Knowledge-Based Systems for Intelligent Patient Monitoring and Management in Critical Care Environments


186.1

186.2

Benoit M. Dawant

Vanderbilt University

186.3

Patrick R. Norris

Vanderbilt University 186.4

Introduction

Introduction

Intelligent Patient Monitoring and Management Signal Acquisition and Low-Level Processing • Data Validation • Signal-to-Symbol Transformation and Giving Advice

Moving Toward Computer Architectures for Real-Time Intelligent Monitoring Discussion and Conclusions

Patient monitoring and management in critical care environments such as intensive care units (ICUs) and operating rooms (ORs) involves estimating the status of the patient, reacting to events that may be life-threatening, and taking actions to bring the patient to a desired state. This complex process includes the interaction of physicians and nurses with diverse data (ranging from clinical observations to labora­tory results to online data) provided by bedside medical equipment. New monitoring devices provide health care professionals with unsurpassed amounts of information to support decision making. Ironi­cally, rather than helping these professionals, the amount of information generated and the way the data is presented may overload their cognitive skills and lead to erroneous conclusions and inadequate actions. New solutions are needed to manage and process the continuous flow of information and provide efficient and reliable decision support tools.

Patient monitoring can be conceptually organized in four layers [Coiera, 1993]: (1) the signal level, which acquires and performs low-level processing of raw data; (2) the validation level, which removes data artifacts; (3) the signal-to-symbol transform level, which maps detected features to symbols such as normal, low, or high; and (4) the inference level, which relies on a computer representation of medical knowledge to derive possible diagnoses, explanations of events, predictions about future physiologic states, or to control actions. In addition to these four layers, medical decision support systems need data interfaces to other clinical information systems as well as carefully designed user interfaces to facilitate rapid and accurate situation assessment by care providers.

A number of systems have been developed to address problems faced by clinicians in critical care environments. These range from low-level signal analysis applications for detecting specific features in monitored signals to complete architectures for signal acquisition, processing, interpretation, and decision support. As always, specificity and generality are conflicting requirements. Systems developed for specific applications are usually successful in their limited domain of expertise. However, the lessons and problem­solving strategies learned in one domain are often difficult to generalize. Conversely, generic architectures aim at providing support for modeling and developing a wide range of applications. They also strive for flexibility, modularity, and ease of expansion. This generality is often at the expense of expertise and performance in specific domains [Uckun et al., 1993]. This chapter reviews some of the problems and proposed solutions associated with the four layers of intelligent monitoring systems.

Intelligent Patient Monitoring and Management

Signal Acquisition and Low-Level Processing

Typical signals found in critical care environments include vital signs (ECG, EEG, arterial pressure, intracranial pressure, etc.) and information provided by therapeutic devices, infusion pumps, and drain­age devices. Modern monitoring devices are also capable of providing derived and computed information in addition to raw data. Heavily instrumented patients frequently have up to 20 medical devices moni­toring them, producing up to 100 pieces of clinically relevant information. These instruments are often stand-alone, and interconnection requires developing dedicated software in-house, usually with substan­tial effort. To address this problem, the IEEE standards committee, IEEE 1073, has been working since 1984 on a standard for medical device data communication in critical care environments called the Medical Information Bus (MIB). Although much of the standard has been adopted by ANSI, and despite long-term efforts by individual institutions [Gardner et al., 1992], a standard adhered to by major medical device manufacturers is still lacking. Today, this absence of interconnectivity remains a major obstacle to the development and implementation of intelligent monitoring systems.

Current bedside monitors typically provide instantaneous values for the monitored variables. To complement this information, numerous algorithms have been proposed to detect features in the signals. In particular, detection of significant trends has received much attention [Avent and Charlton, 1990, Haimowitz and Kohane, 1996]. Methods based on median filters [Makivirta et al., 1991] and fuzzy logic [Sittig et al., 1992; Steinman and Adlassnig, 1994] have also been proposed. More recently, techniques such as sub-band adaptive filtering, chaos analysis [Karrakchou et al. 1996], and wavelet transform [Unser and Aldroubi, 1996] have been investigated. Multivariate and data fusion methods have been used to reveal interactions between signals and applied to problems such as ventricular rhythm tracking [Thoraval and Carrault, 1997]. Traditional waveform analysis also continues to be applied to signals such as intracranial pressure [Czosnyka et al., 1996] and arterial blood pressure [Karamanoglu, 1997]. Interpret­ing the output of these algorithms and assessing their impact on patient care is an ongoing task.

Data Validation

The risk of noise contamination, inadequate wiring, or instrument failure is significant, especially as the amount of sampled data increases. Unreliable information can drastically reduce the practitioner’s ability to rapidly assess and act on monitor data. In addition, false alarms due to erroneous or incomplete data reduce clinicians’ confidence in the instrumentation resulting in alarms being disabled or ignored. Tsien and Fackler [1997] report that after 298 monitored hours in a pediatric ICU, 86% of the 2,942 alarms were found to be false-positive alarms while an additional 6% were deemed irrelevant true alarms.

Methods relying on data redundancy and correlation (thus assuming some level of interconnectivity between bedside instruments) as well as contextual information to eliminate false alarms, validate data, and diagnose malfunctions in the monitoring equipment have been proposed. Rule-based systems [Jiang, 1991; van der Aa, 1990] and neural networks [Orr, 1991] have been developed for detecting faults and malfunctions in the breathing circuit during anesthesia. Other intelligent alarming systems focusing on data validation have been implemented in the operating room, and include rule-based systems for respiratory-circulatory management [Garfinkel et al., 1988] and for patient state-dependent data collec­tion and processing [Arcay-Varela and Hernandez-Sande, 1993]. Rule-based systems have been combined with fuzzy logic [Schecke et al., 1992] and neural networks for false alarm reduction [Navabi et al., 1991]. Laursen [1994] investigated causal probabilistic networks to reduce false alarms in the ICU. In addition to systems specifically designed for data validation and false-alarm rejection, diagnostic and advice-giving systems often include a data-validation step in their overall strategy.

Signal-to-Symbol Transformation and Giving Advice

The next layer in information processing involves transforming numerical values into symbolic infor­mation and using these symbols for diagnosis and giving advice. A representative sample of these systems is discussed below. For a more complete compilation the reader is referred to Uckun [1994]. Although a strict taxonomy of diagnostic and advice-giving systems is difficult to create, systems with so-called shallow and deep knowledge will be distinguished. Shallow knowledge expresses empirically based, experiential knowledge [Mora et al., 1993] generally called heuristics. Reasoning in these systems is mostly associative, and knowledge is usually represented in terms of an IF (antecedent) THEN (action) form. Shallow knowledge systems exhibit a high degree of competence in their limited domain of application, but performance usually degrades quickly near the edge of the domain of expertise. At the other end of the spectrum, deep knowledge refers to knowledge about the structure, behavior, or function of a system. Model-based reasoning involves using this knowledge to diagnose, predict, or explain the behavior of the system over time.

Shallow Knowledge Systems

Typically, these systems perform a specific task and have similar architectures: (1) a module responsible for transforming quantitative data into qualitative or symbolic information, (2) a module capable of translating symbolic information into patient state, and (3) a module designed to provide therapeutic advice based on the estimated patient state.

Ventilator management has received much attention in intelligent monitoring research, following the pioneering efforts of Fagan et al. [1984] on the VM project. VM was developed in the late 1970s as an extension of the rule based MYCIN formalism to interpret online quantitative data in the ICU setting and to provide assistance for postoperative patients undergoing mechanical ventilation. VM addressed the importance of interpreting data differently depending on patient status.

COMPAS [Sittig et al., 1989] was designed to assist in management of patients with adult respiratory distress syndrome (ARDS). COMPAS is a rule-based system incorporating a blackboard architecture with three main types of knowledge sources (KSs): (1) the data-processing KSs compute derived information from the primary data; (2) the data-classification KSs transform numerical information into symbolic information; and (3) the protocol-instruction KSs suggest therapeutic actions. These knowledge sources roughly correspond to the upper three layers of intelligent monitoring systems (see above). The raw data level consists of information provided by clinical or blood gas laboratories, x-ray departments, and respiratory therapists. Although COMPAS is no longer used the knowledge it contains has served as a basis for a system used clinically [Henderson et al., 1991].

The AIRS system provides three levels of decision support to match three phases of ventilation [Summers et al., 1993]: (1) the startup phase matches the patient state to one of 14 predetermined states and suggests initial ventilator settings; (2) the maintaining phase provides context-dependent values for set point and trend alarms; and (3) the weaning phase contains knowledge represented as PROLOG premise-actions rules to provide advice in the weaning process.

Vie-Vent [Miksch et al., 1993] is an advice-giving system for the monitoring and management of mechanically ventilated neonates. It consists of three modules: (1) the data-acquisition and validation module performs simple range checking and measurement correlation; (2) the data-abstraction module translates numerical values into normal, low, and high ranges based on predetermined rules; and (3) the rule-based therapy-planning module proposes ventilator settings based on the qualitative values for pH, pCO2, and PO2 provided by the data abstraction module. These rules are adapted to reflect the site of measurement and the mode of ventilation. Vie-Vent is designed to operate in real-time and it includes a data validation component using time-point, time-interval, and trend-based methods [Horn et al., 1997].

PATRICIA [Moret-Bonillo et al., 1993] is a system designed to assist in monitoring and managing mechanically ventilated patients in ICUs. It consists of two main components: the deterministic module, which transforms incoming information into symbolic ranges, and the heuristic module, which interprets these symbolic ranges using rules to infer patient status and prescribe therapeutic advice. It uses so-called natural contexts, which include demographic information, patient history, and diagnosis information, to provide patient-dependent ranges for the symbolic variables. Based on these symbolic values, the rule — based system performs data validation both in the deterministic and the heuristic modules. It is also capable of detecting “prealarm” situations by predicting a temporal evolution of monitored parameters. PATRICIA is currently working off-line and underwent an extensive retrospective validation [Moret — Bonillo et al., 1997]

Other systems developed for ventilator management include ESTER [Hernandez-Sande et al., 1989], KUSIVAR (developed for the management of patients with ARDS) [Rudowski et al., 1989], and WEAN — PRO (designed for assisting physicians in weaning postoperative cardiovascular patients) [Tong, 1991]. Expert-system methodology also has been applied to the monitoring and management of patients in the cardiac care unit (CCU) [Mora et al., 1993, Sukuvaara et al., 1993b].

As opposed to the systems discussed before, GANESH [Dojat et al., 1992] has been designed for use as a closed-loop controller. Its domain of application is the weaning of patients under pressure-supported ventilation (PSV). Based on the respiratory rate, tidal volume, and end-tidal partial pressure of carbon dioxide, a rule-based controller determines the patient status and action to be taken and then acts on the ventilator. Preliminary clinical evaluations of the system have shown its ability to maintain patients in the region of comfort and to progressively reduce the pressure of weanable patients. Recently, the theory of Event Calculus has been proposed to capture and use temporal knowledge for this application [Chittaro and Dojat, 1997].

Blom [1991] also proposes an expert-system approach for closed-loop control of arterial pressure during surgery. In this approach, heuristic rules supervise a PID controller and adjust its parameters. The result is a robust controller capable of coping with artifacts in the signal, low signal-to-noise ratio, and patient sensitivity to nitroprusside, a drug used to achieve controlled hypotension during surgery. Huang and Rozear [1998] also describe a fuzzy logic-based system to control administration of multiple IV drugs for hemodynamic regulation. It consists of: (1) a fuzzy decision analysis module to assign the patient to one of six possible states using information derived from monitor data; (2) a hemodynamic management module to determine the dosage changes of each drug based on the current state; and (3) a therapeutic assessment module for scheduling drug administration based on drug properties such as pharmacological delay.

Deep Knowledge Systems

Despite its advantage over associative reasoning for explanation, robustness, and prediction, model-based reasoning has been used in few medical systems. Uckun [1992] suggests this may be attributed to the relatively shallow level of knowledge regarding most disease processes and the complexity and inherent variability of human anatomy, physiology, and pathophysiology. These difficulties still challenge our ability to develop accurate quantitative, qualitative, or hybrid models. Despite the difficulties involved, several model-based reasoning systems have been proposed for patient monitoring.

Rutledge and colleagues’ VentPlan [1993] recommends ventilator settings for ICU patients. It has three main components: a belief network, a mathematical modeling module, and a plan evaluator. The belief network computes probability distributions for the physiologic model parameters from quantitative and qualitative information. The model predicts the distribution of future values for the dependent variables based on this data and current ventilator settings, and can simulate the evolution of important variables under various ventilator settings to evaluate proposed therapy plans. The plan evaluator predicts paCO2 and O2 delivery to rank therapy plans using a multiattribute scoring system and to propose ventilator settings.

The patient model component of SIMON [Uckun et al., 1993; Dawant et al., 1993] was built on an extension of the qualitative process theory (QPT) [Forbus, 1984] to diagnose neonates suffering from respiratory distress syndrome (RDS), to predict the patient’s evolution, and to dynamically adjust alarm parameters based on patient state. As opposed to QPT, this approach does not exhaustively predict all distinct states the system may reach, since this is of little use when external variables (i. e., ventilator settings) are not constant. It determines patient status based on the temporal evolution of clinical events, and can modify the model based on discrepancies between observed and predicted values. SIMON underwent a limited retrospective evaluation using neonatal ICU data.

KARDIO [Lavrac et al., 1985] and CARDIOLAB [Siregar et al., 1993] are examples of models developed for model-based ECG simulation, prediction, and interpretation. KARDIO generates qualitative descrip­tions of the ECG signals corresponding to various arrhythmia combinations, based on the relation between the ECG and 30 elementary arrhythmias. Observed ECG can then be compared with the generated descriptions, and the associated arrhythmias can be retrieved from the database. Without a model permitting its automatic generation, the creation of an exhaustive database associating arrhythmias with ECG description would be a daunting, if not impossible, task.

Moving Toward Computer Architectures for Real-Time Intelligent Monitoring

Previously highlighted is the fact that patient monitoring is a complex, multifaceted task, and that much research has been done to facilitate the monitoring tasks of clinicians. But despite such efforts, few of the systems described earlier are in clinical use. None of them have been fielded or tested in other institutions. This may be due to the specificity of these systems and to the difficulty of modifying their structure for slightly different applications. Rather than focusing on specific tasks, other researchers are attempting to develop generic architectures that can support the development and implementation of a wider range of applications. These architectures are designed to support diverse reasoning schemes, guarantee real-time response, and permit the integration of the entire spectrum of tasks required for intelligent patient monitoring. This section describes a few such systems.

GUARDIAN [Hayes-Roth et al., 1992] is a blackboard system. Based on a general architecture for intelligent agents, it has been applied in respiratory and cardiovascular monitoring domains. GUARDIAN emerges from a long history of research on reasoning and problem solving, and much emphasis is put on the generic problem of coordinating and selecting one of possible perceptual, cognitive, or action operations under real-time constraints. It integrates heuristic, structural, or functional knowledge of (1) common respiratory problems, (2) respiratory, circulatory, metabolic, and mechanical ventilator systems, and (3) generic flow, diffusion, and metabolic systems. A system capable of instantiating treat­ment protocols based on (1) current patient context, (2) executing plans and close-loop control actions,

Monitoring the execution of these plans and actions, and (4) modifying plan execution as necessitated by patient response has also been designed to work with GUARDIAN.

The Intelligent Cardiovascular Monitor (ICM) [Factor et al., 199l] is an application of the multitrellis software architecture, which supports modeling of real-time tasks in terms of an acyclic hierarchical network of decision processes. In ICM, the lowest level corresponds to raw data, and processes at this level implement signal processing algorithms to extract features from the signal. Information is abstracted along the hierarchy in a way reminiscent of information flow in a blackboard architecture, until reaching the highest level corresponding to physiologic processes or events. As opposed to GUARDIAN, the process trellis architecture assumes sufficient resources to execute the entire program and meet real-time con­straints. This architecture has also been used for the development of DYNASCENE [Cohn et al., 1990], which models hemodynamic abnormalities in terms of “scenes” and temporal relationships between scenes. Each scene corresponds to one physiologic process (e. g., increased pericardial pressure) and is related to modules for computing specific information from the incoming data. DYNASCENE and ICM do not work in conjunction with patient models and hence are not capable of predicting patient behavior.

SIMON [Dawant et al., 1993] is a framework designed to support the development of real-time intelligent patient monitoring systems. It is organized around three main modules: the patient model, the data-acquisition and abstraction module, and a flexible user interface. The patient model is respon­sible for (1) estimating the physiologic state of the patient, (2) predicting the temporal evolution of the monitored variables, and (3) defining a dynamic monitoring context. The context includes normal and abnormal ranges for monitored variables, expected temporal evolutions of these variables, significant values and patterns to be detected in the incoming signals (events), and display configuration information (i. e., critical information to be displayed). The data abstraction module adjusts alarm limits and thresh­olds based on the current context and implements strategies to detect complex events. These may involve multiple variables and temporal relationships between events detected on each variable. SIMON has been applied off-line to the problem of monitoring neonates in the intensive care unit.

Following the recommendations of the INFORM project [Hunter et al., 1991], Sukuvaara et al. [1994] proposed an object-oriented framework to implement systems based on the blackboard approach to problem solving. Data on the blackboard are separated into four main categories: (1) source data (raw data acquired from monitors, hospital systems, or users), (2) preprocessed variables, (3) physiologically interpreted variables, and (4) patient status. Preprocessing knowledge sources (KSs) span the first two levels and extract features from the incoming data. Transformation KSs span the second and third levels in the blackboard hierarchy, and map values of preprocessed variables onto (low, high) intervals. Patient status assessment KSs get input from physiologically interpreted data and determine the patient state. The system also assumes sufficient resources to execute all its knowledge sources and meet real-time constraints.

186.4 Discussion and Conclusions

Intelligent patient monitoring and management are complex tasks involving all aspects of information processing. They require context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. These systems also need to interface to hospital-wide patient data management systems (PDMS) to gather data from the laboratories, the pharmacy, or radiology. Finally, these systems should include efficient mechanisms to present relevant and timely information to clinical users.

The systems described here illustrate that it is not easy to choose between specificity and generality. Systems that undergo clinical evaluation are typically developed for a very narrow, specific application which lends itself to rule-based approaches. However, these systems are often of limited clinical utility due to their narrow scope. Research aimed at developing deeper models to infer a wider range of behaviors, on the other hand, has failed to produce overwhelming results. Models for predicting patient behavior or response to therapeutic actions are clearly desirable, but currently are only available in very limited or constrained situations. Original modeling methods and approaches have been proposed, but require assumptions that either drastically reduce the scope of the model or make it unrealistic for clinical use.

Complete architectures aim at identifying the essence of patient monitoring tasks and at providing support for the development of a class of systems. These systems should permit experimentation with a number of problem-solving strategies, the integration of diverse reasoning mechanisms, and should ultimately span the entire spectrum of tasks required for efficient and reliable patient monitoring. However, to move from the research laboratory to the clinical arena, these systems will have to be fitted with rich and flexible modeling tools capable of overcoming the complexity of generic architectures. In addition, they will have to interface with a variety of devices and systems, efficiently manage vast amounts of diverse data, and successfully interact with clinical users.

To address the issue of information presentation, web-based technology is being integrated with clinical information systems, and prototypes have been developed to provide integrated clinical user interfaces to ICU patient data [Norris 1997, Nenov 1995] at or close to real time, via a web browser.

However, the fact remains that despite years of research and development and despite the potential of techniques being developed, intelligent monitoring systems have failed to make a significant impact on the quality of patient care. Saranummi [1997] contends that the main reason for this phenomenon is a lack of interaction between clinicians, industry, and academic researchers. Clinicians and academic researchers must interact closely to define the problem, develop solutions, and evaluate these solutions. When this is done, industry needs to be involved to assess the commercial viability of the project. To foster interaction Saranummi advocates the use of new technical platforms that support “plug-in” mod­ules to existing monitoring and PDMS systems. These modules extend the functionality of existing monitoring systems without interfering with routine operation, permitting the in situ evaluation of new technical solutions. Efforts such as the IMPROVE project sponsored by the European Union, aimed at gathering large annotated data sets of ICU patients, could also facilitate the evaluation of novel infor­mation processing methods in close to real world situations [Nieminen et al., 1997].

Coiera [1994] also suggests that current systems are not adapted to their task. He notes that a major effort has been put into developing systems to assist the physician in making diagnoses and taking therapeutic actions. He suggests, however, that the main problem facing health care professionals is not choosing a diagnosis or therapeutic action, but rather establishing a clear picture of the world leading to these choices. Following this observation, Coiera advocates the creation of a “task layer” designed to provide a good match between displayed information and the cognitive processes of physicians and nurses. More research on the way clinicians interact with intelligent monitoring devices is necessary, though, to support the design of these interfaces.

The design of intelligent monitoring systems is thus an ongoing task. Context-based acquisition, processing, analysis, and display of the information are an essential concept in these systems. Elements defining this context are, however, complex and diverse. On the one hand, patient status and history define an interpretation and monitoring context that can be used to determine normal and abnormal limit values, trends, normal relations between observed variables, and significant events to be detected in the signals. Models could automatically establish these contexts, which, in turn, could be used to configure the monitoring system. On the other hand, cognitive tasks faced by the clinician, such as organ system function, treatment course, or resource utilization define the type and form of information presented. This information-processing context may be more difficult to automate and will require development of better interaction mechanisms between the system and its users.

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