1 Introduction
Decision making in primary care is sometimes complex for patients and health professionals. Clinical information with scientific evidence about the various options for diagnosis and treatment is not always clearly available. However, decision-making process involves more that providing information; it means that the patients play an active role in decisions concerning their health, and that they fully engage in the decision-making process.[1]
Shared decision making (SDM) intends to balance the patients’ right of autonomy with the practitioners’ responsibility to protect patients’ safety.[2] SDM is a process within the physician–patient relationship applicable to any clinical action, whether diagnostic, therapeutic, or preventive in nature. It has been studied mainly in the areas of healthy lifestyle and adherence to treatment in chronic diseases, breast and prostate cancer, and palliative care. It is considered a manifestation of patient-centered care, a health care approach that is guided by the patients’ needs instead of the health professionals’ priorities.[3] The components of SDM have many elements in common with patient-centered care, such as providing information about patients’ choices, showing consideration for their values, and decision-making involvement.[4] Its practice is fundamental when all the hoped-for benefits of an intervention cannot be guaranteed or when there is great risk involved. Different instruments have been developed to measure patient participation and how professionals facilitate the involvement of patients in decision making, which has important ethical implications with respect to their autonomy.[5,6] This participation in the decision-making process is possible through a deliberative model of the physician–patient relationship, which involves information exchange and subsequent deliberation in order to achieve the best choice.[7] In this paper, we distinguish “shared decision making” from “informed decision making,” even if they have common characteristics.
Interventions to support SDM either aim to prepare health professionals through actions like coaching or training interventions, or to help the practitioners and patients to proceed with the decision making by implementing procedures such as DA.[8] DA strategies facilitate patients’ decision-making involvement and play an essential role in SDM as informative tools. They contribute to the respect of personal values in the decision-making process by increasing the patients’ knowledge of their conditions and reducing passivity in decision making.[9]
The use of DA can help patients participate in the decisions to improve the quality of the decision-making process and the satisfaction with the chosen option.[10] Benefits from DA compared with usual care have already been described. DA increase knowledge regarding options and reduce the decisional conflict related to feeling uninformed. DA also encourages patients to be actively involved in decision making and provide an accurate perception of the actual risks. The use of DA foster valued-based choices and patient–practitioner communication.[9] A number of barriers to the application of DA by professionals in primary care have been described, such as time restraints, lack of familiarity, and the existence of an of inadequate clinical reporting system that does not allow these tools to be included. Facilitators include automation the use of DA, making them available for patient's prior consultation, and their use by nonclinical personnel.[11]
Evidence has been published that shows that SDM promotes appropriate care, decreases overtreatment, meliorates health outcomes and, by extension, reduces health-care costs.[8] SDM has shown to be effective in many scenarios including Primary Care, Mental Health, Pediatrics, Palliative Care, Medicine, and Surgery.[12] SDM assumes that patients are willing and prepared to choose the best option, although in practice patients are not often in a position to make a good decision and practitioners have to lead the decision-making process. In these cases, Brown and Salmon[2] suggest contextualizing the decision and assessing patients by making judgements of reasonableness. Although many training programs towards improving health care professionals’ competence in SDM have been identified, its routine use remains limited.[13] Boland et al[14] identified barriers in the implementation of SDM beside training, such as low practitioners’ perception of self-efficacy in SDM, time constrains, inappropriate settings, and a lack of team-based approach. Kalsi et al[12] pointed high-quality DA, cultural shift towards a more patient-centered care, and adequate training as challenges in the implementation of SDM.
The benefits from SDM have already been reviewed, but none of these papers focuses on the primary care context. Considering the proven effectiveness and the scarce implementation of SDM, there is a need for summarizing published evidence on the practice of SDM in primary care, considering whether the use of DA with patients treated in primary care, as compared with the usual clinical practice, improve adherence to the treatment, knowledge and awareness of the illness, patients and health professionals’ satisfaction with the intervention, and also reduces decisional conflict.
The aims of this study are: to determine whether SDM using DA in primary care consultations improve adherence to the treatment, knowledge and awareness of the illness, satisfaction of both professionals and patients with the intervention, and reduces decisional conflict. To identify the appropriate tools for decision making in primary care. To assess evidence quality for these tools.
2 Methods
A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.[15] In order to identify primary studies, the following databases were consulted: MEDLINE via PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and NHS Economic Evaluation Database. The PubMed search strategy was “Decision Making” [Mesh] AND “Primary Health Care” [Mesh] AND (Randomized Controlled Trial[ptyp] odds ratio (OR) “before and after” [tiab]) AND (English[lang] OR Portuguese[lang] OR Spanish[lang]) AND (“2007/01/01” [PDAT]: “2020/01/31”[PDAT]). The following search terms were used with the remaining databases: “Shared decision making” AND “Primary care,” with publication limit dates added. Additionally, the reference lists of the selected articles were manually reviewed, and those that met the established inclusion criteria were included. Additionally, the reference lists of the included papers were manually reviewed, in case any study that met the established inclusion criteria had not been identified in the initial search due to the specific search terms used or for being published in journals that are not indexed in the consulted databases.
A literature search was conducted between January 2007 and January 2019.
This systematic revision includes randomized clinical trials that assess DA for shared decision making in primary care. The articles may be written in English, Spanish, or Portuguese. The inclusion criteria were: DA used for any diagnostic or therapeutic intervention in primary care; DA in any format; patients of any age who were assisted in primary care consultations for any disease.
As exclusion criteria it was stablished duplicated references; non access to full text article; not relevant for the aim of the study; and studies with low methodological quality after assessing the risk of bias.
Randomized and controlled clinical trials that included patients using primary care consultations for treatment, diagnosis, prevention, or health promotion activities related to acute or chronic diseases were selected. The Intervention group was programs making use of DA in SDM. The Control group was standard practice, which means that SDM strategies were not used. After discarding duplicates, references were screened according to title and abstract. Then, the full texts of the selected articles were retrieved for assessment. Two researchers selected the studies independently. Discrepancies were resolved by consensus.
One researcher collected the following data through specially designed forms: types of conditions for which SDM was used, health care professionals involved, DA, clinical outcomes of the intervention, adherence to treatment, patients’ knowledge of the different treatment options, adverse effects resulting from the interventions, decisional conflict, satisfaction of professionals and patients. Information obtained after data extraction was analyzed and a narrative synthesis were carried out describing the results.
The assessment of risk of bias was conducted using the Cochrane Collaboration's tool.[16] This tool allows evaluating the studies according to the random sequence generation, the allocation concealment, the blinding of participants and personnel, the blinding of outcome assessment, the incomplete outcome data and the selective reporting. Review authors’ judgements were categorized as “low risk” of bias, “high risk” of bias, or “unclear risk” of bias. The risk was assessed as “unclear risk” when details about methods followed were not described in the article. Studies with a score “high risk” in >3 items were excluded. It is estimated that randomized clinical trials with a medium quality assessment may overestimate the effect size by up to 35%, as compared with those with high quality.[17]
The quality appraisal was performed by 2 researchers independently, and consensus was reached regarding the results. Given the heterogeneity of the interventions and measurement methods, it was not considered appropriate to perform a statistical analysis of the study results. Table 1 shows the methodological quality of the trials included in this review. Blinding of outcomes assessment was the most common bias. This research activity does not involve human subjects or animals. Neither human data have been used. IRB approval has not been required.
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Table 1: Assessment of the methodological quality according to the Cochrane Collaboration's tool.
3 Results
The database search produced 201 references, 15 of which were duplicates. After reading the title and abstract of the identified references, 138 references were discarded for not fulfilling the inclusion criteria. The resulting 48 articles were full-text screened, and 24 were excluded for their poor methodological quality. Finally, 24 studies were included in this review.[18–41]
According to the aim of the review, the results were organized regarding effectiveness of the intervention on adherence to treatment, knowledge and awareness of the disease, absence of conflict, and patients’ and professionals’ satisfaction. The results from the reviewed studies are summarized in Table 2 . Table 3 shows the articles obtained from each database.
Table 2: Characteristics and results from the included clinical trials.
Table 2 (Continued): Characteristics and results from the included clinical trials.
Table 2 (Continued): Characteristics and results from the included clinical trials.
Table 3: List of articles obtained from each database.
3.1 Patients’ conditions
The mean age of the participants in the reviewed studies ranged from 8 years[32] to 73 years.[33] The study population involved adults or older people, except for 1 study on children with asthma.[32] The interventions were performed by family physicians in all the trials, except for 2 where nurses[21] and pediatricians[32] took part. The interventions were used in cancer screening,[22,26,27,31,33,37] type 2 diabetes,[28,35,36] cardiovascular disease,[19,20,21] respiratory disease,[23,25,32] and depression.[18,34]
3.2 Decision aids format
There was great variability in the DA format used among the reviewed studies, including paper,[20,24,28,29,30,34,35,36] video,[26,33,37] and digital formats like web sites and computer-based formats.[19,23,27,32,41] Reporting systems and group meetings were sometimes used, or a combination of both.[31,33]
A paper-based DA implied that an informative sheet was given during the consultation, with a self-report procedure. A personal interview was considered as an encounter and dialogue between the health professional (doctor, nurse, physical therapists) and the patient, but the patient did not necessarily receive written information. When the meeting was in group, this meant >1 patient at the same consultation. It is better called Shared Medical Appointment, understood as a doctor-patient visits in which groups of patients are seen by one or more health care providers in a concurrent session.
Web-based DA referred to online information that was given to the patient, so it could be read by their own at home. Computerized DA meant graphic information, numerical, and information using computer systems. An e-book could be considered a format that uses computer language and that can be used as a tool in DA. Another DA mentioned in the reviewed articles was DVD or video-based techniques. They were commonly used for teaching patients about some medical condition or treatment options. The reviewed results did not show any differences when comparing the strategies.
3.3 Effectiveness regarding clinical outcomes: adherence to the treatment
Studies that did not measure adherence to the treatment, but its consequences, were reviewed.
The intervention reduced the use of antibiotics in cases of acute respiratory infection (relative risk [RR] = 0.48; confidence interval (CI) 95%: 0.34–0.48).[19] In a trial, the use of DA improved osteoporosis treatment (P = .009).
No effects were found in the control of childhood asthma, but admissions to hospital were reduced in 21% after the interventions, and pediatrics consultations were also reduced in 5%.[26]
In the screening programmes, the rate of determination of prostate-specific antigen (PSA) in the prostate cancer screening was reduced (RR = 0.48; CI 95%: 0.14–1.24)[20] and colorectal cancer screening increased in 41% (CI 95%: 29–51).[31] In a trial, prostate cancer screening increased with the intervention, although patients changed their attitudes towards the benefits of determining PSA (P = .008).[27]
Loh et al[18] did not find any differences in adherence to treatment among patients with depression, but they did find greater patient involvement when SDM was used.
Type 2 diabetes patients reduced their HbA1 and body mass index when DA was provided, due to adherence to treatment.[36] In Buhse et al,[39] mean drug adherence rates were high for both groups (80% for antihypertensive and 91% for statin treatment).
Patients who engaged an SDM process reduced their cardiovascular risk because of lifestyle changes. Reported patients’ participation and SDM step was higher when DA was used (P < .001).[20]
3.4 Effectiveness regarding clinical outcomes: knowledge and awareness of the disease
The interventions improved knowledge on medication of depression (OR = 9.5; CI 95%: 0.8–18.2).[28]
In type 2 diabetes, DA improved knowledge (P = .001) and the perception of risk of acute myocardial infarction at 10 years without statins (P = .01) and with statins (P = .08).[35] However, there were no differences regarding the patient's knowledge about the disease (P = .234). Patients in the intervention group reported a significantly higher level of patient involvement (2.92 [SD: 1.21] than the controls (2.44 [SD 1:23]) (difference 0.48; P = .005).[38]
As for awareness of the health status, the perception of cardiovascular risk (P = .001)[21,35] and prostate cancer risk[33] increased when patients went through SDM. In prostate cancer screening, PSA testing was reduced when using SDM, as it was considered a personal decision.[26] On the contrary, colon cancer screening increased, especially among women, after a video-based DA: 68% of intervention group underwent colon cancer screening versus 27% of control group (95% CI: 29–51).[37]
3.5 Effectiveness regarding clinical outcomes: conflict with the decision
Decisional conflict was considered a state of uncertainty about a course of action. Such uncertainty is more likely when a person is confronted with decisions involving risk or uncertainty of outcomes, when high stakes choices with significant potential gains and losses are entertained, when there is a need to make value tradeoffs in selecting a course of action, or when anticipated regret over the positive aspects of rejected options is probable.[19]
Decisional conflict was reduced in the decision that regarded the treatment choice for atrial fibrillation (P < .036)[19] and respiratory infection.[23,25] Patients at cardiovascular risk showed higher confidence about decision making (P = .001)[21] and lower decisional regret[20] when SDM was implemented. There was a significant decrease for decisional conflict and perceived barriers for faecal immunochemical test and colonoscopy in colorectal cancer screening (P < .001).[40]
In contrast, no differences were found in decisional conflict associated to SDM when interviewing patients for prostate cancer screening (P = .620),[22] when dealing with women at osteoporosis risk (P = .725),[24] or when informing type 2 diabetes patients about treatment choices (P = .305).[36]
3.6 Effectiveness regarding clinical outcomes: satisfaction
The satisfaction was greater with the use of DA in choosing the treatment for depression (P = .014)[18,34] in cardiovascular risk management (P = .001),[20,21] the treatment of low back pain (intervention group: 53%, control group: 67%. RR = 1.28 (CI 95%: 0.79–2.03),[30] and the use of statin therapy in diabetes (P = .001).[35] No differences were found among type 2 diabetes patients,[28,36] children with asthma[32] or men interviewed for prostate cancer screening.[26,27] Satisfaction of physicians was measured in only one of the reviewed studies. They were more satisfied with the decision when using a DA tool (RR = 1.64) P = .02.[34]
Patients reported to be more involved in the decision-making process due to SDM for choosing the treatment for depression[18,34] and acute respiratory infection.[25] It was also reported that SDM facilitated better communication between physicians and patients[31] and further discussing the options.[27]Table 4 shows the reports of effectiveness regarding clinical evidence.
Table 4: Effectiveness regarding clinical evidence in the included clinical trials.
3.7 Risk assessment of biases
The Cochrane Collaboration's tool was used to evaluate the risk of bias. Seven trials showed a high risk of bias regarding the “blinding of participants and personnel,” once in “blinding of outcome assessment,” and twice in “incomplete outcome data.” The rest of studies showed a low or uncertain risk of bias.
4 Discussion
This systematic review about the effectiveness of SDM using DA identified an improvement in the satisfaction with the intervention, showing greater patient involvement and better knowledge of the disease, decreasing decisional conflict.
The findings of this review were consistent with the results of other studies on the use of DA for the screening and treatment of specific conditions.[9] There is evidence that the DA improve knowledge of options and reduce decisional conflict when compared with usual care. Knowledge about the different options for diagnosis and treatment is relevant to the clinical context as it helps patients take a more active role in the decisions, improving the risk perception when the options are complex.[42]
SDM using DA in primary care was frequently used in screening programs, mainly for prostate, colon, and breast cancer. The DA used to make decisions in the screening and treatment of oncological processes help choose the less invasive procedures and start treatments earlier.[43] Despite this, oncologists involve patients in decision making less often than they would like.[44] About the use of DA for prostate cancer screening, while the rate of PSA testing was significantly reduced in one trial compared with the control group, it increased in another. Nonetheless, its effectiveness was shown in the increased use of colonoscopy procedures in colon cancer screening.
Few studies assessed the impact of SDM using DA on health outcomes. In one trial where its effectiveness was determined for the control of asthma and quality of life in children, with the reduction in the number of consultations, hospital admissions, visits to the pulmonologist and pediatrician, there were no relevant differences between the groups.[32] More research is needed to know the effectiveness of DA on clinical outcomes of the most common processes treated in primary care consultations. SDM, when put into practice in primary care consultations, improves patients’ knowledge regarding the prevention and treatment of highly prevalent diseases. However, while patients want to play a more active role in decision making,[45] there is no evidence of interventions that improve the participation of health care professionals in SDM.[46] In long term patients, which is the most common patient profile in primary care, a moderate evidence of lack of effect of SDM on medication adherence has been identified, and conflicting evidence for the effectiveness of SDM on the patients’ clinical parameters and health-related quality of life.[47]
Evidence of SDM effectiveness in depression identified in this review is congruent with previous studies. Benefits from the SDM in mental health have been identified such as symptoms reduction, improved self-esteem, increased service satisfaction, improved treatment adherence, improved patient knowledge, increased confidence in decisions, and decreased rates of hospitalization.[48] Due to the complexity inherent to mental health and the lack of decisional capacity of some mental health patients, SDM occurs less frequently than in other medical areas.[49] Hamann et al[50] pointed out self-stigma and shame as barriers for SDM in mental health. These behaviors hinder physician–patient communication and critical attitudes that lead to SDM. Fisher et al[51] reviewed decision making in mental health, particularly in bipolar disorder patients. Findings showed that they desired to get more actively involved, both themselves and their families, in the decisions concerning their treatment. DA was considered a useful tool for informed decision making based on scientific evidence.
Regarding type 2 diabetes patients, this review identified benefits from the use of SDM that are consistent with previous reviews. The meta-analysis conducted by Saheb et al[52] highlighted an association between SDM and decision quality, patient knowledge and patient risk perception in type 2 diabetes. SDM is appropriate for diabetes care because of the impact of treatment in patients’ lifestyle, the lifelong term measures to be adopted, and the multiple treatment options available. SDM allows sharing evidence with patients and engaging them in their choice.[53]
It was found evidence of the usefulness of SDM for the reduction of antibiotic consumption in acute respiratory infections. As Coxeter et al[54] highlighted in their Cochrane review, evidence available to support this finding remains moderate. However, patients reported high decision involvement and self-efficacy, and low decisional conflict when SDM was used in the general practitioners’ consultations for acute respiratory infections.[55] SDM has been particularly suggested for reducing antibiotic prescribing for acute respiratory infections. In these situations, benefits and harms are practically balanced, so patients’ preferences become a priority; they need to be fully informed about evidence in favor and against antibiotic use.[56]
The main limitations of this review are determined by the variability of the DA, the way in which they were applied, and the measurement of outcomes, which made comparing studies difficult. Although the search strategies were broad, they may not have identified all the studies in which SDM appears in primary care. Improving the methodology quality of future clinical trials carried out on DA in primary care is recommended, especially about the double blind and the blind method.
Overall, this review found evidence of SDM effectiveness in improving knowledge about the disease and patients’ options, reducing the decisional conflict and fostering patient satisfaction with the decision process and the final choice in primary care settings. Findings form this review could help facilitate SDM implementation in primary care.
Comparability of results was compromised due to the variability of DA formats included in this review. Although evidence of SDM effectiveness was identified, some of the reviewed studies did not provide solid conclusions. There is a need for more studies to assess the impact of SDM in primary care, health outcomes, and patient quality of life, also for designing and validating DA for treatments and diagnostic tests for chronic conditions.
5 Conclusions
Some decision aids (DA) used in primary care consultations by family physicians and nurses have proven their clinical potential for improving knowledge on the disease, decisional conflict, and professionals and patients’ satisfaction. Future research should assess the effectivity of DA as regards outcomes of the most frequent diseases treated in primary care consultations.
Acknowledgments
This review is part of the research project “Effectiveness of an intervention based on shared decision making for improving the medication of polymedicated chronic patients”, which was awarded the 2016 Esteve Grant for Health Innovation by the Institute of Health Sciences of Aragon.
Author contributions
Conceptualization: Valle Coronado-Vázquez, Carlota Canet-Fajas.
Data curation: Maria Teresa Delgado-Marroquín.
Formal analysis: Carlota Canet-Fajas, Maria Teresa Delgado-Marroquín.
Investigation: Carlota Canet-Fajas, Maria Teresa Delgado-Marroquín, Rosa Magallón-Botaya.
Methodology: Valle Coronado-Vázquez, Maria Teresa Delgado-Marroquín, Macarena Romero-Martín, Juan Gómez-Salgado.
Resources: Rosa Magallón-Botaya, Macarena Romero-Martín, Juan Gómez-Salgado.
Software: Juan Gómez-Salgado.
Supervision: Valle Coronado-Vázquez.
Validation: Valle Coronado-Vázquez, Maria Teresa Delgado-Marroquín.
Visualization: Maria Teresa Delgado-Marroquín, Rosa Magallón-Botaya, Macarena Romero-Martín, Juan Gómez-Salgado.
Writing – original draft: Valle Coronado-Vázquez, Carlota Canet-Fajas, Rosa Magallón-Botaya, Macarena Romero-Martín, Juan Gómez-Salgado.
Writing – review & editing: Valle Coronado-Vázquez, Carlota Canet-Fajas, Macarena Romero-Martín, Juan Gómez-Salgado.
References
[1]. Truglio-Londrigan M, Slyer JT, Singleton JK, et al. A qualitative systematic review of internal and external influences on shared decision-making in all health care settings. JBI Libr Syst Rev 2012;10:4633–46.
[2]. Brown SL, Salmon P. Reconciling the theory and reality of shared decision-making: a “matching” approach to practitioner leadership. Health Expect 2019;22:275–83.
[3]. Jovell A. The natural history of the medical profession under the patient perspective. Monografías Humanitas 2003;(7):23–32.
[4]. Towle A, Godolphin W. Framework for teaching and learning informed
shared decision making. BMJ 1999;319:766–71.
[5]. Elwyn G, Edwards A, Mowle S, et al. Measuring the involvement of patients in shared-decision making: a systematic review of instruments. Patient Educ Couns 2001;43:5–22.
[6]. Ruiz-Moral R. The role of physician-patient communication in promoting patient-participatory decision making. Health Expect 2010;13:33–44.
[7]. Charles C, Gafni A, Whelan T. Decision-making in the physician-patient encounter: revisiting the shared treatment decision-making model. Soc Sci Med 1999;49:651–61.
[8]. Johnson RA, Huntley A, Hughes RA, et al. Interventions to support
shared decision making for hypertension: a systematic review of controlled studies. Health Expect 2018;1:1191–207.
[9]. Stacey D, Légaré F, Lewis K, et al.
Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev 2017;4:CD001431.
[10]. Goldwag J, Marsicovetere P, Scalia P, et al. The impact of
decision aids in patients with colorectal cancer: a systematic review. BMJ Open 2019;9:e028379doi: 10.1136/bmjopen-2018-028379.
[11]. Friedberg MW, Van Busum K, Wexler R, et al. A demonstration of
shared decision making in primary care highlights barriers to adoption and potential remedies. Health Aff (Millwood) 2013;32:268–75.
[12]. Kalsi D, Ward J, Oxon MA, et al. Shared decision-making across the specialties: much potential but many challenges. J Eval Clin Pract 2019;25:1050–4.
[13]. Diouf NT, Menear M, Robitaille H, et al. Training health professionals in
shared decision making: update of an international environmental scan. Patient Educ Couns 2016;99:1753–8.
[14]. Boland L, Lawson ML, Graham ID, et al. Post-training
shared decision making barriers and facilitators for pediatric healthcare providers: a mixed-methods study. Acad Pediatr 2019;19:118–29.
[15]. Urrútia G, Bonfill X. PRISMA declaration: a proposal to improve the publication of systematic reviews and meta-analyses. Med Clin (Barc) 2010;135:507–11.
[16]. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.1.0. The Cochrane Collaboration; 2011.
[17]. Moher D, Pham B, Jones A, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 1998;352:609–13.
[18]. Loh A, Simon D, Wills CE, et al. The effects of a shared decision-making intervention in primary care of depression: a cluster-randomized controlled trial. Patient Educ Couns 2007;67:324–32.
[19]. Thomson RG, Eccles MP, Steen IN, et al. A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with atrial fibrillation: randomised controlled trial. Qual Saf Health Care 2007;16:216–23.
[20]. Krones T, Keller H, Sönnichsen A, et al. Absolute cardiovascular disease risk and shared decisión making in primary care: a randomized controlled trial. Ann Fam Med 2008;6:218–27.
[21]. Koelewijn-van Loon MS, van der Weijden T, Ronda G, et al. Improving lifestyle and risk perception through patient involvement in nurse-led cardiovascular risk management: a cluster-randomized controlled trial in primary care. Prev Med 2010;50:35–44.
[22]. Myers RE, DAkalakis C, Kunkel EJ, et al. Mediated decision support in prostate cancer screening: a randomized controlled trial of decision counseling. Patient Educ Couns 2011;83:240–6.
[23]. Légaré F, Guerrier M, Nadeau C, et al. Impact of DECISION + 2 on patient and physician assessment of
shared decision making implementation in the context of antibiotics use for acute respiratory infections. Implement Sci 2013;8:144doi: 10.1186/1748-5908-8-144.
[24]. Montori VM, Shah ND, Pencille LJ, et al. Use of a decision aid to improve treatment decisions in osteoporosis: the osteoporosis choice randomized trial. Am J Med 2011;124:549–56.
[25]. Légaré F, Labrecque M, Cauchon M, et al. Training family physicians in shared decision-making to reduce the overuse of antibiotics in acute respiratory infections: a cluster randomized trial. CMAJ 2012;184:E726–34.
[26]. Sheridan SL, Golin C, Bunton A, et al.
Shared decision making for prostate cancer screening: the results of a combined analysis of two practice-based randomized controlled trials. BMC Med Inform Decis Mak 2012;12:130doi: 10.1186/1472-6947-12-130.
[27]. Wilkes MS, Day FC, Srinivasan M, et al. Pairing physician education with patient activation to improve shared decisions in prostate cancer screening: a cluster randomized controlled trial. Ann Fam Med 2013;11:324–34.
[28]. Branda ME, LeBlanc A, Shah ND, et al.
Shared decision making for patients with type 2 diabetes: a randomized trial in primary care. BMC Health Serv Res 2013;13:301DOI: 10.1186/1472-6963-13-301.
[29]. Miller MJ, Allison JJ, Cobaugh DJ, et al. A group-randomized trial of
shared decision making for non-steroidal anti-inflammatory drug risk awareness: primary results and lessons learned. J Eval Clin Pract 2014;20:638–48.
[30]. Patel S, Ngunjiri A, Hee SW, et al. Primum non nocere: shared informed decision making in low back pain – a pilot cluster randomised trial. BMC Musculoskelet Disord 2014;15:282.
[31]. Price-Haywood EG, Harden-Barrios J, Cooper LA. Comparative effectiveness of audit-feedback versus additional physician communication training to improve cancer screening for patients with limited health literacy. J Gen Intern Med 2014;29:1113–21.
[32]. Fiks AG, Mayne SL, Karavite DJ, et al. Parent-reported outcomes of a shared decision-making portal in asthma: a practice-based RCT. Pediatrics 2015;135:e965–73.
[33]. Lewis CL, Adams J, Tai-Seale M, et al. Randomized controlled effectiveness trial for psa screening decision support interventions in two primary care settings. J Gen Intern Med 2015;30:810–6.
[34]. LeBlanc A, Herrin J, Williams MD, et al. Decision making for antidepressants in primary care: a cluster randomized trial. JAMA Intern Med 2015;175:1761–70.
[35]. Perestelo-Pérez L, Rivero-Santana A, Boronat M, et al. Effect of the statin choice encounter decision aid in Spanish patients with type 2 diabetes: a randomized trial. Patient Educ Couns 2016;99:295–9.
[36]. Karagiannis T, Liakos A, Branda ME, et al. Use of the diabetes medication choice decision aid in patients with type 2 diabetes in Greece: a cluster randomised trial. BMJ Open 2016;6:e012185doi: 10.1136/bmjopen-2016-012185.
[37]. Reuland DS, Brenner AT, Hoffman R, et al. Effect of combined patient decision aid and patient navigation vs usual care for colorectal cancer screening in a vulnerable patient population: a randomized clinical trial. JAMA Intern Med 2017;177:967–74.
[38]. Sanders ARJ, Bensing JM, Magnée T, et al. The effectiveness of shared decision-making followed by positive reinforcement on physical disability in the long-term follow-up of patients with nonspecific low back pain in primary care: a clustered randomised controlled trial. BMC Fam Pract 2018;19:102doi: 10.1186/s12875-018-0776-8.
[39]. Buhse S, Kuniss N, Liethmann K, et al. Informed shared decision-making programme for patients with type 2 diabetes in primary care: cluster randomised controlled trial. BMJ Open 2018;8:e024004.
[40]. Schwartz PH, Imperiale TF, Perkins SM, et al. Impact of including quantitative information in a decision aid for colorectal cancer screening: A randomized controlled trial. Patient Educ Couns 2019;102:726–34.
[41]. Perestelo-Perez L, Rivero-Santana A, Torres-Castaño A, et al. Effectiveness of a decision aid for promoting colorectal cancer screening in Spain: a randomized trial. BMC Med Inform Decis Mak 2019;19:8doi: 10.1186/s12911-019-0739-6.
[42]. Woolf SH, Chan EC, Harris R, et al. Promoting informed choice: transforming health care to dispense knowledge for decision making. Ann Intern Med 2005;143:293–300.
[43]. Stacey D, Samant R, Bennett C. Decision making in oncology: a review of patient
decision aids to support patient participation. CA Cancer J Clin 2008;58:293–304.
[44]. McCaffery KJ, Jansen J. Pre-operative MRI for women with newly diagnosed breast cancer: perspectives on clinician and patient decision-making when evidence is uncertain. Breast 2010;19:10–2.
[45]. Tariman JD, Berry DL, Cochrane B, et al. Preferred and actual participation roles during health care decision making in persons with cancer: a systematic review. Ann Oncol 2010;21:1145–51.
[46]. Légaré F, Stacey D, Turcotte S, et al. Interventions for improving the adoption of
shared decision making by healthcare professionals. Cochrane Database Syst Rev 2014;CD006732.
[47]. Mathijssen EGE, van den Bemt BJF, van den Hoogen FHJ, et al. Interventions to support
shared decision making for medication therapy in long term conditions: a systematic review. Patient Educ Couns 2020;103:254–65.
[48]. Alguera-Lara V, Dowsey MM, Ride J, et al.
Shared decision making in mental health: the importance for current clinical practice. Australas Psychiatry 2017;25:578–82.
[49]. Beitinger R, Kissling W, Hamann J. Trends and perspectives of shared decision-making in schizophrenia and related disorders. Curr Opin Psychiatry 2014;27:222–9.
[50]. Hamann J, Bühner M, Rüsch N. Self-Stigma and consumer participation in
shared decision making in mental health services. Psychiatr Serv 2017;68:783–8.
[51]. Fisher A, Manicavasagar V, Kiln F, et al. Communication and decision-making in mental health: a systematic review focusing on Bipolar disorder. Patient Educ Couns 2016;99:1106–20.
[52]. Saheb Kashaf M, McGill ET, Berger ZD. Shared decision-making and outcomes in type 2 diabetes: a systematic review and meta-analysis. Patient Educ Couns 2017;100:2159–71.
[53]. Tamhane S, Rodriguez-Gutierrez R, Hargraves I, et al. Shared decision-making in diabetes care. Curr Diab Rep 2015;15:112doi: 10.1007/s11892-015-0688-0.
[54]. Coxeter P, Del Mar CB, McGregor L, et al. Interventions to facilitate
shared decision making to address antibiotic use for acute respiratory infections in primary care. Cochrane Database Syst Rev 2015;2015:CD010907.
[55]. Bakhit M, Del Mar C, Gibson E, et al.
Shared decision making and antibiotic benefit-harm conversations: an observational study of consultations between general practitioners and patients with acute respiratory infections. BMC Fam Pract 2018;19:165doi: 10.1186/s12875-018-0854-y.
[56]. Del Mar CB, Scott AM, Glasziou PP, et al. Reducing antibiotic prescribing in Australian general practice: time for a national strategy. Med J Aust 2017;207:401–6.
