Shared decision-making (SDM) is a treatment decision-making model proposed for humanitarian considerations and the needs of medical ethics (Cathy et al., 1997; Drake and Deegan, 2009; Stiggelbout et al., 2015), where at least one doctor and one patient participate in the process of making a treatment plan, which is based on information sharing. The resulting treatment plan considers the wishes of both parties. Unlike traditional decision-making models, such as the paternalistic model, the informed decision-making model, and the professional-as-agent model, SDM does not give decision-making power to either party but fairly combines the treatment preferences of doctors and patients (Cathy et al., 1997). To some extent, SDM improves the compliance and satisfaction of patients in the treatment process, which increases the effect of treatment (Pieterse et al., 2019; Fiorillo et al., 2020).

The concept of SDM was first proposed by Veatch in 1972 (Veatch, 1972). In 1997, Cathy (Cathy et al., 1997) further clarified its definition and characteristics. After more than 40 years of development, SDM has gradually formed a complete system in the West (Coulter et al., 2011, 2022), including a complete theoretical system (Makoul and Clayman, 2006), patient decision-making aids (Thomson et al., 2007; Elwyn et al., 2013), SDM evaluation tools applied to different scenarios (O'Connor, 1995; Simon et al., 2006; Scholl et al., 2012), and relevant legal and policy support. However, at present, SDM is still a new concept in many countries and regions (Huang et al., 2015), and the clinical practice of SDM is not as effective as expected. Many factors influencing the effectiveness of SDM have been explored. According to Bomhof-Roordink et al.'s (2019) study, which analyzed SDM models presented before September 2019, the exchange of information on treatment plans is key to SDM and also forms the basis for patient participation. Many studies have come to similar conclusions and noted that the medical literacy of the patient has a significant impact on the exchange of information (Shen et al., 2019; Loftus et al., 2020; Alsulamy et al., 2021). Doctors also play a crucial role in the exchange of information. Song and Wu (Song and Wu, 2022) suggest that doctors have a responsibility to elicit preferences from patients, which requires good communication skills. However, a factor that should not be ignored is the need for long-term doctor–patient communication (Beach and Sugarman, 2019; Caverly and Hayward, 2020). However, it is difficult for busy clinicians to find enough time to implement SDM during consultations. Therefore, the current obstacles in SDM practice can be placed into two categories: (1) doctors are under heavy time pressure, which leads to insufficient time for communication between doctors and patients, and (2) subjective factors of both doctors and patients significantly influence decision-making (Covvey et al., 2019; Shinkunas et al., 2020).

To solve the above problems, this study suggests integrating artificial intelligence (AI) into SDM to reduce unnecessary subjectivity in decision-making and the high time cost associated with manual negotiation. This method has been applied in the research of clinical decision support systems (CDSSs) (Osheroff et al., 2004; Magrabi et al., 2019; Yang et al., 2019), which are software that matches the patient's characteristics with existing medical knowledge so as to provide doctors with evaluation suggestions for patients. Such medical knowledge can be obtained from a computerized medical knowledge base or from historical diagnosis data mining using AI. Many studies (Bright et al., 2012; Sutton et al., 2020) have proven that the application of a CDSS can help doctors improve the efficiency of diagnosis and reduce medication errors, thereby reducing department costs and improving service quality. However, in the process of giving advice, a CDSS considers the patient's physiological characteristics instead of the patient's personal preferences. Loftus et al. (2020) suggest that most CDSS methods are black-box models and are in conflict with the concept of patient-centered care. Because patient preferences are not considered in the recommendations, it is possible that the predicted outcomes may differ significantly from the patient's preferences. Other studies (Deegan, 2010; Almario et al., 2018) have proposed computerized decision aids to narrow the knowledge gap between doctors and patients, which would help patients make more informed choices according to their preferences, but the abovementioned time pressure remains unresolved.

To better focus on doctor and patient preferences and reduce time pressures, this study constructed an intelligent negotiation framework to support decision-making based on the SDM model (Elwyn et al., 2012). First, in the model, the doctor informs the patient that reasonable options are available. Second, the doctor lists the options and clearly describes their potential harm and benefits. Finally, the doctor elicits an expression of preference from the patient and judges whether to make a decision or postpone it. Obviously, decision-related choices are directly related to patient preferences. Doctors give professional advice to support patients in decision-making, which reflects a preference derived from medical expertise and diagnostic experience. Therefore, such choices can be considered a problem of negotiation. In order to represent these two individual preferences, an “agent” is used to represent doctors and patients in negotiations, which is the key to automating the negotiation framework.

An agent refers to a computer system in a complex and changeable environment (Wooldridge and Jennings, 1995); it has autonomy and social ability and is able to respond to the environment. An agent is able to express knowledge, belief, intention, and goal-oriented behavior. In other words, it always attempts to retain maximum benefits for participants with the goal of promoting the success of negotiation. Agent-based auto-negotiation helps participants come to an agreement that can bring them as much benefit as possible with reduced time costs (Lomuscio et al., 2003). However, one important factor in a successful negotiation is that the agent adapts its own strategy to the available opponent's information. Participants in SDM have difficulty fully disclosing their preferences, which may be due to failure in building trust between the doctor and patient or the doctor's inadequate conversation skills to make the patient comfortable enough to express a preference. Hence, adding a component of opponent preference predictions to the negotiation framework is necessary.

Agent-based auto-negotiation has been applied in fields such as electronic trade, power trading, resource distribution, and supply-chain planning in recent years. Many negotiation models have been proposed for different domains, mainly focusing on offer evaluation, concession strategies, and opponent models.

Offer evaluations are quite different between linear and non-linear negotiation domains. For continuous and linear negotiation domains, a suitable linear function is usually designed to evaluate the offer, as seen in the study by Amini et al. (2020). However, dealing with the non-linear and discrete negotiation domain is more complicated. Yang and Luo (2019) proposed a method to evaluate offers by ranking demand. Mansour et al. (2022) presented a hybrid negotiation method that adopts different offer-generation mechanisms to tackle both quantitative and qualitative issues. The preference-based method solves quantitative issues by calculating the reservation intervals of the agent, and the fuzzy similarity method solves qualitative issues by finding the most similar counteroffer to the last offer from the opponent. However, these methods do not consider uncertainty in participant preferences.

The concession strategy determines the agent's behavior toward giving up interests, including the opportunity and interval of the concession if conflicts exist between participants. Such methods can be divided into time-based strategies and behavior-based strategies (Faratin et al., 1998). Mirzayi et al. (2021) proposed an opponent-adaptive concession method that creates a concession neighborhood around the target utility of each round, and the radius of the neighborhood growth rate is determined by the negotiation time. Mansour (2020) presented an imitation offer ration tactic that considers both the current concession behavior of the agent and that offered by its opponent.

Many studies use predictions of opponent preferences to accelerate the convergence of incomplete information negotiation and adopt various learning algorithms to improve the accuracy of opponent models, such as Bayesian algorithms (Sim et al., 2008; Pooyandeh and Marceau, 2014; Yi et al., 2021), neural networks (Zafari and Nassiri-Mofakham, 2016), and reinforcement learning (Bagga et al., 2021a). Most of the research and applications of agent-based automatic negotiation models focus on linear values such as electronic market transactions and power transactions. For SDM, there are many non-linear problems. For example, the severity of medical side effects is discrete and difficult to express with a definite value, which means that the agreement of SDM has a large and discrete value space, making it more difficult to learn about the preferences of opponents.

Genetic algorithms (GAs) are also an effective method of promoting agreement in negotiation (Holland, 1975; Matos et al., 1998; Gao and Chen, 2010; de Jonge and Sierra, 2016). GAs use Darwin's “survival of the fittest” theory to simulate the evolution of natural populations in order to find the optimal solution. GAs are efficient in searching and are closer to the global optimum solution when faced with a large solution space (Lambora et al., 2019). Thus, many studies have used them for value-space searches. Bagga et al. (2021b) proposed a method using a GA to find Pareto frontiers in solving the problem of making Pareto optimal bids under uncertain opponent preferences from a multi-objective optimization stance. However, this study does not predict opponent preferences, as it is not an incomplete information negotiation environment. Ayachi et al. (2018) used GA in electronic trading to predict the reservation values and deadlines of their opponents and then adjust the agent's bid strategy based on the predicted opponent model. Choudhary and Bharadwaj (2019) developed a group recommendation system based on multi-agent negotiation, where a GA is used in the negotiation and recommendation-generation phases. First, the GA is employed to find the offer of maximum utility for each agent in the group and then to determine the ranking of the minimum distance from the preferences of all agents. Few studies have used GAs to predict opponent preferences in complex and large negotiation domains, such as multiple issues and non-linear domains. Particularly, in SDM negotiations, a treatment plan often contains multiple linear or non-linear issues.

In summary, to address time pressures while focusing on patient preferences in SDM, this study presents an agent-based negotiation framework using fuzzy constraints and a GA (ANFGA). The chromosome coding method, fitness function, and evolutionary method of the GA are redesigned for the prediction of an opponent model in a complex negotiation domain. The contributions of this study are as follows:

The rest of this article is structured as follows: In Section 2, we describe the definitions of problems and introduce our proposed model, ANFGA. In Section 3, we evaluate the negotiation presented and compare it with other state-of-the-art agents. In Section 4, we conclude the paper and discuss future research directions.

To evaluate the proposed model, we simulate experiments using the ANFGA for multiple negotiations in different scenarios. The measures used to evaluate the model and the results of the experiments are presented in this section.

Two experiments are conducted with three metrics to evaluate our model. The experimental results show that ANFGA has better performance than comparison work in both heavy time pressure and complex negotiation domains. Thus, our model is more adaptable to the real negotiation scenario of SDM. Among the three concession strategies of ANFGA, the results of the first experiment showed that the competition strategy required more negotiation rounds but possessed the highest joint satisfaction, the collaboration strategy needed the fewest rounds but had the lowest joint satisfaction, and the win–win strategy was a trade-off between the former two. These results are in accordance with our assumption that our negotiation strategy is effective in expressing the concession preferences of different participants. In practice, doctors and patients can adopt different concession strategy types depending on their expectations of the outcome. The results of the second experiment showed that the effect of ANFGA in the incomplete information environment is closer to the effect of the complete information environment than the comparison model, which demonstrates that our model can deal well with the problem of incomplete obtaining of opponent preferences in SDM.

SDM is considered an effective method for achieving patient-centered healthcare but is hampered by time constraints and personal subjective factors such as illiteracy of medical knowledge and lack of communication skills in its implementation. To reduce these negative effects and facilitate the implementation of SDM, this study proposes an agent-based auto-negotiation framework that aims for SDM participants to get close to the desired treatment plan with only a vague description of their preferences. For this purpose, to represent the uncertainty of doctor and patient preferences, a fuzzy member function is used to express this information. In addition, the above barriers also leave SDM in an incomplete information environment, with the preferences of the opponent being unavailable, making the SDM unsatisfactory and inefficient. Thus, GA-based opponent preference prediction was added to the negotiation framework, which helps the auto-negotiation model to converge faster and obtain a more satisfying solution. To verify the model, we performed simulated experiments with different information environments and different constraints. From the two metrics, AJS and ANR, it is evident that the proposed model has better performance than the agent without an opponent preference prediction strategy and remains stable under conditions of high time pressure and large solution space. The results also show that this model has promising potential when implementing the SDM between doctors and patients in real medical environments.

In the future, we will continue to maintain close contact with the clinic, collect relevant data, and conduct experiments in a real clinical environment in the next stage of our work. We also aim to improve the convergence and robustness of the model based on the results of its implementation.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by Medical Ethics Committee of Xiamen Hospital of Traditional Chinese Medicine. The patients/participants provided their written informed consent to participate in this study.

K-BL and YL contributed to the conception, design, and first draft of writing. YW completed the code development and performed experiments. F-PH and Y-MY are responsible for data collection and interpretation. PL performed the statistical analysis. All authors contributed to the article revision and approved the final version.