Oct 21
2025
Q&A with EHR Affiliation AI Job Power Management
Synthetic intelligence (AI) is evolving quickly, reshaping the well being IT panorama whereas state and federal governments race to place laws in place to make sure it’s secure, efficient, and accessible. For these causes, AI has emerged as a precedence for the EHR Affiliation. We sat down with EHR Affiliation AI Job Power Chair Tina Joros, JD (Veradigm), and Vice Chair Stephen Speicher, MD (Flatiron Well being), to debate the route of AI laws, the anticipated influence on adoption and use, and what the EHR Affiliation sees as its priorities shifting ahead.
EHR: What are the EHR Affiliation’s priorities within the subsequent 12-18 months, and is/how is AI altering them?
Regulatory necessities from each D.C. and state governments are a major driver for the selections made by the supplier organizations that use our collective merchandise, so numerous the work the EHR Affiliation does pertains to public coverage. We’re at present spending a good quantity of our time engaged on AI-related conversations, as they’re a high-priority subject, in addition to monitoring and responding to deregulatory changes being made by the Trump administration. Different key areas of focus are anticipated modifications to the ASTP/ONC certification program, guidelines that improve the burdens on suppliers and distributors, and dealing to deal with areas of business frustration, such because the prior authorization course of.
EHR: How has the Affiliation tailored since its institution, and what areas of the well being IT business require rapid consideration, if any?
The EHR Affiliation is structured to adapt rapidly to business tendencies. Our Workgroups and Job Forces, all of that are led by volunteers, are evaluated periodically all year long to make sure we’re giving our members an opportunity to satisfy and talk about probably the most urgent subjects on their minds. Most lately, that has meant the addition of latest efforts particular to each consent administration and AI, given the prevalence of these subjects throughout the common well being IT coverage dialog happening at each the federal and state ranges.
EHR: Should you have been to welcome younger healthcare entrepreneurs to tackle the sector’s most urgent challenges, what steering would you provide them?
Well being IT is a good sector for entrepreneurs to give attention to. The work is all the time attention-grabbing as a result of it evolves so rapidly, each from a technological perspective and the truth that public coverage impacting well being IT is getting numerous consideration on the federal and state ranges. There are numerous paths to work within the business, so it’s all the time useful for each entrepreneurs and potential well being IT firm workforce members to have a transparent understanding of the complexities of our nation’s healthcare system and the way the enterprise of healthcare works. Plus, they want an excellent grasp of the more and more crucial function of information in scientific and administrative processes in hospitals, doctor practices, and different care settings.
EHR: What rules are crucial to the secure and accountable growth of AI in healthcare? How do they mirror the Affiliation’s priorities and place on present AI governance points?
One of many first issues the AI Job Power did when it was fashioned was to determine sure rules that we consider are important for guaranteeing the secure and high-quality growth of AI-driven software program instruments in healthcare. These guiding rules must also be a part of the dialog when growing state and federal insurance policies and laws concerning using AI in well being IT.
- Concentrate on high-risk AI purposes by prioritizing governance of instruments that influence crucial scientific choices or add vital privateness or safety threat. Fewer restrictions on different use instances, similar to administrative workflows, will assist guarantee speedy innovation and adoption. This risk-based strategy ought to information oversight and reference frameworks just like the FDA threat evaluation.
- Align legal responsibility with the suitable actor. Clinicians, not AI distributors, keep direct duty for AI when it’s used for affected person care, when the latter supplies clear documentation and coaching.
- Require ongoing AI monitoring and common updates to stop outdated or biased inputs, in addition to transparency in mannequin updates and efficiency monitoring.
- Assist AI utilization by all healthcare organizations, no matter dimension, by contemplating the various technical capabilities of enormous hospitals vs. small clinics. This may make AI adoption possible for all healthcare suppliers, guaranteeing equitable entry to AI instruments and avoiding the exacerbation of the already outsized digital divide in US healthcare.
Our purpose with these rules is to strike a steadiness between innovation and affected person security, thereby guaranteeing that AI enhances healthcare with out pointless regulatory burdens.
EHR: In its January 2025 letter to the US Senate HELP Committee, the EHR Affiliation cited its desire for consolidating regulatory motion on the federal degree. Since then, a flurry of state-level exercise has launched new AI laws, whereas federal regulatory businesses work on discovering their footing beneath the Trump Administration. Has the EHR Affiliation’s place on regulation modified in consequence?
Our desire continues to be a federal strategy to AI regulation, which might eradicate the rising complexity we face in complying with a number of and sometimes conflicting state legal guidelines. Consolidating laws on the Federal degree would additionally guarantee consistency throughout the healthcare ecosystem, which would cut back confusion for software program builders and suppliers with areas in a number of states.
Nonetheless, whereas our place hasn’t modified, the regulatory panorama has. Within the months since submitting our letter to the HELP Committee, California, Colorado, Texas, and a number of other different states have enacted legal guidelines regulating AI that take impact in 2026. Even when the urge for food for legislative motion was there, it’s unlikely the federal authorities might act rapidly sufficient to place in place a regulatory framework that may preempt these state legal guidelines. Confronted with that actuality, we’re engaged on a twin observe of supporting our member firms’ compliance efforts on the state degree whereas persevering with to push for a federal regulatory framework.
EHR: What advantages will likely be realized by focusing laws on AI use instances with direct implications for high-risk scientific workflows?
Centering AI laws on high-risk scientific workflows is smart as a result of they symbolize the next risk of affected person hurt, and that focus would concurrently guarantee room for innovation on lower-risk use instances. Our collective purchasers have many concepts as to how AI might assist them tackle areas of frustration, and that’s the place our member firms subsequently need room to maneuver from growth to adoption extra expediently, unencumbered by regulation—for instance, administrative AI use instances like affected person communication help, claims remittance and streamlining advantages verification, all of which our inside polling exhibits are in excessive demand by physicians and supplier organizations.
A sensible, environment friendly risk-based regulatory framework can be grounded within the understanding that not all AI use instances have a direct or consequential influence on affected person care and security. That differentiation, nonetheless, will not be occurring in lots of states which have handed or are considering AI laws. They have a tendency to categorize every part as high-risk, even when the AI instruments haven’t any direct influence on the supply of care or the danger to sufferers is minimal.
The unintended consequence of this one-size-fits-all strategy is that it stifles AI innovation and adoption. It’s why we consider the higher strategy is granular, differentiating between high- and low-risk workflows, and leveraging present frameworks that stratify threat primarily based on the likelihood of incidence, severity, and optimistic influence or profit. This additionally helps ease the reporting burden on all applied sciences integrated into an EHR which may be used on the level of care.
EHR: The place ought to the final word legal responsibility for outcomes involving AI instruments lie–with builders or finish customers–and why?
That is an attention-grabbing side of AI regulation that continues to be largely undefined. Till lately, there hasn’t been any dialogue about legal responsibility in state rulemaking. For instance, New York grew to become one of many first states to deal with legal responsibility when a invoice was launched that holds everybody concerned in creating an AI software accountable, though it’s not particular to healthcare. California lately enacted laws stating {that a} defendant—together with builders, deployers, and customers—can’t keep away from legal responsibility by blaming AI for misinformation.
Given the criticality of “human-in-the-loop” approaches to expertise use—the idea that suppliers are finally accountable for reviewing the suggestions of AI instruments and making ultimate choices about affected person care—our stance is that legal responsibility for affected person care finally lies with clinicians, together with when AI is used as a software. Current legal responsibility frameworks needs to be adopted for cases of medical malpractice which will contain AI applied sciences.
EHR: Why should human-in-the-loop or human override safeguards be integrated into AI use instances? What are the highest issues for guaranteeing these safeguards add worth and mitigate threat?
The Affiliation strongly advocates for applied sciences that incorporate or public coverage that requires human-in-the-loop or human override capabilities, guaranteeing that an appropriately skilled and educated individual stays central to choices involving affected person care. This strategy additionally ensures that clinicians use AI suggestions, insights, or different data solely to tell their choices, to not make them.
For really high-risk use instances, we additionally help the configuration of human-in-the-loop or human override safeguards, together with different affordable transparency necessities, when implementing and utilizing AI instruments. Lastly, finish customers needs to be required to implement workflows that prioritize human-in-the-loop rules for utilizing AI instruments in affected person care.
Curiously, we’re seeing some states tackle the concept of human oversight in proposed laws. Texas lately handed a regulation that exempts healthcare practitioners from legal responsibility when utilizing AI instruments to help with medical decision-making, offered the practitioner evaluations all AI-generated information in accordance with requirements set by the Texas Medical Board. It doesn’t provide blanket immunity, but it surely does emphasize accountability by way of oversight. California, Colorado, and Utah even have components of human oversight constructed into a few of their AI laws.