[Design and Application of Perioperative Multi-center Data Center].

Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.

Design and Application of Perioperative Multi-center Data Center / 中国医疗器械杂志

Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.

The Impact of Perioperative Data Science in Hospital Knowledge Management.

Conservative practices, such as manual registry have limited scope regarding preoperative, intraoperative and postoperative decision making, knowledge discovery, analytical techniques and knowledge integration into patient care. To maximize quality and value, perioperative care is changing through new technological developments. In this context, knowledge management practices will enable future transformation and enhancements in healthcare services. By performing a data science and knowledge management research in the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015, this paper describes its principal results. This study showed perioperative decision-making improvement by integrating data science tools on the perioperative electronic system (PES). Before the PES implementation only 1,2% of the nurses registered the preoperative visit and after 87,6% registered it. Regarding the patient features it was possible to assess anxiety and pain levels. A future conceptual model for perioperative decision support systems grounded on data science should be considered as a knowledge management tool.

Dual docking robotic surgical staging for high risk endometrial cancer.

OBJECTIVE: The standard of care for patients with high intermediate and high risk endometrial cancer is surgical staging including total hysterectomy with bilateral salpingo-oophorectomy, and pelvic and para-aortic lymphadenectomy. Over the past decade, laparoscopic or robot-assisted minimally invasive surgery has showed many benefits in the management of endometrial cancer. Few studies have specifically assessed the use of minimally invasive surgery for staging of high risk endometrial cancer. The objective of this study was to evaluate the feasibility, the morbidity and oncologic outcomes of dual docking robot-assisted surgical staging of high risk endometrial cancer. METHODS: We conducted a retrospective observational study from January 2014 to March 2016 in patients with high risk endometrial cancer who underwent dual docking robotic hysterectomy with pelvic and paraaortic lymphadenectomy (± omentectomy). Patients' demographics, operative time, conversion rate, intra and postoperative complications, pathologic results, length of stay and survival were analyzed. RESULTS: Twenty patients met the inclusion criteria. Staging surgical procedure was performed robotically with a dual docking in 18 patients. Two patients were converted to laparotomy (1 for bladder extension, 1 for exposure reasons) and no patient had a laparoconversion for complication (conversion rate 10%). One patient was post operatively re-operated within 30 days because of port hernia. In one case, paraaortic lymphadenectomy was not performed because of hemorrhage risk. When the procedure was performed with robot-assisted surgery, the median number of paraaortic nodes was 19.5 (3-45). The median operative time was 240 min (180-300). Eighty-five percent (17/20) of patients were discharged at day 4 or before. The median time to start adjuvant treatments, when indicated, was 5.5 weeks. With a median follow up of 8 months (1-18 months), no tumor recurrence was reported. CONCLUSION: Robotic surgical staging with dual docking in women with high risk endometrial cancer seems to be feasible with few complications. More studies are required to assess the safety of robotic surgery and its impact on survival.

Improving Performance Measures With Perioperative Analytics.

Perioperative nurses and leadership teams across the country strive to improve outcomes, increase operational efficiency, and achieve performance improvement on key measures in the perioperative department. In today's changing health care environment, which involves increasing pressure to do more with less, it is essential for perioperative leaders to understand analytics and how to use analytics to identify, plan, and implement improvement initiatives that will provide the greatest value. This article examines key performance indicators in the OR, barriers to change, and the strategies and processes that perioperative leaders can use to achieve efficiency and performance improvement goals. It also provides an example of a tactical approach to one of the OR's greatest challenges: measuring and improving block utilization.