Modeling Doctor-patient Shared Decision-making as Fuzzy

Constraint-based Agent Negotiation

Yong Liu

, Ping Lu

, Yimin Yang

, Feiping Hong

and Kaibiao Lin

1,* e

School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Department of Pediatrics, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China

Department of Neonates, Xiamen Humanity Hospital, Xiamen, China

yym216@126.com, 136897915@qq.com, kblin@xmut.edu.cn

Keywords: Shared Decision-Making, Fuzzy Constraints, System Structure, Agent Negotiation, Recommendation.

Abstract: Shared decision-making (SDM) has been widely advocated as a new medical decision-making model, but

limited time, uncertain information, and individual differences constrain its application and development.

To facilitate the application of SDM, a multi-issue agent negotiation approach based on fuzzy constraints is

proposed to solve the SDM problem between doctors and patients. The advantages of our approach can be

summarized into three points: 1) a general framework for knowledge representation and problem-solving in

SDM simulation; 2) a feasible system structure that includes negotiation and recommendation model, which

can simulate a real clinical scenario to complete SDM; 3) an efficient negotiation model that can improve

the negotiation efficiency of SDM by alleviating the constraints of issues and time and reducing the impact

of space and emotion. To verify the feasibility and effectiveness of our method, we simulated and solved the

asthma SDM between doctors and patients and then validated its performance under different deadlines and

issues constraints.

1 INTRODUCTION

Evidence-based medicine encourages patients to

participate actively in discussions of diagnosis,

treatment, and follow-up (Hoffmann et al. 2014). On

this premise, as a new medical decision-making

model in which doctors and patients participate and

fully negotiate about diagnosis and treatment, shared

decision-making (SDM) has received extensive

attention. Different from the paternalistic medical

decision-making model and informed consent

decision-making model, SDM is a patient-centered

decision-making model (Weston 2001). It aims to

make decision-making consistent with patients'

values to improve their compliance and strengthen

doctor-patient communication to promote the

harmony of the doctor-patient relationship

(Stiggelbout et al. 2015).

https://orcid.org/0000-0002-6839-4769

https://orcid.org/0000-0001-5666-0348

https://orcid.org/0000-0002-1830-5190

https://orcid.org/0000-0002-9453-1173

https://orcid.org/0000-0003-2648-2960

The studies on SDM mainly focus on the

establishment of theory and the development of the

application. The concept of SDM was first proposed

by Reimann (Reimann 1968) in 1968, defined by

Veatch (Veatch 1972) in 1972, and then perfected by

the American government in 1982 (States 1982).

Since then, many scholars have begun to enrich and

improve relevant theories, such as the Makoul model

(Makoul and Clayman 2006), the Talking model

(Elwyn et al., 2013), and the Stiggelbout model

(Stiggelbout et al. 2015). To promote the clinical

application of SDM, patient decision aids (O'Connor

2000, Poprzeczny et al. 2020), evaluation tools

(Scholl et al. 2012, Barr et al. 2014), auxiliary

standards, and laws (Holmes-Rovner 2007) have

been studied and developed.

Although the theoretical system of SDM has

been mature and widely used in the clinic, there are

still many problems to be solved (Pieterse et al.

2019). For example, uneven distribution of medical

resources, lack of awareness of doctor-patient

communication, limited time of doctors, lack of

medical knowledge of patients, etc. These problems

cannot be completely solved, and we can only

Liu, Y., Lu, P., Yang, Y., Hong, F. and Lin, K.

Modeling Doctor-patient Shared Decision-making as Fuzzy Constraint-based Agent Negotiation.

DOI: 10.5220/0011228100003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 48-55

ISBN: 978-989-758-596-8

reduce the impact of these uncertain factors on the

clinical application of SDM.

The negotiations involved in SDM mainly take

place between doctors and patients. To make SDM

in-network and promote the theoretical study and

clinical application of SDM, a fuzzy constraint-

directed agent-based negotiation (FCAN) model and

a recommendation model for bilateral SDM are

proposed in this paper. We model the shared

decision-making problems (SDMPs) as distributed

fuzzy constraint satisfaction problems (DFCSPs) to

implement SDM and achieve treatment plan

recommendations to objectify SDM. The doctor

agent (DA) and the patient agent (PA) are designed

to interact in the form of offer and counteroffer until

they reach an agreement or withdraw from

negotiations. Then, we match the agreement with the

existing treatment plans by calculating the

recommendation score to achieve the

recommendation of the treatment plans.

The main advantages of this paper can be

summarized into three points. First, it provides a

general framework for knowledge representation and

problem-solving for SDM simulation. Second, a

distributed computing model including negotiation

algorithm and recommendation algorithm is

established, which simulates SDM in the real world

more closely and naturally. Thirdly, an efficient

negotiation model is proposed to relax the

constraints of negotiation issues and time and

improve the negotiation efficiency. In addition, the

negotiation model can effectively reduce the

influence of space and the negotiator's emotions on

negotiation because of its automaticity.

The rest of this paper is organized as follows.

Section 2 generalizes the proposed methods. Section

3 describes the complete process of solving the

SDM problem with the proposed method. It includes

the problem formulation, negotiation and

recommendation strategies, interaction mechanism,

and system structure. Section 4 proves the feasibility

and effectiveness of our method applied to SDM by

an example and comparative test. Section 5

summarizes this paper.

2 METHODS

Study Subjects

The subjects were doctors and patients attending

the treatment for chronic diseases. Significantly, the

eligible patients with light or moderate conditions.

Ethical Requirement

This study was approved by the Medical Ethics

Committee of Xiamen Hospital of Traditional

Chinese Medicine, China, and the approval number

is 2021-K065-01. In addition, all participants

provided written informed consent after a complete

description of the study. There was no financial

compensation for the doctors and the patients.

Study Design

This study was designed as an exploratory study

to solve the problems of doctor-patient SDM

according to the agent negotiation and fuzzy

constraints. Thus, it is necessary to establish an

agent negotiation and recommendation method

based on fuzzy constraints for SDM (Section 3) and

validate it by experiments (Section 4). The proposed

method includes four parts: problem formulation

(Section 3.1), negotiation and recommendation

strategies (Section 3.2), interaction mechanism

(Section 3.3), and system structure (Section 3.4).

The experiments consist of two parts: an illustration

for judging its feasibility (Section 4.1) and a

negotiation performance comparison for evaluating

its effectiveness (Section 4.2).

Data Collection

The experimental data in this paper was related

to childhood asthma and were obtained from the

Department of Pediatrics at Xiamen Hospital of

Traditional Chinese Medicine. It consists of the

preference data of doctors and patients on issues and

the evaluation data of treatment plans provided by

doctors. In addition, the treatment plans come from

the Diagnosis and Treatment Guidelines of Asthma

published in 2016 (The Respiratory Group 2016).

Results Evaluation

The evaluation index of treatment

recommendation is the recommendation score. The

evaluation indicators of negotiation performance are

the combined ASV (the sum of DA's ASV and PA's

ASV) and the number of negotiation rounds.

3 FUZZY CONSTRAINT-BASED

AGENT NEGOTIATION FOR

SDM

In this section, we will introduce our proposed

method in detail. Our problem formulation is

summarized in Section 3.1. It describes the

theoretical basis of modeling SDM as DFCAN.

Section 3.2 introduces the negotiation and

recommendation strategies of our method that is the

behavior framework of the DA and PA. Section 3.3

Modeling Doctor-patient Shared Decision-making as Fuzzy Constraint-based Agent Negotiation

presents the interaction mechanism between the DA

and PA. Section 3.4 develops a system structure for

a prototyped agent-based SDM simulator.

3.1 Problem Formulation

Agent-based negotiation technology has been

successfully applied to solve the problems of

resource allocation (Voos 2006), e-commerce (Ateib

2010), cloud computing (Shojaiemehr et al. 2019),

etc. In clinical practice, SDMP is actually a problem

that needs to be negotiated by doctors and patients.

Thus, based on the agent concept (Wooldridge and

Jennings 1995), SDMP can be transformed into the

agent negotiation problems shown in Figure 1.

Figure 1: Problem conversion.

Furthermore, the real-world clinical environment

is heterogeneous, distributed, and with a great deal

of uncertain and inaccurate information. Therefore,

in our work, SDMP can be modeled as DFCSP. The

task of SDM simulation is to explore a satisfactory

agreement that meets all constraints and then make a

decision on treatment plans. A negotiation for

treatment in SDM can be modeled as a triple

(𝒟,𝒫,ℐ), where 𝒟 and 𝒫 represent the DA and PA,

respectively, and ℐ is the inter-relationships between

the two types of agents. The distributed fuzzy

constraint networks (DFCNs) are defined as follows.

Definition 1: A DFCN (𝑈,𝑋,𝐶) in a SDM

(𝒟,𝒫,ℐ) can be defined as a set of fuzzy constraint

networks (FCN) {𝑁



,...,𝑁



,...,𝑁



}, where 𝑁



(𝑈



,𝑋



,𝐶



) belongs to agent 𝑙, and

𝑈



is the universe of discourse for FCN, 𝑁



;

𝑋



={𝑋





,...,𝑋





,...,𝑋





} is a tuple of 𝑛 non-

recurring objects; and

𝐶



is a set of fuzzy constraints in the FCN, which

includes the internal constraints among objects in 𝑋



and external constraints between agent and its

opponent;

𝑈 is the universe of discourse for DFCN;

𝑋=(𝑈





𝑋



) is a tuple of all non-recurring

objects; and

𝐶=(𝑈





𝐶



) is a set of all fuzzy constraints in

the DFCN.

As stated in Definition 1, the solution to 𝑋



FCN, can be regarded as an intention Π





or Π



indicating that fuzzy set 𝑋



of non-recurring objects

satisfies all fuzzy constraints 𝐶



3.2 Negotiation and Recommendation

Strategies

Following the description of FCSP of SDM, the

FCAN model and recommendation model for SDM

can be described as follows.

Given an issue set 𝐼={𝐼



,𝐼



,…,𝐼



,…,𝐼



} and an

offer (or a counteroffer) 𝑆𝜖Π



, the aggregated

satisfaction value (ASV) about 𝑆 of agent 𝑙 is:

(

𝑆

)

=𝑤



∗𝜇



(𝑆)







(1)

Where 𝜇



(𝑆) is the 𝑖



satisfaction degree

function of 𝑆, fuzzy membership function, 𝑛 is the

number of issues, and 𝑤



is the weight factor for 𝑖



issue.

For an auto-negotiation, the agent will determine

its new behavior state and generate a set of feasible

solutions by reducing its demands to reach an

agreement with its opponent. The feasible solution

set is the solution space, in which agents can explore

to a satisfactory consensus. Given FCN 𝑁, intention

Π, and a new behavior state 𝜀

∗

=𝜀−∆ε, a set of

feasible solutions 𝑃 can be acquired by:

𝑃=Γ

(

𝛱,𝜀

∗

)

={𝑆|(𝑆𝜖𝛱)Λ(𝜀≥Ψ

(

𝑆

)

≥𝜀

∗

)}

(2)

Where, 𝜀 is the behavior state in the last round,

and ∆𝜀 is the concession value.

In the offer exchange round, the agent trends to

select an "optimal" offer from the feasible solution

set to maximize their individual interest. The

selection condition can be defined as:

𝑆

∗

=𝑎𝑟𝑔(𝑚𝑎𝑥

∈

𝐻(𝑆,𝐵))

(3)

Where 𝐻(𝑆,𝐵) is a utility function that can

evaluate the similarity between counteroffer 𝐵 and

feasible solution 𝑆. It can be calculated by:

(

𝑆,𝐵

)

=1−

𝑛



(1−𝐷(

𝐴



,𝐵



))







(4)

Where 𝐴



and 𝐵



are the possibility distributions

of offer 𝐴 and counteroffer 𝐵 over the issue 𝐼



𝜖𝐼,

respectively, and 𝐷 is the distance measure between

a feasible solution 𝐴∈𝑆 (i.e., 𝐴



) and a counteroffer

𝐵 (i.e., 𝐵



The negotiation result between the DA and PA is

an agreement on the value of all issues. However,

the purpose of real SDM is to obtain a treatment

plan that meets the preferences of both sides and

conforms to the patient's condition. Therefore, we

need to translate the results of the negotiations into

treatment plans for doctors and patients to make

decisions. The conversion method is as follows.

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

(

𝑆,𝐵

)

=1−

𝑛



(1−𝐷(

𝐴



,𝐵



))







(5)

Where 𝑤



is the weight of the relevant issues

concerning treatment plans, and 𝑅



(

𝑆

)

∈[0,1] is the

similarity calculation on the negotiation issue level,

that is, the fuzzy membership function related to the

treatment plans.

3.3 Interaction Mechanism

The above-mentioned negotiation process can be

summarized as a universal negotiation and

recommendation algorithm for the DA and PA,

which describes the method adopted by the DA and

PA in the negotiation process. Therefore, based on

the strategies given in Section 3.2, Algorithm 1

presents the details of the interaction process

between the DA and PA.

Initially, the negotiator will send a message with

the initial offer to its opponent. When its opponent

receives the message, it first determines the type of

the message. If it is an "Abort" message, it means

that the other party withdraws from the negotiation,

and the negotiation fails. If it is an "Accept"

message, it means that the other party agrees to

reach an agreement, and the negotiation is

terminated in a successful state. Otherwise, the

opponent will generate a set of feasible solutions and

judge whether it can reach an agreement with the

other party. If it can, it will send an "Accept"

message; if not, it will decide whether to generate

the "Ask" message with a new offer according to the

current time state. For another negotiator, the

response process is similar. The above negotiation

process will be repeated until the negotiation status

is failed or successful.

orithm 1: Interaction Al

orithm

1: negotiation state  "normal"

2: activate Timer 𝑇

3: generate an initial offer 𝐴



or 𝐵



and send it in

"Ask" to the opponent agent

4: Repeat

5: receive a message from its opponent

6: if the message is an "Abort", then

7: negotiation state  "failure"

8: else if the message is an "Accept", then

9: negotiation state  "success"

10: else

11: get a counteroffer 𝐵 or 𝐴 from the received

message

12: generate a new feasible set 𝑃

13: if 𝒟 and 𝒫 reach an agreement, then

14: send an "Accept" message with the agreement 𝑆

15: negotiation state  "success"

16: else if Timer 𝑇 is counting, then

17: generate a new offer 𝐴 or 𝐵 based on feasible

set 𝑃

18: send an "Ask" message with the new offer 𝐴 or

𝐵

19: else

20: negotiation state  "failure"

21: end if

22: end if

23: until the state is "success" or "failure"

3.4 System Structure

In the clinical environment, fuzzy-directed agent-

based automatic negotiation is an open simulation

platform for simulating SDM between doctors and

patients. Figure 2 shows the structure of agent-based

negotiation for simulating SDM. In this figure, based

on the fuzzy description, the SDM Environment

Description Module can transform the clinical

environment (including the description of clinical

situations, disciplines, and rules) into the fuzzy

constraint network.

Figure 2: A system structure of SDM.

In the Negotiation Module, the DA and PA will

comply with the negotiation protocols to solve their

own FCSP by exchanging the offer and counteroffer

in the transformed clinical environment. In the

negotiation process, if the agent cannot reach an

agreement with its opponent in the current round, it

will relax its constraints, adopt a concession

strategy, and explore alternative agreements.

Meanwhile, the agent will update its cognition of the

opponent and the environment and modify its

negotiation status in the next round. The details can

be seen in Sections 3.1 to 3.3.

Modeling Doctor-patient Shared Decision-making as Fuzzy Constraint-based Agent Negotiation

When the DA and PA reach an agreement, the

Recommendation Module is applied to convert the

negotiation result into treatment plans and

accomplish the recommendation of treatment plans.

In this module, the recommendation scores of

treatment plans will be calculated and ranked.

The Log Module records the relevant data of the

Negotiation Module and Recommendation Module

into log files. The negotiation data includes the

process and results, and the recommendation data

includes all the recommended results. The

Reporting Module will generate a detailed report

according to the results of negotiation and

recommendation and send it to doctors and patients,

respectively. Finally, doctors and patients can

evaluate each other and make treatment decisions.

4 EXPERIMENTS

After defining the model and system structure, the

next step is to evaluate it. Given the purpose of our

work, we evaluated our method from the perspective

of feasibility and effectiveness. On the one hand, we

judged its feasibility by an illustration, as shown in

Section 4.1; on the other hand, we evaluated its

effectiveness by negotiation performance, as shown

in Section 4.2. Additionally, the clinical decision-

making scene used in the experiment was the SDM

of doctors and patients on the treatment of childhood

asthma. There are two roles, doctor and patient,

corresponding to the DA and PA, respectively.

4.1 An Illustration for Agent-based

SDM

The following content provides a case to illustrate

the application of the framework proposed in this

paper in simulating and solving SDM problems. For

negotiation, the initial satisfaction threshold of both

DA and PA is set to 1.0, the satisfaction retention

value is 0.0, and the maximum number of rounds is

15. Negotiated issues include cost, effectiveness,

side effects, risk, and convenience (Rivera-Spoljaric

et al. 2014).

According to the negotiation algorithm, the

negotiation is terminated when the DA and PA reach

an agreement, or the negotiator withdraws from the

negotiation, or the negotiation round is exceeded.

After full negotiation, the negotiation result between

the DA and PA is [Cost: 3.78, Effective: 9, Side-

effects: 0.06, Risk: 0.07, Convenience: 9], because

the ASV of DA for the received offer is greater than

its satisfaction threshold.

As mentioned above, the negotiation result is not

the real purpose of SDM. Thus, the

Recommendation Module will be applied to address

the problem of treatment recommendation. The final

recommended results of treatment plans are shown

in Figure 3.

Figure 3: The recommendation scores of treatment plans

in different priority constraints [with the weights of issues

of DA, PA, and their average].

The recommended order of treatment plans is:

En-high dose ICS/LABA + Sustained-release

THP ≽ En-high dose ICS/LABA+LTRA ≽ En-high

dose ICS/ LABA ≽ En-high dose ICS+LTRA ≽ En-

high dose ICS + Sustained-release THP.

Where ICS means inhaled corticosteroid, LABA

is long-acting beta2-agonists, LTRA is leukotriene

receptor antagonists, THP is theophylline, and

ICS/LABA means a combination of inhaled

corticosteroids and long-acting beta2-agonists.

4.2 Negotiation Evaluation

To evaluate the effectiveness of our SDM

negotiation method, we compare it with the Time

model (time-dependent negotiation model) (Faratin

et al. 1998) in terms of negotiation rounds and

combined ASV. All the experimental results were

the average results of 200 repeated experiments. The

first experiment compared the negotiation

performance of agents on different issues but

subjects to the same deadline. The second

experiment compared the negotiation performance

of agents on the same issues but subjects to different

deadlines. Here, the negotiation environment is

defined by the number of issues (between 1 and 9)

and the number of deadlines (between 10 and 30).

The range of these parameters is selected according

to the experience of experts in the SDM field.

As shown in Figures 4 and 5, it can be seen that

when the FCAN model and Time model negotiate

on different issues (the number of issues is between

1 and 9) and subject to the same deadline

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

constraints, the negotiation rounds required by the

FCAN are lower than that of the Time model, and

the combined ASV obtained are higher than that of

the Time model. Figures 4-5 also show that when

the number of issues increases, FCAN and Time

usually need more negotiation rounds, but the

combined ASV may decrease. This corresponds to a

common phenomenon: when negotiation issues

increase, negotiators need more rounds to explore

and reach an agreement, and the final combined

ASV does not necessarily increase.

Figure 4: Average negotiation rounds of agents with

different issues [deadline = 15].

Figure 5: Average combined ASV of agents with different

issues [deadline = 15].

As can be seen from Figures 6-7, when the

negotiation is subject to the deadline constraints

between 10 and 30 and is not affected by the number

of negotiation issues, the number of rounds of the

FACN model is usually lower than that of the Time

model and the combined ASV is usually higher than

that of the Time model. When the deadline is

relaxed, the FCAN and Time can usually obtain a

higher combined ASV after more rounds for

negotiation. In addition, due to deadlines constraints,

the combined ASV obtained by the FCAN and Time

is close, but the number of negotiation rounds

required shows a great difference. This corresponds

to the common phenomenon that when agents

negotiate purely based on time, they need more

rounds to reach a satisfactory agreement.

Figure 6: Average negotiation rounds of agents with

different deadlines [the number of issues = 5].

Figure 7: Average combined ASV of agents with different

deadlines [the number of issues = 5].

In conclusion, when agents are eager to reach an

agreement, whether they are under the pressure of

deadlines or issues, the FCAN model performs

better than the Time model because compared to the

Time model, FCAN can obtain higher combined

ASV in fewer negotiation rounds. This satisfies the

general goal of automatic negotiation, that is, to

obtain a higher combined ASV in fewer rounds.

Therefore, the above experimental results fully

validate the feasibility and effectiveness of our

negotiation model.

5 CONCLUSIONS

A general framework and an open simulation

platform for the fuzzy constraint-based agent

Modeling Doctor-patient Shared Decision-making as Fuzzy Constraint-based Agent Negotiation

negotiation of SDM are presented in this paper.

Based on fuzzy constraints, we intuitively and

effectively transformed SDMP into DFCSP. Then,

in the Negotiation Module, the DA and PA were

designed to simulate the negotiation between doctors

and patients. After that, the Recommendation

Module converted the negotiation results into

treatment plans. Finally, the Reporting Module

provides a detailed report of negotiations and

recommendations to doctors and patients.

Experimental results demonstrate that our

proposed method can successfully implement the

negotiation and recommendation of SDM based on

fuzzy constraints and obtain higher combined ASV

in fewer rounds. Specifically, the presented method

effectively alleviates the constraints of issues and

time on negotiation and significantly improves

negotiation efficiency. Although our method has

received some feasible and effective results, further

exploration is needed, such as the learning

capabilities and convergence analysis and the

negotiation of our proposed method in more

complex scenarios.

ACKNOWLEDGEMENTS

This research was funded by the Science Foundation

of Fujian Province (No. 2021J011188), the Xiamen

Science and Technology Planning Project (No.

3502Z20206073), the Research Project of Xiamen

Overseas Students (No. XRS202017206), and the

Scientific Research Project of Xiamen University of

Technology (No. YKJCX2019108).

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Modeling Doctor-patient Shared Decision-making as Fuzzy Constraint-based Agent Negotiation