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Discrete Wavelet Transform Compression of Clinical Data to Support Rapid Storage and Retrieval

16-Nov-15

Discrete Wavelet Transform as a Tool for Data Analysis

As medical devices are increasingly required to provide for information from the point of care to enterprise electronic health record systems, more patient data collected at the point of care will become available for remote viewing and analysis. In this paper a discrete wavelet transform method is presented for automatically filtering and analyzing data obtained from high acuity bedside physiologic monitors using discrete wavelet transforms, the purpose being to reduce the total amount of data transmitted into the electronic health record, and to facilitate analysis of time-based trends in physiologic data.

Use of Discrete Wavelet Transform Compression

As information technology has been brought into the healthcare enterprise, much of the paper-based record is being supplanted by an electronic record, in which clinicians either record information manually or, in addition, automatically from clinical systems. The electronic medical record is maintained by the health care enterprise and follows the patient throughout all phases of diagnosis and treatment. Furthermore, this medical record is accessible to all authorized clinical personnel. An obvious benefit of this approach is that, unlike the paper record, the electronic medical record can be accessed from many different locations without physically retrieving the patient’s hard copy information from particular departments. Loss of information is nullified, and use of the electronic medical record establishes a standard approach for recording of patient information, so that each department must conform to specific standards in terms of the types and quality of information recorded on each patient. Also, with Web-browser-based medical record viewing, convenience in terms of viewing, together with the reduced delays associated with retrieving the paper recording ensure that clinicians can readily obtain patient information when required. In addition, two-way communication between the enterprise information system and clinical systems enable the error-free retrieval of patient demographic and administrative information (such as medical record number and insurance information) without adding further delay or introducing errors into the patient’s record within the departmental system.

One key difference between the legacy paper record and the electronic medical record is that the paper record remained an intimate device by which the attending nurse monitored and recorded status on the patient: it remained with the patient and the nurse until the patient left the unit. With the introduction of the electronic medical record, data transmitted to that record and viewable by authorized individuals outside of the unit can lack the context of the actual situation in interpreting patient flow sheet results. As society and healthcare move toward a completely automated and electronic medical record environment, it must be mindful of the fact that the introduction of new technologies must never impede quality healthcare [1].

In this paper the author describes a process for augmenting the basic transmission of information from the clinical environment to the medical record by capturing more detailed information that may not be captured during the course of standard flow sheet recording. Patient telemetry is normally recorded within the flow or assessment sheet on regular intervals. Typical intervals range from 15 minutes to an hour, depending on the particular acuity of the patient. However, bedside monitors typically can record very detailed information in fractions of a second. Most of this information is discarded, and much of it can be of no clinical value at these short intervals. However, a trade-off exists in terms of the size of the interval and the capturing of relatively important data from these bedside monitors: make the recording interval too large, and events of relatively short duration but high importance (such as heart rate spikes, or respiratory rate increases) will be missed and never recorded within the electronic medical record. On the other hand, make these cording intervals too small, and the health care enterprise, including the hospital computing network and the size of the medical record, will become cumbersome and filled with much useless information, possibly even rendering the system unusable. One approach to solving this problem is to provide the capability to record detailed information when necessary, but omit when not.

Use of Discrete Wavelet Transform as a Method for Data Compression

Performance and response time is a key metric when communicating raw clinical data from departmental to electronic health systems within a healthcare enterprise.  The objective of this paper was to present a discrete wavelet transform method that can reduce the overall data storage requirements as well as facilitate data analysis of time-varying signals without requiring large scale data mining of the raw information. The discrete wavelet transform (DWT) was selected as a possible filtering mechanism because the DWT preserves both spatial and temporal behavior of a raw data signal. This is a very important feature in the study of medical telemetry, because many processes are not stationary, making the application of traditional signal processing methods (such as Fourier transforms) inappropriate. The creation of a DWT Processing method that exists as an adjunct to the existing departmental information system imposes no additional software features on the existing telemetry system, and operates off of the existing clinical network. Furthermore, the benefits of using the DWT Processor as both a noise filter and as an automatic data filter are affirmed inasmuch as both stationary and non-stationary signals are processed appropriately using the DWT method: stationary signals can be represented by relatively few overall data points in the form of wavelet coefficients, whereas threshold filtering of non-stationary signal can provide accurate reconstruction of raw signals with even a factor of two fewer data points than the original signal. This benefits a potentially congested network and speeds recreation of the original signal by requiring fewer overall calculations to be performed by that enterprise information system.

A copy of the PDF version of the paper is included here:

Discrete Wavelet Transform.

[1]  Patricia Benner, “Beware of Technological Imperatives and Commercial Interests That Prevent Best Practices,” American Journal of Critical Care; 12(5): 469-471, 2003.

An Excel Method for Optimal Assignment of Resources Using the Hungarian Method

16-Nov-15

Optimal Assignment of Resources

Those working in operations research or analytics who are faced with the challenge of performing optimal assignment of resources (for example, assigning sales people to routes to minimize airfare cost), may have the need for the algorithm I am about to discuss. In general, the type of problem described is the assignment of n resources to n tasks such that at most only 1 task can be assigned to 1 resource such that the total cost of assigning the task to resource is minimized.

Optimal Assignment Cost Matrix

Optimal Assignment Cost Matrix

The Hungarian Method (a.k.a. Munkres algorithm) is a method that determines the optimal assignment of a given cost matrix. The cost matrix, as shown above, reflects the relative costs of assigning resources in column j to actors in row i. For small matrices (i.e., 2 or 3 assignees), the solution to determining the overall minimum cost can be computed relatively quickly by hand without the use of an automated algorithm. Yet, when considering larger assignments (e.g.: 4 or more rows and columns), the use of an automated method is necessitated as the computational cost grows as n! (i.e., n-factorial).

That is a big number.

An Excel Macro for Hungarian Method Optimal Assignment

The constraint that the cost matrix must be square (i.e., at MOST 1 column assigned to 1 row) is necessary for the algorithm. There are extensions to the Hungarian method that support rectangular assignment (i.e., selecting the best columns to assign to the rows when the cost matrix is not square). For our purposes, I will present only the nxn example.

The method I will provide to those who contact me directly. Have had a number of people ask me for other methods I have written in the past, and would prefer a direct reach-out versus simply posting on my site. I will share the screen shots of its operation, though.

Here is the cost matrix input--worksheet named "CostMatrix". Two buttons link to running the assignment and resetting the assignment, as shown.

Here is the cost matrix input–worksheet named “CostMatrix”. Two buttons link to running the assignment and resetting the assignment, as shown above.

Pressing the "Run" button will cause the algorithm to execute. The output will be written to an "Output" worksheet. Once the assignment is completed, the assigned entries are displayed in the cost matrix in mauve.

Pressing the “Run” button will cause the algorithm to execute. The output will be written to an “Output” worksheet. Once the assignment is completed, the assigned entries are displayed in the cost matrix in mauve.

Here is a view of the output worksheet. The row and corresponding optimally assigned column are displayed together with the total cost (or residual).

Here is a view of the output worksheet. The row and corresponding optimally assigned column are displayed together with the total cost (or residual).

Here is a sneak peak at the macro. Those interested can contact me here through the Web-Log for details.

Here is a sneak peak at the macro. Those interested can contact me here through the Web-Log for details.

This summarizes the optimal assignment method. It operates in Excel (all versions) and is quite lightweight.

 

Systems Engineering in Critical Care Medicine: An Evolving Field

29-Oct-15

Systems Engineering

As we approach INFORMS 2015, one session theme is health systems engineering. Within the body of this session, the focus on examples of systems methods in medicine is exemplified by the various presentations.

Title slide from Symposium given at the University of Pennsylvania to Systems Engineering Graduate Students & Faculty. (c) 1999

Title slide from Symposium given at the University of Pennsylvania to Systems Engineering Graduate Students & Faculty. (c) 1999

This briefing presented results and research into postoperative critical care medicine. Specifically, weaning of patients who have undergone coronary artery bypass grafting. The parameters and automatically obtained data from bedside medical devices were recorded and analyzed to assess the trajectory of spontaneous breathing recovery over time. The systems engineering aspect involved bringing multiple variables together (integration) and generating a prospective model that was validated on a training set of data, then applied to a test set. The model’s skill in prediction demonstrated that the respiratory state of the patient (measured in terms of respiratory rate, minute volume) evolved from total dependency to near extubatory values with a prediction accuracy of approximately 1 hour (i.e., the ability to predict ahead the patient’s state by 1 hour).

Modeling and Simulation Key Aspects of Systems Engineering

The model demonstrated an application of systems engineering: specifically, the ability to integrate multiple data together and create a high-level model of respiratory state, guided by observations. Also demonstrated was the automated decision support mechanism of a manual process guided by a clinical protocol. In effect, this research demonstrated a control algorithm that provided a feedback response on maintaining mandatory support while responding to patient spontaneous capabilities. Achieving success with the controller is dependent upon having access to real-time clinical observations (both automated measurements from medical devices and clinical observations from staff). Laboratory data was also necessary as this provided the key as to when safe orders to begin weaning could occur. The analysis involved inferring trends and behavior from data; providing for visualization of data to promote visual projection to future state; using raw data to assist in guiding outcomes of weaning performance; and providing a means for linking medical data repositories to achieve access to complete records of data. This research was also conducted in the years prior to the global rollout of electronic health record systems. Thus, much of the data collection needed to be “cobbled” together from raw methods developed in-house.

Data collection were conducted under institutional IRB #570-0 involving live human subjects.

Healthcare Analytics at INFORMS 2015: Kalman Filtering, Medical Device Integration Standards, & Medical Device Actionable Alerts

29-Oct-15

Three analytics presentations and session at INFORMS 2015 on prediction using Kalman filtering; medical device integration standards; and medical device alarms

This coming week, I will be giving three presentations and chairing a panel on the emerging role of health systems engineering, with impact on clinical informatics and analytics. This is a joint session between INFORMS and HAS.

This panel convenes on Tuesday, November 3rd from 16:30-18:00. The formal title of the session is “The emerging role of health systems engineering and its impact on clinical informatics and analytics.”

The three speaker presentations during this panel session will focus:

  1. J. Venella, DNP, RN, “How to make clinically actionable alarms”
  2. J. Zaleski, PhD, CPHIMS, “The Kalman filter and its application to real-time physiologic monitoring of high-acuity patients”
  3. S. Jha, “Predicting the effect of introducing walk in hours on staff workload at a pediatrics practice”

On Wednesday, November 4th, from 11:00-12:30, I will be giving the following presentation:

J. Zaleski, “Why are medical device connectivity standards so elusive?”

This presentation will focus on the history and current pathway toward standardized medical device integration and data communication. Medical devices still remain highly proprietary in terms of interoperability. Health Level Seven (HL7), as a healthcare information standard, only works when medical devices can export data in this common format. Gaps remain between the proprietary, manufacturer-specific language of many devices and the HL7 messaging format. Here we explore approaches for standardizing proprietary equipment around HL7 and related messaging languages and how lack of interoperability impacts patient care.

Finally, on Wednesday, November 4th, from 14:45-16:15, I will be giving the following presentation:

J. Zaleski, “Identifying patients at risk using fuzzy logic”. The use of “big data” for decision making has been a growing area of investigation and usage in healthcare enterprises. This paper shows how fuzzy rules can be used to operate on data obtained from the point of care to assist in clinical decision making, with application to real-time data collection in medical surgical units. This presentation will give an example of patient care management of medical device using data obtained from the patient at the bedside.

Philly INFORMS to hold First Meeting of 2015-2016 Year

27-Sep-15

Philly INFORMS

The Philadelphia Chapter of INFORMS will be holding its first general meeting in October.

To those interested in participating in INFORMS and also located in the Philadelphia, PA area:

Philadelphia INFORMS is holding its first meeting on Friday, October 23rd from 6-8 pm at the Navy Yard (4801 S. Broad Street — Board Room meeting room). Objective of this meeting is the first general meeting of the administrative year 2015-2016.

This is an opportunity for those interested in doing a dry-run of their presentations for the Annual INFORMS Meeting being held Sunday, November 1st, through Wednesday, November 4th.

Those interested in participating, please contact me through this web site or through my email and I will be happy to discuss with you participation and INFORMS in general. This meeting will be an opportunity to network with fellow INFORMS members and to meet the officers in the local Philly Chapter.

John R. Zaleski

INFORMS Philly Chapter President 2015-2016

INFORMS header logo

Medical Device Integration: Growth, Trends & Challenges: An Interview with John Zaleski, PhD, CPHIMS

01-Jul-15


 

HIMSS Blog: Discussing Medical Device Integration Post Publication of John R. Zaleski’s Book “Connected Medical Devices…”

I was interviewed for the HIMSS Blog by the Editors of HIMSS Books and the interview in total is available for viewing at the following link:

Interview with John R. Zaleski: Medical Device Integration: Growth, Trends & Challenges

Why Medical Device Integration Now?

From my recent HIMSS Blog post:

More than half of U.S. hospitals and health systems are planning to purchase and implement a medical device integration (MDI) solution. This is quite a difference from, say, 5 years ago. There are a number of reasons motivating this. Partially, the maturing deployment of electronic health record systems; partially, the maturing of the complexity of integration that requires higher-frequency, higher accuracy, higher fidelity data, such as clinical decision support methods within electronic health record systems; partially, the motivation of Meaningful Use and needs for improvement in patient safety; partially the PP-ACA. Other specific motivations, such as the recognition that improved patient care management can be achieved through better, more accurate data. Furthermore, MDI is an essential element for achieving better patient safety.

Need for Higher Fidelity Data Drove Medical Device Integration Exposure

As a researcher with more than 20 years working with medical devices and as a product developer and inventor, the following trends and major milestones are, in my opinion, the recognition of the value of MDI, which occurred not too long after electronic health record systems became widespread, perhaps not quite 10 years ago; then, the motivating Federal guidelines surrounding Meaningful Use Stages 1 & 2; the PP-ACA also provided some motivation. But, beyond that, my opinion is that receipt of data into the electronic health record systems motivated new ideas about what to do with those data. This, in my opinion, is leading to “higher-level” use cases other than charting. For example, use of the data to assist in improved patient care management and clinical decision making. When I started in this field, I was a graduate student and needed to collect data on live patients whom I was studying to develop methods for weaning from post-operative mechanical ventilation. I was running a study on patients recovering from coronary artery bypass grafting surgery and was following “my” patients from surgery through to extubation from mechanical ventilation in surgical intensive care and general surgery. When I was conducting this study, it was years before the commercial electronic health record system was widely publicized. Furthermore, none of the medical devices with which I was working had any automated data collection capability that was exploited within the hospital system I was working. Hence, I had to write my own code and perform data collection on my own, right at the patient bedside. My purpose was in using the data to develop models of patient state; to better predict time-based changes in physiologic and respiratory parameters, and guided by patient demographics, intakes and outputs, and other information. Yet, what was really lacking was a way to collect this information using an automated, standardized approach. So, I got into the MDI field as it was a necessary utility to meet my ultimate needs: complete data for better clinical decision making.

Tracking an Object in Free-Fall Using the Discrete Kalman Filter

29-Jun-15


 

Modeling Ballistic Free-Fall

I have written previously in several posts on applications of Kalman filtering to estimating and tracking physiologic data:

Tracking Medical Device Data: Example of an Excel Macro-based Kalman Filter

Kalman Filtering of Medical Device Data

Signal data artifact smoothing using the Kalman filter on arterial blood pressure measurements

The white paper, which can be downloaded below, describes the use of the Kalman Filter in tracking the vertical position (altitude) of a spherical object. The purpose of this white paper is to illustrate the system dynamics, control, and tracking algorithm application to a system of known true dynamics. Gauss-Markov noise is applied to the measurements to simulate uncertainty in observing the true position of the object. The mathematics and theory can then be applied to other systems.

Update — May 4th, 2016: Thanks to an astute reader (Jeff Reimer) who discovered an error in my calculation for the cross-sectional area of a sphere. I used the wetted area instead of the cross-sectional area, which is an oversight from my fluid dynamics days over 30 years ago. I have uploaded the paper with correction to Equation 3 (Kalman Filtering of Ballistic Free Fall Object) here:

BallisticFreeFallTracking-update-1

Kalman Filtering System Dynamics

It is necessary first to establish a model of the system dynamics. This is a mathematical representation of the applied external forces that define the motion of the sphere as it descends. Knowing the system dynamics, the accuracy of the prediction of the object’s pathway is increased immensely, particularly if the object is accelerating at a known rate. If the object system dynamics are not known, it is still possible to track the object, with the system dynamics being derived from the observed motion. Yet, the predicted motion of the object and the accuracy with which its position and speed are known from measurement to measurement will not be as accurate and can deviate particularly if external forces cause unpredictable accelerations on the object as it descends. Consider the diagram of Figure 1, in which a sphere of mass, m, and radius, r, is falling under the influence of the two principal forces weight and drag .

Figure 1: Forces on a sphere in ballistic free-fall.

Figure 1: Forces on a sphere in ballistic free-fall.

The attached white paper is developed into a Java program to compute the track of the object as it falls. The purpose of this posting is to provide the capability to those interested in reproducing for academic reasons and for general education. The white paper will be used as the starting point for other posts I intend to write on the tracking of physiological processes, such as heart and lung dynamics.

Evolving from data to knowledge: Using physiologic data to facilitate clinical decisions

22-Jun-15

Better data means better decision making, implying better care

The trend in data collection is that of evolution from episodic, ad-hoc, to more continuous collection of higher frequencies. This higher frequency data collection increases the likelihood that events will be “caught” — hence, better surveillance.

More continuously available information undergirds better care

More continuously available information undergirds better care

“Expert systems are the most common type of clinical decision support system technology in routine clinical use.” [1]

The types of data and uses for expert systems include:

  • Alerts & Reminders
  • Therapy Planning
  • Prescription
  • Data & Information Retrieval
  • Image Recognition & Interpretation

Common Clinical Decision Support System Myths [2]

  • Diagnosis is dominant decision-making issue in medicine: Not “what does patient have,” but, rather, “What should I do?”
  • Clinicians will use expert systems if they can be shown to function at the level of experts: “What do we know?”
  • Clinicians will use standalone tools, so long as they are integrated into clinical workflow.

What is the motivation for better Decision Support?

All across the specialty landscape the prediction for increased demand within the next 20 years is considerable. The motivation for more clinicians is clear. Yet, in addition to increasing the available pool of care providers, improving the capabilities of those in the field through providing better knowledge and decision making capability through access to the right information is key to improved care.

The growing demand and decreasing supply motivates the need for better decision support tools using the available data.

The growing demand and decreasing supply motivates the need for better decision support tools using the available information.

Display is Key to the Right Decisions.

The famous graphic by Charles Minard showing Napolean’s Russian Campaign, is a testament to the genius of his graphic design around 6 data elements: (1)army size, (2,3) position (x,y), (4) direction, (5) temperature, (6) time. Knowing how to display and integrate data into the workflow of the clinician is key to the usability as well as providing a valuable tool for informing on clinical decisions.

Data in 6 variables from Minard (1861) showing Napolean's Russian Campaign. Illustrated are: army size, (x,y) position, direction, temperature, time

Data in 6 variables from Minard (1861) showing Napolean’s Russian Campaign. Illustrated are: army size, (x,y) position, direction, temperature, time

The original presentation by John R. Zaleski is available for download here.

References

[1] Enrico Coiera, Guide to Health Informatics 2nd Edition, Chapter 25, October 2003.

[2] Edward Shortliffe, “Medical Thinking: What Should We Do?” Conference on Medical Thinking, University College of London, June 23rd, 2006.

Modeling Spontaneous Minute Volume in Patients Weaning from Postoperative Mechanical Ventilation

22-Jun-15

A Method for Predicting Spontaneous Minute Volume (SMV) Trajectory in Postoperative Weaning

Weaning is the process by which patients are gradually removed from mechanical ventilation. This post references a legacy article A Method for Predicting SMV on modeling postoperative weaning following the completion of my dissertation. The following figure illustrates the systemic process of surgery followed by transfer of the patient from the operating room (OR) to the surgical intensive care unit (SICU).

Patients re-awaken and resume breathing following coronary bypass grafting procedures in which they experience cessation of breathing as a function of the procedure and require assistance in the form of postoperative mechanical ventilation.

Patients re-awaken and resume breathing following coronary bypass grafting procedures in which they experience cessation of breathing as a function of the procedure and require assistance in the form of postoperative mechanical ventilation.

Study of Mechanically Ventilated CABG Patients

The process of weaning patients from postoperative mechanical ventilation is complex and dependent upon a number of factors that affect the process and viability of a patient to wean. A group of 28 patients who had undergone coronary artery bypass grafting (CABG) in the age range of 28 through 78 years of age were monitored from the time they returned from surgery until extubated from postoperative mechanical ventilation. SMV, the product of respiratory rate and tidal volume, was found to evolve from zero upon patient arrival to between 6 & 10 liters per minute as the effects of the anesthetic abated. A rough model of SMV is shown in the following figure.

Spontaneous Minute Volume qualitative trend versus time.

Typical qualitative trend of the SMV versus time.

The sampled SMV from each patient was binned and all patients who were successfully weaned were found to have measurements in the 6-10 lpm range, as illustrated in the following figure.

Sample patient spontaneous minute volume frequency distribution showing ranges of values just prior to extubation.

Sample patient SMV frequency distribution showing ranges of values just prior to extubation.

and integrated into a distribution function and compared with the distribution function of a normal (Gaussian) distribution. This is illustrated in the following figure.

Sample spontaneous minute volume distribution integrated across all patients and compared with distribution function of a Gaussian.

Sample SMV distribution integrated across all patients and compared with distribution function of a Gaussian.

As can be seen in the illustration, all patients determined to be viable for weaning possessed SMV in the range of 6-10 lpm. In all normal CABG patients, the state variable that was found to evolve over time. In all patients, SMV was immeasurable upon arrival. The method outlined in the article enables critical care staff to predict the SMV trajectory up to 30 minutes into the future, thereby facilitating weaning management and patient care.

Systems Engineering in the Intensive Care Unit: A Model CABG Patient Post-Operative Patient Re-Awakening Time — A Legacy Paper | fentanyl

15-Jun-15

Study of Post-Operative Respiratory Recovery

Paper originally published at INCOSE 1998. Request for reproduction caused me to include the text here. Systems Engineering in the Intensive Care Unit.

In a study of the post-operative recovery of Coronary Artery Bypass Graft (CABG) patients in surgical intensive care units, it was observed that these patients tended to awaken from the effects of anesthesia at varying rates. Because the time at which patients awoke was critical to latter phases of the study, determining the factors which affected the rate of re-awakening were of primary importance. To determine the critical parameters which influenced the re-awakening time of CABG patients, a closed-form model of the patients’ cardiovascular and respiratory systems was constructed to isolate and identify the number of parameters which were thought to affect reawakening time. As a result of this analysis, a mathematical relationship was determined which described the re-awakening time of a patient as a function of several physiological parameters. These included patient body temperature, body mass, body size, and the quantity of anesthetic (fentanyl) administered during surgery.  The work discussed in this paper was performed at the University of Pennsylvania in conjunction with my Ph.D. dissertation requirements.

Key Modeling Parameters

The key input to the process was the amount of anesthesia administered during surgery, and the
key output was the time to re-awaken. Patient physiological parameters affected the reawakening
time by affecting the rate at which the metabolism processes the anesthetic. Therefore, a  mathematical representation of the patient’s rate of metabolism was required to model the
effects of the physiological parameters on reawakening time. The anesthesia, the patient
(system), and the output are represented by the block diagram of Figure 1.

Model of re-awakening CABG patient as a system represented by the parameters mass, height, body temperature and with input the fentanyl dosage and output the re-awakening time.

Figure 1. Model of re-awakening CABG patient as a system represented by the parameters mass, height, body temperature and with input the fentanyl dosage and output the re-awakening time as determined by the time to begin spontaneous breathing.

Knowing when a patient will re-awaken is important to patient care management because
patients must be weaned from post-operative mechanical ventilation. However, before weaning
can occur patients must show that they can sustain spontaneous respiratory load. This cannot happen until the patient’s body temperature has returned to normal and the effects of the anesthesia have worn off sufficiently to enable the respiratory muscles to begin operating autonomously. To facilitate patient care management, it was hypothesized that a model of a patient’s respiratory and cardiovascular systems could be constructed to provide an
estimate of when patients would begin to exhibit spontaneous respiratory response. In brief, it was hypothesized that the re-awakening time, tRA, was a function of the following parameters:

System Equation for Re-Awakening

In deriving the functional form of this model it was necessary to isolate those parameters which were thought to play some role in determining the re-awakening time. These parameters are discussed below.

Patient Age, Mass, Height

Patients varied in ages, mass, and size. Most patients were elderly, as shown in the patient age distribution of Fig. 2. However, the correlation between increased patient age and the need for the surgery is normal as CABG surgery is frequently required in individuals who have experienced many years of arterial plaque buildup.

Patient Age-Frequency Histogram

Figure 2. Patient Age-Frequency Histogram

The functional form of equation (1) indicates that patient mass can be a factor in the reawakening time. To understand the impact of this parameter on re-awakening time it is necessary to understand how changes in patient mass can result in variations in metabolization of the anesthesia. It was hypothesized that a correlation existed between the size of a patient and the patient’s mass. In average people (not abnormally obese or thin), a loose correlation appears to exist, as illustrated in Figure 3. This figure is a scatter plot of patient height versus patient mass, wherein mass varied from approximately 50 kg to 115 kg. Patient height varied between 150 cm and 210 cm. The mass and height of a patient can be related to patient body surface area. Body surface area is used as a normalizing parameter in medicine. Body surface area is incorporated as a normalizing parameter in the relationship between anesthetic dosage and re-awakening time.

Figure 3. Patient height versus mass.

Figure 3. Patient height versus mass.

Patient Body Temperature & Achieving Normothermia

Patients return from surgery cold. The reason for this reduced body temperature is a result of the surgical procedure. Because the body must be at normal temperature so that the heart and the rest of the body functions can operate optimally, reaching normal body temperature normothermia) is key to the re-awakening process. It is only after normothermia is reached that the heart pumps oxygenated blood most efficiently throughout the body. Therefore, patient temperature is a key indicator to achieving efficient cardiovascular performance.
All patients achieved normothermia before the re-awakening process commenced. The average
time to reach normal body temperature, tNORM, was approximately 2.5 hours. Figure 4 is a scatter plot of patient arrival temperature versus time to achieve normothermia. The average temperature profile is shown overlaid on this scatter plot. As can be seen in the figure, the average re-warming time of most patients was found to be in excess of 2 hours.

Figure 4. Patient temperature warming profile.

Figure 4. Patient temperature warming profile.

Deriving the Re-Awakening Model

Patients arrive from surgery dependent on a mechanical ventilator for their breathing. This results from the effect of the anesthesia: it behaves as a muscular inhibitor to ensure that the patient does not move involuntarily during surgery. As the effects of the anesthesia wear off, the patient’s respiratory performance returns with a gradual increase in the amount of air breathed by the patient as a function of time. One measure of this respiratory function is the volume of air breathed by the patient in a given minute. A qualitative relationship between the volume of air breathed in a minute (spontaneous minute volume, Vesp ) and time after surgery is shown in Figure 5.

Figure 5. Evolution of spontaneous minute volume over time.

Figure 5. Evolution of spontaneous minute volume over time.

The re-awakening time was defined by the author as the ability to breathe a steady volume of
air, Vesp, of not less than 1 L/min with 80% confidence. The confidence was determined by monitoring Vesp over a 10 minute interval. If it was found that 80% of all Vesp measurements
exceeded 1 L/min, then the patient was said to be re-awakening. This criterion was selected by the author as an empirical threshold to affirm the reawakening of a patient. Qualitatively, this is illustrated in Figure 6.

Figure 6. Illustration of re-awakening time.

Figure 6. Illustration of re-awakening time.

It was found that once patients began breathing at approximately 1 L/min the volume of air they
continued to breathe spontaneously increased monotonically until they were able to breathe
completely on their own, defined in Figure 5 as breathing between 6 and 10 L/min (depending on body mass). The confidence value of 80% was selected as a threshold for measurements to ensure that the re-awakening of patients was indeed genuine and not spurious or an artifact of a
procedure that may have stimulated the patient to breathe spontaneously. The setting of the threshold on this parameter is of itself a subject of some study. The generalized mathematical relationship between fentanyl dosage and re-awakening time was determined to be most highly correlated to normalized fentanyl dosage, ƒn. This parameter is calculated by computing the ratio of the fentanyl dosage to average patient body surface area. Rollings [Rollings 1984] provides an expression for body surface area, BSA, as a function of patient mass and height:

Equation 2, Body Surface Area

where:

BSA is the patient body surface area measured in square-meters;

m is the mass of the patient, measured in kilograms; and,

h is the patient height, measured in centimeters.

Analysis revealed that a more definitive relationship between anesthetic dosage and time to
reach 1 L/min was achieved by relating this parameter to both the patient body mass and body
surface area, as opposed to body mass alone. Body surface area was selected as an additional
normalizing parameter because the equation defining body mass incorporates an expression
relating to patient height. Patient height affects the size of the body torso, which ultimately affects the size of the lungs and their volume, as well as the cardiovascular system. The relationship between lung volume and body surface area motivates the argument that larger, heavier patients need more anesthetic than smaller, lighter patients. Hence, the patient body mass and surface area (BSA) are used to normalize the dosage. The normalizing parameter, ƒn, is defined as follows:

fentanyl dosing

The regression curve defining the re-awakening time is as follows [1]:

Re-awakening time

A second-order regression was chosen as this represented the lowest order expression with an adequately high correlation coefficient. A plot of the raw data, the mean time to reach 1 L/min., and the curve of equation (4) (solid line) is provided in Figure 7. The upper and lower curves that bound the mean of Figure 7 also define the 1-s standard deviation about the mean value, or 64 minutes. Coincidentally, these bounding curves also define the 80% confidence region for measurements. The plot of Figure 7 provides and indication of when the patient will re-awaken after surgery. Having an estimate of re-awakening time can provide critical care staff with a marker for the time from arrival until the time at which patients will begin breathing on their own. This knowledge provides critical care staff with both a guide for
managing the patients within their care, and as a measuring device with which to determine whether a patient’s progress is normal.

Figure 7. Model to estimate time for patient to begin breathing 1 L/min of air, in comparison with measurements.

Figure 7. Model to estimate time for patient to begin breathing 1 L/min of air, in comparison with measurements.

Discussion

The model of re-awakening time derived here provides a simple and useful tool for estimating patient re-awakening time. This knowledge is vital for the critical care physician as it facilitates the patient care management process, enabling ICU staff to anticipate when and how patients should be evolving during the normal recovery process. It was generally observed that the larger the fentanyl dosage, the longer the time interval until a patient exhibited spontaneous respiratory function. Why? One simple reason is that larger fentanyl doses result in patients who tend to re-awaken more slowly. Patients given heavier doses tended not to re-awaken for perhaps 3-4 hours, and then their respiratory systems responded more slowly. As a result, these patients required ventilatory support for extended periods of time. However, wide variations existed which made the coupling between fentanyl dosage and re-awakening time less significant. What might cause these wide variations? There are a number of reasons. The most obvious is that patients metabolic rates differ, resulting in some patients being able to metabolize
the drug more quickly. Ultimately, by treating the patient as a system of processes, it can be shown that even as complex as the human body is, simple mathematical relationships can be developed to model the outcome of the effects of anesthetic on human respiratory and cardiovascular behavior, and that these simple models can provide a useful tool for physicians to estimate and monitor the expected respiratory responses of patients recuperating from the effects of surgery.

References

[Zaleski 1996] Zaleski, John R. Modeling Post-Operative Respiratory State in Coronary
Artery Bypass Graft Patients: A Methodology for Weaning Patients from Post-Operative Mechanical Ventilation. Ph.D. Dissertation, The University of Pennsylvania, 1996.
[Rollings 1984] Rollings, Robert C. Facts and Formulas. Nashville, TN: By the author, 1984.

Heart Rate Variability (HRV) Analysis Using the Lomb-Scargle Periodogram—Simulated ECG Analysis — Part 2

01-May-15


 

Medical Device Integration, Informatics & Heart Rate Variability (HRV) Analysis

The purpose of the attached white paper and the analysis is to study the use of signal processing analysis on heart rate variability data collected via medical device integration to assess the value of this method for advanced informatics & analytics. The selection of the LSP was made because the ability of this algorithm to operate on data that contains gaps is quite important when considering physiologic and other real-world data.

The use of the Lomb-Scargle Periodogram (LSP) for the analysis of biological signal rhythms has been well-documented. [1,2]

“The analysis of time-series of biological data often require special statistical procedures to test for the presence or absence of rhythmic components in noisy data, and to determine the period length of rhythms.” [3]

“In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or least-squares spectral analysis, LSSA), which estimates a frequency spectrum based on a least squares fit of sinusoid.” [4]

[1] T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series.” Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.

[2] Jozef Púčik, “Heart Rate Variability Spectrum: Physiologic Aliasing and Nonstationarity Considerations.” Trends in Biomedical Engineering. Bratislava, September 16-18, 2009.

[3] T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series”. Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.

[4] Marc in the box, “Lomb-Scargle periodogram for unevenly sampled time series.” Link: http://www.r-bloggers.com/lomb-scargle-periodogram-for-unevenly-sampled-time-series/. Published January 10th, 2013. Accessed 20-April-2015.

This Paper’s Contribution

This paper focuses on the use of the Lomb-Scargle Periodogram to survey Heart Rate Variabililty (HRV). In a preceding analysis, our focus was on the use of signal processing methods, such as the Lomb-Scargle Periodogram, detect power spectral density versus frequency in time-domain signals, such as heart rate variability. The purpose of that analysis was to illustrate the identification of power spectral density associated with time domain signals using a signal processing method known as the Lomb-Scargle Periodogram (LSP). The LSP is deemed a better method for evaluating power spectral density in time-varying signals where there may be missing or data gaps, or irregular measurements. For this reason, it is deemed superior to the discrete Fourier transform for power spectral analysis related to signals processing involving unevenly sample data, which is frequently the case in biology and medicine.

Download the White Paper Here

A copy of the white paper on HRV analysis using Lomb-Scargle Periodogram can be downloaded here. The following figure depicts the power spectrum produced by the Lomb-Scargle Periodogram when applied to a time-varying signal containing several frequencies. The mathematics of the Lomb-Scarlge Periodogram are such that the method can be applied to time signals with gaps or missing data, thereby improving its utility in real-world settings where such gaps may be expected to occur.

Sample Lomb-Scargle Periodogram associated with a signal

Sample Lomb-Scargle Periodogram associated with a signal

John Zaleski on Medical Device Integration: HIMSS Media Interview on Connected Medical Devices

29-Apr-15


 

HIMSS Media Announces New Book on Medical Device Integration: Connected Medical Devices

John Zaleski is interviewed by HIMSS Media (update: #1 best seller at conference 2015) on his new book on the topic of medical device integration. In this book, titled Connected Medical Devices, he describes best practices for medical device integration. This book is intended for the healthcare enterprise that is beginning the process of integrating medical device data into their electronic health record systems. A link to Connected Medical Devices interview is included here:

John R. Zaleski, Ph.D., CPHIMS–HIMSS15 Interview on Connected Medical Devices

Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems

Update: Book of the month for June 2015!

Within a healthcare enterprise, patient vital signs and other automated measurements are communicated from connected medical devices to end-point systems, such as electronic health records, data warehouses and standalone clinical information systems. Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients.  The book begins with a comprehensive overview of the types of medical devices in use today and the ways in which those devices interact, before examining factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations. Offering lessons learned from his own experiences managing MDI rollouts in both operating room and intensive care unit settings, the author provides practical guidance for healthcare stakeholders charged with leading an MDI rollout. Topics include working with MDI solution providers, assembling an implementation team and transitioning to go-live. Special features in the book include a glossary of acronyms used throughout the book and sample medical device planning and testing tools.

About the Author

John Zaleski, PhD, CPHIMS, brings more than 25 years of experience in researching and ushering to market devices and products to improve healthcare. Dr. Zaleski received his PhD from the University of Pennsylvania, with a dissertation that describes a novel approach for modeling and prediction of post-operative respiratory behavior in post-surgical cardiac patients. He has a particular expertise in designing, developing, and implementing clinical and non-clinical point-of-care applications for hospital enterprises. Dr. Zaleski is the named inventor or co-inventor on seven issued patents related to medical device interoperability. He is the author of numerous peer-reviewed articles on clinical use of medical device data, information technology and medical devices and wrote two seminal books on medical device integration into electronic health records and the use of medical device data for clinical decision making.

Using Radiofrequency Identification (RFID) to promote improved patient identification in telemonitoring

27-Apr-15


 

What does RFID and Positive Patient Identification have to do with Telemonitoring, anway?

Chronic ailments, such as congestive heart failure (CHF), diabetes, chronic obstructive pulmonary order, stroke, arthritis and others usually afflict us as we get older. More often than not, we experience more than one of these ailments simultaneously. To facilitate the monitoring and management of these ailments, various types of telemonitoring are being employed to collect vital signs information such as blood pressure, glucose and weight. But, in addition, telemonitoring is being used as a remote method to communicate visually and audibly with nurses and other practitioners, particularly for those patients who are home-bound and infirm. When monitoring vital signs remotely, methods need to be brought to bear to ensure that data are collected from the correct patient, and this is where positive patient identification methods such as barcode or radiofrequency identification (RFID) can come into play.

The original paper, which I wrote a number of years ago, appeared in Practical Patient Care. Subsequent to that time, I opted to create this white paper to highlight key aspects of the subject matter, and to put forth a thought on the use in the home.

RFID for use in medication administration and positive patient identification

The attached paper is an abridgment of an article that originally appeared in Practical Patient Care a number of years ago on the use of RFID technologies in the application of medication administration. The application of barcode and RFID technologies in the areas of medication administration reporting and safety checking suggest there is an expanding role in the use of these technologies at the point of care, regardless of where that point of care might be. In particular, the use of RFID in 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 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. Patients at home could also employ a similar approach to placing their identification on any vital signs information transmitted from there to a remote telemonitoring facility. This would improve the association and linkage between raw numeric vital signs and related observations to a particular identity.

White paper on telemonitoring and positive patient identification

The attached white paper captures my thoughts on the topic and on other technologies, such as telemonitoring.

Autonomic Heart Controller Device Concept

27-Apr-15


 

What is an Autonomic Heart Rate Controller?

The idea for extending the performance of left-ventricular assist devices (LVADs) occurred to me more than 15 years ago. The idea led me to write a white paper at that time which has been maintained and archived on this web site.

Discussions regarding heart rate variability (HRV) caused me to research a conceptual controller for autonomic, 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. Much has transpired over the years in the regard to medical device integration, control and research into physiologic monitoring. Yet, I have not seen any writing in particular associated or closely related to the topic at hand.

Heart Rate Autonomic Control White Paper

Originally written in August 2002, the attached white paper presents a concept for a mechanism to automatically controller for heart rate pacing 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.

Autonomic control is based on the hormonal action of concentrations of catecholamines within the blood stream. These, in turn, influence the sinoatrial node through uptake. The concept laid out in the attached paper “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.

Why Autonomic Control is Potentially Interesting?

The basic premise of left ventricular assist devices (LVAD) and in heart pacing in general is to provide enough contractile force to move blood throughout the system. Most systems these days rely upon vasodilation, which is related to hormones as well as work. Yet, heart contractility is also related to emotion and release of hormones which are unrelated to direct movement or action of the human body. Hence, the idea was to accommodate a more “realistic” concept that took into account not only the contractile or vasodilation / vasoconstriction aspects of the arteries and veins, but also the hormonal changes measured through catecholamine changes in the blood stream. It is recognized that the attached paper is very raw and, clinically, it is somewhat naive. Yet, the objective was to present the idea as a potential starting point for research and, eventually, a product that could extend the performance of existing LVADs to support more human-like, natural behavior of artificial hearts.

Update on measurement of blood enzymes to support autonomic control:

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 utilizing autonomic control to augment heart rate pacing– 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 autonomic 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.

Medical Device Integration can aid Rapid Response and Early Warning

27-Apr-15

What is rapid response and early warning?

The concept of the Rapid Response Team (RRT) is being introduced in many hospitals throughout the United States and Europe as a means of intervening in patient decline and decompensation prior to the onset of a mortal adverse event. Key measures for the early onset warnings that trigger the deployment of such teams are changes in vital signs that may herald the onset of potentially irreversible cardiac, neurologic, respiratory, deterioration in patients, resulting in the potential for “failure to rescue” in the case of adverse events. Availability of accurate, dense and reliably collected data is key to accomplishing this. Hence, medical device integration, or the ability to extract patient care data measured through medical devices at the point of care, is an enabler and enhancement for RRT early warning score calculation.

Early Warning Scores Calculation

The linked white paper captures some my interests and research into this area together with the calculation of early warning scores from certain vital signs, such as heart rate, blood pressure and temperature. A key aspect of early warning score development in support of rapid response is having the data necessary to process through early warning algorithms in order to create scores or measures of trending decompensation. The early warning score (EWS) and modified early warning score (MEWS) employ measurement of blood pressure and heart rate. These two vital parameters are available through physiologic monitors at the point of care. Data from these physiologic monitors can be extracted either through the network or through serial ports associated with these physiologic monitors.

The two monitors shown below measure various vital signs. The Capnostream 20 monitor measures end tidal carbon dioxide, pulse, pulse oximetry and respiratory rate. The Mindray monitor below that measures many other vital signs, including blood pressure–necessary for the early warning score calculation.

Data from these physiologic monitors may be received through serial ports whereupon the data can be translated into more standardized formats (e.g.: HL7) via hardware and software and then sent to rules engines whereby early warning scores may be calculated. These rules can then be used to notify clinical staff, such as members of a clinical rapid response team (RRT) who can then decide whether this information when taken together with other observations and measurements (such as visual observation of the patient and laboratory data) merit intervention.

Capnostream 20 etCO2 monitor

Capnostream 20 etCO2 monitor

Mindray V12 Physiologic Monitor

Mindray V12 Physiologic Monitor

Predictive Analytics enabled by Medical Device Integration

27-Apr-15


 

How can medical device integration enhance prediction?

Data are the heart of decision making. The old adage “what cannot be measured cannot be controlled” is apt here. Historically, clinically quantifiable benefits of connected medical devices within the healthcare enterprise have been measured in terms of time-in-motion studies and workflow relating to time saving associated with accomplish a specific task or end goal. These are valid measures. Yet, the question remains as to whether there is something more tangible clinically that can be used as a measure of effectiveness related to medical device integration. The analysis of data made available from these sources is temporal in nature (i.e., time-varying and collected over time), is multidimensional (i.e., is a vector and represents the patient cardiovascular and respiratory system evolution over time), and is objective if collected automatically from the bedside.

Are Data Access is Key to Improved Prediction and Predictive Analytics?

The attached white paper captures some of the references and measures for improvement relative to medical device integration. Prediction of patient clinical outcome has been the subject of much research and many papers over the years. The difference between large scale data mining and predictive analytics in this context is that data from medical devices are multidimensional time series. Hence, temporal trends in behavior as measured by patient state changes over time provide the ability to track how a patient is “evolving” with time.

The figure below is from a presentation (“FDA Regulatory Submission Prototype Use Case”) I gave at the 2nd Annual Medical Connectivity Conference (San Diego). The figure depicts multiple sources of medical device data, from physiologic monitors to mechanical ventilators to infusion pumps and laboratory systems. The data from each of these medical devices are brought forward to an integrator, whereupon the data can be combined, processed, analyzed and then the output of which can be individual indices, measures, or time predictions. These outputs are, in ensemble, the objective measures of the patient, time-based, and comparative. The scope of the analysis is limited only by the needs and imagination of the researcher and the clinical end user.

What types of algorithms can be fed using data from these sources?

  • Weaning algorithms (i.e., weaning from post-operative mechanical ventilation)
  • Sepsis algorithms (i.e., modified early warning scores combining vital signs, laboratory and visual observations)
  • Respiratory sufficiency assessments and ventilator acquired events (e.g.: ARDS, COPD, PCA management, Extubation Criteria, VAP, etc.)

Many other methods and analysis can be performed, as well, to provide predictive assessments of the patient while in-situ in the critical care, medical surgical, or operating room.

Medical Device Integration -- multiple sources

Medical Device Integration

Legacy INCOSE Healthcare Interest Group: Are Systems Engineering Processes the Key to Treating the Chronically Ill?

27-Apr-15


 

Legacy INCOSE Healthcare Interest Group & Systems Engineering

Respiratory and Cardiovascular Systems

Respiratory and Cardiovascular Systems

Almost 14 years ago I participated and ran the Healthcare Interest Group of the International Council on Systems Engineering (INCOSE). As a result of the new book on connected medical devices, several individuals have approached me asking whether there might be another book in the works. In the figure above, I include an image from a presentation I gave in 1999 at a Systems Engineering at the University of Pennsylvania. The cardiovascular and respiratory systems are two excellent examples of human or animal subsystems that can be modeled at macroscopic (systems level) of microscopic levels.

In reviewing past material I had written over the years, I came upon the following article originally published in INCOSE INSIGHT in April 2002. While some of the material is dated, the message is still accurate:

“In pondering the relationship between systems engineering and medicine, it seems that seems that an analogy for medical treatment exists that mimics effective management of software systems development projects. This analogy derives from the concept of proactive development methodologies in comparison with proactive or preventive, medicine. The essence of the analogy is … taking steps to plan for variance…as opposed to treating the cause of the problem…once symptoms appear.”

“System knowledge relies on many different tools and models for predictive purposes, including identification of normal or abnormal behaviors, or indicators for such behaviors…”

Why Systems Engineering Processes in Medicine?

In the case of systems engineering, we adhere to a set of processes to identify the key requirements of any project or product, and identify the opportunities to mitigate risk, improve quality and patient care. Adherence to processes such as identification of risk, improving quality and providing feedback to clinical and support staff to improve patient care through continuous feedback of what works (and what does not work) are extremely important since one can control only what one can measure.

Furthermore, the application of mathematical rigor, statistical and process control techniques and statistics are key in determining whether measurable benefits are realized through the application of specific workflows or techniques. Hence, the application of systems engineering processes can lead to better or increased predictability, particularly through the application of modeling and simulation and by viewing the patient as a complex system of systems, involving inputs and outputs.

A PDF of the original INCOSE Insight article is included as a link here.

 

Medical Device Integration, the Lomb-Scargle Periodogram, and Heart Rate Variability (HRV) — Part 1

21-Apr-15


 

Medical Device Integration, HRV and Lomb-Scargle Periodogram — What’s the Connection?

Predicting the future accurately is a capability essential to the basic functioning of our lives. Many fields identify the benefits of forecasting behavior. These include, but are not limited to, weather, financial, sales, and defense. In these cases, the objective is to estimate with as high a degree of confidence as possible the expected outcome such that the estimated result will match the actual result, once the actual result occurs.

The accuracy of the predictions is, of course, dependent upon many factors. Some of these factors include the accuracy of the models used to predict the future events, the amount and fidelity of information these models require to ensure accuracy, and the length of time into the future over which the prediction is estimated to be valid.

Medical device integration provides access to the raw data collected for heart rate variability assessment (that is, raw ECG signals). The variability of these signals is well known in terms of diagnostic inference and, hence, the data provide the source for data analysis and predictive assessment. The HRV analysis of the raw signals in the case of the LSP focuses on determining the periodicity of the heart rate, how it changes over time, and given other observations, can be used in concert to assess weather there is an impending issue.

Signal processing of time-varying signals can produce information and knowledge that are useful in diagnosis and analysis of underlying ailments. Hence, one benefit of medical device integration is providing these time-varying signals at relatively high frequency. One technique for determining the frequency of events in measurements — periodic signal behavior — is the Lomb-Scargle Periodogram.

The LSP is a technique that is in a class of predictive analytic algorithms for detecting signal periodicity and identifying frequency occurrence of events in raw data

Lomb-Scargle Periodogram derived from Data Sourced through Medical Device Integration for HRV Assessment

The use of the Lomb-Scargle Periodogram (LSP) for the analysis of biological signal rhythms has been well-documented in the literature. I include a White Paper as the start of my analysis into Heart Rate Variability (HRV) and its calculation for the purpose of alert notification on change. Heart Rate Variability (HRV) has been used as an assessment of the autonomic nervous system, based on sympathetic and parasympathetic tone (SNS versus PSNS). High HRV is indicative of parasympathetic tone. Low HRV is indicative of sympathetic tone. Low HRV has been associated with coronary heart disease and those who have had heart attacks.

Book on Medical Device Integration: “Connected Medical Devices”… Available Now Through HIMSS Media

08-Apr-15


 

About the Author

John Zaleski, PhD, CPHIMS, brings more than 25 years of experience in researching and ushering to market devices and products to improve healthcare. Dr. Zaleski received his PhD from the University of Pennsylvania, with a dissertation that describes a novel approach for modeling and prediction of post-operative respiratory behavior in post-surgical cardiac patients. He has a particular expertise in designing, developing, and implementing clinical and non-clinical point-of-care applications for hospital enterprises. Dr. Zaleski is the named inventor or co-inventor on seven issued patents related to medical device integration and interoperability. He is the author of numerous peer-reviewed articles on clinical use of medical device data, information technology and medical devices and wrote two seminal books on integrating medical device data into electronic health records and the use of medical device data for clinical decision making.

More about the author available through the author’s LinkedIn site. Click on the image to be taken there.

John R. Zaleski, Ph.D., CPHIMS, Author of Connected Medical Devices -- his 3rd book on medical device integration

John R. Zaleski, Ph.D., CPHIMS // LinkedIn Photo Author of Connected Medical Devices — his 3rd book on medical device integration

Author’s 3rd Book on Medical Device Integration — a Practicum

Within a healthcare enterprise, patient vital signs and other automated measurements are communicated from connected medical devices to end-point systems, such as electronic health records, data warehouses and standalone clinical information systems. Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients.  The book begins with a comprehensive overview of the types of medical devices in use today and the ways in which those devices interact, before examining factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations. Offering lessons learned from his own experiences managing MDI rollouts in both operating room and intensive care unit settings, the author provides practical guidance for healthcare stakeholders charged with leading an MDI rollout. Topics include working with MDI solution providers, assembling an implementation team and transitioning to go-live. Special features in the book include a glossary of acronyms used throughout the book and sample medical device planning and testing tools.

Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems

This text on medical device integration (MDI) focuses on the practical aspects of implementing MDI in the hospital. Book contents are as follows:

Introduction

  • The Mechanics of MDI;
  • Medical Device Driver Software;
  • The MDI Intermediary between the Medical Device and the Health IT system;
  • Major MDI Solution Providers;
  • Vendor Agnostic Representation of MDI Solutions;
  • Some Tips on Selecting an MDI Solution; and,
  • Chapter Summary.

Chapter 1: Medical Device Types and Classes Used in Hospital Departments and How They Communicate

  • Healthcare Enterprise Departments most often in need of MDI;
  • Medical Device Topologies;
  • Surgical Services Environments (Operating Room, OR; Post-Anesthesia Care Unit, PACU);
  • Essential OR Data Elements;
  • Parameter Transmission Intervals – OR;
  • Redundant Parameter Transmission;
  • Intensive Care Unit (ICUs) / Critical Care Units (CCUs);
  • Physiologic Monitors;
  • Mechanical Ventilators;
  • Infusion Systems and Tourniquet Pumps;
  • Specialty Medical Devices;
  • Emergency Departments (EDs);
  • Medical Surgical / Step-Down Units;
  • Chapter Summary.

Chapter 2: MDI Solution Acquisition and Implementation

  • Starting the MDI acquisition process: build or buy
  • Building an MDI solution;
  • Acquiring an MDI solution;
  • The Request for Information (RFI) / Request for Proposal (RFP) Process;
  • Communicating Enterprise Requirements to MDI Solution Providers;
  • Medical Device Driver Development & Timelines;
  • Communicating with the Health IT system;
  • Hospital Facilities and Enterprise Networking Requirements;
  • Building the MDI Implementation Team;
  • Project Management;
  • Staging the MDI Solution Implementation;
  • Assembling the MDI Implementation Team;
  • Estimating Timelines for MDI Implementation Completion;
  • Installation;
  • Testing;
  • Transition to go-live;
  • Chapter Summary.

Chapter 3: Semantic Data Alignment and Time Synchronization of Medical Devices

  • Interoperability Continuum;
  • Semantic Harmonization of Medical Device Data;
  • Temporal Alignment of Medical Device Data;
  • Validating Medical Device Data in the Health IT System Patient Chart;
  • Preparation for Go-Live Checklist; and,
  • Chapter Summary.

Chapter 4: Standards Surrounding Medical Device Integration to Health IT Systems

  • Medical Device Standards Specific to Medical Device Integration;
  • Health Level Seven (HL7) Standards Developing Organization;
  • IEEE 11073 Medical / Personal Health Device;
  • Health Level Seven (HL7) Observation Reporting;
  • Conditioning and Translating Connected Medical Device Data for IT System Consumption;
  • Patient Administration;
  • A Few Words About HL7 Fast Health Interoperable Resources;
  • Integrating the Healthcare Enterprise® (IHE);
  • Other Medical Device Integration-Related Standards; and,
  • Chapter Summary.

Chapter 5: Notification, Alerts & Clinical Uses of Medical Device Data

  • Interface Health and Status Notification and Technical Alerts;
  • Clinical Alerts and Notifications;
  • Aperiodic versus Periodic Data Collection;
  • Clinical Uses of Medical Device Data; and,
  • Chapter Summary.

Chapter 6: Patient Identification and Medical Device Association

  • Methods for Patient Identification;
  • Barcode and RFID;
  • Medical Device Association Workflows;
  • Chapter Summary.

Chapter 7: Regulatory and Security Considerations of MDI

  • Medical Device Data Systems (MDDS);
  • Regulatory Classification and Identification of Risk;
  • Medical Device Security;
  • IEC 80001;
  • Software Development Methodologies and Testing;
  • Chapter Summary.

Appendix A.1: Medical Device Quantity Planning Table

Appendix A.2: Testing Tools

Appendix A.3: HL7 Testing Simulator

Ordering Connected Medical Devices Through HIMSS Media

Click on the image to be taken to the HIMSS Media site to order Connected Medical Devices:

John R. Zaleski's 3rd book on medical device integration. Available through HIMSS Media

Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems

 

Tracking Medical Device Data: Example of an Excel Macro-based Kalman Filter

24-Mar-15
Medical Device Data Tracking: Kalman Filter in Orange; Medical Device Data in Blue.

Medical Device Data Tracking: Kalman Filter in Orange; Medical Device Data in Blue.

Excel Spreadsheet Showing Medical Device Data.

Excel Spreadsheet Showing Medical Device Data.

Detail of spin buttons associated with modifying process and measurement noise. Noise is added to simulate inaccuracies in measurements.

Detail of spin buttons associated with modifying process and measurement noise. Noise is added to simulate inaccuracies in measurements.

Tracking Device Data

Microsoft Excel is an extremely powerful computing tool. I have had a number of people contact me privately asking me to include examples of processing algorithms developed over the years. A key algorithm which I use and of which am fond is the Kalman Filter. In the figures above I provide a sample plot of raw data collected from a glucometer obtained through medical device integration with the tracked values (estimated by the Kalman Filter) overlaid. Below this figure are included a screen snapshot of the columns of data and calculations associated with the filter. Finally, the screen snapshot below this includes the macro code associated with the spinner buttons used to adjust the process and measurement noise of the filter.

MS Excel Kalman Filter Model

The code is saved in a macro-enabled workbook (.xlsm), included below as a download below:

Sample_Glucose Filter

Note that the algorithm included in this analysis is for demonstration purposes only to illustrate the calculation process and to show the flexibility and capabilities of the spreadsheet software. Microsoft Excel is an extremely powerful tool and the Author oftentimes uses it for development prior to writing in a more formalized language (such as Java). The ability to develop Macros in addition to the in-built capabilities of the Excel worksheets make it very capable for fairly complex calculations and algorithms.

A nice tutorial on the Filter can be found here on Bilgin Esme’s Blog.

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