Review
Abstract
Background: Patient-centered care promotes the involvement of patients in decision-making related to their health care. The adoption and implementation of shared decision-making (SDM) into routine care are constrained by several obstacles, including technical and time constraints, clinician and patient attitudes and perceptions, and processes that exist outside the standardized clinical workflow.
Objective: We aimed to understand the integration and implementation characteristics of reported SDM interventions integrated into an electronic health record (EHR) system.
Methods: We conducted a scoping review using the methodological framework by Arksey and O’Malley with guidance from the Joanna Briggs Institute. Eligibility criteria included original research and reviews focusing on SDM situations in a real-world clinical setting and EHR integration of SDM tools and processes. We excluded retrospective studies, conference abstracts, simulation studies, user design studies, opinion pieces, and editorials. To identify eligible studies, we searched the following databases on January 11, 2021: MEDLINE, Embase, CINAHL Complete, Cochrane Library including CENTRAL, PsycINFO, Scopus, and Web of Science Core Collection. We systematically categorized descriptive data and key findings in a tabular format using predetermined data charting forms. Results were summarized using tables and associated narratives related to the review questions.
Results: Of the 2153 studies, 18 (0.84%) were included in the final review. There was a high degree of variation across studies, including SDM definitions, standardized measures, technical integration, and implementation strategies. SDM tools that targeted established health care processes promoted their use. Integrating SDM templates and tools into an EHR appeared to improve the targeted outcomes of most (17/18, 94%) studies. Most SDM interventions were designed for clinicians. Patient-specific goals and values were included in 56% (10/18) of studies. The 2 most common study outcome measures were SDM-related measures and SDM tool use.
Conclusions: Understanding how to integrate SDM tools directly into a clinician’s workflow within the EHR is a logical approach to promoting SDM into routine clinical practice. This review contributes to the literature by illuminating features of SDM tools that have been integrated into an EHR system. Standardization of SDM tools and processes, including the use of patient decision aids, is needed for consistency across SDM studies. The implementation approaches for SDM applications showed varying levels of planning and effort to promote SDM intervention awareness. Targeting accepted and established clinical processes may enhance the adoption and use of SDM tools. Future studies designed as randomized controlled trials are needed to expand the quality of the evidence base. This includes the study of integration methods into EHR systems as well as implementation methods and strategies deployed to operationalize the uptake of the SDM-integrated tools. Emphasizing patients’ goals and values is another key area for future studies.
doi:10.2196/59956
Keywords
Introduction
Background
Shared decision-making (SDM) is a model of patient-centered care that encourages patients and clinicians to work together to reach medical decisions by weighing the risks and benefits of various options within the context of the values and goals of the patient []. SDM is the process of communication, deliberation, and decision-making between clinicians and their patients [,]. During this process, treatment options, including risks and benefits, are discussed and patient preferences are explored to inform decision-making. Previous research has shown that SDM increases patient engagement, knowledge, risk comprehension, and participation in decision-making [-]. Furthermore, SDM has been shown to improve patient–health care provider communication, patient satisfaction, compliance, and clinical outcomes [,]. Notably, the US Preventive Services Task Force considers SDM an ethical right of all patients, independent of health outcomes [].
Currently, SDM is not routinely required by large health care payers, such as the Centers for Medicare and Medicaid Services, which is a US federal agency providing health care for patients with low income or older adult patients. Medicare is the largest single payer for health care in the United States []. The Centers for Medicare and Medicaid Services is slowly adopting SDM standards for limited and specific procedures, conditions, and devices, including lung cancer screening, atrial fibrillation, and implantable defibrillators []. US states such as Washington and Vermont are also beginning to mandate SDM for specific conditions and other states such as Connecticut, Massachusetts, Maine, Minnesota, New Hampshire, and Oklahoma are considering legislation [].
The Health Information Technology for Economic and Clinical Health Act was adopted in the United States in 2009, enabling the widespread adoption of electronic health record (EHR) systems across the US health care system []. Leveraging EHR systems to promote the use and uptake of SDM appears to be a logical approach to operationalizing SDM programs. For example, Kuo et al [] found several positive effects of integrating SDM tools within an EHR system, which include improved clinical outcomes, positive lifestyle behavior changes, more deliberation with clinicians, and less decisional conflict. Despite the interest in incorporating SDM into routine care, current research studies identify a variety of obstacles that limit SDM adoption. Some of these obstacles include technical integration issues; logistical and workflow challenges; and psychological impediments, such as uncertainty and legacy belief systems, which continue to impede progress [-]. Integrating SDM tools and processes into EHR systems is often a complex and difficult problem [-].
SDM may involve the use of a decision aid, a type of tool that helps patients consider options for medical decision-making by increasing patient knowledge; illuminating treatment options including risks, benefits, and efficacy; and exploring patient preferences and values [-]. The Ottawa Decision Support Framework provides recommendations and guidelines for health care decisions and standardized checklists for the development and evaluation of decision aids based on the International Patient Decision Aid Standards []. SDM components found within the International Patient Decision Aid Standards include clarifying patient values, coaching and guiding patients when making decisions, and ensuring literacy levels are addressed [-]. Decision aids developed using the Ottawa Decision Support Framework as well as other decision aids outperformed the usual care for performance in a large overview of systematic reviews [,]. Patient decision aids can play a key role in facilitating the occurrence of SDM at the point of care as clinicians may use a decision aid to support SDM conversations. Furthermore, decision aids may provide a way to standardize SDM experiences. Notably, decision aid research developed before the widespread adoption of EHRs [-]. Decision aids are not synonymous with SDM processes as SDM processes may incorporate the use of a decision aid or may use a conversation between patient and clinician without the use of a decision aid. Decision aids typically contain patient education as part of the decision aid content; however, a clinician may choose to verbally educate a patient without the use of a decision aid. Previous research has shown that decision aids integrated into an EHR system show improvement in patient-centered outcomes, including a reduction in decisional conflict. However, there are significant barriers and complexities to overcome when integrating them into commercial EHR systems [-].
Both decision aids and SDM tools still face substantial hurdles to clinician uptake [,,-]. Gaining the attention of health care providers can be difficult as SDM relies on health care provider discretion, time, and required effort [,-]. Integration into the EHR system is recognized as an important strategy as it enables SDM to be part of clinical workflow []. However, integration can also pose challenges because unavoidable health care provider alerts run the risk of high levels of alert fatigue or reactance. SDM tool developers and health care systems must be judicious in their choice of tools to develop and integrate. It is important to understand the characteristics of SDM tools that have been integrated into EHR systems.
Objectives
A literature review revealed limited studies describing the integration and implementation of SDM tools within an EHR workflow [-]. A number of recent systematic reviews have been published on the general topic of SDM [,,-]. These studies focused on a range of SDM topics, including SDM within specific surgical specialties, behavioral theories underpinning SDM, organizational characteristics to support implementation, validated SDM measurement tools, SDM key components, and quality of SDM. However, at the time of the protocol registration, we were unable to identify a systematic or scoping review that specifically addressed the characteristics of SDM tools that have been integrated into an EHR. Thus, this study aimed to understand the characteristics of SDM tools that have been integrated into an EHR system and implemented within a health care system by conducting a scoping review.
Methods
Overview
This scoping review followed methods described by Arksey and O’Malley [], Levac et al [], and the Joanna Briggs Institute (JBI) []. The study protocol is published with the Protocol Registry of Evidence Reviews at the University of Utah. Consensus was achieved on the inclusion and exclusion criteria and primary research question. For transparency and reproducibility, we followed the PRISMA-ScR (Preferred Reporting Items For Systematic Reviews And Meta-Analyses Extension For Scoping Reviews) and PRISMA-S (Preferred Reporting Items For Systematic Reviews And Meta-Analyses Search Extension) reporting guidelines for the scoping review protocol and manuscript [,].
Study Identification
An information specialist developed the search strategies using database-specific subject headings, keywords, and team feedback for the key concepts of SDM, patient engagement, decision support, and EHRs. Example publications were provided to the information specialist for strategy development and term harvesting. The search strategy was evaluated by a librarian coauthor using the Peer Review of Electronic Search Strategies guidelines []. EndNote (version 20; Clarivate) was used to manage citations and remove duplicates. Covidence (Veritas Health Innovation) provided a second pass for duplicate removal. Search strategies are provided in .
The following databases were used to identify eligible studies between 2009 and 2021: MEDLINE (via Ovid platform), Embase (via Embase platform), CINAHL Complete (via EBSCOhost platform), Cochrane Library (via Wiley platform) including CENTRAL (via Wiley platform), PsycINFO (via EBSCOhost platform), Scopus (via Scopus platform), and Web of Science Core Collection (via Clarivate Analytics). No filters other than date limits were applied. The date limit from 2009 corresponds to the adoption of the Health Information Technology for Economic and Clinical Health Act in the United States [].
Study Screening
Covidence software was used for screening. Four researchers independently screened titles and abstracts for inclusion and exclusion criteria and met for discussion. The protocol was modified during this phase to expand inclusion criteria based on discussions among the screeners. For example, after finding a number of design studies and concept papers, we added the requirement that all studies must have SDM tools that have been implemented into a real-world clinical setting. Furthermore, we excluded retrospective studies after identifying studies that focused on a retrospective analysis of clinical notes and activities. Changes to the protocol are listed in 
Textbox 1. Following the screening process, 303 (10%) of the 3017 articles were identified for full eligibility assessment.
Expanded screening criteria were added to the original inclusion and exclusion criteria identified in the research protocol, as noted in Textbox 1. The additional categories were identified and added as the team recognized additional screening criteria as screening commenced.
Inclusion criteria
- Original studies, systematic reviews, and meta-analyses
- Decisions made at the point of care
- Electronic health record–integrated shared decision-making tool
- Intervention implemented into a real clinical setting
- Shared decision-making tool outcomes reported
- Studies written in English
Exclusion criteria
- Retrospective studies
- Conference abstracts
- Limited measures
Eligibility Assessment
A team of 5 researchers, including 2 professors with extensive backgrounds in cognitive and sociotechnical informatics (CW and JMB), 1 informatics researcher experienced in sociotechnical design (TT), an informatics student (JHP), and another informatics student (WR II), met on a weekly basis to discuss inclusion and exclusion criteria and extraction goals for research studies. Initially, each researcher was assigned the same 5 studies to assess for eligibility review. Then, the research team discussed and deliberated on whether each study met the established inclusion criteria until the team achieved consensus. A spreadsheet was created with each evaluation component, including basic study characteristics, integration, and implementation methods. Study characteristics included study type, SDM intervention, clinical domain and conditions, measures, outcomes, integration approaches, and implementation processes. The research team discussed the criteria and achieved consensus for spreadsheet components through discussion, debate, and review. The reviewers then evaluated approximately 10 studies per week as a group, using the spreadsheet to determine criteria for analysis against the study aims. After this, the remaining studies were distributed among the team members for a full review of eligibility. We excluded studies that did not report measurable quantitative outcomes, were not integrated into an EHR system, did not involve a shared decision (with or without a decision aid), and were not in person at the point of care. EHR integration for the purpose of this review ranged from scanned PDFs appended to EHR patient records to full technical integration using semantic interoperability methods.
We identified the studies of tools designed to support SDM that were integrated into the EHR system for evaluation and reported a total of 18 studies, which were included for final review. See PRISMA-ScR 
Figure 1 and .
Review Process
Overview
The research team used the following three-step process to interpret and synthesize the final 18 studies included in the scoping review: (1) data charting, (2) analysis of evidence, and (3) data summary and synthesis. During this process, we performed a detailed review and analysis of the integration methods along with the associated clinical decision support features and then mapped them into a table. Implementation strategies and methods were evaluated and mapped according to the narrative descriptions in the studies.
Data Charting
Data charting was performed according to the methods proposed by Arksey and O’Malley [] and the JBI, as described by Tricco et al [], Levac et al [], and Peters et al []. The template was piloted by the researchers and adjusted based on discussion, debate, and deliberation. Data items included the following: (1) study design, (2) aims, (3) measures, (4) outcomes, (5) intervention type, (6) clinical domain, (7) clinical processes, (8) clinical conditions, (9) EHR integration methods, (10) implementation strategies, (11) SDM goals, and (12) SDM model and components.
Analysis of Evidence
The analysis of the data was then performed to understand and map themes and clusters of notable data according to the updated methodological guidance for scoping reviews []. To do this, the research team discussed the definition along with examples of each extraction category. Following this, one researcher (WR II) performed a preliminary extraction on categories that were unambiguous. Next, the remaining research team members were assigned manuscripts to extract the more complex categories. The team met for iterative discussions, deliberation, and decision-making to achieve consensus on the final extraction items.
Data Summary and Synthesis
We summarized our findings using narrative synthesis and descriptive statistics to describe the characteristics of SDM tools integrated into the EHR systems [,]. For general characteristics, we mapped study types, measures, clinical domains and conditions, and goals of the SDM intervention in a table and calculated frequency counts and percentages []. We then categorized each study according to integration methods and features using descriptive content analysis to summarize narrative findings []. To do this, we analyzed the various methods used in each of the studies to surface and document SDM tools within the EHR systems. We considered integration to have been achieved so long as the SDM tools surfaced in the clinician’s workflow within the EHR systems. We mapped EHR clinical decision support features for each SDM tool according to risk, patient data display, health care provider notification, documentation, and EHR workflow presentation. Then, we analyzed the implementation approaches across studies. To do this, we looked for methods of creating awareness, training programs, and SDM tool buy-in and promotion through clinician leaders. We were then able to organize our results by general characteristics, integration approaches, and implementation methods.
Results
Overview
This scoping review included 18 studies that met the inclusion criteria (Figure 1), including 15 (83%) quantitative and 3 (17%) mixed methods studies. The most commonly used SDM outcome measures are listed in 
Table 1.
| SDM outcome measures of included studies | Studies, n (%) |
| SDM measures | 7 (39) |
| Tool use | 7 (39) |
| Clinical outcomes | 5 (28) |
| Patient satisfaction | 4 (22) |
| Documentation | 4 (22) |
| Clinician satisfaction | 2 (11) |
Of the 18 studies, 7 (39%) measured SDM components, such as patient empowerment, self-efficacy, self-determination, and goal setting. The 7 (39%) studies measuring use rates reported various levels of use; however, predetermined targeted use rate goals were not noted. We found that at least 3 (17%) of the studies achieved a use rate of >50% of the targeted users. In total, 4 (80%) of the 5 studies using clinical outcome measures reported positive improvements based on the SDM intervention [,]. Of the 18 studies, 4 (22%) studies that measured patient satisfaction included elements of communication quality, such as helpfulness, respect for the patient’s opinion, understandability, confidence in the health care team, and worry reduction [-]. Documentation rates increased for all 4 (22%) studies using documentation as the primary outcome measure [-]. A total of 2 (11%) studies that measured clinician satisfaction reported clinician satisfaction at >85% and preference for a systematic approach via a tool at 100% [,]. In total, 4 (22%) included studies incorporated patient goals and values in the SDM process [,,,]. Consistent with previous research, 1 (5%) study reported that obstacles to tool use included a lack of awareness of the tool and low confidence in how to use the tool [].
Goals of SDM Intervention
We categorized the included studies by macrolevel functional goals to include (1) care planning and goal setting (10/18, 56% studies), (2) prevention and screening (4/18, 22% studies), and (3) medical management and treatment (4/18, 22% studies; Table 2). The largest percentage of the care planning and goal setting category was end-of-life planning [,,,], followed by communications [,]. In total, 75% (3/4) of the prevention and screening studies focused on the detection of cardiovascular disease [,,], and 75% (3/4) of the medication management and treatment decision-making studies focused on diabetes and related pharmacotherapy [,,].
| Study | Study type | SDMa intervention | Clinical domain and conditions | Measures | Study outcomes |
| Ballard et al [], 2017 | Quantitative | SCDAb and DMCDAc |
|
| Log file data indicated that 51% of clinicians used the SCDA, and 9% of the clinicians used the DMCDA. Facilitators of decision aid use included clinicians finding them very useful and their impact on treatment decisions. Both facilitators were reported more frequently by clinicians for the SCDA than for the DMCDA (56% vs 30% and 42% vs 22%, respectively). Barriers to use included lack of knowledge of the EMRd link, not finding the decision aids helpful, and time constraints. The use of the tool as intended was low, with many clinicians only discussing decision aid topics that they found relevant. |
| Bose-Brill et al [], 2018 | Quantitative | A previsit planning tool for patients accessed via a patient portal and EHRe-guided point-of-care SDM questions |
|
| In total, 19.5% (39/200) of the patients who received previsit planning responded to the framework. The intervention site had an improvement in new ACP documentation rates (P<.01) and quality (P<.01) among all eligible patients. ACP documentation rates increased by 105% (19/39 to 39/39), and quality improved among all patients who engaged in the previsit planning framework (n=39). Among eligible patients aged between 50 and 60 years at the intervention site, ACP documentation rates increased by 37% (27/96 to 37/96). ACP documentation rates increased by 34% among patients who sent >10 messages per year in the EHR (27/67 to 36/67). |
| Choi et al [], 2019 | Quantitative | EHR reminder to prompt discussions about unintended cardiac defibrillator shocks and deactivation options for EOLg care |
|
| After the interventions, the rates of discussions regarding the deactivation of ICDh improved from 50% to 93% in patients in comfort care and from 32% to 70% in DNRi patients. The rates of deactivated ICDs improved from 45% to 73% in patients in comfort care patients and from 29% to 40% in DNR patients. |
| Chunchu et al [], 2012 | Mixed methods | PCCPj tool to promote self-management planning |
|
| More frequent documentation was noted in 8 problem-solving elements within the EHR, and the number of concerns for a visit was twice as high as in the control group. |
| Coylewright et al [], 2020 | Quantitative | iPDAsk |
|
| For the 8-year analysis period, 1209 clinicians used iPDAs, with 57,116 unique patients in 81,728 visits. Of the clinicians, 76% were physicians, 16% were APPsl, and 8% were other clinicians. There were 2607 unique patient-clinician uses in 2010; 7966 in 2014; and 24,384 in 2017. There was an average increase of 151 new clinicians per year (range 99-302). On average, 82 new clinicians used the iPDAs with at least 1 patient each year (range 56-108), with 657 clinicians for the 8-year period. In total, 54.3% of the clinicians used iPDAs with at least 5 patients. |
| Crosby and Gutierrez [], 2019 | Quantitative | ACP tool with risk identification for patients with late-stage heart failure |
|
| In total, 35% use of templates was reported over 3 months, 100% of templates were documented in EHR, 100% of clinic staff reported inconsistent processes for ACP discussion, and 100% of clinic staff preferred a systematic approach to initiating ACP processes. |
| Denig et al [], 2014 | Quantitative | Diabetes decision aid |
|
| Patient empowerment score increases were 0.1 on a 5-point scale, and drug treatment was intensified in 25% of intervention participants with increased cholesterol levels. |
| Fiks et al [], 2015 | Quantitative | My Asthma clinical decision support tool |
|
| In total, 57% of the parents in the intervention group used My Asthma during at least 5 of the 6 study months. Parents of children with moderate to severe persistent asthma used the portal more often than others; 92% were satisfied with My Asthma. Parents reported that portal use improved their communication with the office. |
| Fossa et al [], 2018 | Quantitative | OpenNotes patient access for SDM |
|
| A total of 6913 patients responded (28% response rate). Patients reading ≥4 clinical notes in the past 12 months were 17% more likely to have top Collaborate scores when compared to those who had not read a note in the previous 12 months (response rate: 1.17, 95% CI 1.04-1.32). |
| Fried et al [], 2017 | Quantitative | TRIMm |
|
| In total, 29.7% of TRIM participants and 15.6% of control participants provided the highest PACICn ratings; this difference was not significant. TRIM improved communication about medications and the accuracy of documentation. |
| Huang et al [], 2020 | Quantitative | TEAo tool with an annual assessment of key transition skills and setting of transition skill goals |
|
| All patients (N=53) performed the transition skills self-assessment and practicum and set transition goals with their health care provider. On a scale of 0 (not helpful at all) to 10 (very helpful), patients reported median (IQR) utility scores of 8 (7-10) for the transition readiness assessment, 9 (7-10) for transition resources provided, and 9 (7-10) for the medical history summary. Most (91%) patients would recommend TEA to other patients. |
| Jouni et al [], 2017 | Quantitative | CHDp genetic risk tool, decision aid for CHD risk estimates, and facilitation of SDM for statin use |
|
| There were no statistically significant differences between the 2 groups in the SDM score, satisfaction with the clinical encounter, and perception of the quality of the discussion or participation in decision-making and physician visit satisfaction scores. |
| Lafata et al [], 2014 | Quantitative | 5Aq framework–guided discussion during primary care visits for CRCr |
|
| In total, 93% of patients received a recommendation for screening (advise), and 53% were screened in the following year. The likelihood of screening increased as the number of 5A steps increased: compared to patients whose visit contained none of the 5A steps, those whose visit contained 1 to 2 steps (ORs 2.96, 95% CI 1.16-7.53), and ≥3 steps (OR 4.98, 95% CI 1.84-13.44) were significantly more likely to use screening. |
| Litzelman et al [], 2017 | Mixed methods | GWt cards and discussions to identify priorities for advanced care planning |
|
| A total of 86 patients’ data indicated that they had engaged in a preferences-for-care process using GW R cards. The top 3 card choices by patients were attending to spirituality and religious concerns, preparing for EOL, and maintaining personal wholeness. |
| Plößnig et al [], 2015 | Quantitative | EMPOWERu SMPv tool to foster self-management of diabetes |
|
| Patients used functions for setting goals (65%) or planning activities (71%). More than half of the patients reported using the charts, the calendar, and the journals, while review (29%) and information material (24%) were of little interest. Initial testing showed an overall high level of empowerment. Improvements in self-efficacy, impact, and self-determination were observable in the German users and meaningful in the Turkish users. |
| Schafer et al [], 2016 | Mixed methods | Pharmacist-led SDM template for medication decisions |
|
| On average, 2 DTPs were identified per consultation and were most commonly related to appropriate compliance (30%), appropriate indication (26%), medication effectiveness (23%), and safety (21%). |
| Sona et al [], 2020 | Quantitative | Family communication tools, including education, decision-making, surrogate decision makers, and advanced directives |
|
| Excellent communication rating was reported by patients at 69% for RNsx and 54% for physicians; 43.5% of patients or families reported liking family care conferences, 52.2% thought family care conferences were helpful, 62.5% had confidence in health care team, 62.5% reported that family communication impacted care of the patient, and 62.5% reported that it reduced worry about family members. |
| Sperl-Hillen et al [], 2018 | Quantitative | CV Wizard, an SDM tool to reduce cardiovascular risk |
|
| Health care provider satisfaction was 85% to 98% for various aspects of the intervention, 10-year cardiovascular risk reduction at 12 months was –2.24%, and use rates were 71% to 77% over the intervention. |
aSDM: shared decision-making.
bSCDA: statin choice decision aid.
cDMCDA: diabetes medication choice decision aid.
dEMR: electronic medical record.
eEHR: electronic health record.
fACP: advanced care planning.
gEOL: end of life.
hICD: implantable cardioverter defibrillator.
iDNR: do not resuscitate.
jPCCP: patient-centered care plan.
kiPDA: integrated patient decision aid.
lAPP: advanced practice provider.
mTRIM: tool to reduce inappropriate medication.
nPACIC: patient assessment of chronic illness care.
oTEA: transition electronic health record activity.
pCHD: coronary heart disease.
q5A: assess, advise, agree, assist, and arrange.
rCRC: colorectal cancer.
sOR: odds ratio.
tGW: Go Wish.
uEMPOWER: Support of Patient Empowerment by an intelligent self-management pathway for patients.
vSMP: self-management pathway.
wDTP: drug therapy problem.
xRN: registered nurse.
Tool objectives were generally a direct function of the use of the SDM tool; for example, an end-of-life planning template may have a stated objective to increase documentation rates for communications. Patient satisfaction–related outcomes, such as decision satisfaction because of a shared conversation, were not noted in 11 (61%) of the 18 studies [,,,-,-].
Promoting SDM Awareness and Adoption
EHR and Workflow Integration
Our study found that workflow integration approaches were highly variable, ranging from scanning paper-based tools into the EHR system to implementing SDM tools with full semantically interoperable data elements surfacing within patient portals and in the clinicians’ EHR workflow (
Figure 2 and Table 3).
| Functional goals and studies | Clinical processes | SDM interventions | SDM goals | EHR integration method and implementation approach | |
| Care planning and goal setting (n=10, 56%) | |||||
| Bose-Brill et al [], 2018 | ACPa | A previsit planning tool for patients accessed via a patient portal and EHR-guided point-of-care SDM questions | To increase the rate of ACP documentation |
| |
| Choi et al [], 2019 | End-of-life planning | EHR clinician alerts and reminders to prompt discussions regarding unintended cardiac defibrillator shocks and deactivation options during end-of-life care | ICDb deactivation discussion |
| |
| Chunchu et al [], 2012 | PCCPd | PCCP tool to promote self-management planning | To increase collaborative self-management planning |
| |
| Crosby and Gutierrez [], 2019 | ACP | ACP tool with risk identification for patients with late-stage heart failure | Risk stratification, life expectancy of <3 years, conversations about preferences, and scanning into EHR |
| |
| Fossa et al [], 2018 | Communication | OpenNotes patient access for SDM | Collaborative decision-making |
| |
| Fried et al [], 2017 | Medication reconciliation | TRIMe | To facilitate clinician and patient medication communications |
| |
| Huang et al [], 2020 | Transition readiness | TEAf tool with annual assessment of key transition skills and setting of transition skill goals | Transition readiness decision-making |
| |
| Litzelman et al [], 2017 | End-of-life planning or ACP | Go Wish cards and discussions to identify priorities for ACP | ACP conversations |
| |
| Schafer et al [], 2016 | Medication reconciliation | Pharmacist-led SDM template for medication decisions | To promote patient and pharmacist SDM with the use of the Ottawa Personal Decision Guide |
| |
| Sona et al [], 2020 | Communication | Family communication tools, including education, decision-making, surrogate decision makers, and advanced directives | To increase the rates of critical care family communications |
| |
| Prevention and screening (n=4, 22%) | |||||
| Coylewright et al [], 2020 | Risk assessment and prevention | iPDAsh | To promote clinician use of iPDAs with patients for SDM |
| |
| Jouni et al [], 2017 | Risk assessment and prevention | CHDi genetic risk tool; decision aid for CHD risk estimates and facilitation of SDM for statin use | Visual comprehension of CHD risk and associated risk reduction with statins |
| |
| Lafata et al [], 2014 | Risk assessment and prevention | 5Ak framework–guided discussion during primary care office visits for CRCl screening | Use of 5As during clinician-patient discussions to support SDM |
| |
| Sperl-Hillen et al [], 2018 | Risk assessment and prevention | CV Wizard: a point-of-care CDS tool to facilitate discussion and SDM to reduce cardiovascular risk | Treatment options discussion per printed document |
| |
| Medical management and treatment (n=4, 22%) | |||||
| Ballard et al [], 2017 | Pharmacotherapy | SCDAm and DMCDAn | SDM for cholesterol reduction treatment options and SDM for diabetes treatment options |
| |
| Denig et al [], 2014 | Pharmacotherapy | Diabetes decision aid | Treatment options discussion |
| |
| Fiks et al [], 2015 | Treatment compliance | My Asthma CDS tool | Patient-reported data collection via the My Asthma tool on the patient portal |
| |
| Plößnig et al [], 2015 | Pharmacotherapy | EMPOWERo SMPp tool to foster self-management of diabetes | Self-management of diabetes |
| |
aACP: advanced care planning.
bICD: implantable cardioverter defibrillator.
cDNR: do not resuscitate.
dPCCP: patient-centered care plan.
eTRIM: tool to reduce inappropriate medication.
fTEA: transition electronic health record activity.
gCDS: clinical decision support.
hiPDA: integrated patient decision aid.
iCHD: coronary heart disease.
jGDMS: generic disease management system.
k5A: assess, advise, agree, assist, and arrange.
lCRC: colorectal cancer.
mSCDA: statin choice decision aid.
nDMCDA: diabetes medication choice decision aid.
oEMPOWER: Support of Patient Empowerment by an intelligent self-management pathway for patients.
pSMP: self-management pathway.
qISO: International Organization for Standardization.
We found that 8 (44%) of the 18 included studies described implementation strategies in sufficient detail to define the approach to operationalizing the SDM processes [,,,,,,,]. In 2 (11%) studies, patient portals were leveraged as part of the SDM process [,]. One patient portal was described as a tethered patient portal, which referred to SDM processes that were partially embedded into the clinicians’ EHR view. This was accomplished by collecting data and then saving them into the patient’s record so that a clinician could view the data from their workflow screen []. One intervention simply scanned paper documents and uploaded them into the patient record or other sections predefined within the EHR [].
Some (5/18, 28%) of the studies noted hybrid approaches to EHR integration, such as a combination of manual data collection with EHR documentation of an SDM process (Table 3). Of the 18 studies, 2 (11%) noted the integration of a URL into the EHR that launched a third-party web-based SDM tool [,]. Other (12/18, 67%) studies included health care provider alerts that presented patient risk factors to facilitate discussion and decision-making. Health care provider alerts may also help capture documentation of the SDM interaction [,].
Determining when to engage a patient in SDM before a clinical encounter is another element of SDM intervention planning. In 1 (6%) study, the engagement interval with patients was extended to previsit preparatory activities []. This included SDM processes that initially engaged the patient through preparatory SDM tools or surveys that were delivered via email, patient portals, or web links or delivered in the waiting room immediately before the clinical encounter [,,,,-,]. Other SDM interventions were designed to be used during the clinical encounter, including EHR-based tools, patient record sharing, communication and conversation guides, patient health plans, and educational printouts for patients [,,,,,-,,]. None of the included studies investigated postclinical encounter SDM tools for ongoing decision affirmation and goal alignment.
Implementation Strategies
A total of 9 (50%) of the 18 included studies described implementation strategies in sufficient detail to define the approach to operationalizing the SDM processes [,,,,,-,]. Methods to make clinicians and patients aware of the tools included email notifications and reminders, links in the EHR to an external third-party SDM tool, paper-based posters displayed in common workspaces, educational presentations, and training (Table 3). Trainings were often conducted in person, but some used prerecorded video. Brief trainings focused on how to use the tool; however, longer trainings included skills for engaging in SDM and sometimes involved role-playing these behaviors. In one case, the required implementation workflow caused users to question the “long-term feasibility” of the SDM intervention due to the additional time and resources required to complete the SDM processes, specifically in the case of advanced care and end-of-life planning for patients with heart failure [].
Clinical adoption was supported by collaboration with physician and nursing leaders for integrating the tools into the clinical workflow, engaging clinicians who initially engaged patients when starting the SDM process, providing feedback at the health care provider or clinic level, and sponsoring lotteries for clinic leadership when tool use achieved a specified level.
Other implementation strategies used to support patient engagement with SDM tools included email reminders to complete surveys, the provision of kiosks in the waiting room to allow patients to access the patient portal, and the provision of paper-based information for patients to read while waiting in the examination room.
We found that there was variance across the targeted users for the SDM tool interaction. In several (7/18, 39%) studies, both patients and clinicians had roles to play within the SDM processes [,,,,,,]. In most cases, the tool was targeted toward the clinician to activate the process and conduct conversations with the patient [,,,,-,,,,].
EHR Clinical Decision Support Features
The SDM interventions identified in this review contained several clinical decision support features. Table 4 describes each of these approaches, along with indicating whether the component was delivered manually or automatically within the EHR system. These features include the following: (1) patient risk alerts, which use patient data to compute personalized risk; (2) patient data display through the retrieval of relevant patient information into an informative display; (3) health care provider notification indicating whether a health care provider has to seek out the SDM tool or whether it surfaces within the EHR automatically based on a set of criteria; (4) presented in the EHR, allowing clinicians to access an SDM tool within the EHR (through several different technical approaches), as opposed to an application that is external to the EHR system; and (5) documentation support, including manual, autogenerated, or template-based clinician notes within an EHR to document that an SDM event occurred. One study used an emoji in the EHR to indicate that the SDM conversation had occurred [].
| Studies | Patient risk calculator+alert | Patient data display | Health care provider notification | Presented in EHR | Documentation | ||||||
| Manual (n=6, 33%) | EHR (n=11, 61%) | Manual (n=7, 39%) | EHR (n=13, 72%) | Manual (n=6, 33%) | EHR alert (n=12, 67%) | No (n=3, 17%) | Yes (n=14, 78%) | Manual (n=10, 56%) | Automatic (n=5, 28%) | Template (n=4, 22%) | |
| Ballard et al [], 2017 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Bose-Brill et al [], 2018 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Choi et al [], 2019 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Chunchu et al [], 2012 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Coylewright et al [], 2020 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Crosby and Gutierrez [], 2019 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Denig et al [], 2014 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Fiks et al [], 2015 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Fossa et al [], 2018 | ✓ | ||||||||||
| Fried et al [], 2017 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Huang et al [], 2020 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Jouni et al [], 2017 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Lafata et al [], 2014 | ✓ | ✓ | ✓ | ✓ | |||||||
| Litzelman et al [], 2017 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Plößnig et al [], 2015 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
| Schafer et al [], 2016 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Sona et al [], 2020 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
| Sperl-Hillen et al [], 2018 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
Discussion
Principal Findings
This review summarizes the findings of 18 studies that investigated approaches to the integration and implementation of SDM tools within EHR systems. Prominent aspects found across studies included functional SDM goals, such as communication, documentation of communication and discussions, and SDM template use. We identified only 4 (22%) randomized controlled trials (RCTs), which may be a reflection of the operational nature of the study measures. In 10 (56%) studies, clinicians were the primary targeted users, whereas 4 (22%) studies targeted patients and 5 (28%) targeted both. Integrating SDM interventions into a clinician’s workflow within an EHR system appears to support improved SDM uptake [,-]. Studies reported multiple EHR and workflow integration approaches, which may be an indication that a dynamic model that is scalable could help guide the adoption of SDM tools and processes across different environments. Future research to understand the utility of adjusting SDM model depth and complexity based on the magnitude of the decision, the complexity of the clinical situation, and the patient preferences is warranted.
Goals of SDM Tools
Most (10/18, 56%) of the included studies focused on SDM tools that were functionally designed to support care planning and goal setting. We also noted 1 (6%) study that used the concept of open medical notes as a precursor to shared care conversations.
Most (4/18, 22%) prevention and screening studies targeted by SDM focused on common conditions, such as the detection of heart disease, colorectal cancer, and other common conditions. Future opportunities for EHR-integrated SDM screening tools include the assessment of social determinants of health for the purpose of supporting individuals with social needs. Furthermore, mental health SDM screening tools offer an important area for prevention and screening to detect anxiety, depression, loneliness, addiction, and suicide risk, as a few examples.
Promoting SDM Awareness and Adoption
Although studies generally lacked sufficient technical detail on the actual integration method into each EHR system, we found considerable variation in how SDM tools are implemented within EHR workflows. The implementation approaches for SDM applications showed varying levels of planning and efforts to promote SDM intervention awareness. For example, 33% (6/18) of the studies did not describe implementation activities for clinicians, whereas other (6/18, 33%) studies described more detailed implementation plans that included training, demonstrations, knowledge queries, and incentives [,,,,,].
In total, 22% (4/18) of the studies included in the review suggested that integrating an SDM intervention into a clinician’s EHR workflow may support increased awareness of the intervention [,,,]. This represents an important step toward supporting clinicians in the adoption and use of SDM tools and processes. Creating awareness, interest, and commitment to SDM tool use is often overlooked and may contribute to waning adoption for otherwise well-designed SDM tools, potentially leading to adoption failure. The implementation plan is a critical component to moving people and the organizational culture toward the adoption of SDM tools. These steps also provide a forum for direct feedback should clinicians lack confidence in the accuracy or efficacy of an SDM tool, as was noted by Ballard et al [].
This review found that established processes and required patient interactions, such as end-of-life care planning protocols, appear to support greater SDM adoption success because they rely on workflows and processes that are already in practice. Adoption through incremental change, such as an evolutionary approach rather than a revolution, may be more likely to gain momentum over time [].
Implications for Future Research
Our review found that only a few randomized clinical studies were noted. Future research studies should assess the effect of SDM in multisite pragmatic RCTs.
This review identified a wide range of approaches to how SDM interventions are organized and delivered within the clinical setting. Future research could explore contextually aware tiered levels of intervention. This is important to standardize SDM care processes within specialties in order to support large-scale adoption of validated SDM tools and methods while addressing practical considerations within the constraints of each specialty.
Our review found that 17% (3/18) of the studies attempted to engage patients before a clinical encounter; however, no studies attempted to engage patients in decision-making after a clinical encounter [,,]. Protracted decisions that include a series of small decisions align well with the use of patient portals to begin preparing patients for the SDM process. Another area for future research could be to understand the efficacy of postclinical encounter engagement to address the ongoing maintenance and reinforcement of decisions that have been made in collaboration with the clinician.
We found limited studies that included the identification and measurement of patient values. Thus, patient involvement in establishing their values and alignment of care with those values appear to be areas for additional focus in future research. Involving patients in goal setting is a key element to support patient engagement, which is essential to help patients manage their decisions outside of the clinician encounter [].
Strengths and Limitations of Approach
Strengths
The review followed rigorous standard scoping review methods set by the JBI, Arksey and O’Malley [], and others [,,,]. We carried out a systematic search of multiple databases developed by a medical librarian and independent article screening and data extraction by at least 2 reviewers, with disagreement resolved through consensus. Our research team included investigators from an academic medical setting with extensive research experience in biomedical informatics, cognitive psychology, human factors, and health care.
Limitations
This scoping review may have missed relevant studies that are not indexed by the databases searched. Furthermore, the search retrieved studies published between January 1, 2009, and January 11, 2021; therefore, studies published outside this time frame were not included in our analysis. We did not search the gray literature or unpublished data sources. SDM studies that lacked an SDM tool, EHR implementation, and an actual clinical setting were not included. Implementation details were highly variable in terms of the approach and the level of detail described in the studies. SDM definitions and interpretations were variable, and we found limited references to validated SDM definitions, such as commonly cited definitions by the US National Institutes of Health or other credible sources.
Conclusions
This scoping review revealed a wide range of approaches to the integration and implementation of SDM tools within an EHR workflow. We found that SDM tool integration methods ranged from simple scan and save methods to fully integrated and operable technical integration with an EHR system. Moreover, implementation approaches were highly variable, ranging from sending an email to clinicians to create awareness to more complete programs involving demonstrations, training, incentives, clinician-led promotion, and feedback sessions. Areas for future research include more RCTs, the development of contextually aware SDM interventions, longitudinal patient engagement with pre- and postvisit interventions, the study of patient-driven goals and values, and the subsequent impact on patient satisfaction levels. Future studies should detail pragmatic and actionable integration and implementation steps to advance the uptake of SDM.
Acknowledgments
The authors would like to acknowledge Hitachi, Ltd (Tokyo, Japan) for partial funding of this project. The funding source played no role in the design, execution, or findings of this study.
During the preparation of this study, the authors used Covidence systematic review software (Veritas Health Innovation) [] for the screening of studies and EndNote (Clarivate) [] for management, citation, and storage of references. Following and concurrent with the use of these tools, the authors reviewed, synthesized, and organized the scoping review content. Figure 2 was created using Drawio app (JGraph Ltd) [].
The views expressed in this paper are those of the authors and do not necessarily represent the position or policy of the US Department of Veterans Affairs or the US government.
Data Availability
The datasets generated and analyzed during this study are available from the corresponding author on reasonable request.
Authors' Contributions
KK, GDF, and CW obtained funding for this project. CW and KK conceptualized the research and recommended the research methodology. JHP led the project administration, with supervision by CW, JMB, and GDF. MMM conducted all data and literature searches. WR II, JHP, CW, JMB, and TT conducted data extraction and synthesis for the final studies. JHP, CW, and TT wrote the original draft of the manuscript. JHP created Figures 1 and and Tables 1-3. CW and JHP created Table 4. All authors reviewed and edited the final manuscript.
Conflicts of Interest
None declared.
Multimedia Appendix 2
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist.
PDF File (Adobe PDF File), 151 KBReferences
- El-Ghazali S, Khan M, Santhirapala R. Shared decision making. In: Perioperative Quality Improvement. Amsterdam, The Netherlands. Elsevier; 2023.
- United States. President's commission for the study of ethical problems in medicine and biomedical and behavioral research. U S Code Annot U S. 1982;Title 42 Sect. 300v as added 1978. [Medline]
- Alston C, Berger Z, Brownlee S, Elwyn G, Fowler FJJ, Hall LK, et al. Shared decision-making strategies for best care: patient decision aids. National Academy of Medicine. Sep 18, 2014. URL: https://nam.edu/perspectives-2014-shared-decision-making-strategies-for-best-care-patient-decision-aids/ [accessed 2021-01-11]
- McCarron TL, Moffat K, Wilkinson G, Zelinsky S, Boyd JM, White D, et al. Understanding patient engagement in health system decision-making: a co-designed scoping review. Syst Rev. Apr 18, 2019;8(1):97. [FREE Full text] [CrossRef] [Medline]
- Hibbard JH, Greene J, Tusler M. Improving the outcomes of disease management by tailoring care to the patient's level of activation. Am J Manag Care. Jun 2009;15(6):353-360. [FREE Full text] [Medline]
- Vaseur RM, Te Braake E, Beinema T, Nijeweme- d'Hollosy WO, Tabak M. Technology-supported shared decision-making in chronic conditions: a systematic review of randomized controlled trials. Patient Educ Couns. Jul 2024;124:108267. [FREE Full text] [CrossRef] [Medline]
- Kuo AM, Thavalathil B, Elwyn G, Nemeth Z, Dang S. The promise of electronic health records to promote shared decision making: a narrative review and a look ahead. Med Decis Making. Nov 2018;38(8):1040-1045. [CrossRef] [Medline]
- Wickramasekera N, Taylor SK, Lumley E, Gray T, Wilson E, Radley S. Can electronic assessment tools improve the process of shared decision-making? A systematic review. Health Inf Manag. May 2023;52(2):72-86. [FREE Full text] [CrossRef] [Medline]
- US Preventive Services Task Force, Davidson KW, Mangione CM, Barry MJ, Nicholson WK, Cabana MD, et al. Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US preventive services task force recommendations. JAMA. Mar 22, 2022;327(12):1171-1176. [CrossRef] [Medline]
- Medicare payment. Centers for Medicare & Medicaid Services. URL: https://www.cms.gov/cms-guide-medical-technology-companies-and-other-interested-parties/payment#:~:text=Medicare%20is%20the%20single%20largest,services%20in%20the%20United%20States [accessed 2024-08-06]
- Merchant FM, Dickert NWJ, Howard DH. Mandatory shared decision making by the Centers for Medicare and Medicaid Services for cardiovascular procedures and other tests. JAMA. Aug 21, 2018;320(7):641-642. [CrossRef] [Medline]
- Lu Y, Elwyn G, Moulton BW, Volk RJ, Frosch DL, Spatz ES. Shared decision-making in the U.S.: evidence exists, but implementation science must now inform policy for real change to occur. Z Evid Fortbild Qual Gesundhwes. Jun 2022;171:144-149. [CrossRef] [Medline]
- Clinical decision support. Assistant Secretary for Technology Policy. URL: https://www.healthit.gov/topic/safety/clinical-decision-support [accessed 2021-01-11]
- Becher S, Holzhüter F, Heres S, Hamann J. Barriers and facilitators of shared decision making in acutely ill inpatients with schizophrenia-qualitative findings from the intervention group of a randomised-controlled trial. Health Expect. Oct 2021;24(5):1737-1746. [FREE Full text] [CrossRef] [Medline]
- Légaré F, Witteman HO. Shared decision making: examining key elements and barriers to adoption into routine clinical practice. Health Aff (Millwood). Feb 2013;32(2):276-284. [CrossRef] [Medline]
- Légaré F, Ratté S, Gravel K, Graham ID. Barriers and facilitators to implementing shared decision-making in clinical practice: update of a systematic review of health professionals' perceptions. Patient Educ Couns. Dec 2008;73(3):526-535. [CrossRef] [Medline]
- Politi MC, Neuman MD. Understanding when, why and how shared decision making is used in routine care. Patient Educ Couns. Jan 2023;106:1-2. [CrossRef] [Medline]
- Aronson PL, Schaeffer P, Ponce KA, Gainey TK, Politi MC, Fraenkel L, et al. Stakeholder perspectives on hospitalization decisions and shared decision-making in bronchiolitis. Hosp Pediatr. May 01, 2022;12(5):473-482. [FREE Full text] [CrossRef] [Medline]
- Scholl I, LaRussa A, Hahlweg P, Kobrin S, Elwyn G. Organizational- and system-level characteristics that influence implementation of shared decision-making and strategies to address them - a scoping review. Implement Sci. Mar 09, 2018;13(1):40. [FREE Full text] [CrossRef] [Medline]
- Cassidy BP, Getchell LE, Harwood L, Hemmett J, Moist LM. Barriers to education and shared decision making in the chronic kidney disease population: a narrative review. Can J Kidney Health Dis. Nov 2, 2018;5:2054358118803322. [FREE Full text] [CrossRef] [Medline]
- Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. Mar 1997;44(5):681-692. [CrossRef] [Medline]
- O'Connor AM, Llewellyn-Thomas HA, Flood AB. Modifying unwarranted variations in health care: shared decision making using patient decision aids. Health Aff (Millwood). 2004;Suppl Variation:VAR63-VAR72. [CrossRef] [Medline]
- Coronado-Vázquez V, Canet-Fajas C, Delgado-Marroquín MT, Magallón-Botaya R, Romero-Martín M, Gómez-Salgado J. Interventions to facilitate shared decision-making using decision aids with patients in primary health care: a systematic review. Medicine (Baltimore). Aug 07, 2020;99(32):e21389. [FREE Full text] [CrossRef] [Medline]
- Ankolekar A, Dekker A, Fijten R, Berlanga A. The benefits and challenges of using patient decision aids to support shared decision making in health care. JCO Clin Cancer Inform. Dec 2018;2:1-10. [CrossRef] [Medline]
- Elwyn G, O'Connor A, Stacey D, Volk R, Edwards A, Coulter A, et al. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ. Aug 26, 2006;333(7565):417. [FREE Full text] [CrossRef] [Medline]
- Hoefel L, O'Connor AM, Lewis KB, Boland L, Sikora L, Hu J, et al. 20th anniversary update of the Ottawa decision support framework part 1: a systematic review of the decisional needs of people making health or social decisions. Med Decis Making. Jul 2020;40(5):555-581. [CrossRef] [Medline]
- Hoefel L, Lewis KB, O'Connor A, Stacey D. 20th anniversary update of the Ottawa decision support framework: part 2 subanalysis of a systematic review of patient decision aids. Med Decis Making. May 2020;40(4):522-539. [CrossRef] [Medline]
- Stacey D, Légaré F, Boland L, Lewis KB, Loiselle MC, Hoefel L, et al. 20th anniversary Ottawa decision support framework: part 3 overview of systematic reviews and updated framework. Med Decis Making. Apr 2020;40(3):379-398. [CrossRef] [Medline]
- Drug and Therapeutics Bulletin. An introduction to patient decision aids. BMJ. Jul 23, 2013;347:f4147. [CrossRef] [Medline]
- Lenert L, Dunlea R, Del Fiol G, Hall LK. A model to support shared decision making in electronic health records systems. Med Decis Making. Nov 2014;34(8):987-995. [FREE Full text] [CrossRef] [Medline]
- Gillum RF. From papyrus to the electronic tablet: a brief history of the clinical medical record with lessons for the digital age. Am J Med. Oct 2013;126(10):853-857. [CrossRef] [Medline]
- Hofstede SN, Marang-van de Mheen PJ, Wentink MM, Stiggelbout AM, Vleggeert-Lankamp CL, Vliet Vlieland TP, et al. Barriers and facilitators to implement shared decision making in multidisciplinary sciatica care: a qualitative study. Implement Sci. Aug 23, 2013;8:95. [FREE Full text] [CrossRef] [Medline]
- Staszewska A, Zaki P, Lee J. Computerized decision aids for shared decision making in serious illness: systematic review. JMIR Med Inform. Oct 06, 2017;5(4):e36. [FREE Full text] [CrossRef] [Medline]
- Lee CN, Sullivan J, Foraker R, Myckatyn TM, Olsen MA, Phommasathit C, et al. Integrating a patient decision aid into the electronic health record: a case report on the implementation of BREASTChoice at 2 sites. MDM Policy Pract. Oct 08, 2022;7(2):23814683221131317. [FREE Full text] [CrossRef] [Medline]
- Scalia P, Ahmad F, Schubbe D, Forcino R, Durand MA, Barr PJ, et al. Integrating option grid patient decision aids in the epic electronic health record: case study at 5 health systems. J Med Internet Res. May 03, 2021;23(5):e22766. [FREE Full text] [CrossRef] [Medline]
- Westerink HJ, Oirbans T, Garvelink MM, van Uden-Kraan CF, Zouitni O, Bart HA, et al. Barriers and facilitators of meaningful patient participation at the collective level in healthcare organizations: a systematic review. Health Policy. Dec 2023;138:104946. [FREE Full text] [CrossRef] [Medline]
- Tong WT, Ng CJ, Lee YK, Lee PY. What will make patients use a patient decision aid? A qualitative study on patients' perspectives on implementation barriers and facilitators. J Eval Clin Pract. Jun 2020;26(3):755-764. [CrossRef] [Medline]
- Pekala KR, Shill DK, Austria M, Langford AT, Loeb S, Carlsson SV. Shared decision-making before prostate cancer screening decisions. Nat Rev Urol. Jun 2024;21(6):329-338. [CrossRef] [Medline]
- Ke Y, Zhou H, Chan RJ, Chan A. Decision aids for cancer survivors' engagement with survivorship care services after primary treatment: a systematic review. J Cancer Surviv. Apr 2024;18(2):288-317. [FREE Full text] [CrossRef] [Medline]
- Mhaimeed N, Mhaimeed N, Mhaimeed O, Alanni J, Burney Z, Elshafeey A, et al. Shared decision making with black patients: a scoping review. Patient Educ Couns. May 2023;110:107646. [FREE Full text] [CrossRef] [Medline]
- Jones JB, Shah NR, Bruce CA, Stewart WF. Meaningful use in practice using patient-specific risk in an electronic health record for shared decision making. Am J Prev Med. May 2011;40(5 Suppl 2):S179-S186. [CrossRef] [Medline]
- Schubbe D, Yen RW, Saunders CH, Elwyn G, Forcino RC, O'Malley AJ, et al. Implementation and sustainability factors of two early-stage breast cancer conversation aids in diverse practices. Implement Sci. May 10, 2021;16(1):51. [FREE Full text] [CrossRef] [Medline]
- Branda ME, Ridgeway JL, Mann D, Wieser J, Gomez Y, Dagoberg A, et al. Healthcare systems collaborating to implement a shared decision-making tool in the electronic health record and build evidence on its adoption and use. Learn Health Syst. Apr 15, 2024;8(Suppl 1):e10418. [FREE Full text] [CrossRef] [Medline]
- Mohindra NA, Garcia SF, Kircher S, Barnard C, Perry LM, Lyleroehr M, et al. Development of an electronic health record-integrated patient-reported outcome-based shared decision-making dashboard in oncology. JAMIA Open. Jul 24, 2024;7(3):ooae056. [CrossRef] [Medline]
- Scalable decision support and shared decision making for lung cancer screening. Agency for Healthcare Research and Quality. URL: https://digital.ahrq.gov/ahrq-funded-projects/scalable-decision-support-and-shared-decisionmaking-lung-cancer-screening [accessed 2025-02-12]
- Lowery J, Fagerlin A, Larkin AR, Wiener RS, Skurla SE, Caverly TJ. Implementation of a web-based tool for shared decision-making in lung cancer screening: mixed methods quality improvement evaluation. JMIR Hum Factors. Apr 01, 2022;9(2):e32399. [FREE Full text] [CrossRef] [Medline]
- Thompson-Leduc P, Clayman ML, Turcotte S, Légaré F. Shared decision-making behaviours in health professionals: a systematic review of studies based on the theory of planned behaviour. Health Expect. Oct 2015;18(5):754-774. [FREE Full text] [CrossRef] [Medline]
- Shinkunas LA, Klipowicz CJ, Carlisle EM. Shared decision making in surgery: a scoping review of patient and surgeon preferences. BMC Med Inform Decis Mak. Aug 12, 2020;20(1):190. [FREE Full text] [CrossRef] [Medline]
- Raj R, Ahuja KD, Frandsen M, Jose M. Older patient considering treatment for advanced renal disease: protocol for a scoping review of the information available for shared decision-making. BMJ Open. Dec 08, 2016;6(12):e013755. [FREE Full text] [CrossRef] [Medline]
- Norful AA, Dillon J, Baik D, George M, Ye S, Poghosyan L. Instruments to measure shared decision-making in outpatient chronic care: a systematic review and appraisal. J Clin Epidemiol. May 2020;121:15-19. [FREE Full text] [CrossRef] [Medline]
- Niburski K, Guadagno E, Mohtashami S, Poenaru D. Shared decision making in surgery: a scoping review of the literature. Health Expect. Oct 2020;23(5):1241-1249. [FREE Full text] [CrossRef] [Medline]
- Doherr H, Christalle E, Kriston L, Härter M, Scholl I. Use of the 9-item shared decision making questionnaire (SDM-Q-9 and SDM-Q-Doc) in intervention studies-a systematic review. PLoS One. Mar 30, 2017;12(3):e0173904. [FREE Full text] [CrossRef] [Medline]
- Corell A, Guo A, Vecchio TG, Ozanne A, Jakola AS. Shared decision-making in neurosurgery: a scoping review. Acta Neurochir (Wien). Sep 2021;163(9):2371-2382. [FREE Full text] [CrossRef] [Medline]
- Bouniols N, Leclère B, Moret L. Evaluating the quality of shared decision making during the patient-carer encounter: a systematic review of tools. BMC Res Notes. Aug 02, 2016;9:382. [FREE Full text] [CrossRef] [Medline]
- Bomhof-Roordink H, Gärtner FR, Stiggelbout AM, Pieterse AH. Key components of shared decision making models: a systematic review. BMJ Open. Dec 17, 2019;9(12):e031763. [FREE Full text] [CrossRef] [Medline]
- Baik D, Cho H, Masterson Creber RM. Examining interventions designed to support shared decision making and subsequent patient outcomes in palliative care: a systematic review of the literature. Am J Hosp Palliat Care. Jan 2019;36(1):76-88. [FREE Full text] [CrossRef] [Medline]
- Ahmad M, Abu Tabar N, Othman EH, Abdelrahim Z. Shared decision-making measures: a systematic review. Qual Manag Health Care. 2020;29(2):54-66. [CrossRef] [Medline]
- Shay LA, Lafata JE. Where is the evidence? A systematic review of shared decision making and patient outcomes. Med Decis Making. Jan 2015;35(1):114-131. [FREE Full text] [CrossRef] [Medline]
- Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [CrossRef]
- Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. Sep 20, 2010;5:69. [FREE Full text] [CrossRef] [Medline]
- Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. Sep 2015;13(3):141-146. [CrossRef] [Medline]
- Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, et al. PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Syst Rev. Jan 26, 2021;10(1):39. [FREE Full text] [CrossRef] [Medline]
- 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. Oct 02, 2018;169(7):467-473. [FREE Full text] [CrossRef] [Medline]
- McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 guideline statement. J Clin Epidemiol. Jul 2016;75:40-46. [FREE Full text] [CrossRef] [Medline]
- Health IT legislation. Assistant Secretary for Technology Policy. URL: https://www.healthit.gov/topic/laws-regulation-and-policy/health-it-legislation [accessed 2021-01-11]
- Peters M, Godfrey CM, Mcinerney P, Munn Z, Trico A, Khalil H. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide, Australia. Joanna Briggs Institute; Jun 2020.
- Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. Oct 2020;18(10):2119-2126. [CrossRef] [Medline]
- Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Guidance on the conduct of narrative synthesis in systematic reviews. CiteSeerX. Apr 2006. URL: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed8b23836338f6fdea0cc55e161b0fc5805f9e27 [accessed 2021-01-11]
- Nick TG. Descriptive statistics. Methods Mol Biol. 2007;404:33-52. [CrossRef] [Medline]
- Lindgren BM, Lundman B, Graneheim UH. Abstraction and interpretation during the qualitative content analysis process. Int J Nurs Stud. Aug 2020;108:103632. [CrossRef] [Medline]
- Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G, et al. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. Sep 01, 2018;25(9):1137-1146. [FREE Full text] [CrossRef] [Medline]
- Sona C, Pollard KA, Schallom M, Schrupp A, Wessman BT. Implementation of a standardized patient/family communication bundle. Crit Care Nurs Clin North Am. Jun 2020;32(2):243-251. [CrossRef] [Medline]
- Fiks AG, Mayne SL, Karavite DJ, Suh A, O'Hara R, Localio AR, et al. Parent-reported outcomes of a shared decision-making portal in asthma: a practice-based RCT. Pediatrics. Apr 2015;135(4):e965-e973. [FREE Full text] [CrossRef] [Medline]
- Jouni H, Haddad RA, Marroush TS, Brown SA, Kruisselbrink TM, Austin EE, et al. Shared decision-making following disclosure of coronary heart disease genetic risk: results from a randomized clinical trial. J Investig Med. Mar 2017;65(3):681-688. [FREE Full text] [CrossRef] [Medline]
- Huang JS, Yueh R, Wood K, Ma S, Cruz R, Boyd N, et al. Harnessing the electronic health record to distribute transition services to adolescents with inflammatory bowel disease. J Pediatr Gastroenterol Nutr. Feb 2020;70(2):200-204. [CrossRef] [Medline]
- Bose-Brill S, Feeney M, Prater L, Miles L, Corbett A, Koesters S. Validation of a novel electronic health record patient portal advance care planning delivery system. J Med Internet Res. Jun 26, 2018;20(6):e208. [FREE Full text] [CrossRef] [Medline]
- Choi DY, Wagner MP, Yum B, Jannat-Khah DP, Mazique DC, Crossman DJ, et al. Improving implantable cardioverter defibrillator deactivation discussions in admitted patients made DNR and comfort care. BMJ Open Qual. Dec 04, 2019;8(4):e000730. [FREE Full text] [CrossRef] [Medline]
- Chunchu K, Mauksch L, Charles C, Ross V, Pauwels J. A patient centered care plan in the EHR: improving collaboration and engagement. Fam Syst Health. Sep 2012;30(3):199-209. [CrossRef] [Medline]
- Lafata JE, Cooper G, Divine G, Oja-Tebbe N, Flocke SA. Patient-physician colorectal cancer screening discussion content and patients' use of colorectal cancer screening. Patient Educ Couns. Jan 2014;94(1):76-82. [FREE Full text] [CrossRef] [Medline]
- 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. Sep 25, 2014;349:g5651. [FREE Full text] [CrossRef] [Medline]
- Crosby EM, Gutierrez KM. Facilitating timely advance care planning: discussions in the ambulatory setting with a heart failure population. J Hosp Palliat Nurs. Oct 2019;21(5):445-452. [CrossRef] [Medline]
- Litzelman DK, Inui TS, Schmitt-Wendholt KM, Perkins A, Griffin WJ, Cottingham AH, et al. Clarifying values and preferences for care near the end of life: the role of a new lay workforce. J Community Health. Oct 2017;42(5):926-934. [FREE Full text] [CrossRef] [Medline]
- Ballard AY, Kessler M, Scheitel M, Montori VM, Chaudhry R. Exploring differences in the use of the statin choice decision aid and diabetes medication choice decision aid in primary care. BMC Med Inform Decis Mak. Aug 10, 2017;17(1):118. [FREE Full text] [CrossRef] [Medline]
- Fossa AJ, Bell SK, DesRoches C. OpenNotes and shared decision making: a growing practice in clinical transparency and how it can support patient-centered care. J Am Med Inform Assoc. Sep 01, 2018;25(9):1153-1159. [FREE Full text] [CrossRef] [Medline]
- Coylewright M, Keevil JG, Xu K, Dodge SE, Frosch D, Field ME. Pragmatic study of clinician use of a personalized patient decision aid integrated into the electronic health record: an 8-year experience. Telemed J E Health. May 2020;26(5):597-602. [CrossRef] [Medline]
- Plößnig M, Kabak Y, Lamprinos I, Pabst A, Hildebrand C, Mantwill S. EMPOWER--pathways for supporting the self-management of diabetes patients. Stud Health Technol Inform. 2015;212:159-166. [Medline]
- Fried TR, Niehoff KM, Street RL, Charpentier PA, Rajeevan N, Miller PL, et al. Effect of the tool to reduce inappropriate medications on medication communication and deprescribing. J Am Geriatr Soc. Oct 2017;65(10):2265-2271. [FREE Full text] [CrossRef] [Medline]
- Schafer KM, Gionfriddo MR, Boehm DH. Shared decision making and medication therapy management with the use of an interactive template. J Am Pharm Assoc (2003). 2016;56(2):166-172. [CrossRef] [Medline]
- Martin RW, Brogård Andersen S, O'Brien MA, Bravo P, Hoffmann T, Olling K, et al. Providing balanced information about options in patient decision aids: an update from the international patient decision aid standards. Med Decis Making. Oct 2021;41(7):780-800. [CrossRef] [Medline]
- Guo KL. DECIDE: a decision-making model for more effective decision making by health care managers. Health Care Manag (Frederick). 2020;39(3):133-141. [CrossRef] [Medline]
- Butler JM, Gibson B, Lewis L, Reiber G, Kramer H, Rupper R, et al. Patient-centered care and the electronic health record: exploring functionality and gaps. JAMIA Open. Oct 29, 2020;3(3):360-368. [CrossRef] [Medline]
- Clavel N, Paquette J, Dumez V, Del Grande C, Ghadiri DP, Pomey MP, et al. Patient engagement in care: a scoping review of recently validated tools assessing patients' and healthcare professionals' preferences and experience. Health Expect. Dec 2021;24(6):1924-1935. [FREE Full text] [CrossRef] [Medline]
- Hadie SN. ABC of a scoping review: a simplified JBI scoping review guideline. Educ Med J. 2024;16(2):185-197. [CrossRef]
- Daudt HM, van Mossel C, Scott SJ. Enhancing the scoping study methodology: a large, inter-professional team's experience with Arksey and O'Malley's framework. BMC Med Res Methodol. Mar 23, 2013;13:48. [FREE Full text] [CrossRef] [Medline]
- Covidence systematic review software. Veritas Health Innovation. URL: https://www.covidence.org/ [accessed 2021-01-11]
- EndNote: Office 2013 compatibility. Clarivate. 2013. URL: https://support.clarivate.com/Endnote/s/article/EndNote-Office-20132016-Compatibility?language=en_US [accessed 2021-01-11]
- Graph DJ. diagrams.net, draw.io, version: 15.5.2. diagrams.net. Oct 14, 2021. URL: https://www.diagrams.net/ [accessed 2025-02-07]
Abbreviations
| EHR: electronic health record |
| JBI: Joanna Briggs Institute |
| PRISMA-S: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Search Extension |
| PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews |
| RCT: randomized controlled trial |
| SDM: shared decision-making |
Edited by A Coristine; submitted 26.04.24; peer-reviewed by K Sajeev, C Bonner, A Coe; comments to author 18.06.24; revised version received 30.10.24; accepted 11.01.25; published 21.02.25.
Copyright©Joni H Pierce, Charlene Weir, Teresa Taft, William Richards II, Mary M McFarland, Kensaku Kawamoto, Guilherme Del Fiol, Jorie M Butler. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.02.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.