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.

Medical Device Data and Modeling for Clinical Decision Making

This work combines much of the experience learned in medical device interoperability and clinical informatics I have gained over the course of the past 20+ years.

I have leveraged work from my Ph.D. and experience in product management of critical care. The device connectivity experience and lessons learned are documented in my first book. Text is available at Amazon.

“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 bed-side clinical decision making…”

Citation: Zaleski, JR: Medical Device Data and Modeling for Clinical Decision Making, Artech House, 2010. ISBN: 978-1-60807-094-7

Contributing Author–Dictionary of Computer Science, Engineering, and Technology

Contributing author to the Dictionary of Computer Science, Engineering, and Technology

Zaleski, JR, (contributing Author), Dictionary of Computer Science, Engineering, and Technology, (CRC Press, Phil Laplante, Editor-in-Chief).

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.

Predicting Sepsis with Early Warning Shock Index

Sepsis hospitalizations increased between 2000 & 2008

Between 2000 and 2008, annual hospitalizations for septicimia increased from 326,000 to 727,000, in which 46% of hospitalized septic patients (patients diagnosed with sepsis or septicimia–blood infections) were admitted through the Emergency Department (ED). [Source: Hall MJ, Williams SN, DeFrances CJ, et al. Inpatient Care for Septicemia or Sepsis: A Challenge for Patients and Hospitals. U.S. Department of Health and Human Services – National Center for Health Statistics. 2011; 62:1-7.]

Predicting the future has never been shown to be possible with 100% certainty. It would seem to be a tautology that uncertainty invades every aspect of life. To a large degree, the inability to predict the future is a direct demonstration of the inability to control or, perhaps more correctly, the inability to fully account for all external influencing events that cause deviation from expected behavior. Thus, establishing certainty in causal relationships (that is, cause and effect) can be difficult.

Such is the case in medicine as in other aspects of life. Over the span of the last 150 years a great deal of research into epidemiology has led to great advances in the ability to predict behaviors based on prior causal evidence. The efficacy of drugs is testament to this statement—just look at penicillin to see a simple example of this. Perhaps the most illustrative example of the use of analysis and modeling to predict or assess the correlation between causal events and outcomes is the famous work of Dr. John Snow relative to identifying the root cause of the cholera outbreak in mid-19th century London. This work presented a cause-and-effect relationship surrounding the presence of cholera in a Broad Street well in 1854. Dr. Snow is identified as one of the fathers of epidemiology. In his analysis he identified the occurrence of cholera in Broad Street and the surrounding environs and traced the cause to a contaminated well. Once the pump handle was removed from the well, the epidemic began to subside. One could argue, perhaps, that early warning as to the likelihood of improvement was the removal of the pump handle on the offending well.

In other areas, though, the ability to predict the causal relationships is much less certain. The battle with cancer in its various forms is testament to this in that the complexity of the cause and effect relationship is not so well understood as to claim that any one approach or cause will guarantee an individual will or will not develop cancer in his or her lifetimes. To be clear, there are correlations and associations based upon past history that establish likely relationships assuming large populations (e.g.: smoking is likely to cause lung cancer). Yet, plenty of individual exceptions exist that violate the general population. My father is an example of an individual who smoked first cigarettes and then a pipe for the vast majority of his life and passed away at the age of 90…of old age. On the other hand, my mother, who did not smoke and yet watched her diet her entire life passed away from breast cancer at the age of 54. However, these individual examples, while outliers, do not provide me, as their progeny, with the rationale or an excuse to engage in destructive behavior. In other words, I am not willing to bet on the gene pool superseding the statistics from the larger population.

Other types of early warning measures (Sepsis and Other Syndromes)

In order to reduce uncertainty in predicting causal relationships it is necessary to improve the fidelity of the models with which the relationships are represented. But, the relationships based upon generalities can provide guidance for expected behavior, and this may be enough to do good, particularly when the expected behavior can target specific outcomes that can mean the difference between life and death. Two examples are given below to illustrate this point.

1) Viability for weaning from mechanical ventilation. Yang & Tobin [Yang KL, Tobin MJ: A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. N Engl J Med 1991; 324:1445-1450] put forth a measure based on the ratio of the respiratory rate to the tidal volume in patients undergoing spontaneous breathing trials. The so-called “rapid shallow breathing index”, or RSBI, is defined as:


In their study, Yang & Tobin determined that this ratio was a good discriminator for weaning success and failure. The threshold RSBI = 105 was identified as the point of demarkation relative to successful weaning (RSBI < 105) and unsuccessful weaning (RSBI > 105). This ratio is used operationally today yet with varying levels of success. However, one of its key benefits is its simplicity. In terms of predicting the causal relationships between successful weaning and failed weaning attempts, the RSBI is, perhaps, moderate to good. Yet, its simplicity lends itself to a general rule-of-thumb that clinicians can opt to use (or ignore) operationally that could motivate the seeking of further, more accurate or telling information that will have a higher, more telling influence on clinical predictability (e.g.: end-tidal CO2, for one).

2) Modified Shock Index and mortality in emergency department patients. Ye-cheung Liu et al. showed that the modified shock index (MSI) (Modified shock index and mortality rate of emergency patients; World J Emerg Med, Vol 3, No 2, 2012) is a superior measure to Shock Index (SI) alone for determining hemodynamic stability. Shock Index is a common measure to assess hypovolemic shock. Shock Index is given by:


this is the ratio of the heart rate (pulse) to the systolic component of blood pressure.

Modified Shock Index is given by:


this is the ratio of heart rate (pulse) to mean blood pressure (MBP), which is given by:

MBP = [(BP-DIAS x 2) + BP-SYS] / 3.

In the referenced study, MSI > 1.3 or MSI < 0.7 is associated with an increased probability of ICU admission and death.

Many more examples exist in which predicting causal relationships can be shown. The absolute predictability of the event is not possible to assess with 100% certainty. But, the ability to identify the likelihood of an event on the basis of the causal relationship is possible to show. In posts to follow we will take a look at the possible uses for these causal relationships in operational care management of patients.

Studies of Shock Index as an Early Warning Marker for Sepsis Onset is Promising

A cross-institution 2011 study presented in The Western Journal of Emergency Medicine (Berger et al., “The Shock Index and Early Recognition of Sepsis in the Emergency Department – A Pilot Study”) conducted by researchers at UC Davis, NY Hospital and Harvard University proffered using the Shock Index (SI) as an early warning marker for sepsis in the emergency department (ED). The objective of the study was to:

“…compare the ability of SI, individual vital signs, and the systemic inflammatory response syndrome (SIRS) criteria to predict the primary outcome of hyperlactatemia (serum lactate ≥ 4.0 mmol/L) as a surrogate for disease severity, and the secondary outcome of 28-day mortality.”

Per the studies presented above, a cohort of adult patients suspected of infection were screened for sepsis using data that included vital signs, laboratory data, and initial serum lactate levels.
Test characteristics were computed for two specific outcomes:
1) hyperlactatemia (identified as a marker for morbidity); and,
2) 28 day mortality (see here and here).
Covariates considered in the analysis for sepsis diagnosis included:
a. heart rate (HR) > 90 beats/min
b. mean arterial pressure (MAP) < 65 mmHg
c. respiratory rate (RR) > 20 breaths/min
Shock Index = HR / SYS-BP
Results of the analysis showed the following causal relationships:
of 2524 patients with complete medical record data considered in the analysis, 290 patients presented with hyperlactatemia and 361 died within 28 days.

Threshold measures for Shock Index as an Early Warning Score

 Patients with SI > 0.7 (representing 15.8%) were 3 times more likely to present with hyperlactatemia than those with normal SI (4.9%). Negative predictive value of SI > 0.7 was 95%. This was identical to that of SIRS. Furthermore, an SI > 1.0 was “the most specific predictor of both outcomes.”

Rapid Response and Earlier Warning of Impending Shock Leads To Better Outcomes Outside the ICU

Rapid Response Teams Intervene Using Early Warning Criteria

Over the past several years Rapid Response Teams (RRTs) have been instituted at a growing number of hospitals to identify patients at risk for rapid decline before patients experience catastrophic events, such as septic or cardiogenic shock, which can lead to death. Ordinarily, if a patient is detected who appears to show signs of increased pulse or breathing difficulty, the RRT can be called for a consult.

Rapid Response Teams arose collaboratively and as a result of empirical study. One study, conducted by Frank Sebat, MD, et al., lead author of a paper in the May 2005 edition of Chest, and featured in the July 2005 issue of Today’s Hospitalist (“How a shock team can detect and treat critical illness earlier” by Edward Doyle; Today’s Hospitalist; July, 2005), showed marked improvement through the use of a shock protocol implemented using several protocols. Sebat et al. stated, at that time, that:

“Most physicians think they recognize critical illness early on but many studies have shown that we really don’t recognize critical illness until it’s in the late stages…the idea of early recognition of critical illness is…not emphasized.”

Cardiogenic and septic shock span many departments and patients

The problem of shock and its identification spans intensive care units, step-down wards and medical / surgical wards. Today’s Hospitalist lists what is often published in the literature on the subject of early recognition of events outside of ICU: treatment outside of ICU can be delayed in part because of a general lack of recognition, poor venous access, inadequate fluid resuscitation, and difficulty in finding bed access in the ICU.

Mortality rates for shock and early warning reference criteria

Mortality rates for septic shock can range in the 30%-60% [1]. In the case of cardiogenic shock (CS), mortality rates can be even higher, and is most commonly caused by damage to the left ventricle resulting from lack of oxygenation resulting from myocardial infarction (MI) [2]. Sebat et al. had further observed:

“A team approach to the resuscitation of patients with shock was first described in 1967…This concept reemerged as the medical emergency team, a group of physicians and nurses that can be activated by frontline nonphysician providers to immediately evaluate and treat patients with significant alterations in vital signs or neurologic deterioration.”

It is interesting to consider the specific criteria employed in the screening and confirmation criteria  setup as part of Dr. Sebat’s protocol. Results from his study showed significant improvements in time to identify and confirm diagnosis; reduction in arrival of intensivist; reduction in time to admit to ICU; reduction in time to place a central line catheter or PA catheter; and reduction in time to antibiotic administration between a control group of patients and those within protocol. Furthermore, the mortality rate between the control group and protocol group of patients was 40.7% and 28.2%, respectively.

An interesting article published in the May Edition of Critical Care Medicine (Gaieski et al., “Benchmarking the Incidence and Mortality of Severe Sepsis in the United States.” CCM May 2013. Volume 41. Number 5. DOI: 10.1097/CCM.0b013e31827c09f8) summarized a survey of sepsis reporting based upon ICD-9 codes established for sepsis, severe sepsis, and septic shock established in 2002-2003, and on the variability in reporting and rate of occurrence during the 6-year period from 2004-2009. Between 2002 and 2003, ICD-9 codes for sepsis, severe sepsis, and septic shock (995.91, 995.92, 785.52) were introduced.

Using ICD sepsis codes, the authors surveyed reports associated with these ICD-9 codes. What they found was that the average annual incidence of sepsis (ICD-9 Code 995.91) was 231 cases of sepsis per 100,000 patients; 144 cases of severe sepsis (ICD-9 Code 995.92); and 95 cases of septic shock (ICD-9 Code 785.52) per 100,000 over this 6-year period.

Sepsis was identified as the 11th leading cause of death in US (Reference: CDC, 2009). Severe sepsis, defined as sepsis associated with new organ dysfunction, hypoperfusion or hypotension, was estimated to cost U.S. healthcare system $24.3B in 2007.

Various studies considered:

Angus et al: 750,000 cases (300 / 100,000 population) and in-hospital mortality rate of 28.6% in 1995.

Martin et al.: 256,000 cases in 2000 (81 / 100,000).

Dombrovskiy et al.: 391,000 cases (134 / 100,000) with an in-hospital mortaility rate of 37.7% in 2003.

Wang et al.: 571,000 annual emergency department (ED) cases nationally between 2001 and 2003.

Gaieski et al. estimated that, for all sepsis codes, an annual increase was observed and the rate of this increaase varied by ICD-9 code: 22.3% for sepsis, 25.3% for severe sepsis, 18.2% for septic shock.

Early warning shock criteria for rapid response

Key screening criteria: systolic blood pressure (SBP) < 90 bpm, mean arterial pressure (MAP) < 66 mmHg, with one or more of the following or normotension with three or more of the following: temperature < 36C, respirations > 20 rpm, altered mental status (anxiety, apathy, agitation, lethargy, stupor, coma), cool extremities or skin mottling, oliguria < 30 mL/h, lactic acid > 2 mmol/L or base excess < -5 mmol/L.

Key confirmation criteria: Required one of the following: administration of >4 L fluid in first 24 hours, use of vasoconstrictors, lactic acid > 2 mmol/L, death (due to hemodynamic instability).

In the aforementioned study, approximately 66% of patients were admitted through the emergency department (ED); 24% were admitted from general wards; remaining 10% were admitted from other locations.


[1] (Sandra Christina Pereira Lima Shiramizo et al.,”Decreasing Mortality in Severe Sepsis and Septic Shock Patients by Implementing a Sepsis Bundle in a Hospital Setting”, http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026790; Nov. 3rd 2011)
[2] The Mayo Clinic. http://www.mayoclinic.com/health/cardiogenic-shock/DS01152/DSECTION=causes

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.

High Touch Beats Technology in Rural Health Telemedicine

Rural Health Benefits from Telemedicine Technology

In Stephanie Baum’s article in MEDCITY News (“A rural physician talks about how to improve connections with patients“), she chronicles the work of rural physician Steve North in Spruce Pine, North Carolina. The question about how to provide telemedical services to patients in rural communities who live distant from hospitals is both asked and answered in this article. Dr. North, who leads the Center for Rural Health Innovation describes a school-based telemedicine program covering two counties in North Carolina. The objective is to provide primary care services to students without missing school, and focuses on providing many services offered by general practitioners (e.g.: flu, ear aches, chronic disease management, medication management, checkups, consultations, and telepsychology).

Those Chronically Ill Reap Benefits from Telemedicine & Telemonitoring

When asked about the biggest challenge faced by Dr. North and similar providers when assisting patients with chronic illnesses, the primary answer was support service access and delivery: nutritionists, food shopping and education. When asked about the right combination of high tech and high touch for patient engagement, the answer is you must have high touch first in order to build a relationship with the patient. It only through the relationship that trust is built and the patient is willing to take the next step. Along these lines, when Dr. North was asked for an example of a practice change he made, a key one was changing from sending letters to patients to speaking directly with them in phone calls. Again, another example of high touch.

As we in the healthcare industry & practitioner community continue to innovate and develop new ways of delivering care, it is worthwhile to take a humble lesson and realize that treatment is about the clinician and the patient. High touch — a very low-technology concept — is the most important in beginning the process of healing the patient. Yet, while high touch is important, high technology can facilitate the virtual “laying on of hands”.

Telemedicine Facilitated by Medical Device Integration

Availability of data from chronically ill patients, such as blood pressure, weight, blood glucose, spirometry, and other information, is vital to ensuring the complete patient picture. Collecting data from the home on patients who are chronically ill or are at risk for chronic ailments serves both the patient and practitioner by enabling the practitioner to review (relatively) unbiased and objective information that can be used for intervention. These data serve the patient by providing an objective rendering of patient state. Security of the underlying data is one issue to be overcome, together with positively identifying the patient. Yet, having objective data facilitates high touch through technology.

Early Warning of Impending Events Through Physiologic Surveillance

Can Data Obtained Through Physiologic Surveillance Help Predict Their Onset?

In the October 2006 issue of Resuscitation, Smith etal. [1] published an article on the benefits of early warning associated with the monitoring and physiologic surveillance of patients in the hospital (ICU, principally). From the abstract of that publication:

“Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs…”

“…It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet…”

From another source [2]:

“Physiologic monitoring systems measure [pulse], blood pressure, …other vitals…Data about adverse events in hospitalized patients indicate…a majority of physiologic abnormalities are not detected early enough to prevent the event, even when some…abnormalities are present for hours before…[occurrence].”

Early Warning Physiologic Surveillance Research

Early warning and physiologic surveillance are not new concepts, whether in the ICU or elsewhere. What, perhaps, has evolved over the past 10 years or so since the formal introduction of the electronic medical record (EMR) is that the automated and complete collection of data normally charted within the EMR is necessary to support such early warning protocols (particularly outside of the ICU environment) so long as the data available are part of an integrated delivery system [3]. That is, complete and contextual data are necessary to promote accurate early warning notifications that can be developed from multiple sources of data, inclusive of physiologic, laboratory, and demographic.

Early warning score predictors can include:

a. vital signs

b. laboratory test results

c. severity of illness scores

d. longitudinal chronic illness burden scores

e. transpired length of hospital stay, and

f. care directives

Escobar [3] reported that of 4,036 events from a cohort of 102,422 patients, modified early warning score c-statistics of 0.709 at 95% confidence. In comparison to EMR-based models, which had a c-statistic of 0.845. Best early warning performance was detected amongst those patients with gastrointestinal (GI) diagnoses (0.841) and worst amongst those with congestive heart failure (CHF) (0.683).

While performance was less robust among the modified early warning models compared with EMR-based models, the performance correlation between the two is encouraging. Perhaps a place exists for the use of early warning protocols and methods which can be based less on the availability of sophisticated information and more on the availability of data readily collected from the bedside.

Improved Physiologic Surveillance through Medical Device Integration

Data required for these models can be obtained automatically through the use of medical device integration from bedside physiologic monitors and other devices at the point of care. The benefits of automated data collection are ensuring complete data collection, mitigation of error due to manual transcription, and regular data updates at pre-defined intervals.


[1] Smith GB, etal., “Hospital-wide physiological surveillance–a new approach to the early identification and management of the sick patient.” Resuscitation. 2006 Oct;71(1):19-28. Epub 2006 Aug 30.

[2] Yoder-Wise, Patricia S., Leading and Managing in Nursing: fifth Edition. Elsevier-Mosby. 2014. ISBN: 978-0-323-24183-0. Page 201

[3] Escobar 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. 2012 Society of Hospital Medicine. DOI 10.1002/jhm.1929. Wileyonlinelibrary.com