Impact of enhanced reimbursement on provider participation a cancer care quality program and adherence to cancer treatment pathways in a commercial health plan.

Malin, Jennifer L.; Nguyen, Ann; Ban, Stacey E.; Willey, Vincent J.; Quimbo, Ralph; Barron, John; Inches, Paula; Spevak, Boris; Fisch, Michael J.

doi:10.1200/jco.2015.33.15_suppl.6571

Cited by 5 publications

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“…To validate the claims-based algorithms, claims and eligibility data were linked at the patient level to information reported by physicians through an online portal built especially for an oncology program that enhances reimbursement to prescribing oncologists for care coordination 15,16. The physician-reported information included patient name (masked before the data were made available to researchers), date of birth, date of treatment, the prescribed anti-cancer drug regimen, the prescribing physician’s National Provider Identifier (NPI), and a number of clinical details unavailable in claims.…”

Section: Methodsmentioning

confidence: 99%

<p>Using claims data to attribute patients with breast, lung, or colorectal cancer to prescribing oncologists</p>

Fishman

Barron

Liu³

et al. 2019

POR

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Background Alternative payment models frequently require attribution of patients to individual physicians to assign cost and quality outcomes. Our objective was to examine the performance of three methods for attributing a patient with cancer to the likeliest physician prescriber of anticancer drugs for that patient using administrative claims data. Methods We used the HealthCore Integrated Research Environment to identify patients who had claims for anticancer medication along with diagnosis codes for breast, lung, or colorectal lung cancer between July 2013 and September 2017. The index date was the first date with a record for anticancer medication and cancer diagnosis code. Included patients had continuous medical coverage from 6 months before index to at least 7 days after index. Patients who received anticancer drugs during the 6 months prior to index were excluded. The three methods attributed each patient to the physician with whom the patient had the most evaluation and management (E&M) visits within a 90-day window around the index date (Method 1); the most E&M visits with no time window (Method 2); or the E&M visit nearest in time to the index date (Method 3). We assessed the performance of the methods using the percentage of the study cohort successfully attributed to a physician, and the positive predictive value (PPV) relative to available physician-reported data on patient(s) they treat. Results In total, 70,641 patients were available for attribution to physicians. Percentages of the study cohort attributed to a physician were: Method 1, 92.6%; Method 2, 96.9%; and Method 3, 96.9%. PPVs for each method were 84.4%, 80.6%, and 75.8%, respectively. Conclusion We found that a claims-based algorithm – specifically, a plurality method with a 90-day time window – correctly attributed nearly 85% of patients to a prescribing physician. Claims data can reliably identify prescribing physicians in oncology.

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Section: Methodsmentioning

confidence: 99%

<p>Using claims data to attribute patients with breast, lung, or colorectal cancer to prescribing oncologists</p>

Fishman

Barron

Liu³

et al. 2019

POR

Self Cite

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“…The HIRE-Oncology clinical database is a product of the Clinical Cancer Quality Program for Anthem (AIM Specialty Health®, Deerfield, IL), which compares planned cancer treatment regimens against evidence-based clinical criteria such as efficacy, toxicity profile, and cost (Malin et al, 2015 ). Clinical information collected for the program is integrated with the medical and pharmacy claims data contained within the HIRD and, for patients with cancer, including lung malignancies, includes: cancer type (ICD-9 or ICD-10 and description); cancer stage; tumor biomarkers; line of treatment (e.g., adjuvant/postoperative; first-line, second-line, third-line, later-line; maintenance); height and weight; treatment regimen details with individual drugs and doses; and Eastern Cooperative Oncology Group (ECOG) performance status.…”

Section: Methodsmentioning

confidence: 99%

Validation of a Case-Finding Algorithm for Identifying Patients with Non-small Cell Lung Cancer (NSCLC) in Administrative Claims Databases

2017

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Objective: To assess the validity of a treatments- and tests-based Case-Finding Algorithm for identifying patients with non-small cell lung cancer (NSCLC) from claims databases.Data sources: Primary data from the HealthCore Integrated Research Environment (HIRE)-Oncology database and the HealthCore Integrated Research Database (HIRD) were collected between June 1, 2014, and October 31, 2015.Study design: A comparative statistical evaluation using receiver operating characteristic (ROC) curve analysis and other validity measures was used to validate the NSCLC Case-Finding Algorithm vs. a control algorithm.Data collection: Patients with lung cancer were identified based on diagnosis and pathology classifications as NSCLC or small-cell lung cancer. Records from identified patients were linked to claims data from Anthem health plans. Three-month pre-index and post-index data were included.Principal findings: The NSCLC Case-Finding Algorithm had an area under the curve (AUC) of 0.88 compared with 0.53 in the control (p < 0.0001). Promising diagnostic accuracy was observed for the NSCLC Case-Finding Algorithm based on sensitivity (94.8%), specificity (81.1%), positive predictive value (PPV) (95.3%), negative predictive value (NPV) (79.6%), accuracy (92.1%), and diagnostic odds ratio (DOR) (78.8).Conclusions: The NSCLC Case-Finding Algorithm demonstrated strong validity for distinguishing patients with NSCLC from those with SCLC in claims data records and can be used for research into NSCLC populations.

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“…Many studies discuss the link between prosocial motivation, which is derived from the opinion of peers or even the community, and interventions that track and share data on performance. Generally, these studies find that tracking performance and providing feedback on it, as done by performance-based financing (PBF) programs, can at least in theory improve performance for pro-socially motivated workers (Peabody et al 2014;Malin et al 2015).…”

Section: Impact Of Pbf On Health Worker Motivation and Satisfaction I...mentioning

confidence: 99%

Improving Effective Coverage in Health: Do Financial Incentives Work?

Walque¹,

Kandpal²,

Wagstaff³

et al. 2022

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Some rights reserved 1 2 3 4 25 24 23 22 This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Nothing herein shall constitute or be construed or considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.

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Impact of enhanced reimbursement on provider participation a cancer care quality program and adherence to cancer treatment pathways in a commercial health plan.

Cited by 5 publications

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<p>Using claims data to attribute patients with breast, lung, or colorectal cancer to prescribing oncologists</p>

<p>Using claims data to attribute patients with breast, lung, or colorectal cancer to prescribing oncologists</p>

Validation of a Case-Finding Algorithm for Identifying Patients with Non-small Cell Lung Cancer (NSCLC) in Administrative Claims Databases

Improving Effective Coverage in Health: Do Financial Incentives Work?

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