Medical Device Alarms Summit
The AAMI Medical Device Alarms Summit was held October 4th & 5th in Herndon, VA at the Hyatt. There will be much published on the AAMI web site in this regard, and much in the way of out-briefs and collateral so I will leave the complete minutes and summary of activities and goings-on to those charged with doing so. However, I am compelled to focus on a few related themes that were referred to by several of the speakers and to which I voiced my opinion publicly during the meeting. I will do so relative to two specific speakers and provide the input that I shared at the conference during the public question and answer forums.
Medical Device Alarms Keynote Presentation
The keynote speaker on medical devices was given between 8:45 and 9:15 by George Blike, MD of the Dartmouth-Hitchcock Medical Center. Early in his presentation, Dr. Blike discussed that not much has changed since the 1999 Institute of Medicine Report To Err is Human was published. In that report, the IOM concluded, based on two studies, that between 44000 and 98000 Americans were killed each year due to preventable medical errors. While the options to diagnose and treat have increased measurably since that time, the complexity of the treatment process undermines the benefits. This is also the case of information complexity, in which the amount of information that is now available in electronic form exceeds the environmental space limitations surrounding the patient.
Medical device alarms, Dr. Blike continued, as a way to redirect attention, about redirecting attention “from something that is less important to something that is more important.”
However, uncertainty plays a large role in medicine, and the uncertainty about the meaning of alarms requires knowing much more than just whether a parameter is outside some norm or threshold. It is about knowing the context surrounding the patient. It is more than managing alarms as “nuisances” in the clinical space. While it is true that clinical staff can become “snow blind” to the continuing cacophony of alarms within the environment, the reason for reducing the alarms is not to reduce the cacophony, but to focus the alarms to redirect attention in a way that truly helps the patient.
Dr. Blike referenced Lucian Leape, MD: “Anesthesia is the only system in healtchare that begins to approach the vaunted six sigma level of perfection that other industries strive for.”
The management of a patient becomes more like a feedback control problem, in which the process blocks of Detect, Diagnose, Treat and Monitor are key to the closed loop system. In an environment where upwards of 40 parameters must be monitored over time (ICU), it becomes a multi-dimensional, multi-parameter feedback control system exercise. In environments such as the ICU where nursing:patient ratios may be 1:2 or 1:1, the care team must be able to detect problems, diagnose cause, treat, and then monitor. As part of this process, the medical device alarms identify deviations of the system state of the patient from the expected state.
Medical Device Alarms: Defining the Problem
The keynote address of Dr. Blike was followed by a panel titled “Defining the Problem: It’s More Than a Nuisance.” James Blum, MD of the University of Michigan Health System and Barbara Drew, RN, PhD of the University of California, San Francisco were the members of this panel. With no disrespect to Dr. Drew, who had an impressive and extremely interesting and informative presentation, I wish to focus on the comments of Dr. Blum because of the message he communicated in terms of systems integration and data management. I resonated most definitely with Dr. Blum’s presentation as it has been a rallying call of my own now for almost 20 years (indeed, a large reason for my entry into the field).
I took some photos of slides, and there are three that speak strongly to me. The first of these is the slide on physiologic monitors, shown below. The key point is that alarms, in general are not “smart”, and this is especially true of physiologic monitoring where, in which there is no “penalty for high sensitivity with low specificity,” and a general lack of data integration. However, the historic and retrospective data is not readily available nor can prospective analysis and projection be performed with the data. Moreover, these data are not integrated with the wealth of other information that provides context on the patient. While it is true that alarms that are of a critical nature (e.g.: ventricular tachycardia or asystole need no other context, there are situations which do (e.g.: O2 Saturation changes, respiratory function changes) and thus it is important to incorporate the context surrounding the patient into medical device alarms.
The second of these charts from Dr. Blum is one that resonates very strongly with me: Electronic Medical Records may be good charting instruments, but in terms of their clinical decision support capacity relative to real-time data, they are very mediocre. The reason I say this is because of the last two bullets on his slides: the data resolution can be limited (critical care charting ~ 15 minute intervals) and suffer from garbage-in:garbage:out. Because modeling of physiological systems often requires high fidelity and high resolution information, sparsely collected data will often miss crucial events that may be seconds in duration or less. For example, assessments of heart rate and respiratory rate variability can be quite important and predictive as to patient stability, as well as critical medical device alarms related to V-TACH or ASYSTOLE. The EMR is simply not equipped to capture these data trends based on the rate of data collection: the likelihood of missing these events all together is simply higher than capturing them at all.
The third and final of these slides is the following titled “Integration.” The essence of the problem with medical device alarms is that they are fairly “one-dimensional” in nature: they typically are associated with the patient care device (PCD) and its function (e.g.: physiologic monitor, infusion pump, mechanical ventilator, etc.) This does not mean that they are univariate, but rather they do not take into account the entire context of the patient and environs, as well as patient history, chemistry, etc. The Integration slide makes the point that multiple systems must be taken together–or fused–to provide an intelligent assessment of what is important versus that which is not.
Medical Device Alarms from a Systems Integration Perspective
The last of the three photos above defines to me the essence of effectively managing medical device alarms is through the application of systems engineering and systems integration disciplines. First, complete and unfettered integration of data, from medical devices through ancillary information systems (lab, PACS, EMR, etc.) are required. Next, laying out the use cases and scenarios related to the types of problems and conditions a patient can experience needs to be done in a holistic way that ignores vendor and device boundaries. This may involve integrating data, user interfaces, and creating methods that “feed” on the data available from multiple sources and assimilate it to produce integrated outputs. The display mechanisms are important but are secondary at this point: dashboards that allow singular access to information (much akin to avionics design) may be appropriate here. However, more important is the overall integration of information to provide predictive modeling, retrospective trending, and for evaluating scenarios on the fly. This takes a longitudinal look at the patient state in terms of everything about the patient. As Dr. Blum identified, there may be 40 parameters (give or take) that form the basic state of the patient.
Medical Device Alarms as a State Space Problem
When I began my career it was in the aerospace field and I focused on state space modeling of complex systems. This state space modeling often involved various forms of filtering, including Kalman and Batch Least Squares filters. The systems integration aspect of this modeling involved evaluating the trend or future state with respect to the current state based upon a system model representation of the entity being modeled. From this model, a projection could be made into the future. As was stated by Dr. Blike and others during the conference, medicine is one field where uncertainty plays a large part, it is perhaps naive to think that one could model the human being in ways that many in the aerospace industry do. However, 20+ years ago when I began my studies at the University of Pennsylvania and began my research into prediction and modeling at the University of Pennsylvania Medical Center, that is precisely what I was doing on a smaller scale with a specific class of patients. The subject of my dissertation was predicting the post-operative respiratory behavior of coronary artery bypass grafting patients, a unique class of patient in surgical intensive care units. Many of the concepts brought up during the Medical Device Alarms Summit resonated with me from the early days of my dissertation. One in particular was the idea of taking multi-source, multi-variate data and massaging into an assessment of outcome. As I did when I conducted my research, multi-source data from laboratory, patient record (history, demographics, etc.) were incorporated into the overall assessment of outcome. I know that as I followed patients from surgery through endotracheal extubation afterwards that I found many interesting relationships once all the data were laid out before me: relationships between re-warming time and patient’s anesthetic dosing; relationships time to begin breathing and time to extubate. The approach took into account the fact that there were uncertainties in the modeling. The objective was to establish a gross, coarse model of behavior by looking at the patient as a “black box.” Higher fidelity warranted more accurate modeling. However, approaching the patient as a system and taking into account all information is one approach I believe is the key to effective medical device alarms management.