have been a big problem!
