Method, system, and predictive computing platform for generating a reduced patient-specific data subset of drug-related adverse reaction alerts
Inventors
Assignees
Publication Number
US-10872079-B1
Publication Date
2020-12-22
Expiration Date
2032-03-08
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Abstract
A platform accessible by a user from a web browser/HMO's electronic medical record (EMR) for providing the user with information regarding a patient's drug regimen as well as generating alerts concerning potential adverse effects to a patient from taking a cluster including a plurality of pharmaceutical preparations and various food supplementals/herbals may be in data communication with and configured to obtain information from at least two databases and at least one tool for processing the cluster of pharmaceutical preparations in accordance with the information to generate the alerts to the user.
Core Innovation
The invention provides a platform accessible via a web browser or integrated within an electronic medical record (EMR) system to offer users information about a patient's drug regimen, generating alerts on potential adverse effects from the combined use of multiple pharmaceutical preparations, including pharmaceuticals, food supplements, and herbals. This platform obtains and processes information from at least two databases and at least one analytical tool to assess drug clusters and produce alerts concerning drug-drug interactions, genetic effects on drug efficacy, and health care burden predictions.
The problem addressed arises from the complexity of poly-pharmacy, particularly in aging patients managing multiple chronic conditions with multiple drugs, including medications treating side effects of other drugs. Existing databases and tools provide empirical data on drug interactions and metabolism but do not efficiently integrate or present this data in a manner that facilitates quick, informed decision-making by physicians working under time constraints. There is a need for a system that effectively consolidates patient-specific data, pharmacological, genetic, and clinical information, and predicts overall health outcomes and costs related to drug regimens, optimizing prescriptions to reduce adverse events and health care expenditures while improving patient well-being.
The invention's platform, exemplified as the DDI+ Platform, integrates databases such as GENELEX™ for genetic and serum-level deviation data and FIRST DATABANK™ for clinical drug-drug interaction data, within a coherent system that processes these data sources. It features tools including a Shared Adverse Side Effect Predictor to identify common side effects from multiple drugs, a Health Care Burden Estimator for forecasting health care costs linked to drug interactions, an Alternative Drug Suggestion Mechanism for proposing safer alternatives, and a Rules & Alerts Engine configurable by users to generate tailored alerts. The entire process is presented via an intuitive graphical user interface accessible through various devices, aiming to enable physicians to efficiently optimize drug regimens with comprehensive, consolidated, patient-specific information.
Claims Coverage
The patent includes two independent claims, a method claim and a system claim, both addressing the transformation of drug-related adverse reaction alert datasets into patient-specific reduced subsets utilizing integrated patient data via a decision algorithm.
Real-time integration of heterogeneous patient data through multiple APIs
Utilizing a plurality of Application Programming Interfaces (APIs) to obtain, in real time over a computer network, diverse patient-related data from multiple disparate data sources in distinct formats, integrating these datasets into a unified patient-related dataset.
Decision algorithm for patient-specific reduction of drug-related adverse reaction alerts
Applying a decision algorithm that, for each drug-related adverse reaction alert, determines the patient's elevated risk based on multiple factors including current and candidate drug intake data, patient-specific parameters, reaction data, and pre-determined severity levels of drug-drug, drug-patient, drug-food interactions, thereby including only alerts where elevated risk exists and removing non-elevated risk alerts to produce a reduced patient-specific alert subset.
Output of reduced patient-specific alert subset via graphical user interface
Causing the output, in real time over the computer network, of the reduced patient-specific subset of drug-related adverse reaction alerts to a graphical user interface on a computing device screen for clinician use.
Use of diverse genetic, pharmaceutical, nutraceutical, and supplement data sources
The plurality of disparate data sources includes at least two selected from genetic data sources (including genetic test data), electronic medical records, pharmaceutical drug databases (both prescription and over-the-counter), nutraceuticals, herbal remedies, and dietary supplements databases to inform decision-making.
The independent claims collectively cover a system and method employing real-time multisource data integration and a decision algorithm to generate reduced, patient-specific drug adverse reaction alerts, output via an intuitive interface, using diverse data including genetic and pharmaceutical databases to enhance alert relevance and clinical utility.
Stated Advantages
The system enables faster and more effective access to relevant clinical and pharmacogenetic data, facilitating physicians' quick comprehension and decision-making within short consultation times.
It predicts and alerts on potential adverse drug interactions and deviations from expected serum drug levels, improving patient safety and treatment efficacy.
The Health Care Burden Estimator informs users about overall healthcare costs associated with drug regimens, enabling cost savings and better resource utilization by minimizing hospital visits, imaging, and other healthcare events.
The platform suggests safe alternative drugs systematically, reducing alerts and improving drug regimen safety while saving physicians time.
User-configurable alert rules and a learning engine allow personalization to clinical judgment, reducing irrelevant alerts and improving usability.
Integration with existing EMRs and access through multiple devices ensure broad usability and convenience.
Documented Applications
Optimizing drug prescriptions for patients receiving multiple drugs to minimize adverse side effects, toxicity, and inefficacy.
Predicting and reducing healthcare expenditures associated with adverse drug interactions including hospital admissions, emergency visits, and diagnostic procedures.
Providing physicians with alert notifications integrated within electronic medical records to improve prescription safety.
Suggesting alternative pharmaceutical treatments based on patient-specific genetic and clinical data to improve treatment outcomes.
Supporting healthcare organizations and providers to manage poly-pharmacy treatments efficiently and cost-effectively.
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