Information technology in critical care: review of monitoring and data acquisition systems for patient care and research

Bedside monitoring tools and advanced signal processing approaches to monitor critically-ill infants

There is a substantial body of literature that supports neonatal monitoring and signal analysis of the collected data to provide valuable insights for improving patient clinical care and to inform new research studies. This comprehensive monitoring approach extends beyond the collection of conventional vital signs to include the acquisition of continuous waveform data from patient monitors and other bedside medical devices. This paper discusses the necessary infrastructure for waveform retrieval from bedside monitors, and explores options provided by leading healthcare companies, third-party vendors or academic research teams to implement scalable monitoring systems across entire critical care units. Additionally, we discuss the application of advanced signal processing that transcend traditional statistics, including heart rate variability in both the time- and frequency-domains, spectral analysis of EEG, and cerebral pressure autoregulation. The infrastructures and signal processing techniques outlined here are indispensable tools for intensivists, empowering them to enhance care for critically ill infants. In addition, we briefly address the emergence of advanced tools for fetal monitoring.

Using electronic health records to classify risk for adverse safety events with ICU patient Mobility: A Cross-Sectional study

BACKGROUND

Integrating early mobility (EM) expert consensus recommendations into an algorithm that uses electronic health record (EHR) data provides an opportunity for ICU nurse decision support.

OBJECTIVE

This study aimed to compare clinical differences in ICU EM algorithm domains among patients with and without documented EM and examine discordance between algorithm classification and documented EM.

METHODS

Secondary analysis of EHR data from adults admitted to the ICU from one health system's electronic data warehouse. We extracted demographic, clinical, and EM data for up to the first three days after ICU admission and applied the algorithm to classify patients as low/high-risk by clinical domain (respiratory, cardiovascular, neurological, activity order, overall) each day. We used the Wilcoxon rank sum test or Fisher's exact test to compare clinical criteria and algorithm classification between patients with and without documented EM.

RESULTS

From a total of 4,088 patients, documented EM increased each ICU day. Patients with EM were more likely to be classified by the algorithm as low-risk than those who stayed in bed each day. While a large proportion of low-risk patients remained in bed each day (813 day 1; 920 day 2; 881 day 3), some patients classified as high-risk had documented EM.

CONCLUSIONS

A significant portion of patients identified as overall low-risk by the algorithm remained in bed, while some high-risk patients achieved EM. Variability between risk definitions and documented patient activity exists and understanding additional factors that nurses use to support EM decision-making is needed.

IMPLICATIONS FOR CLINICAL PRACTICE

EHR data can be used with a mobility algorithm to classify patients at low and high-risk for ICU EM. In the future, with additional refinements, an algorithm may augment clinician decision-making.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

BACKGROUND

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

OBJECTIVE

This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

METHODS

Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

RESULTS

Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

CONCLUSIONS

The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BACKGROUND

This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

METHODS

Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.

RESULTS

A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients.

CONCLUSIONS

The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Cerebral autoregulation in traumatic brain injury: ultra-low-frequency pressure reactivity index and intracranial pressure across age groups

BACKGROUND

The ultra-low-frequency pressure reactivity index (UL-PRx) has been established as a surrogate method for bedside estimation of cerebral autoregulation (CA). Although this index has been shown to be a predictor of outcome in adult and pediatric patients with traumatic brain injury (TBI), a comprehensive evaluation of low sampling rate data collection (0.0033 Hz averaged over 5 min) on cerebrovascular reactivity has never been performed.

OBJECTIVE

To evaluate the performance and predictive power of the UL-PRx for 12-month outcome measures, alongside all International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) models and in different age groups. To investigate the potential for optimal cerebral perfusion pressure (CPPopt).

METHODS

Demographic data, IMPACT variables, in-hospital mortality, and Glasgow Outcome Scale Extended (GOSE) at 12 months were extracted. Filtering and processing of the time series and creation of the indices (cerebral intracranial pressure (ICP), cerebral perfusion pressure (CPP), UL-PRx, and deltaCPPopt (ΔCPPopt and CPPopt-CPP)) were performed using an in-house algorithm. Physiological parameters were assessed as follows: mean index value, % time above threshold, and mean hourly dose above threshold.

RESULTS

A total of 263 TBI patients were included: pediatric (17.5% aged ≤ 16 y) and adult (60.5% aged > 16 and < 70 y and 22.0% ≥ 70 y, respectively) patients. In-hospital and 12-month mortality were 25.9% and 32.7%, respectively, and 60.0% of patients had an unfavorable outcome at 12 months (GOSE). On univariate analysis, ICP, CPP, UL-PRx, and ΔCPPopt were associated with 12-month outcomes. The cutoff of ~ 20-22 for mean ICP and of ~ 0.30 for mean UL-PRx were confirmed in all age groups, except in patients older than 70 years. Mean UL-PRx remained significantly associated with 12-month outcomes even after adjustment for IMPACT models. This association was confirmed in all age groups. UL-PRx resulted associate with CPPopt.

CONCLUSIONS

The study highlights UL-PRx as a tool for assessing CA and valuable outcome predictor for TBI patients. The results emphasize the potential clinical utility of the UL-PRx and its adaptability across different age groups, even after adjustment for IMPACT models. Furthermore, the correlation between UL-PRx and CPPopt suggests the potential for more targeted treatment strategies.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT05043545, principal investigator Paolo Gritti, date of registration 2021.08.21.

The use of information technology to improve interdisciplinary communication during infectious diseases ward rounds on the paediatric intensive care unit

Prospective audit with feedback during infectious diseases ward rounds (IDWR) is a common antimicrobial stewardship (AMS) practice on the Paediatric Intensive Care Unit (PICU). These interdisciplinary meetings rely on the quality of handover, with high risk of omission of information. We developed an electronic platform integrating infection-related patient data (COSARAPed). In the mixed PICU of a Belgian tertiary hospital we conducted an observational prospective cohort study comparing patient handovers during IDWRs using the COSARAPed-platform to those with access only to conventional resources. The quality of handover was investigated directly by assessment if the narrative was in accordance with Situation-Background-Assessment-Recommendation principles and if adequate demonstration of diagnostic information occurred, and also indirectly by registration if this was only achieved after intervention by the non-presenting AMS team members. We also recorded all AMS-recommendations. During a 6-month study period, 24 IDWRs and 82 patient presentations were assessed. We could only find a statistically significant advantage in favor of COSARAPed by indirect evaluation. We registered 92 AMS-recommendations, mainly resulting in reduced antibiotic pressure. We concluded that the IDWR is an appropriate platform for AMS on the PICU and that the utilisation of COSARAPed may enhance the quality of patient handover.

Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis

Objectives

To develop and validate a digital biomarker for predicting the onset of acute kidney injury (AKI) on an hourly basis up to 24 hours in advance in the intensive care unit after cardiac surgery.

Methods

The study analyzed data from 6056 adult patients undergoing coronary artery bypass graft and/or valve surgery between April 1, 2012, and December 31, 2018 (development phase, training, and testing) and 3572 patients between January 1, 2019, and June 30, 2022 (validation phase). The study used 2 dynamic predictive modeling approaches, namely logistic regression and bootstrap aggregated regression trees machine (BARTm), to predict AKI. The mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values across all lead times before the occurrence of AKI were reported. The clinical practicality was assessed using calibration.

Results

Of all included patients, 8.45% and 16.66% had AKI in the development and validation phases, respectively. When applied to testing data, AKI was predicted with the mean AUC of 0.850 and 0.802 by BARTm and logistic regression, respectively. When applied to validation data, BARTm and LR resulted in a mean AUC of 0.844 and 0.786, respectively.

Conclusions

This study demonstrated the successful prediction of AKI on an hourly basis up to 24 hours in advance. The digital biomarkers developed and validated in this study have the potential to assist clinicians in optimizing treatment and implementing preventive strategies for patients at risk of developing AKI after cardiac surgery in the intensive care unit.

The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence

Critical care practice has been embodied in the healthcare system since the institutionalization of intensive care units (ICUs) in the late '50s. Over time, this sector has experienced many changes and improvements in providing immediate and dedicated healthcare as patients requiring intensive care are often frail and critically ill with high mortality and morbidity rates. These changes were aided by innovations in diagnostic, therapeutic, and monitoring technologies, as well as the implementation of evidence-based guidelines and organizational structures within the ICU. In this review, we examine these changes in intensive care management over the past 40 years and their impact on the quality of care available to patients. Moreover, the current state of intensive care management is characterized by a multidisciplinary approach and the use of innovative technologies and research databases. Advancements such as telecritical care and artificial intelligence are being increasingly explored, especially since the COVID-19 pandemic, to reduce the length of hospitalization and ICU mortality. With these advancements in intensive care and ever-changing patient needs, critical care experts, hospital managers, and policymakers must also explore appropriate organizational structures and future enhancements within the ICU.

Exploring the clinical relevance of vital signs statistical calculations from a new-generation clinical information system

New information on the intensive care applications of new generation 'high-density data clinical information systems' (HDDCIS) is increasingly being published in the academic literature. HDDCIS avoid data loss from bedside equipment and some provide vital signs statistical calculations to promote quick and easy evaluation of patient information. Our objective was to study whether manual records of continuously monitored vital signs in the Paediatric Intensive Care Unit could be replaced by these statistical calculations. Here we conducted a prospective observational clinical study in paediatric patients with severe diabetic ketoacidosis, using a Medlinecare® HDDCIS, which collects information from bedside equipment (1 data point per parameter, every 3-5 s) and automatically provides hourly statistical calculations of the central trend and sample dispersion. These calculations were compared with manual hourly nursing records for patient heart and respiratory rates and oxygen saturation. The central tendency calculations showed identical or remarkably similar values and strong correlations with manual nursing records. The sample dispersion calculations differed from the manual references and showed weaker correlations. We concluded that vital signs calculations of central tendency can replace manual records, thereby reducing the bureaucratic burden of staff. The significant sample dispersion calculations variability revealed that automatic random measurements must be supervised by healthcare personnel, making them inefficient.

Evaluation and Application of Ultra-Low-Resolution Pressure Reactivity Index in Moderate or Severe Traumatic Brain Injury

BACKGROUND

The pressure reactivity index (PRx) has emerged as a surrogate method for the continuous bedside estimation of cerebral autoregulation and a predictor of unfavorable outcome after traumatic brain injury (TBI). However, calculation of PRx require continuous high-resolution monitoring currently limited to specialized intensive care units. The aim of this study was to evaluate a new index, the ultra-low-frequency PRx (UL-PRx) sampled at ∼0.0033 Hz at ∼5 minutes periods, and to investigate its association with outcome.

METHODS

Demographic data, admission Glasgow coma scale, in-hospital mortality and Glasgow outcome scale extended at 12 months were extracted from electronic records. The filtering and preparation of time series of intracranial pressure (ICP), mean arterial pressure and cerebral perfusion pressure (CPP), and calculation of the indices (UL-PRx, Δ-optimal CPP), were performed in MATLAB using an in-house algorithm.

RESULTS

A total of 164 TBI patients were included in the study; in-hospital and 12-month mortality was 29.3% and 38.4%, respectively, and 64% of patients had poor neurological outcome at 12 months. On univariate analysis, ICP, CPP, UL-PRx, and ΔCPPopt were associated with 12-month mortality. After adjusting for age, Glasgow coma scale, ICP and CPP, mean UL-PRx and UL-PRx thresholds of 0 and +0.25 remained associated with 12-month mortality. Similar findings were obtained for in-hospital mortality. For mean UL-PRx, the area under the receiver operating characteristic curves for in-hospital and 12-month mortality were 0.78 (95% confidence interval [CI]: 0.69-0.87; P <0.001) and 0.70 (95% CI: 0.61-0.79; P <0.001), respectively, and 0.65 (95% CI: 0.57-0.74; P =0.001) for 12-month neurological outcome.

CONCLUSIONS

Our findings indicate that ultra-low-frequency sampling might provide sufficient resolution to derive information about the state of cerebrovascular autoregulation and prediction of 12-month outcome in TBI patients.

The use of wireless sensors in the neonatal intensive care unit: a study protocol

Background

Continuous monitoring of vital signs and other biological signals in the Neonatal Intensive Care Unit (NICU) requires sensors connected to the bedside monitors by wires and cables. This monitoring system presents challenges such as risks for skin damage or infection, possibility of tangling around the patient body, or damage of the wires, which may complicate routine care. Furthermore, the presence of cables and wires can act as a barrier for parent-infant interactions and skin to skin contact. This study will investigate the use of a new wireless sensor for routine vital monitoring in the NICU.

Methods

Forty-eight neonates will be recruited from the Montreal Children's Hospital NICU. The primary outcome is to evaluate the feasibility, safety, and accuracy of a wireless monitoring technology called ANNE® One (Sibel Health, Niles, MI, USA). The study will be conducted in 2 phases where physiological signals will be acquired from the standard monitoring system and the new wireless monitoring system simultaneously. In phase 1, participants will be monitored for 8 h, on four consecutive days, and the following signals will be obtained: heart rate, respiratory rate, oxygen saturation and skin temperature. In phase 2, the same signals will be recorded, but for a period of 96 consecutive hours. Safety and feasibility of the wireless devices will be assessed. Analyses of device accuracy and performance will be accomplished offline by the biomedical engineering team.

Conclusion

This study will evaluate feasibility, safety, and accuracy of a new wireless monitoring technology in neonates treated in the NICU.

Evaluation and application of ultra-low-frequency pressure reactivity index in pediatric traumatic brain injury patients

PURPOSE

While clinical practice suggests that knowing the cerebral autoregulation (CA) status of traumatic brain injury (TBI) patients is crucial in assessing the best treatment, evidence in pediatric TBI (pTBI) is limited. The pressure reactivity index (PRx) is a surrogate method for the continuous estimation of CA in adults; however, calculations require continuous, high-resolution monitoring data. We evaluate an ultra-low-frequency pressure reactivity index (UL-PRx), based on data sampled at ∼5-min periods, and test its association with 6-month mortality and unfavorable outcome in a cohort of pTBI patients.

METHODS

Data derived from pTBI patients (0-18 years) requiring intracranial pressure (ICP) monitoring were retrospectively collected and processed in MATLAB using an in-house algorithm.

RESULTS

Data on 47 pTBI patients were included. UL-PRx mean values, ICP, cerebral perfusion pressure (CPP), and derived indices showed significant association with 6-month mortality and unfavorable outcome. A value of UL-PRx of 0.30 was identified as the threshold to better discriminate both surviving vs deceased patients (AUC: 0.90), and favorable vs unfavorable outcomes (AUC: 0.70) at 6 months. At multivariate analysis, mean UL-PRx and % time with ICP > 20 mmHg, remained significantly associated with 6-month mortality and unfavorable outcome, even when adjusted for International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)-Core variables. In six patients undergoing secondary decompressive craniectomy, no significant changes in UL-PRx were found after surgery.

CONCLUSIONS

UL-PRx is associated with a 6-month outcome even if adjusted for IMPACT-Core. Its application in pediatric intensive care unit could be useful to evaluate CA and offer possible prognostic and therapeutic implications in pTBI patients.

CLINICALTRIALS

GOV: NCT05043545, September 14, 2021, retrospectively registered.

Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review

BACKGROUND

Nursing data consist of observations of patients' conditions and information on nurses' clinical judgment based on critically ill patients' behavior and physiological signs. Nursing data in electronic health records were recently emphasized as important predictors of patients' deterioration but have not been systematically reviewed.

OBJECTIVE

We conducted a systematic review of prediction models using nursing data for clinical outcomes, such as prolonged hospital stay, readmission, and mortality in intensive care patients, compared to physiological data only. In addition, the type of nursing data used in prediction model developments was investigated.

DESIGN

A systematic review.

METHODS

PubMed, CINAHL, Cochrane CENTRAL, EMBASE, IEEE Xplore Digital Library, Web of Science, and Scopus were searched. Clinical outcome prediction models using nursing data for intensive care patients were included. Clinical outcomes were prolonged hospital stay, readmission, and mortality. Data were extracted from selected studies such as study design, data source, outcome definition, sample size, predictors, reference test, model development, model performance, and evaluation. The risk of bias and applicability was assessed using the Prediction model Risk of Bias Assessment Tool checklist. Descriptive summaries were produced based on paired forest plots and summary receiver operating characteristic curves.

RESULTS

Sixteen studies were included in the systematic review. The data types of predictors used in prediction models were categorized as physiological data, nursing data, and clinical notes. The types of nursing data consisted of nursing notes, assessments, documentation frequency, and flowsheet comments. The studies using physiological data as a reference test showed higher predictive performance in combined data or nursing data than in physiological data. The overall risk of bias indicated that most of the included studies have a high risk.

CONCLUSIONS

This study was conducted to identify and review the diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients. Most of the included studies developed models using nursing notes, and other studies used nursing assessments, documentation frequency, and flowsheet comments. Although the findings need careful interpretation due to the high risk of bias, the area under the curve scores of nursing data and combined data were higher than physiological data alone. It is necessary to establish a strategy in prediction modeling to utilize nursing data, clinical notes, and physiological data as predictors, considering the clinical context rather than physiological data alone.

REGISTRATION

The protocol for this study is registered with PROSPERO (registration number: CRD42021273319).

ROMI: A Real-Time Optical Digit Recognition Embedded System for Monitoring Patients in Intensive Care Units

With advances in the Internet of Things, patients in intensive care units are constantly monitored to expedite emergencies. Due to the COVID-19 pandemic, non-face-to-face monitoring has been required for the safety of patients and medical staff. A control center monitors the vital signs of patients in ICUs. However, some medical devices, such as ventilators and infusion pumps, operate in a standalone fashion without communication capabilities, requiring medical staff to check them manually. One promising solution is to use a robotic system with a camera. We propose a real-time optical digit recognition embedded system called ROMI. ROMI is a mobile robot that monitors patients by recognizing digits displayed on LCD screens of medical devices in real time. ROMI consists of three main functions for recognizing digits: digit localization, digit classification, and digit annotation. We developed ROMI by using Matlab Simulink, and the maximum digit recognition performance was 0.989 mAP on alexnet. The developed system was deployed on NVIDIA GPU embedded platforms: Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. We also created a benchmark by evaluating the runtime performance by considering ten pre-trained CNN models and three NVIDIA GPU platforms. We expect that ROMI will support medical staff with non-face-to-face monitoring in ICUs, enabling more effective and prompt patient care.

Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review

BACKGROUND

Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit.

OBJECTIVE

To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes.

METHODS

The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay.

RESULTS

A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data.

CONCLUSION

With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

Low cost circulatory pressure acquisition and fluid infusion rate measurement system for clinical research

Acquiring patient physiological waveforms is useful for studying hemodynamic management and developing medical monitoring systems. A low cost, Arduino controlled data acquisition system acquires arterial pressure waveforms (Edwards Lifesciences TruWave compatible) and measures fluid infusion rate using hanging scales. This system can be used at the same time as a clinical monitor, enabling recording of patient arterial pressure and fluid delivery for clinical research. The system is powered via a USB connection, which additionally provides serial output, aiding compatibility and customisation. A simple software user interface, developed in Python, shows outputs. Each data acquisition system, including all necessary connection cables costs ~US$90 and is multiple-use.

Optimal Long Distance ECG Signal Data Delivery Using LoRa Technology

Cardiovascular disease is the leading cause of death in the world and the number one killer in Indonesia, with a mortality rate of 17.05%. The target of this research is to increase the range of electrocardiograph (ECG) equipment using LoRa Technology. With LoRa Technology, it is expected that the data transmission process can run effectively and produce an accurate ECG signal and minimal noise. The research method is by sending a heart signal from the ECG simulator by the microcontroller via LoRa Technology which is received by the PC (Personal Computer) and the ECG signal is displayed on the PC display. The most optimal setting will be obtained from the sender-receiver distance and baudrate by measuring data loss and delay. In this study, the simulated cardiac signal from the phantom ECG is fed to an analog signal processing circuit, then the signal is converted to digital and digitally filtered on the microcontroller, then the signal is sent via the LoRa HC-12 Transceiver to a PC with baudrate, distance and barrier settings. The results obtained are that data transmission can be carried out at a distance of 175 meters without a barrier and a distance of 50 meters with a barrier. This remote ECG equipment can detect heart signals and the results can be sent to a PC using LoRa Technology. The implication is that the transmission of ECG signal data via the Lora HC-12 Transceiver media can be carried out optimally at the 9600 baudrate setting.

Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems

Background

One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset.

Materials and methods

This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts’ consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset.

Results

The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset.

Conclusions

We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.

Breaking Barriers in Emerging Biomedical Applications

The recent global COVID-19 pandemic has revealed that the current healthcare system in modern society can hardly cope with the increased number of patients. Part of the load can be alleviated by incorporating smart healthcare infrastructure in the current system to enable patient's remote monitoring and personalized treatment. Technological advances in communications and sensing devices have enabled the development of new, portable, and more power-efficient biomedical sensors, as well as innovative healthcare applications. Nevertheless, such applications require reliable, resilient, and secure networks. This paper aims to identify the communication requirements for mass deployment of such smart healthcare sensors by providing the overview of underlying Internet of Things (IoT) technologies. Moreover, it highlights the importance of information theory in understanding the limits and barriers in this emerging field. With this motivation, the paper indicates how data compression and entropy used in security algorithms may pave the way towards mass deployment of such IoT healthcare devices. Future medical practices and paradigms are also discussed.

Integration of an ICU Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: A Cluster Randomized Controlled Trial (Preprint)

Background

 Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU).

Objective

i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs.

Methods

A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys.

Results

Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs.

Conclusions

 i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs.

Trial Registration

ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698

OUP accepted manuscript

ICU Cockpit: a secure, fast, and scalable platform for collecting multimodal waveform data, online and historical data visualization, and online validation of algorithms in the intensive care unit. We present a network of software services that continuously stream waveforms from ICU beds to databases and a web-based user interface. Machine learning algorithms process the data streams and send outputs to the user interface. The architecture and capabilities of the platform are described. Since 2016, the platform has processed over 89 billion data points (N = 979 patients) from 200 signals (0.5-500 Hz) and laboratory analyses (once a day). We present an infrastructure-based framework for deploying and validating algorithms for critical care. The ICU Cockpit is a Big Data platform for critical care medicine, especially for multimodal waveform data. Uniquely, it allows algorithms to seamlessly integrate into the live data stream to produce clinical decision support and predictions in clinical practice.

The negative impact of interface design, customizability, inefficiency, malfunctions, and information retrieval on user experience: A national usability survey of ICU clinical information systems in Finland

INTRODUCTION

Clinical information systems (CISs) used in intensive care units (ICU) integrate large amounts of patient data every minute, and from multiple systems and devices. Intensive care requires efficient use of information technology to acquire, synchronize, integrate, and analyze data in order to make quick decisions and implement interventions in a timely manner.

OBJECTIVES

To identify factors affecting poor user experience (UX) of CISs used in ICUs in Finland.

METHODS

Data from national Electronic Health Record (EHR) and user experience survey was undertaken in 2017. Those, who used the ICU CIS on a daily or weekly basis were asked supplementary questions and, therefore, comprise a subset of the responses reported in this article.

RESULTS

On a 4-10 scale (i.e., "Fail" to "Excellent"), the mean 'grade' for the principally used ICU CIS was 6.9 (SD 1.3) points. Of the respondents, 119 (57%) were categorized as having good UX. The factors identified as affecting poor UX of the ICU CISs related to poor interface design (OR 7.8; 95% CIs 12.5-24.1; p = 0.001), insufficient customizability (OR 7.2; 95% CIs 1.7-30.6; p = 0.008), the inefficiency of performing routine tasks (OR 4.3; 95% CIs 1.0-18.2; p = 0.044), malfunctions (OR 3.5; 95% CIs 1.2-9.6; p = 0.019), and difficulties in information retrieval (OR 3.0; 95% CIs 1.0-8.8; p = 0.044). The most commonly reported usability problems with the main EHR system and ICU CISs were also identified.

CONCLUSIONS

Overall satisfaction with the principally used ICU CIS was moderate. However, the overall grades varied significantly. Poor interface design, insufficient customizability, inefficiency, malfunctions, and difficulties in information retrieval all affect poor UX.

Connectivity Strategies in Managing a POCT Service

Point of Care Testing is increasingly being used for diagnosis and management of various disease states. Management of different Point of Care instruments at multiple sites can be challenging, particularly when such instruments are operated by non technical staff. Connectivity is critical for optimal management of these services which are intimately linked to operator training and competency and are important in minimising harm to the patient by reducing analytical errors. Furthermore, connectivity improves turn around time leading to faster decision making by physicians. Recent advances in technology such as 5G and artificial intelligence are likely to lead to a greater focus on personalized care as a result of big data analysis and development of algorithms.

The critical care data exchange format: a proposed flexible data standard for combining clinical and high-frequency physiologic data in critical care

Objective.To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis.Approach.A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format-Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools.Main Results.We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset.Significance.Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data.

Disentangling the clinical data chaos: User-centered interface system design for trauma centers

This paper presents a year-long study of our project, aiming at (1) understanding the work practices of clinical staff in trauma intensive care units (TICUs) at a trauma center, with respect to their usage of clinical data interface systems, and (2) developing and evaluating an intuitive and user-centered clinical data interface system for their TICU environments. Based on a long-term field study in an urban trauma center that involved observation-, interview-, and survey-based studies to understand our target users and their working environment, we designed and implemented MediSenseView as a working prototype. MediSenseView is a clinical-data interface system, which was developed through the identification of three core challenges of existing interface system use in a trauma care unit-device separation, usage inefficiency, and system immobility-from the perspectives of three staff groups in our target environment (i.e., doctors, clinical nurses and research nurses), and through an iterative design study. The results from our pilot deployment of MediSenseView and a user study performed with 28 trauma center staff members highlight their work efficiency and satisfaction with MediSenseView compared to existing clinical data interface systems in the hospital.

Validation process of a high-resolution database in a paediatric intensive care unit-Describing the perpetual patient's validation

RATIONALE

High data quality is essential to ensure the validity of clinical and research inferences based on it. However, these data quality assessments are often missing even though these data are used in daily practice and research.

AIMS AND OBJECTIVES

Our objective was to evaluate the data quality of our high-resolution electronic database (HRDB) implemented in our paediatric intensive care unit (PICU).

METHODS

We conducted a prospective validation study of a HRDB in a 32-bed paediatric medical, surgical, and cardiac PICU in a tertiary care freestanding maternal-child health centre in Canada. All patients admitted to the PICU with at least one vital sign monitored using a cardiorespiratory monitor connected to the central monitoring station.

RESULTS

Between June 2017 and August 2018, data from 295 patient days were recorded from medical devices and 4645 data points were video recorded and compared to the corresponding data collected in the HRDB. Statistical analysis showed an excellent overall correlation (R² = 1), accuracy (100%), agreement (bias = 0, limits of agreement = 0), completeness (2% missing data), and reliability (ICC = 1) between recorded and collected data within clinically significant pre-defined limits of agreement. Divergent points could all be explained.

CONCLUSIONS

This prospective validation of a representative sample showed an excellent overall data quality.

IoT MEMS: IoT-Based Paradigm for Medical Equipment Management Systems of ICUs in Light of COVID-19 Outbreak

Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs' medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients' health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs' medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.

Novel Catheter Multiscope: A Feasibility Study

Open Challenges: Continuous monitoring of fundamental cardiovascular hemodynamic parameters is essential to accomplish critical care diagnostics. Today's standard of care measures these critical parameters using multiple monitoring technologies. These state-of-the-art technologies require expensive instrumentation and complex infrastructure. Therefore, it is challenging to use current technologies to accomplish monitoring in a low resource setting.

OBJECTIVE

In order to address the challenges caused by having to use multiple monitoring systems, a point of care monitoring device was developed in this work to provide multiple critical parameters by uniquely measuring the hemodynamic process.

METHODS

To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework was developed to characterize the blood flow dynamics and to obtain critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation method and wavelet-based source separation.

RESULTS

The preliminary results of the acoustic pressure field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique framework was also developed to extract continuous blood pressure from the acoustic pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the hypothesis.

CONCLUSION

The results confirm that catheter multiscope can provide multiple critical parameters with clinical reliability.

SIGNIFICANCE

A novel critical care monitoring system has been developed to accurately measure heart rate, respiratory rate, systolic and diastolic pressures from the blood flow dynamics.

Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study

Our objective in this study was to determine if machine learning (ML) can automatically recognize neonatal manipulations, along with associated changes in physiological parameters. A retrospective observational study was carried out in two Neonatal Intensive Care Units (NICUs) between December 2019 to April 2020. Both the video and physiological data (heart rate (HR) and oxygen saturation (SpO₂)) were captured during NICU hospitalization. The proposed classification of neonatal manipulations was achieved by a deep learning system consisting of an Inception-v3 convolutional neural network (CNN), followed by transfer learning layers of Long Short-Term Memory (LSTM). Physiological signals prior to manipulations (baseline) were compared to during and after manipulations. The validation of the system was done using the leave-one-out strategy with input of 8 s of video exhibiting manipulation activity. Ten neonates were video recorded during an average length of stay of 24.5 days. Each neonate had an average of 528 manipulations during their NICU hospitalization, with the average duration of performing these manipulations varying from 28.9 s for patting, 45.5 s for a diaper change, and 108.9 s for tube feeding. The accuracy of the system was 95% for training and 85% for the validation dataset. In neonates <32 weeks’ gestation, diaper changes were associated with significant changes in HR and SpO₂, and, for neonates ≥32 weeks’ gestation, patting and tube feeding were associated with significant changes in HR. The presented system can classify and document the manipulations with high accuracy. Moreover, the study suggests that manipulations impact physiological parameters.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

OBJECTIVE

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification.

METHODS

IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application.

RESULTS

Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%.

CONCLUSION

This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers.

SIGNIFICANCE

This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

BACKGROUND

Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development.

OBJECTIVE

This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups.

METHODS

This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups.

RESULTS

In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring.

CONCLUSIONS

This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit

Modern intensive care units (ICU) are equipped with a variety of different medical devices to monitor the physiological status of patients. These devices can generate large amounts of multimodal data daily that include physiological waveform signals (arterial blood pressure, electrocardiogram, respiration), patient alarm messages, numeric vitals data, etc. In order to provide opportunities for increasingly improved patient care, it is necessary to develop an effective data acquisition and analysis system that can assist clinicians and provide decision support at the patient bedside. Previous research has discussed various data collection methods, but a comprehensive solution for bedside data acquisition to analysis has not been achieved. In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. We also discuss how the acquired data can be used for patient state tracking. INSMA is being tested in the ICU at University Hospitals Cleveland Medical Center.

Estimation and Tracking of Blood Pressure Using Routinely Acquired Photoplethysmographic Signals and Deep Neural Networks

Supplemental Digital Content is available in the text.

Continuous tracking of blood pressure in critically ill patients allows rapid identification of clinically important changes and helps guide treatment. Classically, such tracking requires invasive monitoring with its associated risks, discomfort, and low availability outside critical care units. We hypothesized that information contained in a prevalent noninvasively acquired signal (photoplethysmograph: a byproduct of pulse oximetry) combined with advanced machine learning will allow continuous estimation of the patient’s blood pressure.

A practical approach to storage and retrieval of high-frequency physiological signals

OBJECTIVE

Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval.

APPROACH

We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications.

MAIN RESULTS

A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second.

SIGNIFICANCE

Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Data Sources and Gateways: Design and Open Specification

Introduction:

With With the proliferation of available ICT services, several sensors and health applications have become ubiquitous, while many applications have been developed to detect certain health conditions and early signs of disease. Currently, all these services operate independently, and the available data is heterogeneous with limited value gained from its exploitation.

Aim:

The Data Sources and Gateways component aims at providing an abstracted and unified API to support the data accumulation from various sources including healthcare organisations, biosensors, laboratories and mobile applications. Meanwhile it tackles connectivity and communication issues with such information sources.

Methods:

The CrowdHEALTH Data Sources and Gateways Service incorporates four main services: The Configuration Service, The DB Connection Handling Service, The File Parsing Service and The RESTful Client Service.

Results:

The initial version of the component design has built upon the requirements collected from the use case participants acting also as data providers.

Conclusion:

These four services presented in this paper guide the implementation of the first version of the Data Sources and Gateways component software prototype. The Data Sources and Gateways component remains to be evaluated within the context of the project and be enriched in order to meet additional end user needs.

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.

Clinical Requirements of Future Patient Monitoring in the Intensive Care Unit: Qualitative Study

Background

In the intensive care unit (ICU), continuous patient monitoring is essential to detect critical changes in patients’ health statuses and to guide therapy. The implementation of digital health technologies for patient monitoring may further improve patient safety. However, most monitoring devices today are still based on technologies from the 1970s.

Objective

The aim of this study was to evaluate statements by ICU staff on the current patient monitoring systems and their expectations for future technological developments in order to investigate clinical requirements and barriers to the implementation of future patient monitoring.

Methods

This prospective study was conducted at three intensive care units of a German university hospital. Guideline-based interviews with ICU staff—5 physicians, 6 nurses, and 4 respiratory therapists—were recorded, transcribed, and analyzed using the grounded theory approach.

Results

Evaluating the current monitoring system, ICU staff put high emphasis on usability factors such as intuitiveness and visualization. Trend analysis was rarely used; inadequate alarm management as well as the entanglement of monitoring cables were rated as potential patient safety issues. For a future system, the importance of high usability was again emphasized; wireless, noninvasive, and interoperable monitoring sensors were desired; mobile phones for remote patient monitoring and alarm management optimization were needed; and clinical decision support systems based on artificial intelligence were considered useful. Among perceived barriers to implementation of novel technology were lack of trust, fear of losing clinical skills, fear of increasing workload, and lack of awareness of available digital technologies.

Conclusions

This qualitative study on patient monitoring involves core statements from ICU staff. To promote a rapid and sustainable implementation of digital health solutions in the ICU, all health care stakeholders must focus more on user-derived findings. Results on alarm management or mobile devices may be used to prepare ICU staff to use novel technology, to reduce alarm fatigue, to improve medical device usability, and to advance interoperability standards in intensive care medicine. For digital transformation in health care, increasing the trust and awareness of ICU staff in digital health technology may be an essential prerequisite.

Trial Registration

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173 (Archived by WebCite at http://www.webcitation.org/77T1HwOzk)

Practical identifiability analysis of a minimal cardiovascular system model

BACKGROUND AND OBJECTIVE

Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume.

METHODS

An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter.

RESULTS

Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation.

CONCLUSIONS

This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable.

Clinical Dashboard in the Intensive Care Unit: Need-Assessment and Survey about Attitudes and Acceptance of Tele-ICU from the Viewpoint of Nurses and Clinicians in the Intensive Care Unit

BACKGROUND

One of the most worrying aspects of medical area in developing countries is the Intensive Care Unit (ICU). This study aimed to evaluate the acceptability of the clinical dashboard by the users, prior to final use and their attitude towards this technology, as well as to examine the specific needs that Tele-ICU technology can cover in the form of a clinical dashboard.

MATERIALS AND METHODS

This study was conducted at Shahid Bahonar Hospital of Kerman, Southeastern Iran, with three ICUs, the first, second, and third sections of which had 10, 12, and 24 beds, respectively. Taking survey and need assessment of care providers, qualitative and quantitative analyses were undertaken to identify key positive and negative themes. The data were analyzed by SPSS software version 18.

RESULTS

About 82% of care providers in the ICU participated in this survey. The number of participants based on the groups in the survey was 98 (81.7%) of the nurses and respiratory therapists group, 20 (80%) from the group of anesthesiologists and 20 (87%) from the group of anesthesiologist assistants who participated in the survey. About 51% of the survey participants completed the description section either partially or totally. On average, among all groups, the group of anesthesiologists had the most and the nurses had the least knowledge about telemedicine and Tele-ICU, whereas the anesthesiologist assistants had the most and the nurses and respiratory therapists group had the least knowledge about clinical dashboards.

CONCLUSION

This study showed that the level of knowledge and awareness of care providers, especially nurses and respiratory therapists in the ICU in terms of telemedicine and Tele-ICU is low and care providers are in doubt that telemedicine technology could have a positive or negative impact on human resource shortages, yet agreed that it would have a negative effect on the privacy of the patients and care providers. In addition, the ICU care providers agree that Tele-ICU can positively affect the quality of patient care, staff satisfaction, reduce the cost of care, and ease and reduce the time for patient counseling. This suggests the need for further research and education of system impact beyond patient outcomes related to this new technology.

Unmasking the Hypovolemic Shock Continuum: The Compensatory Reserve

Hypovolemic shock exists as a spectrum, with its early stages characterized by subtle pathophysiologic tissue insults and its late stages defined by multi-system organ dysfunction. The importance of timely detection of shock is well known, as early interventions improve mortality, while delays render these same interventions ineffective. However, detection is limited by the monitors, parameters, and vital signs that are traditionally used in the intensive care unit (ICU). Many parameters change minimally during the early stages, and when they finally become abnormal, hypovolemic shock has already occurred. The compensatory reserve (CR) is a parameter that represents a new paradigm for assessing physiologic status, as it comprises the sum total of compensatory mechanisms that maintain adequate perfusion to vital organs during hypovolemia. When these mechanisms are overwhelmed, hemodynamic instability and circulatory collapse will follow. Previous studies involving CR measurements demonstrated their utility in detecting central blood volume loss before hemodynamic parameters and vital signs changed. Measurements of the CR have also been used in clinical studies involving patients with traumatic injuries or bleeding, and the results from these studies have been promising. Moreover, these measurements can be made at the bedside, and they provide a real-time assessment of hemodynamic stability. Given the need for rapid diagnostics when treating critically ill patients, CR measurements would complement parameters that are currently being used. Consequently, the purpose of this article is to introduce a conceptual framework where the CR represents a new approach to monitoring critically ill patients. Within this framework, we present evidence to support the notion that the use of the CR could potentially improve the outcomes of ICU patients by alerting intensivists to impending hypovolemic shock before its onset.

Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system

PURPOSE

This study measured the quality of data extracted from a clinical information system widely used for critical care quality improvement and research.

MATERIALS AND METHODS

We abstracted data from 30 fields in a random sample of 207 patients admitted to nine adult, medical-surgical intensive care units. We assessed concordance between data collected: (1) manually from the bedside system (eCritical MetaVision) by trained auditors, and (2) electronically from the system data warehouse (eCritical TRACER). Agreement was assessed using Cohen's Kappa for categorical variables and intraclass correlation coefficient (ICC) for continuous variables.

RESULTS

Concordance between data sets was excellent. There was perfect agreement for 11/30 variables (35%). The median Kappa score for the 16 categorical variables was 0.99 (IQR 0.92-1.00). APACHE II had an ICC of 0.936 (0.898-0.960). The lowest concordance was observed for SOFA renal and respiratory components (ICC 0.804 and 0.846, respectively). Score translation errors by the manual auditor were the most common source of data discrepancies.

CONCLUSIONS

Manual validation processes of electronic data are complex in comparison to validation of traditional clinical documentation. This study represents a straightforward approach to validate the use of data repositories to support reliable and efficient use of high quality secondary use data.

Advocating for Greater Usability in Clinical Technologies: The Role of the Practicing Nurse

Health care, especially ICUs, rely on multiple types of technology to promote the best patient outcomes. Unfortunately, too often these technologies are poorly designed, causing errors, additional workload, and unnecessary frustration. The purpose of this article is to (1) empower nurses with the needed usability and usability testing vocabulary to identify and articulate clinical technology usability problems and (2) provide ideas on ways nurses can advocate to have an impact on positive change related to technology usability within a health care organization.

Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient

OBJECTIVE

Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database.

DESIGN

Descriptive report and analysis of a prospective database.

SETTING

A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada.

PATIENTS

All patients less than 18 years old were included at admission to the PICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient's electronic medical record. The database total volume was 241 GB. The patients' median age was 2.0 years (interquartile range, 0.0-9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database.

CONCLUSIONS

Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.

Implementation of an Evidence-Based Practice Nursing Handover Tool in Intensive Care Using the Knowledge-to-Action Framework

BACKGROUND

Miscommunication during handover has been linked to adverse patient events and is an international patient safety priority. Despite the development of handover resources, standardized handover tools for nursing team leaders (TLs) in intensive care are limited.

AIMS

The study aim was to implement and evaluate an evidence-based electronic minimum data set for nursing TL shift-to-shift handover in the intensive care unit using the knowledge-to-action (KTA) framework.

METHODS

This study was conducted in a 21-bed medical-surgical intensive care unit in Queensland, Australia. Senior registered nurses involved in TL handover were recruited. Three phases of the KTA framework (select, tailor, and implement interventions; monitor knowledge use; and evaluate outcomes) guided the implementation and evaluation process. A postimplementation practice audit and survey were carried out to determine nursing TL use and perceptions of the electronic minimum data set 3 months after implementation. Results are presented using descriptive statistics (median, IQR, frequency, and percentage).

RESULTS

Overall (86%, n = 49), TLs' use of the electronic minimum data set for handover and communication regarding patient plan increased. Key content items, however, were absent from handovers and additional documentation was required alongside the minimum data set to conduct handover. Of the TLs surveyed (n = 35), those receiving handover perceived the electronic minimum data set more positively than TLs giving handover (n = 35). Benefits to using the electronic minimum data set included the patient content (48%), suitability for short-stay patients (16%), decreased time updating (12%), and printing the tool (12%). Almost half of the participants, however, found the minimum data set contained irrelevant information, reported difficulties navigating and locating relevant information, and pertinent information was missing. Suggestions for improvement focused on modifications to the electronic handover interface.

LINKING EVIDENCE TO ACTION

Prior to developing and implementing electronic handover tools, adequate infrastructure is required to support knowledge translation and ensure clinician and organizational needs are met.

Barriers and facilitators to the implementation of an evidence-based electronic minimum dataset for nursing team leader handover: A descriptive survey

INTRODUCTION

There is widespread use of clinical information systems in intensive care units however, the evidence to support electronic handover is limited.

OBJECTIVES

The study aim was to assess the barriers and facilitators to use of an electronic minimum dataset for nursing team leader shift-to-shift handover in the intensive care unit prior to its implementation.

METHODS

The study was conducted in a 21-bed medical/surgical intensive care unit, specialising in cardiothoracic surgery at a tertiary referral hospital, in Queensland, Australia. An established tool was modified to the intensive care nursing handover context and a survey of all 63 nursing team leaders was undertaken. Survey statements were rated using a 6-point Likert scale with selections from 'strongly disagree' to 'strongly agree', and open-ended questions. Descriptive statistics were used to summarise results.

RESULTS AND DISCUSSION

A total of 39 team leaders responded to the survey (62%). Team leaders used general intensive care work unit guidelines to inform practice however they were less familiar with the intensive care handover work unit guideline. Barriers to minimum dataset uptake included: a tool that was not user friendly, time consuming and contained too much information. Facilitators to minimum dataset adoption included: a tool that was user friendly, saved time and contained relevant information. Identifying the complexities of a healthcare setting prior to the implementation of an intervention assists researchers and clinicians to integrate new knowledge into healthcare settings.

CONCLUSION

Barriers and facilitators to knowledge use focused on usability, content and efficiency of the electronic minimum dataset and can be used to inform tailored strategies to optimise team leaders' adoption of a minimum dataset for handover.