My recent participation in an NSF Grant review at PENN on Tuesday and, in particular, Dr. C. William Hanson’s overview briefing during that event, brought to mind some things that he and I had worked on back in the early to mid 1990s. During the course of the presentations, which I found to be very fine and informative, it had occurred to me that basic research is still being conducted in areas that remain relatively untouched or unresolved today, some 16 years later. At the tail end of my dissertation it had been requested by my committee (Dr. Hanson in particular) to lay out key areas of future research that could stand upon what we had done and, in particular, focus on the problem of automation.
Extracts from that long-ago written conclusion read as follows:
“Develop… a larger database of spontaneous minute volume data to verify that it is possible to achieve a more accurate interpolation methodology…”
“Develop…a database of respiratory state data that can be easily recalled for real-time diagnosis and analysis by the critical care staff…by providing an easy way to download this information for…processing would permit the storage of each patient’s data, along with information on patient condition, to be used to classify each patient according to surgery.”
“[A]utomate the weaning process…motivated in part by the need to safely reduce costs and instill more uniformity in the respiratory weaning process across a larger patient population. Moreover, one desirable quality of such an automated methodology is that is would not require the input of such information as patient mass, gender, …anesthetic dosages…to operate effectively. The control system responsible for regulating patient respiratory support should use only that information available through the ventilator…Evidence and trends in patient care exist to suggest that automating post-operative weaning in patients who are not problematic is not only possible, but inevitable as hospitals strive to streamline and reduce the costs associated with critical care medicine.”
The conclusion then goes into the specific algorithm for reducing the support automatically. Undoubtedly, another research focus altogether. Nonetheless, this was written in 1996. Evidence-based medicine is a key objective in the application of treatment today. Furthermore, the databases that were referred to in the research do not (yet) exist today. There are clinical information systems, electronic health records (EHRs) and electronic medical records (EMRs), and varying levels of discrete data that comprise them. But, the level of data richness described in that conclusion still does not exist today.
The one key reason I focused so heavily in my career on medical device connectivity has been because of the inability to uniformly collect the information at the bedside so necessary to support these types of research (and, ultimately, to support the care models related to automated management). While there certainly exist safety concerns surrounding the automated aspect of any process in healthcare, the objective at that time was to provide more uniformity around the process of weaning. Furthermore, respiratory weaning was merely an exemplar to serve as an expression of the larger application of predictive methodologies based upon data normally collected at the bedside.
In order to improve situational awareness it is necessary to have access to the latest information. Part of that situational awareness relates to the observations collected from the patient. Observations in the high acuity spaces comprise the visual, audible and sensory based set that every clinician is familiar with. These are further augmented by historical information, patient demographic, and the training that each clinician receives. Integrate, fuse, or otherwise combine these data together and the situational awareness increases greatly. It is this situational awareness that was sought to be demonstrated through the research at that time and remains an important focus of future research.
Much has happened in the span of time since I graduated from PENN. The IHE, IEEE standards, and interoperability in general have advanced greatly. The research I had begun in the 90s that related to automation and data integration provides potential graduate and undergraduate students seeking to advance their knowledge through the pursuit of both Ph.D.s and Masters degrees with a great opportunity to advance the areas of automation, automated feedback control, and modeling far beyond its current state. While much has been done in the field since I was a student–almost 20 years ago–much remains and I believe the field is on the verge of really taking off, given the advances in interoperability that have occurred to date and the energy and focus on integration of data from various disparate sources within the healthcare domain.
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