Systems

Systems


Real-time chemoreceptor-based blood enzyme tracking is coming of age

3.25.2013 | 0 Comments

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Several years ago I wrote a blog entry on autonomic pacing of the heart using a real-time chemoreceptor-based method (See “A suggested chemoreceptor-based method to control pacing and stroke volume in left ventricular assist devices and for patients undergoing heart transplantation“). A key aspect of this suggested method was the real-time measurement of chemoreceptor chemicals in the blood for controlling the pacing of the heart due to autonomic system changes resulting in changes in stroke volume or heart rate.

Recently, a team at Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland developed an implant to monitor chemicals in the blood. This implant, according to this recent article is reportedly the world’s smallest at 14 mm and measures a maximum of 5 indicators to include troponin, lactase, glucose, ATP, to show whether a heart attack has occurred or to track (in the case of diabetic patients) blood enzymes and protein levels. The information can subsequently be transmitted via Bluetooth to a smartphone for online tracking.

This work is encouraging as it lays the foundation for real-time sensing, data collection and analysis at a level that is necessary for more accurate modeling and monitoring of cardiovascular systems. Such work can lead to earlier detection and more accurate prostheses — particularly, artificial hearts — as well as more accurate clinical decision support methods that can determine whether patients have experienced critical events. I can see applications to earlier stroke detection where time-is-brain. Such real-time chemoreceptor-based methods, when integrated with mobile technology and health information technology, can lead to great advances in patient care management through early warning resulting in early intervention.

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Data are the new soil?

4.03.2012 | 0 Comments

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This blog entry will be relatively brief and will focus on some interesting web sites that I have found. The first of these is a TED video of David McCandless on data visualization. McCandless coined the title phrase of this blog, in which the inference is that interesting information can be mined from data, and the effective display of data can yield not only important associations and correlations, but can provide immense value when displayed appropriately to the human eye.

The second link I found relates to data visualization approaches and is presented by SmashingMagazine.com. Here, some very provocative renderings of user views and user interfaces are provided that can inspire the developer or “data designer”.

Both of these sites also inspire me based upon the use of large quantities of data to manage patients, both from the perspective of large quantities of data and from the perspective of how to deal with data as if drinking from a fire hose, as is the case with medical device alarms and high-frequency data taken from durable medical devices.

 

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Medical Device Connectivity is only an elementary step toward contextual interoperability

3.22.2012 | 0 Comments

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Attending Day 2 of the 22nd Annual AAMI/FDA Summit on Medical Devices.

The importance of medical device connectivity and the medical device data system, or MDDS, ruling taken together with electronic medical record (EMR) technology is, in my mind, driving toward the ultimate goal: how to integrate medical devices as part of the system architecture within the healthcare enterprise to support clinical use cases. The MDDS ruling has, in my mind, had the effect of codifying the methods and mechanisms that relate to communicating durable medical device data to the electronic medical record. Similarly, this has raised the awareness of integrating such information into the EMR charting system. Yet, this is really only the first step in the process.

The interaction of the many systems that clinicians use as part of the care and management of the patient involve interoperability among many verticals in the healthcare environment, to include medical device connectivity. The interaction of data from durable medical devices with the more information-based and clinically specific systems make up the larger medical device data system architecture.

The following figure, referenced from Wikipedia, is one source of the “Interoperability Taxonomy” adapted for presentation during the medical device interoperability presentations.

Levels of Conceptual Interoperability Model (LCIM). Source: Tolk, A. and Muguira, J.A. (2003). The Levels of Conceptual Interoperability Model (LCIM). Proceedings IEEE Fall Simulation Interoperability Workshop, IEEE CS Press

The hierarchy presented in the figure above maps well to medical device connectivity. The first three levels, that is level 0 – level 2, correspond, respectively, to medical devices that are not connected; medical devices that have some form of physical connectivity; and, medical devices that support the ability to syntactically communicate data with other medical devices as well as with information systems using methods such as HL7 messaging. Many hospital systems today that feature medical device connectivity are in the Level 0 to Level 2 range.

Higher levels of interoperability, such as semantic interoperability, are works in progress and organizations such as IHE are engaged in areas of syntactic, semantic and pragmatic interoperability.

The definitions offered per the LCIM model are as follows:

  • Level 0: Stand-alone systems have No Interoperability.
  • Level 1: On the level of Technical Interoperability, a communication protocol exists for exchanging data between participating systems. On this level, a communication infrastructure is established allowing systems to exchange bits and bytes, and the underlying networks and protocols are unambiguously defined.
  • Level 2: The Syntactic Interoperability level introduces a common structure to exchange information; i.e., a common data format is applied. On this level, a common protocol to structure the data is used; the format of the information exchange is unambiguously defined. This layer defines structure.
  • Level 3: If a common information exchange reference model is used, the level of Semantic Interoperability is reached. On this level, the meaning of the data is shared; the content of the information exchange requests are unambiguously defined. This layer defines (word) meaning. There is a related but slightly different interpretation of the phrase semantic interoperability, which is closer to what is here termed Conceptual Interoperability, i.e. information in a form whose meaning is independent of the application generating or using it.
  • Level 4: Pragmatic Interoperability is reached when the interoperating systems are aware of the methods and procedures that each system is employing. In other words, the use of the data – or the context of its application – is understood by the participating systems; the context in which the information is exchanged is unambiguously defined. This layer puts the (word) meaning into context.
  • Level 5: As a system operates on data over time, the state of that system will change, and this includes the assumptions and constraints that affect its data interchange. If systems have attained Dynamic Interoperability, they are able to comprehend the state changes that occur in the assumptions and constraints that each is making over time, and they are able to take advantage of those changes. When interested specifically in the effects of operations, this becomes increasingly important; the effect of the information exchange within the participating systems is unambiguously defined.
  • Level 6: Finally, if the conceptual model – i.e. the assumptions and constraints of the meaningful abstraction of reality – are aligned, the highest level of interoperability is reached: Conceptual Interoperability. This requires that conceptual models are documented based on engineering methods enabling their interpretation and evaluation by other engineers. In essence, this requires a “fully specified, but implementation independent model” as requested by Davis and Anderson; this is not simply text describing the conceptual idea.

 

 

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Informatics: the science and my own vision

3.04.2012 | 0 Comments

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I am an Informatician. There are several definitions of this that are, generally, supportive of one another. Albeit, the definition of biomedical informatics is rather unique, as can be seen below.

For example, Wikipedia defines Informatics (not biomedical informatics) as follows:

…the science of information, the practice of information processing, and the engineering of information systems. Informatics studies the structure, algorithms, behavior, and interactions of natural and artificial systems that store, process, access and communicate information. It also develops its own conceptual and theoretical foundations and utilizes foundations developed in other fields.

The general study of algorithms, structure, behavior and interactions of natural and artificial systems that store, process, access and communication information are all aspects of biomedical informatics, too.

The Free Dictionary provides the following definitions:

…computerized automated delivery and manipulation of information to and by users of computer systems.

…the management of information and knowledge by computers.

…information management; the technology of information storage, retrieval and transmission. Includes on-line access to and editing of data bases, facsimile transmission, optical reading and word processing.

The American Medical Informatics Association has developed a formal definition of Biomedical Informatics:

…Biomedical informatics (BMI) is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.

The list goes on, but I think the point is made: the use of biomedical data for knowledge and scientific inquiry, problem solving and decision making. These are definitely aspects and attributes of the definition that apply to me and my background.

I am a member of AMIA, a member of HIMSS (Health Information Management Systems Society), and other organizations that aim to use health information to foster improved care at the bedside. However, when I started my involvement in this whole arena or field of study I knew of neither and some of the societies that are now in full swing were not even born (IHE, for instance).

My involvement in healthcare informatics began in 1990, and the decision to migrate into this field formally occurred after rather a personal tragedy: my mother’s diagnosis with, and ultimate death from, breast cancer. We all have our stories and reasons why we do things in life. This was one that changed my complete direction. I had been working in the aerospace industry for a major defense contractor (later acquired by yet another defense contractor) and had no intention of changing my field. However, mom’s rather sudden diagnosis with a very virulent form of breast cancer combined with the utter helplessness I felt during her illness and the lack of answers I could obtain from her oncologist, caused me to begin to ask the questions about how such seeming inability to assess and forecast was not a major news story. Furthermore, the inability to locate and access data was, to me, unbelievable. After all, weren’t we dealing with people’s lives here? I compared the relative “order” in my field of aerospace engineering–the existence of standards; the ability to link and integrate disparate systems; the ability to collect data and access multiple sources of information, even sources developed by competing contractors, and could not understand why medicine or healthcare in general was “allowed” to continue this way!

Of course, I was naive. I  understood that there were major challenges in medicine that remained and still remain unsolved (cancer being one of them). Yet, I was coming at this “problem” from the perspective of an engineer’s brain: diagnose the problem, seek the data, formulate a solution approach, execute the solution.

What I saw in this case (so close to me as it was) was the ability to diagnose the problem, collect some of the causal data, but the processes of formulating an effective solution and executing on that solution were completely beyond the capabilities of everyone involved. At the time, at least, there was “guessing”; there were pockets or sources of incomplete data; not all information could be collected and made available to any given clinical end user, etc. Now, to be fair, had my mother been diagnosed in this day & age I believe she may still be around–some very fundamental advances have been made in the field that would have given her more of a fighting chance. However, back then the key approach was “poisoning” the system (note: I had read Dr. Susan Love’s book on breast cancer at that time). Furthermore, the tools at hand for her oncological team were stored in “silos.” There was not much sharing of information outside of the major medical journals. In addition, the oncological team’s ability to seek independent studies or new research to be able to evaluate or even model or statistically assess the effectiveness of experimental treatments was almost impossible to do.

However, the experience placed a focus like a laser beam in my consciousness in terms of providing the tools and access to data to improve the care and prospects of the patient that has not diminished in me since that time. In the aerospace field, collecting information from multiple sources in order to seek better overall knowledge of the “target” was and is done regularly. Often called “multi sensor data fusion”, I carry this term forward from my former field to comparable use in medicine and healthcare as a metaphor for describing the process of collecting data that may paint a picture of “different sides of the elephant” in order to assemble a more complete view of what is actually happening–in other words, to improve situational awareness around the patient. This aspect of informatics is one that, for me, also motivated the need for better and more complete data (medical device connectivity being one source for this type of information).

During my days in research at PENN, collecting data from multiple sources was key to establishing the situational awareness and conditions around the predictive models I had developed for assessing likelihood to wean from post-operative mechanical ventilation. To accomplish this, rich and complete source data from laboratory to medical device information as well as patient demographics and history were necessary. The conditions that establish the usability of the data, its importance and applicability to the environment are, in a microcosm, what was missing from my mother’s experience and that of her oncological team. The ability to source data–even experimental data–from multiple sources and to have the ability to combine and integrate it effectively for the use in “what-if” analysis was completely missing. However, again, going back to the aerospace engineering analogy, this is frequently done as a method for validating design or determining the best approach or possible effects for dealing with unknown situations.

In summary, medicine is much more complex than “rocket science,” but only because of the unknowns. The field of informatics seeks to place the best possible information in the hands of those who are operating on the front lines of battle–the physicians, principally–who are charged with guiding the treatment. If, in this process, I can assist or make a difference to win the battle for the patient, my life will have been well lived.

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Medical Device Alarms and the MIMIC-II Database for Multiparameter Data Modeling

2.06.2012 | 0 Comments

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Some months ago I meant to put this on the blog and it has been sitting in draft form wallowing.

In reference to the medical device alarms topic, there is a very good source for high frequency and fidelity monitoring data. The MIMIC-II database is an excellent source for waveform level modeling data taken from critical care patients and deidentified for use in research. A key reference for the scope of the data found in the database (which is generally available for access at no cost) can be found in the following paper:
Saeed et al:, Multi-parameter Intelligent Monitoring in Intensive Care II (MIMIC-II): A public-access intensive care unit database

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Healthcare IT definitely needs more of these crazy CIOs! | CIO

2.06.2012 | 0 Comments

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So, I was reading Information Week Healthcare and there is a great piece titled “Healthcare IT Needs More Crazy CIOs“. In this article Paul Cerrato talks about MD Anderson Cancer Center’s CIO Lynn Vogel. It turns out that they developed their own EMR, and for many of the reasons I have been discussing with my physician colleagues. One reason stood out bang on the dot:

Most commercial vendors are focused on either routine hospital or physician care, Vogel says, and don’t incorporate the latest clinical research data into patients’ records.

CIO Vogel continues:

In cancer care, that routine just doesn’t work, Vogel says. “It used to be a university would do the research. Five, six, seven or eight years later, it would show up in some clinical practice,” he says. “We want to shorten that. When people have cancer, they don’t want to wait four or five years.” They want to learn about new research right away, so they can enroll in clinical trials. “That’s what drove us,” Vogel says about the center’s work to incorporate research into clinical processes faster.

MD Anderson plans on sequencing all of its patients within the coming years and the vast amount of data requires innovative ways to extract and manipulate it. While this is not outside the realm of existing technology, it may be beyond the capabilities of most EMR systems to provide along with the required flexibility for research.

Glad to see the innovative spark alive and well at a major healthcare provider, and in the person of their CIO!

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Some quick thoughts on medical device alarm research areas of need

2.02.2012 | 1 Comment

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Months ago I wrote about the AAMI Summit on Clinical Alarms. This prompts some ideas for research to mitigate nuisance and general patient medical device alarm problems:

  1. improving signal to noise (specifically, sensitivity / specificity): reducing data overload / information underload would limit “nuisance” alarms;
  2. need for device intelligence: for example, the medical device alarm has context for which awareness and the ability to adapt to a specific situation would reduce the occurrence of artifact;
  3. human-machine interfaces: improved usability through refined/redesigned human / machine sensory interface;
  4. multisource data fusion: integration of data from multiple devices in a manner that provides for a fusion of knowledge and situational awareness that would not be available from these devices separately; and,
  5. historic and prospective trending: deterioration trending of patients over time using historic data as well as models of prospective evolution.

Historic information provides some notion of what is risky for patient: events and information from multiple sources can establish an a prior assessment of whether a patient is experiencing distress. These conditions are not unique and have been seen previously by multiple clinicians over time. Hence, the historical information can be a great filter for establishing what is real and what is not real in terms of alarms and medical device alarm management. Just as automatic feedback control systems provide a feedback to the plant model as to deviations from expected performance, such is analogous with medical device alarms.

Part of the challenge is how to combine the information from multiple sources. This combination of information is not simply co-displaying them in a common format or user interface. It is presenting the data intelligently to the end user, possibly in a processed manner that determines, based on their combination, whether the result is a bona fide alarm condition. This rather cerebral aspect of the processing is the dilemma. Leaving out even the regulatory aspects of the situation, understanding what the processing is actually doing–the “plant” part of the automatic feedback control system–must be represented accurately.

A medical device alarm from a single device provides an indicator with respect to the sensors that are being measured for that one device. For instance, a physiologic monitor that is only measuring pulse and ECG cannot report an alarm on End Tidal CO2 or respiratory rate. On the other hand, a mechanical ventilator cannot report an alarm on heart rate or on ST segment elevation. The two devices report medical device alarms according to their particular sensors and objectives. However, the combination of the two devices can provide corroborating evidence. For instance, an ASYS alarm could be caused bya lead disconnecting from the physiologic monitor or from the patient. But, an ASYS alarm occurring at the same time as an APNEA alarm would provide corroborating evidence. This is rather a tragic example, but one that demonstrates the correlation of two events.

The fusion of multi-source devices can also provide a mechanism for reducing data overload while at the same time increasing useful information is one that I find to be very interesting. There are many research efforts surrounding modeling. Yet, integrated system modeling (multi system integrated assessments) has really yet to be shown (publicly) as viable in medicine–and generally accepted in operational use, not to mention for medical device alarms.

I would like to leave this here for now and follow-up in more detail on these areas. The medical device alarm field is fertile ground for integrated systems engineering application.

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’90s CDS research still valid today?

2.02.2012 | 0 Comments

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My recent participation in an NSF Grant review at PENN on Tuesday and, in particular, Dr. C. William Hanson’s overview briefing during that event, brought to mind some things that he and I had worked on back in the early to mid 1990s. During the course of the presentations, which I found to be very fine and informative, it had occurred to me that basic research is still being conducted in areas that remain relatively untouched or unresolved today, some 16 years later. At the tail end of my dissertation it had been requested by my committee (Dr. Hanson in particular) to lay out key areas of future research that could stand upon what we had done and, in particular, focus on the problem of automation.

Extracts from that long-ago written conclusion read as follows:

“Develop… a larger database of spontaneous minute volume data to verify that it is possible to achieve a more accurate interpolation methodology…”

“Develop…a database of respiratory state data that can be easily recalled for real-time diagnosis and analysis by the critical care staff…by providing an easy way to download this information for…processing would permit the storage of each patient’s data, along with information on patient condition, to be used to classify each patient according to surgery.”

“[A]utomate the weaning process…motivated in part by the need to safely reduce costs and instill more uniformity in the respiratory weaning process across a larger patient population. Moreover, one desirable quality of such an automated methodology is that is would not require the input of such information as patient mass, gender, …anesthetic dosages…to operate effectively. The control system responsible for regulating patient respiratory support should use only that information available through the ventilator…Evidence and trends in patient care exist to suggest that automating post-operative weaning in patients who are not problematic is not only possible, but inevitable as hospitals strive to streamline and reduce the costs associated with critical care medicine.”

The conclusion then goes into the specific algorithm for reducing the support automatically. Undoubtedly, another research focus altogether. Nonetheless, this was written in 1996. Evidence-based medicine is a key objective in the application of treatment today. Furthermore, the databases that were referred to in the research do not (yet) exist today. There are clinical information systems, electronic health records (EHRs) and electronic medical records (EMRs), and varying levels of discrete data that comprise them. But, the level of data richness described in that conclusion still does not exist today.

The one key reason I focused so heavily in my career on medical device connectivity has been because of the inability to uniformly collect the information at the bedside so necessary to support these types of research (and, ultimately, to support the care models related to automated management). While there certainly exist safety concerns surrounding the automated aspect of any process in healthcare, the objective at that time was to provide more uniformity around the process of weaning. Furthermore, respiratory weaning was merely an exemplar to serve as an expression of the larger application of predictive methodologies based upon data normally collected at the bedside.

In order to improve situational awareness it is necessary to have access to the latest information. Part of that situational awareness relates to the observations collected from the patient. Observations in the high acuity spaces comprise the visual, audible and sensory based set that every clinician is familiar with. These are further augmented by historical information, patient demographic, and the training that each clinician receives. Integrate, fuse, or otherwise combine these data together and the situational awareness increases greatly. It is this situational awareness that was sought to be demonstrated through the research at that time and remains an important focus of future research.

Much has happened in the span of time since I graduated from PENN. The IHE, IEEE standards, and interoperability in general have advanced greatly. The research I had begun in the 90s that related to automation and data integration provides potential graduate and undergraduate students seeking to advance their knowledge through the pursuit of both Ph.D.s and Masters degrees with a great opportunity to advance the areas of automation, automated feedback control, and modeling far beyond its current state. While much has been done in the field since I was a student–almost 20 years ago–much remains and I believe the field is on the verge of really taking off, given the advances in interoperability that have occurred to date and the energy and focus on integration of data from various disparate sources within the healthcare domain.

Note: Links to external sites are not to be taken as an endorsement by the author.

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The focus and benefits of big data

2.01.2012 | 0 Comments

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Yesterday I attended an NSF grant status review at the University of Pennsylvania. I had a chance to visit with some old friends of mine and to reacquaint with some new friends. It is striking to me the energy in the area of systems engineering in the medical field that has taken place since I was a student there some 16-20 years ago. When I was a student at PENN, the notion of applying systems engineering to medicine was not even a field: it was the study of one lone student in the department. I am happy to see such growth.

While I cannot get into the specifics of the discussions of the various projects, I will lend my overall impressions vis a vis the types of concepts that are being focused on, and that are a major focus in healthcare and medicine today.

Useful use (as opposed to Meaningful Use) of data within the enterprise is a huge objective in the treatment of patients:

1) using past information and history to assist in reducing the likelihood of readmissions;

2) better management of those patients, particularly in critical care who are likely to suffer from sepsis, ventilator acquired pneumonia, etc.;

3) reduction in the number of medical device alarms that bombard the clinicians;

4) better use of integrated information through application of interoperability standards.

All of these areas are of high value. As I noted in my “outbrief” at the end of the meeting, my views, while highly motivated by pure research, are more governed by the pragmatic from the perspective that I live with customers and their problems, and this motivates me to write on areas that are both pragmatic but motivated by research interests of my own.

The term “Big Data” has often been used, and by itself is nothing more than marketing tripe. To my mind, systems engineering and the various concepts related to it, from situational awareness to systems of systems engineering, has been around a long, long time, primarily as related to the aerospace/defense industry (a former industry of my own). What is becoming more and more clear is that the effective use of data–not just mining it for whatever trend falls out of the net–is the real value. Combining specific information associated with diagnoses and then determining from that which patients are likely to be readmitted; which patients are likely to respond favorably from a certain course of treatment, etc. are outcomes assessments that carry with them great value but also great responsibility.

Pragmatically, most hospital systems that I have been involved with professionally are still at the stage of connecting the dots: getting information from point A to point B. As is typically the case, large academic research institutions are out in front looking at the why and wherefore of what to do with the information–the futuristic. Once something becomes generally accepted in clinical practice, that is when the futuristic becomes commonplace.

Yet, something else is happening now: I see even smaller community hospitals beginning to look at the futuristic from the perspective of how it can assist in the commonplace practice of medicine. It is not just the matter of connecting the dots of how data gets from one point to another; or how certain functions (e.g.: CPOE, eMAR) need to be rolled out into the institution, but what happens with the information after that. Interestingly, a motivation for this is the care and management of patients with chronic ailments and/or co-morbidities. Managing the patient once he or she leaves the hospital is of much more interest, regardless of the size of the institution.

Assisting in this management is the effective study of past history and increasing situational awareness surrounding patients in their home environments as well as in the hospital and how they compare with respect to peers. This is the focus and benefit of big data–where it is going. And we are at the beginning.

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Are we NOW ready to apply systems engineering principles to medicine?

11.10.2011 | 2 Comments

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“Safety is a system” is how Farzad Mostashari, national health IT coordinator, emphasized his stance on electronic medical record (EMR) system software in the wake of the Institute of Medicine (IOM) report on the safety of health information technology and the need for a new agency to oversee regulation of health information technology. This is reported by HealthcareIT News in the wake of the IOM’s report on patient safety and healthcare IT systems. Systems engineering plays prominently in the safety equation between efficient workflow and effective patient care management. As of this writing, I have read through the first 50 pages of the report myself and am making rapid progress. However, of key note are the points in bold right up front that “lack of system interoperability is a barrier to improving clinical decisions and patient safety.”

While much of my focus on integration of medical devices has been the ubiquity with which medical device data can be integrated into clinical systems, the general point of interoperable system architectures (similar to systems of systems architecture) is a key point behind the patient safety and effectiveness argument.

Why?

Because, among other reasons, access to data is restricted when systems are not interoperable. Another reason is that differing semantics (commonality of definitions, lexicon, terms) makes for a healthcare “Tower of Babel,” wherein separate systems are not aligned in their ability to communicate. This is not just physical interoperability–it is semantic interoperability. These problems, among many, are key and require an overarching systems engineering integrator to facilitate architecture and alignment.

In our recent IEEE-AMA paper on liberating medical device data for research, we described the need for semantic interoperability. The problem of aligning separate EMR systems, laboratory systems, registration systems, and medical devices is a system of systems problem: systems engineering. The Department of Defense has recognized for decades the need for systems engineering. The need to meet MIL-SPECs and standards is a tacit requirement of any military system. Why is this not the case in healthcare? There is HL7, but as anyone who has worked with HL7 might indicate, there are many “flavors” of standardization and messaging requirements that can vary by vendor, by application, by enterprise. The idea of an interface control specification or document (ICD) that can be used to establish a common set of requirements among vendors is truly required. Again: systems engineering at work.

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Dictionary of Computer Science, Engineering, and Technology

10.30.2011 | 1 Comment

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Zaleski, JR, (contributing Author), Dictionary of Computer Science, Engineering, and Technology, (CRC Press, Phil Laplante, Editor-in-Chief).
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Systems of Systems Integration

10.29.2011 | 0 Comments

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There’s an old saying: if you want a new idea, read an old book. There’s another saying: those who do not learn from history are doomed to repeat it. Keep these two thoughts in mind as you proceed through this piece.

The healthcare “system” is really a system of systems. Enterprise health information systems (eHIS) are but one component of this system of systems. The integrated whole of the healthcare environment involves the technology, the people providing the care, the people managing the enterprise, the payers, and the workflow peculiarities of the environment in general.

Herein lies the old book. Those working in the aerospace industry are, perhaps, those most familiar with the system of systems integration concept. Systems integration has been a discipline employed by those working in the aerospace and defense fields. In these fields, large-scale systems need to be combined, coexist, and cooperate harmoniously within a larger context or framework. These frameworks, sometimes referred to as system-of-systems (SoS) architecture, are typically used to achieve component and interface commonality to promote reuse across separate and potentially disparate subsystems and components. The Department of Defense (DoD) published a framework [1] for establishing a coordinated approach for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR), whose principal objective was to ensure that architectures created and systems developed by various branches of the DoD would be synergistic and standardized across operational, technical, and organizational boundaries. Chen & Clothier [2] discussed the maintenance of sustainable and controlled SoS evolution. However, their application related to the concept of SoS with respect to military applications, as does the C4ISR, being chartered by the DoD.

Attempts to apply the C4ISR framework to commercial industry abound. Systems Engineering processes and Systems Integration as disciplines are being discussed and applied outside of the DoD domain in telecommunications and healthcare, to name two specific instances. However, even in these industries the main focus of SoS has been on the integration of a single product (that is, the product’s architectural components). This is somewhat different from large-scale, multi-system SoS architecture, in which separate stakeholders and developers, quite possibly outside of the integrating organization, must also participate in the overall solution.

So, what are the parallels to healthcare? Healthcare delivery involves wide-ranging, disparate, seemingly autonomous enterprises: hospitals around the country and around the world. Commonality exists in the form of (fairly) consistent clinical training: medical treatment protocols are the same regardless of where you go in the United States. Basic medicine and its teaching are consistent and uniform worldwide. Yet, the infrastructure to support patients and providers in the delivery of that care can vary from hospital to hospital; enterprise to enterprise; region to region; and country to country. For instance, take any emergency department (ED) in the U.S., and you will see basic medicine being practiced consistently (for the most part). But, depending on the sophistication, financial health, population, and training of the providers and supporting staff, the tools with which care is delivered and managed can be quite different. One ED may have a computerized tracking board for managing patients. Another may have a white board and no computers; yet another may record patients on a simple clipboard. The methods of management are different, but the approaches to care are the same. The benefits derived from more efficient management can be astonishing: lower mortality rates, higher throughput, and higher customer satisfaction.

Standardization across the healthcare enterprise is the subject of efforts by many standards and oversight organizations. One example includes the HL7 standard for healthcare data communication and interoperability standards related to medical device integration with electronic health information systems. But, where healthcare could benefit is by recognizing that this truly represents system of systems integration: each separate healthcare enterprise represents a separate system. The ability to communicate, interoperate, and exchange information among these separate enterprises is the subject and the goal of the system of systems: each autonomous enterprise can interact with its sister enterprise.

So, what are the benefits of achieving this result? One that resonates most closely to home is described in the following scenario. Consider falling ill in a foreign city—regardless of whether in country or globally—and being able to go to the local hospital and have all of your medical records displayed in a format consistent with that display in your home town. The benefit to you is any remote or foreign healthcare enterprise can have the complete detailed record of you. This mitigates errors, reduces the time required to provide treatment, and ensures that your entire history is accurately presented to any clinical user to provide the capability to manage your health better.

This is where history can teach us a lesson: those in the aerospace industry have understood this need for decades. However, the pace of progress has been much slower in healthcare than in the aerospace field. Yet, consider the benefits to patient, provider, insurer. Sometimes the cost of proliferating the not-invented-here attitude can have vast implications which complicate basic care. Healthcare would do well to think outside of its own “box” and draw upon the tools and stride the well-worn paths traversed by others in fields remote to medicine.

[1] C4ISR Architecture Framework, Version 2.0: Report of the C4ISR Architecture Working Group (AWG); 18 December 1997.

[2] Pin Chen, Jennie Clothier, “Advancing systems engineering for systems-of-systems challenges,” pp170-183, Systems Engineering Journal, Volume 6, Issue 3.

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6th Zaleski US Patent Awarded

8.06.2011 | 0 Comments

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Number 6 Zaleski US Patent: US 7,945,457

While trolling the USPTO site this evening I found out that I was just awarded my 6th US Patent. This patent is titled: “Distributed system for monitoring patient video, audio and medical parameter data.”

Zaleski US Patent Abstract:

A distributed patient monitoring system visually monitors patients and patient parameters using multiple portable processing devices. The system includes an authentication processor enabling a user to obtain access authorization to access patient data. A clinical application display image identifies multiple different patients in corresponding multiple different locations and enables a user to select a particular patient. A data processor uses access information in acquiring live video and vital sign parameters of the particular patient from a room associated with the particular patient. A display processor initiates generation of data representing an image sequence comprising a composite image including a first area showing acquired live video of the particular patient and a second area presenting acquired vital sign parameters of the particular patient using the clinical application display image.
 

Zaleski US Patent accessed via USPTO Web Page for US 7,945,457:

The main web site provides the capability to download the images of the actual patent. However, the quick search tool may be used to locate, for those interested. Here is a screen image of the patent:

(Zaleski US Patent 7,945,457)

Significance of this US patent award:

In this US Patent, I discuss the use of video imagery combined with medical device parameter data to provide a more holistic picture of patient care. The significance of this offering and its key differentiator from competition already on market is in the use of an enterprise network for web-based display of imagery through the Internet.

Please view my web site on PhD Dissertation to see benefits of medical device data for patient care and clinical decision making.

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Clinical Informatics Example in ICU Recovery

7.12.2011 | 4 Comments

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Clinical Informatics in intensive care.

Clinical informatics relies on use of real-time data presented in various ways and using various analytics to assist the clinician in assessing status and trajectory of the patient.

A great deal of time on patient care in intensive care units is devoted to management of pulmonary and cardiovascular systems. This is illustrated along with key parameters or measurands in the following slide image:

Pulmonary & Cardiovascular Systems

Medical Device Data Assist in Patient Care Management.

A key objective in managing patients post operatively is maintaining their oxygenation, as shown in the following slide image:

SaO2 versus O2 Partial Pressure Showing Optimal Range.

Laboratory data are necessary to ensure proper blood chemistry, and also provide verification of patient viability to wean. An example of the results of three blood draws is illustrated in the following slide image:

Results of laboratory analysis of three blood draws on a post operative coronary bypass patient.

Pulmonary measurements, such as tidal volume, are among many that can be taken from the mechanical ventilator through serial connectivity and provide a means of monitoring in real-time the respiratory performance of the patient over time. This is illustrated in the following slide image:

Tidal volume and other respiratory function measures assist in identifying patient state and function over time.

Data from Medical Devices Achieved through Connectivity Enable Collection of Clinically Relevant Data.

By collecting and overlaying all such data, measured and acquired in real time from the equipment at the bedside, it is possible to establish a contextual picture for patient state. This is a key aspect of live, bedside clinical decision making. An example of such data are illustrated in the following slide image:

Various measures such as inspired O2 fraction, respiratory rate, tidal volume, and minute ventilation form the basis for establishing pulmonary state. When taken in context with other parameters, these help the clinician with bedside decision making in terms of care management and trajectory.

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A suggested chemoreceptor-based method to control pacing and stroke volume in left-ventricular assist devices and for patients undergoing heart transplantation

10.10.2009 | 10 Comments

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Heart Controller Abstract

I present a concept for autonomic cardiac pacing as a method to augment existing physiological pacing for both ventricular assist devices (VAD) and heart transplantations. The following development represents a vision and reflects an area that has yet to be fully exploited in the field. Therefore, the analysis is meant to be a starting point for further study in this area. Furthermore, an automatic control system methodology for both heart rate and contractile force (stroke volume) of patients having either an artificial left ventricular assist device (LVAD) or who have experienced degenerative performance of the Sinoatrial node is suggested. The methodology is described both in terms of a device and associated operational framework, and is based on the use of the naturally-occurring hormones epinephrine, norepinephrine, and dopamine contained in the return blood flow through the superior vena cava. The quantities of these hormones measured in the blood stream are used to derive a proportional response in terms of contractile force and pacing of the Sinoatrial node. The method of control suggests features normally described using cyclic voltammetry, expert systems, and feedback to pacing an artificial assist device.

Nomenclature

AVN, Atrioventricular Node

Ca, Calcium

CO, Cardiac Output

EPI, Epinephrine

FDA, Food and Drug Administration

K, Potassium

LVAD, Left Ventricular Assist Device

Na, Sodium

NE, Norepinephrine

NHLBI, National Heart, Lung, and Blood Institute

SAN, Sinoatrial Node

SV, Stroke Volume

TAH, Total Artificial Heart

TCI, Threshold Crossing Intervals

VAD, Ventricular Assist Device

 

Background

Artificial assist devices that exist today normally operate by controlling heart rate based on Proprioceptors–position of limbs & muscles during physical activity, or Baroreceptors–monitoring blood pressure in major arteries & veins. None to date operate on the basis of Chemoreceptors: monitoring changes in chemical makeup of the blood stream in direct response to epinephrine production by the adrenal medulla. Such changes are, however, more consistent with the operation of the human heart. For instance, heart rate varies not only according to mechanical movement of limbs, but also as the result of changes in emotion. Such changes manifest themselves as increases and decreases in sympathetic and parasympathetic hormones. Sympathetic hormones (epinephrine, norepinephrine) tend to increase stroke work and heart rate, whereas parasympathetic hormones (acetylcholine) tend to lower heart rate. These two hormones operate to control the resting rate of the heart and its changes as a result of higher-brain center changes (including production of hormone by the adrenal medulla). Benefits of achieving this capability include more naturally-behaving artificial hearts, or, in the case in which a human heart is merely being paced by an assist device, to control normal heart function in relation to changes in hormone production. Thus, the controller described herein provides an adjunct to existing controllers. Specific methods outlining artificial control mechanisms for affecting heart rate and contractility have not specifically been described in the literature, although related concepts have been suggested [1].

Autonomic Heart Regulation

Autonomic regulation of heart rate is controlled via one of the following specific systems within the body [2, 3, 4, 5, 6].

  • Proprioceptors – monitor general movement—position of limbs & muscles during physical activity
  • Chemoreceptors – monitor chemical changes in blood
  • Baroreceptors – monitor blood pressure in major arteries & veins
  • Chemical regulation (Hypoxia, Acidosis, Alkalosis)
  • Hormones (Catecholamines & thyroid)
  • Cations (balance of K+, Na+, Ca2+)

Current artificial heart assist devices operate using feedback from items (1) and (3) alone. Long-term (weeks to months) artificial heart assist devices popularly used for ventricular support, all under the auspices of NHLBI [7], include:

  • Abiomed extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular, introduced in 1988, FDA approved for in-hospital use for low output syndrome.
  • Thoratec extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular approved for in-hospital use for post cardiotomy low output and as a bridge to transplantation.
  • TCI (Heart rate) implantable [8], [9], pulsatile, pneumatically-driven, approved for in-hospital use as a bridge to transplantation. The electrically powered totally implanted configuration with a wearable power source, transcutaneous power lead and vent, is approved for in-hospital as well as out-of-hospital use for bridging to transplantation and is currently used under IDE in the randomized REMATCH trial.
  • Novacor [10] implantable electric, pulsatile, left ventricular (LV), wearable power source, transcutaneous power lead and vent, is approved for in-hospital and out-of-hospital use for bridge to transplantation.

 

Underlying Motivation for the Heart Control Method

The suggested device measures the chemical content of the blood flow past the sinoatrial node, processes the chemical content via cyclic voltammetry [11, 12, 13, 14] to determine the amount of epinephrine (EPI) and norepinephrine (NE), and uses this information to establish the resting and various pacing heart rates of a left ventricular assist device. Furthermore, researchers [15] focusing on advances in heart assist devices have identified characteristics desired for upcoming artificial organs. These include (1) miniaturization, (2) interfaces with nerves for automatic control, (3) control systems that are acceptable for both the living body and the embedded artificial organs, and (4) harmonization with the living body in various ways, including interfaces with higher brain centers and reduction of thrombus (and the associated foreign body rejection issues).

In addition,

“the neuronal and hormonal control of the circulation, including the control of the heart, is mainly effectuated by the autonomic nervous system and its hormonal transmitters, the catecholamines. Autonomic control of the circulation primarily operates through the sympathetic system, though to a slight extent through parasympathetic signals to the heart. These have been lumped together, and there are basically three separate feedback mechanisms in this computational block. These are (1) feedback from the baroreceptor control system; (2) feedback from the peripheral chemoreceptors in the carotid and aortic bodies, and (3) feedback control of the circulator system caused by central nervous system ischemia, that is, ischemia of the vasomotor center in the brainstem. Another input that affects the autonomic nervous system is also included: The activation of the autonomic nervous system during exercise.” [16]

Methods for measuring serum epinephrine levels exist. WPI [17] suggests that sensitive, low noise carbon fiber (CF) and carbon disk (CD) electrodes can be employed in the electrochemical detection of catecholamines (e.g.: EPI, NE, Dopamine). As reported on their Web Site [18], the CF30-500 class of Carbon Fiber Disk Microelectrodes shows current output in Pico Amperes versus Dopamine concentration (nanograms/milliliter). In this analysis, WPI sites work by D. Yeomans and X. T. Wang of the University of Illinois. The analysis shows excellent linearity characteristics for CF filaments ranging in size from 10 to 30 microns to compounds with…detection limit[s] as low as 0.2 nanoMoles. Figure 1 illustrates this linear relationship [19].

Figure 1: Carbon Fiber Current Measurement versus Dopamine Concentration.

Figure 1: Carbon Fiber Current Measurement versus Dopamine Concentration.

WPI reports [20] that “longer CF electrodes…provide higher sensitivity and larger signal to noise ration. They are hence very suitable for in vitro amperometric and differential pulse voltammetry (DPV) in which…voltage scan rates are much lower.”

WPI further suggests [21] “extracellular recordings using CD electrodes (CD-30) in CA1 region of the hippocampus in an anesthetized rat shows ultra-low noise (<5 microVolts).” Voltage response in same region varied between +50 and –100 microVolts with response time less than 5 milliseconds. This is important from the real-time implantable device perspective as changes in responses need to be measured rapidly in order to correctly mimic actual behavior. A suggested bio-sensing device based on those implemented by WPI and others [22, 23] is illustrated in Figure 2.

Figure 2: Author’s biosensing device based on technology suggested by WPI literature to measure current response to catecholamines using cyclic voltammetry.

Figure 2: Author’s biosensing device based on technology suggested by WPI literature to measure current response to catecholamines using cyclic voltammetry.

While WPI stated [24] at the time of publication that there was no existing electrochemical method that exhibited selectivity among the members of the catecholamines or indolamines, others [25], reporting later, suggest that there may be ways to do this.

The Heart Conduction System

Now that the electrochemical mechanism has been suggested, let’s proceed with the automatic control methodology. Figure 3 indicates the hierarchy in which electrical signals and pacing are conducted throughout the human heart [26].

Figure 3: Major heart rate pacing centers.

Figure 3: Major heart rate pacing centers.

The Sinoatrial Node: Primary Heart Pacemaker

The SAN, the primary pacemaker for the heart, is located in the rear wall of the right atrium near the opening of the superior vena cava. The SAN has the fastest rhythm producing, on average, from 60-70 action potentials per minute. SAN pacing overrides all others. The action potential originated within the SAN travels through walls of the atria causing contraction. Internodal pathways connect the SAN to the atrioventricular node (AVN). The AVN is the located in the right posterior portion of interatrial septum. In the absence of SAN pacing, the AVN can take over, although the resting rate is slower (40-60 action potentials per minute). Next, the AV Bundle of His divides into both right and left bundle branches in the ventricular septum and is the only electrical connection between atria & ventricles. The Bundle of His is capable of generating from 30-40 action potentials per minute. The Purkinje fibers (4) are distributed throughout the ventricular myocardium and synchronize ventricular contraction. Ventricular muscles can generate from 20-30 action potentials per minute.

The Mechanics of Catecholamine Measurement

The SAN is that which is affected by stimuli such as adrenaline, exercise, drugs, etc. As discussed, chemoreceptors that detect changes in catecholamine levels (EPI, NE, Dopamine) translate into changes in pacing and contractility. Studies of environmental stress on epinephrine levels in humans show rather discernable relationships between EPI levels and work-related stress [27]. Table 1 depicts the NE levels in male and female managers as a function of time in the workday [28].

Table 1: Norepinephrine Change Before & After Work.

Table 1: Norepinephrine Change Before & After Work.

Catecholamines: Regulate Heart Function

Catecholamines affect the sinoatrial cells as illustrated within the simplified cartoon of Figure 4 [29]. The medullary cardiovascular control center in the brain contains both sympathetic and parasympathetic neurons that act as agonists to control contractility (ventricles), constriction (veins, arterioles), and control secretion of hormones to which the SAN responds. Carotid and aortic baroreceptors also respond to changes in blood pressure which provides feedback to the medullary control center that, in turn, affects contractility, pulse, and vasoconstriction.

Figure 4: Diagram depicting homeostatic control of pulse and contractility.

Figure 4: Diagram depicting homeostatic control of pulse and contractility.

The sympathetic and parasympathetic hormones EPI and acetylcholine are secreted from the adrenal medulla and have the effect of causing increased and decreased pulse and contractility, respectively [30]. NE is a principal neurotransmitter in the sympathetic nervous system and is an a-adrenoceptor agonist [31], implying strong vasoconstrictor response, and, therefore, affects systolic and diastolic blood pressures as well as heart rate and contractility through b1-adrenoceptors [32]. Metabolism of EPI and acetylcholine at the cellular level is illustrated in the diagram of Figure 5. Relationships between stroke volume and contractile force are well known in the literature [33]. Thus, methods of measuring changes in catecholamine concentration must accommodate the combination of both pulsatile rate and contractility. The effects of sympathetic and parasympathetic nervous system on cardiovascular performance are known [34].

Figure 5: Metabolism method of Epinephrine and Acetylcholine at cellular level.

Figure 5: Metabolism method of Epinephrine and Acetylcholine at cellular level.

Changes in sympathetic and parasympathetic hormone affect the arteriolar smooth muscle fibers, the ventricular myocardium, and SAN to affect (as described previously) vasodilation, contractility, and heart rate, respectively.  Studies have quantified the effects of changes in mean plasma catecholamine levels both in vivo and in vitro using techniques such as cyclic voltammetry and blood concentration measurement [35, 36]. Heart pacing set by SAN is regulated by antagonistic mechanisms: primarily through sympathetic innervations (release of NE, EPI – increases rate) and parasympathetic innervations (release of acetylcholine—lowers rate). These innervations act to regulate and control mean arterial pressure through heart rate, stroke volume, and constriction and dilation of arterioles, arteries, and veins. Measuring the amount of EPI, NE, and dopamine in vitro has been performed [37]. One measurement approach is via cyclic voltammetry, details of which are available in the literature. Figure 6 depicts the author’s reconstruction of the relationship between carbon fiber peak anode current and Dopamine levels obtained via in vitro measurement using gold-tipped nanotube electrodes [38]. NE and EPI are released from chromaffin cells. Discrimination between NE and EPI from the same cell has been reported to be possible using slow-scan cyclic voltammetry [39]. Furthermore, the use of amperometry enables sub-millisecond release events to be measured [40]. The mechanism for EPI and NE measurement via chromaffin cell ordinarily involves contacting the cell surface via microelectrode. Furthermore, placing microelectrodes at a distance exceeding several microns can result in significant signal loss [41].

Figure 6: Carbon Fiber Anode Current versus Dopamine Concentration.

Figure 6: Carbon Fiber Anode Current versus Dopamine Concentration.

Suggested Heart Controller Implementation

A methodology is suggested that provides an initial point-of-departure for refinement and iteration. The starting point depends upon the relationship between plasma concentrations of the sympathetic and parasympathetic hormones. Figure 7, Figure 8 and Figure 9 derive a rather simple relationship between HR and NE levels [42].

Figure 7: Heart rate variability with NE concentration, 13 patients prior to, during, and post sleep measurement of plasma NE levels.

Figure 7: Heart rate variability with NE concentration, 13 patients prior to, during, and post sleep measurement of plasma NE levels.

Figure 8: Heart rate variability with EPI concentration, same sample as in previous figure.

Figure 8: Heart rate variability with EPI concentration, same sample as in previous figure.

Figure 9: Plasma NE versus EPI relationship.

Figure 9: Plasma NE versus EPI relationship.

The relationship is somewhat misleading in that the implication is that NE is causative with respect to HR. This is only partially true, Figure 9 illustrates with respect to EPI. Again, the relationship provides only a partial picture. Heart rate is only one component affected. It is worthwhile to point out again that these relationships are not necessarily causative. The association between NE and EPI plasma concentrations is shown in Figure 9. Analysis indicates a linear relationship exists in EPI the ranges specified, and the correlation appears to be quite good, suggesting a relatively simple predictive model. While HR relationship to hormone level suggests a linear dependency within the specified NE range, another study [43], Figure 10 shows a more nonlinear relationship between cardiac output and EPI. This relationship is suggestive of an optimum level of CO change with respect to EPI concentration in dogs. While not conclusive, this relationship serves to illustrate the point that a nonlinear relationship can exist that must be represented in the modeling of plasma hormone levels and the effect on contractility and pulse.

Figure 10: Relationship between cardiac output and EPI levels as a percentage from baseline between 10 & 240 minutes from start of infusion in dogs.

Figure 10: Relationship between cardiac output and EPI levels as a percentage from baseline between 10 & 240 minutes from start of infusion in dogs.

Model Training:

  1. Determine the resting pulse and cardiac output of a patient, where pulse (/min) x SV (liters) = CO (liters/min).
  2. Measure the blood plasma EPI, NE, acetylcholine, and dopamine levels. This establishes the baseline state of the patient.
  3. Measure the patient’s pulse and CO and draw blood samples associated with the plasma levels of the hormones during specific activities, including vigorous exercise, sleeping & awakening. This establishes the training set of inputs (i.e., hormone levels) and outputs (i.e., pulse, CO).
  4. Build the expert system training model that establishes the inclusive range on hormonal input versus output parameters.

Calibrate the voltammetric sensor (invasive component) for measuring real-time anode current versus hormonal concentration. Anode current levels correlate to different voltage samples using cyclic voltammetry. Peak anode currents vary according to hormone level.

Thus, the specific level of each hormone level would be identified on the basis of the sample voltage value. The training mechanism involved relies on taking known inputs (e.g.: catecholamine levels) and measuring outputs, then using these values to develop a training matrix that establishes the transformation between the input and output (e.g., pulse, stroke volume). So, very crudely, this might be represented as follows:

Autonomic Pacing Figure 11

wherein the xform(training) matrix is determined based on the input and output. Note that this is not a single matrix and not this simplistic in representation: an array of inputs and matching outputs will need to be determined that will translate into classes of transformation matrices. Of course, the viability of this approach would need to be determined. Furthermore, the outputs would provide only one component of input determinant to cardiac behavior. The effects of vasoconstriction, for example, must also be accommodated in terms of its effect on arterial pressure and loading.

In Vivo Operations:

  1. Hormonal concentration derived value from cyclic voltammetry defines the input parameters (test parameters) used as input to the feed-forward expert system trained using the training set developed above.

Output pulse and CO in terms of pacing trigger voltage to SAN defines the derived pulse and, thus, the appropriate rate for patient heart function based on catecholamine levels.

Autonomic Pacing Figure 12

This new pacing relationship between input hormonal levels and output pacing can be maintained for a specific patient within a processing chip associated or in proximity to a pacemaker unit. The trained relationship then establishes the expected behavior for a cardiovascular pacing or left ventricular assist device. The equation relating pacing to hormone level can be stored in a secure electronic patient record (for instance) for recall, updated training, or for use in data mining to compare and develop more complex relationships with those of other patients. While this methodology does indeed require validation and refinement, it defines a vision for possible implementation. The very nonlinear relationships among the input and output variables cannot be simply represented using one-dimensional mathematical relationships. Furthermore, mechanical and physical issues remain that will be challenging. For example, biofouling of in vivo electrodes must be overcome and represents a formidable technological challenge [44].

 

Discussion

The preceding describes a rough and partially complete model based on lab research that is suggestive of baroceptor measurement of in vivo catecholamine levels. The motivation behind the approach is the lack of capability in current LVAD technologies that focus on this aspect of autonomic pacing. The methodology and concept would also apply in those cases in which patients may have damaged SANs.

As pointed out, detailed training issues related to generalization to any LVAD, validation of the range of catecholamine concentrations and impacts on pacing is that pulse and contractility are well behaved and do not pose a hazard to the patient, issues related to biofouling of sensors and calibration must be addressed. However, even before considering implementation, concept and technology proof-of-principle must be validated. This will require both human and non-human trials. Operationally, manufacturing and implementation challenges must be overcome.

References:

[1] Y.E. Earm, Y. Shimoni, A.J. Spindler, “A Pace-Maker-Like Current In The Sheep Atrium And Its Modulation By Catecholamines,” J. Physiology (1983), 342, 589-590.

[2] University of South Australia—online learning environment–www.unisanet.unisa.edu.au/Information/12925info/Lecture%20Presentation%20-%20The%20Heart.ppt

[3] I. Kestin, “Control of Heart Rate,” Physiology, 1993, Issue 3, Article 3.

[4] http://courses.washington.edu/conj/bess/spindle/proprioceptors.html

[5] http://en.wikipedia.org/wiki/Chemoreception

[6] http://medical-dictionary.thefreedictionary.com/baroreceptor

[7] NHLBI: “Expert Panel Review of the NHLBI Total Artificial Heart (TAH) Program: June 1998 – November 1999).

[8] CT Lewis et al., “The use of an implantable left ventricular assist device following irreversible ventricular fibrillation secondary to massive myocardial infarction,”  European Journal of Cardio-Thoracic Surgery, Vol 4, 54-56, Copyright 1990 by European Association of Cardio-thoracic Surgery.

[9] Todd J. Cohen, “A Theoretical Right Atrial Pressure Feedback Heat Rate Control System to Restore Physiologic Control to the Rate-limited Heart,” Pacing and Clinical Electrophysiology 7 (4), 671-677, July 1984.

[10] Worldheart Novacor LVAS http://www.worldheart.com/products/novacor_lvas.cfm

[11] http://en.wikipedia.org/wiki/Cyclic_voltammetry

[12] R. Mark Wightman, “Probing Cellular Chemistry in Biological Systems with Microelectrodes,” Science 17 March 2006: Vol. 311 no. 5767, pp. 1570-1574.

[13] Jinwoo Park, et al., “Diamond microelectrodes for use in biological environments,” Journal of Electroanalytical Chemistry, Volume 583, Issue 1, 1 September 2005, pp. 56-68.

[14] D. Bhaskarab, CR Freed, “Changes in arterial blood pressure lead to baroreceptor-mediated changes in norepinephrine and 5-hydroxyindoleacetic acid in rat nucleus tractus solitarius,” Pharmacology And Experimental Therapeutics, Volume 245, Issue 1, pp 356-262, 04/01/1988.

[15] 6th International Micromachine Symposium Special Lecture: “Artificial Heart Research by the Use of Micromachines.” Lecture by Sinichi Nitta, Vice President of Tohoku University and Professor of the Institute of Development for Aging and Cancer

[16] E. Naujokat, U. Kiencke,  “Neuronal and hormonal cardiac control processes in a model of the human circulatory system,” International Journal of Bioelectromagnetism, 2000, Volume 2, Number 2.

[17] “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording.”  World Precision Instruments, March 1998. http://www.wpiinc.com/products/biosensing/carbon-elec/CFM_AppNotes.pdf

[18] http://www.wpiinc.com/products/biosensing/carbon-elec

[19] WPI, “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording,” page 3, Figure 1.

[20] Ibid. page 4

[21] Ibid., page 8

[22] Xueji Zhang, et al., “An Integrated Nitric Oxide Sensor Based on Carbon Fiber Coated with Selective Membranes,” Electroanalysis 2000, 12, No. 14.

[23] Xueji Zhang, Mark Broderick, “Amperometric Detection of Nitric Oxide,” Mod. Asp. Immunibiol 1 (4), 160-165, 2000.

[24] WPI, “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording,” page 10.

[25] Yi-Xin Sun, Sheng-Fu Weng, Xiu Hua Zhang, Yin-Fang Huang,” Simultaneous determination of epinephrine and ascorbic acid at the electrochemical sensor of triazole SAM modified gold electrode,” Sensors and Actuators B: Chemical, Volume 133, Issue 1, 17 January 2006, pages 156-161.

[26] Diagram Copyright Marquette Electronics, 1996.

[27] Ulf Lundberg, “Catecholamines and Environmental Stress,” Summary prepared for the Allostatic Load notebook. Last revised September, 2003. Author sites L. Forsman, “Individual and group differences in psychophysiological responses to stress-with emphasis on sympathetic-adrenal medullary and pituitary-adrenal cortical responses.” Doctoral Dissertation, Department of Psychology, Stockholm University, 1983.

[28] Adapted from U. Lundberg, M. Frankenhauser, “Stress and workload of men and women in high ranking positions,” Journal of Occupational Health Psychology, 4, 142-151, 1999.

[29] G. Monreal, Staff, Cardiothoracic Surgery, The Ohio State University,: MadSci Network: General Biology “How and why does caffeine affect the pulse rate of a person?”, Michael Onken, Washington University, February 2000,

[30] Vicki R. Kee, “Hemodynamic Pharmacology of Intravenous Vasopressors,” Critical Care Nurse, Vol. 23, No. 4, August 2003.

[31] “A drug that binds a receptor of a cell and triggers a response by the cell…Often mimics the action of a naturally occurring substance.” Source: MedicineNet.com

[32] Ibid.

[33] Jeff Isaacson,  “Mammalian Physiology 1”, Lecture 11, UC SanDiego, Lecture 11, Fall 2006. Source Text: Human Physiology, 4th Edition (2006).

[34] University of California at Berkeley lectures on cardiovascular system and heart, 2004.  http://mcb.berkeley.edu/courses/mcb136/topic/Muscle_Cardiovascular/SlideSet2/cardiac.pdf

[35] Christoph Dodt, Ulrike Breckling, Inge Derad, Horst Lorenz Fehm, Jan Born, “Plasma  Epinephrine Concentrations of Healthy Humans Associated with Nighttime Sleep and Morning Arousal,“ Hypertension 1997; 30:71-76.

[36] Spencer E. Hochstetler and R. Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill.

[37] Spencer E. Hochstetler and R. Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill.

[38] Y-H Yun et al., “A nanotube composite microelectrode for monitoring dopamine levels using cyclic voltammetry and differential pulse voltammetry,” Prec. IMechE Vol. 220 Part N: J. Nanoengineering and Nanosystems, 2007.

[39] Spencer E. Hochstetler and Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill, pages 13-20. Biophysics Textbook On-Line, Victor Bloomfield, editor, submitted February 18, 1998.

[40] Ibid., page 13

[41] Ibid., page 22.

[42] Christoph Dodt et al., “Plasma Epinephrine and Norepinephrine Concentrations of Healthy Humans Associated with Nightime Sleep and Morning Arousal,”  Hypertension. 1997;30:71-76.

[43] Michael B. Maron, “Dose-response relationship between plasma epinephrine concentration and alveolar liquid clearance in dogs,” J. Appl Physiol 85:1702-1707, 1998.

[44] Y-H Yun et al., “A nanotube composite microelectrode for monitoring dopamine levels using cyclic voltammetry and differential pulse voltammetry,” Proc. IMechE Vol. 220 Part N: J. Nanoengineering and Nanosystems, 2007.

[Heart Controller is one area of research interest. See my dissertation for my primary interest area in critical care.]

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Healthcare Delivery: A Systems Engineering Challenge

9.18.2009 | 2 Comments

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By John R. Zaleski, PhD

There’s an old saying: if you want a new idea, read an old book. There’s another saying: those who do not learn from history are doomed to repeat it. Keep these two thoughts in mind as you proceed through this piece.

The healthcare “system” is really a system of systems. Enterprise health information systems (eHIS) are but one component of this system of systems. The integrated whole of the healthcare environment involves the technology, the people providing the care, the people managing the enterprise, the payers, and the workflow peculiarities of the environment in general.

300px-Process

Those working in the aerospace industry are, perhaps, those most familiar with the system of systems integration concept. Systems integration has been a discipline employed by those working in the aerospace and defense fields. In these fields, large-scale systems need to be combined, coexist, and cooperate harmoniously within a larger context or framework. These frameworks, sometimes referred to as system-of-systems (SoS) architecture, are typically used to achieve component and interface commonality to promote reuse across separate and potentially disparate subsystems and components. The Department of Defense (DoD) published a framework [1] for establishing a coordinated approach for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR), whose principal objective was to ensure that architectures created and systems developed by various branches of the DoD would be synergistic and standardized across operational, technical, and organizational boundaries. Chen & Clothier [2] discussed the maintenance of sustainable and controlled SoS evolution. However, their application related to the concept of SoS with respect to military applications, as does the C4ISR, being chartered by the DoD.

Attempts to apply the C4ISR framework to commercial industry abound. Systems Engineering processes and Systems Integration as disciplines are being discussed and applied outside of the DoD domain in telecommunications and healthcare, to name two specific instances. However, even in these industries the main focus of SoS has been on the integration of a single product (that is, the product’s architectural components). This is somewhat different from large-scale, multi-system SoS architecture, in which separate stakeholders and developers, quite possibly outside of the integrating organization, must also participate in the overall solution.

So, what are the parallels to healthcare? Healthcare delivery involves wide-ranging, disparate, seemingly autonomous enterprises: hospitals around the country and around the world. Commonality exists in the form of (fairly) consistent clinical training: medical treatment protocols are the same regardless of where you go in the United States. Basic medicine and its teaching are consistent and uniform worldwide. Yet, the infrastructure to support patients and providers in the delivery of that care can vary from hospital to hospital; enterprise to enterprise; region to region; and country to country. For instance, take any emergency department (ED) in the U.S., and you will see basic medicine being practiced consistently (for the most part). But, depending on the sophistication, financial health, population, and training of the providers and supporting staff, the tools with which care is delivered and managed can be quite different. One ED may have a computerized tracking board for managing patients. Another may have a white board and no computers; yet another may record patients on a simple clipboard. The methods of management are different, but the approaches to care are the same. The benefits derived from more efficient management can be astonishing: lower mortality rates, higher throughput, and higher customer satisfaction.

Standardization across the healthcare enterprise is the subject of efforts by many standards and oversight organizations. One example includes the HL7 standard for healthcare data communication and interoperability standards related to medical device integration with electronic health information systems. But, where healthcare could benefit is by recognizing that this truly represents system of systems integration: each separate healthcare enterprise represents a separate system. The ability to communicate, interoperate, and exchange information among these separate enterprises is the subject and the goal of the system of systems: each autonomous enterprise can interact with its sister enterprise.

So, what are the benefits of achieving this result? One that resonates most closely to home is described in the following scenario. Consider falling ill in a foreign city—regardless of whether in country or globally—and being able to go to the local hospital and have all of your medical records displayed in a format consistent with that display in your home town. The benefit to you is any remote or foreign healthcare enterprise can have the complete detailed record of you. This mitigates errors, reduces the time required to provide treatment, and ensures that your entire history is accurately presented to any clinical user to provide the capability to manage your health better.

This is where history can teach us a lesson: those in the aerospace industry have understood this need for decades. However, the pace of progress has been much slower in healthcare than in the aerospace field. Yet, consider the benefits to patient, provider, insurer. Sometimes the cost of proliferating the not-invented-here attitude can have vast implications which complicate basic care. Healthcare would do well to think outside of its own “box” and draw upon the tools and stride the well-worn paths traversed by others in fields remote to medicine.

Systems engineering: it’s not just for aerospace anymore.

 

[1] C4ISR Architecture Framework, Version 2.0: Report of the C4ISR Architecture Working Group (AWG); 18 December 1997.

[2] Pin Chen, Jennie Clothier, “Advancing systems engineering for systems-of-systems challenges,” pp170-183, Systems Engineering Journal, Volume 6, Issue 3.

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