Invented by Casey C. Bennett, Kris Hauser, Team Cognitive Ai Inc
The Team Cognitive Ai Inc invention works as followsThe present invention consists of a method and system for providing decision support to assist in medical treatment decisions. A patient agent software modules processes information about an individual patient. A doctor agent module processes information on a patient’s health status, beliefs about patient treatment, and actual effects of decisions made. A plurality decision-outcomes nodes is created by filtering the information from the patient agent to the doctor agent over time. The nodes then form a patient specific outcome tree. The optimal treatment is determined through the evaluation of the decision-outcomes nodes and a cost-per unit change function. If additional information from the doctor agent or patient agent is available, the steps of filtering, creating and determining are repeated. This allows the system to “reason over time”, continuously updating and learning with new information.
Background for Clinical Decision-making Artificial Intelligence Object Oriented System and Method
Field of Invention
The invention is a decision-making program. The present disclosure is more specific, and relates to artificial intelligence software, computational methods, systems, or devices for clinical medicine decisions-making analysis.
Description of Related Art
The modern healthcare system is characterized by rapidly increasing costs and complexity, a growing number of treatment options, as well as a flood of information that does not reach the front line. This makes it difficult to make the best treatment decisions. The answer to a basic healthcare question, ‘What is wrong with this person’, can be elusive. In the modern age, it is difficult to find clear answers about the best treatment for a particular patient or how to reduce costs and increase efficiency. The growing use of EHRs and the growth of large biomedical datasets are a result of the increasing number of public databases. GenBank and caBig are two of the most important databases to help predict treatment outcomes, reduce side effects, decrease medical errors and costs, and integrate research with practice.
The Online Mendelian Inheritance in Man database lists over 1500 Mendelian diseases whose molecular causes are unknown. Human genetic variation is also a factor in almost all medical conditions. Numerous research groups are aiming to identify genes that are associated with these medical conditions in order improve medical care, as well as better understand gene interactions, pathways, and functions. Patients receive the correct diagnosis and treatment in less than half of cases (at first attempt). The gap between clinical research and the actual practice of care is 13-17 years. This fact suggests that current methods of transferring scientific findings into clinical practice are inadequate. Evidence-based treatments that are derived from this research can be outdated by the time they become widely used and do not always take into account real-world variations, which typically hinder effective implementation. Healthcare costs are on track to exceed 30% of the gross domestic product in 2050 if current growth rates continue. Even in their specialization domain, it is expensive and time-consuming to train a doctor who can understand/memorize the complexity of modern health care. For example, a surgeon’s training takes an average of 10 years, or 10,000 hours.
The present invention involves computational/artificial intelligence (AI) framework addressing these challenges. It provides a simulation to understand and predict the effects of different treatment choices or policies. Simulation modeling can improve decision making and fundamental understanding of healthcare systems and clinical processes, including their elements, interactions and end results. Second, such a framework can be used to create clinical artificial intelligence, which can plan ahead, formulate contingency plans in the event of uncertainty, and adapt to new information as it is received. A simulation AI framework can approximate optimal decisions in complex and unpredictable environments. It may even be able to surpass human decision-making for certain tasks with careful design and formulation. This framework’s success is demonstrated using real patient data taken from an EHR.
Combining AI with clinicians could be the best long-term solution.” Let machines do their jobs and humans do what comes naturally to them. The disclosed systems are designed to maximize both. This technology can be used in many different ways: as enhanced telemedicine systems, automated clinician assistants and next-generation CDSS.
In one embodiment, autonomous AI is embedded in computation devices for patient monitoring and doctor assistance. The information from the patient monitoring device is sent to the doctor-assist devices, which may then influence the doctor by influencing his treatment decisions or beliefs. The AI software analyzes these treatment decisions, and then provides updated results for patient outcome prediction to the doctor. In another embodiment of the invention, these patient monitoring and doctor-assisting computation devices serve as communication devices with web-based AI software which performs analysis. Doctor assisting computation devices can be assisted by databases of information, including electronic health records and personal histories, PACS records or genetic marker records.
In other embodiments, the invention provides a method for providing decision support to assist in medical treatment decisions. A patient agent software modules processes information on a specific patient. The doctor agent software modules processes information regarding a patient’s health status, beliefs about patient treatments and the patent treatment decisions. The method includes filtering the information from the patient agent and into the doctor agent in order to create a plurality decision-outcomes nodes. It also involves creating a patient specific outcome tree using the plurality decision-outcomes nodes. The patient-specific tree allows for the determination of an optimal treatment by evaluating a plurality decision-outcomes nodes using a cost-per-unit change function, and then displaying the optimal treatment. When additional information from the doctor agent or at least one patient agent is available, the steps of filtering, creating and determining are repeated. Cost per unit change includes calculating how much it costs in dollars to get one unit of change (delta), on a specific outcome. The patient agent contains a variety of health status data at multiple times. The doctor agent has a module for receiving rewards/utilities and another module to choose patient treatments to maximize utilities. The nodes of the decision-outcome are updated according a transition model. The method also includes learning, wherein, when additional information becomes available, it is added to a knowledge database used by the patient agent software, the doctor agent, and for determining optimal treatments.
In a second embodiment of the invention the implementation consists of a decision-support system to assist in the decision-making process for medical treatment. The system includes a processor with an associated memory. Program memory is configured to hold instructions that enable the processor to perform certain operations, and storage memory is configured to house data on which the processor performs these operations. Storage memory contains data about a patient. The program memory contains a number of instructions which, when executed by the processor, execute software. The patient agent module of the software is used to process information about a specific patient. The doctor agent module processes information on a patient’s health status, beliefs about at least one treatment effect, or belief in the effects of that treatment. The software filters the information from the doctor agent into the patient agent to create a number of decision-outcomes nodes. It then creates an outcome tree for a specific patient with these nodes. The optimal treatment is determined after evaluating the decision-outcomes nodes using a cost-per unit change function, and then displaying the optimal treatment. After updating the patient specific outcome tree, the optimal treatment is reevaluated if additional information from the patient agent or doctor agent is available. The cost per change unit function calculates the dollar cost to achieve one unit of change in an outcome (delta). The patient agent contains a variety of health status data at multiple times. The doctor agent has a module for receiving rewards/utilities and another module that selects patient treatments to maximize utilities. The transition model is used to update the decision-outcomes nodes. The further learning software module contains a knowledge database where, when new information becomes available, it is added to the knowledge database. This knowledge base is then used by the patient agent, doctor agent, or determining optimal treatment steps.
Another embodiment of the invention is a server that provides decision support to medical treatment decisions. The system consists of a processor, a memory and programmable memory that stores instructions to enable the processor to perform certain operations. The memory includes a storage memory that stores data on which the processor can perform operations. The program memory contains a plurality instructions that, when executed by a processor, enable the processor to perform computer program steps. Receiving evidence-based information about the health status of a patient, doctor beliefs regarding at least one treatment effect and patient treatment decision, filtering the information to create multiple decision-outcomes nodes, and determining the optimal treatment using a scoring function to evaluate the plurality decision-outcomes nodes and outputting the message that includes the optimal treatment.
Such predictive software systems can be used in a number of ways. In one embodiment, these software systems can be tailored to a specific patient so that the attending doctor has additional decision support information for formulating a diagnosis or treatment plan. In a second embodiment, the software system may be centered on a specific type of treatment or health condition. This allows a large population to be monitored while determining general treatment options. In other embodiments, these software systems can be combined with payment systems or reimbursement systems in order to allocate resources. Government or insurance payers. These embodiments can also be implemented by a server that receives and sends information and messages related to the above uses.Click here to view the patent on Google Patents.