Medical Research Papers and Conference Talks Supported by Sickbay™
A Novel Real-Time Patient Monitoring System for Detection of Arrhythmias in Congenital Heart Disease
Feb 2019, Jain, P. et al.
Society of Critical Care Medicine
Automated Event Detection to Improve Patient Care and Quality
July/Aug 2018, Fauss, E., & Patel, R.
Biomedical Instrumentation & Technology
Early Experience with Intravenous Sotalol in Children with and without Congenital Heart Disease
July 10, 2018, Valdés, S. et al.
Escalations to Various Cardiorespiratory Supports after Pediatric Rapid Response Events Are Associated with Unique Preceding Physiologic Patterns
June 2018, Bavare, A. et al.
Pediatric Critical Care Medicine
Hospital Integrates Remote, Real-Time Monitoring Data from Isolation Unit
Mar/Apr 2018, Fauss, E.
Biomedical Instrumentation & Technology
Epinephrine Syringe Exchange Events in a Paediatric Cardiovascular ICU: Analysing the Storm
Mar 2018, Achuff, B. et al.
Cardiology in the Young
An Effective Model of Cerebrovascular Pressure Reactivity and Blood Flow Autoregulation
Jan 2018, Acosta, S., Penny, D., Brady, K., & Rusin, C.
Finding Representative Electrocardiogram Beat Morphologies with CUR
Jan 2018, Hendryx, E., Riviére, B. Sorensen, D., & Rusin, C.
Journal of Biomedical Informatics
Cardiovascular Mechanics in the Early Stages of Pulmonary Hypertension: A Computational Study
Dec 2017, Acosta, S., Puelz, C., Riviére, B., Penny, D., Brady, K., & Rusin, C.
Biomechanics and Modeling in Mechanobiology
A Novel Multimodal Computational System Using Near-Infrared Spectroscopy Predicts the Need for ECMO Initiation in Neonates with Congenital Diaphragmatic Hernia
Oct 12, 2017, Cruz, S. et al.
Journal of Pediatric Surgery
Patient Centric Alarm Management for Improving Care Quality
April 2017, Stein, F.
HIMSS17 Annual Conference Education Sessions
A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study
Jan 2017, Vu, E. et al.
Pediatric Critical Care Medicine
Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients with Severe Traumatic Brain Injury
Sep 2016, Myers, R., Lazaridis, C., Jermaine, C., Robertson, C., & Rusin, C.
Critical Care Medicine
Elevated Diastolic Closing Margin is Associated with Intraventricular Hemorrhage in Premature Infants
July 2016, Rhee, C. et al.
The Journal of Pediatrics
Prediction of Imminent, Severe Deterioration of Children with Parallel Circulations Using Real-Time Processing of Physiologic Data
July 2016, Rusin, C. et al.
The Journal of Thoracic and Cardiovascular Surgery
A Novel Multimodal Computational System Using Near-Infrared Spectroscopy to Monitor Cerebral Oxygenation During Assisted Ventilation in CDH Patients
Jan 2016, Cruz, S. et al.
Journal of Pediatric Surgery
Numerical Method of Characteristics for One-Dimensional Blood Flow
Aug 1, 2015, Acosta, S., Puelz, C., Riviére, B., Penny, D., & Rusin, C.
Journal of Computational Physics
An Effective Model of Blood Flow in Capillary Beds
July 2015, Acosta, S., Penny, D., & Rusin, C.
Predicting the Need for Urgent Intubation in a Surgical/Trauma Intensive Care Unit
Nov 2013, Politano, A. et al.
Breath-by-Breath Analysis of Cardiorespiratory Interaction for Quantifying Developmental Maturity in Premature Infants
Mar 2012, Clark, M.
Journal of Applied Physiology
A New Algorithm for Detecting Central Apnea in Neonates
Jan 2012, Lee, H. et al.
Emma Fauss, CEO and Craig Rusin, CTO
The groundwork for Medical Informatics (MIC) was laid when co-founder, Craig Rusin, PhD, was working as a researcher in the cardiology department at the University of Virginia and needed to leverage high-resolution physiological data to identify indicators of disease and predictors of patient conditions. At the time, no tools were available to aggregate, store, and process the volume of data needed to integrate with an analytics program to achieve his goals. Dr. Rusin solved the problem by building his own grid-computing platform, and the precursor for Sickbay™ was born.
Dr. Rusin is now Associate Professor of Medicine in the Department of Pediatric Cardiology at Baylor College of Medicine and Texas Children’s Hospital and Adjunct Professor in the Department of Computational and Applied Mathematics at Rice University. He and his team, as well as other physicians and researchers across the country, now use Sickbay’s™ Research Tools to expedite algorithm development and conduct studies to identify the precursors of disease. Highlights of Sickbay-enabled peer-reviewed research studies appear above.
The next step in MIC’s journey is unlocking this research from journals and transforming it into clinical reality by creating new, real-time software based predictive monitors. We believe that by creating these transformations, in conjunction with other researchers, physicians, and hospitals that we will be able to realize our vision:
The next step in MIC’s journey is unlocking this research from journals and transforming it into clinical reality by creating new, real-time software based predictive monitors. We believe that by creating these transformations, in conjunction with other researchers, physicians, and hospitals that we will be able to realize our vision: Saving Lives Bit by Bit®.
What Our Customers Have To Say
It is important to look retrospectively at what was done to a patient and look for an inciting event to prevent it from happening in the future. For example, when examining ventilator events like desaturations or loss of EtCO2 that leads to cardiac arrest, the physician examines various signals. Although one can obtain this information from the vent, the timestamps do not align across monitors. When correlating signals from different monitors, you can not get them on the same screen. Sickbay™ Patient Hx allows for all signals at exactly the same time.
- Director of Quality Improvement
In the past if I was worried about a few kids, I would have to call the nurse and ask her for information or walk to the unit and see them. With Sickbay™, I have a new way of observing patients in real-time. This saves me time, allows me to augment the care for the patient that is happening on the floor of the unit and ultimately can provide better patient safety and outcomes. It has revolutionized the way I think about patient monitoring.
- CVICU Physician
At the bedside, the order and trending of patient signals are important. For example, if oxygen drops before blood pressure, it indicates a different issue than blood pressure dropping before oxygen. Patient Hx allows for these patterns to become clearer.
- CVICU Director
I can now remotely view ventilator data for patients. This is something that I previously could only do at the bedside. Now, I can call up a patient of interest on my computer in my office, and observe their recovery or deterioration.
- Director of Anesthesiology
The current patient monitors look for simple patterns and offer limited access to the data they produce. Vendors offer some data recording or data analysis capabilities. However, none offer the scale and power of the Sickbay™ platform.
I recently had a single-ventricle patient that showed a jump in heart rate from 90 bpm to 160 bpm, with arrhythmia. Unrecognized, this event is life-threatening. Fortunately, while reviewing these events with Patient Hx, I was able to apply medications to the patient when I arrived that morning and monitor the intervention remotely.
- Fellow, CVICU
God bless our nursing staff for having to document in the EMR. But I live and die by waveforms. And not just a snippet. I need to see the entire waveform and what led to an event to determine an intervention or root cause. Patient Hx allows that. It should be mandated in every hospital in the country. We just shouldn’t be practicing medicine without it.
- Director of PICU
We have to document a number to justify a procedure. Now I don’t have to worry about writing something down, or remember, I just focus on what I’m doing for the patient and then after the flurry of intervention is over, I can go to Patient Hx to see exactly when it happened and document more accurately.
- CVICU Nurse
In a single minute I have to process over 300 data points and sometimes I process it wrong. Machines aren’t going to make the decisions for me, but I need them to help me interpret all of that data faster so I can make the best decision and not lose patients because of lack of data. Sickbay™ helps me do that.
- Chief of Staff
I recently had a patient that was being transferred from the ICU to step down. During transport the patient crashed and came to the OR. The problem was all patient data had been deleted when they were discharged from the monitor. I needed to know what the order of events were. What started first? Was it cardiac, was it respiratory? Thankfully our hospital had Sickbay™ so I was able to go into Patient Hx and in under 2 minutes figure out the root cause.
- Surgeon and Cardiologist
Patient Hx allows me to look at actual patient trends. Everyone has rose colored glasses when documenting for the chart but the patient looks how they look and the trends and the waveform don’t lie.
- Charge Nurse, ICU
Patient Hx lets me look minute my minute and ask “are there warning signs?” I was looking at a patient near death’s door. At 3 o’clock their heart rate went up, and never came down. Why didn’t it come down? At 7pm, there was an event and the patient passed away. What was going on before then? I need to know when a patient is about to die, so I can prepare. What does that look like? Patient Hx can help me find those answers.
Patient Hx validates what is happening at the bedside. Last week I walked into the room and all the monitors had been shut off.. Looking back on Patient Hx I was able to see the exact time when the monitors were pulled off and also able to see that the shape of the waveform was bad but the numbers were ok. I could tell when the pulse ox stopped picking up and what time we were in the room. The numbers and waveforms made it clear the patient had not had a sentinel event. And I had all the evidence to back this up and even send the complete history to the EMR.
- Nurse Manager, PICU
Strip charting was a constant headache at our hospital. The manual printing and scanning process is of course labor intensive but the bigger issue is trying to get issues to bedside staff and providers faster from our tele techs to expedite intervention. And then there is the reimbursement issue. We had a change in our practices and staffing that was preventing the strips from even being scanned which HIM department quickly realized meant thousands in lost revenue. Automating the process for strip charting with Patient Hx is a game changer. Less manual workload, more money for the hospital, and more important than anything is reduced patient risk.
- Telemetry Charge Nurse