RESUMEN
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.
Asunto(s)
Neoplasias de la Mama/patología , Eficiencia Organizacional/estadística & datos numéricos , Pacientes Ambulatorios/estadística & datos numéricos , Cuidados Preoperatorios/métodos , Evaluación de Procesos, Atención de Salud/estadística & datos numéricos , Estudios de Tiempo y Movimiento , Biopsia con Aguja , Procesamiento Automatizado de Datos/métodos , Procesamiento Automatizado de Datos/estadística & datos numéricos , Femenino , Hospitales de Enseñanza/estadística & datos numéricos , Humanos , Massachusetts , Servicio Ambulatorio en Hospital/estadística & datos numéricos , Sistemas de Identificación de Pacientes/métodos , Sistemas de Identificación de Pacientes/estadística & datos numéricos , Evaluación de Procesos, Atención de Salud/métodos , Servicio de Radiología en Hospital/estadística & datos numéricos , Diseño de SoftwareRESUMEN
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.
Asunto(s)
Eficiencia Organizacional , Quirófanos/organización & administración , Análisis y Desempeño de Tareas , Algoritmos , Biopsia , HumanosRESUMEN
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.