New Book! Coming out late 2014 / early 2015

Implementing Medical Device Connectivity with Electronic Health Records and Data Warehouses: A Practicum for Hospital Enterprise Clinical and Information Technology Implementation Teams. ... Read More

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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). ... Read More

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Integrating Device Data into the Electronic Medical Record

The seminal book on the interoperability of medical device data and health information technology systems. John Zaleski, Ph.D. A book on empirical practice of medical device interoperability, based on years of experience in the field. A Developer’s Guide to Des... Read More

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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  PhD and experience in product management of critical care. The devic... Read More

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Weaning from Postoperative Mechanical Ventilation

Modeling Post-Operative Respiratory State in Coronary Artery Bypass Graft Patients: A Method for Weaning Patients from Mechanical Ventilation. This PhD research developed a model for real-time assessment of patient postoperative recovery and viability for weaning f... Read More

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Early identification of impending events through physiologic surveillance

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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 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.

[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

 

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High Touch Beats High Tech in Rural Health

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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).

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-tech concept — is the most important in beginning the process of healing the patient.

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Using Medical Device Data to Predict Future Patient State

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A keen interest of mine over most of my career has been using data collected at the patient bedside to assist in predicting what was going to happen to that patient over time. 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 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.

To draw an analogy from that field (i.e., aerospace), 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 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??)

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.

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 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 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 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. 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.

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