Patient-physician communication of health and risk information in the management of cardiovascular diseases and diabetes: a systematic scoping and narrative review

doi:10.21203/rs.3.rs-4932591/v1

Background

The communication of health and risk information is an integral and essential part of patient-physician interaction. Effective communication of risk information in the context of chronic diseases such as cardiovascular diseases and diabetes has been shown to improve medication adherence, increase physical activity levels, and improve dietary control. Patients who are able to understand their risk profile and risk factors, are better able to self-manage and work towards modifying their lifestyle behaviours as part of a continuing shared decision-making process with physicians. This scoping review examines the evidence on patient-physician risk communication strategies, approaches and interventions for CVDs and diabetes management in the primary care and secondary outpatient settings.

Methods

A comprehensive database search for quantitative and qualitative studies was conducted in PubMed, Embase, Web of Science, Scopus, CINAHL, PsycINFO, and Cochrane Library from 1^st Jan 2000 to 3rd October 2023. Two reviewers independently performed the screening of articles, studies that specifically report on patient-physician risk communication processes were included. Relevant data were extracted and analysed using descriptive summaries and narrative synthesis. Results are reported in accordance with PRISMA-ScR guidelines. Included articles were appraised for quality following JBI critical appraisal and MMAT tools.

Results

A total of 8378 articles published between Jan 2000 to 3^rd October 2023 were screened. After full text review of 88 articles, a total of 30 articles, consisting of 15 qualitative, 14 quantitative and 1 mixed method studies were included. Common areas of inquiry among included articles on patient-physician risk communication include: (1) understanding and recalling risk information, (2) strategies and approaches used by physicians to communicate risk, and (3) interventions to improve the communication of risk. Studies reveal how physicians use a range of strategies, approaches and interventions to discuss risk with patients with CVD and diabetes. Based on the included articles, we present and discuss each theme narratively in detail.

Conclusion

There is a critical need for further research into risk communication strategies for cardiovascular diseases (CVDs) and diabetes. Research should focus on developing targeted approaches that enhance patients' understanding of their risk profiles. Additionally, it should provide evidence-based guidelines to assist physicians and healthcare professionals in improving risk communication within clinical settings. The goal is to facilitate patient comprehension of their health risks and support sustained lifestyle and behavioural changes through informed, evidence-based methods. Misalignment in communication may lead to confusion, dissatisfaction, and lack of clarity, which may impede the effective management of chronic conditions.

Patient-physician communication

diabetes mellitus

cardiovascular disease

risk communication

risk factor communication

risk presentation

interpretation of risk

decision tools

chronic disease management

shared decision making.

Patient-physician communication is an integral and essential component of clinical care in the management of chronic diseases such as cardiovascular diseases (CVDs) and diabetes. Not only do physicians need to establish rapport and trust with patients during consultations sessions to facilitate the exchange of personal health information, patients have to voluntarily provide details about their medication intake and lifestyle behaviour so that physicians can provide an accurate prognosis for each patient. Guided by the natural history progression, present health status, and specific risk factors or symptoms exhibited by each patient during clinical encounters, physicians aim to accurately evaluate and provide informed recommendations on the next course of action for patients to take.

For chronic conditions such as cardiovascular diseases and diabetes which are heterogeneous and multi-factorial in aetiology (DeFronzo et. al., 2015, Dokken, 2008), physicians need to be cognizant and account for individual as well as general factors when assessing a patient. Individual level factors such as age, gender, the specific stage or duration of condition, an outline of co-morbidities, present physiological ability, and the social background of each patient are evaluated in tandem with standardised measures indicating the progression, or persistence of conditions. These standardised indicators are clinical guidelines recommended by professional bodies (ADA, 2023a, Marx et. al., 2023, Virani et. al., 2023) and are reference points to determine if treatment to target thresholds are achieved and whether a patient’s condition is well managed (ADA, 2023b). Once a patient is assessed, the physician at the primary care or outpatient clinic will prescribe the appropriate medication or lifestyle advice, titrate a patient’s medication, and if required, order additional follow up tests with a clinical laboratory or checks with another specialist doctor to determine the extent and severity of co-morbid complications.

Since the compliance of patients is important in shaping sustained health outcomes, the effective communication of risks is a crucial step in raising self-awareness regarding a patient’s own risk factors, and to convey how inadequately controlled CVD and diabetes risk factors may develop into future complications (Fischer, 2002, Schulberg et. al., 2022). Whilst physicians try to present a realistic description of risks appropriate to each patient, the actual communication process also involves reaching out, connecting, and empathising with a patient’s circumstances and social background at the same time (Zhang et. al., 2024). The negative consequences and dangers of disease risks conveyed by physicians has to be tempered by understanding the unique psychosocial contextual background of each patient, and the personal challenges each patient is facing in managing one’s own health (Hinder, 2012). By showing compassion, care, and listening actively, patients develop greater trust and satisfaction with their physicians, who in turn may be better able to guide patients to control their own health indicators such as haemoglobin A1c, blood glucose, and cholesterol levels (Wollny et. al., 2019). Patients on the other hand, will be more activated to take charge of their own health (Bonds et. al., 2004, Becker and Roblin, 2008).

Despite the clear implications of effective risk communication and the role it plays in chronic disease management, evidence show that dyadic patient-physician risk communication remains less than ideal and faces considerable challenges in practice (Ha, 2010). Effective communication between patients and physicians may be mediated or influenced by patients with low literacy or education levels (Williams et. al., 2002), linguistic factors such as language ability and comprehension (Sudore et. al., 2009), numerical ability (Cavanaugh et. al., 2008), ethnicity or race factors (Johnson et. al., 2004), financial costs concerns (Alexander et. al., 2003), and emotive considerations (Larson and Yao, 2015). From the provider’s perspective, physicians may find it challenging to balance empathy and compassion while communicating risk information in the context of systemic pressures, such as workload and time constraints that inhibit optimal conversations that affect health outcomes (Dugdale, 1999, Ratanawongsa, 2008, Yuguero, 2017). Likewise, interpersonal factors such as the communicative style of the physician (Beck et. al., 2002), tone, nonverbal gestures (Larsen and Smith, 1981), ethnic and cultural concordance between the patient and physician (Cooper et. al., 2003, Shen et. al., 2018) can significantly affect the overall quality of communication or impede the conveyance of risk related information that is increasingly crucial for long term chronic conditions that often requires intensive self-management.

Aims and Rationale

The purpose of this systematic scoping and narrative review is to scope the literature on patient-physician risk communication related to CVDs and diabetes management in the primary care or secondary outpatient setting. Although the communication of risk information is often a key part of patient-physician interaction, this is not often readily reported in studies that examine patient-physician communication or relationships more broadly. Studies often either refer to patient-physician communication in general or refer to risk perceptions, without specifying how actual communicative processes regarding risk information is conveyed, or what interventions has been taken to increase risk awareness of chronic disease risk factors. Other studies refer more generally to the satisfaction or quality of patient-physician relationships, without indicating what interactional processes makes patient-physician relationships satisfactory. We include both CVD and diabetes since both are increasingly prevalent and closely interrelated chronic diseases, and because diabetes is a risk factor for CVDs.

This review aims to map the available literature about:

What aspects of risk communication and understanding of risk is reported in the primary care or outpatient setting?
What communication tools are used to facilitate patient-physician risk communication?
What components of interventions are applied in studies to improve patient-physician risk communication in the clinical setting?
What are gaps in the literature related to patient-physician risk communication?

Protocol Registration and Guidelines

We follow Arksey and O’malley’s (2005) list of stages as an overarching guide in conducting this scoping review. The Joanna Briggs Institute (JBI) guidance was used to develop a scoping review protocol, which has been registered on the Open Science Framework platform (https://doi.org/10.17605/OSF.IO/CQFD5). We use the PICO guideline incorporated in the protocol as a framework to help structure questions in planning for this review, and in starting out with a list of search terms (Schardt et. al., 2007). In reporting the findings of this review, we follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines (Tricco et. al., 2018) [Additional File 1]. During the review of full text articles, we adjusted the list of aims in the protocol to align more closely with the list of articles generated, and so that the scope of questions are more centred on the methods, tools, and interventions used to improve risk communication.

Publication Type and Study Design Inclusion

This scoping and narrative review considers experimental and quasi-experimental study designs including randomized and non-randomized controlled trials, before and after studies, and interrupted time-series studies. Analytical observational studies were considered for screening, including prospective cohort, case-control, analytical cross-sectional, and retrospective cohort studies. All qualitative and mixed methods study types are considered for this review. Protocols, case series, case studies, or case reports were excluded from this review. Editorial, opinion, commentary, review papers, professional guidelines, conference abstracts or reports, and letters were also excluded. Non-English studies were excluded. During the screening process, we decided to include descriptive cross-sectional studies if it informs the study aims.

Database Search and Information Sources

Abstract and full text in English. published between 1^st January 2000 to 3^rd October 2023 were searched from 7 databases, including Pubmed, Embase, Web of Science, Scopus, CINAHL, PsycINFO, and Cochrane Library, with the help of a medical librarian (YM). The librarian tailored the final search strategy for each database. The search terms were refined collaboratively between a reviewer (AC) and the librarian over a few sessions and discussed with a second reviewer (WT) to check that the results were relevant. An initial list of search terms was first implemented on Pubmed, followed by a review of the first 250 results by the first reviewer (AC) to check if the range of articles in the search results were acceptable. The librarian then provided additional suggestions to improve the search terms, such as editing or adding a series of MeSH terms so that all CVD and diabetes articles would be adequately included.

Search terms were grouped into 2 broad categories. The first set of terms covers broadly the span of conditions related to CVD or diabetes, such as "cardiovascular diseases" or “diabetes milletus” using MeSH terms, which includes all associated conditions that fall under each MeSH category. The first set of terms were then combined with a Boolean AND with the second set of terms focusing primarily on patient-physician communication or risk communication, using terms such as “risk communication*” or “patient-provide engagement”. Terms were truncated to include variations to broaden search terms. The final search strategy and full list of search terms applied to all databases can be found in additional file 2.

Screening and Study Selection

After initial implementation of the search terms across databases and duplicates removed by the medical librarian, two reviewers (AC and WT) screened article titles and abstracts independently using Rayyan (Ouzzani et. al., 2016). Reviewers met regularly at monthly intervals to discuss and resolve discordant articles that were included or excluded. Any disagreement was resolved through discussion and by consensus between reviewers. One key screening decision made during multiple meetings were to exclude articles that did not specifically involve a patient-physician interaction or explicitly report a patient-physician communication process, even if risk perception or risk communication is reported as part of the study. Another decision made after discussion was to include studies on risk communication even if participants are recollecting about a patient-physician encounter or who was interviewed/surveyed post-visit that is not necessarily in the clinical setting.

Eligibility Criteria

Inclusion Criteria

Included studies are quantitative or qualitative studies that report on the communication of health and risk information between physicians and patients with cardiovascular diseases or diabetes in the primary or outpatient care setting. Quantitative studies may be observational or an intervention study. Observational studies include studies that examine processes or aspects of risk communication, and any health and behavioural effects that arise because of risk communication processes. Interventions that have the intention to improve risk communication practices that shape or influence behavioural outcomes are included in this study. All qualitative study types utilising different qualitative methodologies are included in this review for screening.

Excluded Studies

We excluded studies where the intended aim of the study or study population is not about CVD or diabetes, or general population studies where populations with CVD or diabetes are not explicitly demarcated. Groups that have an aggregate risk profile that can vary markedly and distinctly for specific sub-groups were excluded, including paediatric, adolescent, pregnancy, natal, postpartum, cancer, dialysis, smoking and tobacco studies, or studies involving institutionalised or formerly institutionalised groups such as veterans, military personnel, prisoners, or students, the rationale being that the emphasis and context of risk communication may differ substantially for these specific groups. We excluded studies conducted in inpatient, emergency department that generally involves acute patients, or palliative care settings. Validation studies of surveys, modelling studies, animal, microbiological, cellular, or genetic studies that don’t typically involve patient-physician communication were excluded. After some deliberation, the study team decided to exclude systematic or other review articles that may use a separate set of selection criteria in the selection of studies.

Data Extraction

Data extraction was conducted by a reviewer (AC), who read through all included articles thoroughly, based on a data extraction template that was revised and updated from the protocol version and edited accordingly as included studies were reviewed. Data was first extracted to a summary data table that report all basic study attributes such as author, year, country, study population brief characteristics, study design or methodological orientation, sample size, data collection method, and study aims and results.

For study population characteristics extracted, we focus on the mean age or age range of the study population, the basic health characteristics of the population, and the region where the population is from, where provided. Study results extracted focused on risk or patient-physician risk communication aspects as reported. For qualitative and mixed methods studies, data was extracted mainly from the results section, while for quantitative studies, results were extracted from both the results section and relevant results from data tables. Interventions for randomised or controlled trials were extracted from the methods section of relevant articles.

The reviewer manually analysed the summary data table and full text to extrapolate relevant details into the key themes of this review and to transfer data to section specific data tables (Tables 1-4), namely: (1) understanding risk in the context of patient-physician communication, (2) strategies and approaches used by physicians in patient-physician risk communication, and (3) interventions to improve the communication of risk information. Theme (1) focuses mainly on patient’s perspectives, (2) focuses mainly on physician perspectives, while (3) summarises the list of interventions components that have been used in enhancing risk communication. A second (WT) and third (SA) reviewer subsequently checked and validated the data extracted independently. Any differences in the data extracted were resolved through discussion and consensus.

Quality Appraisal

A quality appraisal was conducted to check for the quality of the articles selected for this systematic scoping and narrative review. For qualitative articles included, we refer to the Consolidated criteria for reporting qualitative research (COREQ) guidelines (Tong et. al., 2007) to appraise the articles. A reviewer (AC) first assessed all qualitative articles to check if the articles meet the list of criteria provided, a second reviewer (WT) subsequently validated the accuracy of appraisal conducted by the first reviewer, based on a selected number of articles. Any differences were resolved through discussion by both the first and second reviewer.

For quantitative studies, the JBI critical appraisal tool for randomised controlled trials (RCTs) (Barker et.al, 2023), quasi-experimental (Tufanaru et. al., 2017), and cross-sectional studies (Moola et. al., 2015) were used to appraise included studies. Quantitative articles were first reviewed by a reviewer (SA), who is a senior statistician, followed by a second reviewer (AC) , who assessed the quality of selected articles independently. Differences were resolved through discussion. The only mixed methods study included in this review was assessed by a reviewer using the mixed methods appraisal tool (Hong et. al., 2018) A summary of all the studies assessed and a list of criteria checked can be found in additional file 3.

A total of 8378 article titles and abstract were screened from all databases, of which 8348 were excluded based on the eligibility criteria after consensus were reached by the reviewers (AC, WT). 88 articles were sought for retrieval for full text to be read and reviewed independently by both reviewers, after which a further 58 articles were excluded. A total of 30 articles were included for data extraction for this review.

[Figure 1. PRISMA flow diagram]

Description of included studies on patient-physician risk communication

Studies identified were heterogenous in scope and focus. Of the 30 of articles included, 22 articles are related to CVDs (n=22/30, 73.3%), comprising 13 qualitative (n=13/22, 59.1%), 9 quantitative studies (n=9/22, 40.9%), and 1 mixed methods study (n=1/22, 4.5%). Out of 13 qualitative studies, 10 used a semi-structured interview approach to data collection (n=10/13, 76.9%), with 1 study using a qualitative descriptive approach (n=1/13, 7.7%), 1 that conducted focus group discussions (n=1/13, 7.7%), and 1 using both participant observation and interviews (n=1/13, 7.7%). Out of 9 quantitative studies related to CVDs, 7 were randomised controlled or controlled trials (n=7/9, 77.8%), 1 an interrupted time-series study (n=1/9, 11.1%), and 1 a cross-sectional study (n=1/9, 11.1%). 7 out of 30 articles included in this review are related to diabetes (n=7/30, 26.7%), comprising a total of 2 qualitative (n=2/7, 28.6%) and 5 quantitative studies (n=5/7, 71.4%). Out of the 2 qualitative studies, 1 used a semi-structured interview (n=1/2, 50.0%), and 1 a focus group discussion approach (n=1/2, 50.0%). For 5 quantitative studies related to diabetes, 3 studies were randomised controlled trials (n=3/5, 60.0%) and 2 studies were cross-sectional (n=2/5, 40.0%).

Studies selected were conducted in or referred to primary or secondary care settings. Of 22 articles on CVD, 17 studies referred to primary care settings (n=17/22, 77.3%), while 3 occurred in secondary care settings (n=3/22, 13.6%), and 3 in both primary and secondary care settings (n=3/22%). Primary care settings were mainly GPs (n=11/17, 64.7%), family practices (n=3/17, 17.6%), and community health centres (n=1/17, %). 2 studies did not state specifically the actual primary care type (n=2/17, 11.8%). Secondary care settings were mainly atrial fibrillation or transient ischemic attack clinics (n=3/17, 17.6%). 3 studies that included participants from both primary and secondary care settings did not specifically state the exact venue of study; only 1 mentioned primary care setting as GP sites. Out of 7 articles on diabetes, 3 studies referred to primary care settings (n=3/7, 42.9%), and 4 to secondary care settings (n=4/7, 57.1%). Primary care settings were GPs, community health service centres, and 1 which was not stated. Secondary care settings were a teaching hospital, university clinic, a surgery practice, and a diabetes centre. A summary of key characteristics of included studies can be found in Table 1.

Table 1 Key Characteristics of Included Studies

No.	Author (Year) / Country	Study Design / Methodological Orientation*	Data Collection Method	Primary / Secondary Setting	Specific Study Setting	Study Participants (Patients/Physicians /HCPs/Public)**	Sample Size***	Specific CVD/Diabetes condition (if stated)
CVD – Quantitative Articles
1.	Adarkwah et. al. (2016) / Germany	Randomized controlled trial	Surveys	Primary	GPs	Patients	Intervention Group: 157 patients Control Group: 147 patients	N.A.
2.	Adarkwah et. al. (2019) / Germany	Randomized controlled trial	Surveys	Primary	GPs	Patients	Intervention Group: 148 patients (Analysed) Control Group: 146 patients (Analysed)	N.A.
3.	Casebeer et. al. (2009) / USA	Controlled trial	Surveys / Patient Electronic Records	Primary and Secondary	Not stated	Patients	Intervention Group: 355 patients Control Group: 196 patients	N.A.
4.	Goodyear-Smith et. al. (2011) / New Zealand	Cross-sectional survey	Surveys	Primary	Family practice	Patients	934 patients	N.A.
5.	Krones et. al. (2008) / Germany	Randomized controlled trial	Surveys	Primary	Family practice	Patients	Intervention Group: 550 patients (at index consultation) 460 patients (at follow up) Control Group: 582 patients (at index consultation) 466 patients (at follow up)	N.A.
6.	Roach et. al. (2010) / USA	Randomized controlled trial	Surveys	Primary	Not stated	Patients	Intervention Group: 51 patients Control Group: 47 patients	N.A.
7.	Tawfik et. al. (2016) / Egypt	Randomized controlled trial	Surveys	Primary	family medicine outpatient clinic	Patients	Intervention Group: 127 patients (at allocation) 107 patients (at follow up) Control Group: 128 patients (at allocation)	N.A.
8.	Welchen et. al. (2012) / Netherlands	Randomized controlled trial	Surveys	Primary	GPs	Patients	Intervention Group: 132 patients Control Group: 130 patients	N.A.
9.	Williams et. al. (2016) / Indonesia	Interrupted Time Series	Surveys	Primary	Community health centres	Patients	675 patients	N.A.
CVD – Qualitative Articles
10.	Barfoed et. al. (2015) / Denmark	Systematic text condensation	Semi-structured interviews	Primary	GPs	Physicians	10 GPs	N.A.
11.	Bengtsson et. al. (2021) / Sweden	Qualitative content analysis	Semi-structured interviews	Primary	GPs	Physicians	15 GPs	N.A.
12.	Bonner et. al. (2014) / Australia	Framework analysis	Semi-structured interviews	Primary	GPs	Physicians	25 GPs	N.A.
13.	Borg Xuereb et. (2016) / UK	Interpretative phenomenology	Semi-structured interviews	Secondary	AF Clinics	Patients and physicians	11 patients 16 physicians	Atrial Fibrillation
14.	Bridgwood et. al. (2020) / UK	Constant comparative method	Non-participant observation / Semi-structured interviews	Secondary	TIA Clinics	Patients and physicians	15 consultations with 9 stroke specialists observed 15 patients, with 12 accompanying relatives	Transient Ischemic Attack
15.	Durack-Brown et. al. (2003) / France	Not stated	Semi-structured interviews	Primary and Secondary	GPs and specialist consultants	Patients and physicians	27 patients 21 physicians	N.A.
16.	Fisseni et. al. (2008) / Germany	Qualitative content analysis	Semi-structured interviews	Primary	GPs	Physicians, healthcare professionals, public	6 GPs 4 healthcare assistants 12 laypeople	N.A.
17.	Hawking et. al. (2019) / UK	Thematic analysis	Semi-structured interviews	Primary	GPs	Patients	18 patients	N.A.
18.	Honey et. al. (2015) / UK	Qualitative descriptive study	Qualitative Descriptive Interviews	Primary	GPs	Patients	37 patients	N.A.
19.	Polak and Green (2015) / UK	Constant comparative method	Semi-structured interviews	Primary and Secondary	Not stated	Patients	34 patients^	N.A.
20.	Rosal et. al. (2004) / USA	Constant comparative method	Focus group discussions	Primary	Not stated	Physicians	11 physicians	N.A.
21.	Salmasi et. al. (2018) / Canada	Qualitative descriptive study	Semi-structured interviews	Secondary	AF Clinics	Patients and physicians	11 clinicians 10 patients	Atrial Fibrillation
22.	van Steenkiste et. al. (2004) / Netherlands	Constant comparative method	Semi-structured in-depth interviews	Primary	GPs	Physicians	15 GPs	N.A.
CVD – Mixed Methods
23.	Kirby and Machen (2009) / UK	Cross-sectional survey / Thematic analysis	Phase 1: Surveys Phase 2: Focus Group Discussions	Primary	GPs	Patients and physicians	Phase 1 (nationwide survey results reported only) 202 GPs recalled using Factfile (out of 600 GPs from the national sample) 181 GPs who accessed risk calculator Phase 2: 17 GPs 4 practice nurses 1 nurse practitioner	N.A.
Diabetes – Quantitative
24.	Denig et. al. (2014) / Netherlands	Pragmatic randomised controlled trial	Surveys	Primary	GPs	Patients	Intervention Group 225 patients (at allocation) 199 (analysed for primary outcomes) Control Group (usual care) 119 patients (at allocation) 107 patients (analysed for primary outcomes)	N.A.
25.	Huang et. al. (2016) / USA	Pilot randomised trial	Surveys	Secondary	Clinics at a university	Patients	Intervention Group 75 patients (at allocation and analysed) Control Group: 25 patients (at allocation and analysed)	N.A.
26.	Lyles et. al. (2012) / USA	Cross-sectional survey	Surveys	Primary	Not stated	Patients	569 patients	N.A.
27.	Ritholz et. al. (2017) / USA	Cross-sectional survey	Surveys	Secondary	Diabetes centre	Patients	128 adults	N.A.
28.	Rouyard et. al. (2018) / UK	Pilot randomised controlled trial	Surveys	Secondary	Surgery practice	Patients	Intervention Group 20 patients (at allocation) 18 patients (Analysed at follow up) Control Group 20 patients (at allocation) 18 patients (Analysed at follow up)	N.A.
Diabetes – Qualitative
29.	Ledford (2011) / USA	Grounded theory	Semi-structured interviews	Secondary	Teaching hospitals	Physicians	12 physicians	N.A.
30.	Yao et. al. (2022) / China	Thematic analysis	Focus group discussions	Primary	Community health service centres	Patients	22 patients	N.A.

*Given that study design is often symbiotic with methodological approach for qualitative studies than for quantitative studies, we report methodological orientation for qualitative studies in this column.

** For RCTs, patients rather than both patients and physicians or other healthcare professionals, are indicated as study participants in this column if the primary and secondary outcomes of the study are based on data collected from patients.

***For RCTs, sample sizes are stated for analysis and follow up groups where applicable.

^ For Polak and Green (2015), participants were recruited in the community setting who were offered statin as part of primary and secondary prevention. Therefore, we take the assumption that participants are most likely patients as well.

[Table 1 Key characteristics of included studies]

Most studies on patient-physician risk communication were conducted and concentrated in several high-income countries (HICs) in Europe, North America, and Oceania, such as UK (n=7/30, 23.3%), Germany (n=4/30, 13.3%), Netherlands (n=3/30, 10.0%), Denmark (n=1/30, 3.3%), Sweden (n=1/30, 3.3%), France (n=1/30, 3.3%) in Europe; USA (n=7/30, 23.3%), Canada (n=1/30, 3.3%) in North America, and 1 each in Australia and New Zealand respectively. Only 1 (1/30, 3.3%) (Yao et. al., 2022) study was conducted in an upper middle-income country (UMICs) (China), and only 2 (2/30, 6.6%) (Tawfik et. al., 2016, Williams et. al., 2016) conducted in 2 lower middle-income countries (LMICs) (Egypt and Indonesia), suggesting a paucity of research from non-HIC countries. There were no studies from countries in Africa, South America or other parts of Asia other than China. A summary of countries where studies are conducted can be found in Table 2.

Table 2 Summary of countries where studies were conducted

HIC	Continent	Country	Number	Percentage (%)
	Europe	UK	7	23.3%
		Germany	4	13.3%
		Netherlands	3	10.0%
		Denmark	1	3.3%
		Sweden	1	3.3%
		France	1	3.3%
	North America	USA	7	23.3%
	North America	Canada	1	3.3%
	Oceania	Australia	1	3.3%
	Oceania	New Zealand	1	3.3%
UMIC	Asia	China	1	3.3%
LMIC	Africa	Egypt	1	3.3%
LMIC	Asia	Indonesia	1	3.3%
Total			30	100%

Understanding risk in the context of patient-physician communication

CVD related risk information

15 articles, consisting of 8 qualitative (Bonner et. al, 2014, Bridgwood et. al., 2020, Durack-Brown et. al., 2003, Fisseni et. al., 2008, Hawking et. al. 2019, Honey et. al., 2015, Polak and Green et. al., 2015, Salmasi et. al., 2018) 6 quantitative studies (Adarkwah et. al., 2016, Adarkwah et. al., 2019, Goodyear-smith et. al., 2011, Roach et. al., 2010, Tawfik et. al., 2016, Welschen et. al., 2012) and 1 mixed methods study (Kirby et. al., 2009) focused on different aspects of patient-physician risk communication. We summarise and cluster the articles into 3 thematic areas emergent from articles on CVDs: (1) understanding and recalling risk information in the context of patient-physician communication, (2) risk formats and its effects on the risk communication process, and (3) perception of risk information over time.

Understanding and recalling risk information

Many patients tend to perceive risk in binary terms, such as whether they were ‘at risk’ or ‘not’ (Hawking et. al., 2019), or understood future risks in generic, non-numeric terms, even if numeric values were often used to discuss weight, blood pressure and medication dosage (Polak and Green et. al., 2015). A qualitative study of patients with high 10-year CVD risk in the UK found that most patients do not remember receiving explanations about their CVD risk score or what their scores mean (Honey et. al., 2015). As such, most healthcare professionals tend to explain risk narratively rather than describe risk in percentage terms to patients (Honey et. al., 2015). In a study conducted in Vancouver, Canada, physicians describe how atrial fibrillation (AF) patients tend to overestimate their bleeding risk regarding anti-coagulants and have difficulty weighing risk against benefits (Salmasi et. al., 2018). In response to a lack of interest or inability of patients to understand their own risk, physicians communicate an individual’s risk of stroke to a patient less often unless there is a need to do so, such as when patients show resistance towards medications, if there is an unjustified fear of bleeding, or where there is poor understanding towards how medications can reduce risk (Salmasi et. al., 2018).

For patients with asymptomatic conditions, a common problem is recognizing and acknowledging risks that may not be apparent due to a lack of symptoms. A qualitative study to understand the experience of transient ischaemic attack (TIA) patients during consultations sessions find that prior knowledge and health beliefs influences actions taken by patients, and that a lack of symptoms leads toless recognition of risks (Bridgwood et. al., 2020). Many patients, such as those with high cholesterol who do not have manifest symptoms, find their risks unpredictable, unstable and abstract. These patients also have a poor understanding of CVD risk factors and do not perceive hypercholesterolemia to be a risk factor for CVD (Durack-Brown et. al., 2003). Physicians of AF patients find it worrying that patients often associate symptom severity with risk of stroke, who correspondingly believe that having a lack of symptoms implies not being at risk. (Salmasi et. al., 2018)

One intervention study using probabilistic scenarios conducted with GPs, healthcare assistants, and laypeople to test the level of minimum absolute risk required for participants to justify prescribing a hypothetical tablet able to prevent heart attack over 5 years, find that most participants think it makes no difference if a the drug is consumed over 10 years instead or 5, even if the benefit was greater over a longer time period, suggesting challenges in risk estimation even for healthcare professionals (Fisseni et. al., 2008). An intervention study that aims to facilitate the communication of CVD risks between patients and physiciansby providing patients with a tablet computer containing a series of educational modules that patients have to watch prior to consultation sessions, find sthat such an educational intervention makes it easier for patients to speak to their physicians and have a better understanding of why controlling CVD risk factors is important (Roach et. al., 2010).

Different risk formats and its effect on the risk communication process

Multiple studies focused specifically on risk formats and how it shapes the risk communication process. 1 qualitative study that interviewed GPs in New South Wales, Australia, suggests how pragmatic considerations can affect how physicians choose to convey risk. The study finds that physicians prefer using qualitative formats for communicating risk to patients who have lower numeric literacy and who are of lower risk, given how the discussion of numbers with patients may take up a substantial amount of time,. Absolute, relative risk, and risk displayed in a frequency format, were preferred formats used by GPs to convey information to patients who are at high risk or who had a poorly managed CVD condition (Bonner et. al., 2014). For some patients, there was the perception that providing both absolute and relative risk calculations may actually be unnecessary and confusing, since limited information about ways to reduce risk were already received by patients in the first place. Some patients had strong objections to the word ‘absolute’, which was seen as ambiguous and that seem to convey a risk score that was ‘conclusive’ or ‘definitive’ (Kirby et. al., 2009).

There was consensus among patients that risk was generally difficult to understand. In one mixed methods study conducted in the UK to evaluate the use of the JBS2 risk calculator and chart within GP settings, patients do not recall seeing a risk assessment tool used, although they agree that the use of tools can increase confidence in risk assessment and aid patients in understanding risks (Kirby et. al., 2009). Patients prefer a risk calculator that indicates risk in the form of a thermometer rather than paper charts, highlighting how a visual thermometer is more appealing and easier to understand and can even be motivational, although this may cause anxiety to those whose risk is very high (Kirby et. al., 2009). A qualitative study conducted in the UK to understand the experiences of participants presented with a personalised risk report that includes heart age and QRISK2 risk score that indicates a person’s risk of having a stroke or heart attack within the next 10 years, finds that patients tend to recall heart age easier rather than a probabilistic score (Hawking et. al., 2019).

In terms of format preferences, a cross-sectional study examining the preferences of patients attending GP practices in Auckland, New Zealand found that relative risk (n=603/934, 64.5%) was the highest ranked mode of risk presentation preferred, followed by absolute risk (n=131/934, 14.0%), then natural frequencies (n=91/934, 9.7%), when it comes to the format that would help a patient to decide. In this study, relative risk was ranked first by participants who were more numerate (OR = 1.2; 95% CI, 1.0-1.4), those who were more concerned about a heart attack (OR = 1.1; 95% CI, 1.01-1.2), and less by Pacific Islanders (OR = 0.4) or Asian (OR = 0.4) participants (ethnicity overall, 95% CI, 0.7-0.8). Pictures were preferred over numbers by those who had less schooling (OR = 1.2; CI, 1.1-1.3) and by those who were less numerate (OR = 1.1; CI, 1.01-1.2) (Goodyear-smith et. al., 2011).

In a multi-component RCT conducted in Egypt to investigate the accuracy of CVD risk perception among patients with diabetes, patients were provided a combination of absolute and relative risk scores conveyed in percentage and frequency formats, and given advice framed positively by physicians on how to change their risk based on WHO/ISH guidelines. Agreement between perceived and objective CVD scores increased substantially for the intervention group (n=107) (pre-/post- intervention, kappa = 0.271+/-5.2%, p = 0.0 to 0.837+/-4.4%, p = 0.0), compared to the control group (kappa = 0.088/+/-4.5%, p = 0.052 to 0.105+/-4.6%, p = 0.022), which increased only marginally and remained low (Tawfik et. al., 2016).

Perception of risk information over time

Two intervention studies suggests that patient’s perception of risk, although mediated by formatting and visualisation elements that aims to improve understanding, tends to taper over a longer period of time. A non-inferiority RCT conducted in Germany to test a time to event (TTE) format versus emoticons in representing a patient’s 10-year absolute risk of CVD finds TTE to have a stronger effect on risk perception than emoticons (Adarkwah et. al., 2016), although the effect on perception waned somewhat after 3 months (Adarkwah et. al., 2019). Another RCT intervention conducted with Type 2 diabetes patients newly referred to a diabetes care system in the Netherlands, that used a 6-step CVD risk communication method, found that patients in the intervention group were able to estimate their risk of developing CVD more accurately than those in the control group in the short term (appropriateness of risk perception, intervention 0.33 vs. control -0.1, difference = 0.48, CI 0.02 to 0.95 (p = 0.04)), but the effect of risk perception diminished after 12 weeks. The intervention used a combination of tools that include conveying to patients their absolute risk scores calculated using the UK prospective diabetes study (UKPDS) risk engine, with a risk card containing a population diagram, and having positive framed messages of risk conveyed to patients (Welchen et. al., 2012).

Diabetes related risk information

There were a only a small number of studies on patient-physician risk communication focused on diabetes. Five articles, consisting of 2 qualitative (Ledford, 2011, Yao et. al., 2022) and 3 quantitative studies (Huang et. al., 2016, Ritholz et. al., 2017, Rouyard et. al., 2018), focused on how patients may not be provided sufficient information about diabetes related complications and risks by physicians..

Understanding and recalling risk information

One descriptive cross-sectional study describes how only about 23% (32/138) and 14% (19/138) of patients diagnosed with diabetes respectively recall their providers providing them with factual information and warning them about the implications of complications (Ritholz et. al., 2017). The low proportion for this study conducted at a diabetes centre seem to imply a relatively limited number of patients who are conveyed actual risk of complications by physicians.. A focus group discussion conducted with Type 2 diabetes patients from community health service centres in Guangzhou, China, describe how patients understand normal blood glucose and HbA1c levels as reflective of a stable condition, and that higher numbers or fluctuating numbers are a source of worry. Patient participants were not aware that diabetes was a risk factor for CVDs, though they were concerned about diabetes complications. Patients found that consultations with physicians to be too brief and wanted more information about how diabetes can progress and develop further into complications (Yao et. al., 2022). Physicians who treat patients with diabetes, describe engaging more with communicatively active patients (CAP) who are able to recognize and respond to new or evolving medication risk information (Ledford, 2011).

One pilot RCT study with diabetes patients to assess the feasibility of adopting a new risk communication intervention tool based on behavioural and psychological concepts in primary care, focused on diabetes as a risk factor for CVD, find that recall for effective heart age is significantly better than other formats such as 10-year CVD risk, both immediately and 12 weeks after intervention (Rouyard et. al., 2018). Another pilot intervention conducted at 2 clinics in the University of Chicago, USA, find that using a web-based decision support tool can improve risk understanding. The intervention consists of an education support module, a model for calculating life expectancy and risk of developing CVDs, a treatment preference questionnaire, and geriatric screening component consolidated in the form of a personalised report to be delivered by patients prior to a patient’s visit with a physician. The study showed that decisional conflict (DC) are reduced in the intervention group more than the control group (Overall DC score pre-/post- intervention 52.7 +/- 33.0 to 24.5 +/- 26.7, pre-/post- control 51.2 +/- 35.5 to 36.6 +/- 33.8, p=0.07). Although the results for the overall DC scale was not significant, the informed DC subscale which asks about knowledge and risk understanding related to A1c goals is significant (Informed DC subscale score pre-/post- intervention 54.0 +/- 40.1 to 18.3 +/- 33.7, pre-/post- control 56.0 +/- 38.4 to 31.0 +/- 41.0, p=<0.001) (Huang et. al., 2016)

Strategies and approaches used by physicians in patient-physician risk communication

Eight articles (5 CVD, 3 diabetes), consisting of 7 qualitative (Barfoed et. al., 2015, Bonner et. al., 2014, Borg Xuereb et. al., 2016, Honey et. al., 2015, Ledford, 2011, Rosal et. al., 2004, Yao et. al., 2022) and 1 quantitative study (Ritholz et. al., 2017), describe strategies and approaches used by physicians to communicate risk information to patients. Strategies used to convey CVD risk include the use of fear or scare tactics (Bonner et. al., 2014, Borg Xuereb et. al., 2016, Honey et. al., 2015), use of strong language to evoke fear (Rosal et. al., 2004), use of positive language (Bonner et. al., 2014), downplaying risk (Honey et. al., 2015), or use of metaphors and analogies such as associating heart function with an electrical system to simplify risk information and improve patient’s understanding (Borg Xuereb et. al., 2016). Other approaches used include presenting different CVD scenarios to those who are less adherent to medication (Barfoed et. al., 2015), speaking indirectly to patients (Bonner et. al., 2014), intervening strategically during ‘teaching’ moments (Rosal et. al., 2004), emphasising gradual and continuous change (Rosal et. al., 2004), and prioritising discussion points (Rosal et. al., 2004).

For patients with diabetes, strategies used by physicians include setting goals (Yao et. al., 2022), using specific words such as ‘common’ or ‘rare’ to describe risks (Ledford, 2011), avoiding statistics (Ledford, 2011), varying presentation style to different types of patients (Ledford, 2011), and withholding information such as low-level risks that may affect a patient’s medication intake (Ledford, 2011). Additional approaches include using dramatic images such as illustrations of amputations to persuade patients (Yao et. al., 2022). Physicians who provided prompt feedback to patients using clear language and had positive body language were viewed positively by patients (Yao et. al., 2022). diabetes patients, like patients treated for CVD conditions, similarly mentioned how physicians used fear as a motivator to warn about complications (Ritholz et. al., 2017).

The use of fear and scare tactics was a recurring theme to direct patients towards desired behaviour. If physicians perceived patients to be of higher risk but are generally unmotivated about their own health status (Bonner et. al., 2014), the consequences of risky behaviour and complications that may occur in the future (e.g., such as being bed bound) were used by physicians to persuade patients to change (Borg Xuereb et. al., 2016, Rosal et. al., 2004, Ritholz et. al., 2017). If a patient was not ready and receptive to risk information, then physicians would avoid conveying risks directly to avoid alarming or affecting patients negatively (Bonner et. al., 2014). On the other hand, for patients who were motivated or self-activated, a positive strategy is taken to reassure and encourage patients to focus on achievable change (Bonner et. al., 2014), since a communicatively active patient is cognitively ready and can be provided more details about risks (Ledford, 2011).

A summary of articles and strategies and approaches used by physicians for CVDs and diabetes related conditions are described in table 3.

Table 3 Strategies and approaches used by physicians in patient-physician risk communication

No.	Author (Year) / Country	Strategies and approaches used in patient-physician risk communication	Description of strategies and approaches used
CVDs
1.	Barfoed et. al. (2015) / Denmark	Presenting different CVD scenarios	2 different ways of managing compliance by GPs were identified, ‘resigned’ and ‘confrontational’. For the resigned group, compliance is seen as a patient’s choice, which the GP should respect, or is not seen as a problem at all. For the confrontational group, there were dual approaches. Patients were presented with different CVD scenarios that may occur if drug treatment were not adhered to, or there were repeated consultations to reveal barriers in following agreed treatment plans.
2.	Bonner et. al. (2014) / Australia	Positive Scare Tactics Indirect	‘Positive’ strategies were used where GP’s perception of patient’s CVD risk was low and patients were determined in managing their own condition. Approach involves reassuring and motivating patients, focusing on achievable changes. ‘Scare tactic’ strategies were used when a GP’s perception of patient’s CVD risk was high, where patients are unmotivated, with a focus on how future risk may be even higher possibly leading to a CVD event. ‘Indirect’ strategies were used by GPs where patients, who may be of low CVD risk and have lifestyle factors that needs to change, tend to be more resistant, where the conveyance of risk may not be helpful and may elicit a negative response in patients.
3.	Borg Xuereb et. (2016) / UK	Use of metaphors and analogies Use of fear	Physicians used diagrams and metaphors to explain about AF and to convey the risks of stroke and bleeding. Metaphors focused on bodily physiology and analogies to describe bodily function (e.g. Heart function as electrical wiring). A few clinicians mentioned using fear of stroke to persuade patients to make the right decision.
4.	Honey et. al. (2015) / UK	Downplaying risk Use of fear	Patients described healthcare professionals as downplaying their high-risk scores, which affect the level of significance a patient will attribute their own risk. Other risk communication approaches include appealing to fear by emphasizing the worst possible consequences that may occur, and the use of humour to communicate risk.
5.	Rosal et. al. (2004) / USA	Use of strong language to evoke fear Intervening at strategic timepoints Emphasising gradual and continuous change	Strategies used by physicians to motivate lifestyle change include using strong language to evoke fear in patients about risky behaviour or intervening during “teaching moments”. Other strategies include emphasising continuous and gradual change by asking patients to adopt one change at a time.
Diabetes
6.	Ledford (2011) / USA	Use words like ‘common’ or ‘rare’ to present risks Avoid statistics Varying present style to different types of patients	Physicians use various strategies in presenting risk information to patients, such as using words like ‘common’ or ‘rare’, avoiding statistics, personalising presentation to patients, and varying presentation style by medication.
7.	Yao et. al. (2022) / China	Setting goals Using illustrations such as pictures of amputations Prompt provision of feedback Use of clear and frank words Positive body language	There were instances where GPs set goals for patients, although patients may adhere less required than advised. Some patients mentioned that their GPs attempted to use dramatic illustrations such as pictures of amputations to persuade them. Good communication experiences include prompt provision of feedback, use of clear and frank words, and positive body language.

[Table 3 Strategies and approaches used by physicians in patient-physician risk communication]

Communication tools that facilitate patient-physician risk communication

8 articles (7 CVD, 1 diabetes), consisting of 6 qualitative (Barfoed et. al., 2015, Bengtsson et. al., 2021, Bonner et. al., 2014, Borg Xuereb et. al., 2016, Hawking et. al., 2019, van Steenkiste et. al., 2004), 1 quantitative (Lyles et. al., 2012) and 1 mixed methods study (Kirby et. al., 2009), identify tools used to facilitate the communication of CVD risks between patients and physicians. Studies describe the use of personalised risk reports to aid the understanding of risks (Hawking et. al., 2019), that contain pictorial information such as colour codes to indicate CVD risk status with visual depictions of arterial plaques (Bengtsson et. al., 2021). Other tools used include risk charts (Bonner et. al., 2014, Kirby et. al., 2009), risk tables (van Steenkiste et. al., 2004), drawn diagrams (Borg Xuereb et. al., 2016), CVD guidelines (Barfoed et. al., 2015), risk assessment tools (Barfoed et. al., 2015), results from risk calculators such as the JBS2 calculator in the UK (Bonner et. al., 2014, Kirby et. al., 2009), and images such as cholesterol spikes or how the brain looks like during a stroke (Bonner et. al., 2014).For communication tools used by diabetes patients, 1 study found that although most risk factor discussion with physicians still occurs in-person, those who use phone and secure messages to communicate risk factors tend to also have higher in-person provider visits, insulin use and poorly controlled Hba1c (Lyles et. al., 2012).

Interventions to improve the communication of risk information

Interventions related to CVD conditions

Eight interventions were related to CVDs, of which 6 were RCTs (Adarkwah et. al. 2016, Adarkwah et. al. 2019, Krones et. al., 2008, Roach et. al., 2010, Tawfik et. al., 2016, Welschen et. al., 2012), 1 a controlled trial without randomisation (Casebeer et. al., 2009), and 1 an interrupted time series design (Williams et. al., 2016). Most interventions used a combination of training and interventional materials for physicians or healthcare providers, along with reinforcing tools provided to patients that emphasise risk management. Interventions usually involve the use of relative and absolute CVD risk scores generated by risk calculators or algorithms (Krones et. al., 2008, Tawfik et. al., 2016, Welschen et. al., 2012, Williams et. al., 2016), or reports that present CVD risk stratification using colour codes (Casebeer et. al., 2009). A pocket guideline from the National Health Cholesterol Education Program and WHO/ISH guidelines were used by Casebeer et. al. (2009) and Tawfik et. al. (2016) respectively. Additional intervention tools include the use of script like decision aid (Krones et. al., 2008), use of smiley faces (Krones et. al., 2008), emoticons or time to event graphics to visualise risk (Adarkwah et. al. 2016, Adarkwah et. al. 2019), population diagrams (Welschen et. al., 2012), positive framing (Welschen et. al., 2012), risk cards (Welschen et. al., 2012), educational worksheets (Williams et. al., 2016), checklists (Williams et. al., 2016), patient contract or pledges (Casebeer et. al., 2009), and chart stickers (Casebeer et. al., 2009). 1 study used a tablet with multiple educational modules, linked to a patient’s electronic health records (EHR), that requires a patient to access and watch before attending a consultation session with a physician, spread over a period of 5 visits (Roach et. al., 2010).

Five interventions (Casebeer et. al., 2009, Krones et. al., 2008, Roach et. al., 2010, Welschen et. al., 2012, Williams et. al., 2016) required patients to actively respond to the risk information and educational materials provided to them by the physician or health professional. This includes pledging to commit to the medical regimen (statin therapy) of the intervention (Casebeer et. al., 2009), participating in a shared decision-making process with the physician in modifying one’s risk (Krones et. al., 2008), talking to the physician shortly after watching educational modules on a tablet (Roach et. al., 2010), thinking aloud to check if one can self-explain one’s own risk (Welschen et. al., 2012), or completing a checklist to count the number of tick marks one has and the risk category one falls into (Williams et. al., 2016). 1 intervention study, the Heart Health Counts program, sent 5 print mailings over a 4 month period to patients who are new to statin therapy with information focusing on various aspects of CVD risk (Casebeer et. al., 2009).

Before the start of each intervention, training or meeting sessions are sometimes initiated between physicians and other healthcare professionals to familiarise about study aims and protocol (Williams et. al., 2016), discuss the epidemiology of CVD risk calculations (Krones et. al., 2008), explain the meaning of absolute and relative risks (Tawfik et. al., 2016), and discuss practical strategies of how to communicate risk information to patients (Krones et. al., 2008, Tawfik et. al., 2016, Welchen et. al., 2012, Williams et. al., 2016). Sessions are also held to train physicians and healthcare professionals on how to use risk prediction tools such as the adapted Framingham algorithm (Krones et. al., 2008), WHO/ISH CV risk prediction chart (Tawfik et. al., 2016), or the UKPDS risk engine (Welchen et. al., 2012); to discuss the causes and consequences of CVD risk (Welchen et. al., 2012), and discuss the ethics of shared decision making (Krones et. al., 2008). For 1 intervention, training include explaining behavioural theories such as the Theory of Planned Behaviour and Self-Regulation Theory to physicians (Tawfik et. al., 2016), although engagement with theoretical concepts was not common among most interventions included in this review. Role playing was included in some of the intervention training sessions to reinforce learning (Krones et. al., 2008, Williams et. al., 2016). Other than a general outline of training procedures, interventions described in the articles included do not provide more details about the process of training sessions.

Interventions related to diabetes

3 interventions were related to diabetes, consisting of 3 RCTs (Denig et. al., 2014, Huang et. al., 2016, Rouyard et. al., 2018). All the interventions used a personalised decision aid or support tool to present risks to patients in the form of a report (Denig et. al., 2014, Huang et. al., 2016, Rouyard et. al., 2018). 1 pilot intervention was specifically guided by ideas from behavioural economics and psychology to shape the design of intervention, factoring in concepts such as optimistic bias, affect or representative heuristic, risk aversion, present biasness and limited attention span to determine the structure of risk format and description of risk information (Rouyard et. al., 2018). The intervention study, however, did not go into detail about how concepts are translated into risk information. Personalised reports usually include a patient’s screening test results (Denig et. al., 2014, Huang et. al., 2016), including treatment options or recommended actions that can be taken for specific risk factors (Denig et. al., 2014, Rouyard et. al., 2018). Reports also conventionally includes a calculator that estimates life expectancy or risk of developing complications such as developing a heart attack or risk of amputation or blindness (Huang et. al., 2016), or that estimates heart age (Rouyard et. al., 2018), with an education module (Huang et. al., 2016). Risks are described using natural frequencies to convey outcome probabilities relevant to each patient (Denig et. al., 2014, Huang et. al., 2016, Rouyard et. al., 2018). 1 intervention mentioned setting achievable goals for patients to aim towards to (Denig et. al., 2014), while 2 interventions required patients to discuss their treatment options with a physician after a personalised report was received by the patient (Denig et. al., 2014, Huang et. al., 2016).

A summary of articles and associated intervention components described for included articles is described in table 4.

Table 4 Intervention components of studies to improve patient-physician risk communication

No.	Author (Year) / Country	Study Design	Intervention Components	Description of Intervention
CVDs
1.	Adarkwah et. al. (2016) / Germany	Randomized controlled trial	Risk report Risk scores, represented by either emoticons or time to event (TTE) illustrations	Patients were randomized to the emoticons or the TTE illustration group during consultation with GPs who entered a study ID into the decision support software. GPs learned about each patient’s allocation by the illustration displayed by the software, then started a discussion with their patients based on the allocated display (i.e. emoticons or TTE) After consultation, patients fill in a questionnaire covering the immediate outcome assessments. GPs recorded the decision made, such as specific medications, dose adjustments, behavioural measures or no change at all. 3 months later, patients were contacted by telephone to assess their adherence.
2.	Adarkwah et. al. (2019) / Germany	Randomized controlled trial	Same as Adarkwah et. al., 2016	See above, Adarkwah et. al., 2016 A score was developed by the study group to measure adherence as no validated instrument was available for use. For each item, a distinction was made according to whether a patient had been completely adherent, partially adherent, or nonadherent. We labelled the grade of adherence as 2= fully adherent, 1=partially adherent, 0=non-adherent.
3.	Casebeer et. al. (2009) / USA	Controlled trial	Risk report, including colour codes to indicate CVD risk Counselling kit for physicians Patient contract/pledges National Cholesterol Program Pocket guidelines Chart stickers Multiple mailings of educational materials to patients	The goal of the Heart Health Counts (HHC) program is to facilitate statin adherence and provide tools for physicians to increase clarity in CVD risk dialogue. Materials address cardiovascular risk and provide context for patients who might be presented with lab values but may not have a context for severity of risk. The 1-minute health manager and other HHC materials used colour coding with green, yellow, and red to represent cardiovascular risk stratification, and to serve as a call to action for patients who are in the yellow and red zones. Physicians receive a counselling kit including: (1) a set of 1-minute health manager patient education tools used to describe cholesterol risks, (2) patient contracts or pledges designed to confirm a patient's commitment to the prescribed medical regimen, (3) a copy of the National Cholesterol Education Program pocket guidelines and, (4) a set of chart stickers. Following office visit, patients who are new to statin therapy receive 5 HHC mailings over a 4 month period, with the four-color print mailings focused on various aspects of cardiovascular health and cardiovascular risk.
4.	Krones et. al. (2008) / Germany	Randomized controlled trial	Training sessions for physicians, including role-playing and feedback Decision aid with script for physicians Risk report Absolute and relative risk scores, represented by emoticons (smiley faces)	For physicians: Attend 2 Continuing Medical Education (CME) sessions lasting 2 hours each, to discuss the epidemiological background of global CVD risk calculation and ethics of shared decision making, including practical communication strategies. The script-like decision aid was practiced through role playing with participants receiving feedback from peers. After completion of the trial, physicians in the control arm were offered meetings on ARRIBA-Herz and materials. For patients: The patient’s perspective on prevention of CVD was addressed, and patients were invited to a shared decision-making process. Physicians calculated each patient’s absolute risk for stroke and myocardial infarction based on an adapted Framingham algorithm with the decision aid. Individual prognosis was compared with age- and sex-adjusted population risk. For patients in secondary prevention, we assumed about 50% absolute risk for stroke or myocardial infarction in the next 10 years. Individual prognosis was displayed through marked smiley faces (smileys). The possible effects of single or multiple interventions were calculated by applying the specific relative risk reduction on the calculated and demonstrated absolute risk, which was visually supported by smileys being crossed out, i.e., events prevented. Physicians were taught to calculate and show the effect of several preventive measures simultaneously.
5.	Roach et. al. (2010) / USA	Randomized controlled trial	Multimedia educational tool (tablet) with personalised health information and educational videos for patients	The tool uses multimedia technology to educate patients with Type 2 diabetes about their personal CVD risk, explain therapeutic options, and most importantly, motivate them to discuss therapy options with their provider. Program software incorporates personalized health information from the EHR to create personalized risk messages. Effective interaction with the tool does not require computer skills. Patients interact with the computer-based program just before visiting their primary care provider.
6.	Tawfik et. al. (2016) / Egypt	Randomized controlled trial	Training for physicians Risk report Absolute and relative risk scores, in frequency and percentage formats CVD guidelines	Treating physicians receive introductory training on the importance of providing information to patients on CVR including the definition of risk, an explanation of absolute and relative risks, methods of risk communication, self-regulation theory (SRT), theory of planned behaviour (TPB), and a demonstration on how to use the WHO/ISH CVR prediction chart and on how to discuss CVR with patients. Discussion of CVR was based on discussion of having a risk of heart attack and include: (a) providing a clear and simple message on CVD and what actions can be taken to prevent it, (b) explaining in frequency and percentage formats, including absolute and relative risk score for a person with Diabetes as similar age and sex as the patient, (c) explaining to patients how to change their CVR based on WHO/ISH CVD guidelines.
7.	Welchen et. al. (2012) / Netherlands	Randomized controlled trial	Training for nurses and dieticians Absolute risk scores, represented by natural frequencies Risk card, with population diagram Feedback from patients after risk information is conveyed	Diabetes nurses and dieticians receive training for two half-days in learning to perform the intervention. Intervention consists of six steps: 1) Introducing risk communication and explanation about health risks related to Type 2 Diabetes, including the cause and consequences of CVD risk. 2) Communication of absolute risk of developing CVD in the next 10 years using the UK Prospective Diabetes Study (UKPDS) risk engine, explained through natural frequencies format. 3) Visual communication by means of a risk card, with the help of a population diagram. 4) Positive framing explanation that lifestyle changes by the patient can help to reduce the risk. 5) Communication with the patient for a reaction: after finishing the explanation of the risk, the diabetes nurse asked the patient to give a reaction to the information that was given using open questions. 6) Thinking aloud; patient have to explain the risk to him/herself and was encouraged to think aloud.
8.	Williams et. al. (2016) / Indonesia	Interrupted Time Series	Training for physicians and nurses, including role-playing Checklist of modifiable risk factors Risk charts (report) Educational worksheet with patient’s input	Training was conducted with 20 providers on the study aims, intervention protocol, roles, and intervention materials. Trainers than demonstrated the intervention behaviours to the physicians and nurses, after which they role-played and received feedback. During the intervention, nurses assessed patients for the study inclusion criteria and recorded this on the physician intervention material, containing a checklist of modifiable stroke risk factors (i.e., blood pressure, smoking, diet, body weight, physical activity). The checklist in placed in the patient’s charts for the physician to discuss with the patient. Patients receive a one-page educational worksheet covering the same list of stroke risk factors to complete in the waiting room prior to their consultation with the physician. The worksheet asked patients to think about their status on each of the stroke risk factors, then asked them to count the number of check marks and provided an interpretation of “high risk for stroke” / “caution” / “low risk for stroke” in accordance with the count.
Diabetes
9.	Denig et. al. (2014) / Netherlands	Pragmatic randomised controlled trial	Decision aid for patients Personalised risk report Treatment goals and options for patients Risk scores using graph and text, represented by natural frequencies	A decision aid was developed for patients with Diabetes, with individually tailored information on risks and treatment options for multiple risk factors. Key features include a personal status report including test results and current drug treatment; the presentation of tailored information on achievable treatment goals and possible treatment options for specific risk factors; a combination of graphs and text using natural frequencies for outcome probabilities; the presentation of pros and cons of all treatment options; and asking patients to think about treatment options. Should be used by patients before a regular quarterly check-up and discussed together with healthcare provider during consultations to help prioritise treatment that will maximise relevant outcomes.
10.	Huang et. al. (2016) / USA	Pilot randomised trial	Training for physicians Training for patients by research assistants Decision support tool for physicians Personalised risk report Absolute risk scores Treatment preferences for patients	Intervention physicians underwent 1 hour of in-person training on the principles of geriatric diabetes and the use of the decision support tool. Patients met with a research assistant 1 hour prior to physician appointment and were given brief instructions on how to use the tool. Main components of the decision support tool include: 1) an interactive diabetes education module, 2) a simulation model that calculates life expectancy and risk of developing complications, 3) treatment preference elicitation, 4) geriatric condition screening, and 5) a personalized patient printout. Model calculates a range of values including life expectancy, lifetime risk of developing a heart attack (at A1C of 7%), and risk of amputation or blindness (at A1C of 7%, 8%, and 9%). In addition, personalized risks, patient preferences, and geriatric screening results were summarized in the printout. Patients received 2 copies with instructions to give 1 copy to their doctor.
11.	Rouyard et. al. (2018) / UK	Pilot randomised controlled trial	Personalised Risk report Risk scores	A risk communication intervention aiming to target 7 decision-making biases that is known to influence decision-making process of people with T2DM. The intervention aims to improve insight and recall of diabetes-related risks to nudge people with T2DM towards better self-management. Approaches include mitigating optimistic bias, increasing emotional impact and representativeness of risk, taking advantage of people’s loss aversion tendencies and mitigating present biasness, increase people’s limited attention, and personalising risk using a gained framed message.

[Table 4 Intervention components of studies to improve patient-physician risk communication]

Recent trends have emphasized a shift in patient-physician consultations away from a paternalistic model towards a shared decision-making or patient-centred model of clinical care (Davis et. al., 2020), where a patient is viewed as an equal partner in patient-physician interaction actively involved in one’s own management of health (Elwyn et. al., 2012, Epstein et. al., 2010). In shared decision making, patients participate in a deliberative process with their primary physician in making informed choices based on a patient’s values and preferences (Lown et. al., 2009). The articles included in this systematic scoping and narrative review point to some of the salient challenges in shifting towards such a model and focuses on some of the contextual difficulties and implementation challenges in communicating risk information. To the best of our knowledge, this is the first systematic scoping review to focus on patient-physician risk communication in the primary care or outpatient setting for both CVDs and diabetes.

Physicians employ a wide variety of strategies and tools to communicate risk depending on a patient’s attributes and responses. Strategies used by physicians aim to uncover a patient’s propensity in understanding risk information and at the same time, convince or persuade patients who are less self-activated to move positively towards behaviour modification. Tools such as calculator generated risk scores aim to provide a generalised estimate for patients to better understand their own risk profile, while risk reports and other associated toolkits are developed to simplify information, consolidate personal health details, and ease the process of communication so that knowledge can be transmitted more accessibly. In the context of risk for CVDs and diabetes, this involves delicately conveying the probability of negative consequences in the medium to long term while motivating patients.

Some of the communication strategies used by physicians, particularly for less receptive patients, such as downplaying risk or using analogies to describe risk, suggests that avoiding or delaying the conveyance of actual risk information is a strategy used by physicians. An implication is whether such a strategy will lead to either an underestimation of risk or risk avoidance by both the physicians and patients during consultation sessions. Other methods such as communicating indirectly and emphasising gradual change aim to redirect the focus of patients while still communicating risk, while presenting different CVD scenarios involves offering counterfactual scenarios for patients to consider why action should be taken to reduce risk factors. An ongoing challenge is whether appealing to such comparative scenarios is able to convince patients to modify their behaviour. As studies in this review do not correlate different strategies and approaches with outcomes and therefore, it is not possible to evaluate the relative merits of each approach.

Given the diverse approaches among current practices and the range of tools available, there is a need to more thoughtfully combine implementation and behavioural or psychological concepts to build risk communication practices (Avery et. al., 2012, Chew et. al., 2014, Kubzansky, 2018, Laddu et. al., 2021) that can be sustained over the longer term. The range of studies included in this review generally did not report or engage extensively with implementation science principles or behavioural science concepts. Only 1 study was guided by conceptual ideas from behavioural psychology (Rouyard et. al., 2018), and 2 intervention studies incorporated psychological theories as part of training for physicians before the implementation of intervention (Tawfik et. al., 2016, Welschen et. al., 2012). Engaging with implementation science and behavioural concepts may be one way to ensure that strategies are carefully integrated so that approaches and interventions can gain traction with multi-stakeholder groups and can be viable over the longer term (Lewis et. al., 2018, Mazzucca et. al., 2021, Miller et. al., 2021). As exemplified by articles included in this review, interventions can be resource and time consuming, requiring the training of personnel, the use of expertise, and inter-professional collaboration, all of which are costly to maintain, and may affect existing configuration of healthcare processes. A thoughtful approach would identify prevailing gaps, iteratively test different design features, and incorporate feedback mechanisms in tracking the effectiveness of different risk communication approaches to aim for longer term impact.

Gaps in the literature

Articles included in this review underscore some of the fundamental gaps in the existing literature. One notable gap is a lack of studies describing the development of risk information tools in stepwise detail at the pre-intervention stage. Particularly, studies presenting the rationale of why preferred risk information is chosen in the first place, and how the information is piloted with users before implementation or actual use. Understanding risk tools and how it is connected from conception to implementation, can help in the translation and replication of comparable tools across populations as well as aid physicians in communicating risk effectively. One example related to the translation of tools for use is the visual positioning of graphs, symbols, numbers, frequencies or text in different parts of a report. Positioning, in synchronisation with visual or graphical formats and textual information, can influence the uptake and acceptability of risk reports especially where physicians with limited time and occupational constraints are asked to communicate risk information to a wide range of patients.

One methodological related gap is how randomised controlled studies included in this review tend to treat multiple intervention components as conjointly operating together, without stratifying the effects of each component during analysis. Interventions typically include a training program, personalised risk report with risk scores indicating absolute and/or relative risk, personalised recommendations, as well educational toolkits containing items such as educational videos, booklets, risk cards, worksheets and assorted items. Understandably, multi-pronged intervention may be required to elicit a substantive effect on behaviour for potential behavioural change to be realised, but doing so without demarcating the effects of different components may make it hard to know which individual factor influences a patient’s or physician’s change in knowledge or behaviour.

Another gap is the examination of risk communication as it occurs in situ within the clinical setting. If risk communication processes are to be understood naturalistically in daily practice, then data collection methods that rely only on retrospective data collection methods may not fully capture the diverse range of approaches that physicians use and the way patients respond to risk information. It is likely that pragmatic reasons, patient’s privacy, and the traditionally closed set-up of the clinic may make it hard for researchers to observe ongoing clinical encounters, but capturing processes as closely to the clinical setting as possible can allow one to examine processes of communication more closely, particularly specific types of risk information and how such information is associated with different communicative techniques.

Some studies have used video, audio recordings, or participant observations to capture clinical conversations in real time for useful analysis. A comprehensive study by Gidlow et. al. (2021) for instance, conducted in 12 General Practices in the West Midland of England, examined how healthcare assistants or practice nurses communicate cardiovascular risk to patients using the QRISK2 or JBS3 CVD calculator through video recordings, which were subsequently systematically coded and used for simulated recall interviews with patients and practitioners. A diabetes study by Kruse et. al. (2013), although not specifically about the conveyance of risk, used audio recorded clinical encounters to uncover how patients can find it difficult to relate to physicians’ emphasis on quantitative health outcomes during clinical conversations, given caregiving and work commitments that patients have.

As shown by the overall quantity of articles related to CVDs as compared to diabetes, there is an overall lack of studies examining the communication of risk for patients with diabetes. This may be explained by the more conventional use of risk calculators within the clinical setting in helping patients at higher risk of CVDs understand their own risk levels that are based on well-established longitudinal studies, and due to the complexity of diabetes that is caused by a multitude of lifestyle related risk factors such as diet and levels of exercise which are variable and harder to calculate as an aggregate risk.

Strengths and limitations

The strength of this review is to provide a comprehensive and detailed overview of studies that examine patient-physician risk communication in the primary or secondary care setting, for both CVDs and diabetes. Although the range of studies are heterogeneous in focusing on different aspects of risk communication and interventions, the studies collectively highlight some of the common issues that arise and gaps in the literature. Qualitative studies provide depth in uncovering the underlying complexities articulated by patients and physicians in communicating risk information with each other, while quantitative studies show some of the risk format preferences or multi-faceted interventions that have been attempted to improve the communication of risks in the clinical setting, as well as the extent of intervention effectiveness.

This review has focused on the encounter between physicians and patients as it is one of the most common means by which risk information is communicated. However, in so doing, we have not included studies in which patients are given health risk information by healthcare professions other than physicians. Many patients also commonly encounter other HCPs such as nurse practitioners, pharmacists, dieticians, psychologists, community health workers, who may be closely involved in the overall management of the patient’s care and where risk information may be routinely conveyed.

Another limitation of this review is the exclusion of studies that examine risk communication in general or specific populations that do not involve a patient-physician encounter. Given the limited amount of time and interspersed nature of the physician encounter for most non-acute patients, a patient’s notion of risk may invariably be influenced by what occurs prior to or after an encounter with a physician. A patient’s background, disposition, attitudinal factors, and health condition specific literacy, in combination with outside sources of risk information, may enhance or attenuate a patient’s acceptance and understanding of risk in relation to a patient’s condition.

Lastly, a further limitation of this review is the exclusion of review studies, studies that were conducted before 2000, and the exclusion of foreign language studies during the screening process.which may limit the range of articles that may be screened and included for this review.

While multiple research studies have been conducted that explicate various ways in which physicians communicate risk to patients, there is a critical need for further research into risk communication strategies for cardiovascular diseases (CVDs) and diabetes. Research should focus on developing targeted approaches that enhance patients' understanding of their risk profiles. Additionally, it should provide evidence-based guidelines to assist physicians and healthcare professionals in improving risk communication within clinical settings, with the goal of facilitating patient comprehension of their health risks and support sustained lifestyle and behavioural changes through informed, evidence-based methods. Misalignment in communication may lead to confusion, dissatisfaction, and lack of clarity, which may impede the effective management of chronic conditions.

AF

Atrial fibrillation

CVD

Cardiovascular disease

DC

Decisional conflict

GPs

General practitioners

JBS 3

Joint British Societies risk calculator

NHS

National Health Service

TIA

Transient ischaemic attack

TTE

Time to event

UKPDS

United Kingdom prospective diabetes study

WHO/ISH

World Health Organisation/International Society of Hypertension

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This research study is kindly supported and generously funded by the Ng Teng Fong Foundation (Grant Reference: NTF_SRP_P1) and National Healthcare Group as part of the Personalised Cardiometabolic Risk Management program (Predict to Prevent, ‘P2P study’). The P2P study is a population health program that aims to monitor, predict, and delay the risk of macrovascular complications through early risk identification and stratification amongst patients and population groups at high risk of developing cardiovascular disease.

The funding agencies were not involved in the design, planning, screening, analysis or interpretation of the findings of this study, as well as the preparation of this manuscript in any way.

Author’s Contributions

Study design and conceptualisation: AC, WT; Database search and extraction of articles: YM; Screening and review of articles: AC, WT; Data extraction: AC, WT; Study quality appraisal: AC, WT, SA; Drafting and preparation of manuscript: AC; Review of manuscript: AC, WT, SA, RD. All authors approve of the final version of this manuscript.

Acknowledgements

Not Applicable.

Adarkwah CC, Jegan N, Heinzel-Gutenbrunner M, Kuhne F, Siebert U, Popert U, et al. Time-to-event versus ten-year-absolute-risk in cardiovascular risk prevention - does it make a difference? Results from the Optimizing-Risk-Communication (OptRisk) randomized-controlled trial. BMC Med Inf Decis Mak. 2016;16(1):152. 10.1186/s12911-016-0393-1.
Adarkwah CC, Jegan N, Heinzel-Gutenbrunner M, Kuhne F, Siebert U, Popert U, et al. The Optimizing-Risk-Communication (OptRisk) randomized trial - impact of decision-aid-based consultation on adherence and perception of cardiovascular risk. Patient Prefer Adherence. 2019;13:441–52. 10.2147/PPA.S197545.
Alexander GC, Casalino LP, Meltzer DO. Patient-physician communication about out-of-pocket costs. JAMA. 2003;290(7):953–8. 10.1001/jama.290.7.953.
Andreas DC, Abraham NS, Naik AD, Street RL Jr., Sharf BF. Understanding risk communication through patient narratives about complex antithrombotic therapies. Qual Health Res. 2010;20(8):1155–65. 10.1177/1049732310370154.
Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. 10.1080/1364557032000119616.
Avery L, Flynn D, van Wersch A, Sniehotta FF, Trenell MI. Changing physical activity behavior in type 2 diabetes: a systematic review and meta-analysis of behavioral interventions. Diabetes Care. 2012;35(12):2681–9. 10.2337/dc11-2452.
Barfoed BL, Jarbol DE, Paulsen MS, Christensen PM, Halvorsen PA, Nielsen JB, et al. GPs' Perceptions of Cardiovascular Risk and Views on Patient Compliance: A Qualitative Interview Study. Int J Family Med. 2015;2015:214146. 10.1155/2015/214146.
Barker TH, Stone JC, Sears K, Klugar M, Tufanaru C, Leonardi-Bee J, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials. JBI Evid Synthesis. 2023;21(3):494–506. 10.11124/jbies-22-00430.
Beck RS, Daughtridge R, Sloane PD. Physician-patient communication in the primary care office: a systematic review. J Am Board Fam Pract. 2002;15(1):25–38.
Becker ER, Roblin DW. Translating primary care practice climate into patient activation: the role of patient trust in physician. Med Care. 2008;46(8):795–805. 10.1097/MLR.0b013e31817919c0.
Bengtsson A, Lindvall K, Norberg M, Fharm E. Increased knowledge makes a difference! - general practitioners' experiences of pictorial information about subclinical atherosclerosis for primary prevention: an interview study from the VIPVIZA trial. Scand J Prim Health Care. 2021;39(1):77–84. 10.1080/02813432.2021.1882083.
Bonds DE, Camacho F, Bell RA, Duren-Winfield VT, Anderson RT, Goff DC. The association of patient trust and self-care among patients with diabetes mellitus. BMC Fam Pract. 2004;5(1):26. 10.1186/1471-2296-5-26.
Bonner C, Jansen J, McKinn S, Irwig L, Doust J, Glasziou P, et al. Communicating cardiovascular disease risk: an interview study of General Practitioners' use of absolute risk within tailored communication strategies. BMC Fam Pract. 2014;15:106. 10.1186/1471-2296-15-106.
Borg Xuereb C, Shaw RL, Lane DA. Patients' and physicians' experiences of atrial fibrillation consultations and anticoagulation decision-making: A multi-perspective IPA design. Psychol Health. 2016;31(4):436–55. 10.1080/08870446.2015.1116534.
Bridgwood BM, Wilson A, Clarke D, Eborall H. Understanding TIA: an ethnographic study of TIA consultations. Fam Pract. 2020;37(4):530–4. 10.1093/fampra/cmaa004.
Cardona M, Sav A, Michaleff ZA, Thomas ST, Dobler CC. Alignment of Doctors' Understanding of Treatment Burden Priorities and Chronic Heart Failure Patients' Experiences: A Nominal Group Technique Consultation. Patient Prefer Adherence. 2023;17:153–65. 10.2147/PPA.S385911.
Casebeer L, Huber C, Bennett N, Shillman R, Abdolrasulnia M, Salinas GD, et al. Improving the physician-patient cardiovascular risk dialogue to improve statin adherence. BMC Fam Pract. 2009;10:48. 10.1186/1471-2296-10-48.
Cavanaugh K, Huizinga MM, Wallston KA, Gebretsadik T, Shintani A, Davis D, et al. Association of numeracy and diabetes control. Ann Intern Med. 2008;148(10):737–46. 10.7326/0003-4819-148-10-200805200-00006.
Chew BH, Shariff-Ghazali S, Fernandez A. Psychological aspects of diabetes care: Effecting behavioral change in patients. World J Diabetes. 2014;5(6):796–808. 10.4239/wjd.v5.i6.796.
Committee ADAPP. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2023;47(Supplement1):S20–42. 10.2337/dc24-S002.
Committee ADAPP. 6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes—2024. Diabetes Care. 2023;47(Supplement1):S111–25. 10.2337/dc24-S006.
Cooper LA, Roter DL, Johnson RL, Ford DE, Steinwachs DM, Powe NR. Patient-centered communication, ratings of care, and concordance of patient and physician race. Ann Intern Med. 2003;139(11):907–15. 10.7326/0003-4819-139-11-200312020-00009.
Davis K, Schoenbaum SC, Audet AM. A 2020 vision of patient-centered primary care. J Gen Intern Med. 2005;20(10):953–7. 10.1111/j.1525-1497.2005.0178.x.
Denig P, Schuling J, Haaijer-Ruskamp F, Voorham J. Effects of a patient oriented decision aid for prioritising treatment goals in diabetes: pragmatic randomised controlled trial. BMJ. 2014;349:g5651. 10.1136/bmj.g5651.
Dokken BB. The Pathophysiology of Cardiovascular Disease and Diabetes: Beyond Blood Pressure and Lipids. Diabetes Spectr. 2008;21(3):160–5. 10.2337/diaspect.21.3.160.
Dugdale DC, Epstein R, Pantilat SZ. Time and the patient-physician relationship. J Gen Intern Med. 1999;14(Suppl 1):S34–40. 10.1046/j.1525-1497.1999.00263.x.
Durack-Bown I, Giral P, d'Ivernois JF, Bazin C, Chadarevian R, Benkritly A, et al. Patients' and physicians' perceptions and experience of hypercholesterolaemia: a qualitative study. Br J Gen Pract. 2003;53(496):851–7.
Epstein RM, Fiscella K, Lesser CS, Stange KC. Why the nation needs a policy push on patient-centered health care. Health Aff (Millwood). 2010;29(8):1489–95. 10.1377/hlthaff.2009.0888.
Fisher EB, Walker EA, Bostrom A, Fischhoff B, Haire-Joshu D, Johnson SB. Behavioral Science Research in the Prevention of Diabetes: Status and opportunities. Diabetes Care. 2002;25(3):599–606. 10.2337/diacare.25.3.599.
Fisseni G, Lewis DK, Abholz HH. Understanding the concept of medical risk reduction: a comparison between the UK and Germany. Eur J Gen Pract. 2008;14(3–4):109–16. 10.1080/13814780802580247.
Gidlow CJ, Ellis NJ, Cowap L, Riley V, Crone D, Cottrell E, et al. Cardiovascular disease risk communication in NHS Health Checks using QRISK(R)2 and JBS3 risk calculators: the RICO qualitative and quantitative study. Health Technol Assess. 2021;25(50):1–124. 10.3310/hta25500.
Goodyear-Smith F, Kenealy T, Wells S, Arroll B, Horsburgh M. Patients' preferences for ways to communicate benefits of cardiovascular medication. Ann Fam Med. 2011;9(2):121–7. 10.1370/afm.1193.
Ha JF, Longnecker N. Doctor-patient communication: a review. Ochsner J. 2010;10(1):38–43.
Hawking MKD, Timmis A, Wilkins F, Potter JL, Robson J. Improving cardiovascular disease risk communication in NHS Health Checks: a qualitative study. BMJ Open. 2019;9(8):e026058. 10.1136/bmjopen-2018-026058.
Hinder S, Greenhalgh T. This does my head in. Ethnographic study of self-management by people with diabetes. BMC Health Serv Res. 2012;12(1):83. 10.1186/1472-6963-12-83.
Honey S, Hill K, Murray J, Craigs C, House A. Patients' responses to the communication of vascular risk in primary care: a qualitative study. Prim Health Care Res Dev. 2015;16(1):61–70. 10.1017/S1463423613000509.
Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inform. 2018;34:285–91. 10.3233/EFI-180221.
Huang ES, Nathan AG, Cooper JM, Lee SM, Shin N, John PM, et al. Impact and Feasibility of Personalized Decision Support for Older Patients with Diabetes: A Pilot Randomized Trial. Med Decis Mak. 2017;37(5):611–7. 10.1177/0272989X16654142.
Johnson RL, Roter D, Powe NR, Cooper LA. Patient race/ethnicity and quality of patient-physician communication during medical visits. Am J Public Health. 2004;94(12):2084–90. 10.2105/ajph.94.12.2084.
Kirby M, Machen I. Impact on clinical practice of the Joint British Societies' cardiovascular risk assessment tools. Int J Clin Pract. 2009;63(12):1683–92. 10.1111/j.1742-1241.2009.02201.x.
Knowles JW, Zarafshar S, Pavlovic A, Goldstein BA, Tsai S, Li J, et al. Impact of a Genetic Risk Score for Coronary Artery Disease on Reducing Cardiovascular Risk: A Pilot Randomized Controlled Study. Front Cardiovasc Med. 2017;4:53. 10.3389/fcvm.2017.00053.
Krones T, Keller H, Sonnichsen A, Sadowski EM, Baum E, Wegscheider K, et al. Absolute cardiovascular disease risk and shared decision making in primary care: a randomized controlled trial. Ann Fam Med. 2008;6(3):218–27. 10.1370/afm.854.
Kruse RL, Olsberg JE, Shigaki CL, Parker Oliver DR, Vetter-Smith MJ, Day TM, et al. Communication during patient-provider encounters regarding diabetes self-management. Fam Med. 2013;45(7):475–83.
Kubzansky LD, Huffman JC, Boehm JK, Hernandez R, Kim ES, Koga HK, et al. Positive Psychological Well-Being and Cardiovascular Disease: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72(12):1382–96. 10.1016/j.jacc.2018.07.042.
Laddu D, Ma J, Kaar J, Ozemek C, Durant RW, Campbell T, et al. Health Behavior Change Programs in Primary Care and Community Practices for Cardiovascular Disease Prevention and Risk Factor Management Among Midlife and Older Adults: A Scientific Statement From the American Heart Association. Circulation. 2021;144(24):e533–49. 10.1161/CIR.0000000000001026.
Larsen KM, Smith CK. Assessment of nonverbal communication in the patient-physician interview. J Fam Pract. 1981;12(3):481–8.
Larson EB, Yao X. Clinical Empathy as Emotional Labor in the Patient-Physician Relationship. JAMA. 2005;293(9):1100–6. 10.1001/jama.293.9.1100.
Ledford CJ. Contending mediated risk messages: a grounded theory of the physician-patient discussion of a prescription medication's changing risk. Patient Educ Couns. 2011;83(1):14–21. 10.1016/j.pec.2010.04.041.
Lewis CC, Klasnja P, Powell BJ, Lyon AR, Tuzzio L, Jones S, et al. From Classification to Causality: Advancing Understanding of Mechanisms of Change in Implementation Science. Front Public Health. 2018;6:136. 10.3389/fpubh.2018.00136.
Lown BA, Clark WD, Hanson JL. Mutual influence in shared decision making: a collaborative study of patients and physicians. Health Expect. 2009;12(2):160–74. 10.1111/j.1369-7625.2008.00525.x.
Lyles CR, Grothaus L, Reid RJ, Sarkar U, Ralston JD. Communication about diabetes risk factors during between-visit encounters. Am J Manag Care. 2012;18(12):807–15.
Marx N, Federici M, Schütt K, Müller-Wieland D, Ajjan RA, Antunes MJ, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes: Developed by the task force on the management of cardiovascular disease in patients with diabetes of the European Society of Cardiology (ESC). Eur Heart J. 2023;44(39):4043–140. 10.1093/eurheartj/ehad192.
Mazzucca S, Arredondo EM, Hoelscher DM, Haire-Joshu D, Tabak RG, Kumanyika SK, et al. Expanding Implementation Research to Prevent Chronic Diseases in Community Settings. Annu Rev Public Health. 2021;42:135–58. 10.1146/annurev-publhealth-090419-102547.
Mercer MB, Rose SL, Talerico C, Wells BJ, Manne M, Vakharia N, et al. Use of Visual Decision Aids in Physician-Patient Communication: A Pilot Investigation. J Patient Exp. 2018;5(3):167–76. 10.1177/2374373517746177.
Miller CJ, Barnett ML, Baumann AA, Gutner CA, Wiltsey-Stirman S. The FRAME-IS: a framework for documenting modifications to implementation strategies in healthcare. Implement Sci. 2021;16(1):36. 10.1186/s13012-021-01105-3.
Moola S, Munn Z, Sears K, Sfetcu R, Currie M, Lisy K, et al. Conducting systematic reviews of association (etiology): The Joanna Briggs Institute's approach. Int J Evid Based Healthc. 2015;13(3):163–9. 10.1097/XEB.0000000000000064.
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Reviews. 2016;5(1):210. 10.1186/s13643-016-0384-4.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71.
Polak L, Green J. Using quantitative risk information in decisions about statins: a qualitative study in a community setting. Br J Gen Pract. 2015;65(633):e264–9. 10.3399/bjgp15X684433.
Ratanawongsa N, Roter D, Beach MC, Laird SL, Larson SM, Carson KA, et al. Physician burnout and patient-physician communication during primary care encounters. J Gen Intern Med. 2008;23(10):1581–8. 10.1007/s11606-008-0702-1.
Roach P, Klindukhova O, Saha C, Hudson B, Cantrell M, Marrero D. Project RedCar: Cardiovascular Disease Risk Communication for People With Type 2 Diabetes Combining the Power of Electronic Health Records and Computer-Based Multimedia Technology. Diabetes Spectr. 2010;23(3):155–60. 10.2337/diaspect.23.3.155.
Rosal MC, Ockene JK, Luckmann R, Zapka J, Goins KV, Saperia G, et al. Coronary heart disease multiple risk factor reduction. Providers' perspectives. Am J Prev Med. 2004;27(2 Suppl):54–60. 10.1016/j.amepre.2004.04.020.
Rouyard T, Leal J, Baskerville R, Velardo C, Salvi D, Gray A. Nudging people with Type 2 diabetes towards better self-management through personalized risk communication: A pilot randomized controlled trial in primary care. Endocrinol Diabetes Metab. 2018;1(3):e00022. 10.1002/edm2.22.
Salmasi S, Kwan L, MacGillivray J, Bansback N, De Vera MA, Barry AR, et al. Assessment of atrial fibrillation patients' education needs from patient and clinician perspectives: A qualitative descriptive study. Thromb Res. 2019;173:109–16. 10.1016/j.thromres.2018.11.015.
Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inf Decis Mak. 2007;7(1):16. 10.1186/1472-6947-7-16.
Schulberg SD, Ferry AV, Jin K, Marshall L, Neubeck L, Strachan FE, et al. Cardiovascular risk communication strategies in primary prevention. A systematic review with narrative synthesis. J Adv Nurs. 2022;78(10):3116–40. doi.org/10.1111/jan.15327.
Shen MJ, Peterson EB, Costas-Muniz R, Hernandez MH, Jewell ST, Matsoukas K, et al. The Effects of Race and Racial Concordance on Patient-Physician Communication: A Systematic Review of the Literature. J Racial Ethn Health Disparities. 2018;5(1):117–40. 10.1007/s40615-017-0350-4.
Sudore RL, Landefeld CS, Perez-Stable EJ, Bibbins-Domingo K, Williams BA, Schillinger D. Unraveling the relationship between literacy, language proficiency, and patient-physician communication. Patient Educ Couns. 2009;75(3):398–402. 10.1016/j.pec.2009.02.019.
Tawfik MY, Mohamed RA. The impact of communicating cardiovascular risk in type 2 diabetics on patient risk perception, diabetes self-care, glycosylated hemoglobin, and cardiovascular risk. J Public Health. 2016;24(2):153–64. 10.1007/s10389-016-0710-2.
Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57. 10.1093/intqhc/mzm042.
Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73. 10.7326/M18-0850.
Tufanaru C, Munn Z, Aromataris E, Campbell J, Hopp L. Systematic reviews of effectiveness. Joanna Briggs Institute reviewer’s manual. 2017;3.
van Steenkiste B, van der Weijden T, Stoffers HE, Grol R. Barriers to implementing cardiovascular risk tables in routine general practice. Scand J Prim Health Care. 2004;22(1):32–7. 10.1080/02813430310004489.
Virani SS, Newby LK, Arnold SV, Bittner V, Brewer LC, Demeter SH, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2023;148(9):e9–119. 10.1161/CIR.0000000000001168.
Welschen LM, Bot SD, Kostense PJ, Dekker JM, Timmermans DR, van der Weijden T, et al. Effects of cardiovascular disease risk communication for patients with type 2 diabetes on risk perception in a randomized controlled trial: the @RISK study. Diabetes Care. 2012;35(12):2485–92. 10.2337/dc11-2130.
Williams MV, Davis T, Parker RM, Weiss BD. The role of health literacy in patient-physician communication. Fam Med. 2002;34(5):383–9.
Williams PA, Prabandari YS, Burfeind C, Lefebvre RC, LaBresh KA. Impact of a Pilot Intervention to Increase Physician-Patient Communication About Stroke Risk in Indonesia. Health Commun. 2016;31(12):1573–8. 10.1080/10410236.2015.1082456.
Wollny A, Altiner A, Daubmann A, Drewelow E, Helbig C, Loscher S, et al. Patient-centered communication and shared decision making to reduce HbA1c levels of patients with poorly controlled type 2 diabetes mellitus - results of the cluster-randomized controlled DEBATE trial. BMC Fam Pract. 2019;20(1):87. 10.1186/s12875-019-0977-9.
Yao M, Zhang DY, Fan JT, Lin K, Haroon S, Jackson D, et al. The experiences of people with type 2 diabetes in communicating with general practitioners in China - a primary care focus group study. BMC Prim Care. 2022;23(1):24. 10.1186/s12875-022-01632-y.
Yuguero O, Marsal JR, Esquerda M, Soler-González J. Occupational burnout and empathy influence blood pressure control in primary care physicians. BMC Fam Pract. 2017;18(1):63. 10.1186/s12875-017-0634-0.
Zhang X, Li L, Zhang Q, Le LH, Wu Y. Physician Empathy in Doctor-Patient Communication: A Systematic Review. Health Commun. 2024;39(5):1027–37. 10.1080/10410236.2023.2201735.

No competing interests reported.

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Additional File 1 PRISMA ScR Checklist
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Additional File 2 Search Strategy
AdditionalFile3.docx
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Patient-physician communication of health and risk information in the management of cardiovascular diseases and diabetes: a systematic scoping and narrative review

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Abstract

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Background

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1