The Effect of Procedural Changes on the Rate of Clinical Alarms In the Intensive Care Unit

Clinical alarms have become an indispensable part of medical environment, but issues related to alarm artifacts, false alarms, and alarm fatigue have been identified. A national online survey administered to hospitals stated healthcare workers determined that 81% of respondents agreed that alarms occur frequently, 77% agreed that excessive clinical alarms disrupt patient care, and 78% agreed that reduced trust in alarms cause caregivers to disable them (Korniewicz, Clark, & David, 2008). Studies have suggested that preparation of skin of the patient improves electrode-skin contact, thereby resulting in fewer artifacts (Hermens, Freriks, Disselhorst-Klug, & Rau, 2000). Additionally, clinical studies have shown that the electrode-skin interface is frequently overlooked as a major source of artifact affecting many electro-physiologic recordings (Oster, 1998). The purpose of the thesis is to evaluate how the implementation of procedural changes, specifically implementing a patient\u27s chest preparation procedure prior to electrode placement influences the rate of clinical alarms, (i.e., critical or warning cardiac alarms) in an intensive care unit (ICU). Data from clinical alarms were collected from a regional hospital in South Carolina. The data contained the number of clinical alarms recorded with and without nurse administered chest preparation. Functional data analysis was used to evaluate if chest preparation procedure had a significant impact on the rate of clinical alarms produced over an 8-hour shift. The results suggest that there is no significant reduction in the alarm frequency after the implementation of nurse administered chest preparation. However, a nominal decrease in the number of alarms per hour per patient and some preliminary trends were observed during the data analysis that warrants the need for future research in this direction

Decision trees for uncertain data

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/ quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the "complete information" of a data item (taking into account the probability density function (pdf)) is utilized. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted which show that the resulting classifiers are more accurate than those using value averages. Since processing pdfs is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose a series of pruning techniques that can greatly improve construction efficiency. © 2006 IEEE.link_to_subscribed_fulltex

Real-time analysis of physiological data and development of alarm algorithms for patient monitoring in the Intensive Care Unit

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 86-90).The lack of effective data integration and knowledge representation in patient monitoring limits its utility to clinicians. Intelligent alarm algorithms that use artificial intelligence techniques have the potential to reduce false alarm rates and to improve data integration and knowledge representation. Crucial to the development of such algorithms is a well-annotated data set. In previous studies, clinical events were either unavailable or annotated without accurate time synchronization with physiological signals, generating uncertainties during both the development and evaluation of intelligent alarm algorithms. This research aims to help eliminate these uncertainties by designing a system that simultaneously collects physiological data and clinical annotations at the bedside, and to develop alarm algorithms in real time based on patient-specific data collected while using this system. In a standard pediatric intensive care unit, a working prototype of this system has helped collect a dataset of 196 hours of vital sign measurements at 1 Hz with 325 alarms generated by the bedside monitor and 2 instances of false negatives. About 89% of these alarms were clinically relevant true positives; 6% were true positives without clinical relevance; and 5% were false positives. Real-time machine learning showed improved performance over time and generated alarm algorithms that outperformed the previous generation of bedside monitors and came close in performance to the new generation. Results from this research suggest that the alarm algorithm(s) of the new patient monitoring systems have significantly improved sensitivity and specificity. They also demonstrated the feasibility of real-time learning at the bedside. Overall, they indicate(cont.) that the methods developed in this research have the potential of helping provide patient-specific decision support for critical care.b y Ying Zhang.M.Eng

Correlation and real time classification of physiological streams for critical care monitoring.

This thesis presents a framework for the deployment of algorithms that support the correlation and real-time classification of physiological data streams through the development of clinically meaningful alerts using a blend of expert knowledge in the domain and pattern recognition programming based on clinical rules. Its relevance is demonstrated via a real world case study within the context of neonatal intensive care to provide real-time classification of neonatal spells. Events are first detected in individual streams independently; then synced together based on timestamps; and finally assessed to determine the start and end of a multi-signal episode. The episode is then processed through a classifier based on clinical rules to determine a classification. The output of the algorithms has been shown, in a single use case study with 24 hours of patient data, to detect clinically significant relative changes in heart rate, blood oxygen saturation levels and pauses in breathing in the respiratory impedance signal. The accuracy of the algorithm for detecting these is 97.8%, 98.3% and 98.9% respectively. The accuracy for correlating the streams and determining spells classifications is 98.9%. Future research will focus on the clinical validation of these algorithms and the application of the framework for the detection and classification of signals in other clinical contexts

Modellierung eines intensivmedizinischen Behandlungsprotokolls zur Validierung anhand realer Patientendaten

In dieser Arbeit wird ein neuer Ansatz zur Entwicklung intensivmedizinischer Behandlungsprotokolle für den Einsatz am Krankenbett vorgestellt. Im Gegensatz zu bisherigen Ansätzen wird dabei die Modellierung medizinischen Wissens bereits sehr früh anhand von protokollierten medizinischen Verlaufsdaten validiert. Der Schlüssel zu einer weitreichenden Unterstützung medizinischer Experten ist dabei die Formalisierung und Repräsentation als wissensbasiertes System. Nach der Umsetzung eines noch nicht lauffähigen Modellentwurfs in einen solchen Formalismus werden die Vorteile des Vorgehens exemplarisch anhand einiger Experimente mit Verlaufsdaten aufgezeigt