🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 199 (from laksa098)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa199.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.medicaleconomics.com/view/ai-and-employment-law-trends-predictions-and-compliance\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.medicaleconomics.com/view/ai-and-employment-law-trends-predictions-and-compliance\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.medicaleconomics.com\/view\/ai-and-employment-law-trends-predictions-and-compliance","crawl_time":1776349860,"first_indexed_time":1773069947,"http_code":200,"src_unparsed":"com,medicaleconomics!www,\/view\/ai-and-employment-law-trends-predictions-and-compliance s443","src_root_hash":"12219667406561261999","history_drop_reason":null,"meta_title":"AI and employment law: Trends, predictions and compliance | Medical Economics","meta_descriptions":["An attorney specializing in employment law discusses artificial intelligence and noncompetes."],"attrs_boilerpipe_text":"An attorney specializing in employment law discusses artificial intelligence and noncompetes.\nAs health care organizations adopt\nartificial intelligence\n(AI) tools in human resources, employers must ensure that\nreductions in force\n— even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm\nFrier Levitt\n, explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk.\nMedical Economics:\nThis is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict?\nChristopher S. Mayer, J.D.:\nWell, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it.\nBut from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability.\nNewsletter\nStay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.","attrs_markdown":"[![](https:\/\/www.medicaleconomics.com\/logo.webp)](https:\/\/www.medicaleconomics.com\/)\n\nNews\n\n[All News](https:\/\/www.medicaleconomics.com\/news)[Careers](https:\/\/www.medicaleconomics.com\/topics\/careers)[Legal](https:\/\/www.medicaleconomics.com\/topics\/legal)[Money](https:\/\/www.medicaleconomics.com\/topics\/money)[Opinion](https:\/\/www.medicaleconomics.com\/topics\/opinion)[Physicians Financial News](https:\/\/www.medicaleconomics.com\/topics\/physicians-financial-news)[Practice Management](https:\/\/www.medicaleconomics.com\/topics\/practice-management)[Technology](https:\/\/www.medicaleconomics.com\/topics\/technology)\n\nMedia\n\n[Around the Practice](https:\/\/www.medicaleconomics.com\/around-the-practice)[Between The Lines](https:\/\/www.medicaleconomics.com\/between-the-lines)[Clinical Consult](https:\/\/www.medicaleconomics.com\/clinical-consult)[Expert Interviews](https:\/\/www.medicaleconomics.com\/expert-interviews)[Medical Economics Pulse](https:\/\/www.medicaleconomics.com\/medical-economics-pulse)[Medical World News](https:\/\/www.medicalworldnews.com\/)[Off The Chart Podcast](https:\/\/www.medicaleconomics.com\/podcasts)[Physician Report](https:\/\/www.medicaleconomics.com\/physician-report)[PrimaryView](https:\/\/www.medicaleconomics.com\/primaryview)[Shorts](https:\/\/www.medicaleconomics.com\/shorts)[Slideshows](https:\/\/www.medicaleconomics.com\/slideshows)\n\n[Academy](https:\/\/www.medicaleconomics.com\/practice-academy)\n\nConferences\n\n[Conference Coverage](https:\/\/www.medicaleconomics.com\/latest-conference)[Conference Listing](https:\/\/www.medicaleconomics.com\/conference)\n\nMore\n\nPublications\n\n[Medical Economics](https:\/\/www.medicaleconomics.com\/journals\/medical-economics)[Medical Economics Insider](https:\/\/www.medicaleconomics.com\/journals\/medical-economics-insider)[Patient Care Supplement](https:\/\/www.medicaleconomics.com\/journals\/patient-care-supplement)[Sponsored Content](https:\/\/www.medicaleconomics.com\/sponsored-content)[Sponsored Resources](https:\/\/www.medicaleconomics.com\/sponsored-resources)[Supplements And Featured Publications](https:\/\/www.medicaleconomics.com\/journals\/supplements-and-featured-publications)\n\n[CME\/CE](https:\/\/www.medicaleconomics.com\/continuing-medical-education)\n\n[Job Board](http:\/\/jobs.modernmedicine.com\/index.php?action=advanced_search&page=search&keywords=&mult_industries%5B%5D=Family+Medicine&country=&zip_radius=&zip=&position_type=&min_salary=&max_salary=&salary_type=)\n\nDirectories\n\n[Financial Advisor Guide](https:\/\/www.medecadvisory.com\/)\n\n[Subscribe](https:\/\/www.medicaleconomics.com\/subscribe)\n\nMy Account\n\n- News\n- Media\n- [Academy](https:\/\/www.medicaleconomics.com\/practice-academy)\n- Conferences\n- Publications\n- [CME\/CE](https:\/\/www.medicaleconomics.com\/continuing-medical-education)\n- [Job Board](http:\/\/jobs.modernmedicine.com\/index.php?action=advanced_search&page=search&keywords=&mult_industries%5B%5D=Family+Medicine&country=&zip_radius=&zip=&position_type=&min_salary=&max_salary=&salary_type=)\n- [Subscribe](https:\/\/www.medicaleconomics.com\/subscribe)\n- Directories\n\n- [Careers](https:\/\/www.medicaleconomics.com\/topics\/careers)\n  - [Employment Trends](https:\/\/www.medicaleconomics.com\/topics\/employment-trends)\n  - [Advanced Practice Providers](https:\/\/www.medicaleconomics.com\/topics\/advanced-practice-providers)\n  - [Locum Tenens, brought to you by LocumLife®](https:\/\/www.medicaleconomics.com\/topics\/locum-tenens)\n  - [Learning and Development](https:\/\/www.medicaleconomics.com\/topics\/learning-and-development)\n  - [Concierge Medicine 2.0 by Castle Connolly Private Health Partners](https:\/\/www.medicaleconomics.com\/topics\/concierge-medicine-2)\n  - [Burnout and Wellbeing](https:\/\/www.medicaleconomics.com\/topics\/burnout)\n  - [Concierge Medicine](https:\/\/www.medicaleconomics.com\/topics\/concierge-medicine)\n  - [Maintenance of Certification](https:\/\/www.medicaleconomics.com\/topics\/maintenance-of-certification)\n- [Legal](https:\/\/www.medicaleconomics.com\/topics\/legal)\n  - [Insurance](https:\/\/www.medicaleconomics.com\/topics\/insurance)\n  - [Risk Management](https:\/\/www.medicaleconomics.com\/topics\/risk-management)\n  - [Contract Negotiation](https:\/\/www.medicaleconomics.com\/topics\/contract-negotiation)\n  - [Malpractice](https:\/\/www.medicaleconomics.com\/topics\/malpractice)\n- [Medical Device & Technology](https:\/\/www.medicaleconomics.com\/topics\/med-tech)\n  - [Mergers and Acquisitions](https:\/\/www.medicaleconomics.com\/topics\/ma)\n  - [Emerging Technology](https:\/\/www.medicaleconomics.com\/topics\/emerging-tech)\n  - [Earnings and Investment](https:\/\/www.medicaleconomics.com\/topics\/earnings)\n  - [Point of Care](https:\/\/www.medicaleconomics.com\/topics\/point-of-care)\n  - [Leaders and Innovators](https:\/\/www.medicaleconomics.com\/topics\/innovators)\n  - [Device Pipeline and Approvals](https:\/\/www.medicaleconomics.com\/topics\/device-approvals)\n  - [Device Regulation and Policy](https:\/\/www.medicaleconomics.com\/topics\/device-policy)\n  - [Medtech Industry Trends](https:\/\/www.medicaleconomics.com\/topics\/medtech-industry)\n  - [Data and Analytics](https:\/\/www.medicaleconomics.com\/topics\/data)\n  - [Tech Transfer](https:\/\/www.medicaleconomics.com\/topics\/tech-transfer)\n- [Opinion](https:\/\/www.medicaleconomics.com\/topics\/opinion)\n- [Physician's Money Digest](https:\/\/www.medicaleconomics.com\/topics\/physicians-money-digest)\n  - [Investing](https:\/\/www.medicaleconomics.com\/topics\/physicians-financial-news)\n  - [Retirement Planning](https:\/\/www.medicaleconomics.com\/topics\/retirement-planning)\n  - [Estate Planning](https:\/\/www.medicaleconomics.com\/topics\/estate-planning)\n- [Policy](https:\/\/www.medicaleconomics.com\/topics\/policy)\n  - [Politics](https:\/\/www.medicaleconomics.com\/topics\/politics)\n  - [Industry Trends](https:\/\/www.medicaleconomics.com\/topics\/industry-trends)\n  - [Medicare & Medicaid](https:\/\/www.medicaleconomics.com\/topics\/medicare-and-medicaid)\n  - [Pharma Industry](https:\/\/www.medicaleconomics.com\/topics\/pharma)\n  - [Physician Advocacy](https:\/\/www.medicaleconomics.com\/topics\/physician-advocacy)\n- [Practice Finance](https:\/\/www.medicaleconomics.com\/topics\/money)\n  - [Revenue Cycle Management](https:\/\/www.medicaleconomics.com\/topics\/revenue-cycle-management)\n  - [Reimbursement](https:\/\/www.medicaleconomics.com\/topics\/reimbursement)\n  - [Practice Valuation](https:\/\/www.medicaleconomics.com\/topics\/practice-valuation)\n  - [Billing and Collections](https:\/\/www.medicaleconomics.com\/topics\/billing-and-collections)\n  - [Payment Models](https:\/\/www.medicaleconomics.com\/topics\/value-based-care)\n- [Practice Management](https:\/\/www.medicaleconomics.com\/topics\/practice-management)\n  - [Staffing](https:\/\/www.medicaleconomics.com\/topics\/staffing)\n  - [Coding and Documentation](https:\/\/www.medicaleconomics.com\/topics\/coding-and-documentation)\n  - [Leadership](https:\/\/www.medicaleconomics.com\/topics\/leadership)\n  - [Marketing](https:\/\/www.medicaleconomics.com\/topics\/marketing)\n  - [Chronic Condition Management](https:\/\/www.medicaleconomics.com\/topics\/chronic-conditions)\n  - [Vaccines](https:\/\/www.medicaleconomics.com\/topics\/vaccines)\n  - [Patient Relations](https:\/\/www.medicaleconomics.com\/topics\/patient-relations)\n- [Practice Technology](https:\/\/www.medicaleconomics.com\/topics\/technology)\n  - [AI](https:\/\/www.medicaleconomics.com\/topics\/ai)\n  - [Telehealth](https:\/\/www.medicaleconomics.com\/topics\/telehealth)\n  - [Cybersecurity](https:\/\/www.medicaleconomics.com\/topics\/cybersecurity)\n  - [EHRs](https:\/\/www.medicaleconomics.com\/topics\/ehr)\n  - [Remote Patient Monitoring](https:\/\/www.medicaleconomics.com\/topics\/rpm)\n  - [Medical Technology](https:\/\/www.medicaleconomics.com\/topics\/med-technology)\n- [Screening](https:\/\/www.medicaleconomics.com\/topics\/screening)\n- [Special Reports](https:\/\/www.medicaleconomics.com\/topics\/special-reports)\n  - [100 Years of Medical Economics](https:\/\/www.medicaleconomics.com\/topics\/100-years)\n  - [Exclusive Content](https:\/\/www.medicaleconomics.com\/topics\/exclusive-content)\n\nChoose Topic\n\nSpotlight -\n\n[Concierge Medicine Forum](https:\/\/www.medicaleconomics.com\/conference\/concierge-medicine-forum)\\|\n\n[MGMA 2025](https:\/\/www.medicaleconomics.com\/conference\/mgma)\\|\n\n[Malpractice](https:\/\/www.medicaleconomics.com\/topics\/malpractice)\n\nAdvertisement\n\nNews\\|Videos\\|March 9, 2026\n\n# AI and employment law: Trends, predictions and compliance\nAuthor(s)[*Richard Payerchin*](https:\/\/www.medicaleconomics.com\/authors\/richard-payerchin)\n\nFact checked by: [*Keith A. Reynolds*](https:\/\/www.medicaleconomics.com\/authors\/keith-a-reynolds)\n\nAn attorney specializing in employment law discusses artificial intelligence and noncompetes.\n\nAdvertisement\n\nVideo Player is loading.\n\nPlay Video\n\nPause\n\nUnmute\n\nCurrent Time 0:02\n\n\/\n\nDuration 4:42\n\nLoaded: 14\\.01%\n\n0:02\n\nStream Type LIVE\n\nSeek to live, currently behind liveLIVE\n\nRemaining Time \\-4:40\n\n1x\n\nPlayback Rate\n\n- 2x\n- 1\\.75x\n- 1\\.5x\n- 1\\.25x\n- 1x, selected\n- 0\\.75x\n- 0\\.5x\n\nChapters\n\n- Chapters\n\nDescriptions\n\n- descriptions off, selected\n\nCaptions\n\n- captions settings, opens captions settings dialog\n- captions off, selected\n\nAudio Track\n\n- en (Main), selected\n\nFullscreen\n\nThis is a modal window.\n\nBeginning of dialog window. Escape will cancel and close the window.\n\nText\n\nColor\n\nTransparency\n\nBackground\n\nColor\n\nTransparency\n\nWindow\n\nColor\n\nTransparency\n\nFont Size\n\nText Edge Style\n\nFont Family\n\nReset restore all settings to the default values\n\nDone\n\nClose Modal Dialog\n\nEnd of dialog window.\n\nClose Modal Dialog\n\nThis is a modal window. This modal can be closed by pressing the Escape key or activating the close button.\n\nAs health care organizations adopt [artificial intelligence](https:\/\/www.medicaleconomics.com\/topics\/ai) (AI) tools in human resources, employers must ensure that [reductions in force](https:\/\/www.medicaleconomics.com\/topics\/employment-trends) — even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm [Frier Levitt](https:\/\/www.frierlevitt.com\/), explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk.\n\n**Medical Economics:** This is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict?\n\n**Christopher S. Mayer, J.D.:** Well, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it.\n\nBut from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability.\n\n### Newsletter\nStay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.\n\n[Subscribe Now\\!](https:\/\/www.medicaleconomics.com\/subscribe)\n\nAdvertisement\n\n[Employment Trends](https:\/\/www.medicaleconomics.com\/topics\/employment-trends)\n\n\\|\n\n[Careers](https:\/\/www.medicaleconomics.com\/topics\/careers)\n\n\\|\n\n[Staffing](https:\/\/www.medicaleconomics.com\/topics\/staffing)\n\n\\|\n\n[Practice Management](https:\/\/www.medicaleconomics.com\/topics\/practice-management)\n\n\\|\n\n[Contract Negotiation](https:\/\/www.medicaleconomics.com\/topics\/contract-negotiation)\n\n\\|\n\n[AI](https:\/\/www.medicaleconomics.com\/topics\/ai)\n***\n## Related Content\nAdvertisement\n\n[![Off the Chart: A Business of Medicine Podcast - Ep. 142: The legal risks of AI in your practice, with Dan Silverboard, J.D., of Holland & Knight](https:\/\/cdn.sanity.io\/images\/0vv8moc6\/medec\/85d0eed8e593da89df6763e89fd0b2d8731dfc4f-1200x628.jpg?w=350&fit=crop&auto=format)](https:\/\/www.medicaleconomics.com\/view\/the-legal-risks-of-ai-in-your-practice-with-dan-silverboard-j-d-of-holland-knight)\n\n[The legal risks of AI in your practice, with Dan Silverboard, J.D., of Holland & Knight](https:\/\/www.medicaleconomics.com\/view\/the-legal-risks-of-ai-in-your-practice-with-dan-silverboard-j-d-of-holland-knight)\n\nBy[Austin Littrell](https:\/\/www.medicaleconomics.com\/authors\/austin-littrell)\n\nApril 16th 2026\n\n[![Morning Medical Update © batuhan toker - stock.adobe.com](https:\/\/cdn.sanity.io\/images\/0vv8moc6\/medec\/d5350725ac443d4aa0c7af9e4f7f39f533ea1192-6044x4862.jpg?w=350&fit=crop&auto=format)](https:\/\/www.medicaleconomics.com\/view\/team-based-care-model-cuts-blood-pressure-in-high-risk-populations-fda-to-convene-expert-panel-on-wider-access-to-peptides-florida-nursing-assistant-sentenced-in-11-4m-medicare-brace-fraud-scheme-morning-medical-update)\n\n[Team-based care model cuts blood pressure in high-risk populations; FDA to convene expert panel on wider access to peptides; Florida nursing assistant sentenced in \\$11.4M Medicare brace fraud scheme – Morning Medical Update](https:\/\/www.medicaleconomics.com\/view\/team-based-care-model-cuts-blood-pressure-in-high-risk-populations-fda-to-convene-expert-panel-on-wider-access-to-peptides-florida-nursing-assistant-sentenced-in-11-4m-medicare-brace-fraud-scheme-morning-medical-update)\n\nBy[Austin Littrell](https:\/\/www.medicaleconomics.com\/authors\/austin-littrell)\n\nApril 16th 2026\n\n[![MIPS in 2026: A conversation with MIPS expert Holly Black](https:\/\/cdn.sanity.io\/images\/0vv8moc6\/medec\/ad4d05b9434f01cad44c2f5c4385cad9272a5832-1200x628.jpg?w=350&fit=crop&auto=format)](https:\/\/www.medicaleconomics.com\/view\/mips-in-2026-a-conversation-with-mips-expert-holly-black)\n\n[MIPS in 2026: A conversation with MIPS expert Holly Black](https:\/\/www.medicaleconomics.com\/view\/mips-in-2026-a-conversation-with-mips-expert-holly-black)\n\nBy[Austin Littrell](https:\/\/www.medicaleconomics.com\/authors\/austin-littrell)\n\nApril 15th 2026\n\n[![Bone density device gets more funding: ©Osteoboost](https:\/\/cdn.sanity.io\/images\/0vv8moc6\/medec\/0e34efddc3687b87aa4640ac126a0cc8f25e10bf-1417x1214.jpg?w=350&fit=crop&auto=format)](https:\/\/www.medicaleconomics.com\/view\/osteoboost-raises-8m-to-expand-reach-of-bone-density-device)\n\n[Osteoboost raises \\$8M to expand reach of bone density device](https:\/\/www.medicaleconomics.com\/view\/osteoboost-raises-8m-to-expand-reach-of-bone-density-device)\n\nBy[Todd Shryock](https:\/\/www.medicaleconomics.com\/authors\/todd-shryock)\n\nApril 15th 2026\n\n[![Melissa Starowitz: ©Melissa Starowitz](https:\/\/cdn.sanity.io\/images\/0vv8moc6\/medec\/67454cd45d2685cfa81326c52dcd6f6edafee41f-1200x628.png?w=350&fit=crop&auto=format)](https:\/\/www.medicaleconomics.com\/view\/the-future-of-independent-practice-redefining-the-landscape-of-medicine)\n\n[The future of independent practice: Redefining the landscape of medicine](https:\/\/www.medicaleconomics.com\/view\/the-future-of-independent-practice-redefining-the-landscape-of-medicine)\n\nBy[Melissa Starowitz](https:\/\/www.medicaleconomics.com\/authors\/melissa-starowitz)\n\nApril 15th 2026\n\n## Latest CME\n[![From Numbers to Navigation: Translating Data From Diabetes Technologies into Optimized Decision-Making](https:\/\/media.thoughtindustries.com\/thoughtindustries\/image\/upload\/v1\/course-uploads\/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0\/ne4kdmhl7q8h-ACPCGMTICatalog750x361.png) In-Person + Virtual EventFrom Numbers to Navigation: Translating Data From Diabetes Technologies into Optimized Decision-Making April 16, 2026](https:\/\/www.gotoper.com\/courses\/from-numbers-to-navigation-translating-cgm-data-into-optimized-decision-making?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\n[View more](https:\/\/www.gotoper.com\/courses\/from-numbers-to-navigation-translating-cgm-data-into-optimized-decision-making?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\n[![Cases and Conversations™: A Horizon View of Continuous Monitoring Systems for Diabetes Management](https:\/\/d36ai2hkxl16us.cloudfront.net\/thoughtindustries\/image\/upload\/v1\/course-uploads\/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0\/qcag6duoxbzo-ACP25_CGM_TICatalog750x3612x1.png) VideoCases and Conversations™: A Horizon View of Continuous Monitoring Systems for Diabetes Management Diana Isaacs, PharmD, BCPS, BC-ADM, BCACP, CDCES, FADCES, FCCP; Anders Carlson, MD; Jennifer B. Green, MD](https:\/\/www.gotoper.com\/courses\/cases-and-conversations-a-horizon-view-of-continuous-monitoring-systems-for-diabetes-management-n3wp?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\n[View more](https:\/\/www.gotoper.com\/courses\/cases-and-conversations-a-horizon-view-of-continuous-monitoring-systems-for-diabetes-management-n3wp?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\n[![ Progress in Hyperlipidemia Management to Reduce ASCVD Risk: An Illustrated Update](https:\/\/media.thoughtindustries.com\/thoughtindustries\/image\/upload\/v1\/course-uploads\/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0\/tp7povica5jm-CACHyperlipidemiaTICatalog750x361.png) VideoProgress in Hyperlipidemia Management to Reduce ASCVD Risk: An Illustrated Update Nihar R. Desai, MD, MPH; Martha Gulati, MD, MS, FACC, FAHA, MASPC, FESC, FSCCT (hon), FRCP Edin](https:\/\/www.gotoper.com\/courses\/cases-and-conversations-progress-in-hyperlipidemia-management-to-reduce-ascvd-risk-an-illustrated-update?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\n[View more](https:\/\/www.gotoper.com\/courses\/cases-and-conversations-progress-in-hyperlipidemia-management-to-reduce-ascvd-risk-an-illustrated-update?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck)\n\nPrevious slide\n\nNext slide\n\nAdvertisement\n\nAdvertisement\n\n## Trending on Medical Economics\n[1Second Venezuelan physician detained in South Texas as medical groups demand exemption](https:\/\/www.medicaleconomics.com\/view\/second-venezuelan-physician-detained-in-south-texas-as-medical-groups-demand-exemption)\n\n[2Podiatrist let unlicensed salesman perform surgery, then billed Medicare; Novo Nordisk partners with OpenAI to accelerate drug development; a better predictor of hypertension risk – Morning Medical Update](https:\/\/www.medicaleconomics.com\/view\/podiatrist-let-unlicensed-salesman-perform-surgery-then-billed-medicare-novo-nordisk-partners-with-openai-to-accelerate-drug-development-a-better-predictor-of-hypertension-risk-morning-medical-update)\n\n[3Osteoboost raises \\$8M to expand reach of bone density device](https:\/\/www.medicaleconomics.com\/view\/osteoboost-raises-8m-to-expand-reach-of-bone-density-device)\n\n[4Clamoring for ACCESS: New CMS 10-year payment model gets more than 150 applicants](https:\/\/www.medicaleconomics.com\/view\/clamoring-for-access-new-cms-10-year-payment-model-gets-more-than-150-applicants)\n\n[5The future of independent practice: Redefining the landscape of medicine](https:\/\/www.medicaleconomics.com\/view\/the-future-of-independent-practice-redefining-the-landscape-of-medicine)\n\n- [About](https:\/\/www.medicaleconomics.com\/about)\n- [Advertise](https:\/\/www.medicaleconomics.com\/advertise)\n- [Contact Us](https:\/\/www.medicaleconomics.com\/contact-us)\n- [Editorial Staff](https:\/\/www.medicaleconomics.com\/editorial-info)\n- [Job Board](http:\/\/jobs.modernmedicine.com\/index.php)\n- [Terms and Conditions](https:\/\/www.medicaleconomics.com\/terms)\n- [Contributor Guidelines](https:\/\/www.medicaleconomics.com\/contributor-guidelines)\n- [Privacy Policy](https:\/\/www.medicaleconomics.com\/privacy)\n- [Do Not Sell My Personal Information](https:\/\/www.mjhlifesciences.com\/ccpa?domain=www.medicaleconomics.com)\n- Cookie settings\n\nContact Info\n\n259 Prospect Plains Rd, Bldg H  \n Cranbury, NJ 08512\n\n- [![Physicians Financial News](https:\/\/www.medicaleconomics.com\/PhysiciansFinancialNews_black.png)](https:\/\/www.medicaleconomics.com\/topics\/physicians-financial-news)\n- [![Physicians Practice](https:\/\/www.medicaleconomics.com\/physicians_practice.png)](https:\/\/www.physicianspractice.com\/)\n- [![Chief Healthcare Executive](https:\/\/www.medicaleconomics.com\/chief-healthcare-executive-logo.png)](https:\/\/www.chiefhealthcareexecutive.com\/)\n- [![Patient Care Online](https:\/\/www.medicaleconomics.com\/patientcareonline.png)](https:\/\/www.patientcareonline.com\/)\n\n![Physicians Money Digest](https:\/\/www.medicaleconomics.com\/pmdLogo.png)\n\n![Brand Logo](https:\/\/www.medicaleconomics.com\/an_mjhls_brand_ko.webp)\n\n© 2026 MJH Life Sciences®\n\nAll rights reserved.\n\n[Home](https:\/\/www.medicaleconomics.com\/)\n\n![](https:\/\/px.ads.linkedin.com\/collect\/?pid=3143692&fmt=gif)","attrs_readable_markdown":"An attorney specializing in employment law discusses artificial intelligence and noncompetes.\n\nAs health care organizations adopt [artificial intelligence](https:\/\/www.medicaleconomics.com\/topics\/ai) (AI) tools in human resources, employers must ensure that [reductions in force](https:\/\/www.medicaleconomics.com\/topics\/employment-trends) — even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm [Frier Levitt](https:\/\/www.frierlevitt.com\/), explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk.\n\n**Medical Economics:** This is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict?\n\n**Christopher S. Mayer, J.D.:** Well, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it.\n\nBut from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability.\n\n### Newsletter\nStay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

16 hours ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://www.medicaleconomics.com/view/ai-and-employment-law-trends-predictions-and-compliance
Last Crawled	2026-04-16 14:31:00 (16 hours ago)
First Indexed	2026-03-09 15:25:47 (1 month ago)
HTTP Status Code	200
Meta Title	AI and employment law: Trends, predictions and compliance \| Medical Economics
Meta Description	An attorney specializing in employment law discusses artificial intelligence and noncompetes.
Meta Canonical	null
Boilerpipe Text	An attorney specializing in employment law discusses artificial intelligence and noncompetes. As health care organizations adopt artificial intelligence (AI) tools in human resources, employers must ensure that reductions in force — even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm Frier Levitt , explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk. Medical Economics: This is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict? Christopher S. Mayer, J.D.: Well, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it. But from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability. Newsletter Stay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.
Markdown	[![](https://www.medicaleconomics.com/logo.webp)](https://www.medicaleconomics.com/) News [All News](https://www.medicaleconomics.com/news)[Careers](https://www.medicaleconomics.com/topics/careers)[Legal](https://www.medicaleconomics.com/topics/legal)[Money](https://www.medicaleconomics.com/topics/money)[Opinion](https://www.medicaleconomics.com/topics/opinion)[Physicians Financial News](https://www.medicaleconomics.com/topics/physicians-financial-news)[Practice Management](https://www.medicaleconomics.com/topics/practice-management)[Technology](https://www.medicaleconomics.com/topics/technology) Media [Around the Practice](https://www.medicaleconomics.com/around-the-practice)[Between The Lines](https://www.medicaleconomics.com/between-the-lines)[Clinical Consult](https://www.medicaleconomics.com/clinical-consult)[Expert Interviews](https://www.medicaleconomics.com/expert-interviews)[Medical Economics Pulse](https://www.medicaleconomics.com/medical-economics-pulse)[Medical World News](https://www.medicalworldnews.com/)[Off The Chart Podcast](https://www.medicaleconomics.com/podcasts)[Physician Report](https://www.medicaleconomics.com/physician-report)[PrimaryView](https://www.medicaleconomics.com/primaryview)[Shorts](https://www.medicaleconomics.com/shorts)[Slideshows](https://www.medicaleconomics.com/slideshows) [Academy](https://www.medicaleconomics.com/practice-academy) Conferences [Conference Coverage](https://www.medicaleconomics.com/latest-conference)[Conference Listing](https://www.medicaleconomics.com/conference) More Publications [Medical Economics](https://www.medicaleconomics.com/journals/medical-economics)[Medical Economics Insider](https://www.medicaleconomics.com/journals/medical-economics-insider)[Patient Care Supplement](https://www.medicaleconomics.com/journals/patient-care-supplement)[Sponsored Content](https://www.medicaleconomics.com/sponsored-content)[Sponsored Resources](https://www.medicaleconomics.com/sponsored-resources)[Supplements And Featured Publications](https://www.medicaleconomics.com/journals/supplements-and-featured-publications) [CME/CE](https://www.medicaleconomics.com/continuing-medical-education) [Job Board](http://jobs.modernmedicine.com/index.php?action=advanced_search&page=search&keywords=&mult_industries%5B%5D=Family+Medicine&country=&zip_radius=&zip=&position_type=&min_salary=&max_salary=&salary_type=) Directories [Financial Advisor Guide](https://www.medecadvisory.com/) [Subscribe](https://www.medicaleconomics.com/subscribe) My Account - News - Media - [Academy](https://www.medicaleconomics.com/practice-academy) - Conferences - Publications - [CME/CE](https://www.medicaleconomics.com/continuing-medical-education) - [Job Board](http://jobs.modernmedicine.com/index.php?action=advanced_search&page=search&keywords=&mult_industries%5B%5D=Family+Medicine&country=&zip_radius=&zip=&position_type=&min_salary=&max_salary=&salary_type=) - [Subscribe](https://www.medicaleconomics.com/subscribe) - Directories - [Careers](https://www.medicaleconomics.com/topics/careers) - [Employment Trends](https://www.medicaleconomics.com/topics/employment-trends) - [Advanced Practice Providers](https://www.medicaleconomics.com/topics/advanced-practice-providers) - [Locum Tenens, brought to you by LocumLife®](https://www.medicaleconomics.com/topics/locum-tenens) - [Learning and Development](https://www.medicaleconomics.com/topics/learning-and-development) - [Concierge Medicine 2.0 by Castle Connolly Private Health Partners](https://www.medicaleconomics.com/topics/concierge-medicine-2) - [Burnout and Wellbeing](https://www.medicaleconomics.com/topics/burnout) - [Concierge Medicine](https://www.medicaleconomics.com/topics/concierge-medicine) - [Maintenance of Certification](https://www.medicaleconomics.com/topics/maintenance-of-certification) - [Legal](https://www.medicaleconomics.com/topics/legal) - [Insurance](https://www.medicaleconomics.com/topics/insurance) - [Risk Management](https://www.medicaleconomics.com/topics/risk-management) - [Contract Negotiation](https://www.medicaleconomics.com/topics/contract-negotiation) - [Malpractice](https://www.medicaleconomics.com/topics/malpractice) - [Medical Device & Technology](https://www.medicaleconomics.com/topics/med-tech) - [Mergers and Acquisitions](https://www.medicaleconomics.com/topics/ma) - [Emerging Technology](https://www.medicaleconomics.com/topics/emerging-tech) - [Earnings and Investment](https://www.medicaleconomics.com/topics/earnings) - [Point of Care](https://www.medicaleconomics.com/topics/point-of-care) - [Leaders and Innovators](https://www.medicaleconomics.com/topics/innovators) - [Device Pipeline and Approvals](https://www.medicaleconomics.com/topics/device-approvals) - [Device Regulation and Policy](https://www.medicaleconomics.com/topics/device-policy) - [Medtech Industry Trends](https://www.medicaleconomics.com/topics/medtech-industry) - [Data and Analytics](https://www.medicaleconomics.com/topics/data) - [Tech Transfer](https://www.medicaleconomics.com/topics/tech-transfer) - [Opinion](https://www.medicaleconomics.com/topics/opinion) - [Physician's Money Digest](https://www.medicaleconomics.com/topics/physicians-money-digest) - [Investing](https://www.medicaleconomics.com/topics/physicians-financial-news) - [Retirement Planning](https://www.medicaleconomics.com/topics/retirement-planning) - [Estate Planning](https://www.medicaleconomics.com/topics/estate-planning) - [Policy](https://www.medicaleconomics.com/topics/policy) - [Politics](https://www.medicaleconomics.com/topics/politics) - [Industry Trends](https://www.medicaleconomics.com/topics/industry-trends) - [Medicare & Medicaid](https://www.medicaleconomics.com/topics/medicare-and-medicaid) - [Pharma Industry](https://www.medicaleconomics.com/topics/pharma) - [Physician Advocacy](https://www.medicaleconomics.com/topics/physician-advocacy) - [Practice Finance](https://www.medicaleconomics.com/topics/money) - [Revenue Cycle Management](https://www.medicaleconomics.com/topics/revenue-cycle-management) - [Reimbursement](https://www.medicaleconomics.com/topics/reimbursement) - [Practice Valuation](https://www.medicaleconomics.com/topics/practice-valuation) - [Billing and Collections](https://www.medicaleconomics.com/topics/billing-and-collections) - [Payment Models](https://www.medicaleconomics.com/topics/value-based-care) - [Practice Management](https://www.medicaleconomics.com/topics/practice-management) - [Staffing](https://www.medicaleconomics.com/topics/staffing) - [Coding and Documentation](https://www.medicaleconomics.com/topics/coding-and-documentation) - [Leadership](https://www.medicaleconomics.com/topics/leadership) - [Marketing](https://www.medicaleconomics.com/topics/marketing) - [Chronic Condition Management](https://www.medicaleconomics.com/topics/chronic-conditions) - [Vaccines](https://www.medicaleconomics.com/topics/vaccines) - [Patient Relations](https://www.medicaleconomics.com/topics/patient-relations) - [Practice Technology](https://www.medicaleconomics.com/topics/technology) - [AI](https://www.medicaleconomics.com/topics/ai) - [Telehealth](https://www.medicaleconomics.com/topics/telehealth) - [Cybersecurity](https://www.medicaleconomics.com/topics/cybersecurity) - [EHRs](https://www.medicaleconomics.com/topics/ehr) - [Remote Patient Monitoring](https://www.medicaleconomics.com/topics/rpm) - [Medical Technology](https://www.medicaleconomics.com/topics/med-technology) - [Screening](https://www.medicaleconomics.com/topics/screening) - [Special Reports](https://www.medicaleconomics.com/topics/special-reports) - [100 Years of Medical Economics](https://www.medicaleconomics.com/topics/100-years) - [Exclusive Content](https://www.medicaleconomics.com/topics/exclusive-content) Choose Topic Spotlight - [Concierge Medicine Forum](https://www.medicaleconomics.com/conference/concierge-medicine-forum)\\| [MGMA 2025](https://www.medicaleconomics.com/conference/mgma)\\| [Malpractice](https://www.medicaleconomics.com/topics/malpractice) Advertisement News\\|Videos\\|March 9, 2026 # AI and employment law: Trends, predictions and compliance Author(s)[Richard Payerchin](https://www.medicaleconomics.com/authors/richard-payerchin) Fact checked by: [Keith A. Reynolds](https://www.medicaleconomics.com/authors/keith-a-reynolds) An attorney specializing in employment law discusses artificial intelligence and noncompetes. Advertisement Video Player is loading. Play Video Pause Unmute Current Time 0:02 / Duration 4:42 Loaded: 14\.01% 0:02 Stream Type LIVE Seek to live, currently behind liveLIVE Remaining Time \-4:40 1x Playback Rate - 2x - 1\.75x - 1\.5x - 1\.25x - 1x, selected - 0\.75x - 0\.5x Chapters - Chapters Descriptions - descriptions off, selected Captions - captions settings, opens captions settings dialog - captions off, selected Audio Track - en (Main), selected Fullscreen This is a modal window. Beginning of dialog window. Escape will cancel and close the window. Text Color Transparency Background Color Transparency Window Color Transparency Font Size Text Edge Style Font Family Reset restore all settings to the default values Done Close Modal Dialog End of dialog window. Close Modal Dialog This is a modal window. This modal can be closed by pressing the Escape key or activating the close button. As health care organizations adopt [artificial intelligence](https://www.medicaleconomics.com/topics/ai) (AI) tools in human resources, employers must ensure that [reductions in force](https://www.medicaleconomics.com/topics/employment-trends) — even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm [Frier Levitt](https://www.frierlevitt.com/), explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk. Medical Economics: This is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict? Christopher S. Mayer, J.D.: Well, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it. But from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability. ### Newsletter Stay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians. [Subscribe Now\!](https://www.medicaleconomics.com/subscribe) Advertisement [Employment Trends](https://www.medicaleconomics.com/topics/employment-trends) \\| [Careers](https://www.medicaleconomics.com/topics/careers) \\| [Staffing](https://www.medicaleconomics.com/topics/staffing) \\| [Practice Management](https://www.medicaleconomics.com/topics/practice-management) \\| [Contract Negotiation](https://www.medicaleconomics.com/topics/contract-negotiation) \\| [AI](https://www.medicaleconomics.com/topics/ai) *** ## Related Content Advertisement [![Off the Chart: A Business of Medicine Podcast - Ep. 142: The legal risks of AI in your practice, with Dan Silverboard, J.D., of Holland & Knight](https://cdn.sanity.io/images/0vv8moc6/medec/85d0eed8e593da89df6763e89fd0b2d8731dfc4f-1200x628.jpg?w=350&fit=crop&auto=format)](https://www.medicaleconomics.com/view/the-legal-risks-of-ai-in-your-practice-with-dan-silverboard-j-d-of-holland-knight) [The legal risks of AI in your practice, with Dan Silverboard, J.D., of Holland & Knight](https://www.medicaleconomics.com/view/the-legal-risks-of-ai-in-your-practice-with-dan-silverboard-j-d-of-holland-knight) By[Austin Littrell](https://www.medicaleconomics.com/authors/austin-littrell) April 16th 2026 [![Morning Medical Update © batuhan toker - stock.adobe.com](https://cdn.sanity.io/images/0vv8moc6/medec/d5350725ac443d4aa0c7af9e4f7f39f533ea1192-6044x4862.jpg?w=350&fit=crop&auto=format)](https://www.medicaleconomics.com/view/team-based-care-model-cuts-blood-pressure-in-high-risk-populations-fda-to-convene-expert-panel-on-wider-access-to-peptides-florida-nursing-assistant-sentenced-in-11-4m-medicare-brace-fraud-scheme-morning-medical-update) [Team-based care model cuts blood pressure in high-risk populations; FDA to convene expert panel on wider access to peptides; Florida nursing assistant sentenced in \$11.4M Medicare brace fraud scheme – Morning Medical Update](https://www.medicaleconomics.com/view/team-based-care-model-cuts-blood-pressure-in-high-risk-populations-fda-to-convene-expert-panel-on-wider-access-to-peptides-florida-nursing-assistant-sentenced-in-11-4m-medicare-brace-fraud-scheme-morning-medical-update) By[Austin Littrell](https://www.medicaleconomics.com/authors/austin-littrell) April 16th 2026 [![MIPS in 2026: A conversation with MIPS expert Holly Black](https://cdn.sanity.io/images/0vv8moc6/medec/ad4d05b9434f01cad44c2f5c4385cad9272a5832-1200x628.jpg?w=350&fit=crop&auto=format)](https://www.medicaleconomics.com/view/mips-in-2026-a-conversation-with-mips-expert-holly-black) [MIPS in 2026: A conversation with MIPS expert Holly Black](https://www.medicaleconomics.com/view/mips-in-2026-a-conversation-with-mips-expert-holly-black) By[Austin Littrell](https://www.medicaleconomics.com/authors/austin-littrell) April 15th 2026 [![Bone density device gets more funding: ©Osteoboost](https://cdn.sanity.io/images/0vv8moc6/medec/0e34efddc3687b87aa4640ac126a0cc8f25e10bf-1417x1214.jpg?w=350&fit=crop&auto=format)](https://www.medicaleconomics.com/view/osteoboost-raises-8m-to-expand-reach-of-bone-density-device) [Osteoboost raises \$8M to expand reach of bone density device](https://www.medicaleconomics.com/view/osteoboost-raises-8m-to-expand-reach-of-bone-density-device) By[Todd Shryock](https://www.medicaleconomics.com/authors/todd-shryock) April 15th 2026 [![Melissa Starowitz: ©Melissa Starowitz](https://cdn.sanity.io/images/0vv8moc6/medec/67454cd45d2685cfa81326c52dcd6f6edafee41f-1200x628.png?w=350&fit=crop&auto=format)](https://www.medicaleconomics.com/view/the-future-of-independent-practice-redefining-the-landscape-of-medicine) [The future of independent practice: Redefining the landscape of medicine](https://www.medicaleconomics.com/view/the-future-of-independent-practice-redefining-the-landscape-of-medicine) By[Melissa Starowitz](https://www.medicaleconomics.com/authors/melissa-starowitz) April 15th 2026 ## Latest CME [![From Numbers to Navigation: Translating Data From Diabetes Technologies into Optimized Decision-Making](https://media.thoughtindustries.com/thoughtindustries/image/upload/v1/course-uploads/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0/ne4kdmhl7q8h-ACPCGMTICatalog750x361.png) In-Person + Virtual EventFrom Numbers to Navigation: Translating Data From Diabetes Technologies into Optimized Decision-Making April 16, 2026](https://www.gotoper.com/courses/from-numbers-to-navigation-translating-cgm-data-into-optimized-decision-making?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) [View more](https://www.gotoper.com/courses/from-numbers-to-navigation-translating-cgm-data-into-optimized-decision-making?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) [![Cases and Conversations™: A Horizon View of Continuous Monitoring Systems for Diabetes Management](https://d36ai2hkxl16us.cloudfront.net/thoughtindustries/image/upload/v1/course-uploads/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0/qcag6duoxbzo-ACP25_CGM_TICatalog750x3612x1.png) VideoCases and Conversations™: A Horizon View of Continuous Monitoring Systems for Diabetes Management Diana Isaacs, PharmD, BCPS, BC-ADM, BCACP, CDCES, FADCES, FCCP; Anders Carlson, MD; Jennifer B. Green, MD](https://www.gotoper.com/courses/cases-and-conversations-a-horizon-view-of-continuous-monitoring-systems-for-diabetes-management-n3wp?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) [View more](https://www.gotoper.com/courses/cases-and-conversations-a-horizon-view-of-continuous-monitoring-systems-for-diabetes-management-n3wp?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) [![ Progress in Hyperlipidemia Management to Reduce ASCVD Risk: An Illustrated Update](https://media.thoughtindustries.com/thoughtindustries/image/upload/v1/course-uploads/07c0d4e1-23aa-4e1a-8c5f-f2569da581c0/tp7povica5jm-CACHyperlipidemiaTICatalog750x361.png) VideoProgress in Hyperlipidemia Management to Reduce ASCVD Risk: An Illustrated Update Nihar R. Desai, MD, MPH; Martha Gulati, MD, MS, FACC, FAHA, MASPC, FESC, FSCCT (hon), FRCP Edin](https://www.gotoper.com/courses/cases-and-conversations-progress-in-hyperlipidemia-management-to-reduce-ascvd-risk-an-illustrated-update?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) [View more](https://www.gotoper.com/courses/cases-and-conversations-progress-in-hyperlipidemia-management-to-reduce-ascvd-risk-an-illustrated-update?utm_source=medicaleconomics&utm_medium=article&utm_campaign=cmeDeck) Previous slide Next slide Advertisement Advertisement ## Trending on Medical Economics [1Second Venezuelan physician detained in South Texas as medical groups demand exemption](https://www.medicaleconomics.com/view/second-venezuelan-physician-detained-in-south-texas-as-medical-groups-demand-exemption) [2Podiatrist let unlicensed salesman perform surgery, then billed Medicare; Novo Nordisk partners with OpenAI to accelerate drug development; a better predictor of hypertension risk – Morning Medical Update](https://www.medicaleconomics.com/view/podiatrist-let-unlicensed-salesman-perform-surgery-then-billed-medicare-novo-nordisk-partners-with-openai-to-accelerate-drug-development-a-better-predictor-of-hypertension-risk-morning-medical-update) [3Osteoboost raises \$8M to expand reach of bone density device](https://www.medicaleconomics.com/view/osteoboost-raises-8m-to-expand-reach-of-bone-density-device) [4Clamoring for ACCESS: New CMS 10-year payment model gets more than 150 applicants](https://www.medicaleconomics.com/view/clamoring-for-access-new-cms-10-year-payment-model-gets-more-than-150-applicants) [5The future of independent practice: Redefining the landscape of medicine](https://www.medicaleconomics.com/view/the-future-of-independent-practice-redefining-the-landscape-of-medicine) - [About](https://www.medicaleconomics.com/about) - [Advertise](https://www.medicaleconomics.com/advertise) - [Contact Us](https://www.medicaleconomics.com/contact-us) - [Editorial Staff](https://www.medicaleconomics.com/editorial-info) - [Job Board](http://jobs.modernmedicine.com/index.php) - [Terms and Conditions](https://www.medicaleconomics.com/terms) - [Contributor Guidelines](https://www.medicaleconomics.com/contributor-guidelines) - [Privacy Policy](https://www.medicaleconomics.com/privacy) - [Do Not Sell My Personal Information](https://www.mjhlifesciences.com/ccpa?domain=www.medicaleconomics.com) - Cookie settings Contact Info 259 Prospect Plains Rd, Bldg H Cranbury, NJ 08512 - [![Physicians Financial News](https://www.medicaleconomics.com/PhysiciansFinancialNews_black.png)](https://www.medicaleconomics.com/topics/physicians-financial-news) - [![Physicians Practice](https://www.medicaleconomics.com/physicians_practice.png)](https://www.physicianspractice.com/) - [![Chief Healthcare Executive](https://www.medicaleconomics.com/chief-healthcare-executive-logo.png)](https://www.chiefhealthcareexecutive.com/) - [![Patient Care Online](https://www.medicaleconomics.com/patientcareonline.png)](https://www.patientcareonline.com/) ![Physicians Money Digest](https://www.medicaleconomics.com/pmdLogo.png) ![Brand Logo](https://www.medicaleconomics.com/an_mjhls_brand_ko.webp) © 2026 MJH Life Sciences® All rights reserved. [Home](https://www.medicaleconomics.com/) ![](https://px.ads.linkedin.com/collect/?pid=3143692&fmt=gif)
Readable Markdown	An attorney specializing in employment law discusses artificial intelligence and noncompetes. As health care organizations adopt [artificial intelligence](https://www.medicaleconomics.com/topics/ai) (AI) tools in human resources, employers must ensure that [reductions in force](https://www.medicaleconomics.com/topics/employment-trends) — even in small practices — comply with federal protections, including the Age Discrimination in Employment Act, which requires specific disclosures when employees over age 40 are affected. Christopher S. Mayer, J.D., an employment law specialist with the firm [Frier Levitt](https://www.frierlevitt.com/), explains why physicians and practice leaders cannot blindly rely on AI recommendations for layoffs and must review potential disparate impact across protected categories such as age, race and gender to reduce legal risk. Medical Economics: This is still very new, but what is exactly the intersection between human resources and employment law and AI? What are some trends that you're seeing? Or what would you predict? Christopher S. Mayer, J.D.: Well, I mean, certainly any sort of service facing industry or business, they have to carefully address and regulate how their employees use AI, so that a lot of that's going on in terms of adopting policies. But I do think in health care in particular, there's going to be an impact on, as we talked about, folks who do coding, claims, there's, there's going to be a huge impact on that. And that's where I think AI is really going to come into play over time and start to affect it. But from the HR perspective, what HR people have to be mindful of when they're doing a large-scale reduction in force — and for a small provider, a large-scale reduction is three or four employees. It doesn't have to be 50, 60, employees. But the main thing they're going to have to be mindful of, just like you have to be mindful of in any reduction, this is no different than any other reduction that you're doing, is the is the impact that it's going to have across protected categories. What that means is, you still have to make sure, before you go through the layoff, and if you're using an AI tool to tell you who should be eliminated, you're going to have to sit down carefully. And sometimes this should be done through counsel so you can, have the attorney client privilege attached to it, but that's kind of another matter. But look at the impact that it's going to have, and the layoffs that, AI say, has suggested that you do, if it's going to eliminate five or six people over the age of 40 and no one under the age of 40, that's not a good look, and that's a problem. And not only is that a problem in terms of potential liability, but if you're going to do a reduction where you're trying to get a release in exchange for severance, which is a very good business practice, and something that any sophisticated employer does, you're going to have to disclose the age or ages of the people who are impacted by the layoffs, and the ages of the people are not impacted by the layoffs under federal law to get that release. The Age Discrimination Employment Act, which is the federal law I'm talking about, sets forth very particular requirements for a reduction in force that impacts, really more than one employee, a group of employees, and that is one of the things that you have to do. You have to show who by age. You don't have to identify them by name, but by job title and by age, is being impacted. And so if you're seeing as an employer a disparate impact, your employees who are being impacted by that are going to see it too. And not only that, you have to tell them they should go and consult with a lawyer before signing the agreement. So they're going to take that, the lawyer is going to look at that and go, don't sign it, we might be able to sue over this if they're going to go forward with it. So that's a long way of saying, you just have to be very mindful of the impact, and that it's not limited to age. I mean, obviously any category that's protected by law, race, gender, all the protected categories, you have to make sure you're looking at it and determining whether or not it's going to have a disparate impact on your workforce, and if it is and statistically, it doesn't look good. And there are tools that you can plug that data into that will tell you whether it's going to have an improper statistical impact. You might have to make adjustments to the layoff. Now this analysis is more appropriate, as you can imagine, for a large-scale layoff, whereas in a large-scale layoff, if there's a statistical anomaly, that's a problem. You know that. So any sort of statistical anomaly would be sorted out in a large-scale layoff, like, if you're doing five people and three happen to be over the age of 40, well then there may not be much you can do. That's a little different. But if you're doing a large-scale layoff and there's a huge impact on one protected category, that's a problem. But even in a small way, you just have to be mindful of the impact it's going to have, who's going to be affected by it, you can't just blindly accept what the AI tool has told you to do. You have to make sure that you protect yourself from liability. ### Newsletter Stay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.
Shard	199 (laksa)
Root Hash	12219667406561261999
Unparsed URL	com,medicaleconomics!www,/view/ai-and-employment-law-trends-predictions-and-compliance s443