A comparison of vital signs charted by nurses with automated acquired values using waveform quality indices

Delay Between Actual Occurrence of Patient Vital Sign and the Nominal Appearance in the Electronic Health Record: Single-Center, Retrospective Study of PICU Data, 2014-2018

OBJECTIVES

Patient vital sign data charted in the electronic health record (EHR) are used for time-sensitive decisions, yet little is known about when these data become nominally available compared with when the vital sign was actually measured. The objective of this study was to determine the magnitude of any delay between when a vital sign was actually measured in a patient and when it nominally appears in the EHR.

DESIGN

We performed a single-center retrospective cohort study.

SETTING

Tertiary academic children's hospital.

PATIENTS

A total of 5,458 patients were admitted to a PICU from January 2014 to December 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed entry and display times of all vital signs entered in the EHR. The primary outcome measurement was time between vital sign occurrence and nominal timing of the vital sign in the EHR. An additional outcome measurement was the frequency of batch charting. A total of 9,818,901 vital sign recordings occurred during the study period. Across the entire cohort the median (interquartile range [IQR]) difference between time of occurrence and nominal time in the EHR was in hours:minutes:seconds, 00:41:58 (IQR 00:13:42-01:44:10). Lag in the first 24 hours of PICU admission was 00:47:34 (IQR 00:15:23-02:19:00), lag in the last 24 hours was 00:38:49 (IQR 00:13:09-01:29:22; p < 0.001). There were 1,892,143 occurrences of batch charting.

CONCLUSIONS

This retrospective study shows a lag between vital sign occurrence and its appearance in the EHR, as well as a frequent practice of batch charting. The magnitude of the delay-median ~40 minutes-suggests that vital signs available in the EHR for clinical review and incorporation into clinical alerts may be outdated by the time they are available.

Acute vital signs changes are underrepresented by a conventional electronic health record when compared with automatically acquired data in a single-center tertiary pediatric cardiac intensive care unit

OBJECTIVE

We sought to evaluate the fidelity with which the patient's clinical state is represented by the electronic health record (EHR) flow sheet vital signs data compared to a commercially available automated data aggregation platform in a pediatric cardiac intensive care unit (CICU).

METHODS

This is a retrospective observational study of heart rate (HR), systolic blood pressure (SBP), respiratory rate (RR), and pulse oximetry (SpO2) data archived in a conventional EHR and an automated data platform for 857 pediatric patients admitted postoperatively to a tertiary pediatric CICU. Automated data captured for 72 h after admission were analyzed for significant HR, SBP, RR, and SpO2 deviations from baseline (events). Missed events were identified when the EHR failed to reflect the events reflected in the automated platform.

RESULTS

Analysis of 132 054 622 data entries, including 264 966 (0.2%) EHR entries and 131 789 656 (99.8%) automated entries, identified 15 839 HR events, 5851 SBP events, 9648 RR events, and 2768 SpO2 events lasting 3-60 min; these events were missing in the EHR 48%, 58%, 50%, and 54% of the time, respectively. Subanalysis identified 329 physiologically implausible events (eg, likely operator or device error), of which 104 (32%) were nonetheless documented in the EHR.

CONCLUSION

In this single-center retrospective study of CICU patients, EHR vital sign documentation was incomplete compared to an automated data aggregation platform. Significant events were underrepresented by the conventional EHR, regardless of event duration. Enrichment of the EHR with automated data aggregation capabilities may improve representation of patient condition.

Discrete Heart Rate Values or Continuous Streams? Representation, Variability, and Meaningful Use of Vital Sign Data

Documentation and review of patient heart rate are a fundamental process across a myriad of clinical settings. While historically recorded manually, bedside monitors now provide for the automated collection of such data. Despite the availability of continuous streaming data, patients' charts continue to reflect only a subset of this information as snapshots recorded throughout a hospitalization. Over the past decade, prominent works have explored the implications of such practices and established fundamental differences in the alignment of discrete charted vitals and steaming data captured by monitoring systems. Limited work has examined the temporal properties of these differences, how they manifest, and their relation to clinical applications. The work presented in this article addresses this disparity, providing evidence that differences between charting techniques extend to measures of variability. Our results demonstrate how variability manifests with respect to temporal elements of charting timing and how it can facilitate personalized care by contextualizing deviations in magnitude. This work also highlights the utility of variability metrics with relation to clinical measures including associations to severity scores and a case study utilizing complex variability metrics derived from the complete set of monitor data.

Data Entry Automation Improves Cost, Quality, Performance, and Job Satisfaction in a Hospital Nursing Unit

OBJECTIVE

An Automated Data Entry Process Technology tool was developed to free nurses from data entry tasks, thus creating time for patient care and other activities associated with improvements in performance and job satisfaction.

BACKGROUND

Manually transferring data from patient measurement devices to electronic health records (EHRs) is an intensive, error-prone task that diverts nurses from patient care while adversely affecting job performance and employee satisfaction.

METHODS

Performance improvement analytics were used to compare matched sets of manual and automated EHR data entries for 1933 consecutive vital signs records created by 49 RNs and certified nursing assistants in a 23-bed medical-surgical unit at a large tertiary hospital. Performance and quality effects were evaluated via nurses' responses to a postintervention survey.

RESULTS

Data errors decreased from approximately 20% to 0; data transfer times were reduced by 5 minutes to 2 hours per measurement event; nurses had more time for direct patient care; and job satisfaction improved.

CONCLUSION

Data entry automation eliminates data errors, substantially reduces delays in getting data into EHRs, and improves job satisfaction by giving nurses more time for direct patient care. Findings are associated with improvements in quality, work performance, and job satisfaction, key goals of nursing leaders.

Smoothing Effect in Vital Sign Recordings: Fact or Fiction? A Retrospective Cohort Analysis of Manual and Continuous Vital Sign Measurements to Assess Data Smoothing in Postoperative Care

BACKGROUND:

Data smoothing of vital signs has been reported in the anesthesia literature, suggesting that clinical staff are biased toward measurements of normal physiology. However, these findings may be partially explained by clinicians interpolating spurious values from noisy signals and by the undersampling of physiological changes by infrequent manual observations. We explored the phenomenon of data smoothing using a method robust to these effects in a large postoperative dataset including respiratory rate, heart rate, and oxygen saturation (Spo₂). We also assessed whether the presence of the vital sign taker creates an arousal effect.

METHODS:

Study data came from a UK upper gastrointestinal postoperative ward (May 2009 to December 2013). We compared manually recorded vital sign data with contemporaneous continuous data recorded from monitoring equipment. We proposed that data smoothing increases differences between vital sign sources as vital sign abnormality increases. The primary assessment method was a mixed-effects model relating continuous-manual differences to vital sign values, adjusting for repeated measurements. We tested the regression slope significance and predicted the continuous-manual difference at clinically important vital sign values. We calculated limits of agreement (LoA) between vital sign sources using the Bland–Altman method, adjusting for repeated measures. Similarly, we assessed whether the vital sign taker affected vital signs, comparing continuous data before and during manual recording.

RESULTS:

From 407 study patients, 271 had contemporaneous continuous and manual recordings, allowing 3740 respiratory rate, 3844 heart rate, and 3896 Spo₂ paired measurements for analysis. For the model relating continuous-manual differences to continuous-manual average vital sign values, the regression slope (95% confidence interval) was 0.04 (−0.01 to 0.10; P = .11) for respiratory rate, 0.04 (−0.01 to 0.09; P = .11) for heart rate, and 0.10 (0.07–0.14; P < .001) for Spo₂. For Spo₂ measurements of 91%, the model predicted a continuous-manual difference (95% confidence interval) of −0.88% (−1.17% to −0.60%). The bias (LoA) between measurement sources was −0.74 (−7.80 to 6.32) breaths/min for respiratory rate, −1.13 (−17.4 to 15.1) beats/min for heart rate, and −0.25% (−3.35% to 2.84%) for Spo₂. The bias (LoA) between continuous data before and during manual observation was −0.57 (−5.63 to 4.48) breaths/min for respiratory rate, −0.71 (−10.2 to 8.73) beats/min for heart rate, and −0.07% (−2.67% to 2.54%) for Spo₂.

CONCLUSIONS:

We found no evidence of data smoothing for heart rate and respiratory rate measurements. Although an effect exists for Spo₂ measurements, it was not clinically significant. The wide LoAs between continuous and manually recorded vital signs would commonly result in different early warning scores, impacting clinical care. There was no evidence of an arousal effect caused by the vital sign taker.

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data

AIMS OF STUDY

To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores.

MATERIALS AND METHODS

We used an unsupervised approach to investigate the statistical properties of vital signs in an in-hospital patient population and construct an early-warning score from a "development" dataset. We evaluated scoring systems on a separate "validation" dataset. We assessed the ability of scores to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission, or death, each within 24 h of a given vital-sign observation, using metrics including the area under the receiver-operating characteristic curve (AUC).

RESULTS

The development dataset contained 301,644 vital sign observations from 12,153 admissions (median age (IQR): 63 (49-73); 49.2% females) March 2014-September 2015. The validation dataset contained 1,459,422 vital-sign observations from 53,395 admissions (median age (IQR): 68 (48-81), 51.4% females) October 2015-May 2017. The AUC (95% CI) for the mCEWS was 0.868 (0.864-0.872), comparable with the National EWS, 0.867 (0.863-0.871), and other recently proposed scores. The AUC for cCEWS was 0.808 (95% CI, 0.804-0.812). The improvement in performance in comparison to the continuous CEWS was mainly explained by respiratory rate threshold differences.

CONCLUSIONS

Performance of an EWS is highly dependent on the database from which itis derived. Our unsupervised statistical approach provides a straightforward, reproducible method to enable the rapid development of candidate EWS systems.

Integrating Real Time Data to Improve Outcomes in Acute Kidney Injury

Critically ill patients with acute kidney injury requiring renal replacement therapy have a poor prognosis. Despite well-known factors, which contribute to outcomes, including dose delivery, patients frequently miss the target dose and volume removal. One major barrier to effective care of these patients is the traditional dissociation of dialysis device data from other clinical information systems, notably the electronic health record (EHR). This lack of integration and the resulting manual documentation leads to errors and biases in documentation and missed opportunities to intervene in a timely fashion. This review summarizes the technological advancements facilitating direct connection of dialysis devices to EHRs. This connection facilitates automated data capture of many variables - including delivered dose, ultrafiltration rate and pressure measurements - which in turn can be leveraged for data mining, quality improvement and real-time targeted therapy adjustments. These interventions hold the promise to significantly improve outcomes for this patient population.

Multimodal noninvasive monitoring of soft tissue wound healing

Here we report results of non-invasive measurements of indirect markers of soft tissue healing of traumatic wounds in an observational swine study and describe the quantification of analog physiological signals. The primary purpose of the study was to measure bone healing of fractures with four different wound treatments. A second purpose was to quantify soft tissue wound healing by measuring the following indirect markers: (1) tissue oxygenation, (2) fluid content, and (3) blood flow, which were all measured by non-invasive modalities, measured with available devices. Tissue oxygenation was measured by near infrared spectroscopy; fluid content was measured by bipolar bio-impedance; and blood flow was measured by Doppler ultrasound. Immediately after comminuted femur fractures were produced in the right hind legs of thirty anesthetized female Yorkshire swine, one of four wound treatments was instilled into each wound. The four wound treatments were as follows: salmon fibrinogen/thrombin-n = 8; commercial bone filler matrix-n = 7; bovine collagen-n = 8; porcine fibrinogen/thrombin-n = 7. Fractures were stabilized with an external fixation device. Immediately following wound treatments, measurements were made of tissue oxygenation, fluid content and blood flow; these measurements were repeated weekly for 3 weeks after surgery. Analog signals of each modality were recorded on both the wounded (right) hind leg and the healthy (left) hind leg, for comparison purposes. Data were processed off-line. The mean values of 10-s periods were calculated for right-left leg comparison. ANOVA was applied for statistical analysis. Results of the bone healing studies are published separately (Rothwell et al. in J Spec Oper Med 13:7-18, 2013). For soft tissue wounds, healing did not differ significantly among the four wound treatments; however, regional oxygenation of wounds treated with salmon fibrinogen/thrombin showed slightly different time trends. Further studies are needed to establish standards for healthy wound healing and for detection of pathological alterations such as infection. Non-invasive measurement and quantification of indirect markers of soft tissue wound healing support the goals and principles of evidence-based medicine and show potential as easy to administer tools for clinicians and battlefield medical personnel to apply when procedures such as the PET scan are not available or affordable. The method we developed for storing analog physiological signals could be used for maintaining electronic health records, by incorporating vital signs such as ECG and EEG, etc.

A subspace decomposition approach toward recognizing valid pulsatile signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for the prediction of critical intracranial and cerebrovascular pathophysiological variations during the management of many neurological disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is capable of enhancing the quality of ICP signals, recognizing valid (not contaminated with noise or artifacts) ICP pulses and designating the locations of the three ICP sub-peaks in a pulse. This paper extends the algorithm by proposing a singular value decomposition (SVD) technique to replace the correlation-based approach originally utilized in recognizing valid ICP pulses. The validation of the proposed method is conducted on a large database of ICP signals built from 700 h of recordings from 67 neurosurgical patients. A comparative analysis of the valid ICP recognition using the proposed SVD technique and the correlation-based method demonstrates a significant improvement in terms of (1) accuracy (61.96% reduction in the false positive rate while keeping the true positive rate as high as 99.08%) and (2) computational time (91.14% less time consumption), all in favor of the proposed method. Finally, this SVD-based valid pulse recognition can be potentially applied to process pulsatile signals other than ICP because no proprietary ICP features are incorporated in the algorithm.