Managing capacity for virtual and office appointments in chronic care.

Patients living with a chronic disease often require regular appointments and treatments. Due to the constraints on the availability of office appointments and the capacity of physicians, access to chronic care can be limited; consequently, patients may fail to receive the recommended care suggested by clinical guidelines. Virtual appointments can provide a cost-effective alternative to traditional office appointments for managing chronic conditions. Advances in information technology infrastructure, communication, and connected medical devices are enabling providers to evaluate, diagnose, and treat patients remotely. In this study, we build a capacity allocation model to study the use of virtual appointments in a chronic care setting. We consider a cohort of patients receiving chronic care and model the flow of the patients between office and virtual appointments using an open migration network. We formulate the planning of capacity needed for office and virtual appointments with a newsvendor model to maximize long-run average earnings. We consider differences in treatment and diagnosis effectiveness for office and virtual appointments. We derive optimal capacity allocation policies and implement numerical experiments. With the model developed, capacity decisions for office and virtual appointments can be made more systematically with the consideration of patient disease progressions.

Optimizing the robotic surgery team: an operations management perspective.

INTRODUCTION AND HYPOTHESIS: To assess the critical threshold to optimize operating room (OR) time for each surgical team member in robotically assisted sacrocolpopexy (RASCP) and to evaluate the most efficient team compositions. METHODS: All women who underwent RASCP for pelvic organ prolapse (POP) were prospectively entered in a database. Patients having unrelated concomitant surgery were excluded. Our primary outcome measure was total OR time. We utilized factor analysis, regression analysis, and analysis of variance, OR time mapping, and stochastic optimization to identify 'optimal' surgical team configuration. RESULTS: The database included 359 consecutive RASCPs, all performed for stage III-IV POP: 156 (43%) were with total and 44 (12%) supracervical hysterectomies and 159 (44%) post-hysterectomy. Mean age was 58.6 ± 9.3 years. Mean parity was 2.8 ± 1.4, and mean body mass index was 28 ± 4.7 kg/m2. A total of 4 surgeons, 34 first assistants, 20 circulating nurses, 15 surgical technologists, and 59 anesthesiologist/nurse anesthetists were involved. Optimal experience levels for each team member were achieved at the following number of robotic procedures: surgeon 44; first assistant 13; surgical technologist 66; circulating nurse 56; anesthesia provider 46. Our analysis revealed that the surgical technologist and first assistant played the most significant roles within the team. The surgeon was ranked third followed by the circulating nurse and anesthesia provider, respectively. CONCLUSION: Operating time in robotic surgery is multifactorial. Experience of each member of a robotic surgery team is critical. An optimal team can be composed of a variety of combinations of experience levels among the robotic team members.