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e13640 Background: Evidence-based, national guidelines for the diagnosis of recurrent metastatic breast cancer (MBC) recommend confirmation of recurrence with biopsy and reassessment of biomarker status. Real world practice patterns may demonstrate disparities in adherence to guidelines with implications for patients and health systems. Methods: We utilized the Hutchinson Institute for Cancer Outcomes Research (HICOR) data repository that links Washington State cancer registry data to enrollment and claims from the major insurance payers in the state. We identified women > 18 years old diagnosed with recurrent MBC between 2008 and 2017 with evidence of enrollment in a commercial plan (Premera or Regence), Medicare, or Medicaid. Recurrence in Stage I-III patients was detected through identification of ICD 9/10 codes for metastatic disease or resumption of breast cancer systemic therapy (after minimum of 4 months from completion of early breast cancer therapy). Using claims, we identified receipt of and factors associated with biopsy, biomarker re-assessment, and treatment administered at recurrence. Results: We identified 715 patients with recurrent MBC (any ER or HER2 status) with median age of 62 (range 52-73). The majority of the cohort were Caucasian (89%) with the rest comprising of Asian, Black, American Indian, and Hispanic patients. Based on the Rural Urban Commuting Area (RUCA) classification, patients predominantly lived in metropolitan neighborhoods (97%). Approximately 13% of patients lived in areas of high deprivation (area of deprivation index, ADI, 8-10). Patients had either Commercial (53.1%), Medicaid (4.2%), Medicare (29.7%) or multiple (13.0%) insurance. Patients were primarily treated at high volume centers (70.9%), though 23% of patients were seen at low volume centers (2) were more likely to undergo biopsy confirmation (20.3% vs 13.0%, p = 0.02). Biopsy was more often performed in patients receiving care at a high-volume center compared to low-volume center (74.3% vs 18.6%, p = 0.03). First line treatment selection was directly associated with receipt of biopsy and biomarker testing. Hormone therapy only was more common in patients who did not undergo biopsy (62.3% vs 37.7%, p

145 Background: The COVID-19 pandemic disrupted medical care, including routine cancer screening for breast, colorectal, lung and cervical cancers. We aimed to investigate the impact of the pandemic on stage at diagnosis for cancer patients. Methods: Using data from the Washington State SEER records we compared AJCC stage for patients diagnosed with cancer in 2017-2019 to 2020 for two time periods, March to June (initial pandemic months) and July to December (later pandemic months). Patients were included if they were age 18+, diagnosed with a solid tumor, and not diagnosed at autopsy. Results: In the early phase of the pandemic, March – June 2020, there was a shift to cancers being diagnosed at a later stage compared to the same time period in 2017-2019 (Stage III: 13.5% to 14.9%, Stage IV: 16.2% to 19.7%). There was also a decrease in cancer diagnoses for cancers that are often detected through routine screening. As a percentage of all cancer diagnoses, both melanoma (13.2% to 9.8%) and colon cancer diagnoses (7.2% to. 6.7%) decreased during the early pandemic. In the later phase of the pandemic, July to December 2020, the stage at diagnosis showed an indication of returning to pre-pandemic levels with an increase in the proportion of early stage cancers (In situ: 16.6% to 19.3%, Stage I: 38.8% to 41.1%). Stage at diagnosis trends varied by tumor type. For colorectal cancer, the overall number of diagnoses decreased during the initial pandemic months. Stage I diagnoses decreased and Stage IV cancer diagnoses increased in both early and late stages of the pandemic. Conclusions: In Washington State, the COVID-19 pandemic had an impact on stage at diagnosis potentially caused by delays or interruptions in medical care. Additional studies are needed to understand how this shift in stage at diagnosis impacted treatment and outcomes for patients.

e13038 Background: Evidence-based, national guidelines for the management of metastatic breast cancer (MBC) recommend numerous treatment options that do not capture the nuances of real-world practice. Disparities may exist across Washington State with financial implications for patients and health systems. The objective of this study was to assess practice patterns around treatment of ER+/HER2- MBC in actual clinical practice. Methods: We collaborated with Hutchinson Institute for Cancer Outcomes Research (HICOR) to link enrollment and insurance claims records with Washington State cancer registries from 2008-2017. Our cohort comprised of women >18 years old with de novo ER+/HER2- MBC who met enrollment criteria in one of four payors (Premera, Regence, Medicare, or Medicaid). We identified receipt of first line treatment, categorized as CDK4/6 inhibitors plus endocrine therapy (CDKi+ET), chemotherapy (CT), or endocrine therapy alone (ET). We examined factors influencing treatment selection using Fisher's and Kruskal-Wallis tests. Total costs (defined as costs from inpatient and outpatient claims one year after diagnosis) was estimated for patients and payors. Results: We identified 140 patients with de novo ER+/HER2- MBC with median age of 64 (range 28-95). The majority of the cohort were Caucasian (90%) with the rest comprising of Asian, Black, American Indian, and Hispanic patients. Based on the Rural Urban Commuting Area (RUCA) classification, patients predominantly lived in metropolitan neighborhoods (96%). Over 20% of patients lived in areas of high deprivation (area of deprivation index, ADI, 8-10). Patients had either Commercial (40.7%), Medicaid/Medicare (43.6%) or multiple (15.7%) insurance. Our data show that 17 patients (12%) received first line therapy with CDKi + ET, 64 patients (46%) with CT, and 59 patients (42%) with ET alone. Factors influencing treatment selection include age, co-morbidity score, and payor type. Older patients (>65 years old) were more likely to receive ET alone compared to younger patients (56% vs 44%, p value

e18590 Background: The use of platinum agents (PT) in neoadjuvant chemotherapy (CTX) for triple negative breast cancer (TNBC) has been debated in research and practice guidelines. In high risk patients, the addition of PT to standard anthracycline and taxane regimens improves pathological complete response without definitive evidence of survival benefit (Korde JCO 2021). Due to PT’s toxicities and unclear benefit in early stage breast cancer, this study aimed to elucidate the real world practice patterns of PT use and overuse in breast cancer patients. Methods: The cohort was defined as women with stage I-III TNBC (ER, PR, and Her2 negative) who received curative intent surgery between 2011-2018. We utilized the Hutchinson Institute for Cancer Outcomes Research (HICOR) database, which links enrollment and claims records from commercial and public health insurance plans with clinical information from Washington state cancer registries (Manohar JACR 2020). We hypothesized there was overuse of PT CTX in this cohort. Overuse was defined as PT use in stage I, in the adjuvant period (NCCN 2020), and in nonstandard CTX regimens (PT use without taxane and anthracycline (NCCN 2020)). Results: Of the 910 women in the cohort, average age was 63, 90% were white, 682 (75%) received CTX, and 36% had commercial insurance, versus 53% with public insurance (Medicare 45%, Medicaid 8%). Of those receiving CTX, 39% received neoadjuvant and 67% adjuvant. Of those receiving CTX, 85 (13%) received PT, and 28 (4%) received PT without anthracycline. Of those receiving PT, 20% had stage I disease, and of those receiving adjuvant CTX, 43 (9%) received PT in the adjuvant period. Conclusions: We found there was overuse and nonstandard use of PT in this real world data. This overuse was demonstrated by the fact that 20% of women receiving PT had low risk stage I disease, and 9% of women receiving adjuvant CTX received PT in the adjuvant period. Additionally, there was nonstandard use, with 4% of women getting CTX receiving PT without anthracycline. Next steps in this research are to evaluate factors influencing overuse and nonstandard use, as well as complication rates with PT use and overuse.[Table: see text]

6546 Background: Washington was the first US state to experience the COVID-19 pandemic. Transmission risks and patient fears of visiting oncology practices during its onset resulted in rapid adoption of telehealth services. We hypothesized that the pandemic would widen disparities in oncology practice visits between Medicaid and commercially insured patients, resulting higher rates of emergency department (ED) visits during initial treatment. Methods: Linking Washington State SEER records with Medicaid and commercial insurance enrollment and claims records, we compared adults age

6504 Background: Increasing evidence shows that cancer patients (pts) experience financial hardships after diagnosis. Few studies, however, have used objective financial data to estimate the relative risk of adverse financial events (AFEs) in cancer pts versus individuals without cancer. Using a retrospective case-control design, we investigated whether cancer pts are at increased risk of new AFEs, as measured by their credit reports. Methods: Western Washington Surveillance Epidemiology and End Results (SEER) cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion (2012-2020), one of the 3 largest national credit agencies. Controls were age and sex matched to cases and assigned an index date corresponding to the diagnosis (dx) date of the matched case. Individuals with evidence of any AFE in the credit report closest to index/dx date or did not survive to 24 months were excluded. Cases and controls experiencing any of the following AFEs within 24 months were compared, using two-sample z tests: severe (3rd party collections, charge-offs), more severe (tax liens, delinquent mortgage payments), and most severe (foreclosures, repossessions). Multivariate logistic regression models were used to evaluate the association between cancer dx and AFE, adjusting for age, sex, dx year, and available credit 6 months before the index/dx date. Results: A total of 332,825 individuals (84,185 cases and 248,640 controls, mean age 66 (SD 13), 52.7% female) were included. The mean available line of credit in the year before index/dx date was $12,303. AFEs were more common in cases versus controls (Table). After adjusting for age, sex, available credit above or below $12,303, and dx year, cancer dx was significantly associated with any AFE (OR 1.77, 95% CI 1.7-1.85, p65 and higher available baseline credit were associated with decreased risk of any and each category of AFE. Conclusions: Within 24 months from dx, significantly higher proportions of cancer pts experienced AFEs relative to controls. Such events on credit reports have serious and long-lasting consequences on financial status. Studies that link clinical and financial data to investigate the impacts of these events on treatment decisions, quality of life, and clinical outcomes are needed.[Table: see text]

6502 Background: The COVID-19 pandemic dramatically reduced family access to hospitals and created new barriers to home hospice care, raising concerns about how the pandemic has impacted cancer patients’ place of death and end of life home hospice support. Hypothesizing that Medicaid-enrolled cancer patients may be at greater risk of disruptions in end-of-life care compared to commercially insured patients, we examined changes in place of death and home hospice support for Medicaid and Commercial enrollees following the pandemic. Methods: We linked WA State cancer registry records with claims from Medicaid and approximately 75% of commercially insured cancer patients in the state. Patients ages 18-64 with solid-tumor malignancies who died March-June 2020 (COVID) were compared to those who died March-June 2017-2019 (Pre-COVID). Place of death was categorized as hospital, home with hospice, and home without hospice; nursing home deaths were excluded. Given our sample size, we examined differences in the likelihood of place of death with Fisher’s exact tests and multinomial logistic regressions stratified by payer and by COVID period, controlling for age, gender, race, stage, cancer type, and census tract-level neighborhood deprivation. We report marginal effects. Results: In Fisher’s exact analyses, Medicaid but not commercial patients were significantly less like to die in hospital and more likely to die at home without hospice during COVID (Table). In pre-post adjusted analysis of Medicaid patients, the probability of dying in the hospital was 12.3% (p=0.03) percentage points lower during the pandemic versus before, while the probability of dying at home without hospice was 11.1% (p=0.04) greater. Place of death did not change significantly pre-post for commercial patients. In addition, Pre-COVID, the probability of dying in the hospital was 10.7% (p=0.03) greater for Medicaid than commercial patients. During COVID, the probability of dying at home without hospice was 15.8% (p=0.04) greater for Medicaid versus commercial patients but lower for women (ME=20.2%; p=0.01) and colorectal versus breast cancer patients (ME=39.2%; p=0.01). Conclusions: Following COVID, Medicaid patients place of death shifted from hospital to homes, but without an increase in the use of home hospice services. In contrast, place of death and hospice use among commercial patients did not significantly change. This widening disparity in home deaths without hospice services raises concerns that the pandemic disproportionately worsened end of life experience for low income patients with cancer.[Table: see text]

3 Background: Patients with EGFR+ or ALK+ NSCLC benefit from oral TKIs, but high patient OOP TKI costs could negatively impact survival by reducing likelihood of continuing TKI therapy. We assessed the association of high OOP TKI costs with overall survival (OS), medication possession ratio (MPR) and duration of TKI therapy (DOT) in patients with metastatic EGFR+ and ALK+ NSCLC. Methods: We identified patients with EGFR+ and ALK+ NSCLC diagnosed between 01/01/2010 and 12/31/2016 in the Washington State SEER registry using natural language processing, followed by manual confirmation of molecular tests. We linked registry records to commercial and Medicare (including part D) claims. Eligible patients had stage IV NSCLC, sensitizing EGFR mutations or ALK+ by FISH, ≥1 pharmacy claims for EGFR or ALK TKIs, ≥3 months survival from TKI start, and ≥12 months of insurance enrollment post diagnosis. We estimated OOP TKI costs by subtracting the amount paid from the amount allowed in pharmacy claims. We categorized patients by quartiles of monthly OOP costs calculated for the first 3 months of TKI therapy (Q1 < Q2 < Q3 < Q4). We used landmark survival analysis with multivariate Cox regression to test the association of monthly OOP costs in the first 3 months of TKIs and OS starting at 3 months of TKI therapy between Q1-Q3 vs Q4. We used t-tests to compare MPR and DOT for the 1st TKI between Q1-3 and Q4. Results: For 106 eligible patients (median age 69; 67% female; 73% White; 35% Medicare, 85% EGFR+), the median monthly OOP TKI costs at 3 months were $0 (Q1); $1,432 (Q2); $1,798 (Q3); $2,888 (Q4). Mean MPR was 1.20 vs. 1.06 (P = 0.02), and median DOT was 8 vs. 4 months (P < 0.01) for Q1-3 vs. Q4. Median OS was 22 vs. 9 months for Q1-3 vs 4. Compared with Q1-3, Q4 patients had a hazard ratio for death of 1.76 (95% CI = 1.05; 2.94; P = 0.03), adjusted for age, sex, insurance, mutation, income, chemotherapy use, and time to TKI start. There were no statistically significant associations comparing Q1 to Q2-4, or Q1-2 to Q3-4. Conclusions: Higher patient OOP TKI costs are associated with lower number of TKI prescriptions, shorter duration of TKI therapy and inferior survival in advanced EGFR+ and ALK+ NSCLC.

270 Background: Approximately half of cancer patients undergoing outpatient chemotherapy experience unplanned Emergency Department (ED) visits and Inpatient (IP) stays. Current machine learning algorithms that identify high-risk patients are based on pre-treatment variables which can not detect changes in risk over time. Deep learning recurrent neural networks can model complex longitudinal patient histories. This study tests the feasibility of using an interpretable recurrent neural network to predict a patient’s daily likelihood of ED and unplanned IP stays in the six months following chemotherapy initiation. Methods: Medicare and commercial claims data were linked with cancer registry records for patients in Washington State from 2011 to 2017. The study included patients diagnosed with any primary tumor site, excluding leukemia, and treated with chemotherapy. We used the Reverse Time Attention model (RETAIN) with a 1:10 case-control match and included registry elements; diagnoses, procedures, medication, and utilization pre-and post-chemotherapy initiation. Patients were randomly divided into internal training, validation, and test sets (75%, 10%, 15%). Model accuracy was measured by the areas under the receiver operating curve (ROC) and precision-recall curve (PRC), and the Youden sensitivity and specificity. Results: Of the 15,400 eligible patients; 4,037 (26.2%) visited the ED a median of 1 time (6,080 total visits); 5,116 (33.2%) had a median of 1 IP stay (7,839 total stays). Both models had good predictive accuracy: The top 20 predictors for ED visits included 5 chemotherapy regimes, 12 procedures, and 2 tumor characteristics; IP stays included all chemotherapy regimes. Conclusions: The promising performance of RETAIN supports the possibility of building a tool capable of estimating daily hospitalization risk. However, future research, particularly with alternative data sources, may be required to predict hospitalization in a real time clinical setting. [Table: see text]

134 Background: Molecular testing practices and cost-sharing policies may result in delayed initiation of TKI therapy. We assessed predictors of delayed initiation of TKIs in metastatic EGFR+ or ALK+ NSCLC. Methods: We identified patients with EGFR+ and ALK+ NSCLC diagnosed between 01/01/2010 and 12/31/2016 in the Washington State SEER registry using validated natural language processing methods. We linked registry records to commercial and Medicare (including part D) claims. Eligible patients had stage IV NSCLC, sensitizing EGFR mutations or ALK+ by FISH, ≥ 1 pharmacy claims for EGFR or ALK TKIs, and ≥12 months of insurance enrollment post-diagnosis. Potential predictors included age, sex, race, Census-level median household income, urban status, insurance type, comorbidity, histology, mutation type, and receipt of chemotherapy prior to first TKI claim (pre-TKI chemo). We defined time to TKI initiation as the interval from diagnosis to first pharmacy claim for EGFR or ALK TKIs. We fitted Cox regression models to identify predictors of delays in TKI initiation, defining covariates with a P < 0.05 in a final multivariate model as independently associated with delays. Results: For 122 patients (median age 70; 65% female; 74% White; median income $66,580; 98% metropolitan; 35% Medicare; 80% EGFR+; 12% using pre-TKI chemo), the median time to TKI initiation was 6.7 weeks (IQR = 3.9 to 14.0). Independent predictors of TKI delays included male sex (HR = 0.51; 95%CI = 0.34; 0.76); Medicare insurance (HR = 0.32; 95% CI = 0.20; 0.53) and pre-TKI chemo (HR = 0.37; 95% CI = 0.20; 0.66). Median time to TKI initiation was 9.7 vs. 5.8; 7.8 vs. 4.1; and 16.0 vs. 6.3 weeks in male vs. female, Medicare vs. commercial insurance, and pre-TKI chemo (yes vs no), respectively. Conclusions: Male sex, Medicare insurance, and chemotherapy prior to TKI are associated with delays in TKI initiation for EGFR+ and ALK+ stage IV NSCLC patients. Possible explanations include higher prevalence of smoking in males resulting in lower priority for molecular testing, high cost-sharing policies for TKIs in Medicare patients, and prolonged time to obtain molecular test results leading patients to start chemotherapy first.

166 Background: Rural residents are diagnosed at later stages of cancer compared to urban residents, have poorer survival, and face distinct barriers to receiving quality cancer care. ASCO has developed policy initiatives to address rural cancer care; however, little is known about quality of cancer care among patients residing in rural areas. This study examined the impact of rurality on performance metrics, controlling for socioeconomic status and insurance type. Methods: We linked Washington state cancer registry records from 2015-2017 with claims records for two large commercial insurers, Medicare, and Medicaid. Using claims from this database, we generated eight nationally recognized quality measures. Rurality was measured by the Rural-Urban Commuting Area Codes (RUCAs) categorized into 4 levels (Metro, Metro with commute, Micropolitan, Small Town/Rural). Process and outcome measures were adjusted for age, sex, race, comorbidity score, stage, cancer type, marital status, the Area Deprivation Index, and treatment factors where appropriate. Results were stratified by payer type. Results: The table below lists the effect of a patient’s rurality on the quality metric where significant (p

158 Background: Evidence that rural residents compared to urban residents are more likely to be diagnosed at later stages and have worse survival outcomes has prompted recent policy initiatives by ASCO to address the rural cancer care gap. However, rural residents are generally poorer, potentially confounding the cause of these disparities. This study examined the impact of rurality, travel time to oncologist (TTO), and neighborhood deprivation (ND) on stage of diagnosis and 3-year survival in a regional setting. Methods: Cancer registry records for patients in Western Washington were linked with claims from regional commercial insurers, Medicare, and Medicaid at time of diagnosis. The study included adult patients with solid tumors diagnosed between 2012-2014. Rurality was sourced from the Rural-Urban Commuting Area Codes (RUCAs): Metro, Metro with Commute, Micropolitan, Small Town/Rural. ND was measured by the census block Area Deprivation Index from 1 (least) to 10 (most) deprived; TTO from Google Maps (minutes). Multinomial logistic regression measured stage at diagnosis (local, regional, distant). Cox survival models were stratified by insurance type. We adjusted all models for age, gender, race, marital status, cancer type and grade, and hierarchical condition categories. Results: The table below lists the effect size of rurality, TTO, and ND on the outcomes where significant (p

65 Background: Polypharmacy, or the concurrent use of multiple medications, may expose patients to drug-drug interactions and excessive costs. There are limited data on polypharmacy for commercially insured older adults, primarily Medicare Advantage patients, that may have better access to medication management services than Medicare fee-for-service patients. We characterized medication use and out-of-pocket (OOP) medication costs in the last month of life among patients age 65+ who did not enroll in hospice to examine medication use in this population. Methods: We linked enrollment and claims records from two regional commercial insurers to Surveillance, Epidemiology, and End Results (SEER) Cancer Surveillance System and Washington State Cancer Registry records for patients diagnosed with a stage IV malignancy in Washington State between January 1, 2007-December 31, 2016. We calculated OOP costs as the difference between allowed and paid claim amounts and adjusted OOP costs (aOOP) for inflation to 2017 dollars. Results: Among 345 patients with medication claims in their last month, 156 (45%) had a chemotherapy claim. Average age was 74 years (range 65-95), 55% (n = 190) were male, and 150 had lung cancer (44%). Patients averaged 7.7 medication claims (range 1-50); 151 (44%) had 1-4, 99 (29%) had 5-9, and 95 (27%) had 10+. Common symptom-related medications were opioids, benzodiazepines, anti-emetics. Chemotherapy was associated with higher odds of 10+ prescriptions (OR 1.38, 95% CI 1.26-1.51). Excluding four patients with aOOP chemotherapy costs > $14,000, average aOOP costs were $101 for chemotherapy claims (range $0-$8957) and $33 for non-chemotherapy claims ($0-$1993). Costs for those in the highest quartile ranged from $320-$8957 for chemotherapy claims and $100-1993 for non-chemotherapy claims. Conclusions: Most subjects had at least five medication claims in their last 30 days. One-quarter had 10+ claims, which was associated with chemotherapy receipt. Hospice enrollment could reduce OOP costs, as hospice provides symptom-related medications. Interventions facilitating hospice enrollment and reducing chemotherapy use may minimize polypharmacy and cost burden for patients and families.

105 Background: While many quality metrics are generated and reported at a national level, regionally reported metrics may be more meaningful to stakeholders and more amenable to locally-generated interventions aimed at improving quality. We present the lessons learned from adapting national performance reporting standards to our regional setting. Methods: We linked 2014-2016 Washington State cancer registry records with Medicare and commercial insurance claims, capturing about 70% of the state’s cancer patients. A consortium of payers, oncology providers, and patients selected metrics from national initiatives (QOPI, MACRA, OCM, and the Choosing Wisely Campaign). We then searched the National Quality Forum and the National Quality Measures Clearinghouse for published specifications on those metrics. If none were available or there was a lack of consensus, we constructed algorithms with clinical and technical expert review. We calculated clinic-level risk standardized rates (RSRs) using hierarchical regression modeling to adjust for variation in clinic size and constructed quality composites. Results: We generated 13 quality metrics for 36,900 cancer patients cared for at 27 clinics. Our adaptation of national performance measures and implications include: 1) Many treatment process measures had too few numbers to individually report annually requiring us to group quality metrics and cancer types, and report over a three year period. 2) Although we were able to include tumor characteristics (e.g. AJCC stage) from registry records, risk adjustment was challenged by limitations in the number of risk adjustors due to the smaller number of patients. 3) After applying the hierarchical models, risk adjustment had minimal effect on clinic rankings. 4) The small number of clinics and limited range of clinic RSRs were more accurately captured by a quality composite that reflected clinic level differences to the regional average rather than national quintile groupings. Conclusions: Refinement of national metrics is necessary for public reporting in a regional setting. Further methodological development is critical for robust reporting and applications to value based purchasing.

31 Background: Although many quality measures exist in oncology, there have been few efforts to prioritize, measure, and report quality and costs for an entire region. Here, we report the results of a multi-year, multi-stakeholder effort aimed at public reporting of nationally recognized quality metrics for oncology practices in Washington State. Methods: Quality metrics were selected from nationally-recognized measures through a structured process involving oncology providers, health insurance leaders, patient advocates, and policy experts. Cancer registry records from 2014 to 2016 were linked with claims data from two commercial insurers and Medicare, representing approximately 70% of cancer patients in the state. Patients were assigned to oncology clinics using claims data; 27 clinics were large enough for inclusion in at least one measure in the report. Thirteen metrics were combined to produce one clinic-level quality score per measure. Each quality score was mapped to total episode cost per patient. Results were adjusted to account for clinic size, cancer characteristics (e.g. stage), demographics, and comorbidity where appropriate. Results: Conclusions: The Community Cancer Care in Washington State: Quality and Cost Report 2018 is the first publicly accessible report showing clinic-level quality measures linked to cost in oncology. Its ultimate goal is to improve care and lower costs by spurring collaboration, research and innovation.[Table: see text]

104 Background: As quality reports are released for regions and individual clinics, an important question is whether those reports can be used for value based purchasing, particularly for community oncology clinics. We evaluated the reliability of select quality measures and estimated the likelihood that a clinic’s performance would be incorrectly categorized (misclassified) in the top quartile. Methods: We linked 2014-2016 cancer registry records for patients with enrollment and claims from Medicare and two major commercial insurers in Washington State. We calculated risk standardized rates (RSRs) for ED and hospital use during treatment and 3 quality measures for end of life care. Reliability (0-1 scale: 0-unreliabile, > 0.7 good reliability, 1 perfectly reliable) was calculated as signal/(signal + statistical noise) from hierarchical logistic regression modeling for each metric. Misclassification was characterized as the probability of false negative and false positive assignment of clinics to the top quartile of performers in the region. We generated results for 3 and 1 year performance periods. Results: Over the 3 year period, the hospitalization metrics included 7,373 patients, 25 clinics; end of life metrics included 8,165 patients; 24 clinics. Conclusions: Although these metrics had fairly high levels of reliability, approximately one-third of clinics could be incorrectly identified as a top quartile performer. Use of these metrics in value based purchasing should account for potential misclassification to minimize unintended consequences.[Table: see text]

75 Background: Uniformity in receipt of high quality cancer care is imperative to reduce health care disparities. In order to help prioritize efforts aimed at reducing disparities in care, we compared several quality metrics for Washington State cancer patients enrolled in Medicaid and commercial insurance plans. Methods: We linked 2014-2016 Washington state cancer registry records for cancer patients under the age of 65 with enrollment and claims records for the two largest commercial insurers in the state and Medicaid. We then generated thirteen nationally recognized quality measures. Outcome measures were adjusted for age, sex, comorbidity score, stage, cancer site, and treatment factors where appropriate. Process measures were not adjusted. Results: 6,868 commercially insured and 2,379 Medicaid patients are represented in the reported quality measures. Conclusions: Care of Washington state cancer patients enrolled in Medicaid is comparable or superior to commercially insured patients, except for significantly higher ED use during chemotherapy. Further research is needed to understand why Medicaid-enrolled cancer patients utilize the ED at more than double the rate of commercially insured patients.[Table: see text]

823 Background: Few studies have examined how patterns of care relate to clinical consensus guidelines for metastatic colorectal cancer (mCRC) in real-world settings. This study investigated treatment patterns around systemic therapies for older mCRC patients using SEER-Medicare data. Methods: We utilized data from a linkage of national Medicare claims data with records from the 12 Surveillance, Epidemiology, and End Results (SEER) regions for patients diagnosed with stage IV CRC between 2011 and 2014. Other inclusion criteria included adenocarcinoma histology, being > 65 years of age, and continuous Part A/B enrollment 12 months prior to diagnosis. We conducted bivariate analysis and multivariate modeling to evaluate the association between age, race, gender, SEER region, and comorbidity score with receipt of systemic therapy. We examined first and subsequent lines of therapy, and classified them as adherent or non-adherent to NCCN guidelines. Results: Of 102,461 CRC patients, 6,360 (6.2%) patients had evidence of metastatic disease and met all inclusion criteria. 3,155 (49.6%) received no systemic therapy. Of the 2,976 (46.8%) who received systemic therapy with identifiable regimens, FOLFOX/CAPOX was the most common 1L regimen (32.9%), followed by 5-FU or capecitabine alone (26.4%) – both identified as guideline-adherent therapies. Non-guideline adherent regimens (such as use of single-agent oxaliplatin or bevacizumab) were uncommon as 1L therapies (1.4%). 1,553 patients (52.2%) progressed to a 2L therapy, 715 (24.0%) progressed to a 3L therapy, and 320 (10.8%) to a 4L therapy. A large proportion of 2L (26.9%) and 3L (24.2%) treatments contained bevacizumab. 96 patients (3.2%) received regorafenib, representing 6.7% of 3L and 10.6% of 4L therapies. Patients who did not receive systemic therapy were older (mean age of 80.8 vs 74.9) and had multiple comorbidities (63.1% vs 48.4% with Klabunde score > 1). Conclusions: In a Medicare-insured mCRC cohort, patients generally received systemic therapy adhering to consensus guidelines. However, many patients did not receive systemic therapy – suggesting there may be delays in diagnosis and barriers to treatment which lower systemic therapy utilization.

86 Background: National bodies have proposed a number of metrics to measure quality of care at the end of life (EOL). MACRA legislation allows clinics to select the metrics they report to CMS. The self-selection of reported metrics leaves open questions about how representative certain measures, particularly in isolation, may be of overall EOL care in community settings. We examined the consistency of clinical-level performance across three common EOL metrics. Methods: We linked cancer registry records for solid tumor cancer patients diagnosed in Washington State from 2013-2016 with claims from two regional commercial insurers. Representing national recommendations, we compiled 3 EOL quality metrics for each clinic: 1) Chemotherapy in last 14 days of life (DoL); 2) More than 1 ED visit in last 30 DoL; and 3) Admission to ICU in last 30 DoL. Consistency was measured by comparing performance in the top and bottom 3rd across metrics. We compared consistency based on unadjusted rates and risk-standardized rates calculated following CMS methods. Results: The study included 1,535 patients across 12 clinics (median 110 [IQR: 54 – 199] patients/clinic). The clinic rates are below. (See Table.) According to both unadjusted and adjusted rankings, no clinics ranked in the top 3rd across all metrics. Half of clinics (6 of 12) simultaneously ranked in the top 3rd and bottom 3rd of a metric, i.e. high/low performers. The number of high/low performers varied when examining discrete pairs of metrics. The overall pattern was mainly driven by inconsistency between performance in the chemotherapy and ED metrics. Of the other discrete pairs, ICU/Chemo and ICU/ED, clinics were more consistent in performance. Conclusions: We found that clinic performance was not consistently in the highest or lowest tertile across common EOL metrics, suggesting that requiring clinics to report a standard set of metrics may provide a more accurate indication of quality. Furthermore, different population management strategies may be required to improve care targeted by each measure. Future work should focus on the development of multi-dimensional EOL quality performance measures. [Table: see text]

79 Background: Polypharmacy, defined as the concurrent use of multiple medications, may expose patients to drug-drug interactions and excessive medication costs. Patients with advanced cancer may benefit from medication discontinuation when faced with life-limiting illness. While polypharmacy prevalence has been explored in older patients, there are limited data for younger patients with advanced malignancy. To better understand medication use in this population, we characterized medication use and associated out-of-pocket (OOP) costs in the last month of life among commercially insured subjects under age 65 who did not enroll in hospice. Methods: We linked enrollment and claims records from two regional commercial insurers to Surveillance, Epidemiology, and End Results (SEER) Cancer Surveillance System and Washington State Cancer Registry records for patients diagnosed with a stage IV solid tumor malignancy in Washington state between January 1, 2007-December 31, 2016. We excluded patients who did not incur at least 1 claim in their last month. We calculated OOP costs as the difference between allowed and paid claim amounts, adjusted to 2016 dollars. Results: See Table. Among 369 patients, the average age was 56 years (range 23-64) and 51% (n=189) were male. Patients most frequently had lung (n=149, 40%), pancreatic (n=36, 10%) or colorectal cancer (n=32, 9%). Patients had an average of 7 medication claims (range 1-26) in the last month of life; the most commonly observed non-chemotherapy claims were for opioids, benzodiazepines and anti-emetics. Conclusions: A majority of subjects incurred at least 5 medication claims in their last 30 days; almost one-third had 10 or more. Frequent claims were for medication for pain or other symptoms. OOP costs ranged considerably. Future research will focus on developing interventions to assure effective medication use congruent with patient and family preferences and goals of care. [Table: see text]

e15011 Background: Few studies have examined patterns of care relating to guideline-recommended therapies for metastatic colorectal cancer (mCRC) in real-world settings. The goal of this study was to investigate treatment patterns for initial and subsequent therapies for mCRC patients in two large commercial plans in Washington State, USA. Methods: We utilized the Hutchinson Institute for Cancer Outcomes Research linkage of Western Washington Cancer Surveillance System (CSS) SEER data with claims from Regence BlueShield and Premera BlueCross (representing 20% of the WA insured population) for patients diagnosed with mCRC between 2012 and 2016. We identified all patients with Stage IV CRC at diagnosis or stage I-III CRC with subsequent claims suggesting metastatic recurrence. We conducted descriptive analysis and multivariate modeling to evaluate the association between age, race, gender, and rurality with receipt of systemic therapy. We examined 1L (first line) regimens received in the first 15 days of treatment, and classified them as adherent or not to NCCN guidelines. In light of recent FDA approval for regorafenib (9/2012) and TAS-102 (9/2015), we examined use of these therapies for refractory mCRC. Results: Among 4,536 CRC cases identified in the database, 426 patients had evidence of metastatic disease and met all inclusion criteria. 87 (20.4%) received no systemic therapy. Of the 339 (79.5%) who received systemic therapy, FOLFOX was the most common 1L regimen (29.5%), followed by FOLFOX+bevacizumab (12.3%). There was evidence of 1L treatment outside of NCCN guidelines – 8.5% of patients received single-agent bevacizumab, and 8.0% received single-agent oxaliplatin. Patients who did not receive systemic therapy were more likely to be older and live in a large metropolitan area. For subsequent therapies, 24 (7.1%) patients received regorafenib, and 5 (1.5%) received TAS-102. Conclusions: In a commercially-insured mCRC cohort, a majority received 1L guideline-based therapy. Many patients did not receive systemic therapy despite current recommendations - further evaluation of factors contributing to these decisions are warranted. Use of regorafenib and TAS-102 was uncommon.

6582 Background: Although ASCO Choosing Wisely guidelines recommend against routine surveillance testing or imaging for asymptomatic individuals with early-stage breast cancer (ESBC) treated with curative intent, they are frequently performed. Physician specialty and costs associated with surveillance testing and imaging were examined in ESBC patients. Methods: Cancer registry patient records in Western Washington from 2007 to 2015 were linked with claims from two regional commercial insurers. Selected patients had been diagnosed with stage I/II breast cancer and treated with mastectomy or lumpectomy + radiation. Surveillance was considered from the first 4 month gap in treatment (surgery, chemo, radiation) through 13 months or restart of treatment. Evaluation and Management (E&M) and procedure codes for tumor marker (CEA, CA 15-3, CA 27.29) and advanced imaging (PET, CT, bone scan) were identified. Specialty codes were used to determine provider type. Physician visits were matched to tests using E&M codes in the +/- 7 days around each test. Cost included total reimbursed amount from insurers during the surveillance period. Results: During surveillance, 2,193 patients averaged 13.3 physician visits [median: 11, IQR: 8-17]. Oncologists (91%) and PCPs (83%) were the most common specialties with an average of 3.7 visits each. Overall, 37% of patients received tumor marker tests (avg = 2.8 tests/patient) and 17% received advanced imaging (avg = 1.5 images/patient). The mean total cost during the surveillance period was $18,403 (SD $26,640). Costs were higher for those patients who received tumor marker testing or advanced imaging. Conclusions: Patients frequently see oncologists and PCPs during early surveillance. Targeting oncologists to improve appropriate tumor marker testing could have the largest impact on aligning practice with Choosing Wisely recommendations and potentially reducing the financial burden on patients. [Table: see text]

e18337 Background: Our objectives were to determine advanced imaging use (whole body imaging with bone scan, CT, or PET-CT and breast MRI) during breast cancer surveillance, and to identify drivers of potential imaging overuse as outlined by ASCO’s Choosing Wisely initiative. Methods: Cancer registry records for 2923 women diagnosed with primary breast cancer in Washington State from January 1, 2007 to December 31, 2014 were linked with claims data from two regional commercial insurance plans. Inclusion criteria included women with AJCC stage 0-3 disease treated with curative intent. Women without continuous insurance enrollment from 3 months prior to diagnosis until 14 months after diagnosis were excluded. Surveillance began 4 months after the end of primary therapy and lasted for 15 months or until restart of treatment. Women’s (age, race, family history) and tumor (grade, receptor status, stage) characteristics were collected. Evaluation and management codes from claims data were used to determine mammography, advanced imaging, and tumor biomarker use during the peri-diagnostic and surveillance periods. Multivariate logistic regression models were used to identify factors associated with advanced imaging use during surveillance. Results: Of eligible women, 80.0% (2332/2923) received mammography, 16.5% (483/2932) received whole body imaging, and 21.5% (670/2932) received breast MRI during the surveillance period. Whole body imaging was significantly associated with increasing stage of disease (stage 3: OR = 3.39, 95% CI: 2.30-5.02), peri-diagnostic whole body imaging (OR = 1.80, 95% CI: 1.36-2.38), and surveillance tumor biomarker use (OR = 1.83, 95% CI: 1.46-2.31). Significant predictors of surveillance breast MRI included young age ( < 45 years: OR = 2.40, 95% CI:1.78- 3.25), family history (OR = 1.58, 95% CI:1.26-1.98), peri-diagnostic breast MRI (OR = 2.01, 95% CI: 1.56-2.59), and surveillance tumor biomarker use (OR = 1.74, 95% CI:1.41-2.17). Conclusions: Peri-diagnostic use of advanced imaging and surveillance use of tumor biomarkers are associated with advanced imaging use during surveillance, and may represent targets for interventions to increase adherence to Choosing Wisely clinical guidelines.

6505 Background: The Centers for Medicare and Medicaid Services (CMS) released a quality metric for potentially preventable chemotherapy-associated emergency department (ED) use, effective in 2020. This metric excludes diagnoses with emerging evidence for outpatient management, such as proactive symptom management (PSM) and those for ambulatory care sensitive chronic conditions. Little is known about the intersection between potentially preventable ED visits due to cancer vs. other chronic disease. This study characterized the number and costs of ED visits during treatment. Methods: Western Washington cancer registry records from 2011- 2015 were linked with claims from two commercial insurers. Patients with newly diagnosed solid tumors undergoing initial treatment with chemotherapy or radiation were eligible. ED use was tracked one year post treatment initiation. ED diagnosis codes for fields 1-10 from the CMS metric and the PSM literature were labeled “Potentially Preventable” (Pp). Codes from the Agency for Healthcare Research and Quality’s Prevention Quality Indicators (PQI) for Chronic Conditions were labeled “Potentially Preventable-Chronic Disease” (PpChronic). Costs were adjusted to $2016. Results: Of the 7,053 eligible patients, 2,543 (36.1%) visited the ED (median # visits [IQR]: 1 [1-2]). The most commonly listed codes included Pain (1,054 visits) and Dyspnea (279 visits) for Pp, Hypertension-PQI (652 visits) and COPD-PQI (206 visits) for PpChronic, and Diabetes (247 visits) and Hyperlipidemia (181 visits) for the other codes. Spending on ED visits including both potentially preventable cancer and chronic disease diagnoses totalled $706,552 (20% of ED costs). Conclusions: One fifth of ED costs potentially resulted from simultaneous poor cancer symptom and chronic disease management. Future research should explore the role of chronic illness in categorizing which ED visits are potentially preventable during cancer treatment. [Table: see text]

159 Background: Moving cancer care towards a value framework requires patients, providers, and payers to weigh cost, quality, and outcomes in decision-making. Many efforts are underway to help providers and payers make value decisions, but little has been developed for patients. Our regional value in cancer care effort used a claims-registry database to develop quality and cost reports aiming to provide actionable data to all stakeholders. Methods: Reports were generated using cancer registry records for Western WA from 2007-2015 linked with claims from two regional commercial insurers. Patients were presented quality reports on regionally prioritized metrics and the 2012 ASCO Choosing Wisely guidelines on breast cancer surveillance and EOL care. Patients also reviewed cost reports for episodes of care (diagnosis, treatment, end-of-life (EOL)) and out-of-pocket (OOP) cost estimates. Feedback stemmed from 1) stakeholder meetings over a 2-year period, 2) working groups of patients, payers and providers, and 3) an annual regional meeting on value in cancer care. Results: In total, 13 patients provided feedback at one or more outreach event. See table. Conclusions: Reports from a claims-registry database may not support the information needs of patients for care decision-making or representing “value”. Patients desired understanding more about patients “like them” for decision-making. [Table: see text]

226 Background: Early studies of the oncology medical home suggest that intensive outpatient care (e.g. 24-hour phone triage, same-day infusion) reduces emergency department (ED) and inpatient (IP) use during cancer treatment. Little is known about which services are most cost-effective. One strategy is to measure observed variation in ED and IP rates to pinpoint care features associated with low-use clinics. This study examined clinic-level variation in ED and IP use in a community setting. Methods: Cancer registry records for Western Washington from 2011 to 2015 were linked with claims from two regional commercial insurers. Included patients were diagnosed with breast, lung, colorectal, or prostate cancer and treated with chemotherapy or radiation. All ED and IP use was tracked 1 year after treatment start using claims data. Observed clinic rates were measured as the percentage of patients with 1 or more visits. Expected clinic rates were determined from regional average rates weighted by clinic’s cancer-specific stage mix. Observed-to-expected clinic ratios were calculated and the Wilson Score test (95% CI) was used to determine statistically different rates. Results: The 18 clinics included 4,558 eligible patients (median 196 pts/clinic; range: 35-859). Unstaged lung patients had the highest ED rates (38.5%); unstaged breast had the lowest (13.3%). The highest IP rate was among unstaged colorectal (66.7%); the lowest in local breast (11.1%). One clinic had an observed rate that was significantly above its expected rate in both ED only and ED to IP. One clinic was significantly below its expected rate in both ED to IP and IP only. Conclusions: Even after adjusting for cancer-specific stage, there was sizable clinic-level variation in the percentage of patients visiting the ED or IP. Investigation into care delivery features and practice characteristics, along with further risk adjustment, may yield insights into best practices and identify clinics for intervention. [Table: see text]

240 Background: The American Society for Radiation Oncology (ASTRO) ‘Choosing Wisely’ (CW) list notes radiation interventions lacking evidence of benefit in cancer care. This study characterizes adherence to the 2014 CW item to ‘not recommend radiation following hysterectomy in low-risk endometrial cancer’ (Stage I, Grade 1-2). No studies have previously evaluated adherence to this CW item. Methods: SEER records for patients age >65 at first primary diagnosis of Stage I (Grade 1-2) endometrial cancer between 2008-2011 were linked with Medicare files. Included cases had Medicare Part A and B coverage for ≥12 months before/after diagnosis and had a hysterectomy 1 month prior to 4 months following diagnosis. We used logistic regression to evaluate associations between socio-demographic and clinical characteristics (Table 1) and receipt of radiation therapy within 1 year of diagnosis. Results: Among 2,938 women meeting eligibility criteria, mean age was 74, 90% were white, and 55% lived in a ‘big metro’ area (population >1m). The non-adherent fraction was 24%. Among non-adherent cases, 76% received brachytherapy. In adjusted analyses (Table 1), only tumor grade was associated with non-adherence. Conclusions: More than 20% of low-risk endometrial cancer cases in our sample were non-adherent to ASTRO CW recommendations, and this only varied significantly by tumor grade. Therefore, use of radiation therapy following hysterectomy appears to be a widespread issue. This is important because non-adherence may result in side effects or substantial cost without commensurate clinical benefit. This study’s follow-up period was prior to the ASTRO CW list, but similar recommendations existed in ASCO, ESMO and NCCN guidelines at the time—suggesting work is need to align practice with guidelines. Our findings provide a baseline measure against which future studies can measure progress. [Table: see text]

2 Background: There is growing recognition that many emergency department (ED) visits during cancer treatment may be related to poorly controlled disease or treatment-related symptoms and could be prevented. An RCT using the Symptom Tracking and Reporting (STAR) tool for proactive symptom management decreased the percentage of patients admitted to the ED (34% vs. 41%; p=0.02). Little is known about the costs of potentially preventable ED visits in a community setting. This study examined the number and costs of ED visits and their associated diagnoses. Methods: Cancer registry records for patients in Western Washington from 2011 to 2015 were linked with claims from two regional commercial insurers. Patients diagnosed with a solid tumor and treated with chemotherapy or radiation were selected. All ED utilization was tracked for 1 year after the start of treatment. ED-related diagnoses codes were labeled “Potentially Preventable” (PP) if they mapped to the 13 symptom categories targeted by STAR (e.g. pain, nausea) and non-PP otherwise. Costs of ED visits were inflation-adjusted and include claims with ED-related procedure, revenue, and place of service codes. All subsequent inpatient costs were excluded, likely under-estimating total costs. Results: Of the 7,075 eligible patients, 2,543 (35.9%) visited the ED an average of 1.79 times. Pain (720 visits), Dyspnea (279 visits), and Nausea (232 visits) were the most common potentially preventable diagnoses; Hypertension (506 visits), Fever (230 visits), and Diabetes (215 visits) were the most common non-PP diagnoses. $1,134,254 (25.2% of the total ED costs) was spent on PP ED visits. Of PP ED visits 20.3% (178/875) resulted in an inpatient stay. Conclusions: In our community setting, at least one quarter of ED costs were potentially the result of poor symptom management. An investment in better symptom management has a significant opportunity to both improve cancer care and lower total costs.[Table: see text]

29 Background: Cancer care costs are rising, creating concerns about affordability. As a result, delivery systems are creating alternative payment structures to lower costs while maintaining or improving quality. As cancer care delivery often involves multiple provider systems, measuring cost may be difficult. In response, using commercial insurance claims linked to cancer registry records, we constructed broadly applicable, reproducible, clinically relevant episodes to measure costs. Methods: Cancer registry records for patients diagnosed in Western Washington from January 2007-June 2016 were linked with claims from two regional commercial insurers. Patients are age 18+, diagnosed with breast, colorectal (CRC), or non-small cell lung cancer (NSCLC) and enrolled with a single insurance plan. With oncologist input, we constructed three care phases: diagnosis (30 days before diagnosis to first treatment), initial treatment (first treatment through first 4 month treatment gap), and end of life (last 30 days). Costs include all claims paid within the phase (2016 inflation adjusted). Supportive care includes colony-stimulating factors, blood transfusions, antibiotics, antivirals, antifungals, and antiemetics. Results: This study included 8,727 patients at diagnosis, 7,686 during treatment, and 1,736 at end of life. Diagnosis phase averaged 54 days and cost $6,936 (SD $11,761, median $4,021). Treatment averaged 126 days, with costs of $61,148 (SD $75,432, median $35,750). Average end-of-life costs were $15,829 (SD $30,222, median $2,347). The table below provides an example of the variation in costs during the treatment phase using local-stage tumors. Conclusions: Clinically relevant episodes of care and cost measures can be constructed using claims-registry data. This allows for identification of high-cost care categories and areas with large-cost variability, which may be helpful when designing value-based reimbursement programs or identifying areas for potential cost-reduction.[Table: see text]

4 Background: Oncologists, primary care physicians (PCPs), and other clinicians provide care for breast cancer patients following active treatment. Clinical practice guidelines are largely consistent in recommended number of clinic visits and annual mammograms. However, surveys of oncologists and PCPs have found variation in attitudes toward surveillance intensity, perceptions of care responsibility, and adherence to Choosing Wisely guidelines. This study examined if surveillance of patients with early stage breast cancer varied by whether they obtained follow up care with oncologists, PCPs or both. Methods: Cancer registry records for patients in Western Washington from 2007 to 2015 were linked with claims from two regional commercial insurers. Patients were selected if they had been diagnosed with stage I/II breast cancer and treated with mastectomy or lumpectomy + radiation. The surveillance period starts at the first 4 month gap in treatment (surgery, chemo, radiation) through 13 months from gap start or restart of treatment. Evaluation and Management (E&M) codes for visits and procedure codes for biomarker and advanced imaging (PET, CT, bone scan) were identified in claims. Specialty codes were used to determine type of provider seen. Physician visits were matched to tests using E&M codes in the ± 7 days around each test. Results: During surveillance, 2046 patients averaged 12.2 physician visits per patient [median: 10, IQR: 7-15]. Oncologists (92%) and PCPs (82%) were the most common specialties with an average of 4.0 and 4.2 visits respectively. 73% of patients received mammography (avg # exams = 1.6) , 37% biomarkers (avg = 2.7) and 16% advanced imaging (avg = 1.5). The majority of biomarkers and the largest proportion of advanced imaging occurred near an oncology visit. Conclusions: Patients frequently see oncologists and PCPs during early surveillance. Targeting oncologists for intervention on potentially inappropriate biomarker testing could have the largest impact on aligning practice with Choosing Wisely recommendations. [Table: see text]

184 Background: End-of-life (EOL) care for persons with hematologic malignancies is variable and often involves high-intensity services at death approaches, which may not reflect patient or family preferences. We characterized healthcare utilization and associated costs in the last 30 days of life among subjects with leukemia or lymphoma to better understand patterns of care in this population. Methods: We linked enrollment and claims records from two regional commercial insurers to Surveillance, Epidemiology, and End Results (SEER) records for patients diagnosed with leukemia or lymphoma in Western Washington state between January 1, 2007 and December 31, 2015. We developed algorithms to characterize EOL care and calculate costs from both the payer and patient perspective for the last 30 days of life. Costs are derived from paid claims for inpatient, outpatient, and pharmacy utilization. Patient out-of-pocket costs are calculated as the difference between allowed and paid claim amounts. Results: See Table. Conclusions: In this analysis, a majority of subjects usedat least one form of high intensity care in the last 30 days of life, and average out-of-pocket costs were considerable. Future research will focus on developing interventions to assess patient and family preferences for intensity of care to better inform the provision of high-value care in this population. [Table: see text]

186 Background: Studies suggest that end-of-life (EOL) care for persons with cancer in the United States is variable and often misaligned with patient and family preferences. To better understand these issues, we developed reports on high-priority quality indicators and costs at EOL. Methods: Surveillance, Epidemiology, and End Results (SEER) records for solid tumor patients diagnosed with cancer in Western Washington state between 1/1/2007 and 12/31/2015 were linked with enrollment and claims from two regional commercial insurers. Using claims, we then developed algorithms to characterize EOL care for breast, colorectal, and non-small cell lung cancer (NSCLC), including costs of care at 90- and 30-days prior to death. Costs include all claims paid for ED, hospital, outpatient, and pharmacy care. We estimated patient out-of-pocket costs as the difference between allowed and paid claim amounts. Results: See Table. Across the largest 10 clinics in the region there was considerable variability in the average costs of cancer care in the last 90 days of life. The clinic-specific average ranged from $24,532 to $72,931 for breast cancer, $30,495 to $65,975 for colorectal cancer and $23,320 to $59,641 for NSCLC. Conclusions: At the end of life, care for patients with advanced breast, lung, and colorectal cancer is highly variable, costly to patients, and may be misaligned with the goals and preferences for patients and their family members. While the results may reflect both appropriate and unnecessary care, the large variation across clinics suggests opportunities for improvement. Further research is needed to identify factors associated with use of low-value, high-cost services at the end of life. [Table: see text]

6524Background: Many studies suggest that end-of-life (EOL) care in the United States is variable and misaligned with patient and family preferences. Several metrics were characterized as part of a...

282 Background: Palliative care in oncology encompasses a wide array of care, and optimal performance metrics remain uncertain. To address this issue we held a regional meeting that included patient advocates, clinicians, private and public payers and researchers to identify quality indicators for palliative care, discuss palliative care (PC) quality measurement in the region, review preliminary results and metrics for PC, and identify opportunities to improve care. Methods: Regional experts facilitated discussions. Data was presented from a regional insurance claims-cancer registry linked database. Participants were asked to: discuss and prioritize interventions to improve palliative care in oncology and create metric dashboards for healthcare providers. Results: Oncology-specific recommendations for interventions: early and continuous palliative care discussions from diagnosis through EOL; training existing care team on palliative care conversations, through standard certification programs; pair patients with an experienced patient/patient advocate; educate caregivers, patients and providers respectively on how to communicate about PC and care choices; and patient navigation to manage symptoms of advanced cancer. Feedback on prioritization of palliative care/EOL metrics: see Table. Conclusions: A multi-stakeholder approach can be effective for developing metrics and interventions to improve palliative care by providing an understanding of the information needs of the community. [Table: see text]

196 Background: Natural language processing (NLP) has the potential to significantly ease the burden of manual abstraction of unstructured electronic text when measuring adherence to national guidelines. We incorporated NLP into standard data processing techniques such as manual abstraction and database queries in order to more efficiently evaluate a regional oncology clinic’s adherence to ASCO’s Choosing Wisely colony stimulating factor (CSF) recommendation using clinical, billing, and cancer registry data. Methods: Database queries on the clinic’s cancer registry yielded the study population of patients with stage II-IV breast, non-small cell lung (NSCL), and colorectal cancer. We manually abstracted chemotherapy regimens from paper prescription records. CSF orders were collected through queries on the clinic’s facility billing data, when available; otherwise through a custom NLP program and manual abstraction of the electronic medical record. The NLP program was designed to identify clinical note text containing CSF information, which was then manually abstracted. Results: Out of 31,725 clinical notes for the eligible population, the NLP program identified 1,487 clinical notes with CSF-related language, effectively reducing the number of notes requiring abstraction by up to 95%. Between 1/1/2012-12/31/2014, adherence to the ASCO CW CSF recommendation at the regional oncology clinic was 89% for a population of 322 patients. Conclusions: NLP significantly reduced the burden of manual abstraction by singling out relevant clinical text for abstractors. Abstraction is often necessary due to the complexity of data collection tasks or the use of paper records. However, NLP is a valuable addition to the suite of data processing techniques traditionally used to measure adherence to national guidelines.

165 Background: The Hutchinson Institute for Cancer Outcomes Research (HICOR) has adopted a multi-stakeholder approach to identify and prioritize metrics to characterize oncology care in the Western Washington. In March 2015, HICOR released a beta version of an oncology informatics platform, named HICOR IQ, which allows regional payers, providers and patient advocates to view and interact with community-prioritized metrics. Methods: Surveillance, Epidemiology, and End Results (SEER) records for patients diagnosed with cancer in Western Washington state between January 1, 2007 and May 31, 2014 were linked with enrollment and claims from two regional commercial insurance plans. Algorithms were developed to characterize the 2012 ASCO Choosing Wisely (CW) recommendations in the claims-registry database. HICOR partnered with LabKey Software to develop a regional metric reporting platform. Results: The beta version of HICOR IQ displays regional results for the CW recommendations, then allow users to customize the reports by stratifying and/or filtering by different demographic (age, race, gender), clinical (cancer site, stage) and provider (clinic volume) characteristics. In addition, de-identified clinic specific adherence can be plotted to show variation in care throughout the region. A convenience sample of oncology clinic leaders was asked to review and comment on the beta-version. Primary suggestions were as follows: Display clinic-identifying information based on logged in user; Expand the flexibility of the tool to allow for user-created metrics; Keep the tool intuitive and easy to use; Protect the data and identity of patients. Future development of the tool will take an iterative process to allow for continued user feedback. The next planned version of HICOR IQ will incorporate user feedback by displaying clinic-identifying information based on the user and expanding the reporting of regional metrics. Conclusions: It is feasible to create a tool that reports regional adherence rates to oncology metrics. An iterative process that incorporates user feedback is an important step to build a tool that is adopted by the community.

39 Background: In the context of many initiatives aimed at measuring quality and value in cancer care, the Hutchinson Institute for Cancer Outcomes Research (HICOR) has adopted a multi-stakeholder approach to characterize oncology care, prioritize areas for improvement, design programs, and evaluate outcomes. Beginning in 2014, HICOR initiated a process to move towards data transparency in the reporting of regional quality and value metrics. Methods: The HICOR team constructed clinic-level adherence reports for community-prioritized metrics and the 2012 ASCO Choosing Wisely recommendations using a registry-claims linked database. In the fall of 2014, a national external advisory board reviewed methodology for measuring adherence. De-identified regional results were presented at a provider meeting in late 2014 to elicit provider feedback on methodology and on strategies for reporting clinic-identified adherence. Clinics were privately given their own adherence data. In 2015, revised de-identified regional reports were presented at a Value in Cancer Care Summit poster session and made available through HICOR IQ, a regional oncology informatics platform, for further discussion. Results: Results show that no clinic was also the best or worst performing clinic. The table shows the performance by clinic for the 5 Choosing Wisely recommendations. There is now increased demand by clinics to view their own adherence benchmarked with the region as a next step in moving towards full data transparency. Additionally, there is support from provider members in the community to re-identify clinics in order to compare results against their peers. Conclusions: Using an iterative, transparent, multi-stakeholder process, it is feasible build regional consensus towards releasing clinic-level adherence to quality and value metrics. By consulting trusted experts in the field and allowing multiple opportunities to provide feedback, providers are requesting even more transparency in order use the oncology measures to improve care in their practice and the region. [Table: see text]

6612 Background: Approximately 40% of men diagnosed with metastatic prostate cancer will experience ≥ 1 skeletal related events (SREs), defined as a pathological fracture, spinal cord compression, ...

6512 Background: Oncology clinics, health care systems, and payers are considering how to gauge performance for the ASCO Choosing Wisely (CW) program. We sought to develop a clinic-level composite ...

223 Background: Skeletal related events (SREs), defined as pathological fracture, spinal cord compression, surgery or radiotherapy to the bone, occur in nearly half of men diagnosed with metastatic prostate cancer. Accurate assessment of the risk of death associated with SREs is important to making decisions about the use of recently approved treatments, which have been shown to decrease the frequency of skeletal events, yet estimating the impact of SREs on survival presents several methodological challenges given the recurrent time-dependent nature of exposure. Methods: A cohort of men >65years of age, diagnosed with prostate cancer and bone metastasis between January 1, 2004 and December 31, 2009 was identified from the Surveillance Epidemiology and End Results (SEER) registries were linked to Medicare Parts A and B claims. The outcome of interest, death from any cause, was ascertained from SEER and survival time was calculated from the date of metastatic prostate cancer diagnosis. Multivariable Cox proportional hazards models treating the occurrence of an SRE as a time-dependent exposure were used to estimate the hazard ratios (HR) and corresponding 95% confidence intervals (CI) for the association between SRE occurrence, number, and type, and death. Results: Among 3,297 men with metastatic prostate cancer, 40% experienced ≥1 SRE during the observational follow-up period (median 19 months). Compared to men who remained SRE-free, cohort members who had ≥1 SREs had a two-fold higher risk of death (HR 2.2, 95% CI 2.0-2.4). Those whose first SRE was a pathological fracture had a 2.7-fold higher risk of death (HR 2.7, 95% CI 2.3-3.1), followed by spinal cord compression (HR 2.1, 95% CI 1.8-2.5), surgery (HR 1.8, 95% CI 1.5-2.2) and radiotherapy (HR 2.2, 95% CI 1.9-2.4). Compared to those experiencing only one SRE, men who experienced a second SRE of any type had double the risk of death (HR 2.2, 95% CI 1.9-2.6). Conclusions: SREs were associated with ≥50% reduction in overall survival. This finding is consistent across different types of SREs and supports using therapies to prevent or treat SREs in patients with prostate cancer metastatic to the bones.

133 Background: ASCO launched the Choosing Wisely campaign to reduce the use of interventions lacking evidence of benefit to cancer patients, but implementation may be a challenge. This project develops a stakeholder-informed process to prioritize and implement the 2012 ASCO Choosing Wisely (CW) recommendations in oncology clinic settings. Methods: Medical directors from 6 network-affiliated oncology clinics in the Puget Sound region selected 2 CW measures for implementation: (1) No biomarkers/advanced imaging following early breast cancer treated for cure, and; (2) No colony stimulating factor use for low FN risk chemotherapy. Results: Adherence is measured with data from each clinic’s tumor registry, billing records, lab results, pathology reports, and clinic notes. Challenges to measuring baseline adherence included translating non-specific guidelines into discrete data elements (e.g. when does surveillance start?) or required measuring the absence of something, presenting quality control challenges to distinguish between adherence and missing data. Additional challenges are shown in the Table. To address these challenges, the team developed these strategies: (1) creation of specific data elements, (2) mapping clinics separately to the criteria and (3) employing methods such as Natural Language Processing to efficiently collect data from EMR. Conclusions: It is feasible to measure adherence to ASCO CW recommendations with data sources available at oncology clinics. Variability in data platforms among clinics is inherent to the current medical landscape and must be accounted for in successful implementation of cross-clinic programs. [Table: see text]

178 Background: The ASCO Choosing Wisely campaign aims to reduce the use of interventions that lack evidence of benefit in cancer care. This project characterized adherence to the 2012 ASCO Choosing WiselyRecommendations by linking health plan claims data to a regional cancer registry in order to identify areas for research interventions to improve adherence. Methods: Surveillance, Epidemiology, and End Results (SEER) records for patients diagnosed with cancer in Western Washington state between 2007 and 2013 were linked with enrollment and claims from a large regional commercial insurance plan. Using claims and SEER records, algorithms were developed to characterize each Choosing Wisely measure. Results: For the 5 recommendations measured, adherence rates were as follows: (see Table). Conclusions: Using algorithms involving insurance claims and cancer registry records, we found variable adherence to the 2012 ASCO Choosing Wisely recommendations. While 100% adherence is not expected due to limitations of claims data, such records may be useful to identify areas for intervention and estimating potential savings from improved adherence. [Table: see text]

6007 Background: We identified the physicians initially involved in the management of stages III and IV NSCLC, and explored associations of patient and their initial physician factors with referrals to oncologists and subsequent receipt of guideline-based therapies (GBTs) endorsed by the National Comprehensive Cancer Network. Methods: Using a retrospective cohort design, we identified patients with a new diagnosis of stages III and IV NSCLC from 01/01/2000 to 12/31/2005 included in the Surveillance, Epidemiology, and End Results-Medicare database. After collecting patient sociodemographic, tumor, and treatment data, we linked Unique Physician Identifier Numbers (UPINs) from Medicare claims to the American Medical Association Masterfile database to identify the initial physicians and subsequent referrals to cancer specialists, defined as surgeons, radiation oncologists and oncologists. We used logistic regression to explore associations between: 1) patient and initial physician independent variables with referrals to oncologists; 2) referrals to different combinations of cancer specialists with receipt of stage-specific GBTs, adjusted for confounders. The follow-up period was 12 months or up to 12/31/2006. Results: For 28,977 patients, mean age was 75 years, 53% were male, 83% were white, 51% had stage IV, 37% initially saw an internal medicine doctor, 84% saw at least an oncologist, 31% saw all 3 types of cancer specialists, and 44% received GBTs. Younger age, white race, stage IV, higher income, lower co-morbidity index, initial physicians other than family practice doctors, and referral to pulmonologists were associated with higher likelihood of referral to oncologists (P

6007 Background: The risk of bankruptcy for cancer patients is not well characterized and may vary by cancer type, treatment, and personal factors. Linking the Washington State SEER registry with W...