Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies

Improving the acute and perioperative hemodynamic assessment

First, this thesis aimed to extend the evidence on the applicability of hemodynamic monitoring during the perioperative period and after admission to the ICU. Second, we aimed to gain knowledge on how to improve the conduct of studies in perioperative and critical care medicine.We provided an overview of the current evidence for hemodynamic monitoring in perioperative goal-directed therapy. We showed that the studies on this subject showed clinical heterogeneity and risk of bias. Extension of all aspects of hemodynamic monitoring was considered in this thesis. A study was performed on the educated guess of physicians when estimating cardiac output using clinical examination to help improve the reliability of the clinical examination. We showed that physicians at the bed-side mainly consider mottling score and norepinephrine dose when estimating cardiac output. In another study, we demonstrated that blood pressure measurements differ when measured invasively or non-invasively and that these differences may have clinical consequences. We also showed that echocardiography could be performed by novices, but experts are needed to interpret obtained images. We demonstrated that cardiac output measurements vary in critically ill patients when measured with echocardiography or uncalibrated pulse wave analysis.For the second part of this thesis, we demonstrated that various mortality prediction models exist for critically ill patients. Quality of methodology often lacks for these models, and improvements have to be made to help patient care. To help improve the quality of studies, we finally propose that study protocols are prepublished and made available for peer-review before conduct

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Assessment of goal-directed closed-loop management in intensive care medicine

Given an aging population, shortage of nursing staff and a continuously increasing workload, automation in the medical sector is an important aspect of future intensive care. Although automation and machine learning are current research topics, progress is still very limited in comparison to other application areas. Probably one of the most serious problems is data shortage in a heterogeneous landscape of medical devices with limited interfaces and various protocols. In addition, the recording of data or, even more so, the evaluation of automation is limited by a complex legal framework. Given these complications and the sensitive legal nature of medical records, only very limited data is accessible for further analysis and development of automated systems. For this reason, within the context of this thesis various solutions for data acquisition and automation were developed and evaluated concomitant to two clinical studies utilizing a large animal model in a realistic intensive care setting at the University Hospital Tübingen. Foremost, to overcome the problems of data availability and interconnection of medical devices, a software framework for data collection and remote control using a client-server architecture was developed and significant amounts of research data could be collected in a central database. Furthermore, a closed-loop controller based on fuzzy logic was developed and used for management of end-tital CO2, glucose, and other parameters to stabilize the animal subjects during therapy and reduce caregivers’ workload. In addition to the fuzzy controller, closed-loop management for temperature and anticoagulation could be established by developing hardware interfaces for a forced-air warming unit and a point-of-care analysis device, respectively. Besides further reduction of caregivers’ workload, such systems can provide additional patient safety and allow management in settings where human supervision may not be present at all times. One general and encountered problem for closed-loop control in a medical setting is limited availability of measurements, especially if manual blood withdrawals are required. As an initial step to address this problem, measured parameters from other devices as potential surrogates were evaluated in a comparison between different regression approaches. The required training data, a matched set of blood gas and monitoring parameters, was obtained by utilizing a developed algorithm for automated detection of withdrawal events. Yet, besides any specific implementations and analysis, many general aspects regarding the physical implementation of such a system and interaction with caregivers could be evaluated in the experimental setting and might guide further development of clinical automation.Angesichts der alternden Bevölkerung, des Mangels an Pflegekräften und der ständig steigenden Arbeitsbelastung ist Automatisierung ein wichtiger Aspekt zukünftiger Intensivmedizin. Obwohl Automatisierung und maschinelles Lernen aktuelle Forschungsthemen sind, ist der Fortschritt im Vergleich zu anderen Anwendungsbereichen jedoch noch sehr begrenzt. Eines der größten Probleme ist wohl die Datenknappheit in einer heterogenen Medizinproduktelandschaft mit begrenzten Schnittstellen und zahlreichen unterschiedlichen Protokollen. Darüber hinaus sind die Datenerfassung und erst recht die Erprobung einer Automatisierung durch ein komplexes rechtliches Rahmenwerk eingeschränkt. Aufgrund dieser Komplikationen und der sensiblen Rechtslage für Patientendaten sind diese nur sehr begrenzt für weitere Analysen und die Entwicklung automatisierter Systeme zugänglich. Im Rahmen dieser Dissertation wurden daher verschiedene Lösungen zur Datenerfassung und Automatisierung begleitend zu zwei klinischen Studien des Universitätsklinikums Tübingen am Großtiermodell in einer realitätsnahen Intensivstation entwickelt und evaluiert. Um die Probleme der Datenverfügbarkeit und Vernetzung medizinischer Geräte zu lösen, wurde vorrangig ein Software-Framework für die Datenerfassung und Steuerung mittels einer Client-Server-Architektur entwickelt und umfangreiche Forschungsdaten in einer zentralen Datenbank gesammelt. Darüber hinaus wurde ein auf Fuzzy-Logik basierender Regler entwickelt, welcher zur Stabilisierung des endtitalen CO2, Glukose und anderen Parametern verwendet wurde und damit die Arbeitsbelastung der Pflegekräfte reduzieren konnte. Zusätzlich zum Fuzzy-Regler konnten durch die Entwicklung von Hardware-Schnittstellen für Geräte zum Temperaturmanagement mittels luftbasierter Wärmedecken und zur Messung der Blutgerinnung geschlossene Regelkreise aufgebaut werden. Neben einer weiteren Arbeitserleichterung für die Pflegekräfte können solche Systeme zusätzliche Sicherheit für den Patienten bieten und die Anwendung in nicht ständig überwachten Bereichen ermöglichen. Ein allgemeines und auch beobachtetes Problem für Regelkreise im medizinischen Bereich ist die begrenzte Verfügbarkeit von Messwerten, insbesondere bei manuellen Blutentnahmen. Als erster Schritt zur Lösung dieses Problems wurden Messparameter anderer Geräte als potentielle Ersatzparameter mit verschiedenen Regressionsansätzen analysiert und verglichen. Die dazu erforderlichen Trainingsdaten, Paare von Blutgas- und weiteren Vitaldaten, wurden mit Hilfe eines entwickelten Algorithmus zur automatisierten Erkennung von Blutentnahmen erzeugt. Abgesehen von diesen konkreten Anwendungen und Analysen konnten in der experimentellen Evaluation auch viele generelle Aspekte der realen Implementierung eines solchen Systems und die Interaktion mit Ärzten und Pflegekräften untersucht werden und damit der Entwicklung weiterer klinischen Automatisierung dienen

Exploring the pharmacodynamics of multidrug combinations and using the advances in technology to individualise anaesthetic drug titration

In current practice, pharmacokinetic-dynamic (PK/PD) models are frequently used to describe the combined relationship between the time course of drug plasma concentrations (PK) and the time independent relationship between the drug concentration at the receptor site and the clinical effect (PD). This thesis contributes to the knowledge in anaesthetic pharmacology and explores the dose-response relationships of propofol and sevoflurane (with and without the coadministration of remifentanil) in greater detail using PK/PD models. Our studies show that PK/PD models are useful in clinical practice. The concept of neural inertia could have an influence on these models, but is still controversial in humans and it does not break down the essence and applicability of these PK/PD models. Subsequently, we used these models to compare the pharmacodynamics of propofol and sevoflurane (with and without remifentanil) at both a population level as well as at an individual level. This comparison let us describe potency ratios between both hypnotics which is very helpful for anaesthetist when switching between these drugs for any reason during a case. We applied the same PK/PD models and similar potency ratios in clinical practice using the SmartPilot® View, a drug advisory system, to guide anaesthetic drug titration, and we assessed its clinical utility. Finally, we evaluated a novel method to analyse the cerebral drug effect on the EEG using Artificial Intelligence in order to explore the feasibility of whether a single index can quantify the hypnotic effect in a drug-independent way

PERCEIVED IMPACT OF AMBIENT OPERATING ROOM NOISE BY CERTIFIED REGISTERED NURSE ANESTHETISTS

It is widely acknowledged that elevated levels of noise are commonplace in the healthcare environment, particularly in high acuity areas such as the operating room (OR). Excessive ambient noise may pose a threat to patient safety by adversely impacting provider performance and interfering with communication among perioperative care team members. With respect to the certified registered nurse anesthetist (CRNA), increased ambient OR noise may engender distractibility, diminish situation awareness and cause untoward health effects, thereby increasing the possibility for the occurrence of error and patient injury. This research project analytically examines the perceived impact of ambient noise in the operating room by CRNAs. Findings from this study reveal that CRNAs perceive elevated noise to be regularly present in the OR, specifically during the critical emergence phase of the anesthetic. However, CRNAs feel that increased noise only occasionally limits their ability to perform procedures, concentrate and communicate with the perioperative team. OR noise rarely interferes with memory retrieval. CRNAs perceive that noise is sometimes a threat to patient safety but infrequently engenders adverse patient outcomes. CRNAs do not perceive noise in the OR to be detrimental to their health but strongly agree that excessive noise can and should be controlled. Increased ambient OR noise is a veritable reality that may pose a potential threat to patient safety. Further research to identify elevations in noise during critical phases of the anesthetic and delineation of significant contributors to its genesis is warranted