Abstract: Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

Abstract: Methods, systems, and computer-readable media are disclosed herein for generating a disease-agnostic data model that can be used to predict the onset of multiple chronic diseases in individual patients. In an aspect, the data model is made by autonomously selecting features from longitudinal medical records of patients having chronic diseases that will become predictors for the onset of a chronic disease. The features are vectorized around a disease onset date and processed through a recurrent neural network to produce the data model. Then, the data model may leveraged to predict, for new longitudinal medical records that are input, a future time period when another patient is predicted to experience the onset of the chronic disease. The same data model may utilized to make predictions for multiple chronic diseases.

Abstract: Methods, systems, and computer-readable media for creating a model that enables a health system with a population of patients to better understand its patient population by being able to track which patients are being well-managed and which patients may be in need of some type of clinical intervention are provided. The methods, systems, and computer-readable media create a model that enables a health system to assess its patient population in terms of wellness, complexity, and impactability. Wellness provides a measure of resource consumption of patients within the population, complexity measures the long-term complexity of patients within the population, and impactability provides an indication of how in-need a patient is of some type of clinical intervention.

Abstract: The present invention relates to methods and systems for utilizing hierarchical condition category suspected condition models to flag diagnoses for patients which have a high likelihood of requiring documentation in a current year based on previously recorded clinical information. A suspected HCC stratification for a patient may be generated and supporting and competing facts for the HCC stratification may be provided to drive further action for a patient.

Abstract: Methods, systems, and computer-storage media are provided for determining a patient's risk for readmission to an acute care facility after being discharged to an inpatient rehabilitation facility. For instance, upon admission to the inpatient rehabilitation facility, a sampling protocol is initiated where a computer store is accessed on a predetermined schedule to sample a pre-selected set of healthcare data elements for the patient. Logistic regression is executed on the pre-selected set to generate a readmission risk score for the patient. The patient's electronic health record is modified to reflect the generated readmission risk score.

Abstract: The present invention relates to methods, systems, and computer-readable media for tracking prescription refills and determining medication adherence patterns for a patient. Information about a patient's prescriptions is gathered and analyzed to determine patterns of medication adherence for the patient. These patterns may be used to identify whether the patient is at risk for non-adherence and to identify potential barriers to adherence for the patient. The system may also generate recommended clinical interventions to address instances of non-adherence.

Abstract: Methods, systems, and computer-readable media for creating a model that enables a health system with a population of patients to better understand its patient population by being able to track which patients are being well-managed and which patients may be in need of some type of clinical intervention are provided. The methods, systems, and computer-readable media create a model that enables a health system to assess its patient population in terms of wellness, complexity, and impactability. Wellness provides a measure of resource consumption of patients within the population, complexity measures the long-term complexity of patients within the population, and impactability provides an indication of how in-need a patient is of some type of clinical intervention.