The intensive care unit (ICU) of the future in which technology enables dramatic improvements to clinical care while reducing costs has been predicted since the 1970s, but has largely been unrealized.1 In fact, clinicians largely interact with patient data the same way as in the 1950s when ICUs were first introduced. Sensors measure different aspects of patient physiology, with the last 10 seconds of waveform data displayed on a patient monitor. Additional isolated medical devices measure more sophisticated physiological processes that are displayed on its own isolated display.
Very little attention has been given to how to best use data in the ICU. New monitoring technologies are placed next to other devices in already cramped patient rooms, and their data are added to what is already displayed. There is virtually no ability to go back and analyze what has happened over time, no concept of the complex dynamic interactions among all monitored physiological processes. Despite all of the impressive technological advances in the last 60 years, simply determining the average blood pressure over the last few hours is nearly impossible in most ICUs around the world.
The goal of multimodality neuromonitoring is to provide clinicians continuous, real-time assessment of brain physiology to prevent, detect, and attenuate secondary brain injury as well as to improve prognostication of outcome.2 In 2014, the Neurocritical Care Society in collaboration with the European Society of Intensive Care Medicine, the Society for Critical Care Medicine, and the Latin America Brain Injury Consortium organized an international, multidisciplinary consensus conference to help develop evidence-based practice recommendations on bedside physiologic monitoring.3 The development of clinical informatics infrastructure in neurocritical care is not only critical to complying with evidence-based practice recommendations, but will reshape how we view physiological data and potentially our entire approach toward scientific discovery.4 This chapter is designed to help you understand the potential value and barriers to implementing neurophysiologic-centered DSSs in your ICU.
Clinicians may be confronted with more than 200 variables5 during morning rounds, and yet they are not able to judge the degree of relatedness between more than two variables.6 Each variable collected is treated clinically as an individual parameter to control through treatment. However, patient physiology is not composed of independent processes, but dynamic systems of high-dimensional, nonlinear interactions whereby the level of one parameter affects how other parameters relate (see Figure 19-1). In the absence of genuine understanding about these physiological processes, device alarms are set to go off at the most extreme physiologic thresholds. This strategy only alerts clinicians when patients are on the verge of crashing and does little to identify the earliest stages of pathophysiologic processes in patients when conditions are most amenable to treatment. Devices may also produce thousands of false alarms for each patient7 that can lead to alarm fatigue, which results in lower quality of care and sometimes fatal events. The problem is so perverse that The Joint Commission issued a sentinel event alert in April 2013 requiring hospitals to address alarm fatigue.8
Figure 19-1.
High-dimensional nonlinear patient physiology revealed with neuromonitoring. A 56-year-old male patient with a subarachnoid hemorrhage was admitted with Hunt and Hess grade IV and Fisher Grade III. He received neuromonitoring that included intracranial pressure (Integra; Camino, Integra LifeSciences, Plainsboro, NJ) and brain oxygen tension (LICOX; Integra) monitoring. The scatterplots show the relationship between cerebral perfusion pressure (CPP) and brain oxygen tension when end-tidal CO2 ≤ 30 (left) and > 30 (right). A locally weighted regression line is used (black) to represent nonlinear relationships. When end-tidal CO2 ≤ 30 the data suggest that patient maintains a steady oxygenation over a wide range of CPP that is consistent with intact cerebral autoregulation. In contrast, this relationship changes meaningfully when end-tidal CO2 > 30 such that oxygenation appears to increase in a near linear fashion as CPP increases, consistent with impaired cerebral autoregulation.
Our ability to collect patient data far exceeds our intellectual capacity to understand it unassisted9 and greatly contributes to conditions of constant “information overload” that can lead to preventable medical errors.6,10
Implementation of clinical decision support systems (CDSSs) to help us understand the clinical meaning of patient data are essential.11,12 A CDSS is a computer program designed to help clinicians make diagnostic or management decisions 13 and often relies on both patient-specific and knowledge-based information.14 From a critical standpoint, multimodality monitoring as a CDSS is still in its infancy. Patient-specific information from patient monitors and devices is not easy to obtain, manipulate, or visualize. Knowledge-based information is difficult to call from the medical record for the purpose of integrating into a CDSS. The field is starting to evolve rapidly, and realizing the potential benefits of multimodality monitoring will effect an increase in CDSSs.
A neurophysiologic-centered CDSS is justifiable by the improvement in quality of care that can be delivered and by its role in providing detection of avoidable deleterious subclinical events. Systems should elucidate the physiological status of the patient and reveal its impact on other metrics of brain health such as brain oxygenation and/or cerebral metabolism in order to inform treatment decisions. Potential benefits that can be realized by using a patient-specific CDSS15 include automatic early detection of secondary complications before clinical symptoms occurs (eg, sepsis detection16), computerized implementation of clinical protocols (eg, administration of insulin17), and general support of clinical decision making (eg, clinical dashboard18). Tele-ICU systems that provide physiologic trend and abnormal laboratory alerts, as well as addressing daily goals and workflow issues, have been shown to improve patient outcome, while also reducing ICU and hospital length of stay.19
More evidence is needed on how brain monitors can be used to affect patient outcomes. Notably, a neuromonitor must be paired with a treatment strategy in order for its effectiveness to be evaluated in a clinical study.20 Few randomized clinical trials have been successful in critical care,21 and some successful trials have been refuted by subsequent trials.22 Determining the best means to evaluate treatment strategies reliant on neuromonitoring remains critical to moving the field forward and making clear the rationale for such systems.
The biggest barrier to DSSs is a lack of clinical informatics infrastructure to support them. Adoption of electronic health record (EHR) systems is being promoted by the Department of Health and Human Services through the office of National Coordinator for Health Information Technology by means of forums and regulations. The American Recovery and Reinvestment Act of 2009 has also authorized centers for Medicare and Medicaid services (CMS) to provide reimbursement incentives up to $44,000 for each eligible healthcare professional who uses certified EHR systems in a meaningful-use manner. Starting in 2015, financial penalties will be levied until EHR systems are utilized according to the meaningful-use specifications.23–25 ICUs have been migrating from paper-based to computerized charting systems for more than a decade.26 Adoption of EHR systems in acute care hospitals has risen from 12% in 2009 to almost 60% in 2013.24,27
The FDA does regulate medical software as a device. The category of medical software, “software accessories,” is actively regulated by the FDA. Software accessories are attached to (or used with) other medical devices, such as a patient physiologic monitor. For example, systems for digital analysis and graphical representation of electroencephalographic (EEG) data connected to EEG acquisition systems are FDA regulated. In contrast, it is currently unclear how, or to what extent (if at all), stand-alone software should be regulated by the FDA.28 There is the potential for causing harm when using a CDSS,29 and issues regarding liability and negligence with CDSS use are not clear.28 Currently two independent organizations are focusing on these issues.30,31 It is anticipated that the FDA will continue to clarify its intention regarding CDSS regulations.
What kind of data do I need to collect in order to realize any or all of the benefits of neurophysiologic DSSs?
High-resolution physiologic data from the patient monitor and tertiary patient-monitoring devices are the most fundamental data that are required to support neurophysiologic DSSs. Real-time information from infusion pumps about treatments and other intervention information is the second most critical patient-specific data. These two types of data together enable the evaluation of treatment interventions on the patient’s physiological status. Situational awareness about the patient can be further enhanced by integration with laboratory, clinical, nutrition, and other digitally captured data.
EEG monitoring systems are FDA regulated, and traditionally have been separated from neurophysiologic DSSs. EEG systems that integrate patient-monitoring data or make quantitative EEG parameters available for export into neurophysiologic DSSs are now available. Each EEG system will have its own specifications and options, which continue to improve. Transmitting real-time EEG data over hospital networks can be a barrier to integrating these data systems. Kull and Emerson32 have discussed in detail considerations related to EEG monitoring in the ICU.
