Medical Device Plug and Play

Originally posted at, the discussion surrounding plug-and-play medical devices focused on the ability to have true interoperability from a semantic and physical perspective. This post was originally written in 2009 surrounding the need for better medical device plug and play interoperability and integration, in much the same way a USB-enabled accessory purchased for a standard computer is recognized by the drivers once plugged into the computer.

Medical Device Data and Modeling for Clinical Decision Making

Medical Device Data, Modeling & Simulation

This cutting-edge volume is the first book that provides practical guidance on the use of medical device data for bioinformatics modeling purposes. Professionals learn how to develop original methods for communicating with medical devices within healthcare enterprises and assisting with bedside clinical decision making. The book guides in the implementation and use of clinical decision support methods within the context of electronic health records in the hospital environment. Supported with over 100 illustrations, this all-in-one resource discusses key concepts in detail and then presents clear implementation examples to give professionals a complete understanding of how to use this knowledge in the field.

“Medical Device Data and Modeling for Clinical Decision Making” Content Overview:

  • Introduction to Physiological Modeling in Medicine: A Survey of Existing Methods, Approaches and Trends.
  • Simulation and Modeling Techniques.
  • Introduction to Automatic Control Systems Theory and Applications.
  • Physical System Modeling and State Representation.
  • Medical Device Data Measurement, Interoperability, Interfacing and Analysis.
  • Systems Modeling Example Applications.
  • Modeling Benefits, Cautions, and Future Work.


John R. Zaleski Dissertation (1996): Weaning from Postoperative Mechanical Ventilation

Dissertation Title: “Modeling Postoperative Respiratory State in Coronary Artery Bypass Graft Patients: A Method for Weaning Patients from Mechanical Ventilation”

“Physicians, nurses, and other health care workers are facing a problem: provide affordable, quality health care to patients while at the same time satisfy cost constraints imposed on them by insurance companies and government agencies. Cost-cutting measures in many industries, including health care, have resulted in down-sizing “solutions” which achieve their goal of reducing costs by eliminating personnel. This approach, however, can take a physical and psychological toll on those remaining care-providers involved in the daily activity of saving lives. Technology has made an attempt to come to the rescue of these individuals by enabling easy-access to data on patients within their care.”

“Much of this information, though, is in a a raw and unprocessed form, and is generally large in quantity. For the weary health-care provider, the effort involved in viewing and processing this information in real-time can be a deterrent to its use. Few places bombard the health-care provide with more real-time data than the Surgical Intensive Care Unit, or SICU. Patients arrive in the SICU from surgery, their lives dependent on the talents of the critical care staff and the proximity of life-sustaining technologies for survival. While it is important to maintain all of the patient’s physiological functions during the critical 24 hour period following surgery, two of the most vital are heart function and breathing. Whereas heart function is maintained through the careful administration of drugs to reduce the strain of pumping blood through the body, breathing is accomplished directly through the use of a mechanical ventilator which breathes for the patient until spontaneous respiratory function is regained.”

This Ph.D. dissertation documents the research and development of a real-time predictor of patient recovery and viability for weaning from postoperative mechanical ventilation.

Weaning from postoperative mechanical ventilation isa key process in surgical intensive care. According to the SCCM (Source: Society of Critical Care Medicine, 2006), ICU patients occupy only 10% of the inpatient beds, but account for almost 30% of the acute care hospital costs. A key aspect of care in ICUs relates to weaning from postoperative mechanical ventilation.

Employing Medical Device Integration to Help us Age Gracefully and Care for our Health

The aging population, health & wellness

Estimates by the U.S. Census Bureau expect the population of Americans aged 65 and older to increase by more than a factor of two between 2010 and 2050 [1]. At the same time estimates of healthcare expenditure increases between 2007 and 2017 show an increase to nearly 20% of GDP in this period [2]. These estimates were made prior to the recent financial crisis that began during the Fall of 2008. Further compounding this increasing demand and the concomitant increase in costs is the availability of allied healthcare professionals. Some studies [3] identify the likely decrease in the number of physicians entering any number of key specialty areas, including cardiology (20% decrease by 2020), geriatrics (35% of current demand met today), rheumatology (38 day average wait for a new appointment), and primary care (on the verge of collapse). Those of us who are baby boomers are on the leading edge of this demand and, in order to mitigate and minimize the cost impacts on our children, it is our challenge and responsibility to innovate and meet these challenges without passing along unnecessary burdens to our children and grandchildren.

Age-related ailments & managing age-related health issues

For most of us, aging means more frequent and severe afflictions. Taking care of our health by improving diet, exercising, and maintaining an otherwise active lifestyle is essential to ensure a high quality life. Even with increased vigilance chronic ailments can affect us later in life, brought on both by our genetics and consequentially due to the lifestyles we’ve led in our youths. Ailments such as dementia, coronary artery disease, Alzheimer’s, myocardial infarction, congestive heart failure, macular degeneration, osteoporosis, hypertension, chronic obstructive pulmonary disease, diabetes, and others take their toll. Managing chronic diseases is costly from a logistical perspective in terms of time and money. However, even more to the point, effective and quality oversight of patients with chronic ailments requires regular review, screening, and monitoring of patients. This is further complicated by the need to serve patients who lack the means or are physically incapable of leaving their homes for extended periods. Telehealth and remote monitoring are a means by which a case manager—an individual assigned to oversee the care of chronically ill patients within a home-health setting—can review patient information on a regular basis (for example, daily) and support both the patient and the primary care provider. Furthermore, Intensive care units and emergency departments are becoming more crowded. Individuals with insurance are going to EDs because they cannot find satisfaction in terms of prompt scheduling with their gatekeepers (family practitioners). The quantity of individuals with chronic ailments is on the rise (stroke, CHF, diabetes, COPD, etc.) This is in part due to the fact that people are living longer. At the same time the Medicare and SS systems will not be able to sustain the growth in population over age 65. This means that working individuals will increasingly bear the financial burden for us “boomers.” As a result of increased longevity and the fiscal challenges, the retirement age will increase.

Medical device data, and medical device integration to assist in aging and health issues

So, what do we do? Well, several things: first, technology in the form of remote data collection, reporting devices and software will become more prevalent: glucometers, BP cuffs, spirometers and associated software will be more readily available for direct communication with personalized electronic health records. If the purpose of a typical visit is to take BP and diabetic assessments, this can be handled most by collecting data at the point of care (home) and transmitting to the physician’s office for assessment. Such also applies to nursing and assisted living facilities. Next, the technical infrastructure required to transmit and store these data will be required. Paying for this infrastructure could come from a number of sources. One possibility: most everyone nowadays has access to cable television. Cable companies could offer devices that integrate with existing modems to collect and transmit data to the FP, together with complementary emails to next of kin (e.g. “Your mother’s BP as of 8:10 this morning was 145/89”). Other technologies that can be used to evaluate and monitor chronic ailments such as macular degeneration can further reduce costs by providing video cameras at point of care whereby opthalmologists can review retinal changes without requiring an elderly individual to be transported at expense and time to a hospital or office. In addition, support for remote consults via VoIP and video can be supported over the same network. This empowers the remote provider with the ability to interact with the patient All of these technologies are in use in remote pockets around the world today. But, they will become more prevalent. These technology implementations will reduce costs and provide for more personalized care in comfortable settings (homes). Of course, nothing takes the place of the tactile hands-on. But, for routine visits the above will be invaluable. In terms of the software technologies, personalized medicine will become the norm (eventually). Telehealth will be key. But, also, support for automated workflow in the acute care environment will need to be augmented. This means fully integrating all data into the enterprise HIS.

Remote patient monitoring requires medical device integration to facilitate health care management

The U.S. Department of Health and Human Services through its Office of the National Coordinator for Health Information Technology, published operational scenarios focused on providing key information to assist in harmonizing standards on the implementation, certification, and policy implications for robust remote patient monitoring [4]. Included in this assessment are requirements on interacting with personalized health records and enterprise health information systems. The approaches to advancing remote monitoring include both seamless communication from medical devices at the point of care (i.e., in a patient’s home setting) and with a case manager and primary care provider both through electronic transfer, storage, and display of health information and remote video and audio interaction with patients in the same home health setting.

Remote health monitoring to empower the aging population

Technology is not the silver bullet, but those described above are key enablers for remote health monitoring. Of course, the use of technology carries with it the implication that sufficient underlying infrastructure exists. This is not always the case in remote areas of the country. Satellite, cable, and fiber optic technologies are fairly extensive within the continental United States, but pockets and regions exist in which this is not the case. Therefore, a combined effort to extend the communications infrastructure must continue together with a unified effort to standardize and train and “in-service” individual care providers on these technologies must occur. One of the best mechanisms for enabling this is through the local hospitals and their satellite clinics.

So, how long do we have? Well, the sooner the better. Successful telehealth and remote monitoring programs exist throughout the United States and worldwide today. We should ensure that our elected representatives direct healthcare expenditures towards several specific areas to promote growth and alignment to meet the objectives of remote monitoring. These include continuing alignment on electronic personalized health records, expansion of our underlying communications infrastructure, and promoting common standards of communication among these records so that, regardless of location, a patient can communicate his or her information to any physician and allied health professional within the country. In summary: common storage, homogeneous communication, standardized formats.


[1] Source: Population Division, U.S. Census Bureau, August 14th, 2008; Table 12: “Projections of the population by Age and Sex for the United States: 2010 to 2050 (NP2008-T12)”

[2] Cinda Becker, “Slow: Budget Danger Ahead,” Modern Healthcare, March 3rd 2008.

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

“Integrating Device Data” is John R. Zaleski’s First Book on Medical Device Integration into Electronic Medical Record Systems

Automating medical device data collection through medical device integration is a logical extension for the allied health professional; it provides the capability to bring data from medical devices to bear in clinical workflow. However, the need to do so is also consistent with the general direction the healthcare industry is taking towards globalizing the use of electronic medical records (EMRs).

Future generations of vital signs and point-of-care medical devices must interoperate directly and seamlessly with information technology systems to facilitate effective patient care management within the healthcare enterprise.  This is the first book addressing medical device integration with the computer-based patient record in a holistic way.  Readers step into the area of two-way device communication & control and learn best practises from an author known for his brilliant expertise in this field.  It is a fundamental guide for a broad group of people: clinical and biomedical engineers, physicians, bioinformatics practitioners, and vendors.

Providing the essential how-to for medical device integration into the electronic medical record (EMR), health information system (HIS), and computerized patient record (CPR), the book highlights information on data extraction, usually not offered by device vendors. This comprises topics such as the use of third-party software, information on what to do when you develop interfaces on your own, regulatory issues, and how to assure connectivity and access to data.

For physicians, it is a primer and knowledge manual for data integration when applied to clinical care and trials. It gives information on knowledge management and how data can be used statistically and as a tool in patient care management. Furthermore, it impresses upon the reader the quantities of data that must be processed and reduced to make for effective use at the point of care.

Electronic health record developers may learn how medical data integration can be simplified and how software developers may be assisted in the process of communicating vital information to their repositories.

The book is rounded off by a chapter on the future of integration.


  • The Medical Device Integration Landscape
  • Device networking and communication
  • Mechanisms for interfacing and integrating device data
  • Real-time and non-real-time data management
  • Computer-based patient record
  • Correctly associating device data with patients
  • Balancing data quantity with quality: techniques for data analysis and reduction
  • How to display data in a flowsheet
  • Interface software as a medical device
  • The future of medical device integration, Including Device Command & Control
  • Example methods and software


MDDS, Medical Device Integration and Conceptual Interoperability Model

The Linkage: Conceptual Interoperability Model, MDDS and Medical Device Integration

The importance of medical device integration, interoperability and the medical device data system, or MDDS, ruling taken together with electronic health record systems is driving toward the ultimate goal: how to integrate medical devices as part of the system architecture within the healthcare enterprise to support clinical use cases, which improves overall interoperability and communciation. The MDDS ruling has, in my mind, had the effect of codifying the methods and mechanisms that relate to communicating durable medical device data to the electronic medical record. Similarly, this has raised the awareness of integrating such information into the electronic health record system. Yet, this is really only the first step in the process.

The interaction of the many systems that clinicians use as part of the care and management of the patient involve interoperability among many verticals in the healthcare environment, to include medical device connectivity. The interaction of data from durable medical devices with the more information-based and clinically specific systems make up the larger medical device data system architecture.

Interoperability Model

The following figure, referenced from Wikipedia, is one source of the “Interoperability Taxonomy” adapted for presentation during the medical device interoperability presentations.

Levels of Conceptual Interoperability Model (LCIM). Source: Tolk, A. and Muguira, J.A. (2003). The Levels of Conceptual Interoperability Model (LCIM). Proceedings IEEE Fall Simulation Interoperability Workshop, IEEE CS Press
Levels of Conceptual Interoperability Model (LCIM) used to discuss analogies with medical device integration. Source: Tolk, A. and Muguira, J.A. (2003). The Levels of Conceptual Interoperability Model (LCIM). Proceedings IEEE Fall Simulation Interoperability Workshop, IEEE CS Press

The hierarchy presented in the figure above maps well to medical device integration. The first three levels, that is level 0 – level 2, correspond, respectively, to medical devices that are not connected; medical devices that have some form of physical integration; and, medical devices that support the ability to syntactically communicate data with other medical devices as well as with information systems using methods such as HL7 messaging. Many hospital systems today that feature medical device integration are in the Level 0 to Level 2 range.

Higher levels of interoperability, such as semantic interoperability, are works in progress and organizations such as IHE (Integrating the Healthcare Enterprise) are engaged in areas of syntactic, semantic and pragmatic interoperability and play well within the field of medical device integration.

Levels of Conceptual Interoperability, Defined

The definitions offered per the LCIM model are as follows:

  • Level 0: Stand-alone systems have No Interoperability.
  • Level 1: On the level of Technical Interoperability, a communication protocol exists for exchanging data between participating systems. On this level, a communication infrastructure is established allowing systems to exchange bits and bytes, and the underlying networks and protocols are unambiguously defined.
  • Level 2: The Syntactic Interoperability level introduces a common structure to exchange information; i.e., a common data format is applied. On this level, a common protocol to structure the data is used; the format of the information exchange is unambiguously defined. This layer defines structure.
  • Level 3: If a common information exchange reference model is used, the level of Semantic Interoperability is reached. On this level, the meaning of the data is shared; the content of the information exchange requests are unambiguously defined. This layer defines (word) meaning. There is a related but slightly different interpretation of the phrase semantic interoperability, which is closer to what is here termed Conceptual Interoperability, i.e. information in a form whose meaning is independent of the application generating or using it.
  • Level 4: Pragmatic Interoperability is reached when the interoperating systems are aware of the methods and procedures that each system is employing. In other words, the use of the data – or the context of its application – is understood by the participating systems; the context in which the information is exchanged is unambiguously defined. This layer puts the (word) meaning into context.
  • Level 5: As a system operates on data over time, the state of that system will change, and this includes the assumptions and constraints that affect its data interchange. If systems have attained Dynamic Interoperability, they are able to comprehend the state changes that occur in the assumptions and constraints that each is making over time, and they are able to take advantage of those changes. When interested specifically in the effects of operations, this becomes increasingly important; the effect of the information exchange within the participating systems is unambiguously defined.
  • Level 6: Finally, if the conceptual model – i.e. the assumptions and constraints of the meaningful abstraction of reality – are aligned, the highest level of interoperability is reached: Conceptual Interoperability. This requires that conceptual models are documented based on engineering methods enabling their interpretation and evaluation by other engineers. In essence, this requires a “fully specified, but implementation independent model” as requested by Davis and Anderson; this is not simply text describing the conceptual idea.

Medical Device Integration, Interoperablity and MDDS, from HIMSS 2012 Daily Insider

The HIMSS 2012 Daily Insider on Tuesday, February 21st included an article entitled “Agility Through Regulation,” discussing the incentive the final FDA ruling on MDDS has had on enterprise-wide medical device connectivity. This article by Mary Carr discussed the motivation that the MDDS ruling, made by the FDA in February 2011, had on hospitals to begin their migration away from piecemeal, home-grown connectivity solutions and to align on enterprise-wide solutions. The objective in aligning on system-level integration is to foster homogeneous interoperability and connectivity, both at the durable medical device level and informationally through the various healthcare information systems throughout the hospital.

Medical Device Integration & the MDDS ruling by the FDA reads in part:

“…This regulation classifies as class I MDDS only data systems with specific intended uses and functions. Those device data systems that include any uses beyond, or that are for intended uses different from, those identified for an MDDS will remain class III devices. FDA has determined that MDDSs can be regulated as class I devices because general controls provide a reasonable assurance of safety and effectiveness for this device type. In making this determination, FDA has considered that the risks associated with MDDSs are generally from inadequate software quality and incorrect functioning of the device itself. These failures can lead to inaccurate or incomplete data transfer, storage, conversion according to preset specifications, or display of medical device data, resulting in incorrect treatment or diagnosis of the patient. Based on FDA’s knowledge of, and experience with, MDDSs, FDA has determined that general controls will provide a reasonable assurance of safety and effectiveness of MDDSs, such that special controls and premarket approval are not necessary to provide such assurance.

“…Based on the preamble to the proposed rule, and the comments received in response to the proposed rule, FDA is now finalizing the reclassification of medical device data systems from class III to class I. This classification will be codified at 21 CFR 880.6310. To meet the definition of an MDDS under § 880.6310, a data system must be intended for the ‘‘transfer,’’ ‘‘storage,’’ ‘‘electronic conversion * * * in accordance with a preset specification,’’ or ‘‘electronic display’’ of medical device data, ‘‘without controlling or altering the functions or parameters of any connected devices.’’ This classification excludes any data systems with intended uses outside the scope of this rule …

“…an MDDS only communicates medical device data. For purposes of this rule, data that is manually entered into a medical device is not considered medical device data. However, if manually entered data is subsequently transmitted from a medical device as electronic data it will be considered medical device data. A device that then transmits that data or is intended to provide one of the other MDDS functions with regard to that data may be an MDDS. In response to requests for clarification, the use of ‘‘real time, active, or online patient monitoring’’ in the proposed rule has been replaced to indicate that an MDDS is not ‘‘intended to be used in connection with active patient monitoring.’’

Salient points related to the FDA’s position on the integration of durable medical device data:

1) Piecemeal integration (interoperability) leads to silos and organic separation of information. My comment: piecemeal separation is anathema to large enterprises interoperability in which patients need to be transferred around the hospital. This makes for difficulty in sharing of patient information as needed as they roam or are moved from department to department.

2) Silos are often unable to deliver real-time patient data reliably to centralized information systems. My comment: data synchronization to ensure the latest time-aligned data may be absent.

3) Vendor-dependent solutions lead to internal battlegrounds. My comment: this will be a challenge for some time to come. In my opinion, and based on the industry at-large, the right answer is to target enterprise-wide solutions that meet the scalable and flexible needs of the institution and allow for expansion and growth. Eventually, durable medical devices will speak in accordance with common physical and semantic standards, and this problem will go away (see the IHE PCD Wiki).

A personal take on medical device integration & interoperability

In both of my books I have written about the need for ubiquitous medical device data integration. It has taken a long time (decades) to reach the point of some commonality in terms of semantics and messaging since I started out in the field. It will take a while longer to achieve will interoperability of medical devices in the physical connection department. The focus will need to shift on the use of the data for clinical decision making. Some medical devices are beginning to make the shift to network connectivity as their primary physical mode of communication and transmitting HL7 transactions from the machine itself. Many medical devices will need to make this shift. Furthermore, the ability of medical devices to accept commands from external systems is an aspect that needs to happen to support higher-order command and control (C3I) type functions that make use of extended situational awareness around the device and the patient. Again, I believe this is beginning to happen and will continue to happen. It is a matter of time and continued proselytization. The era of data collection from durable medical equipment where I began my healthcare career some 20 years ago has changed some in this department and will change further. I believe we as an industry are on the verge of a breakthrough and great acceleration in this department.

Using Medical Device Data to Predict Future Patient State

Medical Device Data Can Be a Crystal Ball to Predict Patient State

A keen interest of mine over most of my career has been using medical device data collected at the patient bedside through medical device integration middleware to assist in predicting what was going to happen to that patient state over time. By “patient state”, it is meant the patient’s current vitals signs: a representative measure of patient health.

The data that represent patient state represent or reflect the current values of vital signs and observations of the patient at any given time. This is particularly true of technologically-dependent, unconscious patients in critical care units or in surgery. Aside from thinking of data as a “crystal ball” for predicting the future, the concept of using past information to establish an estimate of future patient state–to predict the future patient state–is a very old concept, and individuals in the aerospace industry familiar with the concepts and application of “multisensor” target tracking and prediction (e.g.: Kalman filtering etal.) should be quite familiar with the approach.

Mathematics to Predict Future Patient State Drawn from Ballistic Missile Tracking

In a way, this is rocket science, or an example application of it. To draw an analogy from the aerospace field, the state of a ballistic or powered object in flight is subject to the equations of motion and its powered flight model. Outside observers (or sensors) viewing the ballistic object can assess its immediate future state based on its current trajectory (i.e., where it’s been, what it’s current patient state is). Using the equations of motion comprising external and internal forces, such as gravitational forces, it is possible to determine within some sphere of confidence where the object will be in the immediate future.

This is an example of the application of “rocket science.” Predicting the future state of a human being is not rocket science–it is more difficult! Yet, certain aspects of this analogy can apply and have been applied successfully to diagnosis and treatment of human conditions. Models of various human systems have been created and the expected behavior or response to such models has been observed. To a large degree, this is the basis behind the diagnosis and treatment of illness using drugs and medical devices. Treatment and diagnostic methods have been derived from observations of the effects on human beings. These observations have been developed through controlled studies, clinical trials, and from experimental observation–even by accident (penicillin, anyone??)

Back to Medical Device Data and Patient State Prediction

This brings me to the use of data derived from medical devices, as part of a medical device integration (MDI) implementation within a hospital environment. These data are primarily observations of patient state, almost telemetry-like in context, from physiologic monitoring and other medical devices employed for the maintenance, diagnosis and treatment of patients. Unlike the ballistic missile analogy, telemetry measurements derived from medical devices cannot be treated as standalone or devoid of specific patient context. For instance, pulse measurements within a wide range of variation are potentially meaningless unless combined with non-numeric type information, such as current patient medical condition, past history, whether the patient smokes tobacco or drinks alcohol to excess, and gender. Yet, these contextual pieces of data when combined with numeric data such as pulse, laboratory and other information, can become quite predictive.

What is There To Predict?

This brings us to the concept of developing alerts (sometimes referred to as “smart alerts”) by combining multiple pieces of information together, passing them through a known model, and determining likelihood of a specific outcome:

“A new paradigm in medical care is the constant surveillance of multiple streams of patient information with the foal of early diagnosis of acute and potentially catastrophic illnesses.”[1]

The ability to predict and assess patient state and assess the likelihood of onset based upon past, recurring and contextual information has been termed “syndromic surveillance” in the literature.[2]

The use of such “syndromic surveillance” lends itself to new, more informative types of alerts and alarms that are not merely uni-dimensional in nature. For example, if a patient’s pulse exceeds or drops below a certain value, then notify the rapid response team (RRT) of an event. By combining multiple sources of data into known models of outcome, the likelihood of events occurring can be evaluated. Such approaches are being studied and have been applied to disease conditions related to onset of sepsis, ventilator acquired pneumonia, and other events that have shown to have high morbidity and mortality rates, especially among the very young and very old.[3][4][5]

Medical Device Data Provide A Richer Understanding of Patient State

Medical device data augment the existing electronic medical record system by providing richer, higher-density information that can be updated in seconds and for which certain events are identified rather quickly. For example, changes in cardiopulmonary function which may go undetected in the coarse measurement of hours may reveal critical behaviors over the span of seconds or even minutes. Intensive care patients who are being monitored continuously can fall into this category of patients who are monitored continuously and at relatively high frequency.

Some medical device data measurements need to be combined to provide more telling notification as to the onset of specific conditions. Researchers in several of the referenced articles included at the end of this blog entry have determined that several parameters, when evaluated over time, tend to provide a high level of reliability as to the onset of sepsis hours before the onset actually begins to manifest. Measurements of temperature, heart rate variation, certain laboratory results, and other contextual information have been developed into sophisticated models that, when evaluated together in specific relationship with one another, reveal highly-predictable behaviors and outcomes.

The use of medical device data in support of clinical decision making is still in its infancy. Yet, the possibilities as to use in the clinical setting span far beyond basic clinical charting and post-hoc assessment. As medical device data collection from the bedside becomes more commonplace and the expectations as to the availability of information in real-time grow, new ideas about the use of these data will emerge for patient care management, intervention and prediction. I believe we have just seen the tip of the iceberg.


[1] Herasevich etal., “Connecting the dots: rule-based decision support systems in the modern EMR era.” J Clin Monit Comput DOI 10.1007/s10877-013-9445-6. 28 February 2013.

[2] Ibid.

[3] Escobar, G.J., etal., “Early Detection of Impending Physiologic Deterioration Among Patients Who Are Not in Intensive Care: Development of Predictive Models Using Data From an Automated Electronic Medical Record.” Journal of Hospital Medicine. Vol. 7. No. 5. May/June 2012. pp 388-395.

[4] Sebat, Frank, etal., “A Multidisciplinary Community Hospital Program for Early and Rapid Resuscitation of Shock in Nontrauma Patients.” CHEST / 127 / 5 / May 2005. pp 1729-1743.

[5] Mayaud, Louis, etal., “Dynamic Data During Hypotensive Episode Improves Mortality Predictions Among Patients With Sepsis and Hypotension.” CCM Journal. April 2013. Volume 41. Number 4. pp 954-962.

“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:


  • 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

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.

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.