LEGISLATIVE BUDGET BOARD Austin, Texas FISCAL NOTE, 89TH LEGISLATIVE REGULAR SESSION Revision 1 March 18, 2025 TO: Honorable Lois W. Kolkhorst, Chair, Senate Committee on Health & Human Services FROM: Jerry McGinty, Director, Legislative Budget Board IN RE: SB1822 by Johnson (Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.), As Introduced Estimated Two-year Net Impact to General Revenue Related Funds for SB1822, As Introduced: an impact of $0 through the biennium ending August 31, 2027. The bill would make no appropriation but could provide the legal basis for an appropriation of funds to implement the provisions of the bill. General Revenue-Related Funds, Five- Year Impact: Fiscal Year Probable Net Positive/(Negative) Impact toGeneral Revenue Related Funds2026$02027$02028$02029$02030$0All Funds, Five-Year Impact: Fiscal Year Probable Savings/(Cost) fromDept Ins Operating Acct36 Probable Revenue Gain/(Loss) fromDept Ins Operating Acct36 Change in Number of State Employees from FY 20252026($536,677)$536,6772.12027($491,172)$491,1722.02028($491,172)$491,1722.02029($491,172)$491,1722.02030($491,172)$491,1722.0 Fiscal AnalysisThe bill would require any issuer or utilization review agent that uses an artificial intelligence-based algorithm in conducting utilization review to submit the algorithm and training data to the Department of Insurance (TDI) for review to ensure minimum bias and compliance with clinical guidelines.
LEGISLATIVE BUDGET BOARD
Austin, Texas
FISCAL NOTE, 89TH LEGISLATIVE REGULAR SESSION
Revision 1
March 18, 2025
Revision 1
Revision 1
TO: Honorable Lois W. Kolkhorst, Chair, Senate Committee on Health & Human Services FROM: Jerry McGinty, Director, Legislative Budget Board IN RE: SB1822 by Johnson (Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.), As Introduced
TO: Honorable Lois W. Kolkhorst, Chair, Senate Committee on Health & Human Services
FROM: Jerry McGinty, Director, Legislative Budget Board
IN RE: SB1822 by Johnson (Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.), As Introduced
Honorable Lois W. Kolkhorst, Chair, Senate Committee on Health & Human Services
Honorable Lois W. Kolkhorst, Chair, Senate Committee on Health & Human Services
Jerry McGinty, Director, Legislative Budget Board
Jerry McGinty, Director, Legislative Budget Board
SB1822 by Johnson (Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.), As Introduced
SB1822 by Johnson (Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.), As Introduced
Estimated Two-year Net Impact to General Revenue Related Funds for SB1822, As Introduced: an impact of $0 through the biennium ending August 31, 2027. The bill would make no appropriation but could provide the legal basis for an appropriation of funds to implement the provisions of the bill.
Estimated Two-year Net Impact to General Revenue Related Funds for SB1822, As Introduced: an impact of $0 through the biennium ending August 31, 2027. The bill would make no appropriation but could provide the legal basis for an appropriation of funds to implement the provisions of the bill.
The bill would make no appropriation but could provide the legal basis for an appropriation of funds to implement the provisions of the bill.
General Revenue-Related Funds, Five- Year Impact:
2026 $0
2027 $0
2028 $0
2029 $0
2030 $0
All Funds, Five-Year Impact:
2026 ($536,677) $536,677 2.1
2027 ($491,172) $491,172 2.0
2028 ($491,172) $491,172 2.0
2029 ($491,172) $491,172 2.0
2030 ($491,172) $491,172 2.0
Fiscal Analysis
The bill would require any issuer or utilization review agent that uses an artificial intelligence-based algorithm in conducting utilization review to submit the algorithm and training data to the Department of Insurance (TDI) for review to ensure minimum bias and compliance with clinical guidelines.
Methodology
Based on the analysis of TDI, this estimate assumes that the agency would need an additional 2.1 full-time equivalent positions (FTEs) to implement the provisions of the bill. The agency anticipates needing a Physician FTE ($220,747 each year with 66,003 in estimated benefits) to review the algorithms compliance with clinical guidelines, a Data Architect FTE ($150,773 each year with $45,081 in estimated benefits) to inspect the compliance of the algorithms and training data sets, and 0.1 Attorney FTE ($27,805 for the fiscal year 2026) for the first year of the implementation to write rules and establish legal guidelines for the review. In addition, this estimate assumes other operating costs of $26,193 for the additional staff. These other operating expenses include the additional data storages costs incurred by the agency to be able to receive the large digital files of the artificial intelligence datasets. Due to the self-leveling nature of the Insurance Operating Account 36, any expenditure increases would be reflected in the annual adjustment of the maintenance tax rates for insurance carriers. Therefore, the overall revenue into the Insurance Operating Account 36 will equal overall expenses.
Due to the self-leveling nature of the Insurance Operating Account 36, any expenditure increases would be reflected in the annual adjustment of the maintenance tax rates for insurance carriers. Therefore, the overall revenue into the Insurance Operating Account 36 will equal overall expenses.
Technology
This estimates assumes that the intake of the data training sets would require additional cloud storage costs of $12,000 each year.
Local Government Impact
No significant fiscal implication to units of local government is anticipated.
Source Agencies: b > td > 323 Teacher Retirement System, 327 Employees Retirement System, 454 Department of Insurance, 529 Health and Human Services Commission, 710 Texas A&M University System Administrative and General Offices, 720 The University of Texas System Administration
323 Teacher Retirement System, 327 Employees Retirement System, 454 Department of Insurance, 529 Health and Human Services Commission, 710 Texas A&M University System Administrative and General Offices, 720 The University of Texas System Administration
LBB Staff: b > td > JMc, NPe, CMA, BFa, GDZ
JMc, NPe, CMA, BFa, GDZ