Alarm Fatigue? What a Nuisance!

The alarm problem

“Hospital staff are exposed to an average of 350 alarms per bed per day, based on a sample from an intensive care unit at the Johns Hopkins Hospital in Baltimore.”[1]

Survey says

From the same survey, almost 9 in 10 hospitals indicated they would increase their use of patient monitoring, particularly of Capnography and pulse oximetry, if false alarms could be reduced.[2]

“Of those hospitals surveyed that monitor some or all patients with pulse oximetry or Capnography, more than 65 percent have experienced positive results in terms of either a reduction in overall adverse events or in reduction of costs.”[3]

Attenuating alarm data, and alarm fatigue

The problem with attenuating alarm data is achieving the balance between communicating the essential, patient-safety specific information that will provide proper notification to clinical staff while minimizing the excess, spurious and non-emergent events that are not indicative of a threat to patient safety. In the absence of contextual information, the option is usually to err on the side of excess because the risk of missing an emergent alarm or notification carries with it the potential for high cost (e.g.: patient harm or death).

The purpose of this study (downloadable here) is to look at the mathematics and some of the techniques and options available for evaluating real-time data. The objective is to suggest a dialog for further research and investigation into the use of such techniques as appropriate. Clearly, patient safety, regulatory, staff fatigue and other factors must be taken into account in terms of aligning on a best approach or practice (if one can even be identified). These aspects of alarm fatigue are intentionally omitted from the discussion at this point (to be taken up at another time) so that a pure study of the physics of the parameter data and techniques for analyzing can be explored.

References

[1] Ilene MacDonald, “Hospitals rank alarm fatigue as top patient safety concern”, Fierce Healthcare. January 22, 2014.

[2] Wong, Michael; Mabuyi, Anuj; Gonzalez, Beverly; “First National Survey of Patient-Controlled Analgesia Practices.” March-April 2013, A Promise to Amanda Foundation and the Physician-Patient Alliance for Health & Safety.

[3] Ibid.

“Connected Medical Devices”… Available Now Through HIMSS Media

 

“Connected Medical Devices”

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

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

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.

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

Legacy INCOSE Healthcare Interest Group & Systems Engineering

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.

Predictive Analytics enabled by Medical Device Integration

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.

Autonomic Heart Controller Device Concept

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.

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.