Proactive Patient Acuity:

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.

Predicting Hospital Readmission using Machine Learning:

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.

Multiple Chronic Conditions Study:

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.

Future Research

FARI 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.