Invented by Oliver E. Bent, Sally Simone Fobi Nsutezo, Antoine Nzeyimana, Meenal Pore, Katherine Tryon, Aisha Walcott, International Business Machines Corp

The market for multi-agent consensus resolution and replanning is rapidly growing as industries across various sectors recognize the potential benefits of utilizing this technology. Multi-agent systems refer to a group of autonomous agents that work together to achieve a common goal. Consensus resolution and replanning are crucial components of these systems, enabling agents to make collective decisions and adapt their plans in dynamic environments. One of the key drivers behind the increasing demand for multi-agent consensus resolution and replanning is the rise of complex and interconnected systems. Industries such as transportation, logistics, manufacturing, and healthcare are becoming more intricate, requiring efficient coordination and decision-making among multiple agents. Traditional centralized planning approaches often struggle to handle the complexity and uncertainty inherent in these environments. Multi-agent systems offer a decentralized alternative, where agents can communicate, negotiate, and collaborate to reach a consensus on the best course of action. The transportation industry, for example, can greatly benefit from multi-agent consensus resolution and replanning. With the growing popularity of ride-sharing services and autonomous vehicles, there is a need for efficient route planning and coordination among multiple vehicles. Multi-agent systems can enable real-time communication and collaboration between vehicles, allowing them to dynamically adjust their routes based on traffic conditions, passenger requests, and other relevant factors. This not only improves the overall efficiency of transportation networks but also enhances the passenger experience. Similarly, in the manufacturing sector, multi-agent consensus resolution and replanning can optimize production processes. Agents can communicate and negotiate to allocate resources, schedule tasks, and adapt production plans based on changing demand or supply chain disruptions. This enables manufacturers to achieve better resource utilization, reduce downtime, and respond quickly to market fluctuations. The healthcare industry is another area where multi-agent consensus resolution and replanning can have a significant impact. In hospitals or clinics, multiple agents, such as doctors, nurses, and support staff, need to coordinate their activities to provide efficient and timely patient care. Multi-agent systems can facilitate communication and collaboration among these agents, ensuring that resources are allocated effectively, patient wait times are minimized, and critical decisions are made collectively. As the market for multi-agent consensus resolution and replanning expands, there are several challenges that need to be addressed. One of the key challenges is developing efficient algorithms and protocols for agents to reach a consensus in a timely manner. This requires designing negotiation mechanisms, decision-making rules, and coordination strategies that balance individual agent preferences with the overall system objectives. Another challenge is ensuring the scalability and robustness of multi-agent systems. As the number of agents increases, the complexity of communication and coordination also grows. It is essential to develop scalable architectures and protocols that can handle large-scale systems without compromising performance or reliability. Furthermore, privacy and security concerns need to be addressed when implementing multi-agent consensus resolution and replanning. Agents may need to share sensitive information to make informed decisions, but it is crucial to protect this data from unauthorized access or misuse. Despite these challenges, the market for multi-agent consensus resolution and replanning holds immense potential. As industries continue to embrace automation, artificial intelligence, and decentralized decision-making, the demand for efficient and adaptive multi-agent systems will only grow. Companies that can provide innovative solutions in this space will have a competitive advantage in optimizing complex processes, improving resource utilization, and enhancing overall system performance.

The International Business Machines Corp invention works as follows

Systems and Methods are Provided for Collaborative Decision-Making in Medicine.” The systems can use a distributed record keeping and verification system to solicit suggestions from interested healthcare workers for modifications to a initial healthcare regime. The systems can aggregate all suggested modifications, and then use a consensus algorithm in order to determine which modification is most appropriate.

Background for Multi-agent consensus resolution and replanning

In embodiments, the technical area of the invention is systems and methods for collaborative decision making in medicine.

In modern medicine, interactions are primarily between three parties: the patient, the healthcare worker, and the electronic medical record platform. The patient’s goal is to receive a medical service from the health worker or advice on how to address or avoid a current or potential medical issue. The health worker’s goal is to accurately assess the patient’s needs and provide the appropriate service or advice. The platform’s purpose is to give the health worker a patient’s medical history and to keep records of their interaction for future use. “Often, a patient-health worker interaction results in a treatment regimen that is stored and communicated digitally.

Determining a treatment regimen is not the end of the procedure. In some cases, the patient or health care worker will want to get a second opinion. A review of the original prescribed treatment regimen is often desirable from a policy standpoint, as it can result in a better treatment regime, and also spread information among the medical community. In regions with limited resources (e.g. parts of developing countries, etc.), such a review is often desirable. It could result in an improved treatment regime and also serve to spread information within the medical community. This process is hampered by a number of challenges in resource-constrained regions (e.g., parts of the developing world). In resource-constrained areas, for example, it is difficult to obtain a second medical opinion due to the high costs, lack of diversity among local health professionals and inability to communicate with experts from outside. Health workers who have only a basic level of medical training and knowledge (such as those in resource-constrained areas) may need additional support to prescribe and refine treatment regimens. This is because they lack in-depth knowledge about treating diseases and other medical issues. The lack of medical records and the tracking of interventions by health workers may also hinder these goals’ achievement, particularly in resource-constrained areas.

For at least the reasons above, an improved system to obtain input and consensus regarding medical treatment regimens is desirable.

In some aspects, the invention can be described as a system for obtaining and revising medical opinions in a particular health situation. The system manages and records feedback from health professionals. The system crawls existing guidelines or medical journals to extract pertinent information. The system flags medical doctors in the network who are suitable for a second opinion. The choice of doctor is determined by

Patient Preferences: (e.g. Doctors in the patient’s network); Expertise; Experience (e.g. number of years of practice and seniority); and Efficacy (e.g. doctor’s past success rate at achieving goals through recommendations, e.g. hypertension management). The system aggregates second opinions, resolves the responses, and takes an improvement action (e.g. “Proposing a new treatment regime.

In one aspect, there is a system and method for resolving differences among experts regarding a medication regimen. This allows for consensus resolution and replanning the medication regime. The method involves optimizing the doctors’ consensus regarding a medication regimen.

In one embodiment, “a method for optimizing a health regime” comprises: receiving by a first device a medical regime including a set healthcare tokens representing a set healthcare actions for a patient, with each of these In embodiments:

Transmitting the healthcare regime to a plurality of devices via a distributed networking is what causes the healthcare blockchain to update with the healthcare regimen block;

The healthcare regime block is a function at least of the parameters listed above (i.e. the set of tokens for healthcare, the digital signature and the historical block identification);

The modified healthcare regime block is calculated based on at least these parameters (i.e. the at least proposed modified healthcare tokens, the digital signature of an authorized healthcare worker and the preference factor);

further consists of receiving a number of proposed modified health care tokens, where the number of proposed modified health care tokens represents a variety of modified proposed healthcare action pertaining to the Healthcare regime;

further including: receiving a number of proposed modified healthcare tokens; the plurality representing a variety of modified proposed actions for the healthcare system; calculating a number of modified health care regime blocks for the system, one for each of the multiple proposed modified tokens; and updating the healthcare blockchain with the plurality modified health care regime blocks

further including applying a consensus algorithms to select the most effective modified health regime block out of the plurality;

Further including automatically generating a Message containing a Prescription for a Medication, the medication being based on selected most effective modified Healthcare Regime Block, and transmitting that message to an associated user account with the authoring health care worker for their approval, wherein the approval by the originating healthcare worker automatically transmits a prescription to a prescribing system.

further including automatically generating a messaging comprising a health action based on a selected most effective modified healthcare system block and transmitting the messages via a distributed networks to a patient’s account associated with the user of healthcare;

Further including obtaining a consensus token indicative of the optimization of the healthcare action and based on a set of healthcare tokens

wherein the preferred factor includes an experience factor. The experience factor is calculated as a result of at least one parameter: the experience of the healthcare worker authorized, his/her area of expertise, and the success rate of that healthcare worker authorized;

The preference factor is a compliance factor derived from historical aggregated patient compliance data for at least one modified healthcare action proposed;

The healthcare blockchain also comprises a public-key associated with the authoring health worker, which is operative to allow each device within the plurality networked devices in order to verify the authenticity of the block of healthcare regime;

The healthcare blockchain also comprises a public-key associated with the authorized health worker, which is operative for each device within the plurality networked devices in order to verify the authenticity of the modified block of healthcare regime;

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