BACKGROUND:Hospitals use time-motion studies to monitor process effectiveness and patient waiting. Manual tracking is labor-intensive and potentially influences system performance. New technology known as indoor positioning systems (IPS) may allow automatic monitoring of patient waiting and progress. The authors tested whether an IPS can track patients through a multistep preoperative process.METHODS:The authors used an IPS between October 14, 2005, and June 13, 2006, to track patients in a multistep ambulatory preoperative process: needle localization and excisional biopsy of a breast lesion. The process was distributed across the ambulatory surgery and radiology departments of a large academic hospital. Direct observation of the process was used to develop a workflow template. The authors then developed software to convert the IPS data into usable time-motion data suitable for monitoring process efficiency over time.RESULTS:The authors assigned tags to 306 patients during the study period. Eighty patients never underwent the procedure or never had their tag affixed. One hundred seventy-seven (78%) of the remaining 226 patients successfully matched the workflow template. Process time stamps were automatically extracted from the successful matches, measuring time before radiology (mean +/- SD, 77 +/- 35 min), time in radiology (105 +/- 35 min), and time between radiology and operating room (80 +/- 60 min), which summed to total preoperative time (261 +/- 67 min).CONCLUSIONS:The authors have demonstrated that it is possible to use a combination of IPS technology and sequence alignment pattern matching software to automate the time-motion study of patients in a multidepartment, multistep process with the only day-of-surgery intervention being the application of a tag when the patient arrives.

Object The operating room is rich in digital data that must be rapidly gathered and integrated by caregivers, potentially distracting them from direct patient care. We hypothesized that current desktop computers could integrate enough electronically accessible perioperative data to present a unified, contextually appropriate snapshot of the patient to the operating room team without requiring any user intervention.Materials and methods We implemented a system that integrates data from surgical and anesthesia devices and information systems, as well as an active radiofrequency identification location tracking system, to create a comprehensive, unified, time-synchronized database of all digital data produced by these systems. Next, a human factors engineering approach was used to identify selected data to show on a large format display during surgery.Results A prototype system has been in daily use in a clinical operating room since August 2005. The system functions automatically without any user input, as the display system self-configures based on cues from the primary data. The system is vendor agnostic with respect to input data sources and display options.Conclusion Automatic integration and display of teamsynchronizing data from medical devices and hospital information systems is now possible using software that runs on a personal computer.

Background. Many surgeons believe that long turnover times between cases are a major impediment to their productivity. We hypothesized that redesigning the operating room (OR) and perioperative-staffing system to take advantage of parallel processing would improve throughput and lower the cost of care.Methods. A state of the art high tech OR suite equipped with augmented data collection systems served as a living laboratory to evaluate both new devices and perioperative systems Of care. The OR suite and all the experimental studies carried out in this setting were designated as the OR of the Future Project (ORF). Before constructing the ORF, modeling studies were conducted to inform the architectural and staffing design and estimate their benefit. In phase I a small prospective trial tested the main hypothesized benefits of the ORE reduced patient intra-operative flow-time, wait-time and operative procedure time. In phase H a larger retrospective study was conducted to explore factors influencing these effects. A modified process, costing method was used to estimate costs based on nationally derived data. Cost-effectiveness was evaluated using standard methods.Results. There were 385 cases matched by surgeon and Procedure type in the retrospective dataset (182 ORF, 193 standard operating room [SOR]). The median Wait Time (12.5m ORF vs 23.8 m SOR), Operative Procedure Time (561 m ORF vs 70.5 m SOR), Emergence Time (10.9 m ORF vs 14.5 m SOR) and Total Patient OR Flowtime (79.5 m ORF vs 108.9 m SOR) were all shorter in the Off (P <.05 for all comparisons). The median cost/patient was $3,165 in the OPF (interquartile range, $1,978 to $4,426) versus $2,645 in SORs (interquartile range, $1,823 to $3,908) (P = ns). The potential change in patient throughput for the ORF was 2 additional patients/day. This improved throughput was primarily attributable to a marked reduction in the non-operative time (ie, those activities commonly accounting for "turnover time") rather than facilitation of faster operations. The incremental cost-effectiveness ratio of ORF was $260 (interquartile range, $180 to $283).Conclusion. The redesigned perioperative system improves patient flow, allowing more Patients to be treated per day. Cost-effectiveness analysis suggests that the additional costs incurred by higher staffing ratios in an ORF environment are likely to be offset by increases in productivity. The benefits of this system are realized when performing multiple, short-to-medium duration procedures (eg, < 120 m).

There is a vast array of technical data that is continuously generated within the intensive care unit environment. In addition to physiological monitors, there is information being captured by the ventilator, intravenous infusion pumps, medication dispensing units, and even the patient's bed. The ability to retrieve and synchronize data is essential for both clinical documentation and real-time problem solving for individual patients and the intensive care unit population as a whole. Technical advances that permit the integration of all relevant data into a singular display or "dashboard" may improve staff efficiency, accelerate decisions, streamline workflow processes, and reduce oversights and errors in clinical practice. Critical care nurses must coordinate all aspects of care for one or more patients. Clinical data are constantly being retrieved, documented, analyzed, and communicated to others, all within the daily routine of nursing care. In addition, many bedside monitors and devices have alarms systems that must be evaluated throughout the workday, and actions taken on the basis of the patient's condition and other data. It is obvious that the complexity within such care processes presents many potential opportunities for overlooking important details. The capability to systematically and logically link physiological monitors and other selected data sets into a cohesive dashboard system holds tremendous promise for improving care quality, patient safety, and clinical outcomes in the intensive care unit.

There is a vast array of technical data that is continuously generated within the intensive care unit environment. In addition to physiological monitors, there is information being captured by the ventilator, intravenous infusion pumps, medication dispensing units, and even the patient's bed. The ability to retrieve and synchronize data is essential for both clinical documentation and real-time problem solving for individual patients and the intensive care unit population as a whole. Technical advances that permit the integration of all relevant data into a singular display or “dashboard” may improve staff efficiency, accelerate decisions, streamline workflow processes, and reduce oversights and errors in clinical practice. Critical care nurses must coordinate all aspects of care for one or more patients. Clinical data are constantly being retrieved, documented, analyzed, and communicated to others, all within the daily routine of nursing care. In addition, many bedside monitors and devices have alarms systems that must be evaluated throughout the workday, and actions taken on the basis of the patient's condition and other data. It is obvious that the complexity within such care processes presents many potential opportunities for overlooking important details. The capability to systematically and logically link physiological monitors and other selected data sets into a cohesive dashboard system holds tremendous promise for improving care quality, patient safety, and clinical outcomes in the intensive care unit.

Location tracking systems are becoming more prevalent in clinical settings yet applications still are not common. We have designed a system to aid in the assessment of clinical workflow efficiency. Location data is captured from active RFID tags and processed into usable data. These data are stored and presented visually with trending capability over time. The system allows quick assessments of the impact of process changes on workflow, and isolates areas for improvement.

We have developed a vendor agnostic, full disclosure system for the capture, display, and storage of operative systems data. This system allows door to door capture of data from multiple sources including monitors from competing vendors, integration under a single platform, and storage for future use. Full disclosure functionality includes the ability to retrieve and display archived data including full waveform and trend physiologic data, synchronized to the surgical video and other OR devices and data sources.

When procedures and processes to assure patient location based on human performance do not work as expected, patients are brought incrementally closer to a possible "wrong patient-wrong procedure'' error. We developed a system for automated patient location monitoring and management. Real-time data from an active infrared/radio frequency identification tracking system provides patient location data that are robust and can be compared with an "expected process'' model to automatically flag wrong-location events as soon as they occur. The system also generates messages that are automatically sent to process managers via the hospital paging system, thus creating an active alerting function to annunciate errors. We deployed the system to detect and annunciate "patient-in-wrong-OR'' events. The system detected all "wrong-operating room (OR)'' events, and all "wrong-OR'' locations were correctly assigned within 0.50+/-0.28 minutes (mean+/-SD). This corresponded to the measured latency of the tracking system. All wrong-OR events were correctly annunciated via the paging function. This experiment demonstrates that current technology can automatically collect sufficient data to remotely monitor patient flow through a hospital, provide decision support based on predefined rules, and automatically notify stakeholders of errors.

Background New operating room (OR) design focuses more on the surgical environment than on the process of care. The authors sought to improve OR throughput and reduce time per case by goal-directed design of a demonstration OR and the perioperative processes occurring within and around it. Methods The authors constructed a three-room suite including an OR, an induction room, and an early recovery area. Traditionally sequential activities were run in parallel, and nonsurgical activities were moved from the OR to the supporting spaces. The new workflow was supported by additional anesthesia and nursing personnel. The authors used a retrospective, case- and surgeon-matched design to compare the throughput, cost, and revenue performance of the new OR to traditional ORs. Results For surgeons performing the same case mix in both environments, the new OR processed more cases per day than traditional ORs and used less time per case. Throughput improvement came from superior nonoperative performance. Nonoperative Time was reduced from 67 min (95% confidence interval, 64-70 min) to 38 min (95% confidence interval, 35-40 min) in the new OR. All components of Nonoperative Time were meaningfully reduced. Operative Time decreased by approximately 5%. Hospital and anesthesia costs per case increased, but the increased throughput offset costs and the global net margin was unchanged. Conclusions Deliberate OR and perioperative process redesign improved throughput. Performance improvement derived from relocating and reorganizing nonoperative activities. Better OR throughput entailed additional costs but allowed additional patients to be accommodated in the OR while generating revenue that balanced these additional costs.

BACKGROUND:The Massachusetts General Hospital (MGH) Operating Room of the Future (ORF) project is a test site for evaluating new surgical technologies and processes. Here we evaluate the effect on staff satisfaction and burnout of introducing a set of new technologies.METHODS:Staff satisfaction and burnout were measured via sequential surveys based on the Maslach Burnout Inventory during the introduction of a new technology system. Functional behavior of the OR was measured in terms of flow time (time to transit the OR) and wait time (time to access the OR). These data were gathered using time-motion analysis methods.RESULTS:Significant functional improvements were found in the ORF (more than 35% reduction in flow time and wait time, P < .05). During the same period, more exposure to the ORF resulted in greater sense of personal accomplishment among surgeons, a worse sense of personal accomplishment among nurses, more emotional exhaustion among surgeons, and less emotional exhaustion among nurses. However, the responses for emotional exhaustion were reversed the greater the time from exposure to the ORF. Staff with 6 to 10 years' experience were at highest risk for burnout across all categories. General surgeons experienced more emotional exhaustion than other physicians.CONCLUSIONS:Tracking the response of all users and identifying groups at high risk for burnout when exposed to new systems should be a central part of any new technology project.

Background. Many surgeons believe that long turnover times between cases are a major impediment to their productivity. We hypothesized that redesigning the operating room (OR) and perioperative-staffing system to take advantage of parallel processing would improve throughput and lower the cost of care. Methods. A state of the art high tech OR suite equipped with augmented data collection systems served as a living laboratory to evaluate both new devices and perioperative systems of care. The OR suite and all the experimental studies carried out in this setting were designated as the OR of the Future Project (ORF). Before constructing the ORF, modeling studies were conducted to inform the architectural and staffing design and estimate their benefit. In phase I a small prospective trial tested the main hypothesized benefits of the ORF: reduced patient intra-operative flow-time, wait-time and operative procedure time. In phase II a larger retrospective study was conducted to explore factors influencing these effects. A modified process costing method was used to estimate costs based on nationally derived data. Cost-effectiveness was evaluated using standard methods. Results. There were 385 cases matched by surgeon and procedure type in the retrospective dataset (182 ORF, 193 standard operating room (SOR)). The median Wait Time (12.5 m ORF vs 23.8 m SOR), Operative Procedure Time (56.1 m ORF vs 70.5 m SOR), Emergence Time (10.9 m ORF vs 14.5 m SOR) and Total Patient OR Flowtime (79.5 m ORF vs 108.9 m SOR) were all shorter in the ORF (P .05 for all comparisons). The median cost/patient was $3,165 in the ORF (interquartile range, $1,978 to $4,426) versus $2,645 in SORs (interquartile range, $1,823 to $3,908) (P ns). The potential change in patient throughput for the ORF was 2 additional patients/day. This improved throughput was primarily attributable to a marked reduction in the non-operative time (ie, those activities commonly accounting for "turnover time") rather than facilitation of faster operations. The incremental cost-effectiveness ratio of ORF was $260 (interquartile range, $180 to $283). Conclusion. The redesigned perioperative system improves patient flow, allowing more patients to be treated per day. Cost-effectiveness analysis suggests that the additional costs incurred by higher staffing ratios in an ORF environment are likely to be offset by increases in productivity. The benefits of this system are realized when performing multiple, short-to-medium duration procedures (eg, 120 m). (Surgery 2006;139:717-28.)