Building the IMPROVE Data Library

Association of Data Integration Technologies With Intensive Care Clinician Performance: A Systematic Review and Meta-analysis

IMPORTANCE

Sources of data in the intensive care setting are increasing exponentially, but the benefits of displaying multiparametric, high-frequency data are unknown. Decision making may not benefit from this technology if clinicians remain cognitively overburdened by poorly designed data integration and visualization technologies (DIVTs).

OBJECTIVE

To systematically review and summarize the published evidence on the association of user-centered DIVTs with intensive care clinician performance.

DATA SOURCES

MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, and Web of Science were searched in May 2014 and January 2018.

STUDY SELECTION

Studies had 3 requirements: (1) the study tested a viable DIVT, (2) participants involved were intensive care clinicians, and (3) the study reported quantitative results associated with decision making in an intensive care setting.

DATA EXTRACTION AND SYNTHESIS

Of 252 records screened, 20 studies, published from 2004 to 2016, were included. The human factors framework to assess health technologies was applied to measure study completeness, and the Quality Assessment Instrument was used to assess the quality of the studies. PRISMA guidelines were adapted to conduct the systematic review and meta-analysis.

MAIN OUTCOMES AND MEASURES

Study completeness and quality; clinician performance; physical, mental, and temporal demand; effort; frustration; time to decision; and decision accuracy.

RESULTS

Of the 20 included studies, 16 were experimental studies with 410 intensive care clinician participants and 4 were survey-based studies with 1511 respondents. Scores for study completeness ranged from 27 to 43, with a maximum score of 47, and scores for study quality ranged from 46 to 79, with a maximum score of 90. Of 20 studies, DIVTs were evaluated in clinical settings in 2 studies (10%); time to decision was measured in 14 studies (70%); and decision accuracy was measured in 11 studies (55%). Measures of cognitive workload pooled in the meta-analysis suggested that any DIVT was an improvement over paper-based data in terms of self-reported performance, mental and temporal demand, and effort. With a maximum score of 22, median (IQR) mental demand scores for electronic display were 10 (7-13), tabular display scores were 8 (6.0-11.5), and novel visualization scores were 8 (6-12), compared with 17 (14-19) for paper. The median (IQR) temporal demand scores were also lower for all electronic visualizations compared with paper, with scores of 8 (6-11) for electronic display, 7 (6-11) for tabular and bar displays, 7 (5-11) for novel visualizations, and 16 (14.3-19.0) for paper. The median (IQR) performance scores improved for all electronic visualizations compared with paper (lower score indicates better self-reported performance), with scores of 6 (3-11) for electronic displays, 6 (4-11) for tabular and bar displays, 6 (4-11) for novel visualizations, and 14 (11-16) for paper. Frustration and physical demand domains of cognitive workload did not change, and differences between electronic displays were not significant.

CONCLUSIONS AND RELEVANCE

This review suggests that DIVTs are associated with increased integration and consistency of data. Much work remains to identify which visualizations effectively reduce cognitive workload to enhance decision making based on intensive care data. Standardizing human factors testing by developing a repository of open access benchmarked test protocols, using a set of outcome measures, scenarios, and data sets, may accelerate the design and selection of the most appropriate DIVT.

Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices

The current anaesthesia information management system (AIMS) has limited capability for the acquisition of high-quality vital signs data. We have developed a Vital Recorder program to overcome the disadvantages of AIMS and to support research. Physiological data of surgical patients were collected from 10 operating rooms using the Vital Recorder. The basic equipment used were a patient monitor, the anaesthesia machine, and the bispectral index (BIS) monitor. Infusion pumps, cardiac output monitors, regional oximeter, and rapid infusion device were added as required. The automatic recording option was used exclusively and the status of recording was frequently checked through web monitoring. Automatic recording was successful in 98.5% (4,272/4,335) cases during eight months of operation. The total recorded time was 13,489 h (3.2 ± 1.9 h/case). The Vital Recorder's automatic recording and remote monitoring capabilities enabled us to record physiological big data with minimal effort. The Vital Recorder also provided time-synchronised data captured from a variety of devices to facilitate an integrated analysis of vital signs data. The free distribution of the Vital Recorder is expected to improve data access for researchers attempting physiological data studies and to eliminate inequalities in research opportunities due to differences in data collection capabilities.

CSE database: extended annotations and new recommendations for ECG software testing

Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.

Two open access, high-quality datasets from anesthetic records

OBJECTIVE

To provide a set of high-quality time-series physiologic and event data from anesthetic cases formatted in an easy-to-use structure.

MATERIALS AND METHODS

With ethics committee approval, data from surgical operations under general anesthesia were collected, including physiologic data, drug administrations, events, and clinicians' comments. These data were de-identified, formatted in a combined CSV/XML structure and made publicly available.

RESULTS

Two separate datasets were collected containing physiologic time-series data and time-stamped events for 34 patients. For 20 patients, the data included 400 physiologic signals collected over 20 h, 274 events, and 597 drug administrations. For 14 patients, the data included 23 physiologic signals collected over 69 h, with 286 time stamped comments.

DISCUSSION

Data reuse potentially saves significant time and financial costs. However, there are few high-quality repositories for accessible physiologic data and clinical interventions from surgical cases. De-identifying records assists with overcoming problems of privacy and storing the data in a format which is easily manipulated with computing resources facilitates access by the wider research community. It is hoped that additional high-quality data will be added. Future work includes developing tools to explore and visualize the data more efficiently, and establishing quality control measures.

CONCLUSION

An approach to collecting and storing high-quality datasets from surgical operations under anesthesia such that they can be easily accessed by others for use in research has been demonstrated.

University of Queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research

BACKGROUND

Data recorded from the devices used to monitor a patient's vital signs are often used in the development of displays, alarms, and information systems, but high-resolution, multiple-parameter datasets of anesthesia monitoring data from patients during anesthesia are often difficult to obtain. Existing databases have typically been collected from patients in intensive care units. However, the physical state of intensive care patients is dissimilar to those undergoing surgery, more frequent and marked changes to cardiovascular and respiratory variables are seen in operating room patients, and additional and highly relevant information to anesthesia (e.g., end-tidal agent monitoring, etc.) is omitted from these intensive care databases. We collected a set of high-quality, high-resolution, multiple-parameter monitoring data suitable for anesthesia monitoring research.

METHODS

Vital signs data were recorded from patients undergoing anesthesia at the Royal Adelaide Hospital. Software was developed to capture, time synchronize, and interpolate vital signs data from Philips IntelliVue MP70 and MP30 patient monitors and Datex-Ohmeda Aestiva/5 anesthesia machines into 10 millisecond resolution samples. The recorded data were saved in a variety of accessible file formats.

RESULTS

Monitoring data were recorded from 32 cases (25 general anesthetics, 3 spinal anesthetics, 4 sedations) ranging in duration from 13 minutes to 5 hours (median 105 min). Most cases included data from the electrocardiograph, pulse oximeter, capnograph, noninvasive arterial blood pressure monitor, airway flow, and pressure monitor and, in a few cases, the Y-piece spirometer, electroencephalogram monitor, and arterial blood pressure monitor. Recorded data were processed and saved into 4 file formats: (1) comma-separated values text files with full numerical and waveform data, (2) numerical parameters recorded in comma-separated values files at 1-second intervals, (3) graphical plots of all waveform data in a range of resolutions as Portable Network Graphics image files, and (4) graphical overview plots of numerical data for entire cases as Portable Network Graphics and Scalable Vector Graphics files. The complete dataset is freely available online via doi:102.100.100/6914 and has been listed in the Australian National Data Service Collections Registry.

DISCUSSION

The present dataset provides clinical anesthesia monitoring data from entire surgical cases where patients underwent anesthesia, includes a wide range of vital signs variables that are commonly monitored during surgery, and is published in accessible, user-friendly file formats. The text and image file formats let researchers without engineering or computer science backgrounds easily access the data using standard spreadsheet and image browsing software. In future work, monitoring data should be collected from a wider range and larger number of cases, and software tools are needed to support searching and navigating the database.

A far-view intensive care unit monitoring display enables faster triage

Although nurses perform the majority of the clinical tasks in an intensive care unit, current patient monitors were not designed to support a nurse's workflow. Nurses constantly triage patients, deciding which patient is currently in the most need of care. To make this decision, nurses must observe the patient's vital signs and therapeutic device information from multiple sources. To obtain this information, they often have to enter the patient's room. This study addresses 3 hypotheses. Information provided by far-view monitoring displays (1) reduces the amount of time to determine which patient needs care first, (2) increases the accuracy of assigning priority to the right patient, and (3) reduces nurses mental workload. We developed 2 far-view displays to be read from a distance of 3 to 5 m without entering the patient's room. Both display vital signs, trends, alarms, infusion pump status, and therapy support indicators. To evaluate the displays, nurses were asked to use the displays to decide which of 2 patients required their attention first. They made 60 decisions: 20 with each far-view display and 20 decisions with a standard patient monitor next to an infusion pump. Sixteen nurses (median age of 27.5 years with 2.75 years of experience) participated in the study. Using the 2 far-view displays, nurses more accurately and rapidly identified stable patients and syringe pumps that were nearly empty. Median decision times were 11.3 and 12.4 seconds for the 2 far-view displays and 17.2 seconds for the control display. The 2 far-view displays reduced median decision-making times by 4.8 to 5.9 seconds, increased accuracy in assignment of priority in 2 of 7 patient conditions, and reduced nurses' frustration with the triaging task. In a clinical setting, the proposed far-view display might reduce nurses' mental workload and thereby increase patient safety.

Using "off-the-shelf" tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned

Until now, the creation of massive (long-term and multichannel) waveform databases in intensive care required an interdisciplinary team of clinicians, engineers and informaticians and, in most cases, also design-specific software and hardware development. Recently, several commercial software tools for waveform acquisition became available. Although commercial products and even turnkey systems are now being marketed as simple and effective, the performance of those solutions is not known. The additional expense upfront may be worthwhile if commercial software can eliminate the need for custom software and hardware systems and the associated investment in teams and development. We report the development of a computer system for long-term large-scale recording and storage of multichannel physiologic signals that was built using commercial solutions (software and hardware) and existing hospital IT infrastructure. Both numeric (1 Hz) and waveform (62.5-500 Hz) data were captured from 24 SICU bedside monitors simultaneously and stored in a file-based vital sign data bank (VSDB) during one-year period (total DB size is 4.21TB). In total, vital signs were recorded from 1,175 critically ill patients. Up to six ECG leads, all other monitored waveforms, and all monitored numeric data were recorded in most of the cases. We describe the details of building blocks of our system, provide description of three datasets exported from our VSDB and compare the contents of our VSDB with other available waveform databases. Finally, we summarize lessons learned during recording, storage, and pre-processing of physiologic signals.

ICASP: An Intensive-Care Acquisition and Signal Processing Integrated Framework

This paper presents an intensive-care acquisition and signal processing integrated framework in the area of intensive care units. The framework includes nearly all monitored biosignals in the intensive care, along with metadata and processing results. It is structured on two basic applications, i.e., the acquisition and the database one, running in two different PCs that are connected through a local area network, facilitating real-time data exchange between them. The analytical rundown shows that the proposed framework is a serious effort to give a complete clinical condition of a patient and a form of a diagnostic analysis implement in the intensive care by taking in real-time processing.

Physiologic data acquisition system and database for the study of disease dynamics in the intensive care unit

OBJECTIVE

To describe a real-time, continuous physiologic data acquisition system for the study of disease dynamics in the intensive care unit.

DESIGN

Descriptive report.

SETTING

A 16-bed pediatric intensive care unit in a tertiary care children's hospital.

PATIENTS

A total of 170 critically ill or injured pediatric patients.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

None.

RESULTS

We describe a computerized data acquisition and analysis system for the study of critical illness and injury from the perspective of complex dynamic systems. Both parametric (1 Hz) and waveform (125-500 Hz) signals are recorded and analyzed. Waveform data include electrocardiogram, respiration, systemic arterial pressure (invasive and noninvasive), central venous pressure, pulmonary arterial pressure, left and right atrial pressures, intracranial pressure, body temperature, and oxygen saturation. Details of the system components are explained and examples are given from the resultant physiologic database of signal processing algorithms and signal analyses using linear and nonlinear metrics.

CONCLUSIONS

We have successfully developed a real-time, continuous physiologic data acquisition system that can capture, store, and archive data from pediatric intensive care unit patients for subsequent time series analysis of dynamic changes in physiologic state. The physiologic signal database generated from this system is available for analysis of dynamic changes caused by critical illness and injury.

Human factors error and patient monitoring

A wide range of studies have shown that human factors errors are the major cause of critical incidents that threaten patient safety in the medical environments where patient monitoring takes place, contributing to approximately 87% of all such incidents. Studies have also shown that good cognitively ergonomic design of monitoring equipment for use in these environments should reduce the human factors errors associated with the information they provide. The purpose of this review is to consider the current state of knowledge concerning human factors engineering in its application to patient monitoring. It considers the prevalence of human factors error, principles of good human factors design, the effect of specific design features and the problem of the measurement of the effectiveness of designs in reducing human factors error. The conclusion of the review is that whilst the focus of human factors studies has, in recent years, moved from instrument design to organizational issues, patient monitor designers still have an important contribution to make to improving the safety of the monitored patient. Further, whilst better psychological understanding of the causes of human factors errors will in future guide better human factors engineering, in this area there are still many practical avenues of research that need exploring from the current base of understanding.

Technical description of the IBIS data library. Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery

The IBIS Data Library (DL) is an annotated data library that contains practically all the monitored data and other clinical information from critically ill patients during surgery and in intensive care. The data have been collected at three sites: the intensive care unit of the Kuopio University Hospital, Finland; Royal Brompton Hospital, London, UK; and St. Bartholomew's Hospital, London, UK. The purpose of the DL is to form the basis for development of biosignal interpretation methods in the Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery project in the European Union (EU) BIOMED2 programme (BMH4-97-2570). The DL contains continuous electroencephalography signals, multimodal evoked potential recordings and diagnostic electrocardiography recorded during intensive care and surgery. In addition, signal types similar to those recorded during an earlier project, the EU-BIOMED1 project IMPROVE, are stored in the DL. In addition, trend data from patient monitors, laboratory data, annotations, nursing actions, and medications recorded and stored by a Patient Data Management System (PDMS) during routine care are included. The data obtained routinely are complemented by special annotations made by a physician who observes the patient during the data collection session. Annotations include, for example, assessment of the awareness of the patient and specific events during surgery not recorded routinely by the PDMS. Inclusion of information about the care plan and the aims of the care make the contents of the DL complete. The present paper describes the technical set-up used for recording of the DL and the contents of the DL. The paper also includes an appendix defining a new data format, the extended evoked potentials format, used for storage of sweep data in the DL.

Context related artefact detection in prolonged EEG recordings

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. Although different EEG analysis systems have been described, only few general applicable artefact recognition techniques have emerged. This paper tackles the problem of artefact detection in seven 24 h EEG recordings in the intensive care unit. ICU recordings have received less attention than, e.g. epilepsy monitoring, although recordings in this environment present an interesting application area. The EEG data used here was recorded during the difficult circumstances of an explorative ICU study. The data set includes a diverse set of EEG patterns, as well as EEG artefacts. The study investigates objective artefact detection methods based on statistical differences between signal parameters, using time-varying autoregressive modelling (AR) and Slope detection. In addition to matching the performance of artefact detection against two human observers, the study focuses on the optimal settings for context incorporation by testing the algorithms for different time windows and epoch lengths. Results indicate that a relatively short period (20-40 s) provides sufficient context information for the methods used. The combined AR and Slope detection parameters yielded good performance, detecting approximately 90% of the artefacts as indicated by the consensus score of the human observers.

Digital archival and exchange of events in a simple format for polygraphic recordings with application in event related potential studies

This paper describes a simple method of event encoding as an extension to a previously defined standard format, the European Data Format (EDF). The specification ensures full backward compatibility with the existing definition. By using this extension, the format can be used to store both continuous recordings and selected epochs of recordings. The encoding is performed in a channel of event-codes or in a pseudo-channel for annotations. Standardisation of event encoding is discussed. Decoding of events or annotations from the extended format is implemented at the application level. Existing programs that do not support the new encoding scheme still operate correctly and can simply ignore the new channels in processing 'extended' data files. The event encoding is also compatible with EDF's capability to encode channels of different sampling frequency.