Radiofrequency Identification Utility in Healthcare

4.27.2015 | 0 Comments


The following paper is an abridgment of an article that originally appeared in Practical Patient Care a number of years ago.

The application of barcode and RFID technologies in the areas of medication administration reporting and safety checking is an expanding and accepted use of these technologies at the point of care. In particular, validating and verifying patient, drug, and order identity as part of the “5 Rights” for medication administration checking and infusion pump drug validation via the use of universal product code (UPC) symbol or radio frequency identification (RFID) embedded patient bracelets is a key step in nursing workflow within acute care wards. Verifying patient identity is often accompanied by clinician requests to orally verify patient identity via independent checks for patient name, dates of birth, and social security numbers. The attached white paper captures my thoughts on the topic and on other technologies, such as tele-monitoring.


A Chemoreceptor-Based Sinoatrial Heart Controller Device Concept (originally written August 2002)

4.27.2015 | 0 Comments


Discussions regarding heart rate variability (HRV) caused me to research a conceptual controller for chemoreceptor-based sinoatrial heart control. As HRV is affected by sympathetic and parasympathetic control, this fact reminded me of a paper (unpublished) which I had written 14 years ago. While I included a web-adaptation of this paper very early in the history of the web log, I never included the actual paper itself. Hence, the attached white paper presents a concept for a mechanism to automatically control the pacing (heart rate) and contractile force (stroke volume) of either an artificial left ventricular assist device (LVAD) or a patient’s own heart who has experienced degenerative performance of the Sinoatrial node. The device operates by analyzing the chemical nature of the epinephrine, norepinephrine, and dopamine content of the return blood flow through the superior vena cava and then using this information via cyclic voltammetry, neural network control, and feedback to the pacing device to control the heart rate of the assist device.


Nuvon Speeds Up The Process with Mobile-To-EMR Connections

11.02.2013 | 0 Comments


Posted October 24th in

On the surface, Nuvon’s mission seems to address a problem most would imagine having been addressed years ago: Its technology platform connects wirelessly with all electronic medical record systems to automatically capture and communicate data.

Its technology connects data from mobile devices — tablets, especially — and seamlessly passes along data to EMRs, regardless of whether they’re inside or outside of a hospital. This saves nurses the pain of having to manually enter data, despite the system being electronic.


Is medical device data safe from the prying eyes of the NSA?

6.13.2013 | 0 Comments


An interesting point on the whole issue of connected health and patient privacy is before us. Medical Device & Diagnostic Industry (MDDI) Online published a piece just the other day entitled “Your Medical Device Data is Not Safe from NSA’s Prism.” The implication is that data collected by patients or their providers from medical devices as part of the process of patient care management may not be private — will most likely not be private — as revealed by the latest news of the NSA’s Top Secret Prism Program.

This blog is not political–never intended to be; never will be. Whether you consider Edward Snowden to be a hero, a traitor, or neither, the key point of this revelation is the exposure of information–and the implication for U.S. Citizens’ 4th Amendment Right to be secure in their persons shall not be violated:

The right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated, and no Warrants shall issue, but upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized.

From the medical device data perspective, the issue is to what end does the sharing of this data serve? Some have come out and said that they have no problem with the sharing of their personal information; that they have nothing to hide; and, if in the sharing this provides for greater societal safety, then all the better. I maintain that this has nothing to do with “having something to hide.” This has to do with trust. We are implicitly trusting people with information that is private and, if found to be in the hands of unethical individuals, can be used in nefarious ways. This concern has nothing to do with intentionally wishing to hide information.

I have and continue to be an advocate for the use of data for the good of improving patient care. My concerns are that in the process of exposing information to individuals not authorized to see it, or to whom we have no knowledge, we are throwing the door open to potential vulnerabilities that can ultimately harm the patient. The first credo of the physician is to do no harm. The potential for harm exists, given exposure of health information to unauthorized individuals.

The physics of medical device connectivity

6.07.2013 | 0 Comments


A note to my followers that I am now also a guest blogger at TheConnectingEdge. In this added role, I’ll be sharing experiences, information and knowledge associated with connecting medical devices in the various departments of the hospital, with emphasis on the hardware connectivity. Hope you will check it out. First entry to appear sometime within the next week.


When it comes to mHealth, let physicians complete transactions from anywhere

5.17.2013 | 0 Comments


In the recent mHealth article “Mobility: Key to overcoming barriers to clinical transformation and ACOs“, Matt Patterson asserts that mobile health information technology (mobile health IT) interoperability and data access barriers need to be broken down to achieve the seemingly elusive transformation of care. I agree completely. Two of the key headlines within this article grabbed my attention:

“Bring data to physicians instead of forcing physicians to go to the data.”

Give the physician the data wherever he/she is, and provide this in a manner that makes it simple to interact. This does not mean simply taking the desktop and miniaturizing it for a smart phone. This means a transformation of the experience to support whatever form factor is available for use by the physician that is clear, simple, easy to use and does not cause frustration or impediment. Too often, the “mobile” solutions are nothing more than a miniaturization of the desktop. This miniaturization without proper architecture or adherence to workflow or the peculiarities of the experience can result in frustration and disuse.

“Let physicians close the transaction from wherever they are.”

Allow the complete workflow to be accomplished from wherever the physician is located in whatever form factor is available for use. Closing the loop and providing the capability to turn any geographic location into a working location from and with which orders, vitals, results, notes, images can be viewed, created, analyzed, and interacted is the key to flexibility. To achieve this, interoperability and access to data are essential within the mHealth space.


$8.3B Cost Due To Inefficient Communication? | communications

5.09.2013 | 0 Comments


iHealthbeat has published a summary of a report from the Ponemon Institute indicating that inefficient communications technology results in an estimated $8.3B in lost productivity costs to hospitals.

The survey included 577 health care and health IT professionals at medical facilities. Bed count of these medical institutions ranged from <100 to >500.

Key findings, which are reproduced from the report:

52% of respondents indicated pagers inefficient

39% reported WiFi unavailable

38% reported email inefficient

36% reported text messaging not allowed

28% reported personal mobile devices not allowed

The report contains more interesting and pertinent data related to effects of HIPAA (Health Information Portability and Accountability Act).

In summary, it was estimated that $5.1B is lost annually as a result of decreased physician productivity and decreased time physicians have with patients; $3.2B is lost annually as a result of lengthy patient discharge times.


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

3.25.2013 | 0 Comments


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.


The chain of disruption leads to wireless technologies.

5.01.2012 | 0 Comments


Check out this graphic on Frugaldad.This shows the evolution of technologies over the past several decades. Very interesting.

Personal Technology Infographic"


A new link to an old blog post: Medical Device Open Source Frameworks | Medical device data system

3.26.2012 | 0 Comments


I received a ping to a new comment on an old post I had written on Tim Gee’s site regarding medical device connectivity. The title of the post was Medical Device Open Source Frameworks and in this post I had written about creating ubiquitous connectivity to medical devices through standardized connectivity, similar to the way USB devices operate: plug into a port and they are recognized.


Where is Healthcare’s Information Appliance?

3.12.2012 | 0 Comments


In Donald Norman’s book, “The Invisible Computer”, written back in the late ’90s, he describes the features and functions of “information appliances” — technologies that are tailored towards solving specific problems in everyday lives. A key benefit of information appliances is that they are singular in purpose. For example, a garage door opener could be considered to be an information appliance.

One might argue that the fact that such devices exist is obvious and unremarkable in everyday use. However, the argument made around having such devices singular-of-purpose is just that: they are easy to use, require very little “training,” have few failure modes and are self-intuitive.

We turn now to healthcare and the concept of an information appliance in this field. When Norman wrote his book back in the late ’90s there were no iPhones, no Androids, no iPods (at least those we are familiar with today), and certainly no iPads. Yet, a theme that is often used in describing the ultimately usable device(s) for physicians and nurses, we hear that usability is chief among them. In last week’s Healthcare IT Newsweek piece “12 Integration Capabilities EHRs will need to have” we learn of features and functions that need to be deployed to support single sign on, integration of patient context and awareness, publishing functions to consuming widgets. But, these are all quoted with respect to the assumed rendering device: the personal computer. The personal computer is a generalist’s appliance, not a specialist’s. It is designed to be used for multiple purposes. Hence, when applied toward the specialization of any one problem, it does so only with clunkiness. We are all undoubtedly familiar with problems in upgrading computers; in applying patches; in the intricacies of software installation and configuration. These “generalist appliances” can oftentimes get in the way of the specialist’s purpose because we (all of us) oftentimes spend our time simply figuring out how to use the appliance rather than using the appliance to solve a particular problem for us.

As I was reading Donald Norman’s book, I was thinking about what could be considered to be today’s “information appliances” and how they could be applied to healthcare. My iPhone or iPad immediately came to mind. Albeit, generalist-devices, they have downloadable applications that can make them specialist devices for particular purposes. Then I thought about whether they were really applicable to the healthcare environment: from a clinical perspective, if used by a physician in his or her office or at home that might be the case. But, if use were to be considered at the bedside, there are other issues to deal with: cleaning, dropping, ability to use with a gloved hand, etc. Could the iPhone truly be considered a clinical information appliance at the bedside? I’m not sure it could.

In the 14 or so years since the publication of Norman’s book, much has evolved in terms of technology. Furthermore, the acceptance of technology for clinical practice has certainly evolved enormously. Yet, what has seemingly evolved in a limited way is the usability of technology for clinical practice. Physicians and nurses are all-too-often required to learn to work with, through, or around the quirks of information technology and their requisite appliances rather than having technology that will work with, for and conform to the requirements of the clinical user. This has caused me to think about where is the true clinical information appliance? I do not think it (yet) truly exists.


Some quick thoughts on medical device alarm research areas of need

2.02.2012 | 1 Comment


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.


’90s CDS research still valid today?

2.02.2012 | 0 Comments


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.


Contributing Author–Dictionary of Computer Science, Engineering, and Technology

10.30.2011 | 0 Comments

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

Steve Jobs and the Seven Rules of Success — Applied to Healthcare IT | vision

10.15.2011 | 0 Comments


Have A Vision. Have the Courage to Follow It.

I’m reminded of the film Braveheart during the scene with young William Wallace in which he is asleep and dreaming post the murder of his father at the hands of Edward the Longshanks when, in his dream, his dead father, Malcolm, turns to him and says “Your heart is free. Have the courage to follow it.”

I find that an interesting parallel to one of the rules Steve Jobs stated in his Commencement Address to Stanford graduates several years back, and reiterated in Carmine Gallo’s post in Entrepreneur titled “Steve Jobs and the Seven Rules of Success”:

Rule 1: “Do what you love. … People with passion can change the world for the better.”

From the perspective of healthcare information technology, my view of this applied is don’t do what everyone else is doing just because it is in vogue. As I’ve written in other articles, I believe the key to effective and helpful patient care is incorporating information from multiple sources and looking outside the field for hints and guidance on this. Good ideas from other fields are necessary to enrich the space and can add great value. Systems engineering and integration are but two concepts that I see others beginning to take up in terms of disciplines that are beginning to be applied.

Connect the Dots

Another Steve Jobs concept is connecting things that evade others, or that others ignore. Again, Gallo writes: “…people with a broad set of life experiences can often see things that others miss… Connect ideas from different fields.” This is often easy to do in retrospect: hindsight is 20/20. What this means is that you must expose yourself to a broad range of experiences so that you can look at a problem from outside the field, as described at the end of the last paragraph.

In my own case, when I was preparing to do my research way back in the early ’90s, and I indicated to my Dissertation Committee my goals and objective, I had a member of my Committee say to me privately that he gave me a 30% chance of succeeding. My goal was to develop a model of post-operative weaning in order to demonstrate that a systems engineering modeling approach could be applied to project a state in patients by treating them as a system and creating a model of multi-dimensional inputs. After the successful defense of my dissertation a year or two later the same Committee member said to me that I proved my case. At the time he made the comment I felt very depressed: what was I doing? Was I wrong? Was I biting off more that I could chew? But, as Steve Jobs said, you can only connect the dots looking backwards. You must have faith looking ahead.

Enough said.


Simple Compression Calculations Using MS Excel and Haar Wavelets

10.11.2011 | 4 Comments


 Haar Wavelets

From time to time I have been asked to provide explicit details on the mechanics and methods behind Haar wavelet transforms. The purpose of this post is to walk through two simple examples that demonstrate the use of the Haar transform relative to two one-dimensional signals (time signals). The details of the Haar basis and Haar wavelet transform are available elsewhere. The purpose here is to provide a simple example of how the Haar basis is computed using a simple tool such as an Excel spreadsheet.

 4×4 Wavelet Calculation

Let’s begin with the end product: the following is a 4×4 Haar matrix computed using Microsoft Excel:

Excel calculation of 4x4 Haar Matrix

Excel calculation of 4×4 Haar Matrix

The Haar Matrix, which we will denote Hn, is given as follows for the case of 4 data elements, denoted as Vn:

Haar Matrix 4x4 Definition

Haar Matrix 4×4 Definition

This computes to that shown in the figure above. The Haar wavelet coefficients are computed by first inverting the Haar matrix and multiplying by the output signal vector, Vn:

Definition of Haar Wavelet

Definition of Haar Wavelet

Haar matrix inverse is calculated using Excel and the result is shown in the following figure:

Excel calculation of 4x4 Haar Matrix Inverse

Excel calculation of 4×4 Haar Matrix Inverse

Each cell in the Excel spreadsheet is computed using the following cell entry:

= INDEX(MINVERSE($B$2:$E$5),1,1)

Where $B$2 corresponds the cell corresponding to the first row and column of the original Haar matrix and $E$5 corresponds to the last row and column of the original Haar matrix. The elements (1,1) at the end of the expression must include the components of each cell. So, in the example above, (1,1) represents the element in the first row and column of the matrix inverse. This is place in the first cell of the Excel spreadsheet corresponding to this element. The last row and column element would be (4,4).  So, for example, the cells would be populated as such:

Time and signal vector chosen arbitrarily for this example is as follows:

Sample 4 element signal vector

Sample 4 element signal vector

A plot of this signal is shown in the next figure:

Signal vector plot versus time

Signal vector plot versus time

The wavelet coefficients are computed using the follow expression:

Equation for 4 dimensional Haar wavelet coefficients

Equation for 4 dimensional Haar wavelet coefficients

Excel spreadsheet calculation:Excel spreadsheet calculation of Haar wavelet coefficients

It is possible to cull coefficients on some basis, such as their magnitude with respect to the largest coefficient. We can arbitrarily impose a threshold with respect to the largest coefficient (-4.9497) and remove those coefficients (set to zero) that are at or below this magnitude. Suppose we set a threshold of 30%. The wavelet coefficients with 30% threshold imposed result in the removal of the second coefficient:

Haar wavelet coefficients with 30% value threshold applied

Haar wavelet coefficients with 30% value threshold applied

The signal can be recomputed using this culled set of coefficients. They are calculated using the following expression:

Equation for signal reconstruction

Equation for signal reconstruction

Excel spreadsheet calculation:

Recreated signal with 30% threshold

Excel calculation of signal reconstruction with 30% threshold applied on Haar wavelet values.

A plot of the signal with an overlay plot of the recreated signal using 30% threshold on the wavelet coefficients is displayed in the figure below. Note the comparison between the two signals, indicating some loss of fidelity owing to the removal of the wavelet coefficient. This is a crude representation of the effects of destructive compression on the reconstruction of signals:

Overlay of original signal and reconstructed signal

Signal overlay plot showing original, complete signal and reconstructed signal based upon signal “compression” achieved by applying 30% signal threshold on Haar wavelet values.

 8×8 Wavelet Calculation

The method can be extended easily to any dimension. Let us consider an application of the Haar wavelet transform to an 8×8 Haar matrix:

Haar matrix constructed for 8x8 case

8×8 Haar wavelet matrix

The inverse of this matrix is as follows:

Inverse 8x8 Haar matrix

Matrix inverse of the 8×8 Haar matrix

The base signal is defined as follows:

Base signal containing 8 elements

Arbitrarily chosen 8 element, one-dimensional signal for testing

A plot of this signal provides a convenient visual rendering of the data:

Plot of base 8 element signal

Time series plot of data from base signal

The wavelet coefficients are calculated in precisely the same way as the 4×4 example shown previously:

Haar discrete wavelet coefficients for 8 element signal

Haar wavelet coefficients calculated using MS Excel

Finally, the imposition of signal thresholds (20% and 30%) is shown and the signal is reconstructed in the manner previously described, only extended to an 8×8 Haar matrix. The resulting plot with the wavelet threshold impositions are plotted as overlays in the following figure:

Overlay of original signal and 20%, 30% threshold signals

Plot of original 8 element base signal with overlay plots of reconstructed signal with both 20% and 30% of wavelet coefficients removed based on threshold calculation.

 Links to wavelet and other analytical calculations on this site

My book on modeling medical device data may be found here . Other links, such as the paper on modeling of re-awakening time are also available on this site, for the interested reader. I’ve also included a PDF version of this blog entry for download.


Apple, Healthcare, and Steve Jobs’ Legacy

10.08.2011 | 0 Comments


Tough Week for Blogging on Healthcare

This has been a very tough week, and although I have made a point of attempting to make at least 1 blog entry per day, it simply was not possible due to the business of my schedule and travel. This coming week is no better. However, I felt strongly motivated to take a moment while (finally) at home to make a blog entry while I get ready to pack for my next trip tomorrow… at least my cats still recognize me.

But, Steve Jobs’ death at the age of 56 is a blow in more ways than one. He was a creative genius. I liken him to Nikola Tesla in some ways for his uniqueness as well as his personal behavior–both the oddities and the passion. But, unlike Tesla, Jobs knew how to manage a business: he had both creative genius and business acumen, and this combination is even more rare than creative genius alone. There are many creative geniuses who have brilliant ideas that are manifested as tinkering in their garages, and who die penniless and / or insane. However, it is more rare for the creative genius to recognize and tap into that which can be commoditized and marketed in a way that the public will seek — nay — will run to as a “must have.”

This, to me, is sheer brilliance. I wish I had this talent!

Healthcare and Apple

HealthcareITNews had a piece recently on the legacy Steve Jobs is leaving in terms of the Apple products and their impact and use on healthcare. While healthcare per se was not Apple’s forte, the platforms and the ubiquity of the appliances and the application infrastructure are well designed, easy to use, and provide the perfect infrastructure for deployment of applications as well as usability. I have written on Apple, healthcare and technology in the past. The ability to support web-based applications in a palm-based platform (iPhone) that can support both external device connectivity (through USB) as well as providing camera, high resolution user interface, and the ability to support and share applications through the Apple Story mean that the sky is the limit.

I have spent a fair amount of time writing my own personal applications and learning the iOS SDK and there are enormous possibilities and untapped benefits that have yet to be realized. Many web-based applications from EMRs to software applications ranging from image-based viewing (picture archiving, or PACS) are readily available. The use of the iPhone or the iPad for interactive note taking, electronic medical record interaction, or applications are well represented through the Apple Store.

Healthcare and Medical Device Connectivity using iPad

Apple iPad2 for use in healthcare: showing medical device data

Apple iPad2 (Source:

There are many iPhone and iPad knock-offs out there. All have their relative benefits. But, Apple will always be the first in terms of commercial appeal. Although there are some additional features I would like to see in the iPad (I’m sure Apple is hanging on every word I write), one of the key gaps I currently see is in the lack of multiple USB ports for connecting to external devices. Their single 30-pin interface (image shown below) provides for mapping to USB. However, it is the only hard-wired external interface available from the unit. This limits somewhat the capabilities for tertiary device connectivity given the iPad or iPhone is docked within a docking station or otherwise connected to charging power.

Apple 30-pin interface connector

Apple 30-pin interface connector for use in healthcare connectivity (source:

From a clinical environmental perspective, iPhone and iPad are not of medical grade (i.e., satisfy UL 60601-1-1 requirements, etc.). However, for physician use remotely or for personal use within the healthcare environment at the point of care, these are very capable for private, individual use. However, for general purpose use by staff in environments where the potential to drop the units or where they may receive rough use, these are probably not the best tools for the trade.

Healthcare into the future

Steve Jobs’ legacy may be the passion and creativity of his vision that has been passed on to individuals who can see the application potential of the technologies he created in many areas, not just healthcare. I know from my own perspective that he has inspired me.

Thanks for visiting Medicinfotech.


Alarms, Clinical Decision Support, and the upcoming Alarms Workshop

10.01.2011 | 3 Comments


Medical Device Alarms Summit

The impending Medical Device Alarms Summit has caused me to do some research into the area of medical device alarms in general, and has also caused me to go back and review old papers and research of mine that are related. The conference coordinators provided links to some research material, and this research material also caused me to dig up some references to papers by one of my former advisors, CW Hanson, and colleague Bryan Marshall of PENN. Their paper, “Artificial intelligence applications in the intensive care unit,” (Crit Care Med 2001 Vol. 29, No. 2) is referenced within one of the recommended research articles co-authored by Michael Imhoff and Silvia Kuhls, titled “Alarm Algorithms in Critical Care Monitoring,” (International Anesthesia Research Society, 2006, 0003-2999/06).

Critical Care Medical Device Alarms

Imhoff describes the three classes of medical devices responsible for alarms can be classified into the categories of monitoring, therapeutic to “support or replace failing organs,” and therapeutic to “administer medications and/or fluids to the patient.” While medical devices have evolved in the area of providing closed-loop control in the form of feedback from the patient through sensors, Imhoff describes two key issues that remain in the area of medical device alarms. These are:

1) Identifying conditions for which an alarm needs “to be thrown”, and

2) the consistent and unambiguous annunciation of the alarm in a manner that makes it clear to the end-user that a critical event has occurred and can be differentiated from other such events.

 Classes of medical device alarms

In my work and my experiences, the types of medical device alarms have been legion. Imhoff describes several classes of alarms, and I will further characterize these as clinical versus technical. I view technical alarms as those that identify conditions within the device itself. For example, if a device disconnects from a patient (e.g.: a probe falls off, or a cable disconnects, or the device ceases to communicate with the middleware or interfacing system). These types of alarms, Imhoff explains, can have clinical impact. This, of course makes sense for somewhat obvious reasons.

The second class of alarms are those that are clinical: identifying conditions based on measurements made from the monitoring or therapeutic devices of danger or impending danger (e.g.: heart rate too low or too high, blood pressure too low or too high, O2 saturation too low, etc.). These types of conditions are those for which one expects alarm to be “thrown.” However, as Imhoff puts it, the failure of the technical architecture can result in the inability to detect the clinical conditions. Again, this is quite obvious. Ergo, it is necessary to notify of both technical as well as clinical failure since the failure of the technical will result in the inability to detect the clinical, especially when considering remote monitoring environments.

Medical Device Alarms and False Alarms: Detection & Modeling

Imhoff & Hanson both describe and discuss modeling and prediction techniques for identifying conditions, monitoring and modeling approaches for predicting future trajectory, that involve many different types of techniques. Key among these are artificial neural networks, fuzzy logic, Kalman filtering, Bayesian estimation, least squares filtering, and others.

In my dissertation, “Modeling post-operative respiratory state in coronary artery bypass grafting patients,”  and in the EMBS paper that followed it, found in the EMBS paper “Modeling Spontaneous Minute Volume in Coronary Artery Bypass Graft Patients,” I describe a template-based approach for predicting viability for spontaneous breathing trials. The key points being that, from a regulatory and a predictability perspective, most methods require large amounts of patient data in order to develop reliable and predictable outcomes. Deterministic behavior is required, especially for FDA approval of such methods. Hence, many methods have remained in the realm of research and clinical trials because of this. Nonetheless, the ability to reduce the overall effects of alarm fatigue and better predictability as well as improved forecasting of patient outcome remains a fertile area. I intend to report on the outcome of this workshop and am quite interested in the discussions that will ensue.

For further reading, I will (of course), point the reader to one or both of my books:

Integrating Medical Device Data into the Electronic Medical Record: A Developer’s Guide to Design and a Practitioner’s Guide to Application

Integrating medical device data into the electronic medical record

John Zaleski — Book I

and, Medical Device Data and Modeling for Clinical Decision Making:

Medical Device Data and Clinical Decision Making

John Zaleski — Book II





Single Sign-On; Clinical Context Object Workgroup (SSO; CCOW)

9.27.2011 | 0 Comments


HealthcareITNews reports #1 of the 5 technologies every enterprise needs to be using is single sign on (SSO) and clinical context object workgroup (CCOW) application enablement. Single sign-on in effect enables applications within the environment to share the same user logging and access directory structures across the enterprise. This simplifies user management, ensures security policies are applied consistently across applications, provides a mechanism for role-based access to specific information, and simplifies overall management of the users and maintenance thereof associated with access to specific applications.

CCOW provides the capability to link access to “child” applications from the common context perspective of parent applications. For example, if a patient record view is selected within an EMR and an application normally external to that EMR needs to be launched with the demographics and other context of the selected patient, CCOW provides both a capability to share that patient context but also to enforce specific workflow characteristics that promote patient safety and security.

For instance, if a patient record is selected and the “child” application launches an ultrasound video on that patient, CCOW enablement will restrict the ability to select another patient from within the child or parent application, thereby negating the possibility that multiple patients will be displayed on the screen–a fact that can serve to cause confusion to be avoided plus the very real possibility that information associated with one patient could be misassociated with another patient by the care provider.

Instead, in this situation, CCOW would require that if the parent application (i.e., the EMR) were to be used to select another patient while the child application is currently open on one patient, that child application would be forced to be closed so the possibility of misassociation could not occur. Similarly, once in the child application, a new patient could not be selected from within the child without allowing first the same patient to be selected from the parent.

While somewhat simplistic, this gets to the bare essentials of the concepts of single sign-on and CCOW.


Medicinfotech Now .mobi Enabled

9.22.2011 | 0 Comments


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