Objective We assessed the direct costs of 3 surgical approaches in uterine cancer and the cost impact of incorporating robot-assisted surgery. Methods A cost system that allocates the actual cost of resources used to treat each patient, as opposed to borrowing cost data from a billing system, was used to determine direct costs for patients who underwent surgery for uterine cancer from 2009–2010. These costs included all aspects of surgical care up to 6 months after discharge. Total amortized direct costs (AC) included the capital cost of 3 dual console DaVinci Si platforms with 5 years of service contracts. Non-amortized costs (NAC) were also calculated (excluded capital costs). Modeling was performed to estimate the mean cost of surgical care for patients presenting with endometrial cancer from 2007–2010 Results Of 436 cases (132 laparoscopic, 262 robotic, 42 laparotomy), total mean AC/case was $20,489 (laparoscopy), $23,646 (robot), and $24,642 (laparotomy) (P<0.05 [robot vs laparoscopy]; P=0.6 [robot vs laparotomy]). Total NAC/case was $20,289, $20,467, and $24,433, respectively (P=0.9 [robot vs laparoscopy]; P=0.03 [robot vs laparotomy]). The planned surgical approach in 2007 was laparoscopy-68%, robot-8%, and laparotomy-24% compared to 26%, 64%, and 9%, respectively, in 2010 (P<0.001). The modeled mean AC/case was $21,738 in 2007 and $22,678 in 2010 (+$940). NAC was $21,298 in 2007 and $20,573 in 2010 (−$725). Conclusion Laparoscopy is least expensive when including capital acquisition costs. Laparoscopy and robotic surgery are comparable if upfront costs are excluded. There is cost neutralization with the robot when it helps decrease laparotomy rates.
6508 Background: Electronic decision support is increasingly prevalent in clinical practice. Traditional tools map guidelines into an interactive platform. An alternative method builds on experience-based learning. Methods: Memorial Sloan-Kettering (MSK), IBM and WellPoint teamed to develop IBM Watson – a cognitive computing system leveraging natural language processing (NLP), machine learning (ML) and massive parallel processing – to help inform clinical decision making. We made a prototype for lung cancers using manufactured and anonymized patient cases. We configured this tool to read medical language and extract specific attributes from each case to identify appropriate treatment options benchmarked against MSK expertise, anonymized patient cases and published evidence. Treatment options reflect consensus guidelines and MSK best practices where guidelines are not granular enough to match treatments to unique patients. Analysis and building accuracy is ongoing and iterative. Results: 420 manufactured and 525 anonymized patient cases trained the initial models. Early results show accuracy improvement in NLP and ML in identifying treatment options (Table). All treatment plans were guideline adherent. A proportion of cases showed the need to incorporate tailored treatment plans reflecting MSK’s practice beyond guidelines – e.g. 11% of cases required addressing a site of critical metastasis before initiating guideline supported treatment. Conclusions: IBM Watson is extracting information from free text medical records that supports building ML models to assist in selecting treatments for persons with lung cancers. This tool can select treatment options from consensus guidelines, and, through ML, it will identify personalized treatment plans. Training is ongoing to improve individualized decision making and optimize the web-based tool that connects with IBM Watson. [Table: see text]
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