Project Summary:
In Wisconsin, 40,000 new cancers are diagnosed annually, and 30,000 emergency general surgery (EGS) patients are cared for, 60% of whom undergo surgery. Just as a data-driven systems approach to oncologic care in Wisconsin has been successful, a similar approach is urgently needed for oncology patients suffering from acute surgical emergencies. The long-term trajectory is directly impacted by treatment decisions often made with imperfect information and by non-oncologic providers. Data-driven evidence would provide much needed guidance on the optimal decision making in the acute care setting.
Severe, life-altering treatment decisions such as surgery, altering chemotherapeutics, halting treatments, or transitioning to comfort care are significantly impacted by the underlying cancer stage and prognosis as well as the anticipated outcomes of the acute surgical condition. This co-dependency demands an evidence-based approach to care, which is currently lacking and represents a significant knowledge gap. The knowledge gap is in part due to the way this population of patients challenges existing frameworks for quality assessment and improvement. Individuals with a concurrent cancer diagnosis and EGS condition are difficult to identify both prospectively and retrospectively, have ill-defined outcomes, and significant variability in management across the United States and Wisconsin. Often, they are cared for outside of tertiary care centers and may require transfer and experience delays to definitive treatment. The same challenges that are well described in the EGS population, including high morbidity, are only more pronounced in this population. Ensuring the optimal care of patients with a concomitant cancer diagnosis who develop an acute surgical condition will lead to substantial improvements in cancer outcomes in Wisconsin and across North America.
This project aims to address this knowledge gap through a multi-disciplinary collaboration to computationally identify this high-risk cohort of patients using both structured and unstructured data elements and provide data on the management of two common EGS conditions in patients with cancer: small bowel obstruction and acute cholecystitis. Successful achievement of the project aims will be a significant advance in the care for cancer patients with EGS conditions state-wide with eventual scalability to a national population.