&lt;p&gt;Using claims data to attribute patients with breast, lung, or colorectal cancer to prescribing oncologists&lt;/p&gt;

Fishman, Ezra; Barron, John; Liu, Ying; Gautam, Santosh; Bekelman, Justin E.; Navathe, Amol S.; Fisch, Michael J.; Nguyen, Ann; Sylwestrzak, Gosia

doi:10.2147/por.s197252

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…Each patient was assigned to a primary medical oncologist or a primary gynecologic oncologist based on the specialty of their primary oncologist, defined as the provider with the plurality of outpatient oncology visits in the last year of life. A similar algorithm was found to correctly attribute approximately 85% of patients to their anti-cancer treatment-prescribing oncologist 14. Outpatient visits were identified based on Healthcare Common Procedure Coding System (HCPCS) codes (Online supplemental table 4).…”

Section: Methodsmentioning

confidence: 99%

Intensity of end-of-life care for gynecologic cancer patients by primary oncologist specialty

Hicks-Courant

Kanter

Schapira

et al. 2022

Int J Gynecol Cancer

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ObjectiveThe association of primary oncologist specialty, medical oncology versus gynecologic oncology, on intensity of care at the end of life in elderly patients with gynecologic cancer is unclear.MethodsThis retrospective cohort study used Surveillance, Epidemiology and End Results-Medicare (SEER-M) data. Subjects were fee-for-service Medicare enrollees aged 65 years and older who died of a gynecologic cancer between January 2006 and December 2015. The primary outcome was a composite score for high-intensity care received in the last month of life. Secondary outcomes included invasive procedures and Medicare spending in the last month of life. Simple and multivariable linear and logistic regression analyses evaluated differences in outcomes by primary oncologist specialty. Linear regressions were repeated after creating a more similar control group through nearest-neighbor propensity score matching.ResultsOf 12 189 patients, 7705 (63%) had a medical primary oncologist in the last year of life. In adjusted analyses, patients with a gynecologic versus medical primary oncologist received lower rates of high-intensity end-of-life care (53.9% vs 56.6%; p=0.018). Results were similar for the propensity score-matched cohorts. However, having a gynecologic versus medical primary oncologist was associated with higher rates of invasive procedures in the last month of life (43% vs 41%; p=0.014) and higher Medicare spending ($83 859 vs $74 849; p=0.004).ConclusionsBoth specialties engage in overall high levels of intense end-of-life care, with differences by specialty in aspects of aggressive care and spending at the end of life. Physician-level training could be a target for educational or quality improvement initiatives to improve end-of-life cancer care delivery.

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Section: Methodsmentioning

confidence: 99%

Intensity of end-of-life care for gynecologic cancer patients by primary oncologist specialty

Hicks-Courant

Kanter

Schapira

et al. 2022

Int J Gynecol Cancer

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“…5 Attributing the care of patients to providers has been attempted through a myriad of approaches. [6][7][8] Despite their potential wide-ranging impact, accurately deducing in-person PPIs from within the EHR remains a challenge, [9][10][11] especially in the inpatient setting. 1 From the perspective of medical education, automated PPIs derived from EHR data may help educators understand which patient care experiences add "educational value," informing educational opportunities throughout the spectrum of clinical training.…”

Section: Background and Significancementioning

confidence: 99%

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

et al. 2020

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Objective Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider–patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). Methods We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. Results We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19–278.56; ED: OR = 19.06, 95% CI: 9.53–41.65' inpatient: OR = 2.95, 95% CI: 2.23–3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). Conclusion Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

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“…We assigned the index regimen to the oncologist who provided the plurality of outpatient cancer-related evaluation and management services. 9,[21][22][23] CONTEXT Key Objective What is the association between a national insurer's pay-for-performance program for oncology and changes in prescribing of evidence-based cancer drugs and spending? Knowledge Generated In this differences-in-differences study, which included 25,042 patients with cancer and 1,867 physicians across 14 states, a pay-for-performance program was associated with a 5.1 percentage point increase in prescribing of evidence-based cancer drugs, a $3,339 increase in drug spending, a $253 increase in out-of-pocket expenses, and no significant change in total health care spending.…”

Section: Physician Populationmentioning

confidence: 99%

Association Between a National Insurer’s Pay-for-Performance Program for Oncology and Changes in Prescribing of Evidence-Based Cancer Drugs and Spending

Bekelman

Gupta

Fishman

et al. 2020

JCO

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PURPOSE Cancer drug prescribing by medical oncologists accounts for the greatest variation in practice and the largest portion of spending on cancer care. We evaluated the association between a national commercial insurer’s ongoing pay-for-performance (P4P) program for oncology and changes in the prescribing of evidence-based cancer drugs and spending. METHODS We conducted an observational difference-in-differences study using administrative claims data covering 6.7% of US adults. We leveraged the geographically staggered, time-varying rollout of the P4P program to simulate a stepped-wedge study design. We included patients age 18 years or older with breast, colon, or lung cancer who were prescribed cancer drug regimens by 1,867 participating oncologists between 2013 and 2017. The exposure was a time-varying dichotomous variable equal to 1 for patients who were prescribed a cancer drug regimen after the P4P program was offered. The primary outcome was whether a patient’s drug regimen was a program-endorsed, evidence-based regimen. We also evaluated spending over a 6-month episode period. RESULTS The P4P program was associated with an increase in evidence-based regimen prescribing from 57.1% of patients in the preintervention period to 62.2% in the intervention period, for a difference of +5.1 percentage point (95% CI, 3.0 percentage points to 7.2 percentage points; P < .001). The P4P program was also associated with a differential $3,339 (95% CI, $1,121 to $5,557; P = .003) increase in cancer drug spending and a differential $253 (95% CI, $100 to $406; P = .001) increase in patient out-of-pocket spending, but no significant changes in total health care spending ($2,772; 95% CI, −$181 to $5,725; P = .07) over the 6-month episode period. CONCLUSION P4P programs may be effective in increasing evidence-based cancer drug prescribing, but may not yield cost savings.

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

Cited by 8 publications

References 17 publications

Intensity of end-of-life care for gynecologic cancer patients by primary oncologist specialty

Intensity of end-of-life care for gynecologic cancer patients by primary oncologist specialty

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

Association Between a National Insurer’s Pay-for-Performance Program for Oncology and Changes in Prescribing of Evidence-Based Cancer Drugs and Spending

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