Three-Year Results of a Medicare Advantage Cancer Management Program

Hoverman, J. Russell; Neubauer, Marcus A.; Jameson, Melissa; Hayes, Jad; Eagye, Kathryn J.; Abdullahpour, Mitra; Haydon, W.; Sipala, Maria; Supraner, Amy; Kolodziej, Michael; Verrilli, Diana K.

doi:10.1200/jop.17.00091

Cited by 16 publications

(24 citation statements)

References 20 publications

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“…Alternative payment models, such as the Centers for Medicare and Medicaid Services Oncology Care Model, 8,9 contain financial incentives to enhance clinic-based, ambulatory care systems and prevent avoidable hospitalizations. A number of cancer-focused delivery system innovations have shown promise for reducing hospitalizations during cancer treatment, including patient navigation programs, 10 proactive telephonic nursing, 11 and electronic systems for between-visit symptom reporting. 6,12 Many of these interventions are resource intensive, and targeting outreach interventions to patients at high risk for hospitalization has been identified as a key strategy for preventing unplanned hospitalizations.…”

Section: Introductionmentioning

confidence: 99%

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

Brooks¹,

Uno

Bowles

et al. 2019

JCO Clinical Cancer Informatics

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PURPOSE Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients. PATIENTS AND METHODS We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%). RESULTS There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group. CONCLUSION A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.

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Section: Introductionmentioning

confidence: 99%

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

Brooks¹,

Uno

Bowles

et al. 2019

JCO Clinical Cancer Informatics

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“…18,26,32 All studies were in a single setting. Seven were longitudinal, 18,19,22,26,29,30,32 6 used a pre–post design, 17,20,24,25,27,28 2 were random control trials, 21,23 and 1 a retrospective review. 31 Half (n = 8) included a comparison group.…”

Section: Resultsmentioning

confidence: 99%

“…Six articles included patient- and/or caregiver-reported experience measures and clinical measures. 19,21,22,24,26,27 Five targeted patient and/or caregiver experiences captured through administrative data (rather than direct report) and clinical measures. 20,25,29,31,32 Two included all 3 types of measures: clinical, patient/caregiver-reported experiences, and patient/caregiver experiences captured through administrative data.…”

Section: Resultsmentioning

confidence: 99%

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Improving Care Experiences for Patients and Caregivers at End of Life: A Systematic Review

Quigley

McCleskey

2020

Am J Hosp Palliat Care

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Background: End-of-life care is increasing as the US population ages. Approaches to providing high-quality end-of-life care vary across setting, diseases, and populations. Several data collection tools measure patient and/or caregiver care experiences at end of life and can be used for quality improvement. Previous reviews examined palliative care improvements or available measures but none explicitly on improving care experiences. We reviewed literature on improving patient and/or caregiver end-of-life care experiences. Design: We searched U.S. English-language peer-reviewed and grey literature after 2000 on adult end-of-life care experiences. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for quantitative studies, Enhancing Transparency in Reporting the Synthesis of Qualitative Research approach for qualitative studies, and Assessment of Multiple Systematic Reviews tool for the literature reviews. Setting: Palliative and hospice care. Population: Full-text abstraction of 84 articles, identifying 16 articles. Measures: Patient and/or caregiver end-of-life care experiences (captured through administrative data or direct report). Results: Articles examined palliative care experiences across settings; none studied hospice care experiences. Patients and/or caregivers assessed overall care experiences, clinician–staff interactions, provider communication, respect and trust, timeliness of care, spiritual support, caregiver knowledge of care plans, or bereavement support. Efforts aimed at improving end-of-life care experiences are limited and show mixed results. Conclusions: Literature on improving patient and/or caregiver end-of-life care experiences is emerging and focused on palliative care experiences. Evidence on improving hospice care experiences is lacking. Research on strategies for improving end-of-life care experiences should go beyond overall care experiences to include specific aspects of palliative and hospice care experiences.

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“…Among the Medicare population, where patients are frailer and living with more comorbidities, a 3-year study of a practice-based, clinical pathways program coupled with a patient care-management program identified a cost savings of more than $3 million mainly due to a reduction in medication costs but also from a reduction in inpatient stays and emergency room visits. 5 The 2018 study concluded that "a practice-based program supported by a payer sponsor can reduce costs while maintaining high adherence to treatment pathways and patient satisfaction in older patients. "…”

Section: Pathways To Successmentioning

confidence: 99%

Clinical pathways: reducing costs and improving quality across a network

Neubauer¹

2020

Am J Manag Care

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OVER THE PAST DECADE, the use of clinical cancer pathways has increased. In its 2017 State of Cancer Care in America report, the American Society of Clinical Oncology (ASCO) noted a 42% increase from 2014 to 2016 in practices using a clinical pathways program. 1 This growing trend reflects a need for structured decision support among clinicians, clinical practices, and payer systems. As cancer care becomes more complex and more expensive, these decision-support algorithms offer a mechanism to define best practice, reduce unwarranted variation, and control costs across growing networks. 2-4 At the heart of the pathways movement lies a desire to improve treatment-its outcomes, its tolerability, its efficiency, and its value. Achieving these goals requires commitment not just to an electronic platform but also to a broader pathways program. At the Dana-Farber Cancer Institute (DFCI), we believe that this requires a tripartite dedication to expert content development; integration into physician and practice workflow; and the capture, analysis, and practical use of data (Figure 1). These are, in fact, the same 3 areas identified as key for high-quality pathways programs by the ASCO Pathways Committee. 5 Ultimately, the successful creation and implementation of a pathways program within any institution or network depends on understanding the interdependence of these 3 areas and using each to improve the others.

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Three-Year Results of a Medicare Advantage Cancer Management Program

Abstract: A practice-based program supported by a payer sponsor can reduce costs while maintaining high adherence to treatment pathways and patient satisfaction in older patients.

Cited by 16 publications

References 20 publications

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

Improving Care Experiences for Patients and Caregivers at End of Life: A Systematic Review

Clinical pathways: reducing costs and improving quality across a network

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