Automatic notifications mediated by anesthesia information management systems reduce the frequency of prolonged gaps in blood pressure documentation

Abstract

Background: Arterial blood pressure (BP) measurement at least every 5 minutes is part of the American Society of Anesthesiologists' monitoring standard, but prolonged BP gaps in electronic anesthesia records have been noted. We undertook multicenter studies to determine the frequency of cases with at least 1 interval ≥10 minutes between successive BP measurements and then to ascertain whether educational feedback via an electronic, near real-time notification system alerting providers to the presence of such gaps would reduce their incidence.

Methods: We evaluated 212,706 electronic anesthesia records from 3 large academic centers. We determined the fraction of cases with ≥10-minute BP monitoring gaps at baseline and did a root cause analysis to determine common causes for these lapses. We then designed and implemented automated systems at 2 of the hospitals to notify point-of-care providers immediately after such 10-minute gaps occurred and determined the subsequent impact of this feedback on BP gap incidence, compared with baseline.

Results: At Hospital A, the notification system reduced the incidence of cases with at least 1 BP gap (1.48%± 0.19% SD vs 0.79% ± 0.36% SD, P < 0.0001). At Hospital B, the gap incidence was not significantly altered when notification was provided after a 10-minute gap had already occurred (2.72% ± 0.60% SD vs 2.45% ± 0.48% SD, P = 0.27), but the incidence was reduced when such notification was provided after 6 minutes without a BP reading (2.72% ± 0.60% SD vs 1.54% ± 0.19% SD, P < 0.0001). At Hospital C, where notification was not implemented, the baseline rate of BP gaps was consistent across the preintervention and follow-up periods (7.03% ± 1.27% SD vs 7.13% ± 0.11% SD, P = 0.74). Although monitors disconnected during position change was the most common identifiable cause of BP gaps, reasons for the missing BP measurements were often not documented. During a week when the electronic charting system was temporarily inoperable, no BP gaps were noted on a convenience sample of 500 paper records from Hospital A (99% upper confidence limit = 0.83%).

Conclusions: BP gaps of ≥10 minutes were common in electronic anesthesia records, and their incidence was reduced but not eliminated by near real-time feedback to providers. The American Society of Anesthesiologists' standard for BP documentation every 5 minutes might not be achievable with current practices and technology. Anesthesia information management systems users need to be cognizant of the potential for gaps in BP measurement, take steps to minimize their occurrence, and document an explanation when such failures occur.

Figure 1. Case Example of a 15-Minute Gap in Arterial Blood Pressure Monitoring

Example of a gap in blood pressure monitoring as displayed on an anesthesia information management systems (AIMS) workstation. Blood pressure readings are indicated by the “v” (systolic), “x” (mean), and “^” (diastolic). Here a 15-minute gap in blood pressure monitoring was caused by a failure to initiate the auto-cycle feature of the noninvasive blood pressure cuff.

Figure 2. Data Collection Periods (Baseline and Intervention)

Schema showing the data collection periods for the three hospitals included in the study. Hospital A provided baseline data from two prior calendar years (9/06–4/07, 9/07–4/08), data from the intervention period (9/08–4/09), and data from the follow-up period (8/09 to 10/09). Hospital B provided baseline data from the prior calendar year (5/07 to 5/08) and data from after the initial intervention (7/08 to 4/09). A second intervention was also undertaken at Hospital B (8/09 to 10/09). Hospital C provided baseline data for five years (9/04–7/05, 9/05–7/06, 9/06–7/07, 9/07–7/08, 9/08–7/09) and from the follow-up period (8/09 to 10/09). Note: during the follow-up period the intervention at Hospital A was unchanged, but at hospital B the allowed gap duration was shortened (from 10 minutes to 6 minutes) in a deliberate attempt to improve performance. Hospital C continued to serve as our control, and had no intervention during the follow-up period.

Figure 3

Panel A shows a schematic overview of the function of the automatic chart review system. The dashed line indicates a recursive loop that runs every minute. This system was implemented independently and using different software resources at two centers (Hospitals A and B). An example of the pop-up output is shown in Panel B. This appeared on the anesthesia information management system workstation in the provider's operating room. Additionally, at Hospital A an alphanumeric text page was sent to the individual who had entered their name as the anesthetist for the case.

Figure 4. Distribution of Cases with Gaps by Provider at Hospital A and Hospital B

Distribution of all blood pressure gaps at Hospital A and Hospital B by provider. A blood pressure gap was considered to be present if there was at least one 10 min interval without a blood pressure recorded in the anesthesia information management systems, during the period from induction of anesthesia to the end of surgery / extubation. The number of gaps for each provider with at least one case with a blood pressure gap during the study period (blue bars) is plotted as a percentage of the total number of gaps. Providers were numbered sequentially starting with 1 so that the cumulative number of staff is indicated by the x intercept of each point along the cumulative percentage curve (red line).

Figure 4. Distribution of Cases with Gaps by Provider at Hospital A and Hospital B

Distribution of all blood pressure gaps at Hospital A and Hospital B by provider. A blood pressure gap was considered to be present if there was at least one 10 min interval without a blood pressure recorded in the anesthesia information management systems, during the period from induction of anesthesia to the end of surgery / extubation. The number of gaps for each provider with at least one case with a blood pressure gap during the study period (blue bars) is plotted as a percentage of the total number of gaps. Providers were numbered sequentially starting with 1 so that the cumulative number of staff is indicated by the x intercept of each point along the cumulative percentage curve (red line).

Figure 5. Root Cause Analysis of Arterial Blood Pressure Gaps in 400 Cases at Hospital A and B

Root cause analysis of 400 randomly selected blood pressure gaps (200 at Hospital A, 200 at Hospital B). Three causes accounted for at least 10% of all gaps during the intraoperative period (95% lower confidence limit): indeterminate, position change, and waiting for postanesthesia care unit. *See footnote in the results section.