Novel use of clinical pathways to identify poor prognosis lung cancer patients: Implementation and outcomes
| Autor: | David Michael Jackman, Matthew Rice, Nicholas Virkler, Belinda Desravines, Victoria H. Selesnick, Michael Scalzo, Jocelyn H. Siegel, James A. Tulsky, Joseph O. Jacobson |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Clinical Oncology. 40:1589-1589 |
| ISSN: | 1527-7755 0732-183X |
| Popis: | 1589 Background: Cancer patients in the last year of life have different clinical needs and evolving goals of care. Using our oncology decision-support pathways to help clinicians consistently identify such patients in a systematic and prospective fashion, at a discrete moment in the care trajectory, may be an important step towards matching the care of these patients with their stated goals. Methods: Medical oncologists from each disease group at the Dana-Farber Cancer Institute (DFCI) were tasked with identifying clinical settings in each oncology care pathway associated with an expected median survival of < 12 months. This information was embedded into the underlying data model of the pathways platform, allowing us to determine how often clinicians navigated through each poor prognosis node. Results: From 3/1/20 – 6/30/21, there were 264 navigations in 205 unique lung cancer patients receiving standard of care (i.e., not on clinical trial) for a clinical condition associated with poor prognosis. Overall, the median overall survival from the time of a patient’s first navigation through a poor prognosis node during the defined study period was 6.4 months. Table lists outcomes for each specific setting. Patients with squamous or small cell lung cancer being treated in or beyond the third-line setting had notably poor outcomes, with less than a third of these patients surviving 6 months from the time of navigation. Conclusions: A clinical pathways platform can be a key tool in designating clinical scenarios associated with poor prognosis and identifying patients who may be particularly at risk. Pathways analytics provide real-world evidence corroborating the expected poor prognosis based on published studies and can identify specific clinical subsets for whom specific resources are warranted. By embedding this into the pathways data model, we aim to alert physicians to conduct goals of care conversations, offer supportive care resources, and match patients to appropriate treatment options and clinical trials. [Table: see text] |
| Databáze: | OpenAIRE |
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