Systems Engineering in Critical Care Medicine: An Evolving Field

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.

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

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.

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

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.

Read and Write Color Bitmaps Using Visual Basic inside of Microsoft Excel

Developers working with graphics frequently use applications that can read and transform image files. Whether they are transforming bitmaps into other file formats, or just reading the images in order to create new images from the pixel data, developers in this field regularly work with routines that access the base pixel data. Software methods written in C, C++ and Java are often used to read and write these files. The white paper attached below demonstrates the reading and writing of bitmap files using  Microsoft Excel using Visual Basic. A benefit of this approach is the Visual Basic interpreter inside the Macro editor is used, making the method extremely powerful and simple to code and use — no formal compilers or knowledge of C, C++ or Java is required to implement this set of Visual Basic methods.

The  simple Visual Basic method to read and write bitmaps runs in an Excel spreadsheet, which reads a .bmp file. This method opens, reads, and extracts the pixels of a 24-bit color bitmap, in accordance with a user-specified color. This method then writes the extracted pixels to a new bitmap containing only the selected color specified by the user. This straightforward method requires no compiler.

The original Microsoft Visual Basic bitmap reader/writer method was written in 2002: Read and Write Bitmaps Using Excel.

MD+DI Article: Medical Dvice Integration (MDI): A World of Possibilities

A new article published in Medical Device and Diagnostic Industry (MD+DI) discusses how middleware used for medical device integration (MDI) as part of the Medical Device Data System (MDDS) FDA clearance can migrate medical devices from electronic health record charting to active patient monitoring, for the purpose of intervention and guidance:

Minimally, MDDS middleware needs to be able to retrieve episodic data from a medical device and translate it to a standard format.”