Large Community Oncology Practice Research Articles

The Languages of Love Support Group: Exploring quality of life and relationship satisfaction among couples experiencing cancer.

206 Background: Based on Dr. Gary Chapman’s The Five Love Languages (5LL), love is given and received in different ways. By identifying preferences for reciprocating love, relationships are enhanced through strengthened bonds and improved communication. Study goals were to optimize quality of life and reduce pain through improved dyadic communication via support group curriculum adapted from Dr. Chapman’s work. Methods: A research collaboration was established between an academic pharmacy research team and a large community oncology practice. A group of Masters prepared Social Workers (MSWs) within the oncology practice developed a virtual 6-week, cancer specific curriculum targeting the patient-partner couple (dyad). Participants completed 5LL quiz to determine their preferred love language. In each group session facilitated by MSWs, dyads were educated on each love language meaning (words of affirmation, acts of service, receiving gifts, quality time, physical touch) and potential impact following a cancer diagnosis. Weekly sessions included collective discussion and breakout groups consisting of patients and partners separately. Participants took a researcher-designed, pre- and post-test survey. Participants shared their preferred love language and were encouraged to perform actions accordingly. Dyads were mailed infographics with application examples for each love language and received a welcome starter kit including $50 of items. Results: A pilot group of 24 dyads consisting of 3 cohorts were enrolled in the 6-week intervention. Eleven dyads completed pre- and post-tests (45.8%); 100% of dyads were married, greater than 50% more than 20 years. Greater than 80% of participants were 50 years or older, greater than 70% White; 81.8% of patients female and 63.3% of partners male. Patient relationship satisfaction increased significantly (p<0.10) while trend for improved partner relationship satisfaction was not significant. There was a trend of decreased patient depression and anxiety despite patients’ reported pain increasing significantly (p<0.10). The comments regarding intervention were overwhelmingly positive. Conclusions: Educating dyads within the 5LL virtual support group demonstrated a novel and effective way to provide emotional support and enhance intimate relationships. Through this innovative group, participants learned to identify and express partners’ love language. Pilot intervention shows how this approach is useful in improving relationship satisfaction during the cancer journey. Following group participation, patients also reported increased quality of life and decreased depression and anxiety even while reporting increased pain. Based on this evidence, a couples support group constructed from the 5LL paradigm is a promising approach to improve the quality of life of cancer patients.

Oncology care model cost data for breast, lung, and multiple myeloma: Influence of outpatient rehabilitation therapy services.

14 Background: High value cancer care is defined by access to coordinated, appropriate healthcare services that achieve optimal outcomes at a reasonable cost. Evidence supports outpatient cancer rehabilitation (physical, occupational or speech therapy) as a mechanism to improve outcomes such as health-related quality of life, suggesting that integration of these services is beneficial for cancer survivors. However, the impact of outpatient rehabilitation services on healthcare costs is unknown. We aimed to compare cost segments between those who attended community-based outpatient rehabilitation therapy services (Rehab) versus those who did not (Non-rehab). We hypothesized that healthcare costs would be lower for the Rehab group in the following cost segments: emergency department (ED), part D drugs, home health services, skilled nursing facility services (SNF), inpatient rehabilitation facilities (IRF), and hospice. Methods: This is a retrospective cohort study of Medicare beneficiaries who received chemotherapy at a large community oncology practice that participated in the oncology care model (OCM) program from 2018 to 2021. Two data sources were linked, OCM cost data and the rehabilitation medical record. This yielded sufficient sample sizes for analysis of breast, lung, or multiple myeloma (MM) cancer types. Rehab cases were then matched to Non-rehab cases 3:1 based on cancer type, stage, and sex. Cost variables included total cost of care (TCOC) and cost segments using OCM methodology. Mann-Whitney-Wilcoxon statistic (p<.05) was used for analysis. Results: Cases (N=3,033) primarily had early-stage cancer (0 to 2, 74.8%) and were 70.4±7.3 (Rehab) or 71.6±8.5 years old at diagnosis (Non-rehab). Most had breast cancer (81.2%), followed by lung cancer (11.7%) and MM (7.2%). TCOC was $32,387 and $26,257 for the Rehab and Non-rehab groups, respectively. Costs were significantly lower for the Rehab group in five of the six segments analyzed; IRF costs were lower, but not statistically significant (Table). Conclusions: To our knowledge, this is the first study to explore the influence of real-world, outpatient cancer rehabilitation therapy services on the cost of cancer care. Community-based therapy services were associated with significantly lower costs in ED, part D drugs, home health, SNF, and hospice segments. Future studies examining rehabilitation services as a relatively low-cost supportive care option to meet patient needs and enhance high-value care are warranted. Healthcare segment costs for rehab vs. non-rehab cases. Rehab Non-rehab Between-group Difference Mean, (SD) Mean (SD) p-value ED $85 (260) $104 (372) .019 Part D drugs $4,813 (18,221) $5,675 (18,797) <.001 Home health $433 (1,600) $705 (2,118) <.001 SNF $160 (1,988) $386 (3,943) .046 Hospice $75 (770) $198 (1,529) <.001 IRF $245 (2,275) $384 (3,277) .434

Quality improvement scorecards driving innovation and best practice in a large community oncology practice.

417 Background: Lean Six Sigma Visual Management dashboards, or scorecards, are visual tools designed to drive and improve quality patient care, operationalize best practices, and promote a culture of operational excellence. Practice scorecards create a disciplined problem-solving culture where root causes are identified, and systemic process-based improvements are implemented. Scorecards are created to show full practice data transparency, allowing all employees and sites to see each other’s data. Scorecards are designed so that providers can efficiently review the maximum amount of data, following a Green, Yellow, Red visual cue to signify goal adherence. Methods: Phase 1 (2018 – 2019): Clinical scorecards were created and introduced to all staff on a weekly basis focusing on a few key patient metrics (ex: pain scale, pain care plan, ACP, PHQ-9, teaching/consents, patient portal access). Phase 2 (2020 – 2022): Clinical scorecards were expanded with additional patient care initiatives. New nurse navigator scorecards created (ex: patient teaching and survivorship visits). New physician and APP scorecards created with practice-specific provider metrics. Phase 3 (2023 – 2024): Scorecards updated yearly to match practice quality initiatives. New dashboards for non-clinical employees initiated. Physician and APP compensation changed to include quality metric performance tied to financial incentive. Results: Over the course of 5 years, OHC quality scorecard metrics improved from below national averages (red) to exceeding top-tier threshold goals (green). Scorecard utilization increased across all areas of the business, expanding from one scorecard in 2018 to eight in 2024. Scorecards increased overall practice data transparency leading to increase practice quality metric performance. OHC staff utilize scorecards on a weekly, monthly, and quarterly basis to drive quality improvement adherence. Improved outcomes through full data transparency increased staff participation and ownership in quality metrics and led to achieved shared savings in managed care programs. Example of metrics and results: Patient NCCN Staging 36% (2588/7263) to 96% (10299/10749), Teaching/Consents 37% (423/1147) to 97% (3067/3160), and Oral regimen NCCN entry 69% (586/845) to 98% (1037/1055). Conclusions: Visual Management dashboards or practice scorecards are instrumental in driving and supporting practice change. Scorecards allowed OHC staff to efficiently visualize practice-wide actionable data with eye-catching colors to promote optimal clinical performance. As increased data procurement is on the rise, practices can empower their staff to drive change through data utilization. OHC’s scorecards have proven to bridge the gap to goal, facilitate transparent data sharing, and empower providers and staff toward collaboration and embracing operational excellence.

The impact of oral oncology drugs in the era of value-based care.

15 Background: Oncology drug expenditures are a significant contributor to increasing total cost of care (TCOC). Oral oncolytics (OO), such as small molecule inhibitors (CDK 4/6 inhibitors, tyrosine kinase inhibitors, etc.), and immunomodulators, have revolutionized the treatment landscape for many cancers. We evaluated the patterns of OO use and spend for Medicare patients over time at a large community oncology practice participating in the Center for Medicare and Medicaid Innovation (CMMI)’s Value-based care (VBC) Program, the Oncology Care Model (OCM), with a focus on the seven cancers that are part of a successor model, the Enhancing Oncology Model. Methods: Using CMMI claims for attributed 6-month OCM episodes, we evaluated drug use and expenditures by performance period (PP) from July 2016 – June 2022 (11 PPs), with an emphasis on OO drugs. We used Part D expenditures as a proxy for OO. Results: From 2016-22, average TCOC per PP increased by 15.5% in the 7 EOM diagnoses (breast, lung, colon, prostate cancer, myeloma, lymphoma, and chronic leukemia). Average drug expenditures per PP grew 33% percent and represented 71% of total cost by PP11, up from 61% in PP1. Average expenditures per episode increased by 11.7% for Part B and 82.6% for Part D drugs (p<0.01). Increase in Part D drug expenditures was partly due to an increase in the number of unique oral drugs which grew by 32% over the evaluation period. The greatest increases in Part D drug expenditures occurred in breast cancer (87%) and lymphoma (153.7%). Table reports average OO expenditures per PP, percent change from PP1 to PP11, and the change in the % Part D drugs contributed to TCOC. Conclusions: Increases in drug expenditures represent the greatest impact on TCOC for cancer patients. OO medicines have an increasing role in the treatment landscape and represent a growing focus in VBC models. Utilization and cost of OO medication have a significant bearing on VBC performance. Diagnosis Avg. OO Expenditures PP1 Avg. OO Expenditures PP11 Avg. OO Expenditures Change Contribution of OO to TCOC Change from PP1 - 11 Breast $4,208 $7,869 87.0% 9.9% Leukemia $25,334 $41,128 62.3% 19.9% CRC $1,206 $2,158 78.9% 3.1% Lung $3,650 $3,625 -0.7% -2.5% Lymphoma $4,193 $10,637 153.7% 13.0% Myeloma $25,109 $39,298 56.5% 6.7% Prostate $18,815 $20,114 6.9% 3.2% Total $8,291 $15,140 82.6% 10.8%

The use of telehealth in a large community oncology practice prior to and during the COVID-19 pandemic.

1631 Background:The COVID-19 pandemic prompted implementation of telehealth services in oncology following CMS approval for reimbursement. Using data from a single large US-based community oncology practice, we characterized the adoption of telehealth, evaluated telehealth’s impact on patient transit to the site of care, and the frequency of ER visits and hospitalizations. Methods: A retrospective cohort analysis was conducted using data from January 2019 through June 2022 from the FCS EMR database, IQSS’s RefineIQ database, which captures and standardizes patient treatments for consistent analysis, InfoDive, a reimbursement platform with data on clinic visits, and Oncology Care Model data. The target population included all patients with cancer aged 18 or older under active management at FCS during the study window with at least 60 days of follow-up. Patient disease and demographic characteristics and ER/hospital care visits were compared between telehealth groups, as well as impact of telehealth use on transit time and distance traveled for patients was summarized using descriptive statistics. Results:178,531 patients were analyzed. Median age was 68, with 64% female and 47% white. 97% of patients lived in a metropolitan area, 97% in Florida, and 77% in areas with less than $78,000 median income. Median duration of disease was 41 months, 24% of patients were stage IV and 93% ECOG 0-1. 20% of patients used telehealth, with 81% having only one telehealth use per year. Patients using telehealth, compared to those that did not, were more likely to be white (50% vs 46%), have commercial insurance (30% vs 27%), live in areas where annual median income was > $78,000 (25% vs 22%) and ≥95% of the population graduated high school (31% vs 26%). Telehealth saved a median of 5 miles driven per user annually. Users tended to have more advanced disease than non-users, with a higher proportion of metastatic disease (27% vs 23%), high CMS risk score (16% vs 15%), and line of therapy 2 or greater (10% vs 7%). Users had higher median annual oncology clinic visits both pre- (5 vs 3) and post-telehealth approval (6 vs 5). Hospital admissions after ER visits were more frequent among users both pre- (14% vs 13%) and post-telehealth approval (29% vs 25%). Conclusions: Telehealth was somewhat more likely to be used by patients with cancer in more affluent communities, white patients, patients who had more advanced disease and more frequent clinic and hospital visits. Most patients who used telehealth used it only once, and the low utilization of telehealth had limited impact on patient transit effort to the clinic. This study is limited by its concentration at a single large practice in Florida, the relatively low use of telehealth, retrospective design, and selection bias, as the telehealth population had more advanced disease and likely needed more frequent intervention.

Abstract PO4-02-11: Optimizing Taxane-based Chemotherapy in Breast Cancer: Retrospective study comparing 4 cycles of TC to 6 cycles of TC

Abstract Breast cancer remains one of the most prevalent cancers among women worldwide. First-line chemotherapy options include anthracycline (AC-T) and Taxane (TC) based regimens. While anthracycline-based chemotherapy is most commonly used, the use of anthracycline-sparing regimens is also approved. The US Oncology 9735 trial conducted in the mid 2000s showed that four cycles of docetaxel and cyclophosphamide (TC4) were superior to four cycles of Doxorubicin and cyclophosphamide (AC). The follow-up ABC trials compared six cycles of TC(TC6) to AC-T. This trial showed that while AC-T was superior to TC, the difference was modest. AC-T has remained the standard of care, but TC is an acceptable option. One retrospective analysis involving 143 patients showed TC4 was non-inferior to TC6, and suggested that TC4 had fewer adverse events; however, statistical significance was not reached. The question remains whether six cycles of TC is required, or if four cycles is adequate. Methods: Patient information was retrieved from the EMR of a large private oncology group. The EMR was screened for all breast cancer patients who began treatment with TC between 1/1/2011 and 12/31/2021. Patients were required to have continuous follow-up, documented up to at least 6/30/2022. Only patients receiving TC as the first-line setting were included, while patients with metastatic disease were excluded. Adjusted Kaplan Meier curves for overall survival (OS) and disease-free survival (DFS) were generated. A log-rank test was used to determine the statistical difference between the two curves. OS was defined from the date of the last chemotherapy given to death. DFS is defined from the date of the last chemotherapy given to disease recurrence. The Chi-square analysis test of homogeneity was conducted to evaluate the differences in reported symptoms. Statistical analysis was completed with IBM SPSS version 29. Ethical approval was obtained from The Brooklyn Hospital Center Institutional Review Board. Results: A total of 376 charts were reviewed, out of which 224 met the criteria for inclusion. Of these 191 patients received TC4 and 33 received TC6. TC4 patients were noted to be older than TC6 patients. There were a higher percentage of hormone-positive cancers in the TC4 group while the TC6 group had a larger proportion of triple-negative breast cancers. There was a larger proportion of stage III patients that received TC6 than in TC4. TC6 patients were also noted to receive neo-adjuvant chemotherapy at a substantially higher rate. A greater proportion of patients in the TC4 group received breast-conserving surgery, with radiation than the TC6 group. TC6 had a completion rate of 76 % (Table 1). There was a significant difference in DFS noted between TC4 and TC6 (p = 0.016) with TC6 patients having a higher rate of relapse. No difference was noted in the overall survival. No differences were seen in side effects between TC4 and TC6, however, there was a trend toward high rates of neutropenia, anemia, and neuropathy seen in the TC6 group (Table 2). Conclusions: This real-world retrospective study reviewed patients who received TC in a large community oncology practice. The study reaffirms conclusions seen in previous retrospective studies conducted in the academic setting that there is no difference in overall survival when using six cycles of TC compared to four cycles of TC. Slight detriment was seen in the DFS setting when using TC6 however the small sample size along with the larger proportion of patients with stage III disease make it challenging to interpret this finding. There was a trend towards higher toxicities with TC6 that did not reach significance. Until a prospective randomized study comparing four vs six cycles of TC is conducted no definitive conclusions can be drawn, however, this study contributes to the current body of evidence that four cycles of TC is adequate and could potentially improve the quality of life of patients with early breast cancer. Table. Patient characteristics Patient characteristics including age, hormone status, stage, type of surgery, radiation, and treatment setting. Table. Adverse effects Adverse effect information obtained from physician documentation, and CBC reports documented at time of treatment with TC. Citation Format: Amith Ahluwalia, Michelle Koifman, Jaspreet Nannar, Bhavya Vachhani, Kanwal Ashraf, Maxim Shulimovich. Optimizing Taxane-based Chemotherapy in Breast Cancer: Retrospective study comparing 4 cycles of TC to 6 cycles of TC [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-02-11.

Integration of Genetic Testing and Counseling in Patients With Breast Cancer in a Large, Multisite Community-Based Practice.

Despite data-driven consensus recommendations, there remains significant nonadherence to genetic screening and testing. More than 300,000 patients are diagnosed with breast cancer annually, with one third of these estimated to be eligible for homologous recombination deficiency (HRD)/BRCA testing following National Comprehensive Cancer Network (NCCN) guidelines. Only 35% of eligible patients are referred for genetic counseling. The goal of this project was to apply NCCN guidelines for germline genetic testing to all new patients with breast cancer within a large community oncology practice to improve HRD/BRCA testing. Plan-Do-Study-Act methodology was used, and cycles were built on a proven teaching infrastructure. In cycle 1, providers were educated and directed to use electronic health record (EHR) templates in the setting of an initial diagnosis visit and treatment planning. Discreet data fields were created in the EHR during cycle 2 to streamline and automate the process. Appropriate patients were referred to the genetics team for further evaluation, counseling, and testing. Adherence to the plan was maintained and measured using data analytic reports and chart audits. Of the 1,203 patients with breast cancer eligible for inclusion, 1,200 (99%) were screened according to NCCN guidelines. Of the screened patients, 631 (52.5%) met the referral/testing criteria. In total, 585 (92.7%) of the 631 were referred to a genetic specialist. Seven percent had previous referrals. A total of 449 (71%) patients were acceptable to genetics referral while 136 (21.5%) patients refused. The implemented methods of education, NCCN guidelines imbedded within provider notes, and discreet data fields in the EHR have proven to be highly effective in screening appropriate patients and ordering subsequent genetic referrals.

Building Efficiency and Scaling With a Remote Genetic Counseling Program.

Purpose A third-party telemedicine (TM) genetic counseling program was initiated at a large community oncology practice spanning 35 clinical sites with 110 clinicians and 97 advanced practice providers throughout Tennessee and Georgia. Patients and Methods Appropriate patients were referred through the electronic health record (EHR) based on current National Comprehensive Cancer Network guidelines. A combination of TM and genetic counseling assistants enhanced convenience, broadened access, and decreased no-show rates. Physician education for mutation-positive screening recommendations was provided through deep integration of dedicated genetic counseling notes in the EHR. Results From 2019 to 2022, the program expanded from 1 to 20 clinics with referrals growing from 195 to 885. An average of 82% of patients completed genetic counseling consultations over TM with more than 70% completing genetic testing. The average was 4 to 6 days from referral to consultation. The no-show rate was maintained at less than 7%. In 2023, this model supported all 35 clinics across the state. Conclusion Our program illustrates how remote genetic counseling programs are an effective choice for scaling genetics care across a large community oncology practice. Deep integration of TM genetic counseling within the EHR helps identify patients who are high risk and improves test adoption, patient keep rate, and turnaround time, helping to achieve better patient outcomes.

Building Efficiency and Scaling With a Remote Genetic Counseling Program

Purpose A third-party telemedicine (TM) genetic counseling program was initiated at a large community oncology practice spanning 35 clinical sites with 110 clinicians and 97 advanced practice providers throughout Tennessee and Georgia. Patients and Methods Appropriate patients were referred through the electronic health record (EHR) based on current National Comprehensive Cancer Network guidelines. A combination of TM and genetic counseling assistants enhanced convenience, broadened access, and decreased no-show rates. Physician education for mutation-positive screening recommendations was provided through deep integration of dedicated genetic counseling notes in the EHR. Results From 2019 to 2022, the program expanded from 1 to 20 clinics with referrals growing from 195 to 885. An average of 82% of patients completed genetic counseling consultations over TM with more than 70% completing genetic testing. The average was 4 to 6 days from referral to consultation. The no-show rate was maintained at less than 7%. In 2023, this model supported all 35 clinics across the state. Conclusion Our program illustrates how remote genetic counseling programs are an effective choice for scaling genetics care across a large community oncology practice. Deep integration of TM genetic counseling within the EHR helps identify patients who are high risk and improves test adoption, patient keep rate, and turnaround time, helping to achieve better patient outcomes.

Impact of remote symptom monitoring (RSM) with electronic patient-reported outcomes (ePRO) on hospitalization, survival, and cost in community oncology practice: The Texas Two-Step study.

569 Background: There is rising interest to integrate electronic patient reported outcomes (ePRO) solutions for symptom monitoring to enhance the quality of care in oncology and to support value-based care (VBC) models. Step 1 of the Texas Two-Step Study demonstrated successful implementation of an ePRO system in >200 sites of service of a large community oncology practice. We now report step 2 of our study which evaluates the impact of ePROs in reducing adverse outcomes and cost of care among cancer patients enrolled in CMS’ Oncology Care Model (OCM) VBC program. Methods: This observational study focused on patients with metastatic cancer enrolled in the OCM program at Texas Oncology between July 2020 to December 2020. Patients who enrolled and completed ≥1 survey via the ePRO tool defined the study arm. Patients were matched and balanced for confounders with propensity score matching (PSM). Data used for this analysis included data collected from OCM claims files and the ePRO vendor database. Adverse events (death (D)/hospitalizations (H)/Emergency Department (ED) visits), average H/ED visits per patient, average length of H, total Medicare cost (i.e. Medicare paid amounts for different services except Part D drugs) were study measures. Mann-Whitney U and Chi-Square tests compared continuous and categorical variables, respectively, and multivariable logistic regression was used to adjust the covariates (age, gender, cancer type, therapy line). Results: Out of 831 metastatic cancer patients, 458 matched patients (229 patients per group) were identified through PSM. In our matched cohort, ~52% of patients were male, with a mean age of 74 years. Mean total adverse events were significantly less (0.98 vs 1.41 p-value: 0.007) in the study group (SG) as compared to control (C). We observed decreased rates of H (20% vs 32.5%; p value: <0.05), ED (38.4% vs 42.3%) and D (11.8% vs 16.6%) in the SC compared to C. After adjusting for covariates, the SG had decreased odds of H (aOR: 0.51; 95% CI 0.33, 0.8; p-value 0.002) compared to C. Similar, but not statistically significant, effects were observed in the case of D and ED visits. Average number of H (0.28 vs 0.52, p-value-0.003) and ED visits (0.51 vs 0.75, p value: 0.01) per patient were also lower in the SG as compared to C. In addition, mean H duration per patient (days) was significantly less in the SG as compared to C (1.9 vs 3.2, p value: 0.03). ePRO engagement also reduced the total cost of care (per member per month) by an average of $1146. Conclusions: RSM with ePROs reduced adverse outcomes and cost of care among patients with metastatic cancer in a large multi-site community oncology practice. Additional studies to include a larger patient population are warranted to strengthen these observations. This work suggests that ePRO use in the community setting does improve patient outcomes and enhance VBC.

Clearing the path: Leveraging operational optimization strategies to enhance patient access.

450 Background: The complexities of navigating the healthcare system for an oncology patient can be extensive and overwhelming. Prioritizing ease of access to care is essential. The time and effort required to coordinate visits, treatments, and other appointments can result in time toxicity, limiting the patient’s ability to engage in activities that enhance their quality of life. Minnesota Oncology, a practice within The US Oncology Network, is a large community oncology practice in Minneapolis, MN with 90 providers and 14 sites of care. The leadership team at Minnesota Oncology recognized challenges with patient scheduling as a priority. This was based on patient and staff feedback which included patient satisfaction surveys, significant telephone hold times for scheduling, and missed appointments due to a delay in order processing time. Methods: The quality improvement initiative followed a methodological framework that includes identifying the root causes of challenges patients face related to access to care and the subsequent development and execution of effective improvement solutions. The primary objective of the improvement initiative was to address telephone wait times for scheduling, overall patient satisfaction with scheduling and access, and order processing time. To identify the necessary focus for interventions, a ‘Kaizen’ event was hosted involving front line scheduling and clinical staff. The team aimed to identify workflow redundancies, bottlenecks, and inefficiencies via workflow mapping and root cause analysis methodologies. Leveraging the insights gained from these approaches, the following strategies for improvement were executed: Regionalized resource utilization to maximize productivity; strategic redistribution of workload to improve efficiency; workflow redesign to streamline processes and eliminate waste; and redefinition of key performance indicators for business unit performance management. Results: Through the methodological identification of root cause analyses and a tailored execution approach, results were achieved (table.). Conclusions: The improvements in the identified categories were achieved through strategic operational optimization, without the need for additional resources. This underscores the importance of adopting a culture of methodological continuous improvement in the management of clinic operations and highlights the importance of ongoing improvement efforts in delivering efficient care and achieving favorable business and patient experience outcomes.[Table: see text]

CHIPping in: Clonal hematopoiesis clinics in community oncology.

499 Background: Clonal hematopoiesis of indeterminate prognosis (CHIP) is recently recognized as a pre-malignant condition and independent cardiovascular risk factor with doubled risk of coronary heart disease, cerebrovascular events, and worsened heart failure outcomes. Despite this recognition, multidisciplinary CHIP clinics are near-uniformly associated with tertiary centers where only a minority of oncology patients receive care. Tennessee Oncology is a large community oncology practice with over 200 providers at 35 sites of care throughout Tennessee and North Georgia. Here, we report the characteristics of two years of patients with common CHIP mutations as we prepare to launch our CHIP clinic. Methods: Patients with solid tumors who underwent either tissue- or blood-based comprehensive genomic profiling from May 2021 to May 2023 were reviewed. Patients with incidental discovery of DNMT3A, ASXL1, TET2, JAK2, IDH1/2, SRSF2, U2AF1, SF3B1, and ZRSR2 mutations are classified as having CHIP. High-risk mutation profile was defined as >1 mutation or a single mutation with variant allele frequency (VAF) of ≥10%. In patients sequenced >1x, only initial sequencing was used. The presence of cardiovascular disease or traditional risk factors was based entries into the patients’ “problem list” or use of medications commonly used to treat these conditions. Results: Over a two-year period, 3599 patients underwent sequencing and CHIP mutations were identified in 1069 (29.7%) patients. The median VAF was 1.58% and 585 patients had at least one mutation with a VAF of ≥2%. Median age at sequencing was 69.6 years. Distribution between males/females was similar. Lung cancer was the most common histology (n=328) followed by breast (n=122) and colon (n=121). DNMT3A mutations (n=829) were most frequently seen followed by TET2 (n=353), ASXL1 (n=335), SRSF2/U2AF1/SF3B1/ZRSR2 (n=147), JAK2 (n=138), and IDH1/2 (n=104). Nearly two-thirds of patients had a high-risk mutational profile with >1 mutation (n=479) or VAF ≥10% (n=185). Assessing traditional risk factors for cardiovascular and cerebrovascular disease, essential hypertension and type 2 diabetes were present in 56.4% (n=603) and 30.1% (n=322) of patients, respectively, and a minority of patients had established coronary disease (n=181) or heart failure (n=74). Twenty-three percent (245/1069) had no cardiovascular risk factors. Conclusions: In a large community practice, 29.7% of patients have common CHIP mutations – most of whom are considered high-risk based on current risk stratification criteria. Many patients also have traditional risk factors or established cardiac disease underscoring the strong association between CHIP and cardiovascular disease. A community-based, multi-disciplinary CHIP clinic would enhance these patients’ care and potentially lead to improved outcomes.

Impact of the end of public health emergency oral dispensing flexibilities for at-risk patients in a community oncology practice.

209 Background: Many studies have shown the benefit of medically integrated pharmacies for cancer specialty oral drugs in improving patient adherence and reducing waste compared to external pharmacies. Anti-kickback regulations have limited practice’s ability to deliver prescriptions to patient’s homes from physician offices. During the Covid Public Health Emergency (PHE), flexibility for prescription delivery by a physician owned pharmacy was allowed leading many practices to deliver oral cancer therapies to patients who could not otherwise come to clinics to fill their medications. With the end of the PHE, CMS reversed this flexibility. We sought to determine the impact on patients with this change in CMS regulations. Methods: Utilizing the pharmacy database of prescriptions delivered to a patient’s home for a large statewide community oncology practice in Texas during the PHE, we identified patients with Medicare part D who would no longer be able to utilize their physician’s pharmacy for home delivery of their specialty medications. We determined the distance of those patient’s primary address to the nearest clinic and further matched them to the address deprivation index (ADI) a well-validated approach that rank orders zip codes in deciles from lowest to highest need. Results: We found 970 patients receiving home delivered prescriptions with traditional Medicare Part D. 687 or 71% of patients lived within 20 miles of the nearest clinic. 213 or 22% lived between 20 and 50 miles with 70 or 7% more than 50 miles from the nearest clinic. 60 addresses could not be matched to the ADI database. Focusing on those available addresses in the top half of the ADI at highest need, we found that 40% of patients living within 20 miles of the clinic are at significant risk of disparity vs 67% of those 20-50 miles and 61 % of those living more than 50 miles from the clinic. Conclusions: The removal of the PHE flexibility for home delivery of prescriptions from physician offices will significantly increase the burden on patients in Texas, forcing many to drive long distances to obtain drugs and disproportionately affecting those at greatest risk of disparity. We encourage revising policies to allow medically integrated pharmacies to allow home delivery of cancer specialty drugs to meet CMS stated goals of improving health equity.

Provider messaging for precision treatment (ProMPT): A notification pilot study to inform actionable genomic findings.

448 Background: As an increasing number of biomarker-driven therapies get approved, the burden on the oncology provider to keep track of approved therapies has increased. Further, most of the genomic findings are trapped in unstructured documents and are not easily searchable within the electronic medical record (EMR). We circumvented these issues in a pilot study by creating physician alerts that highlight actionable biomarker-driven therapies and institutional trials, making them easily accessible to oncology providers. Methods: Provider Messaging for Precision Treatments (ProMPT) service is being piloted at a large community oncology practice site with 29 oncology providers. Leveraging the raw genomic data feed from NGS vendors, we perform a weekly query for all the patients with a molecular test signed in the past week. We then examine the diagnoses and the identified mutations for the patients to match FDA approved precision therapies and institutional trials (genomic and diagnosis-based). The findings are recorded in a tabular format and sent weekly to providers via email to assist them in choosing the best therapy for their patients. Results: In the 12 weeks since launch, we have reviewed 1274 mutations from 114 unique patients. In addition, we also highlighted findings that warranted germline counseling/testing (based on the variant allele frequency), additional testing (lack of a clear driver mutation), and biomarkers that may confer resistance to treatment via targeted therapy or immunotherapy. Conclusions: Creating automated services that alleviate provider burden to recall and recommend appropriate therapies by surfacing actionable genomic findings when the NGS results first become available can ensure that all patients receive the best care. Services such as ProMPT are needed to effectively deliver on the promise of personalized medicine.[Table: see text]

Closing the Testing Gap: Standardization of Comprehensive Biomarker Testing for Metastatic Non-Small-Cell Lung Cancer in a Large Community Oncology Practice.

Non-small-cell lung cancer (NSCLC), the leading cause of cancer death in the United States, accounts for 85% of all lung cancer cases. Biomarker testing is an integral part of the care of patients with NSCLC. Despite broad consensus recommendations that all patients with metastatic NSCLC (mNSCLC) undergo comprehensive biomarker testing (comprehensive genomic profiling and PD-L1), testing rates remain suboptimal. The primary goal of this project was to apply National Comprehensive Cancer Network (NCCN) guidelines for comprehensive biomarker testing to all new patients with mNSCLC within a large community practice. Plan-Do-Study-Act methodology was used, with cycle 1 focused on provider education and the creation of a mNSCLC initial consult Note (electronic health record template/McKesson iKnowMed G2) and accompanying order set. Staging, template/order set utilization, and comprehensive biomarker testing rates were recorded while workflow processes were monitored. Cycle 2 centered on improved cancer staging, data analytic reporting, auditing, and reeducation. The comprehensive biomarker testing rates increased from a historic rate of 68% to 92.7% during the 1-year intervention period. The template utilization rate was 71% with complete staging (TNM stage and relevant biomarkers) documented in 40%. Implementation and standardization of comprehensive biomarker testing of patients with mNSCLC in a large multisite community-based oncology practice is feasible and results in significant improvement in comprehensive biomarker testing and reporting. Establishing reliable and measurable tracking metrics to ensure that these new processes are used and maintained can assist in scaling these processes. Efforts to scale this best practice are planned across the US Oncology Network.

Abstract 3383: Liquid biopsy for PDL-1 status by analysis of cfRNA: Clinical data from a large community practice

Abstract PD-L1 testing via immunochemistry has become a widely used tool to predict response to immune checkpoint inhibitor (ICI) therapy in many tumor types. However this marker can be expressed heterogeneously, and can vary with time, treatment effects, and local factors including cytokine expression. Gene expression profiles of immunologically active genes are currently being studied to more accurately predict response to ICI treatment. To evaluate a potentially more straightforward way to accomplish this, we utilized an assay of cfRNA in plasma for PD-L1, utilizing a real-time PCR based assay (Circulogene, Inc, Birmingham, AL). The demonstrated limit of detection for PD-L1 RNA was 1 copy/ul. Results were reported as either not detected, or low at over 1%, or high at over 50%. We used the PCR 30th percentile Ct value corresponding to a tissue IHC PD-L1 of>50%, and the 66th percentile Ct value corresponding to a tissue PD-L1 expression of >1%. We then collected data on over 400 patients over a two year period at a large community oncology practice and at a university clinic (Florida Cancer Specialists and Atrium Health/Wake Forest-Baptist Comprehensive Cancer Center) who had a clinical necessity for molecular testing in a commercially available setting with a CLIA certified lab. In some cases the corresponding tissue PD-L1 testing was also available, and may have been either positive or negative.To date 41 patients have been found to have high levels of cfRNA for PD-L1. Their clinical course has been analyzed using the electronic medical record. Some patients have received standard of care therapy, and some have been given CPI therapy with or without chemotherapy, if the attending oncologist felt this to be the best palliative option. These include tumors such as CRPC, metastatic low grade neuroendocrine carcinoma and HR+ metastatic breast carcinoma, that have rarely been treated successfully with CPI therapy. Tumor types - all are stage IV solid tumor patients: 19 NSCLC, 7 breast carcinoma, 3 colorectal cancer, 3 castrate resistant prostate carcinoma, 9 other tumor types.Data collection for tumor response or clinical benefit on the 41 patients is ongoing and will be presented at the meeting, and compared to published CPI response data using standard tissue IHC testing. Citation Format: Lowell L. Hart, Magali Van den Bergh. Liquid biopsy for PDL-1 status by analysis of cfRNA: Clinical data from a large community practice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3383.

Feasibility of Large-Scale Implementation of an Electronic Patient-Reported Outcome Remote Monitoring System for Patients on Active Treatment at a Community Cancer Center.

The use of digital symptom monitoring with patient-reported outcomes (PROs) has been shown to improve patient outcomes. The evidence of benefit has been largely derived from research studies. The feasibility of adopting this technology in the real-world setting is unknown. We report on the clinical implementation of a proprietary electronic patient-reported outcome (ePRO)-based digital symptom monitoring platform at the Highlands Oncology Group practice, a large community oncology practice. We present here our experience with patient enrollment, engagement, and retention; reasons for discontinued use; proportion of reports generating alerts and containing severe symptoms; and the responses to alerts including nursing telephone consultations and urgent office visits. Over an approximately 17-month period, 923 patients were successfully enrolled. Patients enrolled from June 20, 2020, through November 30, 2021, with follow-up through February 28, 2022. Retention rates at 3, 6, 9, and 12 months were 94%, 88%, 73%, and 67%, respectively, with greater retention at 12 months in patients age 65 years or older. Few patients discontinued use for reasons related to the platform (n = 47; 5%). Of the 25,311 ePRO reports submitted, 49% (n = 12,334) exceeded the predefined alert thresholds and 8% (n = 1,920) included severe symptoms. The nursing team responded within 24 hours by telephone to 31.2% (n = 3,910) of all reports with alerts. Of reports with severe symptoms, 72.7% (n = 1,395) received a call. Only 6.4% (n = 249) of phone calls required an office evaluation within 72 hours of the report. This single-center experience indicates that an ePRO-based digital symptom monitoring platform can be effectively implemented at a large scale with a high level of long-term patient engagement. Most reports could be effectively resolved by nurses, and physician intervention was infrequently required.

Growth and scalability of a palliative care program in a large community oncology practice.

206 Background: Tennessee Oncology (TO) is a large community oncology practice with over 180 oncology providers spanning over 30 clinics throughout Tennessee and northern Georgia. In 2017, TO began embedding palliative care (PC) providers in clinics. However, the program growth was slow and by the end of 2019, TO offered PC services within only five clinics. In early 2020, TO implemented various initiatives to expand access and improve utilization of palliative care. Methods: In May 2020, TO hired a palliative care physician to grow and oversee the program. TO physician leadership established and communicated the importance of PC to providers and began providing feedback to each provider on utilization of PC for metastatic lung and pancreatic cancer patients. These diseases were selected due to poor prognosis, high morbidity, and known benefit of palliative care. Expansion of telemedicine reimbursement helped our PC team offer in person and telemedicine visits. Increasing demand allowed for expansion of the team and hiring of additional physicians, advanced practice providers (APPs), and a PC nurse coordinator to provide triage, follow-up and scheduling for PC providers. Results: Between the end of 2019 and the end of 2021, the average number of PC visits per quarter (averaged across three quarters) increased from 1,279 to 2,480, representing a growth of 194%. During this time, TO provided over 19,600 PC visits for 3,955 unique patients, of which 53% were female and 47% were male. Of visits provided, 40% were performed through telemedicine. The program has grown from five providers to 11 providers (three physicians, eight APPs). The number of clinics offering in person PC services has grown from five to 13. The three most common malignancies associated with patient visits were lung (16%), breast (10%), and colorectal (7%). Conclusions: Embedding palliative care within a large community oncology practice is feasible and can grow rapidly. A combination of in-person and telemedicine visits can expand reach to improve accessibility across a large patient population.

Implementation of a telemedicine-based genetic counseling program in a large community oncology practice.

393 Background: Genetic testing is an integral part of cancer care. To optimally facilitate testing, genetic counselors (GC) provide interpretation and direction for treatments in certain applicable solid tumors. A telemedicine (TM) genetic counseling program was initiated in 2019 at Tennessee Oncology, a large community oncology practice spanning over 30 clinical sites throughout Tennessee and Georgia. Methods: Appropriate patients were identified and referred through the Electronic Medical Record (EMR) for genetic testing based on current National Comprehensive Cancer Network (NCCN) guidelines. All counseling sessions were scheduled over TM to the patient’s home to enhance convenience, broaden access, and decrease no-show rates. Physician education regarding result-dependent appropriate screening recommendations per NCCN for mutation positive patients were provided through email communication and dedicated GC notes in the EMR. Results: Between 2019 and 2021, GC referrals per year grew from 195 to 840. Of these referrals, 84.6% of patients completed GC consultations, all of which were facilitated through TM. Of completed consultations, 81.4% underwent testing. Average time from referral to GC consultation was 8-13 business days. The no-show rate was low (< 7%). This program started at 1 clinic in 2019 and is now offered for patient care in 16 clinics across the state. Conclusions: Our program illustrates how remote GC programs are an effective choice for scaling genetics care across a large community oncology practice. Deep integration of TM-based genetic counseling within the EMR helps identify high-risk patients, improves test adoption, patient keep-rate and turn-around-time therefore helping to better patient outcomes. This quality assurance program is an important part of comprehensive cancer care that we can provide to our patients and their families.

Utilizing data and artificial intelligence to optimize treatment room scheduling and staffing.

436 Background: Tennessee Oncology is a large community oncology practice with over 30 clinics providing 89,000 treatments per year across Tennessee and northern Georgia. Tennessee Oncology’s scheduling application was unable to optimally schedule treatment appointments. This scheduling gap was causing frequent patient delays and employee extended hours. Tennessee Oncology partnered with Smirta, Inc., to develop a data and artificial intelligence (AI) driven scheduling overlay platform that would optimize and simplify cancer treatment scheduling as well as predict scheduling patterns and resource needs. Methods: Named OncoSmart, the scheduling optimization platform ingests historic scheduling data, detailed clinic configuration data including provider and nursing schedules, and available resource data such as treatment room chairs. Utilizing AI, the platform generates optimal scheduling recommendations matching the specific set of services that need to be scheduled. The platform overlays the current scheduling app and provides dynamic, real-time recommendations based on current resource (treatment room, provider, etc.) schedule availabilities and bookings. Tennessee Oncology piloted the scheduling optimization platform at 1 clinic and has currently expanded the pilot to 12 additional clinics. Results: After various ranges of clinic pilot times (6 months to 2 years), Tennessee Oncology treatment volumes have increased by 7%. In parallel to this increase, the optimization platform has helped decrease extended hours by over 32%. The original pilot site has shown major improvement in all 4 primary key performance indicators (KPI): treatment volume +12%; Chair utilization +12%; treatment delay -9%; extended hours -82%. Additionally, using the platform’s predictive analytics capabilities, analyses have been completed to generate optimal treatment scheduling patterns as well as optimal treatment nursing staffing models. Conclusions: Within a short period after deployment, Smirta Inc’s OncoSmart has helped Tennessee Oncology identify better treatment scheduling options for these 13sites. The scheduling optimization platform has proven to be very effective in identifying optimal treatment scheduling strategies and in identifying critical resource bottlenecks. The platform’s clinic management, optimization, nurse assignment, business intelligence, and resource management modules has empowered Tennessee Oncology to better manage critical clinical resources and reduce staff overtime during a period of growth.