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

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.

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

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.

A white paper describing the development of an algorithm written in Visual Basic within MS Excel is contained here: Lomb-Scargle Periodogram

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 mathematics of the Lomb-Scargle 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.

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.