Like a powerful storm, the relational patterns of disease are complex and evolving.
Disease is a complex process. Through advanced relational pattern-based insight, PatientStormTracker is designed to provide a better understanding of abnormal conditions lurking in this complexity, as those conditions form the beginning of a "storm" animated across the screen. PatientStormTracker is designed to identify, track, and display these patterns in relation to the treatment, timing of treatment, and recovery—providing valuable insight for researchers and quality improvement professionals.
How it Works
PatientStormTracker gathers and stores information about a patient's medical state and continuously analyzes for changes. The system identifies time-related dynamic patterns of data related to an adverse condition, determines the severity of pattern changes for multiple physiologic systems (inflammatory, hemodynamic, respiratory, metabolic, and renal), and then displays this information as a storm spreading across visual depictions of the physiologic systems, creating a metaphorical color mapped visualization of a dynamic progression, expansion and relational severity of the pattern changes as they spread across physiologic systems.
Relational pattern analysis of integrated treatment-lab-vitals & fluid balance patterns.
Facilitates retrospective analysis of the dynamic relational patterns.
Provides transcultural visualizations, applicable for worldwide use.
Computational transparency allows medical professionals and patients to see the process creating the depicted “storm”.
Empowers stakeholders by allowing them to see actual clinical conditions in a readily accessible motion-image format.
Enhances retrospective review of quality of care by identifying the relationships of treatments to the actual timing of onset and progression of the clinical conditions.
Facilitates new medical research, allowing the study of dynamic patterns as phenotypes of a complex clinical condition.
Acts as a tool to simplify and improve the medical coding and documentation process.
Case Study 1: Sepsis Hides in the Numbers
This Case Study shows PatientStormTracker as a powerful tool for early detection, by empowering patient’s families at the bedside, prompting early, life-saving response.
Numbers on a Page A boy is taken to the ER, following a visit with his pediatrician. The boy is with his parents. He is vomiting, with a high fever. He complains of pain his leg. At the hospital, the doctors order laboratory testing and review his chart. The numbers on the page show that the boy’s white blood cell count is “high,” but only slightly above the threshold. The doctors conclude (and explain to his parents) that the boy is suffering from an upset stomach and dehydration—likely the flu. The boy is given fluids, takes pain medication, and is promptly sent home.
In the hours and days to follow, the parents notice that the boy’s condition is worsening. They try everything in their power to make him better. The boy’s parents take him to the ER again, and this time he rushed to the ICU to treat a now-irreversible bacterial infection that likely entered his bloodstream through a very small scrape on his arm. There is nothing that the doctors can do at this point, and the boy dies of septic shock—only three days after his first ER admission.
Sepsis on the Screen This is what the boy’s parents and healthcare providers would have seen, using PatientStormTracker, based on the same initial numbers in the doctor’s charts. PatientStormTracker takes the clinical data—the numbers on the page—and analyzes the dynamic, relational patterns of abnormal conditions. This screen-shot of PatientStormTracker shows a septic condition developing in the boy, like a burgeoning storm. The “storm” would have been unmistakable to the boy’s parents at his bedside—empowering them to seek answers and further treatment for their son. This is how early identification can save a life.
Because physicians and families can’t readily “see” the complex condition lurking in the numbers—and oversimplified “sepsis scores” result in alarm fatigue—this tragedy is very common. It has to stop now.
Case Study 2: Tracking and Treating the Ebola Sepsis-like Syndrome
This case study, also published Dr. Lawrence Lynn in Patient Safety in Surgery 2015, 9:1, shows PatientStormTracker as a tool for expert monitoring and advanced research, in the context of the recent Ebola crisis.
Example 1 Screen capture of Zaire Ebola virus infection of a time-lapsed video which was reanimated from data. The left lower panel shows a time map of perturbations of laboratory and vitals datasets divided into systems (the inflammatory system is on the top). The left upper panel is a weather map visualization of the time matrix distortion showing the image at the point of the blue line on the time map. The right panel shows the time series of Ebola viral load and antibody response. An apparent Ebola sepsis like syndrome (ESLS) emerges on day 14 with a precipitous onset (see rapid distortion of the inflammation system on the time map on day 14). While the pattern looks similar to ESLS, it is likely that this distortion was primarily caused by enteric bacterial sepsis.
Example 2 This animation shows a time-lapse of distortions on the time-matrix from day 11 to day 14 of a 30-day case. Patient makes a full recovery.
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