Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals (IPPS) and the Long-Term Care Hospital Prospective Payment System and Policy Changes and Fiscal Year (FY) 2026 Rates; Changes to the FY 2025 IPPS Rates Due to Court Decision; Requirements for Quality Programs; and Other Policy Changes; Health Data, Technology, and Interoperability: Electronic Prescribing, Real-Time Prescription Benefit and Electronic Prior Authorization

Table II displays the results of our analysis of the changes for FY 2026 on estimated average payments per discharge for IPPS operating costs and uncompensated care payments. It presents the impact for the categories of hospitals shown in Table I. It compares the estimated average payments per discharge for FY 2025 with the estimated average payments per discharge for FY 2026, as calculated under our models. It reflects the combined effects of the changes presented in Table I, and therefore the estimated percentage changes shown in the last column of Table II equal the estimated percentage changes in average payments per discharge from Column 7 of Table I.

Table II--Impact Analysis of Changes on Average Payments per Discharge for Operating Costs and Uncompensated

Care

Estimated Estimated

Number of average FY 2025 average FY 2026 FY 2026

hospitals payment per payment per changes (4)

(1) discharge (2) discharge (3)

All Hospitals..................................... 3,033 17,751 18,515 4.3 By Geographic Location:

Urban hospitals............................... 2,372 18,187 18,986 4.4

Rural hospitals............................... 661 12,893 13,269 2.9 Bed Size (Urban):

0-99 beds..................................... 647 12,982 13,360 2.9

100-199 beds.................................. 673 14,325 14,823 3.5

200-299 beds.................................. 406 16,073 16,753 4.2

300-499 beds.................................. 392 17,903 18,658 4.2

500 or more beds.............................. 252 22,804 23,949 5.0 Bed Size (Rural):

0-49 beds..................................... 313 11,002 11,336 3.0

50-99 beds.................................... 180 12,339 12,552 1.7

100-149 beds.................................. 95 12,218 12,634 3.4

150-199 beds.................................. 42 14,079 14,597 3.7

200 or more beds.............................. 31 15,559 16,063 3.2 Urban by Region:

New England................................... 104 19,617 19,935 1.6

Middle Atlantic............................... 274 21,119 22,129 4.8

East North Central............................ 366 17,263 17,812 3.2

West North Central............................ 156 16,846 17,769 5.5

South Atlantic................................ 393 16,104 16,992 5.5

East South Central............................ 141 14,936 15,706 5.2

West South Central............................ 355 16,776 18,070 7.7

Mountain...................................... 180 17,643 18,317 3.8

Pacific....................................... 351 22,188 22,688 2.3 Rural by Region:

New England................................... 19 17,347 17,492 0.8

Middle Atlantic............................... 48 14,481 14,900 2.9

East North Central............................ 106 12,601 12,786 1.5

West North Central............................ 74 12,828 13,302 3.7

South Atlantic................................ 108 12,218 12,647 3.5

East South Central............................ 127 11,264 11,616 3.1

West South Central............................ 116 11,006 11,578 5.2

Mountain...................................... 39 14,739 15,200 3.1

Pacific....................................... 24 17,337 17,612 1.6 Puerto Rico:

Puerto Rico Hospitals......................... 52 13,988 15,587 11.4 By Payment Classification:

Urban hospitals............................... 1,611 15,990 16,677 4.3

Rural areas................................... 1,422 19,107 19,930 4.3 Teaching Status:

Nonteaching................................... 1,756 13,357 13,839 3.6

Fewer than 100 residents...................... 986 15,905 16,563 4.1

100 or more residents......................... 291 26,535 27,842 4.9 Urban DSH:

Non-DSH....................................... 346 13,132 13,575 3.4

100 or more beds.............................. 909 16,804 17,543 4.4

Less than 100 beds............................ 356 12,361 12,940 4.7 Rural DSH:

Non-DSH....................................... 93 16,180 16,327 0.9

SCH........................................... 227 13,778 14,204 3.1

RRC........................................... 863 19,792 20,663 4.4

100 or more beds.............................. 41 17,825 19,316 8.4

Less than 100 beds............................ 198 10,645 10,743 0.9 Urban teaching and DSH:

Both teaching and DSH......................... 527 18,326 19,167 4.6

Teaching and no DSH........................... 58 14,607 15,047 3.0

No teaching and DSH........................... 738 13,871 14,440 4.1

No teaching and no DSH........................ 288 12,240 12,685 3.6 Special Hospital Types:

RRC........................................... 131 13,367 13,780 3.1

RRC that reclassified from urban to rural in 657 20,458 21,382 4.5

accordance with section 1886(d)(8)(E) as

implemented at 42 CFR 412.103................

SCH........................................... 218 13,194 13,643 3.4

SCH that reclassified from urban to rural in 37 15,612 15,992 2.4

accordance with section 1886(d)(8)(E) as

implemented at 42 CFR 412.103................

SCH and RRC................................... 119 14,306 14,729 3.0

SCH and RRC that reclassified from urban to 49 18,018 18,577 3.1

rural in accordance with section

1886(d)(8)(E) as implemented at 42 CFR

412.103...................................... Type of Ownership:

Voluntary..................................... 1,902 17,557 18,231 3.8

Proprietary................................... 724 15,746 16,447 4.4

Government.................................... 406 21,551 22,974 6.6 Medicare Utilization as a Percent of Inpatient

Days:

0-25.......................................... 1,548 19,737 20,791 5.3

25-50......................................... 1,388 15,793 16,268 3.0

50-65......................................... 65 15,374 15,872 3.2

Over 65....................................... 13 12,501 13,061 4.5 Medicaid Utilization as a Percent of Inpatient

Days:

0-25.......................................... 1,917 15,694 16,282 3.7

25-50......................................... 992 20,848 21,816 4.6

50-65......................................... 91 28,395 31,276 10.1

Over 65....................................... 32 30,803 34,517 12.1 FY 2026 Reclassifications:

All Reclassified Hospitals.................... 1,093 18,809 19,606 4.2

Non-Reclassified Hospitals.................... 1,940 16,546 17,272 4.4

Urban Hospitals Reclassified.................. 979 19,685 20,557 4.4

Urban Non-reclassified Hospitals.............. 1,407 15,937 16,621 4.3

Rural Hospitals Reclassified Full Year........ 268 13,129 13,536 3.1

Rural Non-reclassified Hospitals Full Year.... 379 12,546 12,920 3.0

All hospitals that reclassified from urban to 811 20,066 20,956 4.4

rural in accordance with section

1886(d)(8)(E) as implemented at 42 CFR

412.103......................................

Other Reclassified Hospitals (Section 50 12,108 12,236 1.1

1886(d)(8)(B), also known as Lugar hospitals)

In section II.E.4. of the preamble of this final rule, we are continuing to make new technology add-on payments for the technologies listed in the following table in FY 2026 because these technologies would still be considered new for purposes of new technology add-on payments. Under Sec. 412.88(a)(2), the new technology add-on payment for each case would be limited to the lesser of: (1) 65 percent of the costs of the new technology (or 75 percent of the costs for technologies designated as Qualified Infectious Disease Products (QIDPs) or approved under the Limited Population Pathway for Antibacterial and Antifungal Drugs (LPAD) pathway, or for the gene therapies, CasgevyTM (exagamglogene autotemcel) and LyfgeniaTM (lovotibeglogene autotemcel), when indicated and used specifically for the treatment of SCD, which were approved for new technology add-on payments in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69128 through 69135, and 89 FR 69188 through 69196)); or (2) 65 percent of the amount by which the costs of the case exceed the standard MS-DRG payment for the case (or 75 percent of the amount for technologies designated as QIDPs; for technologies approved under the LPAD pathway; or for the gene therapies, CasgevyTM and LyfgeniaTM, when indicated and used specifically for the treatment of SCD, which were approved for new technology add-on payments in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69128 through 69135, and 89 FR 69188 through 69196)). Because it is difficult to predict the actual new technology add-on payment for each case, our estimates in this final rule are based on the applicant's estimate at the time they submitted their original application (or based on updated figures provided during the public comment period) and the increase in new technology add-on payments for FY 2026 as if every claim that would qualify for a new technology add-on payment would receive the maximum add-on payment.

In the following table are estimates for the 27 new technology add-on payments which we are continuing in FY 2026:

FY 2026 Estimates for New Technology Add-On Payments To Continue for FY 2026

FY 2026 NTAP

Technology name Estimated amount (65% Estimated total

cases or 75%) FY 2026 impact

CYTALUX[supreg] (pafolacianine) (lung indication)............ 300 $2,762.50 $828,750.00 EPKINLYTM (epcoritamab-bysp) and COLUMVITM (glofitamab-gxbm) 157 6,504.07 1,021,138.99

*........................................................... AveirTM AR Leadless Pacemaker................................ 245 10,725.00 2,627,625.00 AveirTM Dual-Chamber Leadless Pacemaker...................... 2,250 15,600.00 35,100,000.00 Ceribell Status Epilepticus Monitor.......................... 2,477 913.90 2,263,730.30 DETOUR System................................................ 600 16,250.00 9,750,000.00 DefenCathTM (taurolidine/heparin)............................ 12,000 3,656.10 43,873,200.00 Phagenyx[supreg] System...................................... 294 3,250.00 955,500.00 REZZAYOTM (rezafungin for injection)......................... 795 4,387.50 3,488,062.50 SAINT Neuromodulation System................................. 25 12,675.00 316,875.00 TOPSTM System................................................ 1,200 11,375.00 13,650,000.00 XACDURO[supreg] (sulbactam/durlobactam)...................... 654 13,680.00 8,946,720.00 Annalise Enterprise CTB Triage--OH........................... 271,200 241.39 65,464,968.00 ASTar[supreg] System......................................... 69,000 97.50 6,727,500.00 Edwards EVOQUETM Tricuspid Valve Replacement System.......... 800 31,850.00 25,480,000.00 GORE[supreg] EXCLUDER[supreg] Thoracoabdominal Branch 518 47,238.75 24,469,672.50

Endoprosthesis (TAMBE Device)............................... LimFlowTM System............................................. 561 16,250.00 9,116,250.00 ParadiseTM Ultrasound Renal Denervation System............... 200 14,950.00 2,990,000.00

PulseSelectTM Pulsed Field Ablation (PFA) Loop Catheter...... 3,402 6,337.50 21,560,175.00 Symplicity SpyralTM Multi-Electrode Renal Denervation 55 10,400.00 572,000.00

Catheter.................................................... TriClipTM G4................................................. 150 26,000.00 3,900,000.00 VADER[supreg] Pedicle System................................. 200 28,242.50 5,648,500.00 ZEVTERATM (ceftobiprole medocaril); ABSSSI and CABP 245 5,287.50 1,295,437.50

indications................................................. ZEVTERATM (ceftobiprole medocaril); SAB indication........... 571 16,215.00 9,258,765.00 CASGEVYTM (exagamglogene autotemcel); Sickle Cell Disease 117 1,650,000.00 193,050,000.00

indication.................................................. HEPZATOTM KIT (melphalan for injection/hepatic delivery 149 118,625.00 17,675,125.00

system)..................................................... LYFGENIATM (lovotibeglogene autotemcel))..................... 40 $2,325,000.00 $93,000,000.00

Aggregate Estimated Total FY 2026 Impact................. .............. .............. 603,029,994.79

* These two technologies were determined to be substantially similar to each other and were therefore evaluated

as one application for new technology add-on payments under the IPPS.

In sections II.E.5. and 6. of the preamble to this final rule are 35 discussions of technologies with respect to add-on payments for new medical services and technologies for FY 2026. We note that of the 53 applications (34 alternative and 19 traditional) we received, 18 applicants either withdrew their applications or were not eligible for consideration for new technology add-on payment for FY 2026 (12 alternative and 6 traditional). Of the 35 discussions of technologies in the preamble of this final rule, we are not approving the new technology add-on payment for 8 technologies. This results in a total of 27 new approvals or conditional approvals (5 traditional and 22 alternative) for new technology add-on payments for FY 2026. As explained in the preamble to this final rule, add-on payments for new medical services and technologies under section 1886(d)(5)(K) of the Act are not required to be budget neutral.

As discussed in section II.E.6. of the preamble of this final rule, under the alternative pathway for new technology add-on payments, new technologies that are medical products with a QIDP designation, approved through the FDA LPAD pathway, or are designated under the Breakthrough Device program will be considered not substantially similar to an existing technology for purposes of the new technology add-on payment under the IPPS, and will not need to demonstrate that the technology represents a substantial clinical improvement. These technologies must still be within the 2- to 3- year newness period, as discussed in section II.E.1.a.(1). of the preamble this final rule, and must also still meet the cost criterion.

As fully discussed in section II.E.6. of the preamble of this final rule, we are approving or conditionally approving 22 new technology add-on payments for the alternative pathway applications submitted for FY 2026 new technology add-on payments. The approvals include 20 technologies that received a Breakthrough Device designation from FDA and 2 that were designated as a QIDP by FDA. We did not receive any LPAD applications for add-on payments for new technologies for FY 2026.

Based on information from the applicants at the time of this final rule, we estimate that total payments for the technologies approved under the alternative pathway will be approximately $219 million for FY 2026. Total estimated FY 2026 payments for new technologies that are designated as a QIDP are approximately $7 million, and the total estimated FY 2026 payments for new technologies that are part of the Breakthrough Device program are approximately $212 million.

In the following table, we present detailed estimates for the 22 technologies for which we are approving new technology add-on payments under the alternative pathway in FY 2026:

FY 2026 Estimates for New Technology Add-On Payments for Technologies Under the Alternative Pathway for FY 2026

Pathway (QIDP, LPAD, FY 2026 NTAP

Technology name or breakthrough Estimated amount (65% or Estimated total

device) cases 75%) FY 2026 impact

EMBLAVEOTM (aztreonam-avibactam).... QIDP................... 207 $9,000.68 $1,863,140.76 CONTEPOTM (fosfomycin).............. QIDP................... 573 8,775.00 5,028,075.00 4WEB Medical Ankle Truss System..... Breakthrough Device.... 124 15,275.00 1,894,100.00 AeroPace[supreg] System............. Breakthrough Device.... 550 23,650.90 13,007,995.00 AGENTTM Paclitaxel-Coated Balloon Breakthrough Device.... 9,010 4,013.75 36,163,887.50

Catheter. alfapump[supreg] system............. Breakthrough Device.... 200 21,450.00 4,290,000.00 aprevo[supreg]-C cervical interbody Breakthrough Device.... 400 21,125.00 8,450,000.00

fusion device. CERAMENT[supreg] G.................. Breakthrough Device.... 466 5,687.50 2,650,375.00 Emily's Care Nourish Test System Breakthrough Device.... 11 3,347.50 36,822.50

(Model 1) *. EspritTM BTK Everolimus Eluting Breakthrough Device.... 5,300 6,922.50 36,689,250.00

Resorbable Scaffold System. EUROPATM Posterior Cervical Fusion Breakthrough Device.... 849 80,548.00 68,385,252.00

System. iFuse TORQ TNTTM Implant System..... Breakthrough Device.... 1,392 4,135.95 5,757,242.40 Merit Wrapsody[supreg] Cell Breakthrough Device.... 936 3,770.00 3,528,720.00

Impermeable Endoprosthesis (CIE). Minima Stent System **.............. Breakthrough Device.... 10 22,685.00 226,850.00 MY01 Continuous Compartmental Breakthrough Device.... 2,000 2,112.50 4,225,000.00

Pressure Monitor. PBC Separator with Selux AST System. Breakthrough Device.... 68,149 87.78 5,982,119.22 RECELL[supreg] Autologous Cell Breakthrough Device.... 412 4,875.00 2,008,500.00

Harvesting Device. restor3d TIDALTM Fusion Cage........ Breakthrough Device.... 96 18,196.75 1,746,888.00 ShortCutTM.......................... Breakthrough Device.... 550 9,750.00 5,362,500.00 The WiSE CRT System................. Breakthrough Device.... 209 41,145.00 8,599,305.00 TriVerity Test...................... Breakthrough Device.... 5,755 243.75 1,402,781.25 VITEK[supreg] REVEALTM AST System... Breakthrough Device.... 19,000 81.25 1,543,750.00

Estimated Total FY 2026 Impact.. ....................... .............. .............. 218,842,533.63

* The applicant did not provide an updated volume during the public comment period. Therefore, based on the

volume of claims identified by the applicant in FY 2024 MedPAR data and the expected patient population for

this technology (VLBW infants and neonates), we are imputing a volume of 11 for fewer than 11 estimated cases

for the purposes of estimating the impact of the technology.

** During the public comment period, the applicant stated that Medicare coverage for this technology is

expected to be rare with fewer than 10 anticipated cases (medically complex children) during the new

technology add-on payment period. Therefore, for the purposes of estimating the impact of the technology, we

are imputing a volume of 10.

As fully discussed in section II.E.5. of the preamble of this final rule, we are approving new technology add-on payments for 5 technologies that applied under the traditional pathway for new technology add-on payments for FY 2026. Based on information from the applicants at the time of rulemaking, we estimate that total payments for the technologies for which we are making new technology add-on payments is approximately $139 million for FY 2026.

In the following table, we present detailed estimates for the 5 technologies for which we are approving new technology add-on payments under the traditional pathway in FY 2026:

FY 2026 Estimates for New Technology Add-On Payments for Technologies Under the Traditional

Estimated FY 2026 NTAP Estimated total

Technology name cases amount (65%) FY 2026 impact

AURLUMYNTM (iloprost injection).............................. 300 $28,600.00 $8,580,000.00 BREYANZI[supreg] (lisocabtagene maraleucel).................. 290 316,860.05 91,889,414.50 GRAFAPEXTM (treosulfan)...................................... 368 21,411.00 7,879,248.00 IMDELLTRA[supreg] (tarlatamab-dlle).......................... 692 7,117.50 4,925,310.00 TECELRA[supreg] (afamitresgene autoleucel)................... 55 472,550.00 25,990,250.00

Estimated Total FY 2026 Impact........................... .............. .............. 139,264,222.50

In the following table, we present summary estimates for all new technology add-on payments for FY 2026:

FY 2026 Estimates for New Technology Add-On Payments for FY 2026

Estimated total

Category FY 2026 impact

Technologies Continuing New Technology Add-on $603,029,994.79

Payments in FY 2026................................. Alternative Pathway Applications..................... 218,842,553.63 Traditional Pathway Applications..................... 139,264,222.50

Aggregate Estimated Total FY 2026 Impact......... 961,136,770.92

2. Medicare DSH Uncompensated Care Payments and Supplemental Payment for Indian Health Service Hospitals and Tribal Hospitals and Hospitals Located in Puerto Rico

As discussed in section V.E. of the preamble of this final rule, under section 3133 of the Affordable Care Act, hospitals that are eligible to receive Medicare DSH payments will receive 25 percent of the amount they previously would have received under the statutory formula for Medicare DSH payments under section 1886(d)(5)(F) of the Act. The remainder, equal to an estimate of 75 percent of what formerly would have been paid as Medicare DSH payments (Factor 1), reduced to reflect changes in the percentage of uninsured individuals (Factor 2), is available to make additional payments to each hospital that qualifies for Medicare DSH payments and that has reported uncompensated care. Each hospital that is eligible for Medicare DSH payments will receive an additional payment based on its estimated share of the total amount of uncompensated care for all hospitals eligible for Medicare DSH payments. The uncompensated care payment methodology has redistributive effects based on the proportion of a hospital's amount of uncompensated care relative to the aggregate amount of uncompensated care of all hospitals eligible for Medicare DSH payments (Factor 3). The change to Medicare DSH payments under section 3133 of the Affordable Care Act is not budget neutral.

In this final rule, we are establishing the amount to be distributed as uncompensated care payments (UCP) to DSH-eligible hospitals for FY 2026, which is $7,713,127,500. This figure represents 75 percent of the amount that otherwise would have been paid for Medicare DSH payment adjustments adjusted by a Factor 2 of 62.14 percent. For FY 2025, the amount available to be distributed for uncompensated care was $5,705,743,275 or 75 percent of the amount that otherwise would have been paid for Medicare DSH payment adjustments adjusted by a Factor 2 of 54.29 percent. In addition, eligible IHS/Tribal hospitals and hospitals located in Puerto Rico are estimated to receive approximately $107,842,748.53 in supplemental payments in FY 2026, based on the difference between each hospital's FY 2022 UCP (increased by 35.2 percent, which is the projected change between the FY 2026 total UCP amount and the total UCP amount for FY 2025) and its FY 2026 UCP as calculated using the methodology for FY 2026. If this difference is less than or equal to zero, the hospital will not receive a supplemental payment. For this final rule, the total UCP and supplemental payments equal approximately $7.821 billion. For FY 2026, we are using 3 years of data on uncompensated care costs from Worksheet S-10 of the FYs 2020, 2021, and 2022 cost reports to calculate Factor 3 for all DSH- eligible hospitals, including IHS/Tribal hospitals and Puerto Rico hospitals. For a complete discussion regarding the methodology for calculating Factor 3 for FY 2026, we refer readers to section V.E. of the preamble of this final rule. For a discussion regarding the methodology for calculating the supplemental payments, we refer readers to section V.D. of the preamble of this final rule.

To estimate the impact of the combined effect of the changes in Factors 1 and 2, as well as the changes to the data used in determining Factor 3, on the calculation of Medicare UCP along with changes to supplemental payments for IHS/Tribal hospitals and hospitals located in Puerto Rico, we compared total UCP and supplemental payments estimated in the FY 2025 IPPS/LTCH PPS final rule correction notice (89 FR 68986) to the combined total of the UCP and the supplemental payments estimated in this FY 2026 IPPS/ LTCH PPS final rule. For FY 2025, we calculated 75 percent of the estimated amount that would be paid as Medicare DSH payments absent section 3133 of the Affordable Care Act, adjusted by a Factor 2 of 54.29 percent and multiplied by a Factor 3 calculated using the methodology described in the FY 2025 IPPS/LTCH PPS final rule. For FY 2026, we calculated 75 percent of the estimated amount that would be paid as Medicare DSH payments during FY 2025 absent section

3133 of the Affordable Care Act, adjusted by a Factor 2 of 62.14 percent and multiplied by a Factor 3 calculated using the methodology described previously. For this final rule, the supplemental payments for IHS/Tribal hospitals and Puerto Rico hospitals are calculated as the difference between the hospital's adjusted base year amount (as determined based on the hospital's FY 2022 UCP) and the hospital's FY 2026 UCP.

Our analysis included 2,364 hospitals that are projected to be DSH-eligible in FY 2026. Our analysis did not include hospitals that had terminated their participation in the Medicare program as of January 22, 2025, Maryland hospitals, new hospitals, and SCHs that are expected to be paid based on their hospital-specific rates. The 30 hospitals that are anticipated to be participating in the Rural Community Hospital Demonstration Program were also excluded from this analysis, as participating hospitals are not eligible to receive empirically justified Medicare DSH payments and UCP. In addition, the data from merged or acquired hospitals were combined under the surviving hospital's CMS certification number (CCN), and the non-surviving CCN was excluded from the analysis. The estimated impact of the changes in Factors 1, 2, and 3 on UCP and supplemental payments for eligible IHS/Tribal hospitals and Puerto Rico hospitals across all hospitals projected to be DSH-eligible in FY 2026, by hospital characteristic, is presented in the following table:

Modeled Uncompensated Care Payments * and Supplemental Payments for Estimated FY 2026 DSHS by Hospital Type

FY 2025 estimated FY 2026 estimated

uncompensated care uncompensated care Dollar

Number of payments and payments and difference: FY Percent change

estimated DSHs supplemental supplemental 2025-FY 2026 ($ ***

payments ($ in payments ** ($ in in millions)

millions) millions)

(1) (2) (3) (4) (5)

Total.............................................. 2,364 $5,786 $7,821 $2,035 35.2 By Geographic Location:

Urban Hospitals................................ 1,905 5,456 7,384 1,928 35.3

Other Urban Areas.............................. 984 2,433 3,240 807 33.2

Large Urban Areas.............................. 921 3,023 4,144 1,121 37.1

Rural Hospitals................................ 459 329 437 107 32.6 Bed Size (Urban):

0 to 99 Beds................................... 371 244 310 66 27.3

100 to 249 Beds................................ 775 1,198 1,594 396 33.0

250+ Beds...................................... 759 4,014 5,480 1,466 36.5 Bed Size (Rural):

0 to 99 Beds................................... 344 177 233 56 31.6

100 to 249 Beds................................ 105 122 164 42 34.5

250+ Beds...................................... 10 30 40 9 30.8 Urban by Region:

New England.................................... 85 145 203 58 40.1

Middle Atlantic................................ 222 618 867 249 40.4

South Atlantic................................. 303 576 738 162 28.0

East North Central............................. 105 289 363 75 25.9

East South Central............................. 319 1,406 1,896 490 34.9

West North Central............................. 124 348 471 123 35.2

West South Central............................. 245 1,248 1,729 481 38.6

Mountain....................................... 148 245 348 103 41.8

Pacific........................................ 308 508 670 162 31.9

Puerto Rico.................................... 46 72 98 25 35.1 Rural by Region:

New England.................................... 8 9 11 2 22.7

Middle Atlantic................................ 34 17 25 7 42.2

South Atlantic................................. 70 42 55 13 31.1

East North Central............................. 31 20 27 7 35.3

East South Central............................. 82 95 124 29 31.0

West North Central............................. 104 61 82 21 34.3

West South Central............................. 103 70 92 22 32.4

Mountain....................................... 20 10 13 3 35.1

Pacific........................................ 7 6 7 2 27.6 By Payment Classification:

Urban Hospitals................................ 1,243 2,585 3,469 884 34.2

Large Urban Areas.............................. 661 1,546 2,093 548 35.4

Other Urban Areas.............................. 582 1,039 1,375 336 32.4

Rural Hospitals................................ 1,121 3,201 4,352 1,151 36.0 Teaching Status:

Nonteaching.................................... 1,246 1,412 1,872 460 32.6

Fewer than 100 residents....................... 828 2,086 2,760 674 32.3

100 or more residents.......................... 290 2,288 3,189 901 39.4 Type of Ownership:

Voluntary...................................... 1,509 3,347 4,440 1,093 32.7

Proprietary.................................... 497 811 1,089 278 34.3

Government..................................... 358 1,628 2,292 664 40.8 Medicare Utilization Percent: ****

0 to 25........................................ 1,389 4,478 6,100 1,623 36.2

25 to 50....................................... 948 1,298 1,707 410 31.6

50 to 65....................................... 25 10 13 3 31.3

Greater than 65................................ 2 0 0 0 -100.0 Medicaid Utilization Percent: ****

0 to 25........................................ 1,308 2,479 3,289 810 32.7

25 to 50....................................... 930 2,646 3,582 936 35.4

50 to 65....................................... 93 491 712 221 44.9

Greater than 65................................ 33 169 238 69 41.1

Source: Dobson DaVanzo analysis of 2020, 2021 and 2022 Hospital Cost Reports.

* Dollar UCP calculated by [0.75 * estimated section 1886(d)(5)(F) payments * Factor 2 * Factor 3]. When summed across all hospitals projected to

receive DSH payments, UCP and supplemental payments are estimated to be $5.786 million in FY 2025, and UCP and supplemental payments are estimated to

be $7.821 million in FY 2026. ** For IHS/Tribal hospitals and Puerto Rico hospitals, this impact table reflects the supplemental payments. *** Percentage change is determined as the difference between Medicare UCP and supplemental payments modeled for this FY 2026 IPPS/LTCH PPS final rule

(column 3) and Medicare UCP and supplemental payments modeled for the FY 2025 IPPS/LTCH PPS final rule correction notice (column 2) divided by

Medicare UCP and supplemental payments modeled for the FY 2025 IPPS/LTCH PPS final rule correction notice (column 2) times 100 percent. **** Hospitals with missing or unknown Medicare utilization or Medicaid utilization are not shown in the table.

The changes in projected FY 2026 UCP and supplemental payments compared to the total of UCP and supplemental payments in FY 2025 are driven by increases in Factor 1 and Factor 2. Factor 1 has increased from the FY 2025 final rule's Factor 1 of $10.509 billion to this final rule's Factor 1 of $11.843 billion. Factor 2 has increased from the FY 2025 final rule's Factor 2 of 54.29 percent to this final rule's Factor 2 of 62.14 percent. In addition, we note that there is a decrease in the number of projected DSH-eligible hospitals to 2,364 at the time of the development of this final rule compared to the 2,398 DSHs at the time of development of the FY 2025 IPPS/LTCH PPS final rule (88 FR 58640). Based on the changes, the impact analysis found that, across all projected DSH-eligible hospitals, FY 2026 UCP and supplemental payments are estimated at approximately $7.821 billion, or an increase of approximately 35.2 percent from FY 2025 UCP and supplemental payments (approximately $5.786 billion). While the changes would result in a net increase in the total amount available to be distributed in UCP and supplemental payments, the projected payment increases vary by hospital type. This redistribution of payments is caused by changes in Factor 3 and the amount of the supplemental payment for DSH-eligible IHS/Tribal hospitals and Puerto Rico hospitals. As seen in the previous table, a percent change of less than 35.2 percent indicates that hospitals within the specified category are projected to experience a smaller increase in payments, on average, compared to the universe of projected FY 2026 DSH-eligible hospitals. Conversely, a percentage change greater than 35.2 percent indicates that a hospital type is projected to have a larger increase compared to the overall average. The variation in the distribution of overall payments by hospital characteristic is largely dependent on a given hospital's uncompensated care costs as reported on the Worksheet S-10 and used in the Factor 3 computation and whether the hospital is eligible to receive the supplemental payment.

Urban hospitals, in general, are projected to experience a slightly larger increase in UCP compared to the increase their rural counterparts are projected to experience. Overall, urban hospitals are projected to receive a 35.3 percent increase in payments, while rural hospitals are projected to receive a 32.6 percent increase in payments, which is slightly less than the overall hospital average.

By bed size, rural hospitals with 0 to 99 beds, 100 to 249 beds, and 250+ beds are projected to receive a smaller than average increase of approximately 31.6 percent, 34.5 percent, and 30.8 percent, respectively. Among urban hospitals, the largest urban hospitals, those with 250+ beds, are projected to receive an increase in payments (36.5 percent) that is greater than the overall hospital average. In contrast, smaller urban hospitals with 0-99 beds and 100-249 beds are projected to receive smaller than average increases in payments of 27.3 and 33.0 percent, respectively.

By region, rural hospitals are projected to receive a varied range of payment changes. Rural hospitals in the New England, South Atlantic, East South Central, West North Central, West South Central, Mountain, and Pacific regions are projected to receive smaller than average increases in payments. Rural hospitals in all other regions are projected to receive larger than average increases in payments. Urban hospitals in the South Atlantic, East North Central, East South Central, Pacific regions, and Puerto Rico are projected to receive smaller than average increases in payments, while urban hospitals in all other regions are projected to receive larger than average increases in payments.

By payment classification, hospitals in urban payment areas overall are expected to receive a smaller than average increase in UCP and supplemental payments of 34.2 percent. Hospitals in large urban payment areas are projected to receive a larger than average increase in payments (35.4 percent), while other urban payment areas are projected to receive a smaller than average increase in payments of 32.4 percent. In contrast, hospitals in rural payment areas are projected to receive a larger than average increase in payments of 36.0 percent.

Nonteaching hospitals and teaching hospitals with fewer than 100 residents are projected to receive smaller than average payment increases of 32.6 percent and 32.3 percent, respectively. Teaching hospitals with 100+ residents are projected to receive larger than average payment increases of 39.4 percent. Voluntary hospitals and proprietary hospitals are projected to receive smaller than average increases of 32.7 percent and 34.3 percent, respectively, while government-owned hospitals are expected to receive a larger than average payment increase of 40.8 percent. Hospitals with less than 25 percent Medicare utilization are projected to receive larger than average increases of 36.2 percent, while hospitals with Medicare utilization between 25-50 percent and 50-65 percent are projected to receive smaller than average payment increases of 31.6 percent and 31.3 percent, respectively. There are 2 hospitals with greater than 65 percent Medicare utilization, and the hospitals (250002 and 250017) are projected to have a decrease in payments of 100.0 percent, which reflects the hospitals' projected DSH eligibility. Hospitals with 20-50 percent Medicaid utilization, those with 50-65 percent Medicaid utilization and those with greater than 65 percent Medicaid utilization are projected to receive larger than average increases in payments of 35.4, 44.9 and 41.1 percent, respectively. Hospitals with less than 25 percent Medicaid utilization are projected to receive a smaller than average increase of 32.7 percent.

The impact table reflects the modeled FY 2026 UCP and supplemental payments for IHS/Tribal and Puerto Rico hospitals. We note that the supplemental payments to IHS/Tribal hospitals and Puerto Rico hospitals are estimated to be approximately $107.8 million in FY 2026.

In section V.D. of the preamble of this final rule, we discuss the extension of the temporary changes to the low-volume hospital payment policy originally provided for by the Affordable Care Act and extended by subsequent legislation. Specifically, section 2201 of the Full-Year Continuing Appropriations and Extensions, 2025 further extended the modified definition of low-volume hospital and the methodology for calculating the payment adjustment for low- volume hospitals under section 1886(d)(12) through September 30, 2025. Prior to the enactment of the Full-Year Continuing Appropriations and Extensions, 2025, the temporary changes to the low-volume hospital payment adjustment were set to expire on April 1, 2025. Under the extension provided by section 2201 of the Full- Year Continuing Appropriations and Extensions, 2025, FY 2025 payments to IPPS hospitals are projected to increase by approximately $90 million relative to what the payments would have been in the absence of section 2201.

Beginning October 1, 2025, the low-volume hospital qualifying criteria and payment adjustment will revert to the statutory requirements that were in effect prior to FY 2011, and the preexisting low-volume hospital payment adjustment methodology and qualifying criteria, as implemented in FY 2005, will resume. Therefore, absent further Congressional action, effective for FY 2026 and subsequent years, in order to qualify as a low-volume hospital, a subsection (d) hospital must be more than 25 road miles from another subsection (d) hospital and have less than 200 discharges (that is, less than 200 discharges total, including both Medicare and non-Medicare discharges) during the fiscal year.

Using the same methodology used in developing the quantitative analyses of changes in payments per case discussed previously in section I.G. of this Appendix A of this final rule, based upon the best available data at this time, we estimate the expiration of the temporary changes to the low-volume hospital payment policy effective for discharges occurring on or after October 1, 2025, and subsequent years would decrease aggregate low-volume hospital payments by $375 million in FY 2026 as

compared to FY 2025. This payment estimate was determined based on the estimated payments for the approximately 579 providers that are expected to no longer qualify under the criteria that are effective beginning on October 1, 2025.

Of those 579 hospitals, currently approximately 97 hospitals have a low-volume hospital payment adjustment based on 500 or fewer total discharges, while the remaining approximately 482 hospitals have an adjustment based on having between 500 and 3,800 total discharges. Approximately 55 of the 579 hospitals that currently qualify for a low-volume hospital payment adjustment in FY 2025 have 200 or fewer total discharges. However, the distance information needed to project whether those hospitals are more than 25 road miles from another subsection (d) hospital (instead of 15 road miles), and therefore would continue to qualify for a low-volume hospital payment adjustment for FY 2026, is evaluated by each hospitals' MAC. Therefore, we are unable to estimate how many of these 55 hospitals would continue to qualify for the low-volume hospital payment adjustment for FY 2026.

In section VI.K. of the preamble of the FY 2026 IPPS/LTCH PPS final rule, we are modifying the six readmission measures in the program to include Medicare Advantage (MA) beneficiaries into the patient cohorts and modify the applicable performance period from a 3-year period to a 2-year period beginning with the FY 2027 program year. We are also updating the Extraordinary Circumstance Exception Policy. The remaining policies finalized in FY 2025 IPPS/LTCH PPS final rule (89 FR 69400) continue to apply. We refer readers to TABLE VI.K-04 in section VI.K. of this final rule for an estimate of the financial impact reflecting the newly finalized program updates that will begin in FY 2027.

The Hospital Readmissions Reduction Program requires a reduction to a hospital's base operating diagnosis-related group (DRG) payments to account for excess readmissions of selected applicable conditions and procedures. The table and analysis in this section illustrate the estimated financial impact of the Hospital Readmissions Reduction Program payment adjustment methodology by hospital characteristic for the FY 2026 program year. Hospitals are sorted into quintiles based on the proportion of dual-eligible stays among Medicare fee-for-service (FFS) and managed care stays between July 1, 2021 and June 30, 2024 (that is, the FY 2026 Hospital Readmissions Reduction Program's applicable period, which is the most recently available data at the time of publication of this final rule). Hospitals' excess readmission ratios (ERRs) are assessed relative to their peer group median and a neutrality modifier is applied in the payment adjustment factor calculation to maintain budget neutrality. In this FY 2026 IPPS/LTCH PPS final rule, we are providing an updated estimate of the financial impact using the proportion of dually-eligible beneficiaries, ERRs, and aggregate payments for each condition/procedure and all discharges for applicable hospitals from the FY 2026 Hospital Readmissions Reduction Program applicable period (that is, July 1, 2021, through June 30, 2024).

The results in Table I.G.7.-01 include 2,797 non-Maryland hospitals estimated as eligible to receive a penalty during the performance period. Hospitals are eligible to receive a penalty if they have 25 or more eligible discharges for at least one measure between July 1, 2021, and June 30, 2024. The second column in Table I.G.7.-01 indicates the total number of non-Maryland hospitals with available data for each characteristic that have an estimated payment adjustment factor less than 1 (that is, penalized hospitals).

The third column in Table I.G.7.-01 indicates the estimated percentage of penalized hospitals among those eligible to receive a penalty by hospital characteristic. For example, 77.76 percent of eligible hospitals characterized as non-teaching hospitals are expected to be penalized. Among teaching hospitals, 88.53 percent of eligible hospitals with fewer than 100 residents and 87.76 percent of eligible hospitals with 100 or more residents are expected to be penalized. The fourth column in Table I.G.7.-01 estimates the financial impact on hospitals by hospital characteristic. Table I.G.7.-01 also shows the share of penalties as a percentage of all base operating DRG payments for hospitals with each characteristic. This is calculated as the sum of penalties for all hospitals with that characteristic over the sum of all base operating DRG payments for those hospitals between October 1, 2023, through September 30, 2024 (FY 2024). For example, the penalty as a share of payments for non-teaching hospitals is 0.48 percent. This means that total penalties for all non-teaching hospitals are 0.48 percent of total payments for non-teaching hospitals. Measuring the financial impact on hospitals as a percentage of total base operating DRG payments accounts for differences in the amount of base operating DRG payments for hospitals with the characteristic when comparing the financial impact of the program on different groups of hospitals.

Estimated Percentage of Hospitals Penalized and Penalty as Share of Payments for FY 2026 Hospital Readmissions

Reduction Program by Hospital Characteristic

Percentage of Penalty as a

Number of Number of hospitals share of

Hospital characteristic eligible penalized penalized \c\ payments \d\

hospitals \a\ hospitals \b\ (%) (%)

All Hospitals................................... 2,797 2,304 82.37 0.44 By Geographic Location (n=2,792):

Urban hospitals............................. 2,147 1,802 83.93 0.44

1-99 beds............................... 497 326 65.59 0.43

100-199 beds............................ 626 553 88.34 0.50

200-299 beds............................ 385 354 91.95 0.53

300-399 beds............................ 269 246 91.45 0.45

400-499 beds............................ 117 106 90.60 0.40

500 or more beds........................ 253 217 85.77 0.38

Rural hospitals............................. 645 499 77.36 0.42

1-49 beds............................... 294 195 66.33 0.38

50-99 beds.............................. 184 155 84.24 0.43

100-149 beds............................ 94 85 90.43 0.46

150-199 beds............................ 42 35 83.33 0.43

200 or more beds........................ 31 29 93.55 0.39 By Teaching Status \e\ (n=2,792):

Non-teaching................................ 1,565 1,217 77.76 0.48

Fewer than 100 Residents.................... 933 826 88.53 0.47

100 or more Residents....................... 294 258 87.76 0.37

By Ownership Type (n=2,792):

Government.................................. 388 315 81.19 0.31

Proprietary................................. 608 492 80.92 0.59

Voluntary................................... 1,796 1,494 83.18 0.43 By Safety-Net Status \f\ (n=2,792):

Safety-net hospitals........................ 556 458 82.37 0.41

Non-safety-net hospitals.................... 2,236 1,843 82.42 0.45 By Disproportionate Share Hospital (DSH) Patient

Percentage \g\ (n=2,792):

0-24........................................ 1,051 838 79.73 0.49

25-49....................................... 1,461 1,236 84.60 0.41

50-64....................................... 162 133 82.10 0.41

65 and over................................. 118 94 79.66 0.51 By Medicare Cost Report (MCR) Percentage \h\

(n=2,792):

0-24........................................ 1,371 1,142 83.30 0.36

25-49....................................... 1,354 1,116 82.42 0.52

50-64....................................... 59 38 64.41 0.69

65 and over................................. 8 5 62.50 1.81 By Region (n=2,797):

New England................................. 120 100 83.33 0.68

Middle Atlantic............................. 312 274 87.82 0.55

East North Central.......................... 441 376 85.26 0.43

West North Central.......................... 222 164 73.87 0.29

South Atlantic.............................. 485 424 87.42 0.45

East South Central.......................... 247 219 88.66 0.53

West South Central.......................... 416 324 77.88 0.41

Mountain.................................... 205 144 70.24 0.32

Pacific..................................... 349 279 79.94 0.35

Source: The table results are based on the data used to calculate the FY 2026 payment adjustment factors of

open, non-Maryland, subsection (d) hospitals only. The FY 2026 payment adjustment factors are based on

discharges from July 1, 2021, through June 30, 2024. Although data from all subsection (d) and Maryland

hospitals are used in calculations of each hospital's ERR, this table does not include results for Maryland

hospitals and hospitals that are not open as of the October 2025 public reporting open hospital list because

these hospitals are not eligible for a penalty under the program. Hospitals are sorted into five peer groups

based on the proportion of FFS and managed care dual-eligible stays for the multi-year performance period.

Hospital characteristics are from the FY 2026 IPPS Proposed Rule Impact File. Note: The total number of hospitals with hospital characteristics data may not add up to the total number of

hospitals because not all hospitals have data for all characteristics. Not all hospitals had data for

geographic location, teaching status, ownership type, safety-net status, DSH percentage, and MCR percentage

(n=2,792; missing=5). \a\ This column is the number of applicable hospitals within the characteristic that are eligible for a penalty

(that is, they have 25 or more eligible discharges for at least one measure). \b\ This column is the number of applicable hospitals that are penalized (that is, they have 25 or more eligible

discharges for at least one measure and an estimated payment adjustment factor less than 1) within the

characteristic. \c\ This column is the percentage of applicable hospitals that are penalized among hospitals that are eligible

to receive a penalty by characteristic. \d\ This column is calculated as the sum of all penalties for the group of hospitals with that characteristic

divided by total base operating DRG payments for all those hospitals. Measuring the financial impact on

hospitals as a percentage of total base operating DRG payments in this way allows for comparisons across

hospital characteristics that accounts for differences in the amount of base operating DRG payments for

different groups of hospitals. MedPAR data from October 1, 2023, through September 30, 2024 (FY 2024), are

used to estimate the total base operating DRG payments. \e\ A hospital is considered a teaching hospital if it has an Indirect Medical Education adjustment factor for

Operation PPS (TCHOP) greater than zero. \f\ A hospital is considered a safety-net hospital if it is in the top DSH quintile. \g\ DSH patient percentage is the sum of the percentage of Medicare inpatient days attributable to patients

eligible for both Medicare Part A and Supplemental Security Income (SSI), and the percentage of total

inpatient days attributable to patients eligible for Medicaid but not Medicare Part A. \h\ MCR (Medicare Cost Report) percentage is the percentage of total inpatient stays from Medicare patients.

The Secretary makes value-based incentive payments to hospitals under the Hospital Value-Based Purchasing Program based on their performance on measures during the performance period with respect to a fiscal year. These incentive payments will be funded for FY 2026 through a reduction to the FY 2026 base operating DRG payment amount for hospital discharges for such fiscal year, as required by section 1886(o)(7)(B) of the Act. The applicable percentage for FY 2026 and subsequent years is two percent. The total amount available for value-based incentive payments must be equal to the total amount of reduced payments for all hospitals for the fiscal year, as estimated by the Secretary. In section VI.L.1.b. of the preamble of this final rule, we estimate the available pool of funds for value- based incentive payments in the FY 2026 program year, which, in accordance with section 1886(o)(7)(C)(v) of the Act, will be 2.00 percent of base operating DRG payments, or a total of approximately $1.7 billion. This estimated available pool for FY 2026 is based on the historical pool of hospitals that were eligible to participate in the FY 2025 program year and the payment information from the March 2025 update to the FY 2024 MedPAR file.

The estimated impacts of the FY 2026 program year by hospital characteristic, found in Table I.G.6.-01., are based on historical TPSs and sepsis measure results, and reflect removal of the Health Equity Adjustment as discussed in section VI.L.6. We used the FY 2025 program year's TPSs to calculate the proxy adjustment factors used for this impact analysis. These are the most recently available scores that hospitals were given an opportunity to review and correct. The proxy adjustment factors use estimated annual base operating DRG payment amounts derived from the March 2025 update to the FY 2024 MedPAR file. The proxy adjustment factors can be found in Table 16 associated with this final rule (available via the internet on the CMS website).

The estimated impact analysis shows that, for the FY 2026 program year, the number of hospitals with a positive percent change in base operating DRG (49.25 percent) is lower than the number of hospitals with a negative percent change (50.75 percent). Approximately half of all hospitals experience a percent change in base operating DRG between -1.9 percent and 0.0 percent. On average, urban hospitals in the West North Central region and rural hospitals in the Mountain region have the highest positive percent change in base operating DRG. Urban hospitals in the Middle Atlantic, East South Central, and West South Central regions experience an average negative percent change in base operating DRG. All other regions (both urban and rural) experience an average positive percent change in base operating DRG. As the MCR percent increases, the average percent change

in base operating DRG generally increases, except for the four hospitals with the highest MCR percentage. As DSH percent increases, the average percent change in base operating DRG decreases except for hospitals with greater than 65 DSH percent. On average, non- teaching hospitals have a higher percent change in base operating DRG compared to teaching hospitals.

Table I.G.6.-01--Impact Analysis of Base Operating DRG Payment Amounts

Resulting From the FY 2026 Hospital VBP Program

Average net

Number of percentage payment

hospitals adjustment

By Geographic Location:

All Hospitals................. 2,532 0.169

Urban Area................ 1,984 0.077

Rural Area................ 547 0.500

Missing................... 1 0.466

Urban Hospitals............... 1,984 0.077

0-99 beds................. 364 0.713

100-199 beds.............. 602 0.137

200-299 beds.............. 402 -0.130

300-499 beds.............. 379 -0.244

500 or more beds.......... 237 -0.186

Rural Hospitals............... 547 0.500

0-49 beds................. 212 0.824

50-99 beds................ 178 0.478

100-149 beds.............. 86 0.288

150-199 beds.............. 41 -0.077

200 or more beds.......... 30 -0.267 By Region:

Urban By Region............... 1,984 0.077

New England............... 96 0.103

Middle Atlantic........... 244 -0.095

South Atlantic............ 365 0.025

East North Central........ 311 0.107

East South Central........ 117 -0.131

West North Central........ 131 0.302

West South Central........ 246 -0.002

Mountain.................. 154 0.143

Pacific................... 320 0.246

Rural By Region............... 547 0.500

New England............... 19 0.444

Middle Atlantic........... 41 0.491

South Atlantic............ 90 0.375

East North Central........ 100 0.699

East South Central........ 100 0.121

West North Central........ 68 0.688

West South Central........ 73 0.273

Mountain.................. 32 1.243

Pacific................... 24 0.936 By MCR Percent:

0-25.......................... 1,118 0.086

25-50......................... 1,369 0.222

50-65......................... 38 0.533

Over 65....................... 4 0.474

Missing....................... 3 1.683 By DSH Percent:

0-25.......................... 887 0.418

25-50......................... 1,394 0.059

50-65......................... 146 -0.178

Over 65....................... 104 0.000

Missing....................... 1 0.466 By Teaching Status:

Non-Teaching.................. 1,370 0.360

Teaching...................... 1,161 -0.058

Missing....................... 1 0.466

The actual FY 2026 program year's TPSs will not be reviewed and corrected by hospitals until after the FY 2026 IPPS/LTCH PPS final rule has published. Therefore, the same historical universe of eligible hospitals and corresponding TPSs from the FY 2025 program year have been used for the updated impact analysis in this final rule.

We present the estimated impact of the FY 2026 HAC Reduction Program on hospitals by hospital characteristic based on previously adopted policies for the program. In this final rule, we did not add or remove any measures from the HAC Reduction Program, nor did we finalize any changes to reporting or submission requirements which would have any significant economic impact for the FY 2026 program year. The table in this section presents the estimated proportion of hospitals in the worst-performing quartile of Total HAC Scores by hospital characteristic. Hospitals' CMS Patient Safety and Adverse Events Composite (CMS PSI 90) measure results are based on Medicare fee-for-service (FFS) discharges from July 1, 2022, through June 30, 2024, and version 15.0 of the CMS PSI software. Hospitals' measure results for Centers for Disease Control and Prevention (CDC) Central Line-Associated Bloodstream Infection (CLABSI), Catheter-Associated Urinary Tract Infection (CAUTI), Colon and Abdominal Hysterectomy Surgical Site Infection (SSI), Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, and Clostridium difficile Infection (CDI) are derived from standardized infection ratios (SIRs) calculated with hospital surveillance data reported to the CDC's National Healthcare Safety Network (NHSN) for infections occurring between January 1, 2023,

and December 31, 2024. Hospital characteristics are based on the FY 2026 IPPS proposed rule Impact File.

This table includes 2,891 non-Maryland hospitals with an estimated FY 2026 Total HAC Score based on the most recently available data at the time of publication of this final rule. Maryland hospitals and hospitals without a Total HAC Score are excluded from the table. Actual results for FY 2026 will be determined in the fall of 2025 after a 30-day review and corrections period for hospitals to review their program results. The first column presents a breakdown of each characteristic, and the second column indicates the number of hospitals for the respective characteristic.

The third column in the table indicates the estimated number of hospitals for each characteristic that would be in the worst- performing quartile of Total HAC Scores. For example, with regard to teaching status, 401 hospitals out of 1,620 hospitals characterized as non-teaching hospitals would be subject to a payment reduction. Among teaching hospitals, 210 out of 959 hospitals with fewer than 100 residents and 100 out of 295 hospitals with 100 or more residents would be subject to a payment reduction.

The fourth column in the table indicates the estimated proportion of hospitals for each characteristic that would be in the worst performing quartile of Total HAC Scores and thus receive a payment reduction under the FY 2026 HAC Reduction Program. For example, 24.8 percent of the 1,620 hospitals characterized as non- teaching hospitals, 21.9 percent of the 959 teaching hospitals with fewer than 100 residents, and 33.9 percent of the 295 teaching hospitals with 100 or more residents would be subject to a payment reduction.

Table I.G.7.-01--Estimated Proportion of Hospitals in the Worst-Performing Quartile (>75th Percentile) of the

Total HAC Scores for the FY 2026 HAC Reduction Program

[By hospital characteristic]

Number of Percent of

Number of hospitals in the hospitals in the

Hospital characteristic hospitals worst-performing worst-performing

quartile \a\ quartile \b\

All Hospitals \c\....................................... 2,891 721 25 By Geographic Location (n=2,874): \d\

Urban hospitals..................................... 2,245 510 22.7

1-99 beds....................................... 562 129 23.0

100-199 beds.................................... 645 144 22.3

200-299 beds.................................... 396 85 21.5

300-399 beds.................................... 271 55 20.3

400-499 beds.................................... 118 26 22.0

500 or more beds................................ 253 71 28.1

Rural hospitals..................................... 629 201 32.0

1-49 beds....................................... 276 93 33.7

50-99 beds...................................... 186 59 31.7

100-149 beds.................................... 94 23 24.5

150-199 beds.................................... 42 12 28.6

200 or more beds................................ 31 14 45.2 By Teaching Status \d\ (n=2,874): \d\

Non-teaching........................................ 1,620 401 24.8

Fewer than 100 residents............................ 959 210 21.9

100 or more residents............................... 295 100 33.9 By Ownership (n=2,874):

Government.......................................... 390 146 37.4

Proprietary......................................... 655 94 14.4

Voluntary........................................... 1,829 471 25.8 By Safety-Net Status \e\ (n=2,874): \d\

Safety-net.......................................... 583 162 27.8

Non-safety net...................................... 2,291 549 24.0 By Disproportionate Share Hospital (DSH) Patient

Percentage \f\ (n=2,874):

0-24................................................ 1,103 232 21.0

25-49............................................... 1,465 387 26.4

50-64............................................... 164 45 27.4

65 and over......................................... 142 47 33.1 By Medicare Cost Report (MCR) Percentage (n=2,872):

0-24................................................ 1,455 352 24.2

25-49............................................... 1,350 339 25.1

50-64............................................... 56 14 25.0

65 and over......................................... 11 4 36.4 By Region (n=2,891):

New England......................................... 120 38 31.7

Middle Atlantic..................................... 318 86 27.0

East North Central.................................. 456 118 25.9

West North Central.................................. 227 56 24.7

South Atlantic...................................... 491 97 19.8

East South Central.................................. 248 83 33.5

West South Central.................................. 438 86 19.6

Mountain............................................ 219 45 20.5

Pacific............................................. 374 112 29.9

Source: FY 2026 HAC Reduction Program estimated final rule results are based on CMS PSI 90 data from July 1,

2022, through June 30, 2024, and CDC's NHSN HAI results from January 1, 2023, through December 31, 2024.

Hospital Characteristics are based on the FY 2026 IPPS proposed rule Impact File. Note: The total number of hospitals with hospital characteristic data may not add up to the total number of

hospitals because not all hospitals have data for all characteristics. Not all hospitals had data for

geographic location, teaching status, ownership, Safety-net status, and DSH percent (n=2,874; missing=17), and

MCR percent (n=2,872; missing=19). \a\ This column is the number of non-Maryland hospitals with a Total HAC Score within the corresponding

characteristic that are estimated to be in the worst-performing quartile. \b\ This column is the percent of non-Maryland hospitals within each characteristic that are estimated to be in

the worst-performing quartile. The percentages are calculated by dividing the number of non-Maryland hospitals

with a Total HAC Score in the worst-performing quartile by the total number of non-Maryland hospitals with a

Total HAC Score within that characteristic. \c\ The number of non-Maryland hospitals with a Total HAC Score (N=2,891). \d\ A hospital is considered a teaching hospital if it has an IME adjustment factor for Operation PPS (TCHOP)

greater than zero. \e\ A hospital is considered a Safety-net hospital if it is in the top quintile for DSH percent.

\f\ The DSH patient percentage is equal to the sum of: (1) the percentage of Medicare inpatient days

attributable to patients eligible for both Medicare Part A and Supplemental Security Income; and (2) the

percentage of total inpatient days attributable to patients eligible for Medicaid but not Medicare Part A.

We received no comments on our assumptions regarding these effects.

In section VI.N.2 of the preamble of this final rule for FY 2026, we discussed our budget neutrality methodology for section 410A of Public Law 108-173, as amended by sections 3123 and 10313 of Public Law 111-148, by section 15003 of Public Law 114-255, and most recently, by section 128 of Public Law 116-260, which requires the Secretary to conduct a demonstration that would modify payments for inpatient services for up to 30 rural hospitals.

Section 128 of Public Law 116-260 requires the Secretary to conduct the Rural Community Hospital Demonstration for a 15-year extension period (that is, for an additional 5 years beyond the previous extension period). In addition, the statute provides for continued participation for all hospitals participating in the demonstration program as of December 30, 2019.

While the statute does not call for any new hospitals to join the demonstration, CMMI issued a notice on December 20, 2024, in the Federal Register for a solicitation (89 FR 105049) for up to 10 additional eligible hospitals to participate in the RCHD. Applications were due March 1, 2025. These hospitals have been selected under this solicitation and will be able to participate from May 1, 2025, through June 30, 2028.

Section 410A(c)(2) of Public Law 108-173 requires that in conducting the demonstration program under this section, the Secretary shall ensure that the aggregate payments made by the Secretary do not exceed the amount which the Secretary would have paid if the demonstration program under this section was not implemented (budget neutrality). To ensure budget neutrality, we propose to adopt the general methodology used in previous years, whereby we estimated the additional payments made by the program for each of the participating hospitals as a result of the demonstration, and then adjusted the national IPPS rates by an amount sufficient to account for the added costs of this demonstration. This proposed methodology applies budget neutrality across the payment system as a whole rather than across the participants of this demonstration. The language of the statutory budget neutrality requirement permits the agency to implement the budget neutrality provision in this manner. The statutory language requires that aggregate payments made by the Secretary do not exceed the amount which the Secretary would have paid if the demonstration was not implemented but does not identify the range across which aggregate payments must be held equal.

For this final rule, the resulting amount applicable to FY 2026 is $47,586,847, which we proposed as the budget neutrality offset adjustment for FY 2026. This estimated amount is based on the specific assumptions regarding the data sources used, that is, recently available “as submitted” cost reports and historical and currently finalized update factors for cost and payment.

In previous years, we have incorporated a second component into the budget neutrality offset amounts identified in the IPPS/LTCH PPS final rules. As finalized cost reports became available, we determined the amount by which the actual costs of the demonstration for an earlier, given year differed from the estimated costs for the demonstration set forth in the IPPS/LTCH PPS final rule for the corresponding fiscal year, and we incorporated that amount into the budget neutrality offset amount for the upcoming fiscal year. We have calculated this difference for FYs 2005 through 2018 between the actual costs of the demonstration as determined from finalized cost reports once available, and estimated costs of the demonstration as identified in the applicable IPPS/LTCH PPS final rules for these years.

With the extension of the demonstration for another 5-year period, as authorized by section 128 of Public Law 116-260, we proposed to continue this general procedure. At this time, for the FY2026 final rule, all of the FY2020 finalized cost reports are available and will be reconciled in FY2026. We received no public comments related to the RCHD regulatory impact analysis in the proposed rule. We are finalizing our policies as proposed.

In section VIII.B.2. of the preamble of this final rule, we discuss the implementation of the FCHIP Demonstration, which was authorized under section 123 of the Medicare Improvements for Patients and Providers Act of 2008 (Pub. L. 110-275), as amended by section 3126 of the Affordable Care Act of 2010 (Pub. L. 114-158), and most recently re-authorized and extended by the section 129 of the Consolidated Appropriations Act of 2021 (Pub. L. 116-260). The legislation authorized a demonstration project to allow eligible entities to develop and test new models for the delivery of health care in order to improve access to and better integrate the delivery of acute care, extended care and other health care services to Medicare beneficiaries in certain rural areas. The FCHIP demonstration initial period was conducted in 10 critical access hospitals (CAHs) from August 1, 2016, to July 31, 2019, and the demonstration “extension period” began on January 1, 2022, to run through June 30, 2027. Section 123(g)(1)(B) of Public Law 110-275 required that the demonstration be budget neutral. Specifically, this provision stated that, in conducting the demonstration project, the Secretary shall ensure that the aggregate payments made by the Secretary do not exceed the amount which the Secretary estimates would have been paid if the demonstration project under the section were not implemented. Budget neutrality estimates for the demonstration described in the preamble of this final rule are based on the demonstration extension period.

As described in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69416 through 69419), CMS waived certain Medicare rules for CAHs participating in the demonstration extension period to allow for alternative reasonable cost-based payment methods in the three distinct intervention service areas: telehealth services, ambulance services, and skilled nursing facility/nursing facility services. These waivers were implemented with the goal of increasing access to care with no net increase in costs. As we explained in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69416 through 69419), section 129 of Public Law 116-260 stipulates that only the 10 CAHs that participated in the initial period of the FCHIP Demonstration are eligible to participate during the extension period. Among the eligible CAHs, five elected to participate in the extension period. The selected CAHs are located in two states--Montana and North Dakota--and are implementing the three intervention services.

As explained in the FY 2025 IPPS/LTCH PPS final rule, we based our selection of CAHs for participation in the demonstration with the goal of maintaining the budget neutrality of the demonstration on its own terms meaning that the demonstration would produce savings from reduced transfers and admissions to other health care providers, offsetting any increase in Medicare payments as a result of the demonstration. However, because of the small size of the demonstration and uncertainty associated with the projected Medicare utilization and costs, the policy we finalized for the demonstration extension period of performance in the FY 2025 IPPS/LTCH PPS final rule provides a contingency plan to ensure that the budget neutrality requirement in section 123 of Public Law 110-275 is met.

In the FY 2025 IPPS/LTCH PPS final rule, we adopted the same budget neutrality policy contingency plan used during the demonstration initial period to ensure that the budget neutrality requirement in section 123 of Public Law 110-275 is met during the demonstration extension period. If analysis of claims data for Medicare beneficiaries receiving services at each of the participating CAHs, as well as from other data sources, including cost reports for the participating CAHs, shows that increases in Medicare payments under the demonstration during the 5-year extension period is not sufficiently offset by reductions elsewhere, we will recoup the additional expenditures attributable to the demonstration through a reduction in payments to all CAHs nationwide.

As explained in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69416 through 69419), because of the small scale of the demonstration, we indicated that we did not believe it would be feasible to implement budget neutrality for the demonstration extension period by reducing payments to only the participating CAHs. Therefore, in the event that this demonstration extension period is found to result in aggregate payments in excess of the amount that would have been paid if this demonstration

extension period were not implemented, CMS policy is to comply with the budget neutrality requirement finalized in the FY 2025 IPPS/LTCH PPS final rule, by reducing payments to all CAHs, not just those participating in the demonstration extension period.

In the FY 2025 IPPS/LTCH PPS final rule, we stated that we believe it is appropriate to make any payment reductions across all CAHs because the FCHIP Demonstration was specifically designed to test innovations that affect delivery of services by the CAH provider category. As we explained in the FY 2025 IPPS/LTCH PPS final rule, we believe that the language of the statutory budget neutrality requirement at section 123(g)(1)(B) of Public Law 110-275 permits the agency to implement the budget neutrality provision in this manner. The statutory language merely refers to ensuring that aggregate payments made by the Secretary do not exceed the amount which the Secretary estimates would have been paid if the demonstration project was not implemented and does not identify the range across which aggregate payments must be held equal.

In the FY 2022 IPPS/LTCH PPS final rule (86 FR 45323 through 45328), CMS concluded that the initial period of the FCHIP Demonstration had satisfied the budget neutrality requirement described in section 123(g)(1)(B) of Public Law 110-275. Therefore, CMS did not apply a budget neutrality payment offset policy for the initial period of the demonstration. As explained in the FY 2022 IPPS/LTCH PPS final rule, we finalized a policy to address the demonstration budget neutrality methodology and analytical approach for the initial period of the demonstration. In the FY 2025 IPPS/ LTCH PPS final rule, we finalized a policy to adopt the same budget neutrality methodology and analytical approach used during the demonstration initial period to be used for the demonstration extension period. As stated in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69416 through 69419), our policy for implementing the 5-year extension period for section 129 of Public Law 116-260 follows same budget neutrality methodology and analytical approach as the demonstration initial period methodology. While we expect to use the same methodology that was used to assess the budget neutrality of the FCHIP Demonstration during initial period of the demonstration to assess the financial impact of the demonstration during this extension period, upon receiving data for the extension period, we may update and/or modify the FCHIP budget neutrality methodology and analytical approach to ensure that the full impact of the demonstration is appropriately captured. Therefore, we did not apply a budget neutrality payment offset to payments to CAHs in FY 2026. This policy will have no impact on any national payment system for FY 2026. We received no comments on this provision and therefore are finalizing this provision without modification.

In section XI.A. of the preamble of this final rule, we discuss testing the mandatory episode-based payment model titled the Transforming Episode Accountability Model (TEAM) under the authority of the CMS Center for Medicare and Medicaid Innovation (CMS Innovation Center). Section 1115A of the Act authorizes the CMS Innovation Center to test innovative payment and service delivery models that preserve or enhance the quality of care furnished to Medicare, Medicaid, and Children's Health Insurance Program beneficiaries while reducing program expenditures. The intent of TEAM is to improve beneficiary care through financial accountability for episode categories that begin with one of the following procedures: coronary artery bypass graft, lower extremity joint replacement, major bowel procedure, surgical hip/femur fracture treatment, and spinal fusion. TEAM will test whether financial accountability for these episode categories reduces Medicare expenditures while preserving or enhancing the quality of care for Medicare beneficiaries. We anticipate that TEAM will benefit Medicare beneficiaries through improving the coordination of items and services paid for through Medicare fee-for-service (FFS) payments, encouraging provider investment in health care infrastructure and redesigned care processes, and incentivizing higher value care across the inpatient and post-acute care settings for the episode.

As finalized in the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986), TEAM will be mandatory for acute care hospitals located within mandatory CBSAs and will also include acute care hospitals that were eligible for voluntary opt-in participation.\6\ TEAM will begin on January 1, 2026, and end on December 31, 2030. Payment approaches that hold providers accountable for episode cost and performance can potentially create incentives for the implementation and coordination of care redesign between participants and other providers and suppliers such as physicians and post-acute care providers. We anticipate TEAM will enable hospitals to consider the most appropriate strategies for care redesign, including (1) increasing post-hospitalization follow-up and medical management for patients; (2) coordinating care across the inpatient and post-acute care spectrum; (3) conducting appropriate discharge planning; (4) improving adherence to treatment or drug regimens; (5) reducing readmissions and complications during the post-discharge period; (6) managing chronic diseases and conditions that may be related to the episodes; (7) choosing the most appropriate post-acute care setting; and (8) coordinating between providers and suppliers such as hospitals, physicians, and post-acute care providers.

\6\ Acute care hospitals that participate in the BPCI Advanced or the CJR model, that are not located in a mandatory CBSA selected for TEAM participation, and continue to participate in BPCI Advanced or CJR until the last day of the last performance period or last performance year of the respective model, were eligible to voluntarily opt into TEAM.

Under TEAM, TEAM participants will continue to bill Medicare under the traditional FFS system for items and services furnished to Medicare FFS beneficiaries. The TEAM participant may receive a reconciliation payment from CMS if Medicare FFS expenditures for a performance year are less than the reconciliation target price, subject to a quality adjustment. TEAM will not have downside risk for Track 1, meaning TEAM participants will only be accountable for performance year spending below their reconciliation target price, subject to a quality adjustment, that would result in a reconciliation payment amount. For Track 2 and Track 3, TEAM will be a two-sided risk model that requires TEAM participants to be accountable for performance year spending above or below their reconciliation target price, subject to a quality adjustment, that would result in a reconciliation payment amount or a repayment amount.

TEAM is a mandatory episode-based payment model which will have a direct effect on the Medicare program because TEAM participants will be incentivized to reduce Medicare spending. Additionally, TEAM participants could receive a reconciliation payment amount from CMS or have to pay CMS a repayment amount based on their spending and quality performance. In the FY 2025 IPPS/LTCH PPS final rule (89 FR 70026), we estimated and projected financial impacts of TEAM over the course of the five-year model test. We estimated that on net, TEAM participants would pay CMS $442 million, and that TEAM would save the Medicare program approximately $481 million over the five performance years (2026 through 2030).

In this final rule, we are finalizing several policies that we proposed and finalizing some policies where we solicited comments on policy considerations. We believe most of the policies that are being finalized would not have a material impact on the Medicare savings estimate. For example, we do not anticipate there will be many new hospitals that would be affected by a deferred participation period, nor would capturing an additional quality measure in the model or allowing TEAM participants to use swing-bed arrangements in the 3-Day SNF Rule waiver have a significant effect on Medicare spending or savings. Additionally, many of the proposals that affect the pricing methodology that we are finalizing in this final rule, such as changes to the construction of the prospective trend factor and normalization factors or using a 180-day lookback period for risk adjustment, aim to improve the accuracy of target prices but we do not anticipate they will result in dramatic shifts to the Medicare savings estimate. We noted in the proposed rule that certain policy considerations that we are seeking comment on and not proposing, such as a low volume hospital policy could impact the Medicare savings estimate in magnitude, but we anticipated the direction of the Medicare savings to remain the same. In the proposed rule we stated that generally, Medicare savings estimates are based on the proposed policies to reflect the potential financial implications of the proposals and are not generally updated based on policies that are only soliciting comments. Therefore, in the proposed rule TEAM's financial impact to the Medicare program remained unchanged from the FY 2025 IPPS/LTCH PPS final rule.

While the Medicare savings estimate remained unchanged for TEAM in the proposed rule, we noted in section I.O. of this Appendix, that we assessed the potential financial impact of a low volume policy on the model. Further, we indicated in the proposed rule that we would update the Medicare savings estimate for the final rule to reflect actual TEAM participants participating in the model, inclusive of those hospitals that voluntarily opted into the model, and updated baseline spending assumptions. Additionally, we noted that should a policy that we considered become finalized, such as the low volume hospital policy, we would update the Medicare savings estimate to reflect that policy as well.

Given the policies we are finalizing in this final rule, and our desire to account for all TEAM participants, inclusive of the hospitals that have voluntarily joined the model, we have updated the Medicare savings estimate as a result of implementing TEAM. Table J.G.12-01 shows the projected financial impacts of TEAM over the course of the five-year model test. The first performance year (2026) of TEAM is expected to cost the Medicare program $28 million because we assume most TEAM participants will elect participation in Track 1, which is not subject to downside risk. In performance year 2 (2027), TEAM participants in Track 1 will have no downside risk while TEAM participants in Track 2 and Track 3 will be subject to both upside and downside risk, and we estimate TEAM participants on net (that is, repayment amounts less reconciliation payments) will pay $16 million to CMS, and that TEAM will save the Medicare program $71 million. In performance year 3 (2028), we estimate TEAM participants on net will pay $33 million to CMS, and that TEAM will save the Medicare program $89 million. We estimate that TEAM participants on net will pay CMS $60 million in performance year 4 (2029) and $61 million in performance year 5 (2030), and that TEAM will save the Medicare program $117 million and $119 million for these performance years, respectively. We estimate that CMS will pay TEAM participants $381 million and TEAM participants will pay CMS $469 million, and that TEAM will save the Medicare program approximately $368 million over the 5 performance years (2026 through 2030).

Table J.G.12.--01: Projected Financial Impacts Of Team

[In Millions]

2026 2027 2028 2029 2030

TEAM episode spending.......................... $5,398 $5,478 $5,567 $5,661 $5,751 (+) Reconciliation payment amounts (positive).. $82 $81 $75 $71 $72 (+) Reconciliation repayment amounts (negative) 0 -$97 -$108 -$131 -$133 - Baseline episode spending.................... $5,452 $5,533 $5,623 $5,718 $5,809 Impact......................................... $28 -$71 -$89 -$117 -$119 Impact as % of Baseline........................ 0.5% -1.3% -1.6% -2.0% -2.0%

* These estimates are before financial interactions with Part B premium or the Medicare Advantage program.

(1) Assumptions

We assumed TEAM episode volume is estimated to grow at the same rate as projected Medicare FFS enrollment as indicated in the 2025 Medicare Trustees Report.\510\ We also assumed that TEAM participants are estimated to reduce episode spending by 1 percent as a result of participating in TEAM. We note in the sixth annual evaluation report of the Comprehensive Care for Joint Replacement (CJR) model indicated that CJR resulted in roughly a 3.5 percent reduction in lower extremity joint replacement (LEJR) spending (not including reconciliation payments) for participants in performance year 6.\511\ Since participation in CJR is mandatory in 34 metropolitan statistical areas, and LEJR episodes make up a significant portion of the episodes included in TEAM, the CJR evaluation results appear to be a reasonable proxy for what to expect in TEAM. However, the episode length in CJR is 90 days, whereas in TEAM the finalized length is 30 days. Internal analysis indicated that the 30-day episode is approximately 75 percent as costly as a 90-day episode for LEJR procedures. In addition, post- acute care spending has been declining in recent years for episodes that we are testing in TEAM, which could limit the potential for TEAM participants to achieve significant improvements in efficiency. Thus, we believe that the intervention effect of TEAM on episode spending will be a reduction of 0 to 3 percent (see Table J.G.12-02 for a sensitivity analysis for how the financial impact is affected by changes in this assumption).

\510\ https://www.cms.gov/oact/tr/2025.

\511\ https://www.cms.gov/priorities/innovation/data-and-reports/2024/cjr-py6-annual-report.

We also note that starting from actual episode spending that occurred in the first half of 2023, average baseline spending per episode is estimated to increase by 1.5 percent every year. The national average per episode spending growth for all TEAM episode types in years 2018, 2019, 2022, and 2023 was approximately 1.3 percent. Annual growth rates for each episode type were weighted by spending, and historical experience during 2020 and 2021 were excluded due to possible impacts from the peak of the COVID-19 pandemic. Since some of the historical experience in these years includes Medicare policy changes for LEJR episodes that resulted in surgeries occurring in more efficient care settings, translating to spending decreases that may not be duplicated in future years, the assumed annual trend is slightly greater than the observed average trend from the historical experience.

Additionally, our estimates do not include the impact of TEAM beneficiary overlap with total cost of care models, such as when a TEAM beneficiary is also assigned to a Medicare Shared Savings Program ACO. However, given the precision in the Shared Savings Program projections, we do not anticipate a practical difference in the ACO's shared savings estimates. Nor do we anticipate TEAM beneficiary overlap with total cost of care models having a meaningful effect to TEAM's projected financial impacts, described in Table J.G.12-01.

Because the financial impact is based on projections of spending, the estimates implicitly assume that there will be no meaningful difference between the projected episode spending used to calculate the prospective target prices and actual episode spending, as observed in an internal analysis of simulated reconciliation results using the first three quarters of 2024. This assumption has a large degree of uncertainty, and the actual TEAM financial impacts will be sensitive to this difference. However, some of the financial risk of the projection error is mitigated by the retrospective trend factor. Target prices will still be susceptible to some error risk if the projection error exceeds the retrospective trend factor cap. The direction, magnitude and timing of projection inaccuracies would all affect the overall financial impact estimate.

(2) Sensitivity Analysis

We also performed a sensitivity analysis to assess various intervention effects on TEAM. Overall financial impacts are sensitive to the intervention effect TEAM would have on TEAM participants' episode spending. Table J.G.12-02 includes financial impacts at various intervention effect assumptions (note that negative values indicate savings):

Table J.G.12--02: Team Sensitivity Analysis At Various Intervention Effects

Intervention effect (%) 2026 (%) 2027 (%) 2028 (%) 2029 (%) 2030 (%)

-3.0........................................... -0.7 -1.8 -2.7 -3.1 -3.1 -1.0........................................... 0.5 -1.3 -1.6 -2.0 -2.0

0.0........................................... 1.2 -1.0 -1.0 -1.5 -1.5

The sensitivity is due to the lack of the requirement that TEAM participants participate in downside risk during performance year 1 and the effect that reductions in episode spending during performance years would have on target prices for future performance years.

We believe the refinements to TEAM finalized in this final rule will not materially alter the potential effects of the model on beneficiaries that we had initially indicated in the FY 2025 IPPS/ LTCH PPS final rule (89 FR 70028). We believe the majority of the changes will not alter the effects of the model on beneficiaries because the changes predominantly alter how hospitals interact with the model, rather than how beneficiaries receive care. However, we believe any changes finalized that may have a direct effect on TEAM beneficiaries are positive. In section XI.A.2.b.(3) of the preamble of this final rule, we finalized the policy to include the Information Transfer PRO-PM, specific to episodes initiated in the hospital outpatient department setting, in the quality measure set that will be tied to payment with the belief that doing so will encourage TEAM participants to focus on and deliver improved quality of care for Medicare beneficiaries. We also note in section XI.A.2.f. of the preamble of this final rule that we finalized the policy to allow TEAM participants to use the SNF 3-day rule waiver for TEAM beneficiaries discharged to hospitals and CAHs providing PAC under swing bed arrangements. This finalized policy will help improve beneficiary freedom of choice and access to care, such that beneficiaries in rural or underserved areas could receive PAC services closer to their home.

We welcomed public comments on our impact of TEAM on Medicare beneficiaries. We received no comments and therefore are finalizing this provision without modification.

12. Effects of the Health Data, Technology, and Interoperability: Electronic Prescribing, Real-Time Prescription Benefit, and Electronic Prior Authorization

This final rule implements relevant policy priorities outlined in Executive Orders (E.O.) 12866, 13563, 14221, and 14192. In 1993 E.O. 12866 was issued to ensure that regulations are cost-effective, necessary, and minimally burdensome. To build upon this, E.O. 13563, called for grounding in the best available science to ensure objectivity and transparency in rulemaking. In addition, this final rule reinforces the administration's policy goals set forth in E.O. 14221 to support pricing transparency, automation, patient empowerment through accessible and actionable information. Finally, this rule aligns with E.O. 14192, Unleashing Prosperity Through Deregulation, which seeks to reduce the private expenditures required to comply with federal regulations.

(1) Costs and Benefits

ASTP/ONC has estimated the potential monetary costs and benefits of this final rule for health IT developers, health care providers, patients, and the Federal Government (that is, ASTP/ONC), and have broken those costs and benefits out by section. The impact analysis primarily assesses the costs and benefits of finalized changes to the Certification Program as applicable for certified health IT developers and the health care providers purchasing health IT. We expect the undiscounted costs to developers of certified health IT and health IT purchasers equal to $228 million.

The Certification Program, as described elsewhere in this rule, is voluntary. Developers who present technology for certification do so for varied reasons such as supporting health IT users engaged in quality improvement programs and demonstrating conformance with federally adopted standards. However, we recognize there are real costs associated with any changes to certified health IT and requirements for developers of certified health IT to maintain certification. We estimate these costs to the best of our ability, examining the development tasks and burden associated with each proposal. We also estimate and articulate the expected cost savings and benefits of these proposals. Whereas we estimate the costs and cost savings associated with development tasks for developers of certified health IT, benefits and less-direct costs can be more far reaching--affecting developers directly through standards harmonization and well-delineated processes for technology development while also affecting health care providers, patients, and payers by providing for electronic health information exchange, access to electronic health information, and automation of clinical and administrative processes.

Although participation in the Certification Program is voluntary, we believe that requirements to use certified health IT by Federal programs, to adopt health IT standards, and to make data available to health care providers, patients, and payers provide reasons for developers to present health IT for certification. Certification Program requirements are meant to harmonize health IT development and promote interoperability through common health IT standards and rules of information exchange and access. The benefits described more thoroughly, later in this section, such as those for interoperability that we have described in prior rulemaking (for example, ONC Cures Act Final Rule (85 FR 25642)), are derived from more universal adoption of these standards and from rules that enable data to be electronically recorded, stored, exchanged, and accessed more harmoniously. These actions may remove artificial barriers to information exchange that often result in the duplication of diagnostic and laboratory testing, fragmented care, missing medical record information, and less consumer choice in the healthcare market.512 513

\512\ Jones S.S., Rudin R.S., Perry T., Shekelle P.G. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014 Jan 7;160(1):48-54. doi: 10.7326/M13-1531. PMID: 24573664. https://pubmed.ncbi.nlm.nih.gov/24573664/.

\513\ Everson J., Adler-Milstein J. Sharing information electronically with other hospitals is associated with increased sharing of patients. Health Serv Res. 2020 Feb;55(1):128-135. doi: 10.1111/1475-6773.13240. Epub 2019 Nov 12. PMID: 31721183; PMCID: PMC6980958. https://pubmed.ncbi.nlm.nih.gov/31721183/.

The benefits, both quantifiable and not quantifiable, articulated in this impact analysis have the potential to remove barriers to interoperability and EHI exchange, improve the efficiency of and reduce the administrative overhead involved in health care delivery. These policies first require effort by developers of certified health IT to reflect the policies in their software. Software must then be implemented by end-users to achieve the stated benefits--improving healthcare delivery and improving the overall ability of technology to document, transmit, and integrate EHI across multiple data systems.

Our cost calculations quantify health IT developers' time and effort necessary to implement these policies through new development and administrative activities. Our cost estimates use publicly available data and information to estimate time and effort. We also, where applicable, carry forward cost estimates from prior rulemakings to be consistent in time and effort estimates. Novel cost estimates also use a mix of subject matter expertise and appropriate proxies to quantify costs and cost savings. We note these methods and sources in the tables. We recognize that the costs developers incur as a result of these policies may be passed on to certified health IT end-users. These end-users include but are not limited to the nearly 5,000 non-federal hospitals who provide acute, inpatient care and the over 1 million clinicians who provide outpatient care to all Americans. Official statistics show that nearly all U.S. non-federal acute care hospitals and the vast majority of outpatient

physicians use certified health IT.514 515 These policies affect the technology that all of these health care providers use.

\514\ https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records

\515\ https://www.healthit.gov/data/quickstats/office-based-physician-electronic-health-record-adoption

In our analysis and estimates of costs in the proposed rule, we did not assess the costs that health care providers incur in using certified health IT. Such costs may include changes in how the provider electronically documents information in the medical record, changes to workflow, or the costs incurred by a particular implementation of the technology at a care delivery site. The costs estimated the expected burden on health IT developers in developing and providing the revised technology to their users, not the expected burden on users incurred in implementing and using the revised technology. We noted that the costs and benefits of requirements imposed by Federal agencies on health care providers are often estimated and explained in those rules' regulatory impact analysis. However, in this final rule we have determined it appropriate to consider the costs and benefits for certified health IT purchasers. We believe it is appropriate to include these costs because, while there are HHS programs incentivizing electronic transactions, this final rule is directly applicable for the specific requirements for certified health IT developed by health IT developers to meet the adopted standards and purchased by health care providers.

We have limited data on the fees and costs charged by health IT developers and how those fees and costs are distributed across various health IT purchasers. The estimated costs described for health IT developers are not solely borne by developers of certified health IT and could be passed on to health IT purchasers through health IT developers' licensing, maintenance, and other operating fees and costs, not including additional training to learn the new software and process or workflow changes to integrate the new software into daily practice. Given the ongoing nature of updates made by ASTP/ONC to the Certification Program, health IT developers may have already included the costs associated with making these updates in their existing contracts. Where they have not already been incorporated, these costs may be passed on to health IT purchasers in different ways by developers of certified health IT and across different health care provider organization types. In the section, “Number of End Users that Will Be Impacted by ASTP/ONC's Required Regulations,” we estimate the number of health IT purchasers impacted by these new requirements and the estimated share of total costs quantified in this impact analysis that could be passed on from developers of certified health IT to them. Large integrated healthcare systems may face different fees and other pricing structures than smaller health care provider organizations. The diversity of the healthcare system also limits our ability to accurately model how these costs could be passed on, even if there were data available (much of this data is considered proprietary or trade secrets). Finally, we recognize there may be non-purchase related costs for health care provider use of the certified technology such as staff training. However, the use of the adopted standards within the health IT dramatically limits any necessary manual interaction with the technical processes.

What we can describe with more certainty is the overall impact of these policies on the healthcare system as a whole. These policies affect the certified technology used by health care providers that care for the vast majority of Americans. Nearly all emergency room visits, hospital stays, and regular check-ups are documented and managed using certified health IT. These policies affect the interoperability of EHI for these care events and patients' electronic access to their health information. Certified health IT is a nearly ubiquitous part of U.S. healthcare, and the costs and benefits estimated here encompass the widespread use of these technologies and their impact on all facets of care.

Overall, it is highly speculative to quantify benefits or cost savings associated with the new technical requirements and standards for certification criteria we have proposed in this final rule. Emerging technologies may be used in ways not originally predicted. For example, ASTP/ONC supported the development of SMART on FHIR[supreg], which defines a process for an application to securely request, receive and use data. ASTP/ONC could not have predicted the scale this technical advancement achieved. Today, it is used to support major health IT products and utilized by numerous digital health and technology companies to connect and integrate with health IT products to provide healthcare and other services to health app users.\516\ It is speculative to quantify benefits for specific stakeholders because benefits owing to advancements in interoperability do not necessarily accrue to stakeholders developing and implementing the technologies. Benefits related to interoperability are spread across the healthcare ecosystem and can be considered a societal benefit. We have sought to describe benefits for each of the specific policies, using the best available data and studies to support our analysis.

\516\ Wesley Barker, Natalya Maisel, Catherine E Strawley, Grace K Israelit, Julia Adler-Milstein, Benjamin Rosner, A national survey of digital health company experiences with electronic health record application programming interfaces, Journal of the American Medical Informatics Association, Volume 31, Issue 4, April 2024, Pages 866- 874, https://doi.org/10.1093/jamia/ocae006.

All estimates are rounded to the nearest dollar and all estimates are expressed in 2024 dollars. The wages used to derive cost estimates are from the May 2024 National Occupational Employment and Wage Estimates reported by the U.S. Bureau of Labor Statistics.\517\ Estimates presented in sections titled “Employee Assumptions and Hourly Wage,” “Quantifying the Estimated Number of Health IT Developers and Products,” “Number of End Users that Will Be Impacted by ASTP/ONC's Required Regulations”, and “Comparative Analysis Between Standardized and Non-standardized Application Programming Interfaces” are used throughout.

\517\ BLS. Occupational Employment and Wage Statistics: https://data.bls.gov/oes/#/industry/000000.

In this final rule, we estimate direct benefits wherever research supports such direct estimates of impact. For policies where no such research was identified to be available, we developed estimates based on a reasonable proxy. We note that interoperability can positively impact patient safety, efficacy, care coordination, and improve healthcare processes and other health-related outcomes.\518\ However, interoperability is a function of several factors including the capabilities of the technology used by health care providers. Therefore, to assess the benefits of our policies, we must first consider how to assess their respective effects on interoperability, holding other factors constant.

\518\ Nir Menachemi, Saurabh Rahurkar, Christopher A Harle, Joshua R Vest, The benefits of health information exchange: an updated systematic review, Journal of the American Medical Informatics Association, Volume 25, Issue 9, September 2018, Pages 1259-1265, https://doi.org/10.1093/jamia/ocy035.

Comment: We requested comment on the increase in software licensing costs and other fees resulting from these finalized policies, and if ongoing licensing costs and fees already encompass the costs of meeting new regulations and certification requirements (that is, some or none of the estimated costs of the proposed rule would be passed on to technology end-users). We received no comments regarding the impact on software licensing costs and other fees resulting from these policies.

Response: The final impact analysis updates costs based on new information on the number of products that are likely to require new functionality and the impact of these finalized policies. Cost estimates were updated to reflect wages of software developers as of 2024. Quantified cost savings were updated in this final impact analysis, given new information and availability of data.

(a) Employee Assumptions and Hourly Wage

Unless otherwise noted, we have consistently used the May 2024 National Occupational Employment and Wage Estimates reported by the U.S. Bureau of Labor Statistics (BLS) to calculate private sector employee wage estimates.\519\ These wage estimates are a national average and do not represent any possible regional variation in wages. We also do not account for possible variation in the average wages for software developers in health care IT positions versus IT positions, more generally, which the BLS wage estimate is based upon. We updated average wages used in the proposed rule, which were based upon May 2022 BLS statistics. We most commonly use the mean hourly wage for a Software Developer (Standard Occupational Code: 15-1252), which is $69.50, to measure costs associated with our finalized policies. We have concluded that a 100 percent expenditure on

benefits and overhead is an appropriate estimate based on research conducted by HHS.\520\

\519\ BLS. Occupational Employment and Wage Statistics: https://data.bls.gov/oes/#/industry/000000.

\520\ See U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (ASPE), Guidelines for Regulatory Impact Analysis, at 28-30 (2016), available at https://aspe.hhs.gov/reports/guidelines-regulatory-impact-analysis.

(b) Quantifying the Estimated Number of Health IT Developers and Products

As we described in the HTI-2 proposed rule (89 FR 63498), we do not assume that all developers of certified health IT and their products would be affected by this final rule.\521\ We estimate that, in total, 395 health IT developers will certify 520 health IT products impacted by this final rule. These totals reflect revisions from the original proposals, using up to date data. The analysis and models used to estimate the totals remain the same, as proposed.

\521\ https://www.federalregister.gov/d/2024-14975/p-2051.

We received no comments on our quantification of developers and products affected by this final rule.

(c) Number of End Users That Will Be Impacted by ASTP/ONC's Required Regulations

As previously noted in ASTP/ONC rulemaking (89 FR 63667 and 89 FR 63498), for the purpose of this impact analysis, the population of end users impacted are the number of health care providers that possess certified health IT. Due to data limitations, our analysis is based on the number of hospitals and clinicians who participate in Medicare and who may be required to use certified health IT to participate in various CMS programs, inclusive of those providers who received incentive payments to adopt certified health IT as part of the Medicare EHR Incentive Program (now known as the Medicare Promoting Interoperability Program and the Promoting Interoperability performance category under MIPS).

One limitation of this approach is that we are unable to account for the impact of our provisions on users of certified health IT that were ineligible or did not participate in the CMS EHR Incentive Programs or current Medicare programs (for example, the Medicare Promoting Interoperability Program). For example, in 2017, 78 percent of home health agencies and 66 percent of skilled nursing facilities reported adopting an EHR.\522\ Nearly half of these facilities reported engaging in aspects of health information exchange. However, we are unable to quantify, specifically, the use of certified health IT products among these provider types.

\522\ https://www.healthit.gov/data/data-briefs/electronic-health-record-adoption-and-interoperability-among-us-skilled-nursing.

Despite these limitations, these Medicare program participants represent an adequate sample on which to base our estimates. An analysis of the CMS Provider of Services file for Hospitals and CMS National Downloadable File of Doctors and Clinicians provides a current accounting of Medicare-participating hospitals and practice locations.523 524 In total, we estimated about 4,800 non- Federal acute care hospitals from the Provider of Services file and 1.25 million clinicians (including doctors and advanced nurse practitioners) across over 350,000 practice locations. If we assume that 96 percent of these hospitals and 80 percent of these practice locations use certified health IT, as survey data estimate, approximately 4,600 hospitals and 283,000 practice locations may face some passed-on costs from these requirements.525 526

\523\ https://data.cms.gov/provider-characteristics/hospitals-and-other-facilities/provider-of-services-file-hospital-non-hospital-facilities.

\524\ https://data.cms.gov/provider-data/dataset/mj5m-pzi6.

\525\ https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records.

\526\ https://www.healthit.gov/data/quickstats/office-based-physician-electronic-health-record-adoption.

As detailed in the Accounting Statement, we estimate the total undiscounted costs to developers of certified health IT over a 10- year period to be $228 million or about $577K per developer of certified health IT (n=395) and $438K per certified health IT product (n=580). Replicating prior modeling work (85 FR 25642 and 89 FR 63667), the average hospital user (n=4,600) of certified health IT is expected to face up to $16,519.00 on average additional costs associated with implementing technology that adopt these policies.\527\ The average clinician practice site (n=283,000) will face up to $537.00 on average additional costs associated with implementing technology that adopt these policies.\528\ Described in later sections are the quantifiable cost savings to health IT purchasers of these finalized policies and the average pass-through costs from developers of certified health IT to purchasers.

\527\ Formula: [$228m * (\1/3\)]/4,600.

\528\ Formula: [$228m * (\2/3\)]/283,000.

These costs are not expected to be borne at once. Requirements from this finalized rulemaking may be implemented over several years, so in some cases an individual hospital or health IT purchaser's share of pass-through costs from their health IT developer may be distributed over one or more years. We reiterate that some of these costs may have already been incorporated within existing contracts and thus it is possible that the actual additional costs experienced by hospitals and clinicians may be lower than what is estimated. We do not have insights into proprietary contracts between EHR developers and their clients, and thus cannot speculate on the extent to which the estimated additional costs will be passed on to clients.

It is unknown if the estimated cost savings will have the same distribution. A single clinician may not benefit the same as a single hospital, nor will one hospital benefit the same as another. However, given the same constraints to model costs across different provider types, we choose to assume a similar distribution for benefits as we propose for costs.

(d) Comparative Analysis Between Standardized and Non-Standardized Application Programming Interfaces

Standardization, by its nature, enables predictable, programmable methods of communication between IT systems. When a receiving IT system can ingest, parse, and translate a payload from a transmitting IT system, it can do so automatically with little to no manual intervention. Furthermore, when this process is built on common, industry standards the receiving and transmitting IT systems can be built with this interoperability in mind. The receiving system does not have to predict or intuit the payload's form, structure, and purpose, it knows what it is automatically because the receiving and transmitting systems speak the same computer language and share an information payload that conforms to each system's specifications. For example, one health IT product, built with standards-based application programming interface (API) specifications (developed once and deployed enterprise wide) can be connected to another IT system (for example, an app or information database) that supports these same specifications. Each additional connection has a marginal cost, but each is built on the same foundational infrastructure, enabling more connections at lower marginal costs than if configured using non-standards-based APIs.

In the 2015 Edition Final Rule (80 FR 62602), three API criteria were finalized: 45 CFR 170.315(g)(7), (g)(8), and (g)(9). These were the first API criteria adopted by the Certification Program, and we note that they were finalized as “functional” criteria that did not require conformance to a specific standard or method, beyond implementing a RESTful API, to respond to a query for a patient record. In that rulemaking, we estimated that building each of these APIs would require on average per product approximately 300 to 400 hours in development time. In the ONC Cures Act Final Rule (85 FR 25642), we finalized the “standardized API for patient and population services” criterion in 45 CFR 170.315(g)(10), which replaced the criterion in 45 CFR 170.315(g)(8) and required support for an API using industry standards in place of proprietary methods. We estimated in Table 16 of the ONC Cures Act Final Rule that the effort to replace the functional requirements of the API specified in 45 CFR 170.315(g)(8) and adopt the HL7 Fast Healthcare Interoperability Resources (FHIR) standard specified in the criterion in 45 CFR 170.315(g)(10) would require 1600 to 6000 hours, depending on whether the product already adopted the FHIR standard for its 45 CFR 170.315(g)(8) API (lower bound) or needed to do a complete re-build (upper bound).\529\ We also estimated in Table 16 that it would cost 800 to 1500 hours to adopt the Substitutable Medical Applications, Reusable Technologies (SMART) on FHIR App Launch Framework implementation guide, which standardizes the way in which a requesting application that connects to the standardized API securely accesses data from the FHIR

server.\530\ Together these two standardized approaches to enable patient-level data queries and securely and uniformly respond to those requests amounted to 2400 to 7500 hours of effort or a median of approximately 5,000 hours to replace the functional API with a standards-based API. This is about 15 times the effort of building the functional, non-standardized API, specified in the criterion in 45 CFR 170.315(g)(8) that was finalized in the 2015 Edition Final Rule (80 FR 62602).

\529\ https://www.federalregister.gov/d/2020-07419/p-3033.

\530\ https://build.fhir.org/ig/HL7/smart-app-launch/.

This is a meaningful difference in development effort. Why require this larger additional effort to replace an API with no required standards with one that conforms to one or more standards? Standardization promotes more uniform and predictable access and exchange across many different possible exchange partners (or in computer terms, IT system nodes). If a developer is building an API for a specific, non-scalable purpose to achieve a specific proprietary or internal need, customizing it to align with an industry standard may not be feasible or reasonable. Standards, however, permit multiple uses of the API or many possible users of the API. In the case of the 45 CFR 170.315(g)(10) API, standards were adopted to enable broad implementation across hundreds of certified health IT products to enable use and access by hundreds, if not thousands of application (“app”) developers, health care organization innovators, and entrepreneurial health care providers, seeking to use their standardized data access to create novel applications to treat and care for their patients. Prior to the finalization of the ONC Cures Act Final Rule (85 FR 25642), the potential number of applications that could connect to a health IT product were numerous; standardizing the API would lead to more competition and innovation in the market.\531\ And, within the context of FHIR APIs, competition and innovation have resulted. Studies as early as 2020 show hundreds of distinct apps connecting to health IT products via standards-based and proprietary APIs, and other later studies show nearly all application developers or digital health companies building their applications and services using the FHIR standard by default given the broad availability of FHIR APIs in the market.532 533 Nearly all surveyed companies in the Barker, et al. study reported they were connecting with 2 or more companies and more so if they were FHIR adopters.\534\ These findings show that standardization can enable more connections and broaden the reach of technological innovation across the ecosystem of health care apps and technology.

\531\ Dullabh P, Hovey L, Heaney-Huls K, Rajendran N, Wright A, Sittig DF. Application programming interfaces in health care: findings from a current-state sociotechnical assessment. Appl Clin Inform2020; 11 (1): 59-69.

\532\ Barker W, Johnson C. The ecosystem of apps and software integrated with certified health information technology. J Am Med Inform Assoc. 2021 Oct 12;28(11):2379-2384. doi: 10.1093/jamia/ ocab171. PMID: 34486675; PMCID: PMC8510286.

\533\ Wesley Barker, Natalya Maisel, Catherine E Strawley, Grace K Israelit, Julia Adler-Milstein, Benjamin Rosner, A national survey of digital health company experiences with electronic health record application programming interfaces, Journal of the American Medical Informatics Association, Volume 31, Issue 4, April 2024, Pages 866- 874, https://doi.org/10.1093/jamia/ocae006.

\534\ Ibid.

If an app developer can predictably build their app to connect to one health IT product via a FHIR API, the infrastructure they built once can be used to connect to 2, 3, or more health IT products. And for health IT product developers, enabling easier integrations with their EHR or other health IT system can provide more choice to their customers, and can broaden the product and service offerings available through their platform. Services and tools that the health IT product developer could not provide alone can be provisioned through any number of app developers who can connect and integrate with the health IT product, permitting use of the app directly in the health IT product instance. Furthermore, if a health IT product adopts a standardized API, an innovative company working with the health IT product's competitor can connect to the competitor's health IT products as well, without having to build a custom interface to a proprietary, non-standards-based API.

We find that there are large potential savings for a developer of certified health IT if they adopt this standards-based approach. In our model, we assume certain tasks are necessary for an app to connect to and integrate with a health IT product. We also estimate the effort required by a health IT product to support one or many integrations. In Table I.G.12.-01, we list some common tasks necessary to successfully connect an app to a health IT product via an API. We also provide the expected number of hours required to complete each task via a standards-based and non-standards-based API. For a standards-based API, we assume that the app and health IT product both adopt the same standards-based methods to connect via an API, in this case the FHIR and USCDI standards, as adopted in the 45 CFR 170.315(g)(10) certification criterion. We also assume that, even when utilizing a standards-based API, there is still some effort to integrate. However, when both the app and health IT product support the same standards, many of the tasks can be done at less or no additional effort. This is because the app connecting to the health IT product adopts similar API specifications and a similar data model to fetch data elements from the health IT product. Comparing this effort to a scenario where an app must connect via a non-standards-based API, the effort to connect to the non-standards-based API is greater. This is because the health IT product developer must provide greater support (and the app must expend more effort) to review the novel API's documentation; to map how the app records data elements; and to understand how the corresponding health IT product API makes those or similar data elements available. This effort could be considerable (nearly 60 percent of the entire effort) as mapping data elements is an essential task to programmatically query, fetch, and ingest data via an API. Standardizing APIs and standardizing any data exchange process are essential to reduce the effort to do this mapping.

Table I.G.12.-01--Time on Task for API Integrations, Non-Standards-Based vs. Standards-Based API

Via non- Via standards-

Task Task details standards- based based API

API (hours) (hours)

Review API Documentation................ Get details on specific workflows to 40 8

use the API and how to fetch data

(data resources and endpoints

needed to get specific data

elements). Syntax for the API (to

be used to incorporate into

application code). Process to

discover and connect to API

endpoints. Mapping Data Elements................... Map data received (to be ingested) 150 0

via an API query to the same or

similar data elements in the

developer's application. Authorization........................... What credentials and how to present 20 0

them when requesting data via a

secure API endpoint. Registration............................ Get authorized access to API host's 2 2

non-public facing tools and API

information permitted only after

user is verified and trusted. Testing................................. Conduct testing via sandbox or 40 40

synthetic data to verify successful

API connection and data ingestion

from host server to developer

application.

Total............................... .................................... 252 50

Table I.G.12.-01 shows that it takes about 5 times the effort to connect via a non-standards-based API. However, we also calculated that the base cost to build the infrastructure for the standardized API is nearly 15 times the effort to build a non-

standardized API. How can the standardized API, therefore, be preferred over a non-standardized API if it costs less to do the initial build? The savings are earned as more and more connections between a health IT product and apps are completed. In Table - I.G.12.-02, we compare the costs of building the base API infrastructure for a standards and non-standards-based API, as well as the marginal costs of connecting one or more apps to that health IT product. We find that on average the cost to build the standards- based API infrastructure and complete 24 app integrations would cost about the same as doing so via a non-standards-based API. However, the integration of more apps beyond 24 integrations yields savings, as the incremental costs to connect more apps become more costly via a non-standards-based API than a standards-based API. As the referenced studies and the updated analysis show, there are hundreds of apps (that we know of from public data sources) that currently connect to health IT products with consistent growth year after year.\535\

\535\ https://www.healthit.gov/sites/default/files/2023-05/Insights%20into%20Data%20Sharing%20between%20EHRs%20and%20Apps%20508.pdf.

Table I.G.12.-02--Comparison of Costs To Integrate Apps With a Health IT Product via Standards and Non-Standards-Based APIs

Standards-based Not standards-based Differential costs

Cumulative

Average hours Cumulative Cumulative Cumulative hours for all Cost

\1\ hours Average hours hours hour certified API difference ($)

difference products \2\ \3\

Base build.............................. 5,000 5,000 350 350 4,650 1,088,100 $151,245,900 1 App................................... 50 5,050 250 600 4,450 1,041,300 144,740,700 10 Apps................................. 500 5,500 2,500 2,850 2,650 620,100 86,193,900 24 Apps \4\............................. 1,200 6,200 6,000 6,350 -150 -35,100 -4,878,900 25 Apps................................. 1,250 6,250 6,250 6,600 -350 -81,900 -11,384,100 50 Apps................................. 2,500 7,500 12,500 12,850 -5,350 -1,251,900 -174,014,100 100 Apps................................ 5,000 10,000 25,000 25,350 -15,350 -3,591,900 -499,274,100

Notes: (1) Average hours are the median of the lower (2,400) and upper (7,500) bound hours calculated for the combined 45 CFR 170.315(g)(10) criterion. (2) Total = (Cumulative Hour Difference) x (All Certified API Products, n=234). (3) Total $ = (Cumulative Hours for All Certified API Products) x (Wage Rate Used in this Impact Analysis, $139). (4) On average, the breakeven point for a certified API product to adopt a standards-based API (versus a non-standards-based API) is 24 apps. Formula:

50x + 5000 = 250x + 350; x = 4650/200; x = 23.25.

The growth in new apps and digital health companies coming on the market and number of integrations between these apps and health IT products demonstrates the effectiveness of adopting industry standards to establish software interoperability and promote competition and innovation in the health care market. Standards- based APIs permit less costly and burdensome connections between certified health IT products and third-party apps and services and enable greater predictability in how a single app or digital service can connect to one or many certified health IT products. This model provides evidence for the likely savings that can accrue to certified health IT and third-party developers alike from the adoption and use of standards-based APIs. We replicate this model in our impact analysis, of adopting standards-based electronic prior authorization APIs to show the value of adopting industry standards versus proprietary, non-standards-based methods of exchange.