The Standardized Ratio of Emergency Department Encounters Occurring Within 30 Days of Hospital Discharge for Dialysis Facilities (ED30) is the ratio of the observed number of index hospital discharges that are followed by an emergency department encounter within 30 days for adult Medicare dialysis patients treated at a particular facility to the number of ED encounters that would be expected given the characteristics of the dialysis facility’s patients and the national event rate for dialysis facilities. The time period for the measure calculation is two years. Medicare patients include those with traditional Fee for Service (FFS) Medicare and those with Medicare Advantage. Note that for this measure an “ED encounter” always refers to an outpatient encounter that does not end in a hospital admission, but does include observation stays. This measure is calculated as a ratio but can also be expressed as a rate.
Note that for this measure an “ED encounter” always refers to an outpatient encounter that does not end in a hospital admission, but does include observation stays. This measure is calculated as a ratio but can also be expressed as a rate.
Since the previous endorsement cycle, we have made the following changes:
- Denominator: the measure now includes Medicare Advantage (MA) patients that had previously been excluded due to lack of outpatient claims data.
- Risk Adjustment: the measure includes Medicare Advantage status at the time of index discharge as a covariate in the risk adjustment model.
Measure Specs
General Information
Emergency department encounters within 30 days of an index discharge are an important indicator of care coordination, care transitions, and quality of life. In the general population, studies have shown higher risk of an emergency department encounter subsequent to a discharge from an inpatient hospitalization or an outpatient emergency department encounter[1]. This has been demonstrated in the end-stage kidney disease (ESRD) population as well with 27% of patients being treated in an ED within 30 days of hospital discharge, most frequently for congestive heart failure [2].
More than half (55.0%) of all patients with ESRD visit the ED during their first year of dialysis, and patients with ESRD have a mean of 2.7 ED visits per patient-year [4]. This rate is 6-fold higher than the national mean rates for US adults in the general population [4]. Furthermore, the Lovasik study notes that among Medicare beneficiaries with ESRD, 30% of hospital admissions that originate in the ED are for diagnoses that are often dialysis related such as complications of vascular access, congestive heart failure/fluid overload, septicemia, and hyperkalemia. A study by Zhang and colleagues [5] reported that rates of ED visits among patients on thrice weekly in-center hemodialysis vary by dialysis schedule (Mon/Weds/Fri; Tues/Thurs/Sat) and by day of week. For example, the ED visit rate (without hospital admission) was highest on the day following the longer interdialytic interval over the weekend (Mondays), suggesting an association with facility structure and treatment schedule.
Cohen and colleagues [6] reported that missed dialysis treatments are associated with an over two-fold higher risk of an ED visit, suggesting an opportunity for dialysis facilities to establish or strengthen facility practices that can help to reduce skipped treatments through increased communication, care coordination, and patient education. This in turn has the potential to reduce avoidable ED visits.
The CMS Centers for Medicare and Medicaid Innovation’s Comprehensive End Stage Renal Disease (ESRD) Care model emphasizes care coordination as a central feature of care delivery in order to reduce utilization and improve outcomes. During the second performance year, the original Wave 1 cohort of ESCOs (ESRD Seamless Care Organizations) experienced about a 3% reduction in ED use relative to the period before the CEC model was launched [7].
As reported by the USRDS, the unadjusted ED visit among HD patients (Medicare Advantage and FFS) remained relatively stable with around 1.2 – 1.5 visits per patient-year, and from 0.8 to 1.1 per patient-year for PD patients [3]. Measures of the frequency of ED encounters subsequent to a hospital discharge may help dialysis facility efforts to prevent emergent unscheduled care and to help control escalating medical costs, for example through greater care coordination and post-discharge transitional care. Specifically, dialysis facility activities such as evaluation of patient target weight or medication reconciliation and review may help reduce the risk of ED encounters after hospital discharge. This measure complements existing measures targeting care coordination (such as the Standardized Readmission Ratio) by identifying impactful events that can be influenced by dialysis facility care.
Inclusion of Medicare Advantage Patients
Legislative changes that became effective in January 2021 removed barriers that had previously prevented ESRD patients from enrolling in Medicare Advantage (MA) plans. In the subsequent years, there have been a substantial increase in the number of ESRD beneficiaries covered by MA plans, now approaching 45% of the dialysis population. Unlike FFS beneficiaries, MA outpatient encounters and administrative records had not been readily available for analyzing facility quality, and so MA patients had been excluded from the previously endorsed version of this measure. With the recent availability of Part C Medicare Advantage encounter data, MA patients can now be included in this measure. Adjustment for MA coverage is important in order to control for potential difference in outcomes related to coverage type since there is wide variation in the frequency of MA patients at the dialysis facility level. In addition, our internal analyses indicate that MA patients have significantly higher rates of ED encounters compared to FFS patients.
While the currently endorsed version of this measure does not include MA patients, this submission includes MA patients by: (1) incorporating Part C outpatient encounter data to identify ED visits for MA patients, (2) addition of all prevalent comorbidity from Part C encounter data in comorbidity adjustment, and (3) adjusts for MA coverage by including a time dependent covariate.
References:
- Hastings NS., Oddone EZ., Fillenbaum G, Shane R J., Schmader KE. Frequency and predictors of adverse health outcomes in older Medicare beneficiaries discharged from the emergency department. Med Care. 2008 Aug;46(8):771-7
- Harel, Z.;Wald, R.;McArthur, E.;Chertow, G. M.;Harel, S.;Gruneir, A.;Fischer, H. D.;Garg, A. X.;Perl, J.;Nash, D. M.;Silver, S.;Bell, C. M. Rehospitalizations and Emergency Department Visits after Hospital Discharge in Patients Receiving Maintenance Hemodialysis. J Am Soc Nephrol. 2015 26(12):3141-50 doi:10.1681/ASN.2014060614
- United States Renal Data System. 2024 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2024.
- Lovasik BP, Zhang R, Hockenberry JM, Schrager JD, Pastan SO, Mohan S, Patzer RE. Emergency Department Use and Hospital Admissions Among Patients With End-Stage Renal Disease in the United States. JAMA Intern Med. 2016 Oct 1;176(10):1563-1565.
- Zhang S, Morgenstern H, Albertus P, Nallamothu B, He K, and Saran R. Emergency department visits and hospitalizations among hemodialysis patients by day of the week and dialysis schedule in the United States. PLOS ONE. https://doi.org/10.1371/journal.pone.0220966 August 15, 2019.
- Cohen DE, Gray KS, Colson C, Van Wyck DB, Tentori F, Brunelli SM. Impact of Rescheduling a Missed Hemodialysis Treatment on Clinical Outcomes. Kidney Med. 2019 Dec 11;2(1):12-19. doi: 10.1016/j.xkme.2019.10.007. PMID: 32734224; PMCID: PMC7380431.
- Marrufo G, Negrusa B, Ullman D, Hirth R, Messana J, Maughan B, Nelson J, Lindsey N, Gregory D, Svoboda R, Melin C, Chung A, Dahlerus C, Nahra T, Jiao A, McKeithen K, and Gilfix Z. Comprehensive End-Stage Renal Disease Care (CEC) Model. Performance Year 2 Annual Evaluation Report. Prepared for: Centers for Medicare & Medicaid Services. September 2019. https://innovation.cms.gov/Files/reports/cec-annrpt-py2.pdf
Data are derived from the EQRS patient-specific clinical and administrative data, including ESRD patient list, CMS-2728 Medical Evidence Form, CMS-2746 Death Notification Form, and patient admission and discharge data, from all Medicare certified dialysis facilities, the Medicare Enrollment Database (EDB), and Medicare claims data.
In addition, the database includes transplant data from the Scientific Registry of Transplant Recipients (SRTR), data from the Nursing Home Minimum Dataset, and the provider and survey and certification data from the Internet Quality Improvement and Evaluation System (iQIES) data.
Information on hospitalizations is obtained from Medicare inpatient and skilled nursing claims Standard Analysis Files (SAFs), and past-year comorbidity data are obtained from multiple claim types (inpatient, home health, hospice (Part A only), skilled nursing facility claims).
Fee-for-service (FFS) Medicare Part A (inpatient) and Part B (outpatient and physician supply) claims for dialysis patients are included in the current database; additionally, the database now incorporates Part C Medicare Advantage (MA) data for the MA enrollees. This database ensures that hospital, outpatient dialysis, and other billable services under Medicare – whether FFS or MA – are captured.
Numerator
The observed number of index acute care hospital discharges during the two-year period that are followed by an emergency department encounter within 4–30 days of the discharge among eligible adult Medicare patients at a facility.
Index Discharges
We use Medicare Part A inpatient claims to identify acute hospital discharges. This source includes inpatient claims for Medicare FFS patients and shadow claims for Medicare Advantage patients (Part C claims are not included). Among these acute hospital discharges, all live discharges of eligible patients in a calendar year are considered eligible for this measure. Those that do not meet one of the index discharge exclusion criteria described in the next section are considered index discharges.
Assignment of Index Discharges to Facilities
Index discharges are attributed to the facility of record on the day of discharge for the patient. That is, if the patient transfers dialysis facilities at the time of hospital discharge, it is the new facility that is assigned the index discharge.
Emergency Department Encounters
Emergency department (ED) encounters are identified from Medicare FSS and Medicare Advantage outpatient claims using revenue center codes that indicate an ED visit (0450, 0451, 0452, 0453, 0454, 0455, 0456, 0457, 0458, 0459, 0981). Note that this means that we include both outpatient ED visits and those that result in an observation stay, but not those that result in a hospital admission. Outpatient ED claims that have overlapping or consecutive dates of service are combined and considered as a single ED encounter. To further ensure that these outpatient ED encounters are distinct from those associated with hospitalizations, we exclude ED encounters where there is an inpatient claim that has dates of service included in any of the same time period covered by the ED encounter.
An ED encounter “follows” the index discharge only if there is no intervening inpatient hospitalization. In other words, if after hospital discharge there is another inpatient hospitalization and then an ED encounter within the time frame, the original index discharge is not counted as having been followed by an ED encounter. If eligible, the second hospitalization could become a new index discharge. The measure does not count the number of ED encounters after each index discharge, but instead determines whether or not there is at least one such encounter. If there are multiple ED encounters during days 4-30 after an index discharge, only the first ED encounter during that time is relevant to determining whether or not the index discharge is counted as having been followed by an ED encounter. ED encounters that occur before the 4th day after index discharge are not considered.
The 4-30 day time frame was selected to harmonize with the Standardized Readmission Ratio) that also uses the same time period after an index hospitalization. This time interval was selected in response to providers and stakeholders concerns that there may be up to 72 hours before a patient is seen at the facility after hospital discharge.
The time period for the measure calculation is two calendar years, meaning that index discharges must occur during the two calendar year period. The subsequent ED encounters may occur during the two calendar years or the first 30 days of the following calendar year. The first 30-days of the following calendar year is needed to have a complete lookback period for ED encounters occurring 4-30 days after an index discharge in the prior month of December of the second calendar year.
Denominator
The expected number of index hospital discharges that are followed by an emergency department encounter within 4-30 days among eligible adult Medicare dialysis patients at the facility during the two-year reporting period. The expected value is the result of a risk-adjusted predictive model that accounts for the characteristics of the patients, the dialysis facility, and the discharging hospitals.
We use Medicare Part A inpatient claims to identify acute hospital discharges. This source includes inpatient claims for Medicare FFS patients and shadow claims for Medicare Advantage patients (Part C claims are not included). Among these acute hospital discharges, all live discharges of eligible patients in a calendar year are considered eligible for this measure. See Numerator Details section above for definitions of index discharges, patient assignment, and ED encounters.
General Inclusion Criteria for Dialysis Patients
To be eligible for the measure a patient must be an adult (aged 18 or more) Medicare dialysis patient with more than 90 days of ESRD treatment on date of index discharge. This minimum 90-day period assures that patients are eligible for Medicare, either as their primary or secondary insurer, and that follow-up is complete. Thus, the measure excludes ED encounters during the first 90 days of ESRD as well as patients who die or recover kidney function during that time period. The 90 days of ESRD are counted without regard to which facility, or the number of facilities, where a patient received their dialysis treatments. The date of index discharge is considered day 0 when identifying ED visits within 4-30 days of discharge.
In order to assure completeness of information on ED encounters for all patients included in the analysis, we restrict to Medicare patients who are either enrolled in Medicare Advantage or who reach a certain threshold of Medicare outpatient dialysis and inpatient claims indicating that Medicare is the primary payor. Specifically, months within a given dialysis patient-period are used for the calculation when the patient is enrolled in Medicare Advantage or meets the criterion of being within two months after a month with either: (a) $1200+ of Medicare dialysis claims OR (b) at least one Medicare inpatient claim.
Expected Calculation
We calculate each dialysis facility’s expected number of index hospital discharges during the two-year period that are followed by an ED encounter within 4-30 days of the discharge. The expected number is calculated by fitting a model with random effects for discharging hospitals, fixed effects for facilities, and regression adjustments for a set of patient-level characteristics. We compute the expectation for the given facility assuming ED encounter rates corresponding to an “average” facility with the same patient characteristics and same discharging hospitals as this facility. Model details are provided in subsequent sections.
Exclusions
Index Discharge exclusions that are implicit in the denominator definition include discharges for which the patient:
- Has had ESRD for 90 days or less at time of discharge
- Is less than 18 years of age at the time of discharge
- Non-Medicare primary insurance at the time of discharge
We also exclude discharges and emergency department encounters for which the patient was actively enrolled in hospice at any time during the calendar month of the discharge date or ED encounter admit date. The hospice exclusion is needed because hospice patients are considered to be under the purview of hospice care givers and may have other reasons for Emergency Department use.
Additionally, we exclude hospital discharges that:
- Do not result in a live discharge
- Are against medical advice
- Include a primary diagnosis for cancer, mental health or rehabilitation (see below for excluded CCSs)
- Are from a PPS-exempt cancer hospital
- Are followed within three days of discharge by the patient being transplanted, discontinuing dialysis, recovering kidney function, being lost to follow-up, having another hospitalization, or having an emergency department visit
- Death in hospital: We determine a patient’s death date from a number of sources including CMS Medicare Enrollment Database, CMS form 2746, OPTN transplant follow-up form, EQRS database, Social Security Death Master File, and Inpatient Claims. In addition, if the discharge status on the index discharge claim indicates death and the death date occurs within 5 days after discharge, we consider this a death in the hospital.
- Discharged against medical advice: We determine discharge status from the inpatient claim.
- Certain diagnoses: The primary diagnosis at discharge is available on the inpatient claim. We group these diagnoses into more general categories using AHRQ’s Clinical Classification Software (CCS; see http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp for descriptions of each CCS). The excluded CCSs for a primary diagnosis for cancer, mental health or rehabilitation are shown below.
- Cancer: 42, 19, 45, 44, 17, 38, 39, 14, 40, 35, 16, 13, 29, 15, 18, 12, 11, 27, 33, 32, 24, 43, 25, 36, 21, 41, 20, 23, 26, 28, 34, 37, 22, 31, 30
- Psychiatric: 657, 659, 651, 670, 654, 650, 658, 652, 656, 655, 662
- Rehab for prosthesis: 254
- PPS-exempt cancer hospitals: The following hospitals are listed as PPS-exempt cancer hospitals in the Federal Register (http://www.gpo.gov/fdsys/pkg/FR-2011-07-18/html/2011-16949.htm): 050146, 050660, 100079, 100271, 220162, 330154, 330354, 360242, 390196, 450076, 500138
- Are followed within three days of discharge by the patient being transplanted, discontinuing dialysis, recovering kidney function, being lost to follow-up, having another hospitalization, or having an emergency department visit. We determine transplant status from OPTN, EQRS, and dialysis claims, and discontinuation of dialysis or recovery of kidney function from EQRS.
Measure Calculation
The numerator for a facility is the observed number of hospital discharges followed by an ED encounter within 30 days of discharge. The denominator for the same facility is the expected number of hospital discharges followed by an ED encounter with 30 days adjusted for the characteristics of the patients, the dialysis facility, and the discharging hospitals. The measure for a given facility is calculated by dividing the numerator by the denominator.
See ED30_Flowchart_Final_508.pdf attached to 1.18a for more details.
Not applicable for this measure
There is not a minimum sample size needed to calculate the performance score. Public reporting of this measure on DFCC would be restricted to facilities with at least 11 eligible index discharges to ensure stable estimates and for the measure to comply with restrictions on reporting of potentially identifiable patient information related to small cell size.
Supplemental Attachment
Measure Record
Point of Contact
Not applicable
Wilfred Agbenyikey
Baltimore, MD
United States
Jonathan Segal
UM-KECC
Ann Arbor, MI
United States
Importance
Evidence
Among Medicare beneficiaries, 30% of hospital admissions that originate in the ED are for diagnoses that are often dialysis related such as complications of vascular access, congestive heart failure/fluid overload, septicemia, and hyperkalemia [1]. Recent research points to many additional opportunities to further reduce unnecessary ED use in this population. Programs developed to impact dialysis provider practices have been shown to improve intermediate outcomes (reduced catheter vascular access [3], small solute adequacy, anemia management volume overload [1], hospitalization, and mortality. Post-hospitalization transition care management can be effective in reducing return visits to the ED by focusing on evaluation of target weight and fluid balance, medication reconciliation, and assistance in post-hospitalization follow up [12].
Cohen and colleagues [9] reported that missed dialysis treatments are associated with an over two-fold higher risk of an ED visit, suggesting an opportunity for dialysis facilities to establish or strengthen facility practices that can help to reduce skipped treatments through increased communication, care coordination, and patient education. This, in turn, has the potential to reduce avoidable ED visits. Given the association between missed dialysis treatments and increased risk of an ED visit [4], dialysis facility interventions that improve person-centered care and adherence to the treatment schedule would be expected to decrease ED utilization. Other interventions, such as telehealth, have been demonstrated to reduce ED utilization in high-risk dialysis patients [5].
Zhang and colleagues [10] reported that rates of ED visits among patients on thrice weekly in-center hemodialysis vary by dialysis schedule (Mon/Weds/Fri; Tues/Thurs/Sat) and by day of week. For example, the ED visit rate (without hospital admission) was highest on the day following the longer interdialytic interval over the weekend (Mondays), suggesting an association with facility structure and treatment schedule.
In the general population, outpatient ED visits were reported to have increased more slowly for Medicare patients being treated by patient-centered medical home practices when compared to non-patient-centered medical homes [6]. PCMH provide comprehensive care and treat all the patient’s clinical and mental and health issues; rely on care coordination across providers and provide expanded access to care around the clock (AHRQ; https://www.ahrq.gov/ncepcr/research/care-coordination/pcmh/define.html). A comparable, promising example that may reduce ED use among ESRD dialysis patients is the prior CMS Centers for Medicare and Medicaid Innovation’s Comprehensive End Stage Renal Disease (ESRD) Care model that emphasized care coordination as a central feature of care delivery in order to reduce utilization and improve outcomes. During the second performance year, the original Wave 1 cohort of ESCOs (ESRD Seamless Care Organizations) experienced about a 3% reduction in ED use relative to the period before the CEC model was launched [11].
Finally, low health literacy has been associated with increased use of ED services [7] and some studies have indicated that patient education interventions can reduce ED utilization [8].
In-Line References:
[1] Emergency Department Use Among Adults Receiving Dialysis. Ronksley PE, Scory TD, McRae AD, MacRae JM, Manns BJ, Lang E, Donald M, Hemmelgarn BR, Elliott MJ. JAMA Netw Open. 2024 May 1;7(5):e2413754. doi: 10.1001/jamanetworkopen.2024.13754.
[2] Defragmentation of care in complex patients with ESKD improves clinical outcomes. Moore N, Roer D.Am J Manag Care. 2024 May 1;30(5):e165-e168. doi: 10.37765/ajmc.2024.89544.
[3] Fast track dialysis: Improving emergency department and hospital throughput for patients requiring hemodialysis. O'Donnell C, Molitch-Hou E, James K, Leong T, Perry M, Wood D, Masud T, Thomas B, Ross MA, Franks N.Am J Emerg Med. 2021 Jul;45:92-99. doi: 10.1016/j.ajem.2021.02.035. Epub 2021 Feb 22.
[4] Insurance Status and Emergency Department Visits Associated With Hemodialysis in Texas West J, Chan HK, Molony DA, Robinson DJ, Foringer JR, Wang HE. JAMA Netw Open. 2020 Feb 5;3(2):e1921447. doi: 10.1001/jamanetworkopen.2019.21447.
[5] Effects of Early Frequent Nephrology Care on Emergency Department Visits among Patients with End-stage Renal Disease Chen YY, Chen L, Huang JW, Yang JY. Int J Environ Res Public Health. 2019 Mar 31;16(7):1158. doi: 10.3390/ijerph16071158.
[6] Failed Target Weight Achievement Associates with Short-Term Hospital Encounters among Individuals Receiving Maintenance Hemodialysis Assimon MM, Wang L, Flythe JE. J Am Soc Nephrol. 2018 Aug;29(8):2178-2188. doi: 10.1681/ASN.2018010004. Epub 2018 May 23.
[7] Burden of Emergency Medical Services Usage by Dialysis Patients Bartolacci J, Goldstein J, Kiberd B, Swain J, Vinson A, Clark D, Tennankore KK. Prehosp Emerg Care. 2018 Nov-Dec;22(6):698-704. doi: 10.1080/10903127.2018.1454558. Epub 2018 Apr 19.
[8] Patterns of emergency department utilization by patients on chronic dialysis: A population-based study. Komenda P, Tangri N, Klajncar E, Eng A, Di Nella M, Hiebert B, Strome T, Lobato de Faria R, Zacharias JM, Verrelli M, Sood MM, Rigatto C. PLoS One. 2018 Apr 17;13(4):e0195323. doi: 10.1371/journal.pone.0195323. eCollection 2018.
[9] Lovasik, B.P., et al., Emergency Department Use and Hospital Admissions Among Patients With End-Stage Renal Disease in the United States. JAMA Intern Med, 2016. 176(10): p. 1563-1565.
[10] Centers for Disease Control and Prevention. National hospital ambulatory medical care survey: 2011 emergency department summary tables. http://www.cdc.gov/nchs/fastats/injury.htm 2011 [cited 2017 January 9].
[11] Ng LJ, Chen F, Pisoni RL, Krishnan M, Mapes D, Keen M, Bradbury BD. Hospitalization risks related to vascular access type among incident US hemodialysis patients. Nephrol Dial Transplant. 26(11):3659-66, 2011
[12] Wingard RL, McDougall K, Axley B, et al. Right TraC™ post-hospitalization care transitions program to reduce readmissions for hemodialysis patients. Am J Nephrol. 2017;45(6):532–539. doi:10.1159/000477325
General References:
[13] Chan, K. E.;Thadhani, R. I.;Maddux, F. W. Adherence barriers to chronic dialysis in the United States. J Am Soc Nephrol. 2014 25(11):2642-8 doi:10.1681/asn.2013111160
[14] Minatodani, D. E.;Berman, S. J. Home telehealth in high-risk dialysis patients: a 3-year study. Telemed J E Health. 2013 19(7):520-2 doi:10.1089/tmj.2012.0196
[15] Pines, J. M.;Keyes, V.;van Hasselt, M.;McCall, N. Emergency department and inpatient hospital use by Medicare beneficiaries in patient-centered medical homes. Ann Emerg Med. 2015 65(6):652-60 doi:10.1016/j.annemergmed.2015.01.002
[16] Green, J. A.;Mor, M. K.;Shields, A. M.;Sevick, M. A.;Arnold, R. M.;Palevsky, P. M.;Fine, M. J.;Weisbord, S. D. Associations of health literacy with dialysis adherence and health resource utilization in patients receiving maintenance hemodialysis. Am J Kidney Dis. 2013 62(1):73-80 doi:10.1053/j.ajkd.2012.12.014
[17] Morgan, S. R.;Chang, A. M.;Alqatari, M.;Pines, J. M. Non-emergency department interventions to reduce ED utilization: a systematic review. Acad Emerg Med. 2013 20(10):969-85 doi:10.1111/acem.12219
[18] Cohen D, Gray K, Colson C, Van Wyck D, Tentori F, and Brunell S. Impact of Rescheduling a Missed Hemodialysis Treatment on Clinical Outcomes. Kidney Med. 2(1):12-19. Published online December 11, 2019
[19] Zhang S, Morgenstern H, Albertus P, Nallamothu B, He K, and Saran R. Emergency department visits and hospitalizations among hemodialysis patients by day of the week and dialysis schedule in the United States. PLOS ONE. https://doi.org/10.1371/journal.pone.0220966 August 15, 2019.
[20] Grecia Marrufo, Brighita Negrusa, Darin Ullman, Richard Hirth, Claudia Dahlerus Jennifer Wiens, Ariana Ackerman, Daniel Gregory, Kelsey Bacon, Jonathan Segal, Yi Li, Tammie Nahra, Amy Jiao, Joseph Gunden, Kathryn Sleeman, Daniel Strubler, Katherine B. McKeithen, and Rebecca Braun. Association of the comprehensive end-stage renal disease care model with Medicare payments and quality of care for beneficiaries with end-stage renal disease. JAMA Intern Med. 2020;180(6):852–860. doi: 10.1001/jamainternmed.2020.0562.
[21] Han, Gregory et al.Emergency Department Utilization Among Maintenance Hemodialysis Patients: A Systematic Review. Kidney Medicine, Volume 4, Issue 2, Feb 2022
Measure Impact
During the 2015 ED Technical Expert Panel (TEP) which included 3 patient members, some TEP members cited care fragmentation and lack of ownership over patient outcomes that often occur within the U.S. health care system contribute to avoidable ED use. They noted that many dialysis patients rely heavily on their nephrologists (versus primary care physicians) for more comprehensive as well as primary care due to their frequent interactions as part of the regular dialysis treatment schedule. There was agreement that better communication among providers including the dialysis facility is needed to avoid preventable ED visits.
The TEP agreed that ED encounters that do not result in admission are not well monitored as a quality indicator. Panelists recommended both the development of a measure of overall ED use that did not result in an admission along with a measure focused on ED visits occurring shortly after an inpatient discharge. The ED30 measure would provide facilities with a more complete picture of their performance on key clinical outcomes of mortality, hospitalization, readmission, and ED usage.
Performance Gap
Data for Table 1 are from the data described in 1.25 for the years 2022-2023. The total number of dialysis facilities included in the performance scores was 7,927, and the total number of index discharges was 801,844.
See ED30_Table 1_508.pdf attached to 2.4a for table and text for this question
Care Gaps
Closing Care Gaps
This domain is optional for the Spring 2026 cycle.
Feasibility
Feasibility
All the data incorporated into our database come from structured data. Data collection for this measure is accomplished via data sources including EQRS, a web-based and electronic batch submission platform maintained and operated by CMS contractors, Medicare Claims, and other supplemental data sources (see Section 1.25 Data Source Details). Publicly reported measures like this one are reviewed on a regular basis by dialysis facility providers and rare instances of inaccurate or missing data are present (based on comments received during facility previews). For this measure maintenance submission, Medicare Advantage patients are now being included in the measure through the addition of Medicare Advantage Part C encounter data.
As the data required for this measure are already part of routine data collection, no additional costs or burden are anticipated.
Public reporting of this measure on DFCC would be restricted to facilities with at least 11 eligible index discharges to comply with restrictions on reporting of potentially identifiable patient information.
No feasibility challenges have been identified that resulted in a change to the measure. Changes to the measure were made to include Medicare Advantage patients that had previously been excluded and do not affect the feasibility profile.
Proprietary Information
Scientific Acceptability
Testing Data
Data are derived from an extensive national ESRD patient database explained in more detail in question 1.25, encompassing the years 2022 - 2023.
January-December 2022 - 2023
None
See Section 7.1 Supplemental Attachment for ED30_5.1.3_Final_508.pdf, which contains the table and text for this question
See Section 7.1 Supplemental Attachment for ED30_5.1.4_Final_508.pdf, which contains the table and text for this question
Reliability
A key metric for ED30 is the inter-unit reliability (IUR), which quantifies the proportion of total variation in a measure that is attributable to true differences between facilities, rather than to random variation. By definition, IUR ranges from 0 to 1, with higher values indicating that most of the observed variation in the quality measure reflects actual differences in facility performance—thereby implying higher precision in comparing facilities.
However, due to the ratio form of ED30, directly estimating the within-facility variance is not straightforward. We use a bootstrap-based approach to estimate this component of variability.
Let T1,…,TN represent the ED30 values for N facilities. For each facility i with ni observations, we draw bootstrap samples with replacement from its patients (we found B=100 to be sufficient based on numerical experiments). For each sample, we compute the corresponding bootstrapped ED30s, denoted of T*i,1, … T*i,B. We then compute the sample variance of these bootstrapped ED30s for each facility, denoted Si*2.
An estimate of the within-facility variance of ED30, namely, σ t,w2, is given by the bootstrap variance:
St,w2= ΣNi=1[(ni-1) S i*2]/ ΣNi=1(ni-1).
Calling on formulas from the one-way analysis of variance, an estimate of the overall variance of Ti is
St2= ΣNi=1[ni (Ti -Ť)2]/ [n’(N-1)],
where
Ť = Sni Ti / Sni
is the weighted mean of the observed ED30 and
n’ = (Sni - Sni2/Sni)/(N-1)
is approximately the average facility size (number of observations per facility). Note that St2 is the total variation of ED30 and is an estimate of σ b2 + σ t,w2, where σ b2 is the between-facility variance, the true signal reflecting the differences across facilities. Thus, the estimated IUR, which is defined by
IUR = σ b2 /( σ b2 + σ t,w2),
can be estimated with (St2- St,w2)/St2.
Note: ED30 calculations were restricted to facilities with at least 11 index discharges to ensure stable estimates and comply with restrictions on reporting of potentially identifiable patient information related to small cell size.
The IUR for ED30 in 2023 is 0.557, which means that over half of the variation can be attributed to between-facility variation.
The value of IUR indicates a moderate degree of reliability. When stratified by facility size, we find that, as expected, larger facilities have greater IUR.
See Pages 2-5 of ED30_Table 2a_Table 2b_IUR info_Final_508.pdf attached to 5.2.3a for more information about ED30 IUR.
See 5.2.3a for ED30_Table 2a_Table 2b_IUR info_Final_508.pdf attachment, which contains the table and text for this question
See 5.2.3a for ED30_Table 2a_Table 2b_IUR info_Final_508.pdf attachment, which contains the table and text for this question
Validity
To validate ED30, we first stratified facilities into the ‘better than/as expected’ ‘and ‘worse than expected’ categories of the ED30 ratio. Next, we calculated mean performance scores for several quality measures: Standardized Mortality Ratio (SMR), Standardized Transfusion Ratio (STrR), Standardized Fistula Rate (SFR), Percentage of Prevalent Patients Waitlisted (PPPW), Standardized Readmission Ratio (SRR), and Standardized Emergency Department Visit Ratio (SEDR). We then compared mean performance scores across the combined strata of ‘better than/as expected’ and ‘worse than expected’ performance categories for ED30.
We expect better mean performance on the above quality measures for facilities classified as ‘better than/as expected’ for ED30 compared to facilities classified as ‘worse than expected.’ Compared to facilities that perform ‘worse than expected’, facilities that perform ‘better than/as expected’ on ED30 are likely to have more successful care coordination and other processes of care in place that may help patients avoid an ED visit in the vulnerable period following a recent discharge:
- SMR: We expect to observe a lower mean standardized mortality ratio for facilities in the ‘better than/as expected’ category for ED30 compared to facilities classified as ‘worse than expected.’ Facilities with a higher rate of ED utilization after hospitalization may not have care processes in place to support these transitions in care.
- STrR: We expect to observe a lower mean standardized transfusion event ratio for facilities in the ‘better than/as expected’ category for ED30 compared to facilities classified as ‘worse than expected.’ Facilities that have a low STrR likely have processes of care in place to support robust anemia management and care transitions for patients recently discharged, compared to facilities with a higher STrR.
- PPPW: We expect to observe a higher mean standardized percentage of prevalent patients on the waitlist for facilities in the ‘better than/as expected’ category for ED30 compared to facilities classified as ‘worse than expected.’ Facilities that have a higher standardized percentage of patients on the transplant waitlist suggest they may have more robust processes to coordinate care outside of the dialysis facility with other providers and the transplant center, compared to facilities with lower percentages. This includes the facility taking steps to ensure patients maintain sufficient health status in order to be placed on the waitlist. Therefore, facilities that have higher standardized waitlist percentages are likely deploying effective care coordination and care transition processes that are expected to also reduce the likelihood that patients recently discharged will experience an acute event resulting in an ED visit.
- SEDR: We expect to observe a lower mean standardized emergency department visit ratio for facilities classified as ‘better than/as expected’ for ED30 compared to facilities classified as ‘worse than expected’ since both measures are a reflection of outpatient ED use. However, the measures represent two different aspects of dialysis patients’ emergency department use that assess complementary elements of facility care. A low SEDR, corresponding to low overall emergency department encounter rates, indicates that the facility has processes (e.g. patient/staff education, assistance with primary care, frequent evaluation of target weight) in place to avoid the need for unscheduled acute care. A low ED30 indicates that a facility is successful in managing the transition of care (e.g. medication reconciliation, evaluation of target weight, assistance with follow up appointments) that occurs after a hospital discharge.
See 5.3.4a for ED30_5.3.4_Final_508.pdf, which contains the table and text for this question
On average the standardized mortality ratio was 4% higher than the national average for facilities that were ‘worse than expected,’ and 1% lower from the national average (SMR = 0.99) for facilities that were ‘better than/as expected’ for ED30.
On average the standardized transfusion event ratio was 1% higher than the national average for facilities classified as ‘worse than expected’ while the ‘better than/as expected’ classification group of facilities were 5% lower than the national average. This suggests that facilities which have lower numbers of transfusion events likely have better processes of care in place to support robust anemia management and other care processes to support patients after they are discharged, thus reducing patient utilization of the ED post-discharge.
The mean facility standardized percentage of patients waitlisted (PPPW) in facilities classified as ‘better than/as expected’ was 16.00% compared to 13.67% in facilities classified as ‘worse than expected,’ suggesting that facilities that have higher rates of patients on the transplant waitlist may have more robust processes to coordinate care outside of the dialysis facility with other providers. These facilities are likely deploying more effective care coordination and other care processes that may reduce the likelihood of patients utilizing the ED for many acute care needs.
The SEDR ratio on average for facilities classified as ‘better than/as expected’ for ED30 was close to the national average (0.99), while facilities classified as ‘worse than expected’ had an SEDR ratio 38% higher than the national average. These results reinforce that both ED30 and SEDR assess complementary elements of care that are likely reflected by internal processes that support greater access to care and other clinical triaging of patients that may be experiencing onset of an acute event, which may help reduce patient utilization of the ED post-discharge for preventable acute care needs.
Taken together these results provide validation support for ED30. Performance on key quality measures that were expected to be related to ED use post-discharge was also related to facility flagging in the respective ‘better than/as expected’ or ‘worse than expected’ categories.
Risk Adjustment
See 5.4.2a for ED30_Conceptual Model_5.4.2_Final_508.pdf, which includes both the conceptual model as well as the conceptual model rationale (which contains a table)
See 5.4.3a for ED30_5.4.3_Final_508.pdf, which contains the table and text for this question
See 5.4.4a for ED30_5.4.4_Final_508.pdf, which contains the table and text for this question
The model's ability to distinguish between patients who will and will not have an ED visit within 4-30 days of discharge was measured using the Area Under the Receiver Operating Characteristic (AUC) curve. The predicted AUC value is 0.661, which indicates the model has fair discriminatory power. This means the model is effective at differentiating between patients with higher and lower risk of an ED visit. Specifically, if a patient who was admitted to the ED after an inpatient discharge and a patient who was not are randomly selected, the model will correctly identify which patient was admitted 66.1% of the time.
Please see 5.4.5a for calibration and discrimination testing results found in ED30_5.4.5a_Final_508.pdf
See Section 7.1 Supplemental Attachment for ED30_5.4.6_Final_508.pdf, which contains the text and table for this question
Use & Usability
Use
Dialysis Facility Care Compare helps patients find detailed information about Medicare-certified dialysis facilities. They can compare the services and the quality of care that facilities provide.
United States. All Medicare-certified dialysis facilities who are eligible for the measure and have at least 11 index discharges are included in the measure calculation for the program. For the October 2024 Dialysis Facility Compare refresh, ED30 results were reported for 420,627 index discharges in 7,245 facilities.
Facility level, Dialysis Facilities
The Dialysis Facility Reports (DFRs) are provided as a resource for characterizing selected aspects of clinical experience at this facility relative to other caregivers in this state, End Stage Renal Disease (ESRD) Network, and across the United States. Since these data could be useful in quality improvement and assurance activities, each state’s surveying agency may utilize the DFRs as a resource during their survey and certification process. Measures included in the DFRs are updated annually and available to dialysis facilities to review and submit comments prior to their release to State Survey Agencies and Regional Offices in September of each year.
United States. All Medicare-certified dialysis facilities who are eligible for the measure and have at least 11 index discharges are included in the measure calculation for the program. For the FY 2025 Dialysis Facility Reports, ED30 results were reported for 189,806 index discharges in 6,016 facilities.
Facility level, Dialysis Facilities
This measure is best suited for an accountability program that focuses on End Stage Renal Disease (ESRD) patients. Specifically, ESRD patients with Medicare coverage (either traditional Medicare or a Medicare Advantage Plan) would be the target population. Programs that focus on the dialysis facility as the accountable entity are ideal, as opposed to programs that focus on the Nephrologist or provider. As such, this is an outpatient measure with limited adjustments for social risk factors. However, additional adjustments could be made at the program level based on the needs or design of the program.
Usability
As described in the logic model there are multiple resources facilities have available which can help them prevent avoidable ED events. These include:
- Reconcile medications after hospitalization to avoid medication errors and ensure that any changes in medications at hospital discharge have occurred.
- Assessment of current dialysis prescription, in particular target weight and any changes in target weight that occurred after index admission.
- Assist with post-hospitalization follow up with either primary care providers or specialists.
- Tracking patients who do not achieve target weight for improved fluid management by offering additional or prolonged dialysis treatments
- Identification of patients with missed/shortened treatments and counseling or removal of barriers (e.g. assistance with transportation) to improve adherence to dialysis prescription
- Establish relationships with outpatient vascular access centers so that efficient management of vascular access problems (e.g. access thrombosis or malfunction) can be achieved without reliance on the Emergency Department
- Regular review and training refresher for infection control
- Educate patients about when to receive care in the ED vs. dialysis clinic or primary care and who to contact if questions or concerns arise between treatments
For DFCC, feedback can be provided any time through contacting the dialysisdata.org helpdesk. Preview periods allow for specific times for facilities to review and comment on measure calculations and provide an opportunity to request a list of patients included in the measure calculation.
Comments received during DFCC preview periods tend to be technical in nature, asking for clarification on how the SEDR is calculated for particular facilities, including questions about patient assignment and application of risk adjustment criteria.
The revisions made to the measure specifications during this maintenance review were not directly in response to specific feedback received during public reporting (which, as described above, was more general in nature).
Based on enrollment information from the Medicare Enrollment Database (EDB), the percentage of ESRD dialysis beneficiaries enrolled in Medicare Advantage (MA) has steadily increased over time. From 12% in 2010, the proportion rose to 22% by 2020. Prior to 2020, there was an annual increase of approximately 1%. However, since 2021, the annual increase has been more than 5%.
The growth in ESRD beneficiaries joining MA plans carries significant implications for the metrics used to assess dialysis facility performance. Contrary to the data from Fee-For-Service (FFS) Medicare beneficiaries, MA outpatient encounters and administrative records had not been readily available for the purposes of analyzing facility quality, except for internal CMS use in risk adjustment and performance assessment.
See Section 7.1 Supplemental Attachment for ED30_6.2.4_Final_508.pdf, which contains the text and table for this question
None
Comments
Staff Preliminary Assessment
CBE #3566 Staff Assessment
Importance
Strengths:
- A clear logic model is provided, depicting the relationships between inputs (e.g., Quality improvement staff and clinical data systems, activities (e.g., identification of high-risk patients and conducting of root cause analysis for common emergency department (ED) visits and staff training program for infection prevention, and desired outcomes (e.g., increased awareness among providers of avoidable ED visits and reduced overall average of unplanned outpatient ED encounters that occur between 4-30 days. This model demonstrates how the measure's implementation will lead to the anticipated outcomes.
- The problem this measure addresses presents a significant burden for dialysis patients, with 27% of patients being treated in the ED within 30 days of hospital discharge often for serious conditions such as heart failure.
- Data from the End Stage Renal Disease Quality Reporting System (EQRS) from 2022-2023 show a performance gap, with decile ranges from 0.42 in the lowest decile and 1.80 in the highest decile indicating variation in measure performance.
Limitations:
- The literature review includes studies and reports that are more than 5 years old and in some cases more than 10 years old. The submission could be strengthened by including more recent and higher quality empirical studies.
- The patient input obtained through a technical expert panel (TEP) is more than 10 years old (2015). The submission could be strengthened by expanding the discussion of how meaningfulness was established and providing more feedback.
Rationale:
- This new/maintenance measure meets all criteria for 'Met' for importance due to the significance of the problem it addresses and well-articulated logic model.
Closing Care Gaps
The developer did not address this optional domain.
Feasibility Assessment
Strengths:
- All required data elements are routinely generated during care delivery, and required elements are available from digital or electronic sources. The developer described that the recent inclusion of Medicare Advantage patients does not affect data availability. The developer stated that no feasibility issues were found requiring adjustment of the final measure’s specifications.
- The developer noted that there are no additional costs or burden associated with data collection and data entry, validation, and analysis as all data are routinely generated during care delivery.
- The developer described how all required data elements can be collected without risk to patient confidentiality, including limiting public reporting of the measure on Dialysis Facility Care Compare to facilities with at least 11 eligible index discharges to avoid reporting potentially identifiable patient information.
- There are no fees, licensing, or other requirements to use any aspect of the measure (e.g., value/code set, risk model, programming code, algorithm).
Limitations:
- None identified.
Rationale:
- This maintenance measure meets all criteria for 'Met' for feasibility due to its well-documented feasibility assessment, clear and implementable data collection strategy, and transparent handling of patient confidentiality, burden, licensing, and fees.
Scientific Acceptability
Strengths:
- Data used for reliability testing were sourced from EQRS patient-specific clinical and administrative data during the year 2023.
Limitations:
- The developer conducted bootstrap inter-unit reliability (IUR) at the accountable entity-level. Between 30% and 40% of accountable entities meet the expected threshold of 0.6 for accountable entity-level reliability. Around 30% of accountable entities had reliability below 0.4, and the developer provided an interpretation of and a rationale for these results, stating that because dialysis facilities in the United States are extremely small they lack sufficient sample size to achieve the reliability threshold of 0.6. The developer provides a rationale for why facilities should be included in the measure to help with the overall quality of care provided by dialysis facilities.
Rationale:
- This maintenance measure is rated as 'Not Met But Addressable' for reliability because the current reliability metrics do not meet the established thresholds for this measure, indicating potential issues with the consistency and accuracy of the results across different settings and populations. However, the identified limitations are deemed addressable, as the developer may consider aggregating data across multiple years to increase sample size. Data were sourced for year 2022 in addition to year 2023 which was used for reliability testing. By addressing this issue, there is potential to enhance the reliability.
Strengths:
- The developer performed the required validity testing for this maintenance measure; specifically, they performed accountable entity validity testing at the level the measure is specified (dialysis facility). The data used for validity testing come from facilities that reported the measure in 2022 and 2023 (7,890 and 7,753 facilities, respectively; combined number not provided).
- The developer performed a classification analysis comparing the meaure to four other measures reported by many dialysis facilities, where the mean performance on each of the comparator measures was calculated for dialysis facilities that performed "better than or as expected" vs. "worse than expected" on the ED30 measure. The developer hypothesized that better performance on the ED30 measure (where a lower score = better performance) would be associated with lower scores on the standardized mortality ratio (SMR), the standardized transfusion ratio (STrR), and the standardized ED visit ratio (SEDR), and higher scores on the percentage of patients waitlisted for transplant (PPPW). The developer's reasoning for each of these hypothesized relationships was detailed in the submission, and a common theme is the likely effectiveness of care coordination and transition processes in facilities with better than expected performance and its affect on performance across these measures.
- The developer reported findings largely in line with their hypotheses; RC ED30 was not significant.
- The developer conducted statistical risk adjustment, based on a conceptual model, selecting risk factors that are present at the start of care, have a significant association with the outcome, and vary in prevalence across measured entities. The model has acceptable calibration.
Limitations:
- The developer did not provide an interpretation for the lack of significant association between ED30 and STrR.
- The developer reported a c-statistic of 0.661, indicating moderate model discrimination.
Rationale:
- This maintenance measure is rated as ‘Met’ for validity because the developer performed the required validity testing for this measure, and validity testing results support a strong inference of validity for the measure, confirming that the measure accurately reflects performance on quality and can distinguish good from poor performance.
- The risk adjustment methods used are appropriate and demonstrate variation in the prevalence of risk factors across measured entities. The model performance is acceptable.
Use and Usability
Strengths:
- The measure is currently used in the Dialysis Facility Care Compare (DFCC) and Dialysis Facility Reports (DFRs) Public Reporting Programs.
- Attributes of a suitable program for this measure are described, and these include an accountability program that focuses on End Stage Renal Disease (ESRD) patients with Medicare coverage as the target population and the dialysis facility as the accountable entity.
- The developer provided a summary of how accountable entities can use the measure results to improve performance. Specifically, identifying patients with missed/shortened treatments and who don’t achieve target weight for improved fluid management, refresher training for infection control, and education for patients around when to use the emergency department (ED) and who to contact for questions or concerns between treatments. These possible actions are reflected in the measure’s logic model.
- Stakeholders can submit questions about the measure at any time through an online helpdesk. The developer noted that only clarification questions have been received. The developer noted that the feedback received did not lead to any changes in the measure specifications.
- The developer reported no unexpected findings.
- The developer reported changes in performance from 2019-2023, in which the overall performance score improved from 0.17 to 0.14 (for this measure, a lower score is better) which supports the argument that this measure is usable.
Limitations:
- None identified.
Rationale:
- This new measure is rated ‘Met’ for use and usability because there is a clear plan for use in at least one accountability application, and the measure provides actionable information for improvement. The developer reported that no potential unintended consequences were identified.
Committee Independent Review
#3566 ED following Hospital Discharge (Dialysis Facilities)
Importance
Closing Care Gaps
Not addressed
Feasibility Assessment
Already in use
Scientific Acceptability
~30% of accountable entities are small facilities with inadequate sample size to support adequate reliability. The developer asserts the need for outcome measures in this population to hold facilities accountable and describes the impact of the metric on the reliability of the QIP overall. This could be strengthened by sharing the impact of the metric on the reliability in the program as a whole rather than a standalone metric.
Sufficient validity testing and results. Lack of correlation between Standardized Transfusion Ratio and the ED visit measure is inconsistent with the evidence presented which asserts that consistent transfusion is tied to reduced utilization of services. This could be driven by the fact that the ED visit measure excludes ED visits that convert to admissions. This could be strengthened by assessing the impact of this aspect of the measure; which was implemented to prevent "double counting" admissions with the readmission measure but could have broader implications on interpretation and in turn improvement efforts in this measure.
Use and Usability
Already in use; developer shared some modest improvement since implementation.
Summary
An important measure for the ESRD population. Would like to understand how it interacts with other measures within the ESRD QIP and the role it plays in that program as some measure design decisions were dictated by other measures in the ESRD QIP.
3566- Standardized Ratio of ED30 for dialysis centers
Importance
Is there any more recent literature/research on this measure? Would be helpful to understand why using patient feedback >10 years old? Are there any updates available? Has this measure been used by patients since implementation and has it aided in increased transparency?
Closing Care Gaps
this is an optional section for maintenance measure
Feasibility Assessment
existing measure, data already collected/reported
Scientific Acceptability
The measure, as presented, presents potential issues with the consistency / accuracy of the results across different settings and populations, including urban/rural, small/large centers, and population socioeconomic characteristics.
Use and Usability
Would be helpful to see more on the current use of the measure and its impact on outcomes.
Summary
Would like to see more recent feedback in use of the measure and more recent studies analysis included. While an existing measure, it lacks scientific acceptability, however it is addressable.
The measure documentation…
Importance
Closing Care Gaps
See detailed comments regarding validity and reliability.
The documentation includes distributions of performance scores and reliability analyses, but the submitted materials provide more limited evidence that differences in ED30 scores correspond to meaningful differences in care quality, care coordination practices, patient experience, mortality, hospitalization outcomes, or other clinically relevant outcomes. The existence of variation alone does not establish that the measure is effectively distinguishing higher-performing from lower-performing facilities.
Feasibility Assessment
Scientific Acceptability
While ED30 incorporates patient-level risk adjustment through the calculation of expected events, the measure is ultimately expressed as an observed-to-expected ratio rather than a fully risk-standardized rate with reliability adjustment. Consequently, facilities with lower discharge volumes may experience greater performance volatility, as a small number of additional emergency department encounters can have a disproportionate impact on the measure score. Although CMS applies minimum case thresholds and risk adjustment, questions remain regarding whether the methodology adequately accounts for statistical uncertainty among smaller dialysis facilities and whether a hierarchical risk-standardized approach would improve measure reliability and fairness.
The measure as I understand may not account for factors outside the control of dialysis facilities, may not fully adjust for patient complexity and social risk, assumes that all post-discharge ED encounters represent poor-quality care, and may have limited ability to distinguish true performance differences from statistical variation among smaller facilities. Collectively, these concerns raise questions regarding whether observed differences in performance accurately reflect dialysis facility quality.
Use and Usability
Summary
The measure documentation includes distributions of performance scores and reliability analyses, but the submitted materials provide more limited evidence that differences in ED30 scores correspond to meaningful differences in care quality, care coordination practices, patient experience, mortality, hospitalization outcomes, or other clinically relevant outcomes. The existence of variation alone does not establish that the measure is effectively distinguishing higher-performing from lower-performing facilities.
(No subject)
Importance
Closing Care Gaps
Optional domain
Feasibility Assessment
Does not appear to be any additional reporting burden
Scientific Acceptability
Need additional data to increase sample size
Required testing completed
Use and Usability
Data provided can provide clear actionable information
(No subject)
Importance
Although some of the evidence is a little older, the prevalence and cost of the condition and outcome are important enough that they warrant a measure in this space.
Closing Care Gaps
Not adressed
Feasibility Assessment
No concerns
Scientific Acceptability
Although this is a maintenance measure with an established history of use in CMS accountability programs, too many facilities fall below the 0.60 threshold. I recognize that the developer provides a reasonable explanation that dialysis facilities are much smaller than hospitals and that achieving reliability above 0.60 for most facilities would require facility sizes exceeding what is typically observed nationally. The supplemental analysis shows that a facility would need approximately 125 patients to achieve an IUR of 0.60, while the median facility has only 91 patients. The measure also includes public reporting thresholds, small-facility adjustments within CMS programs, and contributes to broader composites that reduce random variation. Still, the results are too low for too many facilities.
The developer provides empirical construct-validity testing demonstrating that facilities performing better on ED30 also perform better on several related quality measures. Facilities classified as better or as expected on ED30 had lower mortality rates, lower transfusion rates, higher waitlist percentages, and substantially lower overall emergency department utilization than facilities classified as worse than expected. These relationships were all in the expected direction and support the conclusion that ED30 is measuring meaningful differences in facility performance rather than random variation.
The developer cites evidence that emergency department use following hospitalization is frequently related to dialysis-specific conditions, including fluid overload, vascular access complications, electrolyte abnormalities, and missed treatments, all of which may be influenced by dialysis facility practices such as medication reconciliation, target-weight assessment, care coordination, patient education, and post-discharge follow-up. The validity argument is strengthened by consistent findings from dialysis care coordination programs that have demonstrated reductions in ED use.
The risk-adjustment model appears appropriate and demonstrates acceptable performance. The reported AUC of 0.661 indicates fair discrimination, meaning the model is reasonably capable of distinguishing higher-risk from lower-risk patients, although discrimination is not particularly strong.
Use and Usability
No concerns
(No subject)
Importance
Closing Care Gaps
Optional domain, developer should address the care gap.
Feasibility Assessment
Scientific Acceptability
agree with staff that updated research studies should be utilized to be 5 years or less.
Use and Usability
I recommend approval
Importance
I agree with Staff Assessment. In particular, the problem this measure addresses presents a significant burden for dialysis patients, with 27% of patients being treated in the ED within 30 days of hospital discharge often for serious conditions such as heart failure.
The literature review should indeed be updated.
Closing Care Gaps
As mentioned in Staff Assessment, the developer did not address this optional domain.
Feasibility Assessment
In particular. the staff assessment noted that all required data elements are routinely generated during care delivery, and required elements are available from digital or electronic sources.
Scientific Acceptability
I agree with Staff Assessment on this topic.
I agree with Staff Assessment on this topic. In particular, I agree with the developer's reasoning in that there is likely improved effectiveness of care coordination and transition processes in facilities that correlates with better than expected performance and this has impact on performance across these measures discussed.
Use and Usability
I agree with the Staff Assessment , I particular I see a clear plan for use in at least one accountability application, and the measure provides actionable information for improvement.
Summary
I recommend approval for this measure.
Public Comments
Need to update comorbidity adjustment method
"Methodology for prevalent comorbidity selection: We began the selection process with the 283 AHRQ CCS groupers for calendar year 2015."
This approach is antiquated. The Clinical Classification Software (CCS) is a dead grouper that was designed for ICD-9-CM and is no longer supported or recommended by AHRQ for ANY purpose. The current diagnosis grouper is the Clinical Classification Software Refined (CCSR), which was purpose-built for ICD-10-CM. Unlike CCS, CCSR is updated annually and therefore properly handles the 5,000 or more new ICD-10-CM codes that have been introduced over the past decade. For example, 1504 new codes were introduced in 2023 alone, and these codes cannot be handled by a dead grouper such as CCS. The CCSRs are designed for backward compatibility to October 2015, and must therefore be used for any purpose where CCS would have been used prior to that date. Please refer to these websites or consult with the AHRQ HCUP Research Tools team for further information.
https://www.icd10data.com/ICD10CM/Codes/Changes/New_Codes?year=2025
https://hcup-us.ahrq.gov/tools_software.jsp
https://hcup-us.ahrq.gov/toolssoftware/ccsr/ccs_refined.jsp
ASN Comments on CBE ID 3566
ASN supports expanding the denominators for CBE 3565 and CBE 3566 to include Medicare Advantage (MA) beneficiaries. With MA enrollment among ESRD patients now exceeding 50% and continuing to grow, inclusion of these patients is essential to ensure that quality measures fully and accurately represent the entire ESRD population[i]. ASN also supports inclusion of MA status as a covariate in the measures’ risk adjustment models, as this could help account for some differences in patient demographics and utilization patterns between MA and traditional Fee-for-Service Medicare.
At the same time, ASN urges CMS to maintain oversight of known limitations in MA encounter data. ASN has consistently raised concerns regarding the completeness and transparency of MA data, challenges that have also been documented by the Medicare Payment Advisory Commission (MedPAC)[ii]. As these measures are implemented for public reporting and accountability programs, CMS must carefully monitor MA data completeness and timeliness to prevent distortions in facility-level performance scores.
[i] United States Renal Data System. 2025 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2025
[ii] https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs… source/reports/jun19_ch7_medpac_reporttocongress_sec.pdf