Establishing development strategies and improvement paths for decision coach competencies in shared decision-making using an integrated accessibility–performance analysis and network relation map approach

Abstract

Background

A decision coach (DC) is a trained healthcare professional who provides non-directive support to patients in health-related decision-making. The decision coach role can be integrated into shared decision-making (SDM) processes, but is not an inherent component of SDM, as both may be applied independently depending on the clinical context. Numerous trained DCs are nurses who have transitioned from their roles as patient educators to knowledgeable, skilled decision facilitators. Thus, developing core competencies has become increasingly crucial for DC training, especially patient-centered care in nursing education. This study aimed to evaluate DC competencies in SDM by examining accessibility, performance, and perceived directional relationships among competency domains.

Methods

This mixed-methods study combined a narrative literature review and expert consultations to identify key DC competency criteria. Subsequently, a cross-sectional questionnaire survey was administered to healthcare professionals at two hospitals in Taiwan between April 2023 and February 2024. Accessibility–performance analysis (APA) and the decision-making trial and evaluation laboratory (DEMATEL)-based network relation map (NRM) approach were applied to evaluate competency accessibility, performance, and perceived directional relationships across domains.

Results

A total of 149 healthcare professionals, including physicians, nurses, and other clinical staff, completed the valid questionnaires. The reliability of the overall criteria was measured at 0.965. The findings suggest that the professional knowledge (PK) aspect demonstrated perceived directional associations with the outcome evaluation (OE), process management (PM), and guidance skills (GS) aspects within the network structure, whereas the GS aspect was more likely to be associated with other competency domains. Each of the four aspects has a criterion critical to improving DCs’ competencies: value (PK1), listening skill (GS2), shared decision-making resources (PM3), and practical decision-making results (OE3).

Conclusion

From healthcare professionals’ perspectives, professional knowledge was the competency domain most closely associated with perceived directional relationships among other competency aspects within the network structure. The integrated APA-NRM approach delivers evidence-based prioritization and practical pathways for developing DC competencies and guiding training strategies.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12911-026-03680-1.

Background

Shared decision-making (SDM) is an interactive process in which patients and healthcare professionals collaborate to make appropriate medical decisions by integrating clinical evidence with patient preferences [1, 2]. The Ottawa Decision Support Framework provides a theoretical foundation for decision support and includes various interventions such as decision aids, SDM, and decision coaching, which may be used independently or in combination [3, 4]. Decision coaching refers to non-directive support provided by trained healthcare professionals to help patients prepare for decision-making, including understanding options, clarifying values, and communicating preferences [3, 5]. Importantly, decision coaching is conceptually distinct from SDM and is not a mandatory component of all SDM interventions, although it may be integrated into SDM processes [6, 7]. Decision coaches (DCs) may include nurses, social workers, pharmacists, psychologists, and other healthcare professionals [3, 8].

Previous studies have identified key roles of decision coaching, including assessing decisional conflict, supporting patients in decision-making, facilitating communication, and monitoring decision processes [5, 9]. Decision coaching also supports patients in understanding evidence, clarifying values, and overcoming barriers to implementation [5, 10]. A scoping review by Rahn et al. further refined the concept of decision coaching and emphasized its role as non-directive support provided by qualified healthcare professionals to prepare patients for health-related decisions [11]. These findings highlight the multifaceted nature of decision coaching within SDM.

Various frameworks and studies have explored competencies related to decision coaching and SDM, including communication skills, use of patient decision aids (PtDAs), and training interventions [6, 12, 13]. The Ottawa Decision Support Framework outlines competencies for supporting patients throughout the decision-making process [4], while systematic reviews have examined the effectiveness of decision coaching interventions and associated competencies [14]. Additional research highlights the importance of communication, active listening, and relationship-building as core competencies [12]. Although training interventions have been shown to improve knowledge and skills, their impact on clinical outcomes and patient participation remains variable [13]. Furthermore, implementing decision coaching in practice is complex and affected by multiple factors, including professional roles, contextual conditions, and organizational support [15].

Despite these advances, several challenges remain in integrating decision coaching into routine clinical practice, including limited awareness, insufficient training, time constraints, and lack of organizational support [6, 16]. Moreover, there is currently no widely accepted framework to systematically define, evaluate, and prioritize DC competencies. In this study, competency is defined as the integrated application of knowledge, skills, and professional attitudes that enable healthcare professionals to perform effectively in clinical contexts. Accordingly, DC competencies refer to the capabilities required to support patients in making informed, value-based decisions. Therefore, this study aimed to evaluate DC competencies in SDM by examining accessibility, performance, perceived directional relationships, and potential improvement pathways among competency domains.

Methods

Study design and analytical framework

This study used stepwise mixed-methods procedure to identify, evaluate, and prioritize DC competencies in SDM practice, as illustrated in Fig. 1. The primary empirical component of the study was a cross-sectional questionnaire survey, whereas the narrative literature review and expert consultation were conducted as preliminary processes for competency framework development.

Fig. 1.

Stepwise procedure of the integrated accessibility–performance analysis (APA) and network relation map (NRM) approach for decision coach competency development

First, preliminary competency elements were identified through a narrative literature review focused on decision coaching, SDM competency development, and patient-centered care. Second, the competency framework was refined through expert consultation and questionnaire development. Third, accessibility and performance were evaluated using accessibility–performance analysis (APA). Fourth, perceived directional relationships among competency domains and criteria were examined using the decision-making trial and evaluation laboratory (DEMATEL)–based network relation map (NRM) approach. Fifth, the results from APA and NRM were integrated to formulate competency improvement strategies. Finally, potential competency improvement pathways were developed based on the integrated analytical results. Because the purpose of this study was to explore competency gaps and perceived directional relationships among competency domains rather than test predefined hypotheses, an integrated APA–NRM approach was adopted. APA was used to identify competency gaps, whereas NRM was used to examine perceived directional relationships and support the prioritization of competency development pathways.

The study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines, with the corresponding checklist available in Supplementary File S1.

Literature review and framework development

A narrative literature review was conducted to identify preliminary key DC competencies in SDM practice. The research team used keywords such as “shared decision-making,” “decision-making support,” “competencies,” “knowledge,” “skills,” and “attitudes” to search for relevant literature in databases such as PubMed and ScienceDirect. This review aims to establish a conceptual foundation by identifying core competency domains related to decision-making support, SDM frameworks, and existing assessment methods. This review focuses on synthesizing key themes rather than conducting an exhaustive systematic literature review.

Preliminary competency aspects and criteria were initially identified through the literature review. To refine the preliminary framework, four experts (three attending physicians and one clinical pharmacist) from the hospital’s SDM group participated in structured consultations. The experts reviewed the proposed competency domains and criteria, discussed their appropriateness for decision coaching practice, and provided recommendations regarding the inclusion, modification, merging, and classification of competency elements. The research team incorporated this feedback and revised the framework through several rounds of discussion until consensus was reached.

To further organize and interpret the competency framework, the Context-Input-Process-Output (CIPP) evaluation model [17] and the Knowledge, Skills, and Attitudes (KSA) model [18] were used as conceptual references. These models helped structure the competency domains, although the final framework was developed through an integrative process that combined evidence from the literature review, expert consultations, and theoretical considerations. The final framework comprised four competency aspects—professional knowledge (PK), guidance skills (GS), process management (PM), and outcome evaluation (OE)—and 16 evaluation criteria, which formed the basis of the questionnaire used in the empirical analysis. Each aspect includes four criteria, as shown in Table 1.

Table 1.

The illustrations of aspects/criteria for competency development in decision coaches

Aspect/Criteria	References
1. Professional knowledge (PK) (Derived from literature review and expert consultation; conceptually aligned with the “knowledge” domain of the KSA framework)
1.1 Value (PK1): Understand the importance of shared decision-making (SDM) and recognize the value of SDM.	[19, 20]
1.2 Concept (PK2): Patient-centered care promotes mutual respect and communication between doctors and patients and provides the best quality of patient care.	[19, 20]
1.3 Timing (PK3): Know the situation suitable for initiating SDM.	[19, 21, 22]
1.4 Evidence-based knowledge (PK4): Have basic evidence-based medical knowledge to enhance decision-making and guidance capabilities.	[5, 23, 24] Expert
2. Guidance skills (GS) (Primarily aligned with the “skills” domain of the KSA framework and informed by communication and decision support literature)
2.1 Explanation skill (GS1): Ability to explain decision-making content in clear, simple language that patients can understand.	[15, 22] Expert
2.2 Listening skill (GS2): Listen attentively, accept with flexibility and openness, genuinely understand the other party’s needs, and be able to focus on what the patient considers most important.	[25–28] Expert
2.3 Communication skills (GS3): Express care and empathy for patients through communication and establish trust, honesty, and respect between doctors and patients.	[4, 25, 29, 30]
2.4 Teamwork skills (GS4): Cooperate with healthcare team members to jointly assess and coordinate.	[4, 25, 31, 32]
3. Process management (PM) (Conceptually informed by the “process” dimension of the CIPP model and clinical workflow considerations in SDM)
3.1 Invite participation in decision-making (PM1): Invite patients and their essential relatives (such as family members or primary caregivers) to participate in the decision-making process.	[15, 19] Expert
3.2 Discuss decision-making problems (PM2): Gently provide all options without bias and be able to analyze the advantages and disadvantages of various options.	[15, 19]
3.3 Shared decision-making resources (PM3): Use patient decision aids to share sufficient information to enhance patients’ understanding of medical choices.	[4, 6, 33, 34]
3.4 Guide participation in decision-making (PM4): Provide customized information to help patients think, increase their sense of participation in decision-making, and promote patients’ independent decision-making.	[15, 21] Expert
4. Outcome evaluation (OE) (Aligned with the “product” dimension of the CIPP model, focusing on outcomes and decision quality)
4.1 Confirmation of values and preferences (OE1): Help patients organize their thoughts about decision-making, confirm patient values, preferences, fears, and expectations, and consider patient emotions and life experiences.	[5, 15, 25, 35]
4.2 Monitoring and resolving conflicts (OE2): Monitor factors that may affect the implementation of decisions (e.g., motivation, self-efficacy, obstacles), resolve decision-making conflicts promptly, and screen implementation needs.	[4, 5, 14]
4.3 Practical decision-making results (OE3): Keep track of decision-making progress, be able to choose, discuss, or postpone decisions with patients or family members, reach a consensus with them, select decision-making plans together, and complete follow-up arrangement tracking.	[4, 5, 35]
4.4 Psychological support (OE4): Because patients (or family members) are worried about regrets that may result from decision-making, we can accompany them all the way to guide them to return to normalcy and reduce the level of decision-making anxiety.	[14, 25] Expert

Questionnaire development and psychometric testing

The questionnaire underwent psychometric testing to evaluate its content validity and internal consistency. Four experts assessed the questionnaire items based on grammar, wording, clarity, and relevance to the competency evaluation model. They were asked to express their ideas about the relevancy of each item to the evaluation model of the DCs on a 3-point scale (1. not essential; 2. useful but not essential; 3. essential). Then, we calculated the content validity ratio (CVR). The entire instrument’s content validity index (CVI) is the mean CVR for all retained items. A CVR of 0.78 or higher with three or more experts could be evidence of good content validity, and a CVI exceeding 0.80 is preferred [36]. After examining the CVI for content validity, the CVI was computed as 0.936. Further, the questionnaire was validated through the pre-testing of 6 expert respondents in the SDM group. These respondents comprised four doctors, one nursing supervisor, and one clinical pharmacist. The internal consistency of the data was evaluated using the Cronbach alpha test, where for checking the reliability of the questionnaire, a value of alpha greater than 0.7 is acceptable [37].

Setting and participants

The survey was conducted between April 2023 and February 2024. Participants were recruited through institutional communication channels, including internal announcements and professional networks within the participating hospitals. Snowball sampling was used to recruit participants with relevant SDM experience through professional networks. Eligible participants included healthcare professionals (e.g., physicians, nurses, and related staff) involved in SDM practices within the participating institutions. A total of 170 healthcare professionals from two branches of Chang Gung Memorial Hospital in Taiwan, specifically one medical center and one regional hospital, were eligible to participate in the study. All participants had prior exposure to SDM through institutional training programs or clinical practice. Formal certification as DCs was not required; therefore, participants’ experience in decision coaching varied depending on their professional roles and involvement in SDM-related activities. The Institutional Review Board at Chang Gung Memorial Hospital approved this study (IRB No: 202300200B0C601). Before the survey, participants were provided with an information sheet explaining the study purpose, the voluntary nature of participation, and the anonymity of responses. Participants were also informed of their right to withdraw from the study at any time without any consequences. Completion of the questionnaire was considered as implied consent. The survey was administered online and completed anonymously. No incentives were provided for participation.

Because this study employed an exploratory cross-sectional design and focused on APA and DEMATEL-based analyses of competency relationships, a formal statistical power calculation was not performed. The sample size was determined based on the number of eligible healthcare professionals involved in SDM practices within the participating institutions during the study period and by reference to previous DEMATEL-related healthcare studies using comparable sample sizes [22, 35, 38–42].

Data collection

An online survey of health professionals at two Chang Gung Memorial Hospital branches was performed. The survey included items on the respondents’ basic information, evaluated competencies accessible to DCs and the level at which they were performed, and examined perceived directional relationships among different competency aspects and criteria. Survey items were adapted from literature reviews and expert consultations. Respondents were asked to evaluate: (1) accessibility: the degree to which each competency is available or supported in practice, (2) performance: the level at which each competency is performed, and (3) perceived directional relationships: the degree to which one competency is perceived to be directionally associated with another within the network structure. The English version was translated from the original Chinese questionnaire for reporting purposes. The full questionnaire (Chinese and English versions) is provided in Supplementary File S2 and S3.

To reduce potential response bias, participation was voluntary, and questionnaire responses were collected anonymously. Standardized questionnaire items and psychometric testing procedures were also used to improve measurement consistency and content validity.

Data analysis

Descriptive statistics summarized participant characteristics and questionnaire responses. Reliability was assessed using Cronbach’s alpha to determine the questionnaire’s internal consistency. The APA method evaluated the accessibility and performance status of competency domains and criteria. The DEMATEL-based NRM approach examined perceived directional relationships among competency domains and criteria. Subsequently, APA and NRM results were integrated to identify priority competency areas and potential development pathways. Microsoft Excel was used for the APA analysis, and MATLAB for the NRM analysis.

Accessibility–performance analysis

The accessibility–performance analysis (APA) method, adapted from the importance–performance analysis (IPA) framework proposed by Martilla and James [43], was applied to evaluate competency gaps. Respondents rated each competency criterion using an 11-point Likert scale ranging from 0 to 10. For the accessibility indicator (AI), higher scores reflected greater perceived accessibility of the competency in clinical practice. For the performance indicator (PI), higher scores indicated better perceived performance in SDM practice. Overall mean AI and PI scores were used as reference points to position competency criteria within a two-dimensional matrix. These mean scores were then used to rank the competency domains and criteria by relative accessibility and performance levels. Based on these values, criteria were classified into four quadrants: high–high (H, H), low–high (L, H), low–low (L, L), and high–low (H, L). These quadrant classifications facilitated the identification of relative competency strengths, maintenance areas, observation areas, and priority areas for improvement.

DEMATEL-based network relation map analysis

The DEMATEL-based network relation map approach was applied to investigate the perceived directional relationships among competency domains and criteria within the network. Unlike regression models or causal statistical analyses, the DEMATEL approach assesses directional associations using a direct-relation matrix derived from respondents’ questionnaire ratings. The resulting network structure enabled exploration of the relative positions and interrelationships among competency domains and criteria.

Based on previous research methods [44, 45], this study outlined the four stages of the DEMATEL technique: (1) the average direct relation matrix was constructed based on respondents’ ratings, (2) the matrix was normalized to obtain the normalized direct relation matrix, (3) the total relation matrix was calculated to capture both direct and indirect directional associations, (4) prominence (d + r) and relation (d − r) values were computed to identify the relative positions of factors within the perceived network structure. Based on the DEMATEL results, an NRM was constructed to visualize the perceived directional relationships among the factors. The NRM is derived from the DEMATEL method and serves as a graphical representation of the interrelationships among variables [46]. For readers unfamiliar with the APA and DEMATEL-based NRM approaches, Supplementary File S4 provides step-by-step calculation examples, illustrative matrices, and graphical demonstrations of the analytical procedures. References 44 and 45 provide additional methodological details.

Integration of accessibility–performance analysis and network relation map results

The APA and DEMATEL-based NRM results were integrated to identify priority competency areas and potential development pathways for DC competencies. Potential improvement pathways were derived by combining the APA ranking results with the perceived directional relationships identified through the NRM analysis. This integrated approach was intended to support exploratory competency development strategies rather than establish causal relationships among competency domains.

Results

Respondents’ survey of background information and its reliability analysis

Questionnaires were collected from 170 health professionals, yielding 149 valid responses, for a response rate of 87.6%. Non-participation was primarily due to non-response or incomplete questionnaire submission. Only questionnaires with complete responses were included in the final analysis; therefore, there were no missing data for the analyzed variables. The demographic characteristics of the participants (20 men and 129 women) are shown in Table 2. Of the participants, 80.5% were over 40 years of age. About two-thirds of the participants (67.8%) worked in a medical center, and the rest worked in a regional hospital. More than half of the participants (56.4%) reported having experience in more than six SDM cases. Nearly three-quarters of the participants (73.2%) had job duties that required decision-making guidance. The participants included 25 doctors, 33 nursing supervisors, 62 nurse practitioners, and 29 other personnel (including clinical nurses, case managers, and dietitians).

Table 2.

The background information of valid samples

Variables		n (%)*
Gender	Male Female	20 (13.4) 129 (86.6)
Age	Less than 30 years 30 to 39 years 40 to 49 years 50 to 59 years Over 60 years	2 (1.3) 27 (18.1) 87 (58.4) 30 (20.1) 3 (2.0)
Workplace Institutions	Medical center Regional hospital	101 (67.8) 48 (32.2)
Different levels of health providers	Doctors Nursing supervisors Nurse practitioners Others (including nurses, case managers, dietitians)	25 (16.8) 33 (22.1) 62 (41.6) 29 (19.5)
SDM experience cases	< 6 times 6–10 times > 10 times	65 (43.6) 20 (13.4) 64 (43.0)
Work as a decision coach	Yes No	109 (73.2) 40 (26.8)

*May not add up to 100% due to rounding

Cronbach’s alpha defines the reliability of the aspects’/criteria’. The reliability of the AI was 0.975, and the reliability of the PI was 0.980. The reliability of the AI and PI was higher than the proposed Cronbach’s alpha (Cronbach = 0.7). Thus, the AI and PI were highly consistent. The reliability of all aspects was 0.965, which is higher than the proposed Cronbach’s alpha; hence, all aspects were highly consistent. The reliability of the PK aspect was 0.969, and the GS aspect reliability was 0.976. The reliability of the PM aspect was 0.980, and that of the OE aspect was 0.982, which is higher than the proposed Cronbach’s alpha; hence, the criteria in these aspects were highly consistent, as shown in Table 3.

Table 3.

The analysis of reliability (Cronbach α)

Items	Aspects/criteria	Alpha
Accessibility indicator (AI)		0.975
Performance indicator (PI)		0.980
Entire aspects of the competence evaluation		0.965
Each aspect	Professional knowledge (PK)	0.969
	Guidance skills (GS)	0.976
	Process management (PM)	0.980
	Outcome evaluation (OE)	0.982

Note: Cronbach’s alpha values for all constructs exceeded 0.7, indicating acceptable internal consistency

Accessibility–performance analysis – network relation map approach

Table 4 presents the integrated results of the APA and NRM analyses. The APA results indicate that professional knowledge (PK), process management (PM), and outcome evaluation (OE) fall into the low–low (L–L) quadrant, suggesting critical gaps in both accessibility and performance and therefore representing priority areas for improvement. In contrast, guidance skills (GS) are located in the high–high (H–H) quadrant, indicating relatively strong performance that should be maintained. The NRM analysis also revealed that PK and OE exhibited positive directional association values, whereas GS and PM exhibited relatively negative ones within the perceived network structure (Fig. 2). Specifically, PK demonstrated directional associations with OE, PM, and GS; OE demonstrated directional associations with PM and GS; and PM was directionally associated with GS. These findings indicate that PK occupies a central position within the DC competency network (Fig. 2, right). Based on the combined findings, four improvement strategies were identified. GS corresponds to Strategy A (maintaining current performance), whereas PK, PM, and OE correspond to Strategy C (sequential reinforcement), emphasizing the need to strengthen core competencies in a prioritized manner.

Table 4.

The improvement strategy for competence development in decision coaches

Entire aspects	APA			NRM			IS
	AI	PI	(AI, PI)	d + r	d-r	(R, D)
Professional knowledge (PK)	-0.275	-0.700	L, L	132.024	0.702	D (+,-)	C
Guidance skills (GS)	1.365	1.468	H, H	132.573	-0.650	ID (-,-)	A
Process management (PM)	-0.061	-0.212	L, L	130.143	-0.355	ID (-,-)	C
Outcome evaluation (OE)	-1.029	-0.556	L, L	132.120	0.304	D (+,-)	C

Notes: The improvement strategies (IS) contain four classes: strategy A (keeping situation), strategy B (observing circumstance), strategy C (sequential reinforcement), and strategy D (immediate reinforcement)

Abbreviations: PK = professional knowledge; GS = guidance skills; PM = process management; OE = outcome evaluation; AI = accessibility indicator; PI = performance indicator; APA = accessibility—performance analysis; NRM = network relation map; IS = improvement strategies

Fig. 2.

The APA–NRM analysis of competence development in decision coaches

Evaluating suitable improvement paths by ranking standardized accessibility and performance across aspects

The co-primary paths of accessibility and performance indicators were integrated by ranking the aspects and establishing suitable improvement paths. In the analysis of available improvement paths, AI was ranked as GS > PM> PK > OE, and PI was ranked as GS > PM> OE > PK. The available improvement paths of AI and PI were combined, and no suitable improvement paths were identified for DCs’ competency development, as indicated in Table 5.

Table 5.

The suitable improvement paths for competency development of decision coaches

Entire Aspects of the Competence Assessment
	AI (Accessibility indicator)	PI (Performance indicator)
Rank	GS[1] > PM[2] > PK[3] > OE[4]	GS[1] > PM[2] > OE[3] > PK[4]
Available improvement paths	1. PK[3]→GS[1] {N} 2. PK[3]→PM[2]→GS[1] {N} 3. PK[3]→OE[4]→GS[1] {Y} 4. PK[3]→OE[4]→PM[2]→GS[1] {Y}	1. PK[4]→GS[1] {N} 2. PK[4]→PM[2]→GS[1] {N} 3. PK[4]→OE[3]→GS[1] {N} 4. PK[4]→OE[3]→PM[2]→GS[1] {N}
Suited improvement paths	-

Abbreviations: PK = professional knowledge; GS = guidance skills; PM = process management; OE = outcome evaluation; AI = accessibility indicator; PI = performance indicator

Sub-aspect development strategies and suitable improvement paths

Professional knowledge aspect

The APA results (Table 6; Fig. 3) indicate that PK1 criteria (Value) is located in the high–low (H–L) quadrant, suggesting that direct reinforcement is required, whereas PK2 criteria (Concept) falls in the high–high (H–H) quadrant, indicating that its current performance should be maintained. In contrast, PK3 (Timing) and PK4 (Evidence-based knowledge) are positioned in the low–low (L–L) quadrant, highlighting the need for sequential reinforcement to address gaps in both accessibility and performance.

Table 6.

The improvement strategy for the decision coaches’ competence development is stratified by four criteria

Criteria/aspects	APA			NRM			IS
Professional knowledge (PK)	AI	PI	(AI, PI)	d + r	d-r	(R, D)
Value (PK1)	1.032	-0.576	H, L	87.614	0.732	D (+,+)	D
Concept (PK2)	0.836	1.543	H, H	86.985	-0.767	ID (+,-)	A
Timing (PK3)	-0.683	-1.365	L, L	86.108	-0.370	ID (+,-)	C
Evidence-based knowledge (PK4)	-2.005	-1.512	L, L	86.684	0.405	D (+,+)	C
Guidance skills (GS)	AI	PI	(AI, PI)	d + r	d-r	(R, D)
Explanation skill (GS1)	1.228	0.853	H, H	138.679	-0.376	ID (+,-)	A
Listening skill (GS2)	1.032	1.100	H, H	138.032	0.585	D (+,+)	A
Communication skills (GS3)	1.277	1.494	H, H	140.302	-0.001	ID (+,-)	A
Teamwork Skills (GS4)	0.542	0.558	H, H	138.192	-0.208	ID (+,-)	A
Process management (PM)	AI	PI	(AI, PI)	d + r	d-r	(R, D)
Invite participation in decision-making (PM1)	0.248	0.804	H, H	232.897	-0.888	ID (+,-)	A
Discuss decision-making problems (PM2)	0.542	0.262	H, H	233.607	0.177	D (+,+)	A
Shared decision-making resources (PM3)	-0.438	-0.724	L, L	232.810	0.446	D (+,+)	C
Guide participation in decision-making (PM4)	-0.536	-0.921	L, L	234.749	0.265	D (+,+)	C
Outcome evaluation (OE)	AI	PI	(AI, PI)	d + r	d-r	(R, D)
Confirmation of values and preferences (OE1)	-0.585	-0.280	L, L	140.923	-0.804	ID (+,-)	C
Monitoring and resolving conflicts (OE2)	-1.516	-1.217	L, L	140.115	-0.213	ID (+,-)	C
Practical decision-making results (OE3)	-0.879	0.065	L, H	141.349	0.803	D (+,+)	B
Psychological support (OE4)	-0.095	-0.083	L, L	140.653	0.214	D (+,+)	C

Notes: The improvement strategies (IS) contain four classes: strategy A (keeping situation), strategy B (observing circumstance), strategy C (sequential reinforcement), and strategy D (immediate reinforcement)

Abbreviations: PK = professional knowledge; GS = guidance skills; PM = process management; OE = outcome evaluation; AI = accessibility indicator; PI = performance indicator; APA = accessibility—performance analysis; NRM = network relation map; IS = improvement strategies; PK1 = Value; PK2 = Concept; PK3 = Timing; PK4 = Evidence-based knowledge (PK4); GS1 = Explanation skill; GS2 = Listening skill; GS3 = Communication skills; GS4 = Teamwork Skills; PM1 = Invite participation in decision-making; PM2 = Discuss decision-making problems; PM3 = Shared decision-making resources; PM4 = Guide participation in decision-making; OE1 = Confirmation of values and preferences; OE2 = Monitoring and resolving conflicts; OE3 = Practical decision-making results; OE4 = Psychological support (OE4)

Fig. 3.

The decision coaches’ competency development map for the professional knowledge (PK) aspect

The NRM analysis further indicated that PK1 and PK4 demonstrated positive directional association values (d − r > 0), suggesting that these criteria held central positions within the perceived competency network structure. PK1 exhibited the strongest directional association within the professional knowledge aspect, while PK4 was closely linked to PK1 in the network. Additionally, PK3 displayed directional associations with both PK1 and PK4. These results underscore PK1’s prominent position within the professional knowledge competency aspect.

Based on the integrated APA-NRM results, the priority order of accessibility (AI) was PK1 > PK2 > PK3 > PK4, while performance (PI) was ranked as PK2 > PK1 > PK3 > PK4. By combining these rankings, three suitable improvement paths were identified (PK1→PK2→PK3; PK1→PK4→ PK3; PK1→PK4→PK2→PK3) (Table 7). These results suggest that strengthening core criteria can facilitate the improvement of less-developed competencies through sequential directional association pathways.

Table 7.

The suitable improvement paths for competency development of decision coaches

Professional knowledge (PK) aspect
	AI (Accessibility indicator)	PI (Performance indicator)
Rank	PK1[1] > PK2[2] > PK3[3] > PK4[4]	PK2[1] > PK1[2] > PK3[3] > PK4[4]
Available improvement paths	1. PK1[1]→PK2[2] {Y} 2. PK1[1]→PK4[4]→PK2[2] {Y} 3. PK1[1]→PK3[3]→PK2[2] {Y} 4. PK1[1]→PK4[4]→PK3[3]→PK2[2] {Y}	1. PK1[2]> PK2[1] {N} 2. PK1[2]→PK4[4]→PK2[1]{Y} 3. PK1[2]→PK3[3]→PK2[1]{Y} 4. PK1[2]→PK4[4]→PK3[3]→PK2[1] {Y}
Suited improvement paths	2. PK1→PK2→PK3 ; 3. PK1→PK4→PK3 ; 4. PK1→PK4→PK2→PK3
Guidance skills (GS) aspect
Rank	GS3[1] > GS1[2] > GS2[3] > GS4[4]	GS3[1] > GS2[2] > GS1[3] > GS4[4]
Available improvement paths	1. GS2[3]→GS1[2] {N} 2. GS2[3]→GS4[4]→GS1[2] {Y} 3. GS2[3]→GS3[1]→GS1[2] {Y} 4. GS2[3]→GS3[1]→GS4[4]→GS1[2] {Y}	1. GS2[2]→GS1[3] {Y} 2. GS2[2]→GS4[4]→GS1[3] {Y} 3. GS2[2]→GS3[1]→GS1[3] {Y} 4. GS2[2]→GS3[1]→GS4[4]→GS1[3] {Y}
Suited improvement paths	2. GS2→GS4→GS1; 3. GS2→GS3→GS1; 4. GS2→GS3→GS4→GS1
Process management (PM) aspect
Rank	PM2[1] > PM1[2] > PM3[3] > PM4[4]	PM1[1] > PM2[2] > PM3[3] > PM4[4]
Available improvement paths	1. PM3[3]→PM1[2] {N} 2. PM3[3]→PM2[1]→PM1[2] {Y} 3. PM3[3]→PM4[4]→PM1[2] {Y} 4. PM3[3]→PM4[4]→PM2[1]→PM1[2] {Y}	1. PM3[3]→PM1[1] {N}2. PM3[3]→PM2[2]→PM1[1] {N}3. PM3[3]→PM4[4]→PM1[1] {Y}4. PM3[3]→PM4[4]→PM2[2]→PM1[1] {Y}
Suited improvement paths	3. PM3→PM4→PM1; 4. PM3→PM4→PM2→PM1
Outcome evaluation (OE) aspect
Rank	OE4[1] > OE1[2] > OE3[3] > OE2[4]	OE3[1] > OE4[2] > OE1[3] > OE2[4]
Available improvement paths	1. OE3[3]→OE1[2] {N} 2. OE3[3]→OE2[4]→OE1[2] {Y} 3. OE3[3]→OE4[1]→OE1[2] {Y} 4. OE3[3]→OE4[1]→OE2[4]→OE1[2] {Y}	1. OE3[1]→OE1[3] {Y} 2. OE3[1]→OE2[4]→OE1[3] {Y} 3. OE3[1]→OE4[2]→OE1[3] {Y} 4. OE3[1]→OE4[2]→OE2[4]→OE1[3] {Y}
Suited improvement paths	2. OE3→OE2→OE1; 3 OE3→OE4→OE1; 4. OE3→OE4→OE2→OE1

Abbreviations: PK = professional knowledge; GS = guidance skills; PM = process management; OE = outcome evaluation; AI = accessibility indicator; PI = performance indicator; PK1 = Value; PK2 = Concept; PK3 = Timing; PK4 = Evidence-based knowledge (PK4); GS1 = Explanation skill; GS2 = Listening skill; GS3 = Communication skills; GS4 = Teamwork Skills; PM1 = Invite participation in decision-making; PM2 = Discuss decision-making problems; PM3 = Shared decision-making resources; PM4 = Guide participation in decision-making; OE1 = Confirmation of values and preferences; OE2 = Monitoring and resolving conflicts; OE3 = Practical decision-making results; OE4 = Psychological support (OE4)

Guidance skills aspect

The APA results (Table 6; Fig. 4) show that all guidance skills (GS) criteria are located in the high–high (H–H) quadrant, indicating strong accessibility and performance that should be maintained. The NRM analysis further showed that GS2 (Listening skill) demonstrated a positive directional association value (d − r > 0), indicating a relatively central position within the guidance skills network structure. GS2 showed strong directional associations with GS3 (Communication skills), whereas both GS2 and GS3 were connected to GS4 (Teamwork skills) within the perceived network structure. In addition, GS1 (Explanation skill) demonstrated directional associations with GS2, GS3, and GS4. These findings suggest that improvements in foundational listening-related skills can strengthen overall guidance competencies.

Fig. 4.

The decision coaches’ competency development map for the guidance skills (GS) aspect

Based on the integrated APA–NRM results, the priority order of accessibility (AI) was GS3 > GS1 > GS2 > GS4, while performance (PI) was ranked as GS3 > GS2 > GS1 > GS4 (Table 7). Three suitable improvement paths were identified (GS2 → GS4 → GS1; GS2 → GS3 → GS1; GS2 → GS3 → GS4 → GS1). These findings suggest that GS2 serves as a critical starting point, and that strengthening interconnected skills can facilitate the progressive improvement of guidance competencies.

Process management aspect

The APA results (Table 6; Fig. 5) indicate that PM1 (Invite participation in decision-making) and PM2 (Discuss decision-making problems) are located in the high–high (H–H) quadrant, suggesting that their current accessibility and performance should be maintained. In contrast, PM3 (Shared decision-making resources) and PM4 (Guide participation in decision-making) fall within the low–low (L–L) quadrant, highlighting priority areas requiring sequential reinforcement.

Fig. 5.

The decision coaches’ competency development map for the process management (PM) aspect

The NRM analysis showed that PM2, PM3, and PM4 had positive directional association values (d − r > 0), indicating central roles within the process management network. PM3 had the strongest directional association and close connections with PM4, while PM2 was linked to both PM3 and PM4. PM1 was associated with PM2, PM3, and PM4. These results show interconnected process management competencies in the network.

Based on the integrated APA–NRM results, the priority order of accessibility (AI) was PM2 > PM1 > PM3 > PM4, while performance (PI) was ranked as PM1 > PM2 > PM3 > PM4 (Table 7). Two suitable improvement paths were identified (PM3→PM4→PM1; PM3→PM4→PM2→PM1), suggesting that strengthening core process-related competencies can facilitate the sequential improvement of higher-level competencies.

Outcome evaluation aspect

The APA results (Table 6; Fig. 6) indicate that OE1 (Confirmation of values and preferences), OE2 (Monitoring and resolving conflicts), and OE4 (Psychological support) are located in the low–low (L–L) quadrant, suggesting critical gaps in both accessibility and performance that require sequential reinforcement. In contrast, OE3 (Practical decision-making results) is positioned in the low–high (L–H) quadrant, indicating relatively strong performance but limited accessibility, and thus requiring continued observation and support.

Fig. 6.

The decision coaches’ competency development map for the outcome evaluation (OE) aspect

The NRM analysis indicated that OE3 and OE4 had positive directional association values (d − r > 0), signifying relatively central positions within the outcome evaluation network structure. OE3 exhibited the strongest directional association and maintained close network connections with OE4. OE2 demonstrated directional associations with both OE3 and OE4. Additionally, OE1 showed directional associations with OE2, OE3, and OE4. These data suggest interconnected relationships among outcome evaluation competencies within the perceived network structure.

Based on the integrated APA–NRM results, the priority order of accessibility (AI) was OE4 > OE1 > OE3 > OE2, while performance (PI) was ranked as OE3 > OE4 > OE1 > OE2 (Table 7). Three suitable improvement paths were identified (OE3→OE2→OE1; OE3→OE4→OE1; OE3→OE4→OE2→OE1), suggesting that strengthening key outcome evaluation competencies can facilitate the progressive improvement of related criteria.

Overall, the integrated APA–NRM findings suggest that certain core competencies occupy relatively central positions within the perceived competency network and may serve as important considerations for prioritizing DC competency development. These findings provide a systematic and exploratory basis for DC competency development planning in SDM practice.

Discussion

This study applied an integrated accessibility–performance analysis and network relation map approach to evaluate accessibility, performance, and perceived directional relationships among DC competencies in SDM. The results suggest that professional knowledge (PK) demonstrated the strongest perceived directional associations within the competency network structure, with key criteria identified across all aspects, including value (PK1), listening skill (GS2), shared decision-making resources (PM3), and practical decision-making results (OE3). These findings provide an exploratory and structured basis for prioritizing competency development strategies.

Professional knowledge aspect

The identified improvement paths (e.g., PK1→PK2→PK3; PK1→PK4→PK3; PK1→PK4→PK2→PK3) consistently originate from SDM values (PK1), highlighting their foundational role. These paths suggest that reinforcing SDM values supports the development of conceptual understanding, evidence-based knowledge, and appropriate timing of SDM. This is consistent with prior findings that SDM is conceptually simple but challenging to implement in practice [24]. Evidence-based medicine (EBM) and SDM are complementary components of patient-centered care [47], yet they are often taught separately, limiting their integration. Integrating SDM into EBM training is feasible and supports the incorporation of patient values into clinical decision-making [48]. DC plays a key role in bridging EBM and SDM, enabling clinicians to recognize when SDM should be initiated and to communicate evidence effectively, thereby improving decision quality and patient involvement [47].

Guidance skills aspect

The improvement pathways (e.g., GS2→GS3→GS1; GS2→GS4→GS1) indicate that listening skill (GS2) serves as the key entry point for developing other guidance competencies. These pathways suggest a progressive relationship in which active listening enhances communication and teamwork, ultimately improving explanatory skills. This aligns with previous research emphasizing the importance of listening and communication in patient-centered care [26–28], as well as their role in fostering teamwork and collaboration [31, 32]. Together, these findings highlight listening-centered training as a critical strategy for strengthening guidance competencies.

Process management aspect

The identified improvement paths (e.g., PM3→PM4→PM1; PM3→PM4→PM2→PM1) suggest that the use of SDM resources, particularly PtDAs, serves as a key starting point for enhancing process management competencies. These pathways indicate that strengthening decision support tools can facilitate guided participation, improve discussion of options, and ultimately enhance patient involvement in decision-making. This is consistent with prior evidence showing that PtDAs improve patient engagement and participation [33, 34].

Outcome evaluation aspect

The improvement pathways (e.g., OE3→OE2→OE1; OE3→OE4→OE1) indicate that practical decision-making outcomes (OE3) act as the primary driver of outcome evaluation competencies. These paths suggest that strengthening decision outcomes can enhance conflict resolution, psychological support, and alignment with patient values and preferences. Previous studies have shown that effective conflict resolution improves decision quality [49], while decision outcomes and emotional support contribute to patient satisfaction and engagement [50, 51]. These findings highlight the importance of integrating evaluation, emotional support, and value clarification in SDM.

Comparison with previous studies and practical implications

Previous studies have applied DEMATEL and related methods to explore healthcare decision-making and competency development. For example, DEMATEL has been used to identify key factors influencing vaccine hesitancy [38], training effectiveness [39], and SDM-related competencies [22, 35]. Our previous work also applied a hybrid multi-criteria decision-making model to explore physician competencies in SDM [40]. In addition, various training programs have focused on developing DC competencies through decision aids, coaching sessions, communication training, and participatory approaches [13, 25, 52]. Consistent with these studies, our findings support the use of structured, multi-criteria approaches to identify key competencies and prioritize improvement strategies. In practice, training programs should emphasize foundational competencies and adopt sequential development pathways to enhance SDM implementation. Unlike conventional competency surveys that only rank importance or performance, the APA–NRM approach also shows the network structure among competency domains. This insight can help educators and healthcare organizations find key leverage competencies. These may affect the development of others, providing a more systematic basis for planning competency development.

From a practical perspective, the findings of this study provide actionable guidance for enhancing DC competencies. The identified key competencies and perceived directional relationships can support the prioritization of training content, while the structured improvement pathways offer a framework for designing stepwise competency development strategies. In clinical settings, these findings may help healthcare institutions strengthen communication, decision support, and patient engagement in SDM, thereby improving the quality of patient-centered care.

Limitations of the study

This research has several limitations. First, data were collected exclusively from two branches of the institution, comprising a single medical center and a regional hospital in southern Taiwan. As a result, the findings may not be generalizable to other health care settings or countries. The extent to which these results can be applied to research on the competence of DCs is yet unknown. Second, the cross-sectional and self-reported nature of the data restricts the ability to establish causal relationships. The directional relationships among competencies identified by DEMATEL are based on participants’ perceptions and should not be interpreted as empirical causal pathways.Third, the use of snowball sampling for respondent recruitment requires caution, as the sample may not represent the broader population of healthcare professionals in Taiwan. Fourth, the majority of respondents were nurse practitioners and nursing supervisors, thereby narrowing the data to primarily non-physician healthcare professionals. Consequently, proposals for implementing SDM may be incomplete. Finally, the study did not address DCs’ competencies in overseeing specific or complex diseases. Nevertheless, the integrated APA–NRM approach delivers a structured exploratory framework for assessing competency priorities and potential development pathways in SDM.

Future research directions

Future studies should further validate the proposed competency framework across diverse healthcare settings and various professional groups. Multicenter and cross-cultural research could enhance the generalizability of these findings. Longitudinal and intervention-based designs are recommended to examine the impact of competency development strategies on SDM practices over time. Incorporating patient perspectives would provide a more comprehensive understanding of DC competencies in patient-centered care. Additionally, integrating quantitative methods could clarify the interrelationships among competency domains and assess the applicability of the framework in varied healthcare environments. Further research should also investigate the development of DC competencies in specific clinical scenarios, particularly in response to changes following the post-COVID-19 era.

Conclusions

This study identified key competency gaps, analyzed perceived directional relationships, and proposed structured improvement pathways for DC competencies in SDM. This study applied an integrated APA–NRM approach to analyze competency gaps and perceived directional associations among DC competencies in SDM. The findings identify key competencies and suggest potential improvement pathways that may support structured competency development. However, these improvement paths are derived from perceived directional relationships using the DEMATEL method and should be interpreted as exploratory and heuristic rather than empirically validated. More importantly, this study demonstrates the feasibility of applying the integrated APA–NRM approach to competency assessment in SDM-related healthcare education, providing a methodological reference for future competency-based research. Further studies are needed to validate these findings through empirical testing, longitudinal designs, and multi-source data. From a practical perspective, the findings of this study can support the design of structured training programs and competency development strategies for DCs. By prioritizing key competencies and following identified development pathways, healthcare institutions may enhance the implementation of SDM and improve patient-centered care.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AI

Accessibility indicator

APA

Accessibility–performance analysis

CIPP

Context, Input, Process, Product

CVI

Content validity index

CVR

Content validity ratio

DC

Decision coach

DEMATEL

Decision-making trial and evaluation laboratory

EBM

Evidence-based medicine

GS

Guidance skill

IPA

Importance–performance analysis

KSA

Knowledge, Skills and Attitudes

NRM

Network relation map

OE

Outcome evaluation

PK

Professional knowledge

PI

Performance indicatory

PM

Process management

PtDA

Patient decision aid

SDM

Shared decision making

Author contributions

S-F Yu contributed to the study design, data analysis and interpretation, and manuscript drafting and revision for this study. Y-C Lin conducted the survey. H-T Yu and C-L Lin contributed to conceptualizing the study, data interpretation, and critical manuscript revision. All authors read and approved the final manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Requests should include a brief description of the research purpose and will be subject to approval in accordance with institutional regulations.

Declarations

Ethical approval

Ethics permission was granted by the Institutional Review Board of the Chang Gung Memorial Hospital (IRB No: 202300200B0C601). Before conducting the questionnaire, we provided all participants with a participant information sheet to explain that their answers would be anonymized so that no individual could be identified, that answers would be used only to analyze survey results, and that the questionnaire was voluntary. Because participation was anonymous and voluntary, consent was implicitly obtained by completing the questionnaire.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.