30-Day Unplanned Readmissions for Cancer Patients

CBE ID

3188

1.5 Project

Cost, Resource Use, and Efficiency

Endorsement Status

Endorsed

1.0 New or Maintenance

Maintenance

1.1 Measure Structure

Single Measure

Previous Endorsement Cycle

Full Year 2017

Is Under Review

Yes

Next Maintenance Cycle

Spring 2025

1.6 Measure Description

30-Day Unplanned Readmissions for Cancer Patients measure is a cancer-specific measure. It provides the rate at which adult cancer patients have an unplanned readmission within 30 days of discharge from an acute care hospital. The unplanned readmission is defined as a subsequent inpatient admission to a short-term acute care hospital, which occurs within 30 days of the discharge date of an eligible index admission and has an admission type of “emergency” or “urgent.”

Measure Specs

General Information

1.7 Measure Type

Outcome

1.3 Electronic Clinical Quality Measure (eCQM)

1.8 Level of Analysis

Facility

1.9 Care Setting

Hospital: Inpatient

1.10 Measure Rationale

Hospital readmission, for any reason, is disruptive to patients and caregivers, costly to the healthcare system, and puts patients at additional risk of hospital-acquired infections and complications. Readmissions are also a major source of patient and family stress and may contribute substantially to loss of functional ability, particularly in older patients.

Some readmissions are unavoidable and result from inevitable progression of disease or worsening of chronic conditions. However, readmissions may also result from poor quality of care or inadequate transitional care. Transitional care includes effective discharge planning, transfer of information at the time of discharge, patient assessment and education, and coordination of care and monitoring in the post-discharge period. Numerous studies have found an association between quality of inpatient or transitional care and early (typically 30-day) readmission rates for a wide range of conditions.^1-8

Throughout medicine, randomized controlled trials have shown that improvement in the following areas can directly reduce readmission rates: quality of care during the initial admission; improvement in communication with patients, their caregivers and their clinicians; patient education; predischarge assessment; and coordination of care after discharge.^9-24 Despite these isolated successful interventions, the overall national readmission rate remains high, with a 30-day readmission following nearly one fifth of discharges. Furthermore, readmission rates vary widely across institutions.^25-27 Both the high baseline rate and the variability across institutions speak to the need for a quality measure to prompt more concerted and widespread action.

Existing studies in cancer have largely focused on post-operative readmissions, reporting readmission rates between 6.5% and 25%. For many cancer patients, readmission following hospitalization may be preventable and should be addressed to lower costs and improve patient outcomes.^28-30 The Alliance of Dedicated Cancer Centers (ADCC) recognized the need for an oncology-specific unplanned readmission measure because this population was excluded from most existing measures, and because planned readmissions are often used in clinical pathways for cancer patients. In 2014, the ADCC proposed the 30-Day Unplanned Readmissions for Cancer Patients measure as an accountability measure for the PPS-Exempt Cancer Hospitals Quality Reporting Program (PCHQR). The measure was initially developed by the Comprehensive Cancer Centers for Quality Improvement (C4QI), a national group of academic medical centers that collaborate to measure and improve the quality of cancer care in their institutions. C4QI’s members have utilized this claims-based, cancer-specific unplanned readmissions measure since 2012. It is designed to reflect the unique clinical aspects of oncology and to provide a comprehensive measurement of unplanned readmissions in cancer patients. It considers patients with an admission type of “emergency” or “urgent” within 30 days of an index admission as an unplanned readmission. It excludes readmissions for patients readmitted for chemotherapy or radiation therapy treatment or with disease progression. Using this measure, hospitals can better identify and address preventable readmissions for cancer patients.

References

Frankl SE, Breeling JL, Goldman L. Preventability of emergent hospital readmission. American Journal of Medicine. Jun 1991;90(6):667-674.
Corrigan JM, Martin JB. Identification of factors associated with hospital readmission and development of a predictive model. Health Services Research. Apr 1992;27(1):81-101.
Oddone EZ, Weinberger M, Horner M, et al. Classifying general medicine readmissions. Are they preventable? Veterans Affairs Cooperative Studies in Health Services Group on Primary Care and Hospital Readmissions. Journal of General Internal Medicine. Oct 1996;11(10):597-607.
Ashton CM, Del Junco DJ, Souchek J, Wray NP, Mansyur CL. The association between the quality of inpatient care and early readmission: a meta-analysis of the evidence. Med Care. Oct 1997;35(10):1044-1059.
Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Archives of Internal Medicine. Apr 24 2000;160(8):1074-1081.
Courtney EDJ, Ankrett S, McCollum PT. 28-Day emergency surgical re-admission rates as a clinical indicator of performance. Annals of the Royal College of Surgeons of England. Mar 2003;85(2):75-78.
Halfon P, Eggli Y, Pr, et al. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Medical Care. Nov 2006;44(11):972-981.
Hernandez AF, Greiner MA, Fonarow GC, et al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. May 5 2010;303(17):1716-1722.
Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. Jun 15 1994;120(12):999-1006.
Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. Jama. Feb 17 1999;281(7):613-620.
Krumholz HM, Amatruda J, Smith GL, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. Journal of the American College of Cardiology. Jan 2 2002;39(1):83-89.
van Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post-discharge visits on hospital readmission. Journal of General Internal Medicine. Mar 2002;17(3):186-192.
Conley RR, Kelly DL, Love RC, McMahon RP. Rehospitalization risk with secondgeneration and depot antipsychotics. Annals of Clinical Psychiatry. Mar 2003;15(1):23-31.
Coleman EA, Smith JD, Frank JC, Min S-J, Parry C, Kramer AM. Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. Journal of the American Geriatrics Society. Nov 2004;52(11):1817-1825.
Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. Mar 17 2004;291(11):1358-1367.
Jovicic A, Holroyd-Leduc JM, Straus SE. Effects of self-management intervention on health outcomes of patients with heart failure: a systematic review of randomized controlled trials. BMC Cardiovasc Disord. 2006;6:43.
Garasen H, Windspoll R, Johnsen R. Intermediate care at a community hospital as an alternative to prolonged general hospital care for elderly patients: a randomized controlled trial. BMC Public Health. 2007;7:68.
Mistiaen P, Francke AL, Poot E. Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic meta-review. BMC Health Services Research. 2007;7:47.
Courtney M, Edwards H, Chang A, Parker A, Finlayson K, Hamilton K. Fewer emergency readmissions and better quality of life for older adults at risk of hospital readmission: a randomized controlled trial to determine the effectiveness of a 24-week exercise and telephone follow-up program. Journal of the American Geriatrics Society. Mar 2009;57(3):395-402.
Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. Feb 3 2009;150(3):178-187.
Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. Journal of Hospital Medicine. Apr 2009;4(4):211-218.
Weiss M, Yakusheva O, Bobay K. Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Medical Care. May 2010;48(5):482-486.
Stauffer BD, Fullerton C, Fleming N, et al. Effectiveness and cost of a transitional care program for heart failure: a prospective study with concurrent controls. Archives of Internal Medicine. Jul 25 2011;171(14):1238-1243.
Voss R, Gardner R, Baier R, Butterfield K, Lehrman S, Gravenstein S. The care transitions intervention: translating from efficacy to effectiveness. Archives of Internal Medicine. Jul 25 2011;171(14):1232-1237.
Keenan PS, Normand SL, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circulation. Sep 2008;1(1):29-37.
Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circulation. Mar 1 2011;4(2):243-252.
Lindenauer PK, Normand SL, Drye EE, et al. Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. Journal of Hospital Medicine. Mar 2011;6(3):142-150
Bell JF, Whitney RL, Reed SC, et al. Systematic Review of Hospital Readmissions Among Patients With Cancer in the United States. Oncol Nurs Forum. 2017;44(2):176-191. doi:10.1011/17.ONF.176-191
Brown EG, Burgess D, Li CS, Canter RJ, Bold RJ. Hospital readmissions: necessary evil or preventable target for quality improvement. Ann Surg. 2014;260(4):583-591. doi:10.1097/SLA.0000000000000923
Johnson PC, Xiao Y, Wong RL, et al. Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer. J Oncol Pract. 2019;15(5):e420-e427. doi:10.1200/JOP.18.00595

1.11 Measure Webpage

http://example.com

1.20 Types of Data Sources

Claims Data

1.25 Data Source Details

Medicare Limited Dat Set (LDS) Standard Analytic Files (SAF), 2020-2022

Master Beneficiary Summary File

Fee-For-Service Inpatient (IP) Claim File

Inpatient Revenue Center File

Numerator

1.14 Numerator

This outcome measure demonstrates the rate at which adult cancer patients have unplanned readmissions within 30 days of discharge from an eligible index admission. The numerator includes all eligible unplanned readmissions to any short-term acute care hospital within 30 days of the discharge date from an index admission that is included in the measure denominator. Readmissions with an admission type (UB-04 Uniform Bill Locator 14) of “emergency = 1” or “urgent = 2” are considered unplanned readmissions within this measure. Readmissions for patients with progression of disease and for patients with planned admissions for treatment are excluded from the measure numerator.

1.14a Numerator Details

This outcome measure demonstrates the rate at which adult cancer patients have unplanned

readmissions within 30 days of discharge from an eligible index admission. The numerator includes all eligible unplanned readmissions to any acute care hospital within 30 days of the discharge date from an index admission that is included in the measure denominator.

Readmissions with an admission type (UB-04 Uniform Bill Locator 14) of “emergency = 1” or “urgent = 2” are considered unplanned readmissions within this measure. Readmissions for patients with progression of disease (using a principal diagnosis of metastatic disease as a proxy) and for patients with planned admissions for treatment (defined as a principal diagnosis of chemotherapy or radiation therapy) are excluded from the measure numerator.

If a patient has more than one unplanned admission (for any reason) within 30 days after discharge from the index admission, only one is counted as a readmission for calculating the measure. The outcome is a dichotomous yes or no indicating if each admitted patient has an unplanned readmission within 30 days. However, if the first readmission after discharge is considered planned, any subsequent unplanned readmission is not counted as an outcome for that index admission because the unplanned readmission could be related to care provided during the intervening planned readmission rather than during the index admission.

The numerator algorithm and associated code tables are attached (Data Dictionary).

Denominator

1.15 Denominator

The denominator includes inpatient admissions for all adult Fee-for-Service Medicare beneficiaries where the patient is discharged from a short-term acute care hospital with a principal or secondary diagnosis (i.e., not admitting diagnosis) of malignant cancer within the defined measurement period.

1.15a Denominator Details

The denominator includes inpatient admissions for all adult Fee-for-Service Medicare beneficiaries where the patient is discharged from an acute care hospital with a

principal or secondary diagnosis of malignant cancer within the defined measurement period.

The denominator algorithm and associated code tables are attached (Data Dictionary). Briefly, admissions are included if all of the following criteria are met:

Enrolled in Medicare fee-for-service (FFS) for the 60 days prior to the date of admission and during the index admission.

Rationale: The 2-month prior enrollment criterion ensures that the comorbidity data used in risk adjustment can be captured from inpatient claims data in the 2 months prior to the index admission. Enrollment during the index admission is needed to qualify for the cohort and to ensure availability of data from the index admission for risk adjustment.

Aged 65 or over.

Rationale: Medicare beneficiaries younger than 65 are not included in the measure because they are considered to be too clinically distinct from Medicare beneficiaries who are 65 or older.

Discharged alive from a non-federal short-term acute care hospital.

Rationale: It is only possible for patients to be readmitted if discharged alive.

Not transferred to another acute care facility.

Rationale: Hospitalizations that result in a transfer to another acute care facility are not included in the measure because the measure’s focus is on admissions that result in discharge to a non-acute care setting (for example, to home or a skilled nursing facility).

1.15d Age Group

Adults (18-64 years)

Older Adults (65 years and older)

Measure Calculation

1.13a Attach Data Dictionary

3188_PCH_36_DataDict_PQMmaintenance_052025.xls

1.16 Type of Score

Rate/proportion

1.17 Measure Score Interpretation

Better quality = Lower score

1.18 Calculation of Measure Score

Below we provide the individual steps to calculate the measure score:

Define Cohort

Apply the inclusions/exclusions criteria to construct the measure cohort. See Tab 3: Denominator Calculation of the Data Dictionary; Tab 4: Denominator Codes; and Tab 5: Denominator Exclusion Codes of the Data Dictionary.

Identify discharges meeting the inclusion criteria described in Tab 3 and Tab 4.
Exclude admissions meeting any of the exclusion criteria described in Tab 3 and Tab 5.

Define outcome

Derive the measure outcome of 30-day readmission, by identifying a binary flag for an unplanned hospital visit within 30 days of index admission as described above.

Define risk variables

Use patients’ historical and index admission claims data, as well as fields (Age, Sex) from the Master Beneficiary Summary File to create risk-adjustment variables.

- Revenue Center Codes – ICU admission

- Diagnostic Related Group (DRG) codes – Surgical admission

- ICD-10 CM codes – BMT, Solid tumor, metastatic, comorbidities (following algorithm to calculate Elixhauser comorbidity index, including comorbidity specific application of POA indicator; with cancer related condition groups replaced with specific cancer specific risk indicators)

- Claim inpatient admission type code – Admission via ER

- Previous Inpatient claims within 60 days – Prior admissions

- Age variable recoded into dichotomous indicators based on 5 year increments (65-69, 70-74, 75-79, 80-85, 85+)

Measure score calculation

Estimate a mixed effects hierarchical logistic regression model (mHLM) to produce a standardized risk ratio (SRR), calculated as the ratio of the number of “predicted” readmissions to the number of “expected” readmissions at a given hospital. The mHLM is adjusted for age groups, gender, comorbidities, length of stay, prior admissions, solid tumor vs hematologic disease, metastatic cancer, surgery admission, ICU admission, bone marrow transplant, and a hospital-specific effect. Details about the risk-adjustment methodology are in section 5.4.5.

1.19 Measure Stratification Details

CMS initiated stratified reporting by dual eligibility status for PCHQR in 2024 (please see 2024 CMS Disparities Methods FAQs (07/19/24), at https://qualitynet.cms.gov/pch/measures/readmissions/resources). Associated codes are in the attached Data Dictionary.

We compared the difference in observed readmission rates between dual eligible and non-dual eligible patients for hospitals. See Supplemental Table 1.19.

1.21a Data Collection Tool URL(s)

http://example.com

1.26 Minimum Sample Size

Based on the signal to noise reliability values calculated for 5.4.2, we determined a minimum case count of 50 index admissions per year helped stabilize the reliability values above .7.

Importance

Evidence

2.1 Attach Logic Model

3188 Logic Model Attachment.docx

2.2 Evidence of Measure Importance

Cancer is the second leading cause of death in the United States, with 2,041,910 new cancer cases and 618,120 cancer deaths projected to occur in the United States in 2025.¹ It is now the leading cause of death among adults aged 40 to 79 years as well and in 21 states.² Cancer disproportionately affects older Americans, with 86% of all cancers diagnosed in people 50 years of age and older.¹ The overall cost of cancer treatment in the US was $183 billion in 2015, and conservative projections indicate that these costs will increase 34% to $246 billion by 2030.³ Given the current and projected increases in cancer prevalence and costs of care, it is essential that healthcare providers look for opportunities to lower the costs of cancer care.

Reducing readmissions after hospital discharge has been proposed as an effective means of lowering healthcare costs and improving the outcomes of care. Unnecessary hospital readmissions negatively impact cancer patients by compromising their quality of life, by placing them at risk for health-acquired infections, and by increasing the costs of their care. Furthermore, unplanned readmissions during treatment can delay treatment completion and, potentially, worsen patient prognosis.

Preventing these readmissions improves the quality of care for cancer patients. Numerous studies have examined all-cause readmissions and readmissions for specific conditions, and randomized controlled trials demonstrate reduced readmission rates through the following: improvement of quality of care during the initial admission improvement in communication with patients, their caregivers, and their clinicians; patient education; pre-discharge assessment; and coordination of care after discharge. Evidence that hospitals have been able to reduce readmission rates through these quality-of-care initiatives illustrates the degree to which hospital practices can affect readmission rates. Successful randomized trials have reduced 30-day readmission rates by 20-40%.^4-16 Hospital processes that reflect the quality of inpatient and outpatient care such as discharge planning, medication reconciliation, and coordination of outpatient care have also been shown to reduce readmission rates.¹⁷

A 2017 systematic review by Bell et al identified comorbidities, gender, older age, more advanced cancer (identified by stage, tumor size, and/or lymph node involvement), low socioeconomic status, unmarried status, race, dual eligible insurance status, and residence in low population areas, rural areas, or the Midwest or South as associated with higher readmission rates. They also found that surgical factors, such as postoperative complications and operative methods, were associated with higher readmission rates, as were longer and shorter index hospital stays and high and low hospital volume. Other characteristics of the index hospitalization associated with higher rates included having a medical (versus surgical) discharging physician, greater travel distance, discharge to a place other than home, and emergent admission.¹⁸Existing studies in cancer have largely focused on post-operative readmissions, reporting readmission rates between 6.5% and 25%. Brown et al. (2014) concluded that 33% of readmissions within seven days of the index hospitalization were for issues deemed potentially preventable by the authors, including nausea, vomiting, dehydration, and postoperative pain, with improved discharge follow-up, care coordination, and palliative care.¹⁹Johnson et al. (2019) Found premature discharges and inadequate outpatient follow-up to be common contributors to avoidable readmissions. Ensuring timely follow-up appointments and comprehensive discharge planning can mitigate this risk.²⁰

All-cause and disease-specific unplanned readmissions rates have been adopted by the Centers for Medicare & Medicaid Services (CMS) as key indicators of inpatient quality care. Additionally, Medicare began reducing payments to hospitals with excess readmissions in October 2012, as mandated in the Patient Protection and Affordable Care Act of 2010. However, cancer has lagged behind these conditions in the development of validated readmission rates. In 2012, the Comprehensive Cancer Center Consortium for Quality Improvement, or C4QI (a group of academic medical centers that collaborate to measure and improve the quality of cancer in their centers), began development of a cancer-specific unplanned readmissions measure: 30-Day Unplanned Readmissions for Cancer Patients. The ADCC identified this ongoing work as a potential accountability measure for the PCHQR. Both groups recognize the importance of measuring unplanned readmissions as an indicator of the quality of hospital-based oncology care and have designed the 30-Day Unplanned Readmissions for Cancer Patients measure accordingly.^5,6 This measure is intended to reflect the unique clinical aspects of oncology patients and to yield readmission rates that more accurately reflect the quality of cancer care delivery, when compared with broader readmissions measures.

REFERENCES

1. Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10-45. doi:10.3322/caac.21871.

2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7-30.

3. Mariotto, A. B., Enewold, L., Zhao, J., Zeruto, C. A., & Yabroff, K. R. (2020). Medical care costs associated with cancer survivorship in the United States. Cancer Epidemiology, Biomarkers & Prevention: a Publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 29(7), 1304–1312. https://doi.org/10.1158/1055-9965.EPI-19-1534

4. Patel PH, Dickerson KW. Impact of the Implementation of Project Re-Engineered Discharge for Heart Failure patients at a Veterans Affairs Hospital at the Central Arkansas Veterans Healthcare System. Hosp Pharm. 2018;53(4):266‐271. doi:10.1177/0018578717749925.

5. Jack BW, Chetty VK, Anthony D, Greenwald JL, Sanchez GM, Johnson AE, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150(3):178-87.

6. Coleman EA, Smith JD, Frank JC, Min SJ, Parry C, Kramer AM. Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc 2004;52(11):1817-25.

7. Courtney M, Edwards H, Chang A, Parker A, Finlayson K, Hamilton K. Fewer emergency readmissions and better quality of life for older adults at risk of hospital readmission: a randomized controlled trial to determine the effectiveness of a 24-week exercise and telephone follow-up program. J Am Geriatr Soc 2009;57(3):395-402.

8. Garasen H, Windspoll R, Johnsen R. Intermediate care at a community hospital as an alternative to prolonged general hospital care for elderly patients: a randomised controlled trial. BMC Public Health 2007;7:68.

9. Koehler BE, Richter KM, Youngblood L, Cohen BA, Prengler ID, Cheng D, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med 2009;4(4):211-218

10. Mistiaen P, Francke AL, Poot E. Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic metareview. BMC Health Serv Res 2007;7:47.

11. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med 1994;120(12):999-1006.

12. Naylor MD, Brooten D, Campbell R, Jacobsen BS, Mezey MD, Pauly MV, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. Jama 1999;281(7):613-20.

13. van Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post-discharge visits on hospital readmission. J Gen Intern Med 2002;17(3):186-92.

14. Weiss M, Yakusheva O, Bobay K. Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Med Care 2010;48(5):482-6.

15. Krumholz HM, Amatruda J, Smith GL, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. Jan 2 2002;39(1):83-89.

16. Nelson EA, Maruish ME, Axler JL. Effects of Discharge Planning and Compliance With Outpatient Appointments on Readmission Rates. Psychiatr Serv. July 1 2000;51(7):885-889.

17. Fisher ES, Wennberg JE, Stukel TA, Sharp SM. Hospital Readmission Rates for Cohorts of Medicare Beneficiaries in Boston and New Haven. New England Journal of Medicine. 1994;331(15):989-995.

18. Bell JF, Whitney RL, Reed SC, et al. Systematic Review of Hospital Readmissions Among Patients With Cancer in the United States. Oncol Nurs Forum. 2017;44(2):176-191. doi:10.1011/17.ONF.176-191

19. Brown EG, Burgess D, Li CS, Canter RJ, Bold RJ. Hospital readmissions: necessary evil or preventable target for quality improvement. Ann Surg. 2014;260(4):583-591. doi:10.1097/SLA.0000000000000923

20. Johnson PC, Xiao Y, Wong RL, et al. Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer. J Oncol Pract. 2019;15(5):e420-e427. doi:10.1200/JOP.18.00595

Performance Gap

2.4 Performance Gap

Data source: Inpatient claims via the Medicare Limited Dat Set (LDS) Standard Analytic Files (SAF), 2020-2022

Unadjusted performance scores by decile for CY 2022 are provided in Table 1. 2021 performance scores and adjusted scores for 2021 and 2022 are attached in the 2.4.a attachment.

Table 1. Performance Scores by Decile

Performance Gap, 30-Day Unplanned Readmissions for Cancer Patients Measure
	Overall	Minimum	Decile_1	Decile_2	Decile_3	Decile_4	Decile_5	Decile_6	Decile_7	Decile_8	Decile_9	Decile_10	Maximum
Mean Performance Score	15.2%	0%	0%	0.6%	9.0%	12.8%	14.8%	16.4%	17.7%	19.4%	22.3%	39.4%	39.4%
N of Entities	4333		434	434	434	433	433	433	433	433	433	433
N of Persons / Encounters / Episodes	646,500		2,137	3,760	21,792	65,953	95,032	137,322	140,636	108,031	63,132	8,705

2.4a Attach Performance Gap Results

3188 2.4a Performance Gap Results Attachment.docx

Equity

Equity

Equity

3.1 Contributions Toward Advancing Health Equity

See supplemental Table 5.1.4 and section 5.1.4. This analysis revealed higher unplanned 30-day readmission rates for cancer patients who were all other races compared to white patients (16.45% for White patients; 19.17% for Black patients; 18.31% for Asian patients; 16.60% for Hispanic patients; 18.23% for Native American patients; and 18.55% for other races). Patients who were dual eligible during the measurement period had a higher readmission rate (19.01%) compared to those who were never dual eligible (16.43%).

The literature regarding association between social risk factors and cancer-specific readmissions suggests that low income, older age, unmarried status, and language barriers are social risk factors associated with readmissions.^1-4 A 2017 recent systematic review by Bell et al identified comorbidities, gender, older age, low socioeconomic status, unmarried status, race, dual eligible insurance status, and residence in low population areas, rural areas, or the Midwest or South as associated with higher readmission rates.⁵

References

Goel AN, Raghavan G, St John MA, Long JL. Risk Factors, Causes, and Costs of Hospital Readmission After Head and Neck Cancer Surgery Reconstruction. JAMA Facial Plast Surg. 2019 Mar 1;21(2):137-145. doi: 10.1001/jamafacial.2018.1197. PMID: 30418467; PMCID: PMC6439803.
Wilbur MB, Mannschreck DB, Angarita AM, Matsuno RK, Tanner EJ, Stone RL, Levinson KL, Temkin SM, Makary MA, Leung CA, Deutschendorf A, Pronovost PJ, Brown A, Fader AN. Unplanned 30-day hospital readmission as a quality measure in gynecologic oncology. Gynecol Oncol. 2016 Dec;143(3):604-610. doi: 10.1016/j.ygyno.2016.09.020. Epub 2016 Sep 21. PMID: 27665313.
Sadowski DJ, Warner H, Scaife S, McVary KT, Alanee SR. 30-day all-cause hospital readmission after cystectomy: no worse for rural Medicare residents. Urol Oncol. 2018 Mar;36(3):89.e7-89.e11. doi: 10.1016/j.urolonc.2017.11.013. Epub 2017 Dec 14. PMID: 29249273.
Chen MM, Orosco RK, Harris JP, Porter JB, Rosenthal EL, Hara W, Divi V. Predictors of readmissions after head and neck cancer surgery: A national perspective. Oral Oncol. 2017 Aug;71:106-112. doi: 10.1016/j.oraloncology.2017.06.010. PMID: 28688676.
Bell JF, Whitney RL, Reed SC, et al. Systematic Review of Hospital Readmissions Among Patients With Cancer in the United States. Oncol Nurs Forum. 2017;44(2):176-191. doi:10.1011/17.ONF.176-191

Scientific Acceptability

Testing Data

5.1.1 Data Used for Testing

Medicare Limited Dat Set (LDS) 100% Standard Analytic Files (SAF), Q4 2020, CY 2021, CY 2022

Master Beneficiary Summary File

Fee-For-Service Inpatient (IP) Claim File

Inpatient Revenue Center File

5.1.2 Differences in Data

We tested minimum case counts of 5 to 50 in increments of 5, and opted for a minimum case count of 50 annual index admissions for reliability. We then also maintained that threshold for validity testing and the finalized hierarchical logistic regression mixed model covariates.

5.1.3 Characteristics of Measured Entities

4,462 hospitals were included in this analysis, including the 11 PPS exempt cancer hospitals. There were 1,356,010 admissions, with a mean of 304 admissions per hospital (standard deviation= 603). See Supplemental Table 5.1.3 in Section 7.1.

5.1.4 Characteristics of Units of the Eligible Population

Supplemental Table 5.1.4 in Section 7.1 provides characteristics of the patients with the 1,356,010 eligible hospital admissions included in the maintenance analysis. Briefly, there were 227,439 30-day unplanned readmissions. Readmission rates were slightly higher for men compared with women (17.24% vs 16.23%, respectively). Readmission rates were higher for all other races compared to white patients (16.45% for White patients; 19.17% for Black patients; 18.31% for Asian patients; 16.60% for Hispanic patients; 18.23% for Native American patients; and 18.55% for other races). Readmission rates were highest for the 75-79 age group (17.45%). Patients who were dual eligible during the measurement period had a higher readmission rate (19.01%) compared to those who were never dual eligible (16.43%).

Reliability

5.2.1 Level(s) of Reliability Testing Conducted

Accountable entity level (i.e., measure score) (e.g., signal-to-noise analysis)

5.2.2 Method(s) of Reliability Testing

To calculate accountable entity level reliability of the risk standardized readmission rate, we applied signal to noise analysis, taking the variance of the hospital random effect intercepts, estimated via the hierarchical logistic regression, as the ‘signal’ value (hospital to hospital variance) and each hospital’s estimated measurement error variance as ‘noise’ (hospital-specific error). Thus, each hospital’s signal to noise reliability for the risk adjusted outcome is calculated as signal/(signal+noise).

5.2.3 Reliability Testing Results

After limiting the dataset to hospitals with 50 or more index admissions in a calendar year, reliability estimates for are .7 or greater. Mean reliability for the hospital cohort meeting that threshold is 0.938 and 0.935 in 2021 and 2022 respectively.

5.2.3a Attach Additional Reliability Testing Results

3188 5.2.3a Reliability Testing Results Attachment.docx

5.2.4 Interpretation of Reliability Results

Testing done via the test-retest method for the original measure submission found that hospitals with a minimum annual case count of 50 admissions produced consistent and stable results while maintaining as many hospitals as possible for the measure calculations. We opted to reevaluate this cutoff point, applying annual minimum index admissions thresholds from 5 to 50 in increments of 5 to the cohort of index admissions, refitting the hierarchical logistic random intercepts model to that dataset, then calculating signal to noise reliability based on the estimated variance of the hospital random intercepts fit to that cohort (signal) and each hospital’s estimated measurement error variance (noise).

Our findings corroborated the previous findings, suggesting that an annual minimum of 50 index admissions produced reliability estimates above .7 for all hospitals included in the dataset while minimizing the removal of hospitals from reporting.

Table 2. Accountable Entity Level Reliability Testing Results by Denominator, Target Population Size

Accountable Entity-Level Reliability Testing Results
	Overall	Minimum	Decile_1	Decile_2	Decile_3	Decile_4	Decile_5	Decile_6	Decile_7	Decile_8	Decile_9	Decile_10	Maximum
Reliability	0.935	0.772	0.849	0.880	0.908	0.924	0.938	0.952	0.962	0.972	0.980	0.989	0.989
Mean Performance Score	17.3%		17.3%	17.3%	17.3%	17.3%	17.3%	17.4%	17.4%	17.3%	17.3%	17.5%
N of Entities	2,008		201	201	201	201	201	201	201	201	200	200
N of Persons / Encounters / Episodes	614,217	50	11,669	15,793	20,830	25,978	33,124	43,242	55,925	75,046	106,083	226,581

Validity

5.3.1 Level(s) of Validity Testing Conducted

Accountable entity level (i.e., measure score) (e.g., criterion validity)

5.3.2 Type of Accountable Entity Level Validity Testing Conducted

Empirical validity testing at the accountable entity-level (e.g., criterion validity, construct validity, known groups analysis)

5.3.3 Method(s) of Validity Testing

Initial empirical validity testing compared the 30-Day Unplanned Readmissions for Cancer Patients measure with CMS’ Hospital-Wide All-Cause Readmission (HWR) measure. At that time, the TEP selected the HWR measure given gaps in cancer-specific process or outcome measures suitable for this purpose. While the two measures have different target populations, they both utilize Medicare claims administrative claims data and assess unplanned readmissions within thirty days of hospital discharge. Additionally, the 30-Day Unplanned Readmissions for Cancer Patients measure was modeled after the HWR measure where possible.

The hypothesized relationship was that better performance (i.e., lower hospital-level rates) on the HWR measure should be associated with better performance (i.e., lower hospital-level rates) on the 30-Day Unplanned Readmissions for Cancer Patients measure. Moderate positive correlation was expected, given that the measures assess similar healthcare practices related to patient care, but mutually exclusive patient populations.

For this maintenance analysis, we tested the same hypothesis, with moderate positive correlation expected.

5.3.4 Validity Testing Results

Using the results of 30-Day Unplanned Readmissions for Cancer Patients for CY 2022 compared with the HWR values published for the period Q3 2022 – Q2 2023, 1,995 hospitals had data for both measures. We found an overall correlation of .402 (95% CI: .365, .439) (p<0.001).

5.3.5 Interpretation of Validity Results

As expected, we found a statistically significant, moderate, positive correlation (0.402) between the 30-Day Unplanned Readmissions for Cancer Patients measure and the HWR measure.

Risk Adjustment

5.4.1 Methods Used to Address Risk Factors

Statistical case-mix adjustment model

Stratification by risk factor category

5.4.2 Conceptual Model Rationale

During the initial measure development, we identified potential risk factors for the 30-Day Unplanned Readmissions for Cancer Patients measure using the following methods:

Review of the literature; and,
Convening a multidisciplinary workgroup of:
Physician subject-matter experts from cancer hospitals to identify patient-level risk adjustors that are clinically-relevant for unplanned readmissions in patients with cancer;
Data analysts with experience in complex analyses of hospital data, quality measurement, and quality improvement, with a specific focus on cancer conditions;
Experienced coders to advise on the selection and completeness of code lists for the measure; and,
Analytics experts with experience in statistical testing methods and in creating predictive models for unplanned readmissions.

In total, 25 patient-level variables were evaluated for potential inclusion in the risk adjustment model. The list of potential risk adjustors was then compared to the data elements available in administrative claims data. Since this measure is to be implemented using claims data only, 7 clinical and SDS variables (Supplemental Table 5.4.2, Group A in Section 7.1) that are not well-defined in claims data were not included in this model. Additionally, 2 variables (Supplemental Table 5.4.2, Group B in Section 7.1) were unavailable in our measure testing dataset. The list of potential risk adjustors was then refined to include only variables not in the control of the hospital, as the goal of this model is to adjust for patient-specific factors only. This eliminated 1 variable (Supplemental Table 5.4.2, Group C). Finally, 1 SDS variable (“Race”) was removed (Supplemental Table 5.4.2 in Section 7.1, Group D). Joynt et al. found that racial disparities in readmissions were related to patient race and the site of care, suggesting an opportunity to reduce disparities in care;² thus, we removed the variable to ensure that the risk adjustment model would not mask disparities in care. This process yielded 14 risk factors (Supplemental Table 5.4.2, Group E in Section 7.1) to be evaluated for fit in the risk adjustment model. Throughout this process, all potential risk factors were determined by careful review with workgroup members. They reflect clinically-relevant decisions and alignment with coding practices and analytical standards to ensure accurate assessments of patient-level risk factors present at the index admission and outside the control of the hospital.

The complete list of potential risk factors identified through the workgroup’s review, with the workgroup’s assessment, is in attached Supplemental Table 5.4.2 in Section 7.1.

For measure maintenance, we re-evaluated clinical and social risk factors. First, we re-reviewed published literature. The literature regarding association between social risk factors and cancer-specific readmissions is mostly limited to readmissions following surgical procedures and results are varied; however, studies suggest that low income, older age, unmarried status, and language barriers are social risk factors associated with readmissions.^1-4 A 2017 recent systematic review by Bell et al identified comorbidities, gender, older age, more advanced cancer (identified by stage, tumor size, and/or lymph node involvement), low socioeconomic status, unmarried status, race, dual eligible insurance status, and residence in low population areas, rural areas, or the Midwest or South as associated with higher readmission rates. They also found that surgical factors, such as postoperative complications and operative methods, were associated with higher readmission rates, as were longer and shorter index hospital stays and high and low hospital volume. Other characteristics of the index hospitalization associated with higher rates included having a medical (versus surgical) discharging physician, greater travel distance, discharge to a place other than home, and emergent admission.⁵

A panel of experts - including physician subject-matter experts from cancer hospitals, quality measurement experts, and analytic experts – re-reviewed the original variables, updated literature, and discussed potential changes. Generally, the original clinical and SES variables and methods were supported for maintenance. Age and gender were maintained as risk factors for our maintenance modeling. Race remained excluded from modeling, as described above. Also, for this maintenance analysis, we removed the dual eligibility variable from the risk model, as CMS initiated stratified reporting by dual eligibility status for PCHQR in 2024 (please see 2024 CMS Disparities Methods FAQs (07/19/24), at https://qualitynet.cms.gov/pch/measures/readmissions/resources). Finally, we removed the ‘Discharge to’ variables (Discharged to Home; Discharged to Hospice) based on expert feedback that these are substantively within the control of the hospital. The expert panel reviewed the updated statistical results produced for maintenance, as described in 5.4.4.

References

Goel AN, Raghavan G, St John MA, Long JL. Risk Factors, Causes, and Costs of Hospital Readmission After Head and Neck Cancer Surgery Reconstruction. JAMA Facial Plast Surg. 2019 Mar 1;21(2):137-145. doi: 10.1001/jamafacial.2018.1197. PMID: 30418467; PMCID: PMC6439803.
Wilbur MB, Mannschreck DB, Angarita AM, Matsuno RK, Tanner EJ, Stone RL, Levinson KL, Temkin SM, Makary MA, Leung CA, Deutschendorf A, Pronovost PJ, Brown A, Fader AN. Unplanned 30-day hospital readmission as a quality measure in gynecologic oncology. Gynecol Oncol. 2016 Dec;143(3):604-610. doi: 10.1016/j.ygyno.2016.09.020. Epub 2016 Sep 21. PMID: 27665313.
Sadowski DJ, Warner H, Scaife S, McVary KT, Alanee SR. 30-day all-cause hospital readmission after cystectomy: no worse for rural Medicare residents. Urol Oncol. 2018 Mar;36(3):89.e7-89.e11. doi: 10.1016/j.urolonc.2017.11.013. Epub 2017 Dec 14. PMID: 29249273.
Chen MM, Orosco RK, Harris JP, Porter JB, Rosenthal EL, Hara W, Divi V. Predictors of readmissions after head and neck cancer surgery: A national perspective. Oral Oncol. 2017 Aug;71:106-112. doi: 10.1016/j.oraloncology.2017.06.010. PMID: 28688676.
Bell JF, Whitney RL, Reed SC, et al. Systematic Review of Hospital Readmissions Among Patients With Cancer in the United States. Oncol Nurs Forum. 2017;44(2):176-191. doi:10.1011/17.ONF.176-191

5.4.2a Attach Conceptual Model

3188 5.4.2a Conceptual Model Attachment.pdf

5.4.3 Variable Distribution Across Measured Entities

Variables tested for risk/case-mix adjustment included:

Age
Sex
Admitted via Emergency Department
Comorbidities
Length of stay greater than 3 days
Metastatic disease
Admissions in the prior 60 days
Intensive care unit (ICU) stay
Solid tumor vs hematologic cancer diagnosis

Descriptive statistics are in Table 5.4.3 in the attachment.

Surgical admissions and One or more comorbidities were excluded from the model fitting process due to high tetrachoric correlation. Dual eligibility was not included in the risk adjustment model but is considered for risk stratification.

5.4.4 Risk/Case-Mix Adjustment Modeling and/or Stratification Results

Statistical results are in Table 5.4.4 in the attachment.

Testing variables for Tetrachoric correlation, we removed:

- 1 or More Comorbidities (.98 with 2 or More Comorbidities)

- Surgical Admission (-.62 with Admission via ER)

After reviewing the fitted hospital-level random intercepts hierarchical logistic regression model with our TEP, we opted to maintain all other covariates.

5.4.4a Attach Risk/Case-mix Adjustment Modeling and/or Stratification Specifications

3188 5.4.4a Risk:Case-mix Adjustment Modeling Specifications Attachment .pdf

5.4.5 Calibration and Discrimination

The Hosmer-Lemeshow Goodness-of-Fit test statistic (G = 10) is a Chi-square with 8 degrees of freedom: 470.59 (p< 0.0001). While this is a significant result indicating potential issues of fit with the model, the H-L test can be overpowered for large datasets (n> 25,000), potentially magnifying relatively small differences between observed and predicted rates.

The c-statistic gives a measure of how well the model discriminates between patients with or without an unplanned readmission compared. Our c-statistic was 0.601 (95% CI: 0.5993-0.6018). The decile plot attached in 5.4.5a shows relatively close predicted and observed rates within each risk decile.

5.4.5a Attach Calibration and Discrimination Testing Results

3188 5.4.5a Calibration and Discrimination Testing Results Attachment .docx

5.4.6 Interpretation of Risk/Case-mix Factor Findings

Our c-statistic of 0.601 (95% CI: 0.5993-0.6018) suggests that there are risk covariates not captured in our model that could be useful in understanding the rates of unplanned readmissions for cancer patients; however, additional variables identified by experts are not available in claims (see 5.4.2). The decile plot attached in 5.4.5a shows relatively close predicted and observed rates within each risk decile, with the largest magnitude difference in the lowest risk decile, observed: 8.1%, predicted: 9.5%, a difference of 1.4%. The magnitude of the remaining differences are 0.7% or lower. The spread between the lowest risk decile (observed: 8.1%, predicted: 9.5%) and the highest risk decile (observed: 25.4%, predicted: 26.0%) suggests the model may be adequate in controlling for differences in patient-level risk factors.

5.4.7 Final Approach to Address Risk Factors

Statistical risk adjustment model with risk factors

Stratification by risk factor category

Use & Usability

Use
Usability

Use

6.1.1 Current Status

In use

6.1.3 Current Use(s)

Public Reporting

Quality Improvement (Internal to the specific organization)

6.1.4 Program Details

Name of the program and sponsor

PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program, Centers for Medicare and Medicaid Services (CMS)

Geographic area and percentage of accountable entities and patients included

Eleven hospitals across the nation that are granted as PPS-Exempt Cancer Hospitals. In the 7/1/2022 - 6/30/2023 PCHQR reporting period, there were a total of 20,371 cases in the 11 hospitals for this measure.

Applicable level of analysis and care setting

Hospital inpatient reporting program

Usability

6.2.1 Actions of Measured Entities to Improve Performance

The outcome of unplanned hospital visits following discharge from an inpatient admission is a widely accepted measure of care quality. The 30-Day Unplanned Readmissions for Cancer Patients measure provides the opportunity to improve the quality of care for patients with cancer and to lower rates of adverse events that result in unplanned readmission after an inpatient stay.

Across medicine, there are evidence-based interventions that can reduce readmission rates. These interventions often address inadequate transitions of care, including patient education at discharge and coordination of outpatient care. For example, a 2021 systematic review that analyzed 60 trials, including 19 randomized controlled trials, concluded, in agreement with prior systematic reviews, that interventions that focus on communication at discharge were statistically significantly associated with lower rates of hospital readmissions (Becker et al., 2021). Within the 19 trials,10 focused on medication counselling, and six focused on patient education about their condition: the other three focused on other specific communication strategies. A 2022 systematic review found that post-discharge care including home care, telephone, and/or clinic visits resulted in lower rates of readmission compared with “usual care” for cardiac patients (Chauhan & McAlister, 2022). A systematic review published in 2023 pooled the results from 73 different studies to compare transitional care interventions with different levels of complexity and their impact on improving outcomes and found that low- and medium-complexity interventions were the most effective at reducing 30-day readmissions (Tyler et al., 2023). Study authors found that compared with usual care, readmission rates were reduced by 18 percent to 55 percent for these types of interventions. Complexity was categorized by the number of components of the intervention, and the number of stages of the hospitalization that the intervention was implemented. Finally, CMS has published a guide for hospitals, aimed at leadership, staff, and clinicians, which outlines effective strategies for reducing readmissions and reducing disparities. Strategies covered in the guide include: ensuring that patients understand discharge instructions and have appropriate follow-up visits, improving accessibility (transportation) for post-discharge care, ensuring patients have a primary care provider, starting post-discharge visit planning early in the discharge process, ensuring transfer of information to the post-discharge provider, and strategies to address language barriers and low health literacy (CMS Office of Minority Health, 2024).

For cancer patients specifically, Brown et al. (2014) concluded that 33% of readmissions within seven days of the index hospitalization were for issues deemed potentially preventable by the authors, including nausea, vomiting, dehydration, and postoperative pain, with improved discharge follow-up, care coordination, and palliative care. Johnson et al. (2019) Found premature discharges and inadequate outpatient follow-up to be common contributors to avoidable readmissions. Ensuring timely follow-up appointments and comprehensive discharge planning can mitigate this risk.

References

Becker, C., Zumbrunn, S., Beck, K., Vincent, A., Loretz, N., Müller, J., Amacher, S. A., Schaefert, R., & Hunziker, S. (2021). Interventions to Improve Communication at Hospital Discharge and Rates of Readmission: A Systematic Review and Meta-analysis. JAMA network open, 4(8), e2119346. https://doi.org/10.1001/jamanetworkopen.2021.19346
Chauhan, U., & McAlister, F. A. (2022). Comparison of Mortality and Hospital Readmissions Among Patients Receiving Virtual Ward Transitional Care vs Usual Post discharge Care: A Systematic Review and Meta-analysis. JAMA network open, 5(6), e2219113. https://doi.org/10.1001/jamanetworkopen.2022.19113
CMS Office of Minority Health (2024). Guide for Reducing Disparities in Readmissions. Accessed April 23, 2024; https://www.cms.gov/about-cms/agency-information/omh/downloads/omh_readmissions_guide.pdf
Tyler, N., Hodkinson, A., Planner, C., Angelakis, I., Keyworth, C., Hall, A., Jones, P. P., Wright, O. G., Keers, R., Blakeman, T., & Panagioti, M. (2023). Transitional Care Interventions From Hospital to Community to Reduce Health Care Use and Improve Patient Outcomes: A Systematic Review and Network Meta-Analysis. JAMA network open, 6(11), e2344825. https://doi.org/10.1001/jamanetworkopen.2023.44825
Brown EG, Burgess D, Li CS, Canter RJ, Bold RJ. Hospital readmissions: necessary evil or preventable target for quality improvement. Ann Surg. 2014;260(4):583-591. doi:10.1097/SLA.0000000000000923
Johnson PC, Xiao Y, Wong RL, et al. Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer. J Oncol Pract. 2019;15(5):e420-e427. doi:10.1200/JOP.18.00595

6.2.2 Feedback on Measure Performance

The ADCC’s Quality Workgroup provides ongoing review and discussion regarding reporting of the 30-Day Unplanned Readmissions for Cancer Patients in the PCHQR program. Members of ADCC’s Quality Workgroup and Physician Advisory Group served as the TEP for maintenance of this measure. Further, discussions with CMS staff and analytic vendor for this measure in PCHQR informed the maintenance efforts.

CMS receives feedback on all its measures through the publicly available Q&A tool on Quality Net. As the measure steward, the ADCC has not received any substantive feedback on this measure as implemented in the PCHQR program.

6.2.3 Consideration of Measure Feedback

Modifications made based on feedback from the groups described above are noted throughout this submission. Briefly, these include:

updating the Data Dictionary to reflect coding changes
modifications to the cohort / denominator analysis, to exclude patients admitted with a psychiatric primary diagnosis and exclude patients admitted for rehabilitation (based on TEP guidance and to be consistent with other hospital readmission measures)
modifications to risk adjustment variables. QWG members proposed and the TEP ultimately rejected a suggestion to add a variable for DNR status present on admission. TEP members supported removing the ‘discharge to’ variables based on feedback that this variable is within the control of the measured entity. TEP members supported removing dual eligibility from the risk adjustment model after understanding CMS’ reporting of the measure with stratification based on dual eligibility status.

6.2.4 Progress on Improvement

This measure has been publicly reported in the PCHQR program in 2023 and 2024. For results published in 2023 (reflecting a measurement window of 10/1/21-9/30/22), the national rate was 20.8; 1 of 11 hospitals were better than the national rate, 1/11 were worse than the national rate, and 9/11 were no different from the national rate. For results published in 2024 (reflecting a measurement window of 10/1/22-9/30/23), the national rate was 20.2; 1 of 11 hospitals were better than the national rate, 10/11 were no different than the national rate. With only 2 years of publicly reported data it is difficult to make conclusions about progress on improvement; however, national rates and individual performance slightly improved from 2023 to 2024.

6.2.5 Unexpected Findings

There were no unintended impacts during implementation of this measure on patients or in care delivered by hospitals.

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