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(active tab)
- Numerator
- Denominator
- Exclusions
- Measure Calculation
- Supplemental Attachment
- Point of Contact
General Information
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
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
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.
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
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.
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).
Exclusions
The measure excludes index admissions for patients who meet any of the following criteria:
- Patients discharged against medical advice (AMA)
- Patients discharged with a planned readmission
- Patients having missing or incomplete data
- Patients not admitted to an inpatient bed
- Patients admitted for primary psychiatric diagnoses
- Patients admitted for rehabilitation
The following index admissions are excluded from the measure denominator:
- Patients discharged against medical advice (AMA), identified using the discharge disposition indicator in claims data. Rationale: Providers did not have the opportunity to deliver full care and prepare the patient for discharge.
- Patients discharged with a planned readmission. Rationale: Not an unplanned readmission.
- Patients having missing or incomplete data
- Patients not admitted to an inpatient bed
- Patients admitted for primary psychiatric diagnoses, identified by a principal diagnosis in one of the specific AHRQ CCS categories listed in the attached data dictionary. Rationale: Patients admitted for psychiatric treatment are typically cared for in separate psychiatric or rehabilitation centers which are not comparable to acute care hospitals.
- Patients admitted for rehabilitation, identified by the specific ICD-10 diagnosis codes included in CCS 254 (Rehabilitation care; fitting of prostheses; and adjustment of devices). Rationale: These admissions are not typically admitted to an acute care hospital and are not for acute care.
Associated code tables for denominator exclusions are in the attached Data Dictionary.
Measure Calculation
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.
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.
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.
Supplemental Attachment
Point of Contact
Not applicable
Jack Kolosky
Houston, TX
United States
Kristen Landrum
KM Healthcare Consulting
Wilmington , NC
United States
Importance
Evidence
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.18 Existing 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.19 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.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
Measure Impact
Hospital readmission, for any reason, is disruptive to patients and caregivers, costly to the healthcare system and policy holders, and puts patients at additional risk of hospital-acquired infections and complications. During development of the 30-Day Unplanned Readmissions for Cancer Patients measure, developers engaged five patient/caregiver advisory representatives, who provided feedback during conceptualization and development. All five agreed that the measure is meaningful and provides information valuable in making care decisions.
Moreover, a study conducted at the Hospital of the University of Pennsylvania and Penn Presbyterian Medical Center surveyed 197 oncology patients who were readmitted within 30 days of discharge to identify patient-perceived factors contributing to readmission. The most commonly reported challenges included difficulty with activities of daily living, feeling unprepared for discharge, and difficulty adhering to medications. The study highlights the importance of understanding patient perspectives to improve transition-of-care processes and reduce unplanned readmissions.1
Reference
- Kangovi S, Evans TL, Mitra N. Patient-reported oncology readmission factors. J Clin Oncol. 2012;30(34_suppl):48. doi:10.1200/jco.2012.30.34_suppl.48
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.
| 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 |
Equity
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
Feasibility
Feasibility
This claims-based measure analyzes data generated or collected by and used by healthcare personnel during the provision of care, coded by someone other than person obtaining original information (e.g., DRG, ICD-10 codes on claims).
An analysis of missing data in defining the denominator found 0% missing data elements in a population of more than 1.5 million patients.
Medicare claims data are used for this measure. Claims-based measures are feasible to implement comprehensively and efficiently for large populations of patients.
Data for claims-based measures are readily available, making the measure feasible to implement and report.
Medicare claims data are used for this measure. Access to Medicare claims files are limited and controlled.
Medicare claims data are used for this measure. Claims-based measures are feasible to implement comprehensively and efficiently for large populations of patients.
Data for claims-based measures are readily available, making the measure feasible to implement and report. Our testing work proved that it is feasible to calculate the measure, as specified.
Proprietary Information
Scientific Acceptability
Testing Data
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
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.
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.
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
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).
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.
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.
| | 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
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.
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).
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
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
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.
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.
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.
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.
Use & Usability
Use
Hospital inpatient reporting program
Usability
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_read…
- 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
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.
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.
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.
There were no unintended impacts during implementation of this measure on patients or in care delivered by hospitals.
Comments
Staff Preliminary Assessment
CBE #3188 Staff Preliminary Assessment
Importance
Strengths
- Cancer is a leading cause of death, with significant projected increases in cases and treatment costs. Reducing readmissions is crucial for lowering healthcare costs and improving patient outcomes. Studies show that quality-of-care initiatives, such as communication and coordination, can reduce readmission rates by 20-40%.
- The 30-Day Unplanned Readmissions for Cancer Patients measure, developed by Consortium of Comprehensive Cancer Centers for Quality Improvement (C4QI) and Alliance of Dedicated Cancer Centers (ADCC), aims to reflect oncology patients' unique needs and improve care quality. Feedback from patient representatives confirms its meaningfulness. A study at the University of Pennsylvania highlights patient challenges, emphasizing the need for improved transition-of-care processes. Randomized clinical trials and systematic meta-analyses identify a range of practices that have successfully reduced 30-day readmissions, such as interventions aimed at improving communication between patients, caregivers, and providers, patient education, coordination of per-discharge assessment, and post-discharge care.
- Performance data from 2022 show some variation in readmission rates, with a mean score of 17.3% and a decile range of 16.7% to 18.1%.
- During the development of the 30-Day Unplanned Readmissions for Cancer Patients measure, five patient and caregiver advisory representatives were engaged for feedback. They unanimously agreed that the measure is meaningful and provides valuable information for making care decisions.
Limitations
- The logic model provided does not clearly articulate the relationships between inputs, activities, and outcomes. The submission could be strengthened by clearly defining these elements, ensuring that they are also captured in the narrative text.
- In addition, most of the studies cited are 10 to 20 years old or older, and the submission could be strengthened with more recent evidence, or noting that no more recent evidence exists.
Rationale
- Cancer readmissions are costly and impact patient outcomes. The 30-Day Unplanned Readmissions for Cancer Patients measure aims to improve care quality. Data form 2022 show a 17.3% readmission rate, and patient representatives support its importance for care decisions. However, this maintenance measure is rated as 'Not Met But Addressable' due to an unclear logic model and lack of recent evidence. The submission could be strengthened by a more comprehensive logic model and updated evidence.
Closing Care Gaps
Strengths
- The developer cites literature suggesting that patient characteristics such as low income, older age, unmarried status, and language barriers contribute to higher readmission rates. The developer also conducted an analyses showing higher unplanned 30-day readmission rates for cancer patients of all other races compared to White patients (16.45%), including Black patients (19.17%), Asian patients (18.31%), Hispanic patients (16.60%), Native American patients (18.23%), and other races (18.55%). Dual eligible patients had a higher readmission rate (19.01%) compared to non-dual eligible patients (16.43%).
Limitations
- The developer provided evidence of gaps in care related to the measures focus for certain subgroups, but they did not clearly articulate how the measure will close gaps in care. The developer also did not describe the methods used for analyses and the submission could be strengthened by conducting testing for statistical significance or alignment with literature. The developer also does not provide recommended actions entities can take to close care gaps.
Rationale
- While the measure attempts to assess gaps in care across various subgroups, it is unclear why those subgroups were included in the analysis and not others from the literature cited. The submission could also be strengthened by provided statistical testing of differences across identified subgroups and by providing recommended actions to close care gaps.
Feasibility Assessment
Strengths
- All required data elements are routinely generated during care delivery, and required elements are available in claims, which is an electronic data source, in structured fields. The measure is easily implemented with no data collection or confidentiality issues, with 0% missing data. Lastly, this measure is not proprietary and has no proprietary components.
Limitations
- None identified.
Rationale
- This maintenance measure meets all criteria for 'Met' due to the data elements being derived in structured fields from electronic sources during the normal process of care. The measure has minimal impact on provider burden and there are no concerns with patient confidentiality. The measures is also not proprietary. These factors collectively ensure that the measure can be implemented effectively and sustainably in a real-world healthcare setting.
Scientific Acceptability
Strengths
- The developer performed the required reliability testing for this maintenance measure, namely, they conducted accountable entity-level (“measure score”) reliability testing at the level for which the measure is specified. Data sources used for reliability analysis are adequately described and include Medicare claims data for the periods of CY2021 and CY2022 (analyzed separately). The entities included in the analysis were characterized by accountable entities with at least 50 index admissions. There were 676,880 admissions across 2,074 qualifying entities for CY2021 and 614,217 admissions across 2,008 qualifying entities for CY2022.
- The developer conducted split-half reliability testing at the accountable entity-level. More than 70% of accountable entities meet the expected threshold of 0.6 at the levels for which the measure is specified.
Limitations
- The developer has demonstrated that more than 70% of accountable entities with at least 50 index admissions meet the expected threshold of 0.6 based on 614,217 patients across 2008 facilities. However the importance table has more than twice as many entities (4333) but only slightly more patients (646500) which suggests that more than 1/2 the entities may not have at least 50 index admissions.
Rationale
- The developer performed the required reliability testing for this maintenance measure and results demonstrate sufficient reliability at the accountable entity-level.
Strengths
- Validity: The developer provides an Importance Table and Logic Model, providing a plausible causal association between the entity response to the measure and the measure focus. Empirical support for ruling out confounders includes adequate reliability (N>50), risk adjustment, and a correlation with a related outcome measure with construct overlap (Hospital-Wide All-Cause Readmission (HWR)). Empirical support for ruling in responsible mechanisms includes several empirical studies and reports (e.g. high-quality care during initial admission, effective discharge planning, patient and caregiver education, post-discharge follow-up, coordination across settings (transitional care), structured medication counseling at discharge, addressing basic needs (e.g., transportation, activities of daily living [ADLs], palliative care), screening for readiness for discharge).
- Risk adjustment (RA): The developer conducted statistical risk adjustment, based on a conceptual model, selecting risk factors that have a significant correlation with the outcome. The developer applied stratification to measure results based on dual eligibility status, ensuring comparisons across measured entities account for differences in patient characteristics.
Limitations
- Validity: The logic model was not particularly robust, and the developer may want to consult the CBE guidance on logic models. Residual risk for confounders includes fair correlation (r=.40) that cannot rule out confounding. The developer does not provide a strong justification for the construct overlap, or a quantitative explanation of the degree of construct overlap. Therefore, the explanation is not a justification for the magnitude of the correlation and leaves 60% of the causal factors for the risk-adjusted performance unexplained. Residual risk for confounders not ruled out includes (patient clinical characteristics, high symptom burden, end-of-life care complexity, variation in institutional practices and discharge thresholds). There is no direct empirical support for whether the responsible mechanisms account for the association between the entity response to the measure and the measure focus. Residual risk for a responsible mechanism includes the potential counter-acting mechanisms (availability and implementation of transitional care interventions, access to social supports and services (e.g., palliative care, transportation, help with ADLs)).
- RA: The developer reported a c-statistic of 0.601, indicating moderate discrimination. Developer also included risk factors in the model that do not have a statistically significant association with the outcome.
Rationale
- MET justification (validity): The measure developer provides some support for the causal claim that the entity response to the measure is causally related to the measure focus. The developer provides empirical support for ruling out confounders (always with some residual risk of unstated or unexamined confounders) and for ruling in responsible mechanisms (always with some residual risk that the explicit mechanisms are only partially responsible for the measure focus).
Conditions might reflect a more explicit consideration of potential confounders and ruling out such confounders as causal explanations of the variation among entities, groups, and over time. Conditions might also reflect a more explicit consideration of responsible mechanisms and ruling in such mechanisms as causal explanations of the variation among entities, groups, and over time. - MET justification (RA): The case-mix adjustment methods used are appropriate, demonstrate variation in the prevalence of case-mix factors across measures entities, and show the impact of adjustment for providers at high or low extremes of risk. The model performance is acceptable. Stratification was applied to manage differences due to patient characteristics.
Use and Usability
Strengths
- This maintenance measure is currently used in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program, Centers for Medicare and Medicaid Services (CMS).
- The developer also cites various interventions, such as effective communication at discharge and post-discharge care, which significantly lower readmission rates. Systematic reviews further highlight the effectiveness of low- and medium-complexity transitional care interventions. The developer also notes that CMS provides a guide for hospitals to reduce readmissions, emphasizing patient understanding, follow-up care, and addressing barriers like transportation and language.
- The developer notes that the ADCC's Quality Workgroup and Physician Advisory Group, serving as the TEP, provide ongoing review and discussion for the 30-Day Unplanned Readmissions for Cancer Patients measure in the PCHQR program. Feedback is gathered through CMS's Q&A tool, but no substantive feedback has been received to-date. Modifications based on feedback include updates to the Data Dictionary, cohort analysis adjustments, and changes to risk adjustment variables, such as removing dual eligibility and 'discharge to' variables.
- The measure was publicly reported in the PCHQR program for 2023 and 2024. In 2023, the national rate was 20.8, with 1 of 11 hospitals better, 1 worse, and 9 no different from the national rate. In 2024, the national rate improved to 20.2, with 1 hospital better and 10 no different from the national rate. Although only two years of data are available, there is a slight improvement in national rates and individual performance. No unintended impacts were observed during implementation.
Limitations
- The submission could be strengthened with a description of how accountable entities can use the performance score results to conduct quality improvement initiatives to better their score.
Rationale
- The maintenance measure is utilized in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program by CMS. It highlights interventions like effective communication at discharge and post-discharge care to lower readmission rates. The ADCC's Quality Workgroup and Physician Advisory Group oversee the measure, with feedback gathered through CMS's Q&A tool. Modifications include updates to the Data Dictionary and risk adjustment variables. Public reporting in 2023 and 2024 shows slight improvements in national rates, with no unintended impacts observed. The submission could be enhanced by detailing how accountable entities can use performance scores for quality improvement initiatives.
Committee Independent Review
Measure not supported
Importance
Although there is literature supporting the use of hospital readmission measures in general, it is unclear whether a cancer-specific measure would identify additional gaps and lead to different interventions.
Closing Care Gaps
Unclear whether there are interventions specific to closing gaps relating to cancer readmissions rather than overall hospital readmissions.
Feasibility Assessment
agree
Scientific Acceptability
agree
Low correlation coefficient with the other readmission measure questioned validity.
Use and Usability
agree
Summary
Value over existing readmission measures unclear
Please see comments above in…
Importance
Cancer is a leading cause of death that is anticipated to increase in number, complexity, and costs. It is important to address readmissions especially those due to communication and transitions of care. As noted in preliminary reviews there is variability in performance and practices. More current evidence and fully understanding the definitions of what is included in the models would strengthen the model. Would also like to see additional information specific to metastatic cancer and readmissions.
Closing Care Gaps
It has been noted that there are associations between social risk factors and cancer-specific readmissions for low income, older age, unmarried, and language barriers that are associated with readmissions. Also noted that dual eligible insurance rural areas and residences in low population areas are associated with higher risks of readmissions. The developer did show gaps but did not provide any evidence on how to close the gaps. This would be important to include to strengthen this measure.
Feasibility Assessment
Claims based measure. Measure is from structured fields from electronic sources that occur during routine care.
Scientific Acceptability
Reliability testing for this measure--accountable entity-level was provided
For validity, overall has fair correlation (r=.40) and not clear on how confounding variables were addressed.
Additionally, the c-statistic is 0.602 for moderate discrimination. Not clear how the factors that were included in the model were chosen. would like to better understand how the causal explanations impact variations in the groups and over time.
Use and Usability
This measure uses the PPS-Exempt Cancer Hospital Quality Reporting Program-CMS Measure and provides a guide for hospitals to reduce readmissions. Feedback was gathered through the CMS Q&A and no specific feedback. Measure has been publicly reported for 2023 and 2024. Some improvements noted.
Summary
Please see comments above in each section
This Measure Should Not Be Endorsed
Importance
Although it is highly desirable to measure the quality of care for cancer patients, this is not a valid or reliable measure of the quality of care. Use of this measure could mislead patients about the quality of care provided by individual hospitals.
Closing Care Gaps
This measure provides no feedback to a hospital as to why its readmission rates are higher or lower than another hospital’s, and the developer does not appear to have done any analysis to determine whether higher or lower rates are due to potentially avoidable factors.The developer reports that the average readmission rate for the cancer-specific hospitals was over 20%, while the average for all hospitals was 14.6%. No information is provided as to whether the risk adjustment methodology adequately captures the factors leading to this large difference.
Feasibility Assessment
Met
Scientific Acceptability
Met
The measure only includes problems treated during an inpatient admission to the hospital, not problems treated during an observation stay or in an emergency department. If a complication can be successfully treated in an emergency department or if the treatment is classified as an observation stay, the complication will not be counted. Other measures use a broader definition of post-discharge complications and this measure could also.
The measure includes all admissions to the hospital, regardless of whether the admission was related to the patient’s cancer or to their cancer treatment, or whether the admission could be prevented by actions that the hospital or the patient’s cancer team could take. A study cited by the developer found that only 39% of readmissions were potentially avoidable.
The risk adjustment methodology only uses information available in claims data, when it is well known that many of the key characteristics of cancer are not recorded in claims data. Table 5.4.2 shows that many important risk adjusters were not included the model because they were not available. For example, there is no adjustment for the distance the patient lives from the hospital; as a result, this measure could unfairly indicate that hospitals serving a more rural population provide poorer-quality care. If 61% of readmissions are not preventable, and the methodology does not adjust for this, any differences between hospitals will likely be due to factors they cannot affect.
Use and Usability
The measure provides no useful information about differences in hospital performance. As shown in Table 2, 80% of the risk-adjusted readmission rates ranged from 17.1% to 17.6%. With an average of 149 patients per hospital, this represents a difference of less than 1 readmission per hospital.
The measure is currently being used to compare the 11 PPS-exempt cancer hospitals, but no information is provided to demonstrate that the methodology is appropriate for those hospitals. All of the modeling and testing information is based on data from all Medicare hospitals, even though the 11 cancer-specific hospitals treat a different population of patients. The developer reports that the average readmission rate for the cancer-specific hospitals was over 20%, while the average for all hospitals was 14.6%. No information is provided as to whether the risk adjustment methodology adequately captures the factors leading to this large difference.
No information was provided regarding how many hospitals would be considered to have higher or lower performance that is sufficiently different to report, but it appears that most hospitals would be classified as “no different from average.” The developer reports that 10 of the 11 cancer-specific hospitals were determined to be no different from average in 2022-23, but no information is provided as to why the 1 determined to be better than average had better outcomes.
Summary
This is not a valid, reliable, or useful measure of the outcomes of care for patients with cancer.
First, it only measures problems treated during an inpatient admission to the hospital, not problems treated during an observation stay or in an emergency department.
Second, the measure includes all admissions to the hospital, regardless of whether the admission was related to the patient’s cancer or to their cancer treatment, or whether the admission could be prevented by actions that the hospital or the patient’s cancer team could take.
Third, the risk adjustment methodology only uses information available in claims data, when it is well known that many of the key characteristics of cancer are not recorded in claims data.
A study cited by the developer (Johnson PC et al. “Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer,” Journal of Oncology Practice 2019.) found that only 39% of readmissions were potentially avoidable. If 61% of readmissions are not preventable, and the methodology does not adjust for this, any differences between hospitals will likely be due to factors they cannot affect.
The measure is currently being used to compare the 11 PPS-exempt cancer hospitals, but no information is provided to demonstrate that the methodology is appropriate for those hospitals.
The measure provides no useful information about differences in hospital performance, since most hospitals differ by less than 1 readmission per year.
Will not endorse for stated use and purpose.
Importance
No comment
Closing Care Gaps
No comment
Feasibility Assessment
No comment
Scientific Acceptability
We have found via hematology oncologists that BMT patients require additional levels of segmentation for appropriate adjustment.
The reliability and validity of the models need to be reassessed with more recent data. Hopefully, the model performance would improve if doing so; however, with the evidence provided, this is a poorly performing measure and should not be used for public reporting or value based payment.
The statistics for the validity are old and indicate poor model performance in risk adjustment. The face validity while helpful is not sufficient to move this forward for approval.
Use and Usability
This should only be used for point in time estimates of performance. It is not designed for quality improvement use (eg, drilling into populations), nor is it of sufficient scientific validity to report publicly or use in VBP programs.
Summary
see above comments
Public Comments
3188: 30-Day Unplanned Readmissions for Cancer Patients
The American Medical Association (AMA) is increasingly concerned that measures focused on unplanned readmissions may be leading to negative unintended patient consequences and no longer capture the appropriate patient population due to the structure and timeframe of these measures (Graham, 2018; Gupta, 2017). For example, the literature is beginning to show that readmission measures, which are based on administrative claims, may be contributing to increased mortality and we are concerned that this measure could also yield similar results (Gupta, 2017). We ask that the committee carefully review the potential unintended consequence that this measure may have on patient outcomes.
In addition, the measure specifications and definitions are not aligned with other readmission measures used in the Centers for Medicare & Medicaid Services (CMS) programs. For example, the denominator criteria and exclusions differ, and this measure does not appear to use the CMS-defined Planned Readmissions Algorithm. We believe that measures that have a similar intent, particularly when used in CMS quality programs, should use the same definitions and criteria.
References:
Graham, Kelly. Et al (2018). Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients. Ann Intern Med. Doi. 10.7326/M17-1724.
Gupta, Ankar, et al. Association of the Hospital Readmissions Reduction Program Implementation with Readmission and Mortality Outcomes in Heart Failure. JAMA Cardiol. 2017. doi:10.1001/jamacardio.2017.4265. Published online November 12, 2017.