Effect of the COVID-19 Pandemic on Place of Death Among Medicaid and Commercially Insured Patients With Cancer in Washington State

PURPOSE

The COVID-19 pandemic-related disruptions in health care delivery might have affected end-of-life care in patients with cancer. We examined changes in place of death and hospice support for Medicaid and commercially insured patients during the pandemic.

PATIENTS AND METHODS

We linked Washington State cancer registry records with claims from Medicaid and two commercial insurers for patients with solid tumor age 18-64 years. The study included 322 Medicaid and 162 commercial patients who died between March 2017 and June 2019 (pre-COVID-19), along with 90 Medicaid and 47 commercial patients who died between March and June 2020 (COVID-19). Place of death was categorized as hospital, hospice (home or nonhospital facility), and home without hospice. Place of death was compared using adjusted multinomial logistic regressions stratified by payer and time period (pre-COVID-19 v COVID-19). The clinical and sociodemographic factors associated with dying at home without hospice were examined, and adjusted marginal effects (ME) are reported.

RESULTS

In the adjusted pre-COVID-19 analysis, Medicaid patients were more likely than commercially insured patients to die in hospital (48% v 36%; adjusted ME, 11%; P = .02). In the pre-COVID-19/COVID-19 analysis, Medicaid patients' place of death shifted from hospital (48% v 32%; ME, -16%; P < .01) to home without hospice (19.9% v 38.0%; ME, 16.5%; P < .01). However, there were no statistically significant changes pre-COVID-19/COVID-19 for commercial patients. As a result, during COVID-19, Medicaid patients were more likely than commercial patients to die at home without hospice (38% v 22%; ME, 16%; P = .04) as were male versus female patients (ME, 16%; P < .01).

CONCLUSION

The pandemic might have disproportionately worsened the end-of-life experience for Medicaid enrollees with cancer. Attention should be paid to societal and health system factors that decrease access to care for Medicaid patients.

Patient (pt) experience with a smartphone application (app) and biosensor for remote symptom monitoring during systemic cancer therapy: Qualitative findings from initial testing

443

Background: Up to half of pts with cancer receiving systemic therapy have unplanned emergency department or hospital admission during treatment. ML41539 is a prospective observational study of an app to collect electronic patient-reported outcomes (ePROs) and a wearable biosensor (Samsung Galaxy Watch3) to evaluate the use of a remote monitoring system to improve symptom management and reduce unplanned admissions (ISRCTN25569053). Here, we report the initial feasibility and usability results for the ePRO app and biosensor. Methods: The study includes a vanguard phase (n = 32) to obtain early feedback on pt experience of the app and biosensor. After 2 weeks of continuous biosensor monitoring and daily ePRO reporting, 10 pts participated in semi-structured telephone interviews conducted by trained study staff. Open-ended questions addressed usability and technology-driven facilitators and barriers. Pts were also asked about ease of use and biosensor comfort on a 10-point Likert scale (higher scores reflected better usability and comfort). Transcripts were summarized using a FITTE-adapted framework and coded in ATLAS.ti 22.0.11.0. Coded data were summarized and consolidated into matrices to identify areas for improvement in future study phases. Results: Participants were approached until the target of 10 completed interviews was met. Respondents resembled the overall study population with regards to gender, race, ethnicity, age, and adherence to wearing the sensor and completing daily ePROs. For ease of use, the average rating among participants was 9 for the app (range 8.5-10) and 9.5 for the biosensor (range 7-10). Biosensor comfort was rated 7 on average (range 3-9). The most common words used to describe the app were “easy,” “simple,” and “straightforward.” Sixty percent of pts said that the watch needed to be charged too often and that their experience could be improved by offering more sensor functions and allowing pts to view and track their ePRO responses and biometric data. Sixty percent of pts also supported using these technologies to monitor symptoms during cancer therapy, stating that it was a good idea or useful, especially if it prevented hospitalization or increased communication with their care team. Conclusions: Overall, pts reported positive experiences wearing the sensor and using the ePRO app to report their symptoms during the 2-week observation period. Ease of use was high for both the app and biosensor. A majority reported dissatisfaction with the battery life of the biosensor. Most stated that they would prefer more opportunities to interact directly with the devices and to monitor their own symptoms by viewing collected ePRO and biometric data. Building this type of functionality into the app and sensor for future study phases could help pts engage more with the devices and potentially improve adherence. Clinical trial information: ISRCTN25569053.

Feasibility and usability of an electronic patient (pt)-reported outcome (ePRO) smartphone application (app) and biosensor for pts with cancer undergoing systemic therapy

444

Background: Almost half of the nearly 370,000 pts with cancer who receive chemotherapy in the United States each year experience Emergency Department (ED) visits and unplanned inpatient (IP) stays during treatment, largely due to poorly controlled symptoms. Recent studies show that using PROs in oncology practice can improve symptom management and pt outcomes. This study examines the feasibility and usability of a PRO app paired with a biosensor to identify pts at high risk of ED and IP visits during systemic therapy. Methods: ML41539 is an ongoing prospective observational study evaluating the feasibility and usability of a clinic-provided app and smartwatch biosensor for monitoring pts undergoing systemic cancer therapy (ISRCTN25569053). Key inclusion criteria are 18–80 years old, Eastern Cooperative Oncology Group Performance Status 0–2, biopsy-proven solid tumor diagnosis (excluding non-melanoma skin cancer), and scheduled to receive a first dose of intravenous or oral cancer therapy as an initial or new line of therapy. Exclusion criteria include receiving radiation or hormone therapy only, residing in a skilled nursing facility, or participating in another clinical trial. The app collects 15 common treatment-related symptoms (PRO-CTCAE) daily. Usability and satisfaction were assessed with the modified mHealth App Usability Questionnaire (mMAUQ: score 1-7; higher score indicates better app usability and satisfaction) and the modified Quebec User Evaluation of Satisfaction with Assistive Technology (mQUEST 2.0: score 1-5; higher score indicates better sensor satisfaction). We report planned analysis results of the first 32 pts in the vanguard phase of the trial. Pts wore the biosensor and recorded symptoms on the app for 2 weeks. Results: Thirty-two pts from three Washington State community oncology clinics consented to the vanguard phase. One pt was not onboarded; two dropped out before completing the 2-week observation period. The mean age was 60 years; 68% were women. The most common cancer types were breast (41%), colorectal (13%), endometrial (9%), and melanoma (9%). Of the 29 pts, 59% completed all daily ePRO assessments and 55% wore the sensor every day during the 2-week period. The overall adherence rate was 91% (370/406 assessments) for ePRO and 86% (349/406 biosensor days) for the biosensor. The average mMAUQ score was 6.25 (n = 26); the average mQUEST score was 4.02 (n = 25). Conclusions: Pts receiving systemic cancer therapy had relatively high adherence to a daily digital monitoring system that included an ePRO app and biosensor. Participants expressed moderately high usability of the app and satisfaction with the biosensor. The results support the feasibility of monitoring pts with an app and biosensor. Future studies to assess adherence and data completeness for full courses of systemic therapy are needed. Clinical trial information: ISRCTN25569053.

Can order entry systems improve oncology practice? The TrACER Experience

339

Background: Primary prophylactic colony stimulating factors (PP-CSF) are prescribed to patients undergoing chemotherapy to reduce the risk of febrile neutropenia (FN). Prior research suggested poor adherence to PP-CSF prescribing relative to national guidelines. Accordingly, the objective of the TrACER study was to examine whether a guideline-based standing order entry (SOE) system for PP-CSF improves use and reduces FN. TrACER also included a substudy to evaluate the effectiveness of PP-CSF for patients receiving intermediate risk chemotherapy, where evidence of benefit is weaker. Methods: We conducted a patient-informed, cluster randomized trial among 32 oncology clinics from the NCI Community Oncology Research Program. Patients age ≥18 with breast, colorectal or non-small cell lung cancer initiating cancer therapy were enrolled. Clinics were randomized 3:1 to the implementation of a guideline-based PP-CSF SOE or usual care. Automated orders for PP-CSF were added for high-risk regimens and an alert not to use PP-CSF was included for low-risk regimens. Risk was based on National Comprehensive Cancer Network guidelines. A secondary 1:1 randomization for intermediate risk-regimens assigned 16 intervention sites to either SOE to prescribe or an alert to not prescribe PP-CSF. Results: 2,946 patients were enrolled (2287 intervention, 659 usual care). PP-CSF use among high-risk patients was high and not significantly different between arms (89.2% SOE; 95.8% usual care). FN rates for the SOE and usual care arms were 6.1% and 4.2% and not significantly different. The FN rate among high-risk patients not receiving PP-CSF was 14.9%. Among the 585 patients receiving low-risk regimens, PP-CSF use was low and not different between arms (6.3% SOE, 5.5% usual care). FN rates did not differ between the SOE system (1.5%) and usual care (0.8%). In contrast, for the intermediate risk substudy, rates of PP-CSF use were substantially higher among sites randomized to SOE (37.1% vs 9.9%, OR = 5.91(95% CI 1.77-19.70; p = 0.0038), and rates of FN were low and identical between arms (3.7% vs 3.7%). Similarly, FN rates did not differ between intermediate-risk patients that did or did not receive PP-CSF, irrespective of assignment. Conclusions: Implementation of a guideline-informed SOE system did not impact PP-CSF use or FN rates in high- and low-risk patients, where evidence supporting PP-CSF is stronger, and had a significant impact on PP-CSF use but not FN rates among intermediate risk patients, where evidence of benefit is weak. Overall, adherence to PP-CSF for low- and high-risk chemotherapy was much better than predicted based on evidence available at trial design. SOE interventions may be more useful in situations where more uncertainty of benefit exists. The pragmatic trial design provides high-quality evidence that had previously been lacking on the use and performance of PP-CSF in real world settings across the spectrum of FN risk. Clinical trial information: NCT02728596.

Intervention non-adherence in a pragmatic randomized trial of a standardized order entry for colony stimulating factor prescribing

374

Background: Pragmatic trials evaluate the effectiveness of interventions in routine practice conditions. Pragmatic trials have high generalizability, but the treatment effect can be influenced by nonadherence to the intervention of interest. We conducted a pragmatic, cluster-randomized trial to test whether a guideline-based standing order entry (SOE) system improves use of primary prophylactic colony stimulating factor (PP-CSF) prescribing for patients receiving myelosuppressive chemotherapy. Clinics were assigned to the SOE or usual care. We investigated variability in adherence to the intervention. Methods: TrACER was a patient-informed, cluster randomized trial among 32 oncology clinics from the NCI Community Oncology Research Program. Clinics were randomized 3:1 to a guideline-based PP-CSF SOE or usual care (primary study). Among SOE intervention sites, automated orders for PP-CSF were included for regimens at high risk for febrile neutropenia (FN) and an alert not to use PP-CSF for low FN risk. A secondary 1:1 randomization assigned the 24 intervention sites to either SOE to prescribe or an alert to not prescribe PP-CSF for patients receiving intermediate FN risk-regimens. Providers were allowed to override the standing orders for individual patients. Results: Overall, 8 sites (659 patients) were randomized to usual care and 24 sites (2287 patients) to the intervention; 12 (1296 patients) were randomized to the intermediated risk SOE intervention and 12 (991 patients) to the alert not to prescribe PP-CSF. PP-CSF use among patients receiving high FN risk treatment was high and not different between arms (89.2% SOE; 95.8% usual care), however rates of PP-CSF use by site ranged from 48.6% to 100%. Among those receiving low FN risk regimens, PP-CSF use was low and not different between arms (6.3% SOE, 5.5% usual care), however PP-CSF use ranged from 0% to 19.4% across sites randomized to the alert to not prescribe. In the intermediate risk sub-study, PP-CSF was higher among sites randomized to SOE vs. the alert not to prescribe PP-CSF (37.1% vs 9.9%, OR = 5.91, 95% CI 1.77-19.70; p = 0.0038). However, there was considerable variability in adherence to intervention assignment: PP-CSF use ranged from 0% to 75% among sites randomized to SOE, and despite an alert to not prescribe, PP-CSF rates ranged among sites from 0% to 33%. FN rates were low and similar in both arms. Conclusions: In this randomized pragmatic trial aimed at improving PP-CSF prescribing, there was substantial variability in site adherence to the intervention assignment. While the ability to opt-out of the intervention is a feature of pragmatic trials, careful pre-study planning to estimate nonadherence is critical to ensure adequate power to detect an effect. Understanding reasons for intervention opt-outs will is also inform future pragmatic studies aimed at improving adherence to practice guidelines. Clinical trial information: NCT02728596.

Evaluating the feasibility of using an electronic patient-reported outcome (ePRO) smartphone application (app) and biosensor by patients with cancer undergoing systemic treatments

TPS1599

Background: Almost half of the nearly 370,000 patients with cancer who receive chemotherapy in the United States each year experience Emergency Department (ED) visits and unplanned hospital inpatient (IP) stays during treatment, largely due to poorly controlled symptoms. Recent studies have shown that utilizing PRO information in oncology practice can improve symptom management and patient outcomes. This study aims to examine the feasibility and usability of a PRO app paired with a biosensor to identify patients who are at high risk for ED and IP visits. Methods: This prospective, pragmatic, observational study will evaluate the feasibility and usability of a clinic-provided smartphone app and smartwatch biosensor for monitoring patients undergoing systemic cancer treatment. Eligible patients are 18–80 years old, ECOG PS 0–2, have a biopsy-proven solid tumor diagnosis of cancer (excluding non-melanoma skin cancer), and are scheduled to receive the first dose of intravenous (IV) or oral cancer therapy as an initial or new line of treatment. Patients should be able to provide informed consent, wear the biosensor daily, and complete the app ePRO survey and questionnaires in English. Study exclusion criteria include receiving radiation or hormone therapy only, residing in a skilled nursing facility, participating in another clinical trial, current pregnancy, and wearing pacemakers, implantable cardioverter defibrillators, cochlear implants, and/or neurostimulator devices. The app collects PROs (PRO-CTCAE), app usability and satisfaction (modified mHealth App Usability Questionnaire [mMAUQ]) and patient satisfaction with the biosensor (modified Quebec User Evaluation of Satisfaction with Assistive Technology [QUEST 2.0]). The study is divided into two phases: (1) vanguard (N = 30); (2) operational (N = 70). Patients will be asked to wear the biosensor and enter PROs into the app daily for a 2-week (vanguard) or 6-week period (operational). The vanguard sample size allows for the recruitment of ̃10 patients at each of the three participating oncology community clinics as is standard for initial device and software testing and development. Study endpoints for feasibility include: (1) vanguard – patient recruitment and protocol adherence, completeness of data capture, app usability, user satisfaction of biosensor; (2) operational – validity of self-reported hospital visits, feasibility of using electronic case report forms. Data collected from the vanguard will inform modifications to the app for the operational phase. The operational phase sample size is sufficient to assess data capture completion and clinical trial recruitment procedures in diverse practice settings (e.g., low volume vs. high volume, rural vs. urban). Clinical trial information: ISRCTN25569053.

Stage at cancer diagnosis during the COVID-19 pandemic in western Washington state

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.

Medicaid patients more likely to die at home without hospice during the pandemic versus before, exacerbating disparities with commercially insured patients

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]

Disparity in telehealth and emergency department use among Medicaid and commercially insured patients receiving systemic therapy for cancer in Washington State following the COVID-19 Pandemic

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 <65 with new solid tumor malignancies who received systemic treatment at academic and community oncology practices. Persons starting therapy March – June 2020 (COVID) were compared with those starting therapy March-June 2017-2019 (Pre-COVID). Poisson regressions were used to evaluate differences in oncology practice office visits and telehealth visits. Logistic regressions were used to evaluate the likelihood of at least one ED admission among patients starting systemic therapy pre- and post-COVID. Results: Among patients who met inclusion criteria (652 Commercial, 349 Medicaid), Medicaid enrollees had more advanced disease and more comorbidity versus commercial enrollees. In unadjusted analysis of E&M and telehealth service visit codes, office-based visits fell for both insurance groups (Table) while telehealth service visits (negligible pre-COVID) were higher for commercial versus Medicaid enrollees post-COVID. The proportion of persons with ≥ 1 ED visit during therapy fell for both insurance groups. In Poisson models, Medicaid enrollees had significantly fewer total visits (P=0.001) and fewer telehealth visits (p<0.001) compared commercial enrollees during the COVID period. In the logit models, ED visits trended lower for both groups after COVID (OR 0.53 95% CI 0.279 to 1.008). Among Medicaid enrollees, persons ages 40-49 and breast cancer patients were more likely to visit the ED. Among the commercially insured, persons with 2 or more comorbidities were more likely to visit the ED. The pre-post COVID change in likelihood of an ED visit was not significantly different between insurance groups (p=0.355). Conclusions: In Washington State, the COVID-19 pandemic created a substantial disparity in access to office-based and telehealth care for low-income patients receiving systemic therapy for new cancers. Reduced oncology practice visits among Medicaid patients did not widen existing disparities in utilization of emergency care.[Table: see text]

Cancer diagnosis and adverse financial events: Evidence from credit reports

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, p<0.0001), severe AFEs (OR 1.94, 95% CI 1.85-2.03, p<0.0001), more severe AFEs (OR 1.23, 95% CI 1.12-1.36, p<0.0001), and most severe AFEs (OR 1.46, 95% CI 1.16-1.86, p=0.0016). Age >65 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]

Results of a pilot study to increase adherence to ASCO G-CSF recommendations at community clinics

19

Background: An ASCO 2012 Choosing Wisely recommendation cautions against the use of primary prophylactic colony stimulating factors (PP-CSF) for chemotherapy regimens with <20% risk of febrile neutropenia (FN). Our pilot aimed to test the feasibility and impact of using academic detailing and an automated CSF ordering system on CSF prescribing at 6 regional community oncology clinics in Washington State. Methods: The intervention was 1) academic detailing for oncologists during a regular staff meeting and 2) reconfiguring ordering systems to show FN risk and PP-CSF recommendation in a passive alert and include or exclude PP-CSF standing orders with >20% or ≤20% FN risk regimens, respectively. Clinic tumor registries were queried for patients with stage II-IV breast, non-small cell lung, or colorectal cancer starting first-line chemotherapy pre- or post-intervention. PP-CSF use, FN risk factors, and chemotherapy regimens matching the study protocol were manually abstracted. A regimen order was coded as adherent if it is low (<10%) FN risk without PP-CSF use; intermediate (10-20%) FN risk without PP-CSF use or FN risk factors are present; or high (>20%) FN risk with PP-CSF use. Results: The intervention was successfully implemented at 4 out of 6 participating clinics. The remaining 2 clinics were transitioning from paper to electronic orders in 1-2 years or using a system managed by a non-participating hospital. Adherence across the four implementing clinics increased after the intervention. *=p<0.05 Implementation of the CSF order-entry intervention was successful across a variety of ordering systems, including paper-based systems. Overall, while adherence prior to the intervention was high for these clinics, the order entry systems significantly improved adherence. Population characteristics and data availability may account for variation in adherence. Conclusions: An intervention with both academic detailing and ordering system presets may help increase adherence to Choosing Wisely recommendations. [Table: see text]

A pilot study to increase adherence to ASCO Choosing Wisely recommendations for breast cancer surveillance at community clinics

18

Background: ASCO 2012 Choosing Wisely recommends against serum tumor marker tests and advanced imaging for breast cancer survivors who are asymptomatic for recurrence. Our pilot aimed to measure and raise adherence to this recommendation through a patient video at regional community clinics. Methods: Eligibility for study included patients with 1+ long-term follow-up visit within 3 months of end of treatment in the pre- or post-intervention period: Clinic tumor registries were queried for stage I-IIIA breast cancer patients treated with curative intent. The intervention included 1) academic detailing for oncologists at a regular meeting and 2) a video about the recommendation shown to patients at end of active treatment. Surveillance data was manually abstracted. We define adherence as no asymptomatic tests in the first 13 months of surveillance. Results: Advanced imaging adherence was high before and after the intervention (99-100%). Tumor marker (TM) adherence is in the table. Six of seven providers with low (<60%) TM adherence before the intervention maintained low TM adherence after. One provider with low TM adherence and all 3 providers with moderate (60-90%) TM adherence increased to high TM adherence (>90%). TM adherence was better among patients who viewed the video (130 of 145, 90%) than those who did not (452 of 556, 81%) in the post-intervention period. The higher TM adherence among patients who viewed the video and the wide range in adherence among providers suggest that tumor marker use may be both patient- and provider-driven. Population characteristics may explain some of the variation in adherence. Conclusions: While adherence to recommendations regarding high-cost imaging was high, wide variation in tumor marker adherence among providers and high baseline adherence for advanced imaging suggests that interventions targeting surveillance testing may wish to focus primarily on tumor markers and provider outreach. [Table: see text]

Baseline cancer worry and tumor marker testing among earlier-stage breast cancer patients participating in a Choosing Wisely pilot

153

Background: We developed a patient-facing video aimed at raising early breast cancer survivors’ adherence to ASCO’s Choosing Wisely recommendation against surveillance tumor marker testing. To understand the impact of the video on cancer worry regarding recurrence, we surveyed breast cancer survivors before and after viewing the video. Methods: Women with stage I-IIIA breast cancer (N=246) treated at six regional community clinics were surveyed prior to viewing a video at the start of surveillance, then again at follow-up one year later (N=171). Both surveys included the Cancer Worry Scale (CWS-8 items, 4-point Likert scale). Tumor marker (TM) testing during surveillance was collected for 728 patients and linked to surveys among those who provided consent (N=105). Results: Most women (77%) were white age 50+. Among women who completed both questionnaires (N=153), the average CWS summary score was 17.1 at baseline (range=9-29) and 16.9 at follow-up ( p=0.48, range=8-30). Women who did not view the video (A) and those with high baseline cancer worry (B) who viewed the video had similar rates of TM testing (19%) compared to patients with low baseline cancer worry (C) who viewed the video (3%). Cancer worry is highly correlated with the decision to use of TM testing. Viewing an informational video that provided evidence-based advice on follow-up and testing did not impact cancer worry. Enrollment among eligible patients was impacted by challenges to proactively identify and consent patients during their transition to surveillance. Conclusions: Patients with high baseline cancer worry may need different or additional guidance beyond an educational video during their transition to surveillance for breast cancer. [Table: see text]

Using deep learning with registry linked claims data to predict hospitalization during chemotherapy: Feasibility study

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]

Do rural cancer patients receive lower quality cancer care? Assessing the impact of rurality on oncology practice performance measures

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<0.05). Where rurality did not impact the performance measure, results are left blank. Conclusions: After controlling for socioeconomic status and payer type, quality of cancer care for rural cancer patients was not consistently poorer compared to urban patients. These results suggest that lower survival among rural patients may be due to factors beyond quality of care.[Table: see text]

Patient and provider perceptions of emergency room visits during chemotherapy

183

Background: Emergency room (ER) visits during chemotherapy are costly. To identify potential causes of ER visits, we interviewed oncology patients and providers about symptoms and other factors related to ER visits. Methods: People with cancer (n = 19) and oncology physicians and nurses (n = 11) were interviewed using a semi-structured protocol. Interviews were transcribed. The study team developed a codebook and interviews were coded deductively. Four of the interviews were double coded. Results: Patients reported communicating with their physicians mostly by telephone. Patients reported a variety of distressing symptoms from chemotherapy including infection, mouth sores, distress, fatigue, neuropathy, gastrointestinal (GI) effects. Pain, fever, neuropathy, and dizziness were symptoms that motivated ER visits. Patients reported that contact with their care team and having a specific plan for symptoms helped prevent ER visits. Patients also reported that going through chemotherapy the first time was harder because they did not know what to expect and the patient education can be overwhelming. Providers did not always have formal protocols for proactively contacting patients but those that did contacted patients early in treatment. Providers reported GI symptoms, pain, shortness of breath, dizziness, and infection as causes of ER visits. They reported difficulties assessing symptoms due to reporting inaccuracies and reticence to share details. Providers cited financial insecurity, remote living, and lack of social support as factors increasing risk of ER visits. Providers reported some patients would not call frequently enough while a smaller subset called more frequently than providers perceived as needed. Conclusions: Results suggest contact with the care team is crucial for preventing ER visits during chemotherapy. Symptoms causing ER visits were consistent with previous literature

Impact of rurality versus neighborhood deprivation on stage at diagnosis and survival: A regional analysis

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<0.05). Conclusions: In Washington State, neighborhood deprivation, not rurality, was largely associated with later stage at diagnosis and poorer survival. Regional stakeholders need to carefully examine the local sources of cancer care disparities to effectively target interventions.[Table: see text]

Polypharmacy and medication costs at end of life among commercially insured adults age 65 and older with cancer

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.

Impact of clinic characteristics on the adoption of a guideline-based standing order algorithm and patient accrual in the pragmatic cluster-randomized trial SWOG S1415CD (NCT02728596)

60

Background: The S1415CD intervention requires the integration of guideline-based prescribing recommendations and standing orders for primary prophylactic colony stimulating factors into existing chemotherapy order systems at community practices within the National Cancer Institute’s Community Oncology Research Program. We looked at the impact of clinic level characteristics on the length of time needed to successfully adopt the intervention and subsequent patient accrual. Methods: We calculated the length of time between randomization and intervention completion for each intervention arm clinic and classified them as short onset (2-5 months, N = 5), medium onset (6-8 months, N = 12) or long onset (10-12 months, N = 7). We compared baseline survey responses about clinic characteristics to onset times. Results: Type of EMR software and the number of chemotherapy regimens reconfigured for the trial had no effect on onset time. All short and medium onset clinics placed orders through an EMR, while 5 of 7 long onset clinics used paper orders. Long onset clinics had less reported nurse involvement in the reconfiguration workflow (change initiation, approval, fulfillment and dissemination) at 14% of clinics vs. 25% of medium onset and 75% of short onset clinics. The average weekly patient accrual rates observed after intervention completion were 1.0 in the short onset (range 0.6-1.5), 0.8 in the medium onset (0.2-1.3) and 0.6 in the long onset (0.1-2.0). Conclusions: When recruiting clinics for trials that require health record system changes, it may be helpful to consider aspects of the system modification workflow such as type of hospital departments involved, as clinics with less nursing involvement may take longer to complete the changes. The inclusion of clinics using different EMR software did not impede onset, but clinics using paper may require more time. Length of onset had no meaningful impact on weekly accrual rates; however, it did determine when clinics could start recruitment, affecting the total number of months clinics could recruit during the study accrual period. Clinical trial information: NCT02728596.

The influence of patient engagement on the design and implementation of a clinical trial to improve cancer care delivery

223

Background: We have engaged 10 patient partners in the development and implementation of S1415CD, a five-year pragmatic clinical trial currently in year 3 assessing the effectiveness of a guideline-based colony stimulating factor standing order intervention (NCT02728596). Patient partners serve as part of a 21-person External Stakeholder Advisory Group (ESAG), which also includes providers, payers and guidelines experts. This abstract explores the influence of patient partners on the design, tools and implementation of S1415CD Methods: Patient partners advise the study team on protocol development, patient-facing materials and implementation challenges over four teleconferences each year, annual in-person meetings and targeted email communication. All patient partner input from 2014-2017 was tracked, collected and reviewed for impact on the trial. Results: Input from patient partners led to the refinement of the study’s patient-reported outcome (PRO) survey questions, the creation of a highly utilized patient brochure, and the formation of talking points for clinic staff to help explain the study. Patient partners in conjunction with high performing sites helped develop strategies for sites with lower patient accrual to optimize the approach and consent of study participants. Conclusions: The sustained engagement of patient partners in S1415CD ensured patient-centeredness in trial design and guided the development of PRO surveys and relevant, high quality patient-facing materials. Drawing on experiential knowledge and insights from their roles as caregivers and advocates, patient partners provided valuable feedback that influenced patient approach and engagement in the study. Embedding patient partners in the research continuum has catalyzed critical discussions and problem solving among the patient partners and study team, which has led to patient-centered solutions to study challenges. Clinical trial information: NCT02728596.

Washington State Community Cancer Care Report: Implications for value-based purchasing

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]

Lessons from Reporting National Performance Measures in a Regional Setting: Washington State Community Cancer Care Report

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.

From A to Xeloda: Practical considerations for implementing a chemotherapy regimen ordering system intervention

57

Background: SWOG S1415CD (NCT02728596) is a pragmatic trial comparing outcomes of colony stimulating factor (CSF) use in usual care with care that uses guideline-informed standing CSF orders for protocol chemotherapy regimens. To develop the regimen list, we reviewed, reconciled, and compiled a comprehensive list of 77 NCCN-recognized core regimens and 40 biologic variants for breast, non-small cell, and colorectal cancer, meant to capture a broad population and variety of practices and settings. The 24 intervention sites chose regimens representative of the 3 febrile neutropenia (FN) risk categories (high, intermediate, low) from the list to reconfigure in prescription ordering systems. The current analysis seeks to determine whether providing a comprehensive list of regimens resulted in increased enrollment of patients. Methods: For each site, we compared how many core regimens the site reconfigured to their average weekly rate of enrollment from date of first patient enrolled to 4/1/2018, controlled for cooperative group type and site-reported volume of breast, non-small cell lung, and colorectal cancer patients. For each regimen, we determined its relative popularity by tallying how many sites chose to reconfigure it for the trial. Results: Sites reconfigured an average of 56% (range: 19%-100%) of core regimens on our list. The 18 most popular core regimens chosen by 83-96% of sites provided enrollment coverage across all FN risk categories for all intervention sites and accounted for 91% of enrollment, although enrollment among those regimens varied widely (0-18% of all patients enrolled). Reconfiguring more regimens did not correlate with higher average weekly enrollment (r < 0.1). Conclusions: Studies that wish to limit the number of regimens may want to focus on the most popular regimens as identified by committee or preliminary polling. Starting from a comprehensive set of regimens for a pragmatic trial focusing on chemotherapy ordering systems takes more time to compile and vet, is more difficult to implement in databases, is a burden to sites to review and reconfigure in ordering systems, and does not necessarily translate to increased enrollment rates. Clinical trial information: NCT02728596.

Implementing an EHR guideline–based intervention in paper ordering systems

56

Background: Studies show Colony Stimulating Factor (CSF) prescribing is inconsistent with national guidelines. SWOG trial S1415CD is a pragmatic study comparing outcomes of CSF use in usual care with care that uses guideline-informed standing CSF orders. The intervention includes embedded CSF orders based on Febrile Neutropenia (FN) risk and system note wording describing guideline recommendations. Of 24 intervention sites, five (20.8%) use paper, presenting the challenge of translating an electronic health record (EHR) intervention to paper order entry systems (OES). Methods: Paper sites were surveyed on their chemotherapy OES to inform translation of the intervention. We worked with sites to develop a workflow consistent with the key principles of the intervention: Recommendation language 1) is present before prescribing CSF, 2) reaches all study-authorized orders, 3) reaches all patients on study-authorized orders, and 4) chemotherapy and CSF are ordered before the patient is enrolled. Results: Each site had a distinct workflow that was reworked (Table 1). Paper sites took 4-10 months (average 7.6) for implementation; EHR sites took 1-10 months (average 4.5). Conclusions: Implementing an EHR-centric intervention in paper sites is feasible with appropriate time and resources built into a project to account for the unique challenges. As paper sites represented one fifth of the intervention sites, it is vital to include paper sites in OES interventions for generalizability of results. Clinical trial information: NCT02728596. [Table: see text]

Comparing quality of care for Medicaid and commercially insured patients with cancer in Washington State

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]

The Washington State Community Cancer Care Report: A multi-stakeholder effort to characterize quality of care and costs for Washington State oncology practices

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]

Polypharmacy and out-of-pocket medication costs in the last month of life among commercially insured patients with advanced cancer: Insights from linking a regional cancer registry and insurance claims

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]

How do clinics perform across multiple end of life metrics?

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]

Patterns in provider types and cost of surveillance testing in early-stage breast cancer patients: A regional study

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]

The role of chronic disease in the costs of potentially preventable emergency department use during treatment: A regional study

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]

Clinic level variation in emergency department and inpatient utilization in a community setting

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]

Development of cancer care episodes to measure costs for breast, colorectal, and non-small cell lung cancer

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]

Patient engagement on claims-registry reports of cost and quality

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]

Costs of potentially preventable emergency department use during cancer treatment: A regional study

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]

Out of system (OOS) costs for oncology clinics treating patients with breast, colorectal, and non-small cell lung cancer in Washington state

5

Background: As payers move from fee-for-service to episode-based reimbursement, there is a need for oncology providers to accurately measure in- and out-of-system resource use and cost for patients under their care. Medicare assigns management of a patient to only one provider, yet delivery systems may assume contractual responsibility for a patient with cancer’s entire episode costs, including care received outside of their system. Accordingly, the goal of this study was to estimate OOS care for patients with breast, colorectal (CRC), and non-small cell lung cancer (NSCLC). Methods: Cancer registry records for patients with breast, CRC, or NSCLC diagnosed in Western Washington State from January 2007 to June 2016 were linked with claims from two regional commercial insurers. The analysis focused on initial treatment phase: first day of treatment (surgery, radiation, chemotherapy) through the first 4-month gap in treatment. Patients were assigned an oncology provider group by identifying the clinic Tax ID Number (TIN) with the most Evaluation & Management (E&M) claims with a cancer diagnosis. Claims were considered in system if the TIN matched the assigned clinic. Costs included claims paid to all providers (adjusted to 2016 dollars). Results: The study included 7,686 newly diagnosed patients with breast, CRC, or NSCLC. The average cost for the initial treatment phase was $61,147/patient (SD $75,432, median $35,750). Nearly 31% of claims paid (mean $18,684, SD $32,649) were out of system. Among OOS costs, 24% were for inpatient care, 68% were for outpatient care, and 8% were for outpatient pharmacy. Conclusions: Among newly diagnosed patients with breast, CRC, or NSCLC, approximately 1/3 of costs for the initial treatment period stemmed from OOS care. Developing best practices for the reporting and management of OOS will be critical for organizations to succeed under episode-based reimbursement plans.[Table: see text]

Patterns of surveillance testing in commercially insured patients with breast cancer across provider types: A regional study

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]

Using cancer registry records linked with health insurance records to measure costs and services at end-of-life

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]

Health care utilization and costs at end of life among patients with leukemia or lymphoma in a regional cancer registry-insurance claims linked database

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]

Regional initiative to use data transparency to improve cancer care

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]

HICOR IQ: Developing a regional oncology metric reporting platform

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.

Value in cancer care: Regional initiative to improve care through data reporting and interventions

34

Background: In the context of many initiatives aimed at measuring quality and value in cancer care, the Hutchinson Institute for Cancer Outcomes Research (HICOR), partnered with community members to launch a regional, stakeholder-driven initiative to define and report value metrics for cancer care for Washington State. Region-wide Summits were held in 2014 and 2015. Participants included local healthcare delivery organizations, patient advocacy groups, payers, and policymakers. The 2014 Summit identified priority metrics; these metrics were reported at the 2015 Summit. Methods: For the 2015 Summit, HICOR staff developed algorithms to measure adherence to the community-prioritized metrics using a claims-registry linked database. Metrics spanned diagnosis, treatment, continuing, and end-of-life (EOL) phases of care. After reviewing adherence at the clinic-level and for the region, attendees were invited to attend break-out sessions for metrics where there was the largest variation: hospital and ED use during treatment, hospital and ED use at EOL, and breast cancer surveillance. Within the breakout sessions, participants were asked to identify barriers to adherence and possible interventions to improve care. After discussion, participants individually ranked the top 3 interventions and estimated expected improvement to be gained by successful implementation of the intervention Results: Table. Working groups were formed to develop detailed protocols for implementable interventions. Conclusions: Using an iterative, transparent, multi-stakeholder process, it is feasible build regional consensus to identify and prioritize value metrics in cancer care, and to develop consensus regarding approaches to improve adherence to those metrics. [Table: see text]

Measuring adherence to a Choosing Wisely recommendation in a regional oncology clinic

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.

Measuring adherence to 2012 Choosing Wisely recommendations: SEER Registry linked with health plan data

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]

Regional initiative to define, collect, and report value metrics in cancer care

25

Background: In the context of numerous national initiatives aimed at measuring quality and value in cancer care, the Hutchinson Institute for Cancer Outcomes Research, in partnership with local healthcare delivery organizations, patient advocacy groups, payers, and policymakers launched a regional, stakeholder-driven initiative to define 3-5 “value-based metrics” for cancer care for Washington State. Methods: Representatives from major cancer care delivery organizations, patient advocacy groups, payers, and policymakers were invited to participate in a day-long Value Summit. Attendees were tasked with identifying metrics that considered both costs and outcomes. Trained facilitators helped participants identify metrics for 9 domains: appropriate use of effective therapies, adherence to best practices, survival, comprehensive disease management, efficiency of care, hospice/palliative care, patient and family satisfaction with care, patient reported outcomes/preferences and safety. After the initial list was generated, attendees were then asked to rank the metrics on the basis of feasibility to collect, clinical relevance, ability to act on, meaningfulness to multiple stakeholders, and willingness to report statewide. Attendees were then asked to participate in 3 domain-specific facilitated breakout sessions to prioritize the top metrics for each domain. Breakout sessions reported top metrics for a final group prioritization exercise. Following the Summit, attendees provided feedback on the final rankings. The metrics were then presented at a Town Hall style meeting for public comment. Results: Over 70 participants, representing 20 different organizations, identified 750 unique metrics from 9 domains. The prioritization process yielded 3 areas of interest, with 2 specific metrics within each: end of life and palliative care (metric 1, metric 2); adherence to best practices (metric 3, metric 4); and coordinated and efficient care (metric 5, metric 6). Follow-up surveys of Summit attendees and the Town Hall forum showed widespread support for these metrics Conclusions: Using an iterative, transparent, multi-stakeholder process, it is feasible build regional consensus around value metrics in cancer care.

Measuring baseline adherence to Choosing Wiselyrecommendations in regional oncology clinics: Challenges and strategies

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]

Stakeholder-informed “Choosing Wisely” implementation project in regional oncology clinics

207

Background: ASCO launched the Choosing Wisely campaign to reduce the use of interventions that lack evidence for their use in clinical cancer care. The recommendations have strong support, but lack an implementation plan. The objective of this project is to develop a stakeholder-informed process to improve adherence to ASCO Choosing Wisely within an experimental context. Methods: Participants include Medical Directors from 7 oncology clinics and 1 commercial insurer within the Puget Sound region, and the SEER Puget Sound Cancer Registry. The project consists of 3 phases (1) prioritization, (2) design, (3) implementation and monitoring. For phase 1, Medical Directors were surveyed via e-mail to prioritize the recommendations with the following criteria: importance for improving the value of cancer care; impact of adherence; urgency; and feasibility of implementing an intervention. Participants met via teleconference to discuss survey results, review regional utilization data, and design of interventions. Participants were surveyed again for a final ranking. In phase 2, participants discussed options for interventions and study designs. Results: Initially, the highest ranked recommendations were advanced imaging in staging of local stage prostate cancer, surveillance of local and regional stage breast cancer, and colony stimulating factor (CSF) use for low risk chemotherapy. After discussion, breast cancer surveillance and CSF prescribing ranked the highest. Participants requested utilization and cost-impact data from the health insurer for the top 2 choices. Conclusions: Using a transparent, multi-stakeholder process, it is feasible to implement programs to improve adherence to ASCO Choosing Wisely.