{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:53:09Z","timestamp":1781650389255,"version":"3.54.5"},"reference-count":40,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"6","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Oncol Pract"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Cancer care is highly interdependent, but optimal timing and sequencing is challenging. Teamwork is a promising approach to optimize care timing and sequencing. We previously showed that the 4R Oncology model fostered a high-functioning team and enabled interdependent care optimizations in lung and breast cancers in a community health system. Herein, we evaluated whether the optimizations and 4R clinic implementation resulted in actual timing\/sequencing improvements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We activated optimizations and implemented Care Sequences in practice. Care Sequences are 4R tools for patients and clinicians who guide optimal roadmap and timing\/sequencing of care. In each cancer, we compared metrics for 11 types of interdependent care using chi-square analyses between the intervention cohort (lung cancer n = 138, breast cancer n = 208), diagnosed post-4R, and the historical cohort (lung cancer n = 173, breast cancer n = 268), diagnosed pre-4R. We used multiple regression analysis to determine factors influencing a composite Optimization Index, a patient-level measure of overall interdependent care optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Intervention and historical cohorts were comparable in patient characteristics and care received. In each cancer, timing\/sequencing for all care types improved, six of them significantly, including the timing of lung surgery (88%\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      72%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .02), lung biomarker results (81%\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      63%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .04), breast gene expression results (70%\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      34%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), and endocrine therapy start (89%\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      78%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .03). Optimization Index was significantly higher in the intervention than historical cohorts (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001 in each cancer, mean = 0.82\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      0.68 in lung cancer; 0.81\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      0.68 in breast cancer). 4R contributed to this increase twice as much as all patient characteristics and care received combined.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>4R is effective in improving timing and sequencing of interdependent care via establishing high-functioning teams and facilitating relevant optimizations. Our study provides a roadmap for other institutions developing high-functioning teams and optimizing timeliness of care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/op-25-01277","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T12:00:07Z","timestamp":1780142407000},"page":"978-989","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing Cancer Care Delivery Through High-Functioning Teams: Results of Implementing the 4R Oncology Model in Lung and Breast Cancers"],"prefix":"10.1200","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-2301","authenticated-orcid":true,"given":"Julia R.","family":"Trosman","sequence":"first","affiliation":[{"name":"Center for Business Models in Healthcare, Glencoe, IL"},{"name":"Northwestern University Feinberg School of Medicine, Chicago, IL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3918-0048","authenticated-orcid":true,"given":"Christine B.","family":"Weldon","sequence":"additional","affiliation":[{"name":"Center for Business Models in Healthcare, Glencoe, IL"},{"name":"Northwestern University Feinberg School of Medicine, Chicago, IL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5368-2696","authenticated-orcid":true,"given":"Elizabeth","family":"Linehan","sequence":"additional","affiliation":[{"name":"Department of Hematology Oncology, The Permanente Medical Group, San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffery B.","family":"Velotta","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Oakland Medical Center, Oakland, CA"},{"name":"Kaiser Permanente Bernard J. 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