ideal case, connected to a central control system in the ... control the dose for each individual patient [5] [6]. ..... Cuidados Intensivos para el control remoto de.
DISTRIBUTED ARCHITECTURE SYSTEM FOR MONITORING INTENSIVE CARE PATIENTS Miguel A. Pereira1, Javier Pereira2, Alberto Curra2, Raquel Rivas2, Gerardo Baños1, Alejandro Pazos2, Jorge Teijeiro2 (1) Service of Anesthesia, Resuscitation and Intensives cares. Meixoeiro Hospital. Vigo Meixoeiro s/n. 32600. Vigo. Pontevedra Spain {miguel.angel.pereira.loureiro, gerardo.banos.rodriguez}@sergas.es (2) Centre of Medical Informatics and Radiological Diagnostic (IMEDIR). Faculty of Health Science. University of A Coruna Campus de Elvina. 15071. A Coruna Spain {javierp, acurra, imedir, apazos, jtv}@udc.es
ABSTRACT The main goal of an Intensive Care Unit (ICU) is to stabilize patients who are in critical condition. To this end, patients’ vital signs are recorded using monitoring systems that employ different types of sensors. The attending physician analyzes these records and, based on their medical knowledge and experience, administer different medications to improve the patient’s condition. The infusion pumps used to administer these medications, as well as the monitoring devices that show the patient’s vital signs, are usually at the patient’s bedside or, in the ideal case, connected to a central control system in the ICU. This implies that the physician should be physically present in the ICU in order to oversee the decisionmaking process. In addition, it is necessary that the physician be provided with an easy-to-use (usable) interface to the monitoring system such that the information is displayed in an optimal manner to efficiently support medical decision-making. In this paper we describe the design and implementation of a system architecture whose objective is to both capture ICU patient data using the MECIF protocol as well as to display the information to the attending physician irrespective of his/her location via mobile devices (Tablet PCs and PDAs) connected to a dedicated server. Through this remote interface, the physician will be able to visualize the patient’s vital signs and subsequently decide on the appropriate treatment course by remotely controlling the pumps that administer medication to the patient
1. Introduction The development of computing and information technologies in recent years in the medical domain has significantly helped clinicians in their decision-making tasks. Specifically, a greater amount of information and a more comprehensive set of clinical parameters describing the patient’s condition, have enabled the clinician to improve his/her ability to adjust the treatment course as a function of the patient’s condition [1]. When a patient is admitted to the ICU (either following surgery or due to trauma or a serious medical condition), the patient’s vital signs vary continually, requiring the attending physician to make time-critical decisions and to dynamically modify the doses of different medications being given to the patient. This type of medical attention requires that the clinicians be appropriately (and highly) trained, and that constant vigilance be placed on the parameters being shown by the monitoring devices that continuously update the patient’s condition, since the patient’s condition in turn completely depends on his/her reaction to the medication being administered [2]. One initial advance in the monitoring of critical patients was the development of techniques for the analysis of data and warning of extreme or grave conditions using alarm systems. This was followed by the development of methods for the interpretation of patient data and the assessment of clinical parameters using complex computational formulae. These advances gave rise to the emergence of alarm systems based on pre-determined thresholds and limits, systems for the identification of arrhythmias, interpretation of measured and estimated respiratory parameters, etc. [3]. The development of monitoring systems that were increasingly complex and comprehensive led to the inclusion of increasing amounts
KEY WORDS Intensive Care Unit, ICU, Medical Informatics, CORBA, MECIF, Infusion Pumps
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continuous stream of clinical data, especially when the number of patients in the ICU is relatively high [8] [9]. With this in mind, we are exploring a novel mechanism to aid clinicians in their decision-making duties at the Hospital do Meixoeiro. At this medical centre, there are three ICUs. One of these units is exclusively dedicated to patients with cardiac pathologies. When a patient is admitted into this unit for post-surgery recovery, his/her condition is monitored continuously in order to reestablish the patient’s stability. A variety of clinical parameters are recorded, depending on the type of surgery performed. During recovery, the following parameters are monitored: cardiac frequency (CF), invasive arterial pressure (IAP), oxygen saturation (Sat02%), and central venous pressure (CVP). In some cases, hemodynamic data are also monitored. Using a pulmonary vein catheter, values are obtained for the cardiac performance (CP) o cardiac index (CI = CP/weight), and capillary pressure (PCP). Following cardiac surgery, it is frequently necessary to
of clinical information. This additional information permits increased accuracy but, at the same time, also complicates the decision-making process. At present, the majority of monitoring processes use safety systems and alarms in case of extreme or dangerous conditions, which require immediate response on the part of the attending clinician in order to stabilize the patient [4]. A greater number of commercially available monitoring systems appear each day, incorporating additional data such that the physician can make a more comprehensive and more precise clinical decision. At the same time, there is an increasing number of systems and digital techniques for administering medication using infusion pumps that can specifically control the dose for each individual patient [5] [6]. With the availability of a more comprehensive set of clinical information and more precise infusion pumps, the treatment can be optimally adjusted for each patient based on the attending clinician’s overall training and experience [7].
Visualization Integration Control
Acquisition
Communication Patient Monitoring System
RS232
RS232
Infusion Pump
Figure 1. Schematic diagram of the elements of the system administer medication with the objective of helping the myocardium to maintain the arterial pressure required to keep the appropriate blood perfusion level. These medications belong to a family of pharmacological agents knows as amines, and the more common ones include dobutamine, noradrenalin, fenilefrine, adrenaline, dopamine, and milrinone. Each one of these pharmaceuticals has a specific dose based on the data shown by the monitoring devices, which in turn describe the condition and specific needs of each patient [10] [11] [12] [13].
This procedure, however, presents some limitations and challenges that are worth addressing, such as: the human involvement in decision-making can sometimes incur errors; the decision-making process does not always include all of the relevant parameters; it frequently is the case that decisions must be made in an extremely rapid fashion, faster than the time required by a human to assess all of the pertinent information; clinicians are often under duress and experiencing significant workload, which can affect his/her decision-making abilities; and it becomes increasingly difficult to process or memorize the
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mobile device such that he/she can remotely accept or reject the decisions suggested by the system, thereby remotely controlling the action of the infusion pumps at the patient’s bedside. The system is therefore expected to assist the physician directly in the clinical environment, while at the same time reducing his/her workload. Figure 1 illustrates a schematic diagram of the different elements of the system.
Another type of treatment used for these patients are the vasodilators, fundamentally nitroglycerine and nitroprusiate. These pharmaceuticals are especially suitable for patients who undergo revascularization surgery, or for vasodilatation in patients with a history of hypertension. Vasodilators demonstrate pharmacokinetics and pharmacodynamics that make them appropriate for these types of patients [14]. In response to the data that is dynamically obtained from the patient, either one or another type of medication is administered. When the patient progressively recovers and his/her myocardial functions improve, the amount of medication is reduced. The doses as well as the type of pharmaceuticals to be administered are constantly modified according to the evolution of the patient data. Decisions regarding changes in administered dosage first require an evaluation of the patient’s signs and clinical data, such that the clinical condition closely follows the physiological condition. These decision-making processes are complex, and are based on the evolution and dynamic changes that may occur in the patient data. The reasoning behind the decision-making process is based on control models that are actually standardized and follow an established set of rules and clinical guidelines set forth by various scientific and medical organizations; these
Computer 1. ICU (1 each patient)
2. Implementation and Equipment The approach previously described makes it possible to dynamically acquire patient information from the monitoring systems. We have implemented this approach with the Philips CMS monitoring system, using a PC to control an Alaris IVAC TIVA infusion pump. The PC also records both the evolution of the patient’s condition as well as the medication being administered. The system has been implemented in a Windows XP platform using JAVA and the Eclipse development environment. The Postgres program has been used to create the database, while the Tomcat applications server was employed to manage the information pages. The Model-ViewControler architectural model has been used to develop the system, which allows the system model to be
Computer 3 Client browser
Computer 2. Central Server
Figure 2. Overview of the distributed architecture of the system guidelines, in turn, represent the well recognized body of medical knowledge and previously established protocols that have evolved from clinical studies, experience, and outcomes centred on the similar patient cases [15]. These guidelines and protocols define the most probable patient diagnosis at each moment in time. Hence, the patient data form the basis on which the ICU attending physician decides whether to change one or any of the medications. In this work, we describe a system that is capable of acquiring the data obtained by ICU monitors. The system stores the acquired data in database that is designed to form part of an expert system (currently under development). The expert system is a rule-based (production) system that would process the acquired data and subsequently propose a course of action (or courses of action) to the attending physician. These decisions will be transmitted via an application server to the physician’s
separated from the user interface.
3. System Architecture The system consists of five subsystems: four independent subsystems work in a cooperative fashion, while a fifth subsystem is dedicated to information visualization: Communications Subsystem This element oversees communications functions and consists of hardware tailored to the clinical devices in use. Acquisition Subsystem This subsystem is in charge of acquiring the different patient clinical parameters via the vital signs monitor. Control Subsystem This unit is devoted to the control of the infusion pumps, such that the medication dose can be 87
Control subsystems are designed to accommodate other devices and protocols such that the system can have wider application. The Integration Subsystem acts as a manager between the Acquisition and Control Subsystems, and makes the functionality and actions of these two subsystems available to the Visualization Subsystem. The Integration Subsystem is designed to support different ways of accomplishing its tasks. In addition, this subsystem stores all of the information regarding the patient’s evolution (changes in condition over time) and his/her treatment (data which are provided by the Acquisition and Control modules). It is important to stress the temporal nature of all of this information, since it evolves and changes dynamically over time, and as a result this temporal dimension is reflected in the tiered architecture of the database created by the system. The Visualization Subsystem builds on the Integration Subsystem in order to offer the user all of the information regarding the functionality, data, and actions associated
increased or decreased at each moment of time (control is achieved via an RS232 interface) Integration Subsystem This element provides general access to all the modules and records the information in the database. In addition, this subsystem integrates the various tools and modules that would ultimately be used in formulating a suggested course of action. Visualization Subsystem This building block is designed to show the clinician the patient’s condition in terms of all the relevant parameters and data that the clinician him/herself initially specified. In addition to vitals signs, the subsystem also shows the evolution of the treatment that has been given to the patient and the concomitant response and changes that have dynamically occurred.
Figure 3. Prototype of a visualization interface with the system in general. The information is provided to the user in a simple, ordered, and transparent fashion, with usability utmost in mind. In this fashion, the user can easily view, analyze, and assess the patient’s condition, thereby enabling the user to make decisions at any point in time [15].
The Acquisition and Control Subsystems were developed independently of each other. The Communications Subsystem supports both of these subsystems in order to exchange information regarding the clinical devices in use. Although the current implementation is designed for the use of specific devices (and their particular protocols, such as the Alaris TIVA pumps), the Acquisition and
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carefully be taken into account in interface design, which should follow a consistent mental model. Thus, interface design is both a vital and time-consuming aspect of system design that should necessarily involve the ultimate users of the system. With this in mind, the graphical interface was the result of a cycle of prototype design and redesign in which a group of clinical users were highly involved from the beginning phases of development. The initial prototype used in the interface design was a mockup (without true functionality) separate from both the actual system architecture and any Web-based application. This prototype underwent successive iterative testing and redesign by the user base. The resulting interface, shown in figure 3, shows the data specified by the users (although this represents a subset of the information that can be displayed). It should be noted that the quality of the interface is not necessarily a direct function of neither the amount of data that is displayed, nor of the overall look of the display, but of the type of display that the users feel is most useful and helpful. In this case, the users felt that a subset of the information was crucial and most useful in assessing the overall patient condition and in terms of decision-making. To implement the actual interface resulting from the iteratively improved mock-up prototype into a form suitable for Web-based application, it was necessary to use various tools to achieve this redesign. Initially, the AJAX environment was employed, but this did not yield acceptable results. Finally, a Java Applet was used to create the interface (although AJAX was still used for the questionnaires that must be completed prior to initiating remote monitoring of a patient). The main interface is displayed in Figure 3. The upper portion of the display (with black background) shows the values of the patient parameters being monitored. The different colours were carefully selected by the users to represent different sets or ranges of medically important values. The blue rectangles in the lower left portion represent the infusion (pump) devices. An attempt is made to simulate the manual manipulation of these devices as requested by the users. Next to this information, the display shows the data pertaining to the changes in dose administered by the pumps, which the clinical users consider to be an extremely important type of information [17].
4. Results and Discussion Distributed Architecture and Centralized Database The system is compatible with “mobile clients” that can transmit and receive information remotely, and which can also remotely control the infusion pumps. By “mobile clients” we mean a variety of devices: portable (notebook) PCs, tablets, PDAs, Smartphones, and other devices connected via the hospital intranet or the Internet. This overall scheme is illustrated in Figure 2. It is again seen that the system must support data acquisition and actions (such as controlling the infusion pumps) at bedside. In addition, since there will be more than one patient being monitored, the system must also be capable of controlling data obtained from multiple patients, which in turn requires a (possibly dedicated) server. Together, these requirements imply that certain elements of the system must reside at patients’ bedsides, while other elements are centralized in a server that can support communications with mobile clients. Consequently, the system design is based on the concept of distributing the Acquisition and Control Subsystems such that part of these subsystems are physically located at patients’ bedsides and other parts of these subsystems are contained within the applications servers, where the Integration Subsystem also resides. And, in order to fully exploit the power and usefulness of such a system, it is important to include a Web-based dimension in its architecture. These characteristics are illustrated in Figure 2, which shows how the Acquisition and Control Subsystems are subdivided into two components that communicate via a CORBA distributed-object communications platform. The resulting architecture is functional, powerful, and dynamic. By endowing the system with a distributed architecture, the Acquisition and Control Subsystems can effectively become independent modules. This, in turn, makes it possible to have a centralization of the data in the applications server coupled with the Integration Subsystem. This architecture can support both stand-alone visualization subsystems as well as the option of webbased visualization for remote interactions. Visualization As is the case with any system that is oriented for human usage, this subsystem should be the product of usercentered design and undergo usability testing to insure efficiency, precision, and ease of use [16]. It is well established that many clinical applications have failed due to poor interface design. Given the workload and timepressure with which clinicians are faced, clinical applications should incorporate user interfaces that reduce workload, are incorporated into the work environment in a natural fashion, and that can facilitate the tasks to be performed by the clinicians, ideally reducing the amount of time that would be devoted to certain tasks. Factors such as color usage, numeric display, message formats, and other information-rich visual characteristics should
3. Conclusion A system architecture has been described for the acquisition of ICU patient data. The information is obtained from a bedside, patient monitoring system, and is subsequently processed and stored in a centralized database designed to reflect the time-sensitive nature of the information. The information is provided remotely to the responsible medical personnel via a specialized interface, such that the clinician can assess both the treatment that is currently being administered to the patient as well as the dynamically evolving response to the treatment. In this manner, the clinician can decide how to proceed and whether to modify the treatment. If 89
the treatment is modified, the system would both enforce as well record these changes remotely. The architecture consists of five subsystems that work synchronously and cooperatively. The subsystems address the acquisition of the patient condition (and all relevant parameters); control of the infusion pumps; communications between the system and the various infusion devices; integration of all of the communications, data manipulations, and actions; and a user interface designed by the clinical users to insure optimal usability. The flexibility of the architecture makes it possible to maintain communications between distributed components at bedside, in a centralized server, and at remote devices. The application was implanted following an MVC (Model-View-Controller) architecture using design models, which will facilitate continuous maintenance and updating of the system. At present, the system is undergoing extensive testing in the ICU of the Hospital do Meixoeiro in Vigo, Spain.
[7]
[8]
[9]
[10]
Acknowledgements [11]
We would like to thank Norberto Ezquerra for his helpful comments. This work was supported in part by the Spanish Carlos III Health Institute (FIS-PI061524), and Vice-chancellor of Research, grants for mobility PR2006-0459 and SAB2005-0153, grants from the General Directorate of Research of the Xunta de Galicia (Ref. PGIDIT04PXIC10503PN and PGIDIT04-PXIC10504PN).
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