Improving operating room productivity via parallel anesthesia processing.

PURPOSE: Parallel processing of regional anesthesia may improve operating room (OR) efficiency in patients undergoes upper extremity surgical procedures. The purpose of this paper is to evaluate whether performing regional anesthesia outside the OR in parallel increases total cases per day, improve efficiency and productivity. DESIGN/METHODOLOGY/APPROACH: Data from all adult patients who underwent regional anesthesia as their primary anesthetic for upper extremity surgery over a one-year period were used to develop a simulation model. The model evaluated pure operating modes of regional anesthesia performed within and outside the OR in a parallel manner. The scenarios were used to evaluate how many surgeries could be completed in a standard work day (555 minutes) and assuming a standard three cases per day, what was the predicted end-of-day time overtime. FINDINGS: Modeling results show that parallel processing of regional anesthesia increases the average cases per day for all surgeons included in the study. The average increase was 0.42 surgeries per day. Where it was assumed that three cases per day would be performed by all surgeons, the days going to overtime was reduced by 43 percent with parallel block. The overtime with parallel anesthesia was also projected to be 40 minutes less per day per surgeon. RESEARCH LIMITATIONS/IMPLICATIONS: Key limitations include the assumption that all cases used regional anesthesia in the comparisons. Many days may have both regional and general anesthesia. Also, as a case study, single-center research may limit generalizability. PRACTICAL IMPLICATIONS: Perioperative care providers should consider parallel administration of regional anesthesia where there is a desire to increase daily upper extremity surgical case capacity. Where there are sufficient resources to do parallel anesthesia processing, efficiency and productivity can be significantly improved. ORIGINALITY/VALUE: Simulation modeling can be an effective tool to show practice change effects at a system-wide level.

A Coordinated Patient Transport System for ICU Patients Requiring Surgery: Impact on Operating Room Efficiency and ICU Workflow.

PURPOSE: Transfer of intensive care unit (ICU) patients to the operating room (OR) is a resource-intensive, time-consuming process that often results in patient throughput inefficiencies, deficiencies in information transfer, and suboptimal nurse to patient ratios. This study evaluates the implementation of a coordinated patient transport system (CPTS) designed to address these issues. METHODS: Using data from 1,557 patient transfers covering the 2006-2010 period, interrupted time series and before and after designs were used to analyze the effect of implementing a CPTS at Mayo Clinic, Rochester. RESULTS: Using a segmented regression for the interrupted time series, on-time OR start time deviations were found to be significantly lower after the implementation of CPTS (p < .0001). The implementation resulted in a fourfold improvement in on-time OR starts (p < .01) while significantly reducing idle OR time (p < .01). CONCLUSION: A coordinated patient transfer process for moving patient from ICUs to ORs can significantly improve OR efficiency, reduce nonvalue added time, and ensure quality of care by preserving appropriate care provider to patient ratios.

An IP-based healthcare provider shift design approach to minimize patient handoffs.

The new Accreditation Council for Graduate Medical Education (ACGME) duty-hour standards for residents and fellows went into effect in 2011. These regulations were designed to reduce fatigue-related medical errors and improve patient safety. The new shift restrictions, however, have led to more frequent transitions in patient care (handoffs), resulting in greater opportunity for communication breakdowns between caregivers, which correlate with medical errors and adverse events. Recent research has focused on improving the quality of these transitions through standardization of the handoff protocols; however, no attention has been given to reducing the number of transitions in patient care. This research leverages integer programming methods to design a work shift schedule for trainees that minimizes patient handoffs while complying with all ACGME duty-hour standards, providing required coverage, and maintaining physician quality of life. In a case study of redesigning the trainees' schedule for a Mayo Clinic Medical Intensive Care Unit (MICU), we show that the number of patient handoffs can be reduced by 23 % and still meet all required and most desired scheduling constraints. Furthermore, a 48 % reduction in handoffs could be achieved if only the minimum required rules are satisfied.

Recovery bed planning in cardiovascular surgery: a simulation case study.

Recovery beds for cardiovascular surgical patients in the intensive care unit (ICU) and progressive care unit (PCU) are costly hospital resources that require effective management. This case study reports on the development and use of a discrete-event simulation model used to predict minimum bed needs to achieve the high patient service level demanded at Mayo Clinic. In addition to bed predictions that incorporate surgery growth and new recovery protocols, the model was used to explore the effects of smoothing surgery schedules and transferring long-stay patients from the ICU. The model projected bed needs that were 30 % lower than the traditional bed-planning approach and the options explored by the practice could substantially reduce the number of beds required.

Improving patient access in nuclear medicine: a case study of PET scanner scheduling.

We used the systems engineering technique of discrete event simulation modeling to assist in increasing patient access to positron emission tomographic examinations in the Department of Nuclear Medicine at Mayo Clinic, Rochester. The model was used to determine the best universal slot length to address the specific access challenges of a destination medical center such as Mayo Clinic. On the basis of the modeling, a new schedule was implemented in April 2012 and our before and after data analysis shows an increase of 2.4 scans per day. This was achieved without requiring additional resources or negatively affecting patient waiting, staff satisfaction (as evaluated by day length), or examination quality.

Optimizing the patient transport function at Mayo Clinic.

In this article, we report on the implementation of a computerized scheduling tool to optimize staffing for patient transport at the Mayo Clinic. The tool was developed and implemented in Microsoft Excel and Visual Basic for Applications and includes an easy-to-use interface. The tool allows transport management to consider the trade-offs between patient waiting time and staffing levels. While improved staffing efficiency was a desire of the project, it was important that patient service quality was also maintained. The results show that staffing could be reduced while maintaining historical patient service levels.

Using simulation modeling to improve patient flow at an outpatient orthopedic clinic.

We report on the use of discrete event simulation modeling to support process improvements at an orthopedic outpatient clinic. The clinic was effective in treating patients, but waiting time and congestion in the clinic created patient dissatisfaction and staff morale issues. The modeling helped to identify improvement alternatives including optimized staffing levels, better patient scheduling, and an emphasis on staff arriving promptly. Quantitative results from the modeling provided motivation to implement the improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient time in the clinic was reduced.

Highlights from the third annual Mayo Clinic conference on systems engineering and operations research in health care.

In August 2010, the Third Annual Mayo Clinic Conference on Systems Engineering and Operations Research in Health Care was held. The continuing mission of the conference is to gather a multidisciplinary group of systems engineers, clinicians, administrators, and academic professors to discuss the translation of systems engineering methods to more effective health care delivery. Education, research, and practice were enhanced via a mix of formal presentations, tutorials, and informal gatherings of participants with diverse backgrounds. Although the conference promotes a diversity of perspectives and methods, participants are united in their desire to find ways in which systems engineering can transform health care, especially in the context of health care reform and other significant changes affecting the delivery of health care.

Modeling patient service centers with simulation and system dynamics.

We report on the use of simulation modeling for redesigning phlebotomy and specimen collection centers (or patient service centers) at a medical diagnostic laboratory. Research was performed in an effort to improve patient service, in particular to reduce average waiting times as well as their variability. Discrete-event simulation modeling provided valuable input into new facility design decisions and showed the efficacy of pooling sources of variation, particularly patient demand and service times. Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of service centers eventually resulted in unanticipated performance problems. We show how a system dynamics model might have helped predict these implementation problems and suggest some ways to improve results.

Rolling horizon appointment scheduling: a simulation study.

Health-care consumers continue to be frustrated with long waits, especially when an appointment has been made. However, providers who book appointments are under increasing pressure to maximize utilization so that revenues will be increased and costs reduced. Thus, scheduling appointments involves opposing forces that are difficult to manage. This challenge is addressed in a rolling-horizon environment with fluctuating demand loads. These two issues have not been explored previously in the appointment-scheduling research. Two management policies are considered: overload rules (OLR) and rule delay (RD). The former considers different scheduling methods (overtime, double booking) when demand loads are high, and the rule delay policy considers when to implement the overload rules. These methods are explored for six different demand patterns/loads and evaluated with a variety of client and server-oriented measures. The results show that managers of appointment scheduling systems must carefully consider which measures are most important to them since the best choices of OLR and RD vary substantially by measure. Good choices also depend on the general type of client demand pattern. Thus, to consider the various tradeoffs between client and server measures a matrix is developed that outlines good choices for each scenario.