Automated Correction of Room Location Errors in Anesthesia Information Management Systems

BACKGROUND:Anesthesia information management systems (AIMS) and operating room information management systems (ORIMS) are both used in operating rooms (OR). Anesthesia providers use AIMS to document their care in near real-time, including milestone events, and these systems automatically record vital signs from patient monitors. Circulating nurses use ORIMS primarily to document procedural information. Because of automatic documentation, AIMS would be ideal platforms for OR managerial decision support if the correct locations of cases in progress were known accurately. Trust is diminished if recommendations are poor. METHODS:We compiled room location error rates from prior analyses of ORIMS data. Data from 24 consecutive 4-wk periods (45,459 cases) were analyzed from one hospital where both ORIMS and AIMS data were available. The actual location of cases was inferred from the physical location of the workstation recording the majority of pulse oximetry saturations. These were compared to the listed location in the AIMS and the final corrected location in the ORIMS. The scheduled and final ORIMS locations were compared to determine how often location changes were updated before the start of anesthesia. The location of cases was inferred in near real-time by using the identifier of the AIMS workstation transmitting pulse oximetry saturated electrocardiogram heart rate, and end-tidal CO2 partial pressures. RESULTS:Location error rates ranged from 0% to 7.5% at 42 hospitals. The error rate at the studied hospital was just 0.4%, showing that the hospital was suitable for investigation. The 0.4% error rate was based on cases listed as overlapping in the same OR, and thus under-estimated the actual error rate in the ORIMS (1.0%). With education, there was a decrease in the moved cases in the ORIMS whose location was not changed before the start of anesthesia (9.3%–2.0%, P < 10−5). Despite the significant improvement (P < 10−5) in the error rate between the AIMS listed and actual locations, the residual AIMS real-time error rate was 4.1% of cases. Use of vital sign data reduced errors to <0.1%. CONCLUSIONS:Education can only modestly improve the accuracy of OR locations in ORIMS and AIMS data. The actual location can be inferred, either in near real-time or afterwards, from the AIMS workstation transmitting vital sign data. This addresses the fundamental problem of cases having more than one location during the course of anesthetic care (e.g., holding area, block room, OR, and postanesthesia care unit), which cannot be determined from scheduled ORIMS or listed AIMS locations.

[1]  Bethany Daily,et al.  Automatic Detection and Notification of “Wrong Patient—Wrong Location” Errors in the Operating Room , 2005, Surgical innovation.

[2]  Franklin Dexter,et al.  Uncertainty in Knowing the Operating Rooms in Which Cases Were Performed Has Little Effect on Operating Room Allocations or Efficiency , 2002, Anesthesia and analgesia.

[3]  Michael M. Vigoda,et al.  The Medicolegal Importance of Enhancing Timeliness of Documentation When Using an Anesthesia Information System and the Response to Automated Feedback in an Academic Practice , 2006, Anesthesia and analgesia.

[4]  Stephen F Spring,et al.  Automated Documentation Error Detection and Notification Improves Anesthesia Billing Performance , 2007, Anesthesiology.

[5]  Marina Krol,et al.  The Effect of an Interactive Visual Reminder in an Anesthesia Information Management System on Timeliness of Prophylactic Antibiotic Administration , 2007, Anesthesia and analgesia.

[6]  Franklin Dexter,et al.  Coordination of Appointments for Anesthesia Care Outside of Operating Rooms Using an Enterprise-Wide Scheduling System , 2007, Anesthesia and analgesia.

[7]  AkkeNeel Talsma,et al.  An Anesthesia Information System Designed to Provide Physician-Specific Feedback Improves Timely Administration of Prophylactic Antibiotics , 2006, Anesthesia and analgesia.

[8]  Marina Krol,et al.  Development of a Module for Point-of-care Charge Capture and Submission Using an Anesthesia Information Management System , 2006, Anesthesiology.

[9]  Franklin Dexter,et al.  Operating Room Managerial Decision-Making on the Day of Surgery With and Without Computer Recommendations and Status Displays , 2007, Anesthesia and analgesia.

[10]  J. Ledolter,et al.  Validation of Statistical Methods to Compare Cancellation Rates on the Day of Surgery , 2005, Anesthesia and analgesia.

[11]  R. Epstein,et al.  The Impact of Service-Specific Staffing, Case Scheduling, Turnovers, and First-Case Starts on Anesthesia Group and Operating Room Productivity: A Tutorial Using Data from an Australian Hospital , 2006, Anesthesia and analgesia.

[12]  John D. Lee,et al.  Trust, self-confidence, and operators' adaptation to automation , 1994, Int. J. Hum. Comput. Stud..

[13]  Yan Xiao,et al.  Making Management Decisions on the Day of Surgery Based on Operating Room Efficiency and Patient Waiting Times , 2004, Anesthesiology.

[14]  J. Ledolter,et al.  Estimating the Incidence of Prolonged Turnover Times and Delays by Time of Day , 2005, Anesthesiology.

[15]  K. Tremper,et al.  Electronic Reminders Improve Procedure Documentation Compliance and Professional Fee Reimbursement , 2007, Anesthesia and analgesia.

[16]  Marie T Egan,et al.  Auto Identification Technology and Its Impact on Patient Safety in the Operating Room of the Future , 2007, Surgical innovation.

[17]  Jesse M. Ehrenfeld,et al.  Real-Time Checking of Electronic Anesthesia Records for Documentation Errors and Automatically Text Messaging Clinicians Improves Quality of Documentation , 2008, Anesthesia and analgesia.

[18]  Yan Xiao,et al.  An Algorithm for Processing Vital Sign Monitoring Data to Remotely Identify Operating Room Occupancy in Real-Time , 2005, Anesthesia and analgesia.

[19]  Yan Xiao,et al.  The Use of Distributed Displays of Operating Room Video When Real-Time Occupancy Status Was Available , 2008, Anesthesia and analgesia.

[20]  R. Epstein,et al.  Optimizing second shift OR staffing. , 2003, AORN journal.

[21]  R. L. Coleman,et al.  Using an Anesthesia Information Management System as a Cost Containment Tool: Description and Validation , 1997, Anesthesiology.