Abstract: The personal pandemic proximity index system and method include a data ingestion pipeline configured to receive location data associated with disease-positive cases, a data processing module configured to clean and process the received data, a personal pandemic proximity module configured to determine a numerical personal pandemic proximity index value associated with an individual having an interaction at an address relative to the location data, and a graphical user interface configured to present, to a patient care team, the numerical personal pandemic proximity index value, which may include a modified workflow to limit the spread of disease.

Abstract: What is disclosed is a computerized system and method that include a data ingestion logic module that automatically receive, cleanse, and process data associated with a plurality of patients to create a plurality of holistic patient records that include identity and demographic metrics associated with each patient, clinical metrics associated with each patient, clinical utilization metrics associated with each patient, social determinants of health metrics associated with each patient at the blockgroup level, and social determinants of health metrics associated with each patient at the census track level.

Abstract: A system comprises a data ingestion logic configured to receive pandemic data associated with susceptible, exposed, infected, recovered, and vaccinated people in a community; a data analysis logic configured to weigh, normalize, calculate, or use artificial intelligence to analyze the received pandemic data; and a dashboard configured to visually present values for a plurality of indicators; wherein the data analysis logic includes a herd immunity module determining values associated with an accurate estimation of herd immunity.

Abstract: The personal pandemic proximity index system and method include a data ingestion pipeline configured to receive location data associated with disease-positive cases, a data processing module configured to clean and process the received data, a personal pandemic proximity module configured to determine a numerical personal pandemic proximity index value associated with an individual having an interaction at an address relative to the location data, and a graphical user interface configured to present, to a patient care team, the numerical personal pandemic proximity index value, which may include a modified workflow to limit the spread of disease.

Abstract: A community vulnerability index dashboard includes a data ingestion logic module configured to automatically receive real-time and non-real-time data from a variety of sources including education agencies, law enforcement, health services agencies, healthcare agencies, medical insurance agencies, housing and transportation agencies, childcare licensing agencies, and non-emergency citywide services. The dashboard includes a data processing module that extracts and process the ingested data, and a data analysis logic module that analyzes the processed data to determine values for indicators and an overall vulnerability index value based on the values of the plurality of indicators to provide insight into the lives of residents living in a community on a block group level. The dashboard includes a data presentation dashboard interface to display an interactive choropleth map of the overall vulnerability index and indicator values on a block group level for the community of interest.

Abstract: A machine learning system for healthcare applications comprises a data ingestion pipeline configured to automatically receive patient data including stored data from an EHR database and real-time data from a plurality of data sources, the data including, EHR records, claims data, and social determinants of health data; a data processing module configured to clean, extract, and process the received patient data; at least one predictive model configured to analyze the cleaned and processed data and determine a risk score for each patient; a configuration file defining the predictive model execution parameters; a tuning module configured to adjust parameters of the predictive model, including variables, thresholds, and coefficients; a retraining module configured to make further adjustments of the predictive model to remove inherent data biases; and a dashboard and reporting module configured to present the risk score to a patient care team.

Abstract: A lightweight clinical pregnancy preterm birth predictive system includes a data store configured to receive and store patient data consisting of only health insurance claim data associated with a plurality of patients, a predictive model including a plurality of weighted risk variables and risk thresholds, a risk logic module configured to identify a pool of pregnant patients and to apply the predictive model to the patient data of the pool of pregnant patients to determine a risk score for each pregnant patient to identify at least one patient who is at risk for preterm birth, and a data presentation module operable to present notification and information to an intervention coordination team about the identified at least one high-risk patient.

Abstract: A patient care plan system includes a repository of patient data including real-time clinical and non-clinical data that include data generated as a result of at least one treatment received by the patient provided by an outside service provider; at least one predictive model configured to analyze clinical and social factors derived from the patient's data to determine a risk score associated with the particular medical condition; a patient care plan module configured to selectively extract data from the patient's data to generate a targeted patient data summary including data that are indicative of quality metrics associated with the at least one treatment received by the patient, and organize and format the extracted data into a patient care plan; and a payment interface module configured to transmit the patient's care plan to a payor in exchange for payment for the at least one treatment received by the patient.