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ADVANCING MEDICAL KNOWLEDGE THROUGH DATA RESEARCH
Following “successful” open-heart surgery, Florence A. Rothman died of unrecognized post-operative complications. Her sons, Steven and Michael Rothman, identified this tragic outcome as a problem of the healthcare delivery system. Since that time, they have worked to prevent similar mistakes from happening to others. The Rothmans envisioned a means of detecting medical decline earlier, allowing for greater opportunity to initiate treatment and save patients who might otherwise descend into profound sickness, suffering and death.
With the cooperation of Sarasota Memorial Hospital and the support of the Sarasota Memorial Healthcare Foundation and the Greenfield Foundation, they utilized innovative techniques to analyze data from the electronic medical record (EMR), ultimately developing a universal health acuity score, the Florence A. Rothman Patient Monitoring Index (RI), that tracks each patient’s condition in real time.
This outside-the-box thinking led to new techniques in Data Science, seminal “big data” research, publication in peer-reviewed medical journals, and the enhancement of EMR software to graph every patient’s condition in real time. Currently the Florence A. Rothman Patient Monitoring Index(Ri) is used nationwide by more than 30 major hospitals and academic medical centers. In 2016 two major hospitals reported a 30% reduction in mortality following rigorous use of the Ri by nursing personnel. More recently adapted for use in non-acute care settings, the Rothman Index converts “mountains” of data into real-time information to providers, patients and families throughout the healthcare continuum.
Recognizing that a more readily available and clearer understanding of a patient's EMR would address other clinical issues, Steven Rothman established the Florence A. Rothman Institute (FARI), a not-for-profit 501c3 organization in Sarasota, Florida. Today, FARI conducts and supports medical research focusing on patient data, including analytic tools developed by the Rothmans.
By applying data science to medical conditions, FARI's researchers have published a groundbreaking study that validates the strong correlation of nursing assessments with mortality. Other studies include the value of risk-based versus demographically standardized reference intervals for routine laboratory test results, and improved accuracy in identifying hospitalized patients who are experiencing life-threatening clinical deterioration.
As a pioneer in the rapidly evolving field of "big data" medical research, FARI plans to uncover potentially life-saving information that is now buried in the vast deluge of individual patient EMRs. By efficiently probing these patient records, FARI's researchers expect to find early warning signs of serious diseases and disorders, opening the door for novel treatment strategies.
Current Initiatives FARI:
Our original investigations during the invention of the Rothman Index were based on the associations between various in-hospital patients’ clinical measurements and the risks of mortality one year after discharge. We discovered that there was an increased risk of mortality associated with values of certain laboratory tests that were considered “normal” by the usual reference levels. This led us to further testing and proposal of a new methodology for determining reference levels based on clinical risks rather than population norms as has been the case. Abstracts include “A New Theory for Reference Intervals and Analyte Test Reporting based on Clinical Risks derived from Readily- Available EMR Data” which was recognized as a “Best Abstract in the Informatics Division of the American Association of Clinical Chemistry” and “High” Normal” Potassium Poses Mortality Risk for All Patients”. Four papers are currently in process.
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Develop and Validate a Model to Identify Alzheimer’s Disease and other Dementias using Electronic Medical Record Data: A Feasibility Study
The Alzheimer’s Disease (AD) study is designed to develop and validate an index to identify undiagnosed patients and those at risk for developing AD and other dementias using EMR data collected at Sarasota Memorial Hospital since 1999. Secondary objectives are to gain a deeper understanding of the clinical correlates and disease progression of AD and to identify medicines not prescribed for AD that may provide some protective or disease modifying impact. We anticipate that application of our data science approach in hospitalized patients will reveal unknown clinical and pathophysiological correlates that provide insight into factors potentially involved in susceptibility or resilience to AD and other dementias. Gaining a better understanding of disease correlates may suggest strategies for primary and secondary prevention.
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Proactive Patient ACuity sTewardship (PACT) trial
Primary purpose is to evaluate the (a) difference in clinical outcomes, e.g., 30 day ReAdmissions (30 dReAm) and supportive care consult rates before and after implementation of respective prompts in the Electronic Medical Record (EMR) utilizing Rothman Index (RI) monitoring thresholds for patients undergoing SMH hospitalist care and (b) 30 dReAdm rate in patients who continue to be monitored with RI after discharge to SMH-Nursing Rehabilitation Center. A historical cohort will be retrospectively matched with the prospective sample in PACT trial. Secondary purpose of the study is to demonstrate that findings derived from RI monitoring protocols also can contribute to building a “learning health system” that can leverage EMR data science methods to characterize clinical patterns before positive and adverse clinical events and to identify trends to improve patient care and safety across the acute/post- acute care continuum.
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Electronic medical records contain text composed by hospital employees; this text often describes medical and socio-economic information that appears nowhere else in the electronic medical record. This data has historically been ignored by data analysts, as unstructured text is uniquely challenging to analyze: phrasing differs across authors and misspellings and punctuation errors are frequent.
By using a combination of Word2Vec (developed by Google) and a convolutional neural network on our data, we were able to develop a model that predicts 30-day readmission more precisely than other well-established models.
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Clinicians are encouraged to follow evidence-based guidelines in managing their patients’ conditions, and frequently they must rely on guidelines that have been designed for a single chronic condition. The presence of Multiple Chronic Conditions (MCC) creates many challenges for clinicians, including the need to decide what evidence to use in making clinical decisions and the need to consider patients’ context and personal preferences in relation to clinical decision-making. This study will examine the records of patients with multiple chronic conditions (MCC) to test the feasibility of characterizing their clinical pathophysiological presentation using cross-sectional and longitudinal data to encourage a change from an approach focused on single chronic diseases to an integrated approach that systematically generates practice-based evidence to inform quality improvement, clinical research, “institutional learning” about the population served, and on-going clinical practice guideline development and updates.
Learn MoreFARI has established relationships with other nonprofit entities such as hospitals, medical schools, and research institutions. We collaborate with these institutions to conduct the above research, prepare publications, and apply the new methodologies of data analysis to various other areas in medicine. In summary, FARI is doing world-class medical research that is already having an impact on the way healthcare is delivered. FARI's research and methodologies are making it possible to achieve better outcomes, as well as cast new light on medical issues.
We are Committed To Helping Doctors Help Their Patients
The Florence A. Rothman Story...Tragedy to Triumph.Read More
Steven Rothman, with his brother Dr. M. J. Rothman, are the inventors of Florence A. Rothman Patient Monitoring Index Read More
Dr. Finlay began his career at a private pulmonary and sleep medicine practice in Sarasota. He is a Fellow of the American College of Chest PhysiciansRead More
Robert Smith has over 40 years of medical research experience in academic and federal settings. He has been on the faculty of medicine at Tulane University Read More
Professor Emeritus of Surgery at Emory University Read More
Alan B. Solinger, PhD, consulted as a senior scientist for the Florence A. Rothman Institute in Sarasota, FL. Originally a theoretical astrophysicist Read More
G. Duncan Finlay, M.D., Chairman
Darlene Arbeit
Janis Cohen
Alan Grindal, M.D.
Steven I Rothman
David Siegle, M.D.
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The F.A.R. Institute
1803 Glengary Street, Sarasota, FL 34231
Email: contact@farinstitute.org
© 2017 The F.A.R Institute. All Rights Reserved.