BACKGROUND
Cancer is fast-becoming one of the most expensive chronic conditions in the United States, and hospital care accounts for nearly a quarter of this spending. 1 Unplanned emergency department (ED) visits and inpatient admissions (ADM) can derail treatment schedules, compromise outcomes, and add substantial out-of-pocket cost. Acknowledging this reality, the Center for Medicare and Medicaid Services (CMS) developed the Outpatient Chemotherapy Quality Measure (OP-35) to measure potentially preventable acute hospital visits for patients receiving chemotherapy. 2 This metric was first incorporated into Medicare's Hospital Outpatient Quality Reporting (OQR) Program, is attached to a penalty of up to 2% on Medicare reimbursement, and puts forth an important hypothesis: can performance measurement reshape the role of hospital care in cancer care delivery? Although the measure has been applied since 2018, and its rapid uptake underscores an important clinical need, we notice many problems with the measure specification that hinders its goal of reducing avoidable hospital care.
OP-35 is a claims-based measure that aggregates up to three-hundred discharge diagnosis codes representing 10 potentially avoidable conditions (anemia, dehydration, diarrhea, emesis, fever, nausea, neutropenia, pain, pneumonia, and sepsis). It separately tallies ED visits and ADM for these conditions when they occur within 30 days of outpatient chemotherapy administration. 3 It requires a full year of claims to accrue, plus additional time for compilation, so it was first reported in 2020 for performance year 2018. Adapting from OP-35, other similar quality measures have been adopted for Prospective Payment System (PPS)-Exempt Cancer Hospitals (PCH): PCH-30 (for ADM) and PCH-31 (for ED) and into CMS' 2023 value-based payment program, the Enhancing Oncology Model (EOM). 4
FINANCIAL PENALTIES FOR FLAWED MEASURES HAVE UNINTENDED CONSEQUENCES
Despite its widespread adoption across policy and practice, very little about this measure has been investigated. Similar Medicare-level efforts at financially incentivizing quality improvement should be instructive to the OP-35 effort. Since 2010, via the Hospital Readmissions Reduction Program, Medicare has been attaching reimbursement penalties to hospitals that have too many readmissions within 30 days of a hospital discharge for certain conditions (chronic obstructive pulmonary disease, myocardial infarction, congestive heart failure, pneumonia, coronary artery bypass graft surgery, total hip and total knee arthroplasty). 5 Despite this, across the program and within conditions, it is not clear whether the program has resulted in improved care quality or reduced costs. Indeed, some studies suggest potential for increased mortality in some conditions. Rather, a more consistent finding of the program evaluation has been that it penalized hospitals for the social disadvantage of the patients they serve. 6
This experience should serve as a cautionary tale. While OP-35 attempts to risk-adjust based on certain clinical factors, it also does not adjust for social risk factors. OP-35 also contains other cognitively dissonant facets that deserve more attention.
INCONSISTENCIES IN AVOIDABLE VISIT DEFINITION
The OP-35 undergoes annual revision, mostly adding and removing diagnosis codes, but the exact specifications also differ across the different quality programs, limiting cross-site comparison and longitudinal evaluation. The annual respecification OP-35 undergoes makes it difficult for hospitals to evaluate data longitudinally and hinders its utility.
The Hospital OQR Program, PPS-Exempt Cancer Hospital Quality Reporting Program (PCHQR), and EOM chemotherapy measures apply only to Medicare fee-for-service (FFS) beneficiaries. All three programs require patients to have continuous Medicare Part A and Part B enrollment 30 days after outpatient chemotherapy treatment. 3 , 7 , 8 However, only the Hospital OQR and PCHQR programs require patients to have 1 year of continuous Part A and Part B enrollment before treatment, which excludes 10% and 12% of eligible patients, respectively. 7 , 8
The Hospital OQR and PCHQR programs also include any cancer diagnosis except leukemia, whereas the EOM version only counts seven qualifying cancer types, including leukemia. The programs also use different code definitions. Both the Hospital OQR and PCHQR programs use ICD-10 and procedure codes (Healthcare Common Procedure Coding System), but the EOM uses drug codes as well as diagnosis and procedure codes (Table 1 ). 3 , 7 , 8
TABLE 1.
Comparison of Measure Definitions of Chemotherapy Quality Reporting Across Medicare Programs
NOTE. OP-35, the thirty-fifth hospital OQR program measure for chemotherapy quality; PCH-30, the 30th PPS-exempt cancer hospital quality reporting measure for hospital admissions after outpatient chemotherapy; PCH-31, the 31st PPS-exempt cancer hospital quality reporting measure for ED visits after outpatient chemotherapy; EOM-1, the first EOM quality measure for hospital admissions and ED visits after outpatient chemotherapy. Abbreviations: C-ART, chimeric antigen T-cell receptor; ED, emergency department; EOM, enhancing oncology model; HCPCS, healthcare common procedure coding system; ICD-10, international classification of diseases, tenth revision; Medicare FFS, Medicare fee for service; NDC, national drug code; OQR, outpatient quality reporting; PPS, prospective payment system.
| Medicare Quality Reporting Program | Chemotherapy Measure | Organizations | Code Sets | Inclusion Criteria | Exclusion Criteria | ||
| Diagnosis | Medicare Enrollment Before First Outpatient Chemotherapy Treatment | Medicare Enrollment After First Outpatient Chemotherapy Treatment | |||||
| Hospital OQR program | OP-35 | Hospital outpatients | ICD-10 HCSPC | Medicare FFS beneficiaries with any diagnosis of any cancer, excluding leukemia | Continuous enrollment in Part A and Part B Medicare FFS 1 year before | Continuous enrollment in Part A and Part Medicare FFS 30 days after | Leukemia |
| Prospective payment system-exempt cancer hospital quality reporting program | PCH-30/PCH-31 | PPS-exempt cancer hospitals | ICD-10 HCSPC | Medicare FFS beneficiaries with any diagnosis of any cancer, excluding leukemia | Continuous enrollment in Part A and Part B Medicare FFS 1 year before | Continuous enrollment in Part A and Part Medicare FFS 30 days after | Leukemia |
| EOM | Respecified OP-35/EOM1 | Oncology physician group practices | ICD-10 HCSPC NDC | Medicare FFS beneficiaries with a diagnosis of one of seven cancer types: Breast cancer, chronic leukemia, lung cancer, lymphoma, multiple myeloma, prostate cancer, small intestine/colorectal cancer | None | Continuous enrollment in Part A and Part Medicare FFS 30 days after | CAR-T therapy |
As a result, while these measures have distinct differences in technical specification, they are close enough conceptually to be conflated together, doubling down on inconsistencies resulting from heterogeneity in definition. Ultimately, this affects the measure's functionality and ability to facilitate improvement in quality and care delivery.
The variation in OP-35 definitions across the quality programs also creates ambiguity around what exactly OP-35 measures. Different organizations purport to be using a standardized metric but are tracking different visits. Indeed, of the six most recent studies applying OP-35 to measure avoidable hospital visits, only one applied every facet of the official OP-35 definition. 9 - 14
TOO COMPLICATED TO BE PRACTICAL
The inconsistencies in measurement stem not only from differing definitions but also highlight the overall complexity of the main definition. While Medicare is laudably transparent about the technical specifications, including variables and coefficients for risk adjustment for ED visits and inpatient admissions, this mostly demonstrates just how complicated it is to reproduce within a local electronic health record (EHR) to inform real-time changes. 2
In addition, the inclusion of only Medicare FFS beneficiaries in OP-35 reveals other potential limitations. First, the majority (51.2%) of Medicare enrollees are now enrolled in Managed Medicare and thus are excluded from the official OP-35 measure. 15 Second, there is heterogeneity in payers among patients treated by a practice. Applying the measure to a wider group of payers or conversely partially applying to only a practice's Medicare FFS beneficiaries has different implications—both of which dilute the mission of data-driven quality improvement. While some may point to the potential benefits of spillover from Medicare-led initiatives, when the measure captures different cancer incidences and populations based on insurance type, it is unclear how tangible this spillover effect may be.
CLINICAL SHORTCOMINGS
OP-35 also makes some statistically sound, but clinically incongruous decisions: it only counts the first ED or inpatient admission—subsequent hospital visits of any kind by the same patient during the same 30-day span are excluded. A clinician might wonder whether multiple hospital visits from the same patient, especially the subsequent visits, are, in fact, more potentially avoidable than the first one.
OP-35 also divides measure reporting into an ED visit rate separate from the inpatient admission rate. Furthermore, it groups observation stays—which are often brief inpatient visits—with the ED visit rate, and not the inpatient visit rate. The reasoning for these decisions is based on the cost difference between the types of visits. However, this ignores the reality that most inpatient admissions begin with an ED arrival and that it is not apparent how to differentially prevent ED visits, observation stays, and inpatient admissions for the same avoidable condition.
There are other questions about the utility of OP-35: it has not yet been clinically validated, leading to potential discrepancy between the clinical and policy definitions of potentially avoidable. Clinicians may agree that certain OP-35 avoidable conditions, such as emesis, could be treated in an urgent care or outpatient setting, whereas others, such as hematemesis, are not very avoidable. Other work highlights how clinicians have differing perspectives of the potential avoidability of ED and hospital visits among their patients with cancer. 16
Separately, amalgamating 10 different conditions (anemia, dehydration, diarrhea, emesis, fever, nausea, neutropenia, pain, pneumonia, and sepsis) into a summative percentage does not provide clinicians with enough information regarding the reasons for potentially avoidable hospital care. Great effort is required to replicate the measure even when claims are provided (which is only done for EOM) to gain a deeper understanding of the constitutive conditions driving differences in OP-35 rates. The diagnosis code also obscures unbilled contacts only available in the EHR: for example, it is unclear whether admissions are due to clinical errors (eg, lack of antiemetic prescriptions) or gaps in clinical workflow (eg, patient did not call a nurse triage line and self-referred to the ED).
In sum, the variation in OP-35 specification, lack of clinical validation, questionable reliability in the EHR, and general limitations with claims-based measures suggest that OP-35 may not yet be ready for tracking and performance-based payments.
EOM: HAVE ANY LESSONS BEEN LEARNED FROM THE ONCOLOGY CARE MODEL?
The incoherence surrounding OP-35 perhaps points to a more fundamental issue: there is not a clear sense of how systems can effectively reduce acute care utilization after outpatient chemotherapy. The EOM builds off a previous CMS model, the Oncology Care Model (OCM), which similarly incentivized participating organizations to improve quality and reduce costs of cancer care through performance-based payments. 4 , 17 The OCM spanned 2016-2022, but despite provider financial incentives, support for practice redesign, and almost all OCM participants having a goal to reduce unplanned hospital visits, OCM participants did not have significantly lower ED visits or inpatient admissions. 18
Yet, the EOM requires participating organizations to incorporate similar interventions that have not been linked to reductions in potentially avoidable acute care utilization. Both the EOM and the OCM require redesign activities, including 24/7 clinician access and patient navigation services. There is broad acceptance that these services are likely useful to patients. But the impact of these services on the OP-35 measure is unknown, 19 and it seems premature to be applying both these widely in the hopes that it will reduce potentially avoidable hospital care.
A WAY FORWARD
Reducing potentially avoidable acute hospital visits is necessary to improve patient quality of life and outcomes while reducing costs, but the OP-35 measure has many limitations that hobble it as an effective tool to drive this change. Providers are asked to reduce chemotherapy-related acute care visits but are not given timely access to data, and OP-35's applicability within the real-time EHR is unknown. The quality improvement adage is that one cannot change what is not measured. Here, we seem to be measuring something, but nobody can be sure what it is.
Providers may be discouraged from participating in CMS initiatives such as the EOM. 20 It is notable that the number of organizations participating in the EOM is only one third of those participating in the OCM and many of those that did not opt to participate are large oncology provider groups, 4 , 17 , 19 rather than academic or other multispecialty groups. With competing priorities and a greater volume of noncancer patients, hospitals may choose to prioritize initiatives that have stronger evidence and are associated with higher cost-savings than reducing acute care utilization after chemotherapy.
Effectively reducing potentially avoidable acute hospital care requires improving the OP-35. First, the metric must be clinically validated. Additional changes should include standardization of coding definitions across Hospital OQR, PCHQR, and EOM programs to facilitate cross-site comparison, and the diagnosis code definition should be slow to change. Validation of the measure should include Medicare Advantage, commercially insured beneficiaries, as well as Medicaid and the uninsured. Finally, an EHR-based version of the measure may be most useful for facilitating quality improvement.
It is also worth considering the variety of factors that contribute to acute hospital care after chemotherapy. Research is needed to investigate other potential drivers of acute care visits, such as patient decision making around going to the ED instead of considering oncology urgent care, making an outpatient clinic appointment, or calling a nurse triage line. Individual patient decisions may be driven by differential access to cancer team advice or even informal advice from the family and friends. Further work is also needed to delineate complications of chemotherapy from cancer or care delivery processes. Ultimately, more granular data and research efforts are needed to determine effective strategies for reducing chemotherapy-related acute care visits although this can only be done with adequate metrics.
ACKNOWLEDGMENT
The content is solely the responsibility of the authors and does not necessarily represent the official views of Texas Health Resources, University of Texas Southwestern Medical Center, The University of Texas MD Anderson Cancer Center, or the American Cancer Society. None of these entities had a role in the design and conduct of the study; collection, management, and analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
SUPPORT
Dr. Hong was supported by R01CA282242 from the National Cancer Institute, Texas Health Resources Clinical Scholars Program, and P30CA142543 from the National Cancer Institute.
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Dissonance in the Sole Quality Measure for Outpatient Chemotherapy, OP-35
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/cci/author-center .
Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians ( Open Payments ).
Ryan W. Huey
Honoraria: Vizient, Clinical Care Targeted Communications LLC
Research Funding: GRAIL (Inst)
Travel, Accommodations, Expenses: Clinical Care Targeted Communications LLC, Vizient
Arthur S. Hong
Consulting or Advisory Role: Janssen (I), AbbVie (I), Novartis (I), UCB (I)
Speakers' Bureau: Janssen (I), AbbVie (I)
Research Funding: Amgen (I), Incyte (I), AbbVie (I), Janssen (I), Phoenicis (I)
Travel, Accommodations, Expenses: Janssen (I), AbbVie (I)
No other potential conflicts of interest were reported.
Ryan W. Huey
Honoraria: Vizient, Clinical Care Targeted Communications LLC
Research Funding: GRAIL (Inst)
Travel, Accommodations, Expenses: Clinical Care Targeted Communications LLC, Vizient
Arthur S. Hong
Consulting or Advisory Role: Janssen (I), AbbVie (I), Novartis (I), UCB (I)
Speakers' Bureau: Janssen (I), AbbVie (I)
Research Funding: Amgen (I), Incyte (I), AbbVie (I), Janssen (I), Phoenicis (I)
Travel, Accommodations, Expenses: Janssen (I), AbbVie (I)
No other potential conflicts of interest were reported.
AUTHOR CONTRIBUTIONS
Conception and design: Mahima Akula, Arthur S. Hong
Financial support: Arthur S. Hong
Administrative support: Arthur S. Hong
Provision of study materials or patients: Arthur S. Hong
Collection and assembly of data: All authors
Data analysis and interpretation: Mahima Akula
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
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