Impact of robotic surgery on patient flow and resource use intensity in ovarian cancer.

There is an emerging focus on the role of robotic surgery in ovarian cancer. To date, the operational and cost implications of the procedure remain unknown. The objective of the current study was to evaluate the impact of integrating minimally invasive robotic surgery on patient flow, resource utilization, and hospital costs associated with the treatment of ovarian cancer during the in-hospital and post-discharge processes.

Incorporating robotic surgery into the management of ovarian cancer after neoadjuvant chemotherapy.

Introduction: With the rapid uptake of robotic surgery in surgical oncology, its use in the treatment of epithelial ovarian cancers is being evaluated. Complete cytoreduction represents the goal of surgery either at primary cytoreduction or after neoadjuvant chemotherapy in the setting of interval cytoreduction. In selected patients, the extent of disease would enable minimally invasive surgery.

Improving post-stroke health outcomes: can facilitated care help?

Objectives: The objectives of this study were (1) identifying the patterns of post-stroke care, (2) determining the care-provider and patient characteristics associated with optimal management of post-stroke care and (3) estimating the potential influence of various facilitated care policies on outcomes.

Methodology: The 3946 subjects included in the study were admitted to one of Quebec's acute-care hospitals with confirmed diagnosis of stroke and subsequently discharged to their home. The records related to fee-for-service billings of this sample were obtained for the 3 months following discharge and used to define the care-provider path for each stroke survivor.

On the significance of reducing the need for stroke patients to visit the emergency department.

We studied the care-provider paths followed by 3,946 patients in Quebec in 2001. We showed that the patients flow during the three months preceding discharge from hospital can be represented by a Markov model with memory. This model enables study of four major scenarios to improve health outcomes, workloads and cost efficiency in the overall community-based care delivery system.