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What are the mechanisms that support healthcare professionals to adopt assisted decision-making practice? A rapid realist review.

ABSTRACT: BACKGROUND:The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) establishes a right to legal capacity for all people, including those with support needs. People with disabilities have a legal right to be given the appropriate supports to make informed decisions in all aspects of their lives, including health. In Ireland, the Assisted Decision-Making (Capacity) Act (2015) ratifies the Convention and has established a legal framework for Assisted Decision Making (ADM). The main provisions of the Act are not yet implemented. Codes of Practice to guide health and social care professionals are currently being developed. Internationally, concerns are expressed that ADM implementation is poorly understood. Using realist synthesis, this study aims to identify Programme Theory (PT) that will inform ADM implementation in healthcare. METHODS:A Rapid Realist Review using collaborative methods was chosen to appraise relevant literature and engage knowledge users from Irish health and social care. The review was led by an expert panel of relevant stakeholders that developed the research question which asks, 'what mechanisms enable healthcare professionals to adopt ADM into practice?' To ensure the PT was inclusive of local contextual influences, five reference panels were conducted with healthcare professionals, family carers and people with dementia. PT was refined and tested iteratively through knowledge synthesis informed by forty-seven primary studies, reference panel discussions and expert panel refinement and consensus. RESULTS:The review has developed an explanatory PT on ADM implementation in healthcare practice. The review identified four implementation domains as significant. These are Personalisation of Health & ADM Service Provision, Culture & Leadership, Environmental & Social Re-structuring and Education, Training & Enablement. Each domain is presented as an explanatory PT statement using realist convention that identifies context, mechanism and outcome configurations. CONCLUSIONS:This realist review makes a unique contribution to this field. The PT can be applied by policymakers to inform intervention development and implementation strategy. It informs the imminent policy and practice developments in Ireland and has relevance for other worldwide healthcare systems dealing with similar legislative changes in line with UNCRPD.

SUBMITTER: Davies C

PROVIDER: S-EPMC6909502 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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What are the mechanisms that support healthcare professionals to adopt assisted decision-making practice? A rapid realist review.

Davies Carmel C Fattori Francesco F O'Donnell Deirdre D Donnelly Sarah S Ní Shé Éidín É O Shea Marie M Prihodova Lucia L Gleeson Caoimhe C Flynn Áine Á Rock Bernadette B Grogan Jacqueline J O'Brien Michelle M O'Hanlon Shane S Cooney Marie Therese MT Tighe Marie M Kroll Thilo T

BMC health services research 20191212 1

<h4>Background</h4>The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) establishes a right to legal capacity for all people, including those with support needs. People with disabilities have a legal right to be given the appropriate supports to make informed decisions in all aspects of their lives, including health. In Ireland, the Assisted Decision-Making (Capacity) Act (2015) ratifies the Convention and has established a legal framework for Assisted Decision Makin ...[more]

PMID: 31831003

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Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Project description:BackgroundIn this study, we sought to assess healthcare professionals' acceptance of and satisfaction with a shared decision making (SDM) educational workshop, its impact on their intention to use SDM, and their perceived facilitators and barriers to the implementation of SDM in clinical settings in Iran.MethodsWe conducted an observational quantitative study that involved measurements before, during, and immediately after the educational intervention at stake. We invited healthcare professionals affiliated with Tabriz University of Medical Sciences, East Azerbaijan, Iran, to attend a half-day workshop on SDM in December 2016. Decisions about prenatal screening and knee replacement surgery was used as clinical vignettes. We provided a patient decision aid on prenatal screening that complied with the International Patient Decision Aids Standards and used illustrate videos. Participants completed a sociodemographic questionnaire and a questionnaire to assess their familiarity with SDM, a questionnaire based on theoretical domains framework to assess their intention to implement SDM, a questionnaire about their perceived facilitators and barriers of implementing SDM in their clinical practice, continuous professional development reaction questionnaire, and workshop evaluation. Quantitative data was analyzed descriptively and with multiple linear regression.ResultsAmong the 60 healthcare professionals invited, 41 participated (68%). Twenty-three were female (57%), 18 were specialized in family and emergency medicine, or community and preventive medicine (43%), nine were surgeons (22%), and 14 (35%) were other types of specialists. Participants' mean age was 37.51 ± 8.64 years with 8.09 ± 7.8 years of clinical experience. Prior to the workshop, their familiarity with SDM was 3.10 ± 2.82 out of 9. After the workshop, their belief that practicing SDM would be beneficial and useful (beliefs about consequences) (beta = 0.67, 95% CI 0.27, 1.06) and beliefs about capability of using SDM (beta = 0.32, 95% CI -0.08, 0.72) had the strongest influence on their intention of practicing SDM. Participants perceived the main facilitator and barrier to perform SDM were training and high patient load, respectively.ConclusionsParticipants thought the workshop was a good way to learn SDM and that they would be able to use what they had learned in their clinical practice. Future studies need to study the level of intention of participants in longer term and evaluate the impact of cultural differences on practicing SDM and its implementation in both western and non-western countries.

Project description:Involvement of many variables, uncertainty in treatment response, and inter-patient heterogeneity challenge objective decision-making in dynamic treatment regime (DTR) in oncology. Advanced machine learning analytics in conjunction with information-rich dense multi-omics data have the ability to overcome such challenges. We have developed a comprehensive artificial intelligence (AI)-based optimal decision-making framework for assisting oncologists in DTR. In this work, we demonstrate the proposed framework to Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications by developing an interactive software tool entitled Adaptive Radiotherapy Clinical Decision Support (ARCliDS). ARCliDS is composed of two main components: Artifcial RT Environment (ARTE) and Optimal Decision Maker (ODM). ARTE is designed as a Markov decision process and modeled via supervised learning. Given a patient's pre- and during-treatment information, ARTE can estimate treatment outcomes for a selected daily dosage value (radiation fraction size). ODM is formulated using reinforcement learning and is trained on ARTE. ODM can recommend optimal daily dosage adjustments to maximize the tumor local control probability and minimize the side effects. Graph Neural Networks (GNN) are applied to exploit the inter-feature relationships for improved modeling performance and a novel double GNN architecture is designed to avoid nonphysical treatment response. Datasets of size 117 and 292 were available from two clinical trials on adaptive RT in non-small cell lung cancer (NSCLC) patients and adaptive stereotactic body RT (SBRT) in hepatocellular carcinoma (HCC) patients, respectively. For training and validation, dense data with 297 features were available for 67 NSCLC patients and 110 features for 71 HCC patients. To increase the sample size for ODM training, we applied Generative Adversarial Networks to generate 10,000 synthetic patients. The ODM was trained on the synthetic patients and validated on the original dataset. We found that, Double GNN architecture was able to correct the nonphysical dose-response trend and improve ARCliDS recommendation. The average root mean squared difference (RMSD) between ARCliDS recommendation and reported clinical decisions using double GNNs were 0.61 [0.03] Gy/frac (mean [sem]) for adaptive RT in NSCLC patients and 2.96 [0.42] Gy/frac for adaptive SBRT HCC compared to the single GNN's RMSDs of 0.97 [0.12] Gy/frac and 4.75 [0.16] Gy/frac, respectively. Overall, For NSCLC and HCC, ARCliDS with double GNNs was able to reproduce 36% and 50% of the good clinical decisions (local control and no side effects) and improve 74% and 30% of the bad clinical decisions, respectively. In conclusion, ARCliDS is the first web-based software dedicated to assist KBR-ART with multi-omics data. ARCliDS can learn from the reported clinical decisions and facilitate AI-assisted clinical decision-making for improving the outcomes in DTR.

Project description:BackgroundGiven the complexity and variety in treatment options for advanced chronic kidney disease (CKD), shared decision-making (SDM) can be a challenge. SDM is needed for making decisions that best suit patients' needs and their medical and living situations. SDM might be experienced differently by different stakeholders. This study aimed to explore clinical practice and perspectives on SDM in nephrology from three angles: observers, patients and healthcare professionals (HCPs).MethodsAn explanatory sequential mixed methods design was used. First, in the quantitative part of the study, outpatient consultations with patients with advanced chronic kidney disease (eGFR < 20 ml/min) were video recorded and SDM was assessed using the OPTION5 instrument. Subsequently, in the qualitative part, patients and HCPs reflected on their own SDM behaviour during individual stimulated recall interviews which were analysed using deductive thematic content analysis.ResultsTwenty nine consultations were recorded and observed in seven hospitals. The mean SDM score was 51 (range 25-80), indicating that SDM was applied to a moderate extent. The stimulated recall interviews with patients showed that they rely on the information provision and opinion of HCPs, expect consistency and support, and desire a proactive role. They also expect to be questioned by the HCP about their SDM preferences. HCPs said they were willing to incorporate patients' preferences in SDM, as long as there are no medical contraindications. They also prefer patients to take a prominent role in SDM. HCPs ascribe various roles to themselves in supporting patients' decision-making.ConclusionsAlthough SDM was applied by HCPs to a moderate extent, improvement is needed, especially in helping patients get the information they need and in making sure that every patient is involved in SDM. This is even more important given the complex nature of the disease and the relatively high prevalence of limited health literacy among patients with chronic kidney disease.

Project description:Importance: Early mobilization, out-of-bed activity, is a component of acute stroke unit care; however, stroke patient heterogeneity requires complex decision-making. Clinically credible and applicable CPGs are needed to support and optimize the delivery of care. In this study, we are specifically exploring the role of clinical practice guidelines to support individual patient-level decision-making by stroke clinicians about early mobilization post-stroke. Methods: Our study uses a novel, two-pronged approach. (1) A review of CPGs containing recommendations for early mobilization practices published after 2015 was appraised using purposely selected items from the Appraisal of Guidelines Research and Evaluation-Recommendations Excellence (AGREE-REX) tool relevant to decision-making for clinicians. (2) A cross-sectional study involving semi-structured interviews with Australian expert stroke clinicians representing content experts and CPG target users. Every CPG was independently assessed against the AGREE-REX standard by two reviewers. Expert stroke clinicians, invited via email, were recruited between June 2019 to March 2020.The main outcomes from the review was the proportion of criteria addressed for each AGREE-REX item by individual and all CPG(s). The main cross-sectional outcomes were the distributions of stroke clinicians' responses about the utility of CPGs, specific areas of uncertainty in early mobilization decision-making, and suggested parameters for inclusion in future early mobilization CPGs. Results: In 18 identified CPGs, many did not adequately address the "Evidence" and "Applicability to Patients" AGREE-REX items. Out of 30 expert stroke clinicians (11 physicians [37%], 11 physiotherapists [37%], 8 nurses [26%]; median [IQR] years of experience, 14 [10-25]), 47% found current CPGs "too broad or vague," while 40% rely on individual clinical judgement and interpretation of the evidence to select an evidence-based choice of action. The areas of uncertainty in decision-making revealed four key suggestions: (1) more granular descriptions of patient and stroke characteristics for appropriate tailoring of decisions, (2) clear statements about when clinical flexibility is appropriate, (3) detailed description of the intervention dose, and (4) physical assessment criteria including safety parameters. Conclusions: The lack of specificity, clinical applicability, and adaptability of current CPGs to effectively respond to the heterogeneous clinical stroke context has provided a clear direction for improvement.

Project description:BackgroundThis project engages patients and physicians in the development of Decision Boxes, short clinical topic summaries covering medical questions that have no single best answer. Decision Boxes aim to prepare the clinician to communicate the risks and benefits of the available options to the patient so they can make an informed decision together.MethodsSeven researchers (including four practicing family physicians) selected 10 clinical topics relevant to primary care practice through a Delphi survey. We then developed two one-page prototypes on two of these topics: prostate cancer screening with the prostate-specific antigen test, and prenatal screening for trisomy 21 with the serum integrated test. We presented the prototypes to purposeful samples of family physicians distributed in two focus groups, and patients distributed in four focus groups. We used the User Experience Honeycomb to explore barriers and facilitators to the communication design used in Decision Boxes. All discussions were transcribed, and three researchers proceeded to thematic content analysis of the transcriptions. The coding scheme was first developed from the Honeycomb's seven themes (valuable, usable, credible, useful, desirable, accessible, and findable), and included new themes suggested by the data. Prototypes were modified in light of our findings.ResultsThree rounds were necessary for a majority of researchers to select 10 clinical topics. Fifteen physicians and 33 patients participated in the focus groups. Following analyses, three sections were added to the Decision Boxes: introduction, patient counseling, and references. The information was spread to two pages to try to make the Decision Boxes less busy and improve users' first impression. To try to improve credibility, we gave more visibility to the research institutions involved in development. A statement on the boxes' purpose and a flow chart representing the shared decision-making process were added with the intent of clarifying the tool's purpose. Information about the risks and benefits according to risk levels was added to the Decision Boxes, to try to ease the adaptation of the information to individual patients.ConclusionResults will guide the development of the eight remaining Decision Boxes. A future study will evaluate the effect of Decision Boxes on the integration of evidence-based and shared decision making principles in clinical practice.

Project description:BackgroundWork participation is an important determinant of public health; being unemployed leads to a decrease in an individual's health. In the Netherlands, people with a work disability can apply for disability benefits, in which people also receive support to return to work (RTW). A method, currently used in the medical sector, that can include both the perspective of the reintegration professional and of the individual in the process of RTW, is shared decision making (SDM). In this article we explore to what extent reintegration professionals currently use SDM, and to what extent they prefer to use SDM in their ideal interaction with clients.MethodsWe performed semi-structured interviews with fourteen reintegration professionals from four different municipalities. The transcripts were coded according to content analysis, applying open and axial coding.ResultsReintegration professionals emphasised the importance of having a good relationship with clients, of building trust and collaborating as a team. They did not inform their clients that they could be part of the decision-making process, or discussed a shared goal. Although professionals did emphasise the importance of aligning their approach with the preferences of the client and though they tried to offer some choice options, they did not mention available options, discussed the pros and cons of these options or evaluated decisions with their clients. Furthermore, they did not mention any of these aspects in their ideal interaction with clients.ConclusionsSDM has a potential value, because all professionals underline the importance of having an alliance with clients, collaborating as a team, and striving to align their approach with the preferences of the client. However, professionals currently perform a limited set of SDM steps. Additional knowledge and skills are needed for both reintegration professionals and municipalities so that professionals can consider and reflect on the value of using SDM, or SDM steps, in supporting RTW. Providing clients with knowledge and skills seems necessary to facilitate both self-management and SDM.

Project description:BACKGROUND:Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds. OBJECTIVE:The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview. METHODS:A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models. RESULTS:The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015. CONCLUSIONS:The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

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What are the mechanisms that support healthcare professionals to adopt assisted decision-making practice? A rapid realist review.

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What are the mechanisms that support healthcare professionals to adopt assisted decision-making practice? A rapid realist review.

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