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1.  Impact of surgical case order on perioperative outcomes for robotic-assisted radical prostatectomy 
Urology Annals  2014;6(2):142-146.
Since its introduction, there have been many refinements in the technique and implementation of robotic-assisted radical prostatectomy (RARP). However, it is unclear whether operative outcomes are influenced by surgical case order. We evaluated the effect of case order on perioperative outcomes for RARP within a large health maintenance organization.
Materials and Methods:
We conducted a retrospective review of RARP cases performed at our institution from September 2008 to December 2010 using a single robotic platform. Case order was determined from surgical schedules each day and surgeries were grouped into 1st, 2nd and 3rd round cases. Fourth round cases (n = 1) were excluded from analysis. We compared clinicopathological variables including operative time, estimated blood loss (EBL), surgical margin rates and complication rates between groups.
Of the 1018 RARP cases in this cohort, 476 (47%) were performed as 1st round cases, 398 (39%) 2nd round cases and 144 (14%) 3rd round cases by a total of 18 surgeons. Mean operative time was shorter as cases were performed later in the day (213 min vs. 209 min vs. 180 min, P < 0.0001) and similarly, EBL also decreased with surgical order (136 mL vs. 134 mL vs. 103 mL, P = 0.01). Transfusion rates, surgical margin rates and complication rates did not significantly differ between groups. Patients undergoing RARP later in the day were much more likely to have a hospital stay of 2 or more days than earlier cases (10% vs. 11% vs. 32%, P = 0.01).
Surgical case order may influence perioperative outcomes for RARP with decreased operative times and increased length of hospital stay associated with later cases. These findings indicate that select perioperative factors may improve with ascending case order as the surgical team “warms up” during the day. In addition, 3rd round cases can increase hospital costs associated with increased lengths of hospital stay. Knowledge of these differences may assist in surgical planning to improve outcomes and limit costs.
PMCID: PMC4021655  PMID: 24833827
Case order; cost; morbidity; robotic radical prostatectomy
2.  Achieving proficiency with robot-assisted radical prostatectomy: Laparoscopic-trained versus robotics-trained surgeons 
Canadian Urological Association Journal  2013;7(11-12):E711-E715.
Initiating a robotics program is complex, in regards to achieving favourable outcomes, effectively utilizing an expensive surgical tool, and granting console privileges to surgeons. We report the implementation of a community-based robotics program among minimally-invasive surgery (MIS) urologists with and without formal robotics training.
From August 2008 to December 2010 at Kaiser Permanente Southern California, 2 groups of urologists performing robot-assisted radical prostatectomy (RARP) were followed since the time of robot acquisition at a single institution. The robotics group included 4 surgeons with formal robotics training and the laparoscopic group with another 4 surgeons who were robot-naïve, but skilled in laparoscopy. The laparoscopic group underwent an initial 7-day mentorship period. Surgical proficiency was measured by various operative and pathological outcome variables. Data were evaluated using comparative statistics and multivariate analysis.
A total of 420 and 549 RARPs were performed by the robotics and laparoscopic groups, respectively. Operative times were longer in the laparoscopic group (p = 0.002), but estimated blood loss was similar. The robotics group had a significantly better overall positive surgical margin rate of 19.9% compared to the laparoscopic group (27.8%) (p = 0.005). Both groups showed improvements in operative and pathological parameters as they accrued experience, and achieved similar results towards the end of the study.
Robot-naïve laparoscopic surgeons may achieve similar outcomes to robotic surgeons relatively early after a graduated mentorship period. This study may apply to a community-based practice in which multiple urologists with varied training backgrounds are granted robot privileges.
PMCID: PMC3840530  PMID: 24282463
3.  A Semantic Web Management Model for Integrative Biomedical Informatics 
PLoS ONE  2008;3(8):e2946.
Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.
Methodology/Principal Findings
The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MDAnderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.
The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis.
PMCID: PMC2491554  PMID: 18698353

Results 1-3 (3)