Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

Abstract: The present disclosure is directed to methods and apparatus for centralized de-identification of protected data associated with subjects. In various embodiments, de-identified data may be received (1102) that includes de-identified data set(s) associated with subject(s) that is generated from raw data set(s) associated with the subjects. Each of the raw data set(s) may include identifying feature(s) that are usable to identify the respective subject. At least some of the identifying feature(s) may be absent from or obfuscated in the de-identified data. Labels associated with each of the de-identified data sets may be determined (1104). At least some of the de-identified data sets may be applied (1108) as input across a trained machine learning model to generate respective outputs, which may be compared (1110) to the labels to determine a measure of vulnerability of the de-identified data to re-identification.

Abstract: Implementations set forth herein relate to a peer-to-peer search system for patient medical records for leveraging benefits of identifying similar patient cases in a secured singular network. The peer-to-peer search system can use a distributed ledger to securely correlate similar instances of medical data, located in various other systems, to a globally accessible network that is available via the peer-to-peer search system. For instance, medical data from various sources can be hashed by a hash technique, such as locality-sensitive hashing, and stored in a hash database. When the hash database is queried via the peer-to-peer search system, hash data corresponding to query results can be provided and, optionally, ranked according to similarities between a hashing of input query to a hashing of documents embodying the query results.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: The present disclosure is directed to centralized de-identification of protected data associated with subjects in multiple modalities based on a hierarchal taxonomy of policies and handlers. In various embodiments, data set(s) associated with subject(s) may be received. Each of the data set(s) may contain data points associated with a respective subject. The data points associated with the respective subject may include multiple data types, at least some of which are usable to identify the respective subject. For each respective subject: a classification of each of the data points may be determined in accordance with a hierarchal taxonomy; based on the classifications, respective handlers for the data points may be identified; and each data point of the plurality of data points may be processed using a respective identified handler, thereby de-identifying the plurality of data points associated with the respective subject.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.