Patient Decision Support Interventions Research Articles

Understanding Symptom Self-Monitoring Needs Among Postpartum Black Patients: Qualitative Interview Study.

Pregnancy-related death is on the rise in the United States, and there are significant disparities in outcomes for Black patients. Most solutions that address pregnancy-related death are hospital based, which rely on patients recognizing symptoms and seeking care from a health system, an area where many Black patients have reported experiencing bias. There is a need for patient-centered solutions that support and encourage postpartum people to seek care for severe symptoms. We aimed to determine the design needs for a mobile health (mHealth) patient-reported outcomes and decision-support system to assist Black patients in assessing when to seek medical care for severe postpartum symptoms. These findings may also support different perinatal populations and minoritized groups in other clinical settings. We conducted semistructured interviews with 36 participants-15 (42%) obstetric health professionals, 10 (28%) mental health professionals, and 11 (31%) postpartum Black patients. The interview questions included the following: current practices for symptom monitoring, barriers to and facilitators of effective monitoring, and design requirements for an mHealth system that supports monitoring for severe symptoms. Interviews were audio recorded and transcribed. We analyzed transcripts using directed content analysis and the constant comparative process. We adopted a thematic analysis approach, eliciting themes deductively using conceptual frameworks from health behavior and human information processing, while also allowing new themes to inductively arise from the data. Our team involved multiple coders to promote reliability through a consensus process. Our findings revealed considerations related to relevant symptom inputs for postpartum support, the drivers that may affect symptom processing, and the design needs for symptom self-monitoring and patient decision-support interventions. First, participants viewed both somatic and psychological symptom inputs as important to capture. Second, self-perception; previous experience; sociocultural, financial, environmental, and health systems-level factors were all perceived to impact how patients processed, made decisions about, and acted upon their symptoms. Third, participants provided recommendations for system design that involved allowing for user control and freedom. They also stressed the importance of careful wording of decision-support messages, such that messages that recommend them to seek care convey urgency but do not provoke anxiety. Alternatively, messages that recommend they may not need care should make the patient feel heard and reassured. Future solutions for postpartum symptom monitoring should include both somatic and psychological symptoms, which may require combining existing measures to elicit symptoms in a nuanced manner. Solutions should allow for varied, safe interactions to suit individual needs. While mHealth or other apps may not be able to address all the social or financial needs of a person, they may at least provide information, so that patients can easily access other supportive resources.

Patient decision support interventions for candidates considering elective surgeries: a systematic review and meta-analysis.

The increase in elective surgeries and varied postoperative patient outcomes has boosted the use of patient decision support interventions (PDSIs). However, evidence on the effectiveness of PDSIs are not updated. This systematic review aims to summarize the effects of PDSIs for surgical candidates considering elective surgeries and to identify their moderators with an emphasis on the type of targeted surgery. Systematic review and meta-analysis. We searched eight electronic databases for randomized controlled trials evaluating PDSIs among elective surgical candidates. We documented the effects on invasive treatment choice, decision-making-related outcomes, patient-reported outcomes, and healthcare resource use. The Cochrane Risk of Bias Tool version 2 and Grading of Recommendations, Assessment, Development, and Evaluations were adopted to rate the risk of bias of individual trials and certainty of evidence, respectively. STATA 16 software was used to conduct the meta-analysis. Fifty-eight trials comprising 14981 adults from 11 countries were included. Overall, PDSIs had no effect on invasive treatment choice (risk ratio=0.97; 95% CI: 0.90, 1.04), consultation time (mean difference=0.04min; 95% CI: -0.17, 0.24), or patient-reported outcomes, but had a beneficial effect on decisional conflict (Hedges' g =-0.29; 95% CI: -0.41, -0.16), disease and treatment knowledge (Hedges' g =0.32; 95% CI: 0.15, 0.49), decision-making preparedness (Hedges' g =0.22; 95% CI: 0.09, 0.34), and decision quality (risk ratio=1.98; 95% CI: 1.15, 3.39). Treatment choice varied with surgery type and self-guided PDSIs had a greater effect on disease and treatment knowledge enhancement than clinician-delivered PDSIs. This review has demonstrated that PDSIs targeting individuals considering elective surgeries had benefited their decision-making by reducing decisional conflict and increasing disease and treatment knowledge, decision-making preparedness, and decision quality. These findings may be used to guide the development and evaluation of new PDSIs for elective surgical care.

Communicating Uncertainty in Benefits and Harms: A Review of Patient Decision Support Interventions.

Interventions designed to help people deliberate and participate in their healthcare choices frequently describe uncertainty in potential benefits and harms. This uncertainty can be generalized to aleatory, or first-order uncertainty, represented by risk estimates, and epistemic, or second-order uncertainty, represented by imprecision in the risk estimates. The aim of this short communication was to review how patient decision support interventions (PDSIs) describe aleatory and epistemic uncertainty. We reviewed PDSIs available online in five repositories and extracted all the uncertainty statements. A framework was developed and each statement was classified by presentation of uncertainty (aleatory and epistemic). Overall, we reviewed 460 PDSIs from eight main developers, which included 8956 uncertainty statements. When describing first-order, aleatory uncertainty, almost all PDSIs included at least one qualitative statement, such as 'treatment may cause side effects'. Forty-four percent of PDSIs included at least one natural frequency, such as '2 in 100 people have side effects'. Second-order, epistemic uncertainty was also most often communicated qualitatively; notably, nearly half of all PDSIs did not communicate epistemic uncertainty at all. Few PDSIs communicated epistemic uncertainty in quantitative terms conveying imprecision, e.g. risk ranges. We found considerable heterogeneity in both the extent and manner in which aleatory and epistemic uncertainties are communicated in PDSIs. This variation is predominately explained by a lack of evidence and consensus in risk communication, particularly for epistemic uncertainty. This study highlights the need for more empirical research to understand not only the outcomes of communicating uncertainty in PDSIs but also the reasons for this variation in uncertainty communication.

Development of a Practical Model for Targeting Patient Decision Support Interventions to Promote Colorectal Cancer Screening in Vulnerable Populations

Development of a Practical Model for Targeting Patient Decision Support Interventions to Promote Colorectal Cancer Screening in Vulnerable Populations

Development of a Practical Model for Targeting Patient Decision Support Interventions to Promote Colorectal Cancer Screening in Vulnerable Populations.

We sought to develop a practical model for predicting probability of colorectal cancer (CRC) screening completion in a diverse safety-net population and a subsequent framework for targeting screening promotion interventions. Data used to construct the models was prospectively collected from a CRC screening intervention. Models were cross-validated by randomly splitting the data into training and validation samples. The predicted probabilities of screening completion from the final model were trichotomized into framework groups and cross-validated by defining cut-points in the training sample, applying them to the validation sample, and comparing across samples for homogeneity. The final model included demographic and simple psychosocial measures and predicted screening behavior adequately (AUROC=0.63). The framework groups (cut-points 0.62 (low), 0.74 (medium), and 1.0 (high)) were homogeneous across the two samples. The model and framework may be useful for designing and delivering targeted interventions to promote CRC screening. Future research should validate the framework groups.

Implementation of Patient Decision Support Interventions in Primary Care: The Role of Relational Coordination.

The benefits of patient decision support interventions (DESIs) have been well documented. However, DESIs remain difficult to incorporate into clinical practice. Relational coordination (RC) has been shown to improve performance and quality of care in health care settings. This study aims to demonstrate how applying RC theory to DESI implementation could elucidate underlying issues limiting widespread uptake. Five primary care clinics in Northern California participated in a DESI implementation project. We used a deductive thematic approach guided by behaviors outlined in RC theory to analyze qualitative data collected from ethnographic field notes documenting the implementation process and focus groups with health care professionals. We then systematically compared the qualitative findings with quantitative DESI distribution data. Based on DESI distribution rates, clinics were placed into 3 performance categories: high, middle, and low. Qualitative data illustrated how each clinic's performance related to RC behaviors. Consistent with RC theory, the high-performing clinic exhibited frequent, timely, and accurate communication and positive working relationships. The 3 middle-performing clinics exhibited high-quality communication within physician-staff teams but limited communication regarding DESI implementation across the clinic. The lowest-performing clinic was characterized by contentious relationships and inadequate communication. Limitations of the study include nonrandom selection of clinics and limited geographic diversity. In addition, ethnographic data collected documented only DESI implementation practices and not larger staff interactions contributing to RC. These findings suggest that a high level of RC within clinical settings may be a key component and facilitator of successful DESI implementation. Future attempts to integrate DESIs into clinical practice should consider incorporating interventions designed to increase positive RC behaviors as a potential means to improve uptake.

A Randomized Controlled Effectiveness Trial for PSA Screening Decision Support Interventions in Two Primary Care Settings.

Decision support interventions (DESIs) provide a mechanism to translate comparative effectiveness research results into clinical care so that patients are able to make informed decisions. Patient decision support interventions for prostate-specific antigen (PSA) have been shown to promote informed decision making and reduce PSA testing in efficacy trials, but their impact in real world settings is not clear. We performed an effectiveness trial of PSA decision support interventions in primary care. A randomized controlled trial of three distribution strategies was compared to a control. Participants included 2,550 men eligible for PSA testing (76.6 % of the eligible population) and 2001 survey respondents (60.1 % survey response rate). The intervention groups were: 1) mailed the DESI in DVD format, 2) offered a shared medical appointment (SMA) to view the DESI with other men and discuss, and 3) both options. We measured PSA testing identified via electronic medical record at 12 months and DESI use by self-report 4 months after the intervention mailing. We found no differences in PSA testing across the three distribution strategies over a year-long follow-up period: 21 %, 24 %, 22 % in the DESI, SMA, and combined group respectively, compared to 21 % in the control group (p = 0.51). Self-reported DESI use was low across all strategies at 4 months: 16 % in the mailed DESI group, 6 % in the SMA group, and 15 % in the combined group (p = < 0.0001). Mailing PSA decision support interventions or inviting men to shared medical appointments unrelated to a primary care office visit do not appear to promote informed decision making, or change PSA testing behavior.

Minimum standards for the certification of patient decision support interventions: Feasibility and application

ObjectivePatient decision support interventions are not currently subject to standardized quality control. The current study aims to assess the feasibility of applying a proposed set of minimum standards (previously developed as part of a possible certification process) to a selection of existing patient decision support interventions. MethodsA convenience sample of interventions selected from those included in the 2009 Cochrane systematic review of patient decision aids was scored by trained raters using the International Patient Decision Aids Standards (IPDAS) instrument. Scores were then evaluated against the published proposed minimum standards. ResultsTwenty-five out of thirty included interventions met all qualifying criteria while only three met the proposed certification criteria. The changes required for an intervention to meet the proposed certification standards were relatively minor. There was considerable variation between raters’ mean scores. ConclusionsMost interventions did not meet the certification criteria due to lack of information on modifiable items such as update policy and funding source. Practice implicationsSpecifying minimum standards for patient decision support interventions is a feasible development. However, it remains unclear whether the minimum standards can be applied to interventions designed for use within clinical encounters and to those that target screening and diagnostic tests.

Clinicians' perceptions of digital vs. paper-based decision support interventions.

Despite extensive evidence on the value of patient decision support interventions (DESIs), there is no consensus on optimal DESI formats. Assessing clinicians' perceptions about DESI formats can help facilitate their adoption. The aim of this study was to assess clinicians' perceptions of DESIs formats and potential use in practice. Semi-structured qualitative interviews were conducted with doctors from diverse practice areas (internal medicine, OB/GYN, surgery, medical oncology, emergency medicine) and elicited perceptions toward patient DESIs formats (digital vs. paper) and timing of administration. Questions also elicited beliefs underlying attitudes, perceived social norms and self-efficacy for using DESIs and the feasibility of doing so. Data analysis was conducted using a thematic analysis approach. Participants identified strengths of both more comprehensive digital and shorter paper-based tools and thought they could complement each other. Participants consistently expressed the advantages of using DESIs outside the consultation to supplement clinical discussions about cancer decisions given the amount of information to discuss during these emotion-laden conversations. Participants felt that patients with older age and lower socio-economic status were more likely to use a paper-based compared with a digital DESI. Participants also noted challenges related to reliable resources such as computers and Internet in the practice setting, which would be necessary for implementing the digital DESIs on site. Clinicians' perceptions and opinions about value of DESIs can vary widely across doctor, patient and clinic characteristics. A one-size-fits-all approach to implementation might not be feasible, suggesting that flexible approaches to providing decision support for patients are needed to drive broader adoption.

Undetermined impact of patient decision support interventions on healthcare costs and savings: systematic review

Objective To perform a systematic review of studies that assessed the potential of patient decision support interventions (decision aids) to generate savings. Design Systematic review. Data sources After registration with...

"Many miles to go...": a systematic review of the implementation of patient decision support interventions into routine clinical practice

"Many miles to go...": a systematic review of the implementation of patient decision support interventions into routine clinical practice

"Many miles to go …": a systematic review of the implementation of patient decision support interventions into routine clinical practice.

BackgroundTwo decades of research has established the positive effect of using patient-targeted decision support interventions: patients gain knowledge, greater understanding of probabilities and increased confidence in decisions. Yet, despite their efficacy, the effectiveness of these decision support interventions in routine practice has yet to be established; widespread adoption has not occurred. The aim of this review was to search for and analyze the findings of published peer-reviewed studies that investigated the success levels of strategies or methods where attempts were made to implement patient-targeted decision support interventions into routine clinical settings.MethodsAn electronic search strategy was devised and adapted for the following databases: ASSIA, CINAHL, Embase, HMIC, Medline, Medline-in-process, OpenSIGLE, PsycINFO, Scopus, Social Services s, and the Web of Science. In addition, we used snowballing techniques. Studies were included after dual independent assessment.ResultsAfter assessment, 5322 abstracts yielded 51 articles for consideration. After examining full-texts, 17 studies were included and subjected to data extraction. The approach used in all studies was one where clinicians and their staff used a referral model, asking eligible patients to use decision support. The results point to significant challenges to the implementation of patient decision support using this model, including indifference on the part of health care professionals. This indifference stemmed from a reported lack of confidence in the content of decision support interventions and concern about disruption to established workflows, ultimately contributing to organizational inertia regarding their adoption.ConclusionsIt seems too early to make firm recommendations about how best to implement patient decision support into routine practice because approaches that use a ‘referral model’ consistently report difficulties. We sense that the underlying issues that militate against the use of patient decision support and, more generally, limit the adoption of shared decision making, are under-investigated and under-specified. Future reports from implementation studies could be improved by following guidelines, for example the SQUIRE proposals, and by adopting methods that would be able to go beyond the ‘barriers’ and ‘facilitators’ approach to understand more about the nature of professional and organizational resistance to these tools. The lack of incentives that reward the use of these interventions needs to be considered as a significant impediment.

Patchy ‘coherence’: using normalization process theory to evaluate a multi-faceted shared decision making implementation program (MAGIC)

BackgroundImplementing shared decision making into routine practice is proving difficult, despite considerable interest from policy-makers, and is far more complex than merely making decision support interventions available to patients. Few have reported successful implementation beyond research studies. MAking Good Decisions In Collaboration (MAGIC) is a multi-faceted implementation program, commissioned by The Health Foundation (UK), to examine how best to put shared decision making into routine practice. In this paper, we investigate healthcare professionals’ perspectives on implementing shared decision making during the MAGIC program, to examine the work required to implement shared decision making and to inform future efforts.MethodsThe MAGIC program approached implementation of shared decision making by initiating a range of interventions including: providing workshops; facilitating development of brief decision support tools (Option Grids); initiating a patient activation campaign (‘Ask 3 Questions’); gathering feedback using Decision Quality Measures; providing clinical leads meetings, learning events, and feedback sessions; and obtaining executive board level support. At 9 and 15 months (May and November 2011), two rounds of semi-structured interviews were conducted with healthcare professionals in three secondary care teams to explore views on the impact of these interventions. Interview data were coded by two reviewers using a framework derived from the Normalization Process Theory.ResultsA total of 54 interviews were completed with 31 healthcare professionals. Partial implementation of shared decision making could be explained using the four components of the Normalization Process Theory: ‘coherence,’ ‘cognitive participation,’ ‘collective action,’ and ‘reflexive monitoring.’ Shared decision making was integrated into routine practice when clinical teams shared coherent views of role and purpose (‘coherence’). Shared decision making was facilitated when teams engaged in developing and delivering interventions (‘cognitive participation’), and when those interventions fit with existing skill sets and organizational priorities (‘collective action’) resulting in demonstrable improvements to practice (‘reflexive monitoring’). The implementation process uncovered diverse and conflicting attitudes toward shared decision making; ‘coherence’ was often missing.ConclusionsThe study showed that implementation of shared decision making is more complex than the delivery of patient decision support interventions to patients, a portrayal that often goes unquestioned. Normalizing shared decision making requires intensive work to ensure teams have a shared understanding of the purpose of involving patients in decisions, and undergo the attitudinal shifts that many health professionals feel are required when comprehension goes beyond initial interpretations. Divergent views on the value of engaging patients in decisions remain a significant barrier to implementation.

Barriers and facilitators to routine distribution of patient decision support interventions: a preliminary study in community‐based primary care settings

A growing body of literature documents the value of decision support interventions (DESIs) in facilitating patient participation in preference sensitive decision making, but little is known about their implementation in routine care. This study explored barriers and facilitators to prescribing DESIs in primary care. Four community-based primary care practices across Los Angeles County serving diverse low and middle income populations participated. The first phase focused on implementing DESI prescribing into routine care. Weekly academic detailing visits served to identify barriers to DESI prescribing, generate ethnographic field notes and record DESI prescriptions. The second phase explored the impact of a financial incentive on DESI prescribing. At the project's conclusion, each physician completed an in-depth interview. The four practices prescribed an average of 6.5 DESIs a month (range 3.6-9.2) during Phase I. The financial incentive increased DESI prescribing by 71% to 11.1 per month (range 3.5-21.4). The estimated percentages of patients who viewed the DESI were 37.9 and 43.9% during Phases I and II, respectively. Qualitative data suggest that physician buy-in with the project goal was crucial to DESI distribution success. Competing demands and time pressures were persistent barriers. The effects of the financial incentive were mixed. This study confirmed the importance of physician engagement when implementing DESIs and found mixed effects for providing financial incentives. The relatively low rate of DESI viewing suggests further research on increasing patient uptake of these interventions in routine practice is necessary.

Why do clinicians not refer patients to online decision support tools? Interviews with front line clinics in the NHS

ObjectiveTo assess whether clinical teams would direct patients to use web-based patient decision support interventions (DESIs) and whether patients would use them.DesignRetrospective semistructured interviews and web server log analysis.Participants and...

The design of patient decision support interventions: addressing the theory–practice gap

Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory-practice gap and to examine how a range of selected decision-making theories could inform the design and evaluation of decision support interventions. We reviewed the decision-making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory-based outcome measures. Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy-trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational-emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory-building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory-practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions. Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.

Conducting implementation research in community‐based primary care: a qualitative study on integrating patient decision support interventions for cancer screening into routine practice

Despite a growing body of evidence supporting the efficacy of patient decision support interventions (DESI), little is known about their implementation in community-based primary care practices. The goal of this study was to explore the feasibility of integrating the use of DESIs for cancer screening in primary care practices serving patients from diverse backgrounds and learn more about the potential barriers and facilitators of integration. 12 community-based primary care practices in metropolitan Los Angeles. Qualitative field notes documented the roles played by staff and physicians in accomplishing project goals, the impact of the programmes on the clinical work-flow in the practices and other noteworthy observations. Practices that were better able to integrate the project had adequate clinic infrastructure, a relatively well-matched patient pool, and positive work and patient care environments. The remaining identified components, including staff facilitation and the physician's role accounted for higher level differences between the clinics, acting as barriers and facilitators that distinguished practices that were able to work independently from those that required more assistance and, to a lesser extent, those clinics that did and those that did not meet the project goals. This study suggests that implementation of DESIs to be used immediately before a consultation is feasible if the practice infrastructure can provide sufficient basic accommodation and physician and staff are dedicated to patient care goals that are implicit in the use of these tools. Overall, the physician's role appeared to be the most important factor in determining whether project integration was successful.

Appraisal of primary outcome measures used in trials of patient decision support

Objective To appraise instruments used as primary outcome measures in trials measuring the effectiveness of patient decision support interventions. Methods Primary outcome measures were identified in trials of patient decision aids included in the 2003 Cochrane Review. Instruments were appraised for: use in calculating sample size, appropriateness, reliability, validity, responsiveness, precision, interpretability, acceptability, and feasibility. Results Of the 35 trials, there were 35 unique primary outcome measures and 8 instruments were appraised. Actual or preferred choice was the primary outcome measure in 18 trials. Two instruments met at least 6 of 8 appraisal criteria: Control Preference Scale ( n = 2 trials) and Decisional Conflict Scale ( n = 5 trials). The Decision Conflict Scale was used to calculate sample size in 4 trials. Conclusion Decision was the most consistent outcome measure. Most publications provided inadequate detail for appraising the instruments. Four instruments (Decisional Conflict, Control Preferences, Genetic Testing Knowledge Questionnaire, and McBride's Satisfaction with Decision) measured one or more International Patient Decision Aid Standards criteria for evaluating effectiveness. Practice implications Selecting relevant and high quality outcome measures remains challenging and is an important area for further research in the field of shared decision making.

Adoption and sustainability of decision support for patients facing health decisions: an implementation case study in nursing

BackgroundEffective interventions prepare patients for making values-sensitive health decisions by helping them become informed and clarifying their values for each of the options. However, patient decision support interventions have not been widely implemented and little is known about effective models for delivering them to patients. The purpose of this study was to describe call centre nurses' adoption of a decision support protocol into practice following exposure to an implementation intervention and to identify factors influencing sustainable nursing practice changes.MethodsExploratory case study at a Canadian province-wide call centre guided by the Ottawa Model of Research Use. Data sources included a survey of nurses who participated in an implementation intervention (n = 31), 2 focus groups with nurses, interviews with 4 administrators, and a document review.ResultsTwenty-five of 31 nurses responded to the survey measuring adoption of decision support in practice. Of the 25 nurses, 11 had used the decision support protocol in their practice within one month of the intervention. Twenty-two of the 25 intended to use it within the next three months. Although some nurses found it challenging to begin using the protocol, most nurses reported that it: a) helped them recognize callers needing decision support; b) changed their approach to handling these calls; and c) was a positive enhancement to their practice. Strategies identified to promote sustainability of practice changes included integration of the decision support protocol in the call centre database, streamlining the patient decision aids for easier use via telephone, clarifying the administrative direction for the call centre's program, creating a call length guideline specific for these calls, incorporating decision support training in the staff development plan, and informing the public of this enhanced service.ConclusionAlthough most nurses adopted the decision support protocol for coaching callers facing values-sensitive decisions, to sustain practice changes, interventions are required to manage barriers in the practice environment and integrate decision support into the organization's policies, resources, and routine activities.