Introduction

Shared decision-making (SDM) is a collaborative approach where medical decisions are jointly formulated, considering both the physician’s expertise and the patient’s preferences (Murray et al., 2007). Originating over 40 years ago in developed regions like Europe and the US, SDM has been institutionalized through policies such as the UK’s “Working for Patients” White Paper (Langan, 1990) and the US’s Patient Protection and Affordable Care Act (Law, 2010).

Throughout its evolution, various evaluation tools tailored to different medical scenarios have been introduced (Zhao et al., 2018). For instance, the OPTION (Observing Patient Involvement in Decision Making) scale was employed to assess SDM quality during lung cancer screenings (Brenner et al., 2018), and the iSHARE questionnaire was used for oncology co-decision-making (Bomhof-Roordink et al., 2022). Tools have also been developed for specific stages of the decision process, such as the pre-decision selection preference survey (Schaede et al., 2017), the decision-making participation scale (Elwyn et al., 2003), and the post-decision regret scale (Xu et al., 2020). Furthermore, instruments have been designed for specific decision-makers, like the Nurse Participation Questionnaire (Krairiksh and Anthony, 2001), the SDM-Q for patient participation (Simon et al., 2006), and the SDM-Q-Doc for physician participation (Rodenburg-Vandenbussche et al., 2015). While scale surveys are the predominant assessment method (Scholl et al., 2011; Simon et al., 2007), other techniques like questionnaires (Frosch and Kaplan, 1999) and interviews (Shay and Lafata, 2014) are also utilized.

SDM epitomizes the “patient-centered” clinical decision-making model. Ensuring high-quality medical decision-making is paramount in healthcare delivery (Stiggelbout et al., 2012). However, measuring SDM quality remains challenging and contentious. It necessitates considering not only the perspectives of physicians and patients but also external factors like cultural values and policies. Both physicians and patients, as primary decision-makers, are influenced by various factors in their respective systems. For physicians, these might include gender, professional rank, education, and experience, while for patients, factors like health status, economic situation, insurance coverage, and disease severity play a role. These systems exhibit intricate multivariate coupling relationships.

The degree of coupling coordination, initially derived from the capacity coupling coefficient model in physics (Tang et al., 2018), serves as a model for evaluating the coordination level between systems (Wang and Tang, 2018). The coupling degree symbolizes the interaction between systems; a higher value indicates a stronger system interaction. Conversely, the coupling coordination degree signifies the coordination relationship between systems, with a higher value denoting a superior coordination level between systems. Over the past decade, this method has been routinely employed to gauge the integration degree between various systems, including the economy, agriculture, urbanization, industry, and transportation (Shao and Fang, 2021; Wang et al., 2021; Xu et al., 2021).

In this study, the coupling coordination degree method is utilized to quantitatively determine the alignment in SDM between doctors and patients. This approach enhances existing evaluation tools, aiming to quantitatively measure the SDM’s coupling coordination level. The goal is to align decision-making preferences between healthcare professionals and patients, reducing potential decision-making disparities. Based on this analysis, strategic recommendations are proposed to advance the SDM paradigm.

Materials and methods

Questionnaire development and validation

Background and rationale

Medical decision-making is a nuanced process that integrates a plethora of factors, both clinical and non-clinical. Treatment effect, for instance, evaluates the primary outcome of medical interventions, assessing their effectiveness in alleviating symptoms or halting disease progression (Elwyn et al., 2012). Concurrently, medical treatments can have unintended consequences or side effects, and even treatments with high efficacy can have adverse reactions (Zhang et al., 2017). The treatment costs of medical interventions are also significant, encompassing both immediate costs and potential subsequent expenses (Drummond et al., 2015). Additionally, the entirety of the patient’s experience, from clinical outcomes to interactions with healthcare providers and the emotional impact of the treatment, plays a role in decision-making (Coulter and Collins, 2011).

Decisions are influenced by various factors, such as the patient’s health condition, familial context, local medical advancements, and prevailing circumstances. Consequently, four pivotal factors—treatment efficacy, side effects, cost, and patient experience—are identified as central in SDM (Liu and Yan, 2019).

Questionnaire structure and operational definitions

For this study, specialized questionnaires were crafted for both medical staff and patients. Their development was rooted in an extensive literature review (Liu and Yan, 2019) and expert consultations.

The questionnaire is bifurcated into two sections: one captures demographic and professional details, while the other focuses on medical decision-making. To ensure clarity in our study, we provided explicit operational definitions for the key variables:

  • Treatment Efficacy: The effectiveness of a treatment in achieving its intended outcomes. Participants allocated a percentage to signify the importance they place on treatment effect when considering medical decisions.

  • Side Effects: The potential adverse reactions or unintended consequences of a treatment. Participants assigned a percentage to indicate the weight they give to potential side effects in their decision-making process.

  • Cost: The financial implications, both immediate and long-term, of a treatment or intervention. Participants designated a percentage to reflect the significance of cost considerations in their medical decisions.

  • Patient Experience: The holistic view of a patient’s journey, from the effectiveness of the treatment to their interactions with healthcare professionals. Participants attributed a percentage to represent the importance of overall patient experience in their decision-making.

Both the medical staff and patient questionnaires follow the aforementioned logic. Each factor is denoted by a percentage, reflecting its significance or priority in the decision-making process. The cumulative total of these percentages amounts to 100%, ensuring a comprehensive representation of all factors.

Validation and refinement

An initial pilot study, involving 10 doctors and 10 patients, was undertaken to gauge the clarity and pertinence of the questionnaires. Insights from this preliminary phase guided subsequent refinements. To ensure content validity, both questionnaires were subjected to a rigorous expert review process. Detailed breakdowns of both questionnaires are available in Annex 1 (Medical Staff Questionnaire) and Annex 2 (Patient Questionnaire).

Data collection and sampling

Data for this study were collected from online questionnaires, completed by 210 doctors and 248 patients across three tertiary hospitals in Guangzhou, China, between September and December 2021. Before initiating data collection, a power analysis was performed using G-power software. This analysis, predicated on a projected medium effect size of 0.5, an alpha level of 0.05, and a power of 0.90, suggested a requisite sample size of roughly 140 participants to discern statistically significant differences in the primary outcomes. The actual participant count in this study surpassed this estimate, bolstering the robustness of the results.

To qualify for the study, participants had to be at least 18 years old, free from psychiatric disorders, cognitive impairments, or altered consciousness, and willing to engage in the research survey. For unbiased sampling, a detailed list of all eligible doctors and patients was assembled. Using a computerized system, each participant was assigned a unique random identifier, ensuring objective selection. Administrators responsible for disseminating the questionnaires were granted access solely to the list of selected participants. After providing their consent, participants could retrieve the questionnaire by scanning a QR code through WeChat, a prominent messaging and social media platform in China.

During the preliminary phase of data analysis, responses that were inconsistent or incomplete were discarded. Exclusion criteria included the absence of crucial data or indications of survey disinterest, such as consistently identical answers. The study’s ethical guidelines were overseen by the institutional ethics committee and received approval under the IRB number (Number Redacted). All participants were thoroughly briefed on the study’s objectives and methods, ensuring they gave informed consent.

Methods

The coupling coordination degree method is a recognized tool for evaluating coordination in multivariate systems, particularly within the social sciences. However, between 2010 and 2018, a notable number of studies were found to have misapplied this model, particularly in the utilization of its calculation formulas (Wang et al., 2021).

In response to these challenges, Wang and associates proposed modifications to the conventional model, addressing its validity concerns and its tendency for oversimplification. By recalibrating the interpretative conventions of the coupling degree C, the updated model provides a more nuanced depiction of the interactions among subsystems (Wang et al., 2021). Moreover, this adaptation is specifically designed to align more closely with research methodologies in the social sciences. Consequently, the modified model, when juxtaposed with its predecessor, showcases improved empirical results and broader applicability.

In the context of this research, the study leverages Wang’s refined algorithm to derive a bespoke calculation formula, suited for assessing SDM between medical professionals and patients. The detailed formulation is as follows:

The values representing doctors’ and patients’ inclinations towards specific medical decisions are normalized to eliminate the impact of dimensional variations. The normalization is achieved using the following formula:

$${U_{aj}}^\prime = \frac{{U_{aj} - min\left( {U_{aj}} \right)}}{{max\left( {U_{aj}} \right) - min\left( {U_{aj}} \right)}}$$
(1)

Where Ua is the medical decision tendency value (original value), and Ub is the patient decision tendency value (original value).

$${U_{bj}}^{\prime} = \frac{{U_{bj} - min\left( {U_{bj}} \right)}}{{max\left( {U_{bj}} \right) - min\left( {U_{bj}} \right)}}$$
(2)

In the formula, j = 1,2,3, and 4 represent, in order, treatment effect, treatment cost, treatment side effects, and treatment experience. Using the normalization, the medical side’s decision tendency value is Uaj′ and the patient side’s is Ubj′, and the interval is [0,1].

To calculate the coupling coordination degree of the four types of medical decision-making tendency values further, Uaj′ and Ubj′ are calculated as the mean value, which is \({{\bar{U}}_{aj}}^{\prime}\) and \({{\bar{U}}_{bj}}^\prime\). Because the doctor and patient systems are the decision-making subjects of SDM, the number of systems is n = 2, and the coupling degree calculation formula is shown in (3):

$$C_j = \frac{{2\sqrt {{U_{aj}}^\prime {U_{bj}}^\prime } }}{{{U_{aj}}^\prime + {U_{bj}}^\prime }}$$
(3)

Where Cj is the degree of doctor–patient SDM coupling and Tj is the degree of doctor–patient SDM coordination.

In the context of China’s healthcare system, fostering a harmonious doctor–patient relationship has been a priority. SDM plays a pivotal role in achieving this harmony (Chen and Li, 2022). SDM emphasizes the equal involvement of both the doctor and the patient in the medical decision-making process, ensuring that both parties’ perspectives, values, and preferences are considered and respected. The rationale behind assigning equal weights (0.5) to both doctors and patients in the decision-making process is rooted in the philosophy of mutual respect and collaboration. In the traditional doctor–patient dynamic, the doctor often assumes a more dominant role, potentially overshadowing the patient’s voice. However, in the modern healthcare paradigm, especially in the context of China’s emphasis on harmonious relationships, it is imperative to recognize the patient as an equal partner in their care (Melbourne et al., 2011). Therefore, both doctors’ and patients’ decision tendency values were assigned a value of 0.5 to reflect that they are of equal importance in SDM. As a result, it can see in (4) that α = β = 0.5 and the exact formula is:

$$T_j = \alpha {{\bar{U}}_{aj}}^\prime + \beta {{\bar{U}}_{bj}}^\prime$$
(4)

In conclusion, the doctor–patient SDM coupling coordination degree is Dj, as indicated by the formula in (5), which reads as follows:

$$D_j = \sqrt {C_j \times T_j}$$
(5)

Results

Descriptive analysis of medical decision-making choice tendency

In this research, a total of 458 participants were enrolled, with an equal distribution between medical staff and patients. The statistical analysis of the normalized medical decision tendency values \({{{\boldsymbol{U}}}}_{{{{\boldsymbol{aj}}}}}^\prime\) and \({{{\boldsymbol{U}}}}_{{{{\boldsymbol{bj}}}}}^\prime\) for each of the four categories is presented in Table 1. This table delineates the minimum, maximum, mean, median, and standard deviation for each category.

Table 1 Descriptive analysis of medical decision-making choice tendency.
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Figure 1 illustrates the mean tendency values for the four medical decision categories. Doctors place the highest emphasis on the treatment effect, reflected by a tendency value of 0.454. They then prioritize treatment costs (0.357), side effects (0.317), and treatment experience (0.213) in that order. On the other hand, patients also prioritize the treatment effect with a value of 0.364, followed by treatment costs (0.334), side effects (0.221), and treatment experience (0.208). Initial analysis highlights that both doctors and patients consider the treatment effect as the primary factor in decision-making. However, there are noticeable differences in their preferences for other categories. To delve deeper into the alignment and coordination between the decision-making tendencies of doctors and patients, the study utilizes the coupling coordination degree method, offering a comprehensive understanding of their interconnected preferences.

Fig. 1: Line graph of four types of medical decision choice tendency (mean value).
figure 1

This line graph presents the mean values of four distinct medical decision choice tendencies. The x-axis represents the different categories of medical decisions, while the y-axis indicates their respective mean values. The graph provides insights into the prevailing tendencies in medical decision-making and highlights any significant shifts or trends. Figure permissions: the figure included in this manuscript was created by the author(s) and is hereby released under the Creative Commons Attribution 4.0 International License. No external permissions were required.

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Analysis of coupling coordination degree of medical decision-making choice tendency

Medical decision-making, especially in the context of SDM, is a system in its own right. Just as any system is influenced by a myriad of interconnected factors and variables, SDM is shaped by clinical evidence, patient preferences, socio-economic determinants, and the dynamics between healthcare professionals and patients. The essence of a system lies in its components and their interactions, and SDM is no exception. Given that SDM operates as a system, it is logical and appropriate to apply the coupling coordination degree method, a tool designed for systems analysis, to study it. Consequently, existing literature provides established classification standards that can be referenced (Sun et al., 2019). The highest possible score was 10 for excellent coordination, and the lowest possible score was 1 for extreme disorder. The specific classification criteria are shown in Table 2.

Table 2 Classification criteria of coupling coordination degree.
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In this study, the doctor–patient SDM coupling degree “C value,” coordination degree “T value,” and coupling coordination degree “D value” are calculated using the coupling coordination degree method. The calculation steps are shown above. With the help of the average normalized value of the original data, the results of the doctor–patient SDM coupling coordination degree are displayed in Table 3.

Table 3 Coupling coordination degree of doctor–patient SDM.
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In this research, the coordination level of doctor–patient SDM was assessed using the coupling coordination degree method. The decision-making tendencies were categorized into four aspects based on the principle of optimization: treatment effect, treatment cost, treatment side effects, and treatment experience. The subsequent analysis of the medical decision-making tendencies for both doctors and patients revealed the following coupling degree “C values”: 0.999, 0.9721, 0.996, and 0.999. Their corresponding coupling coordination degree “D values” were 0.6375, 0.5299, 0.5704, and 0.4586. The coupling degree signifies the intensity of interaction between systems, and a higher value indicates a stronger interaction. The results demonstrated a pronounced coupling degree (>0.9) in medical decision-making tendencies between doctors and patients, suggesting a robust interactive relationship. The coupling coordination degree measures the harmony between systems, with a higher value denoting superior coordination (Wang and Tang, 2018). Based on the data, the coordination level for treatment effects was the most prominent (7 = Primary coordination). This was followed by treatment costs and side effects (6 = Barely coordination), with treatment experience trailing (5 = On the verge of disorder). These empirical insights lead to several recommendations to enhance the coordination in doctor–patient decision-making.

Discussion

Dissecting coordination levels in shared medical decisions

Data from Table 1 and Fig. 1 indicates that both physicians and patients prioritize treatment effect in their decision-making. However, distinct preferences emerge in the other categories. Physicians seem to be more attuned to the cost of treatments, potentially due to institutional guidelines or policy-driven factors (Ubel et al., 2013). Conversely, patients exhibit heightened concerns about side effects, possibly shaped by personal narratives or shared experiences (Zafar et al., 2013). Both cohorts appear to give lesser weight to the patient-centric experience, suggesting that while it is valued, it may not be the primary factor in medical decisions (Epstein and Street, 2011).

Building on this foundation, the empirical analysis results presented in Tables 2 and 3 further elucidate these dynamics. The coupling coordination degree approach offers profound insights into SDM in healthcare. Utilizing the coupling coordination degree approach, it becomes evident that the coordination value of 7 for “Treatment effect” suggests a shared emphasis between clinicians and patients on tangible health outcomes, reflecting an inherent human desire for certainty in critical decisions (Hammond, 2000; Tversky and Kahneman, 1974). However, the lower coordination values for “Treatment costs,” “Treatment side effects,” and “Treatment experience” indicate potential divergences in perceptions. These discrepancies might arise from differing risk perceptions, economic considerations, and past experiences. Overall, the coupling coordination degree approach provides a lens through which the complexities of SDM in healthcare, influenced by psychological and behavioral factors, can be better understood.

In summation, while Treatment effect is a shared emphasis, the divergences in other decision-making factors underscore the intricate nature of healthcare. Recognizing these differences is pivotal for fostering effective doctor–patient communication and enhancing SDM processes (Charles et al., 1997).

Collaborative decision-making with a “patient first” approach

A decision preference questionnaire revealed that 62.5% of patients are in favor of SDM (Mazur et al., 2005). This indicates a growing trend where patients are voicing their preferences, and the healthcare industry is transitioning towards a more “patient-centered” approach. However, this approach is still in its nascent stages (Elwyn et al., 2000). The significance of SDM quality evaluation tools is evident, especially when considering their role during the implementation phase and the need for quantitative benchmarks.

The empirical analysis results of this study suggest that t the coordination between doctors and patients, in terms of medical decision-making preferences, is suboptimal. Particularly, the coordination in treatment experience is notably low. This could be interpreted as a lack of concern from medical professionals towards the distress patients undergo during diagnosis and treatment. Factors influencing medical professionals’ empathy towards their patients have been studied (Elayyan et al., 1995), and organizational culture emerges as a significant impediment. Like all industries, healthcare institutions come with their unique challenges and opportunities. The prevailing ethos, values, and cultural nuances of an institution can significantly influence its stakeholders (Klingle et al., 1995). Thus, fostering an environment conducive to collaborative decision-making between physicians and patients becomes paramount.

Prioritizing the patient’s treatment experience is essential, given the emphasis of the “patient first” principle on recognizing and upholding the dignity of the patient (Liu and Yan, 2019). A collaborative framework for decision-making between physicians and patients is proposed, rooted in this principle. The initial step involves sensitizing medical personnel to the significance of service, urging them to view patients as unique individuals with distinct diagnoses, lifestyles, and backgrounds. The next step focuses on enhancing the quality of care provided by nursing staff, especially to critically ill patients. Data (Hofhuis et al., 2008) indicates that a significant proportion of critically ill patients, post-admission to the ICU (intensive care unit), grapple with sleep disorders and psychological issues. Thus, top-tier nursing care becomes crucial to mitigate the physical and emotional distress of patients. Lastly, the overarching objective is to elevate patient satisfaction levels. A meta-analysis (Batbaatar et al., 2017) has shown that the caliber of compassionate care extended by healthcare providers directly impacts patient satisfaction. Therefore, championing a “patient-centered” ethos in medical services can significantly enhance patient contentment during the clinical diagnosis and treatment phases.

Establishing a collaborative decision-making mechanism anchored in the optimization principle

The empirical results of this study, when interpreted through the lens of the optimization principle for clinical diagnosis and treatment, underscore an existing imbalance between physicians and patients. The coordination quality, whether in terms of medical decision-making preferences, treatment effects, costs, side effects, or experiences, has not achieved an optimal level. Studies (Hamann et al., 2012) have indicated that physicians value patients’ active participation in decision-making. Such involvement aids in understanding patient needs, reducing doctor–patient conflicts, and minimizing communication expenses. On the other hand, while Western countries tend to adopt a partnership consultation style (Claramita et al., 2013), some regions in Southeast Asia often diverge. The communication in these areas can be influenced by disparities in social status and education, leading to a more paternalistic approach.

To address these challenges, a collaborative decision-making mechanism, grounded in the optimization principle, is proposed. This mechanism aims to mitigate the potential cultural influences that can disrupt effective communication. Firstly, enhancing the coordination level of SDM becomes pivotal. When physicians have insights into their patients’ decision-making tendencies, they can bridge the information gap, fostering a more balanced relationship. Secondly, it’s essential to establish responsive channels for patients. This ensures that both parties reach a consensus before initiating a treatment plan. Furthermore, this approach advocates for a quantifiable coordination level across the four dimensions: treatment effect, cost, side effects, and experience.

AI-enhanced collaborative platform for physician–patient decision-making

In the age of AI, intelligent decision coordination platforms are emerging (Razzaque, 2020). SDM, a collaborative system, establishes a smart platform for doctor–patient SDM, gathering data from both parties. Interviews with 12 experienced clinicians revealed three main functionalities for the decision support system: task execution, patient status, and early warning (Mastrianni et al., 2022). This research suggests incorporating an AI algorithm into the platform to anticipate the coupling coordination level in doctor–patient SDM. If a severe disorder is approaching, the platform will alert medical staff when the coordination degree is between 0.0 and 0.2. It will also inform both the doctor and patient about potential imbalances when the coordination degree ranges from 0.2 to 0.5.

Utilizing the coupling coordination degree method, a decision-making platform for doctors and patients has been crafted. Firstly, it can optimize the management of strained healthcare resources during crises like the Covid-19 pandemic (Jana et al., 2022). Secondly, it can deter medical violence. Past data (Pan et al., 2015) indicates that a significant portion of medical violence incidents in China stemmed from dissatisfaction with treatment, services, or high costs. In such a challenging medical landscape, this intelligent platform, guided by comprehensive coupling and coordination, predicts patients’ medical decision-making tendencies. By considering factors like treatment effect, cost, side effects, and experience, the platform can streamline communication and foster evidence-based medical decisions.

Conclusions

SDM plays a pivotal role in the clinical decision-making process, emphasizing the synergy between doctors and patients. The coordination level of SDM is a direct reflection of the quality of decisions made. This research introduced an innovative approach to assess the coordination level of SDM through the lens of the coupling coordination degree. The objective is to optimize this coordination level, thereby enhancing the quality of decisions and fostering a more harmonious doctor–patient relationship.

Nevertheless, this research is not without its limitations. The current SDM coupling coordination evaluation model might not capture all system elements, potentially missing out on key stakeholders like patient families. Future surveys should consider incorporating these additional decision-making subjects to craft a more holistic and authoritative evaluation model, mirroring the intricacies of real-world medical services more accurately.