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Background: Measuring and improving outcomes is a central element of value-based health care. However, selecting improvement interventions based on outcome measures is complex and tools to support the selection process are lacking. The goal was to present strategies for the systematic identification and selection of improvement interventions applied to the case of aortic valve disease and to combine various methods of process and outcome assessment into one integrated approach for quality improvement., Methods: For this case study a concept-driven mixed-method approach was applied for the identification of improvement intervention clusters including: (1) benchmarking outcomes, (2) data exploration, (3) care delivery process analysis, and (4) monitoring of ongoing improvements. The main outcome measures were long-term survival and 30-day mortality. For the selection of an improvement intervention, the causal relations between the potential improvement interventions and outcome measures were quantified followed by a team selection based on consensus from a multidisciplinary team of professionals., Results: The study resulted in a toolbox: the Intervention Selection Toolbox (IST). The toolbox comprises two phases: (a) identifying potential for improvement, and (b) selecting an effective intervention from the four clusters expected to lead to the desired improvement in outcomes. The improvements identified for the case of aortic valve disease with impact on long-term survival in the context of the studied hospital in 2015 include: anticoagulation policy, increased attention to nutritional status of patients and determining frailty of patients before the treatment decision., Conclusions: Identifying potential for improvement and carefully selecting improvement interventions based on (clinical) outcome data demands a multifaceted approach. Our toolbox integrates both care delivery process analyses and outcome analyses. The toolbox is recommended for use in hospital care for the selection of high-impact improvement interventions.

Background: Value-based healthcare (VBHC) is a concept that focuses on outcome measurement to contribute to quality improvement. However, VBHC does not offer a systematic approach for implementing improvement as implementation science does. The aim is to, firstly, investigate the implementation of improvement initiatives in the context of VBHC and secondly, to explore how implementation science could be of added value for VBHC and vice versa., Methods: A case study with two cases in heart care was conducted; one without the explicit use of a systematic implementation method and the other one with the use of the Implementation of Change Model (ICM). Triangulation of data from document research, semi-structured interviews and a focus group was applied to evaluate the degree of method uptake. Interviews were held with experts involved in the implementation of Case 1 (N = 4) and Case 2 (N = 7). The focus group was held with experts also involved in the interviews (N = 4). A theory-driven qualitative analysis was conducted using the ICM as a framework., Results: In both cases, outcome measures were seen as an important starting point for the implementation and for monitoring change. Several themes were identified as most important: support, personal importance, involvement, leadership, climate and continuous monitoring. Success factors included intrinsic motivation for the change, speed of implementation, complexity and continuous evaluation., Conclusion: Application of the ICM facilitates successful implementation of quality- improvement initiatives within VBHC. However, the practical use of the ICM shows an emphasis on processes. We recommend that monitoring of outcomes be added as an essential part of the ICM. In the discussion, we propose an implementation model that integrates ICM within VBHC.

Value Based competition in Health Care (VBHC) has become a guiding principle in the quest for high quality health care for acceptable costs. Current literature lacks substantial ethical evaluation of VBHC.In this paper we describe how a single-minded focus on VBHC may cause serious infringements upon at least four medical ethical principles: 1) it tends to neglect patients' personal values; 2) it ignores the intrinsic value of the caring act; 3) it disproportionately replaces trust in professionals with accountability, and 4) it undermines solidarity.Health care needs a next step in VBHC. We suggest a 'Values-Driven Health Care' (VDHC) approach that a) takes patients' personal values as prescriptive and guiding; b) holds a value account that encompasses health care's intrinsic (gift) values; c) is based upon intelligent accountability that supports trust in trustworthy professionals, and d) encourages patients to raise their voices for the shared good of health care.

Background: Using outcome measures to advance healthcare continues to be of widespread interest. The goal is to summarize the results of studies which use outcome measures from clinical registries to implement and monitor QI initiatives. The second objective is to identify a) facilitators and/or barriers that contribute to the realization of QI efforts, and b) how outcomes are being used as a catalyst to change outcomes over time., Methods: We searched the PubMed, EMBASE and Cochrane databases for relevant articles published between January 1995 and March 2017. We used a standardized data abstraction form. Studies were included when the following three criteria were fulfilled: 1) they relied on structural data collection, 2) when a structural and comprehensive QI intervention had been implemented and evaluated, and 3) impact on improving clinical and/or patient-reported outcomes was described. Data on QI strategies, QI initiatives and the impact on outcomes was extracted using standardized assessment tools., Results: We included 21 articles, of which eight showed statistically significant improvements on outcomes using data from clinical registries. Out of these eight studies, the Chronic Care Model, IT application as feedback, benchmarking and the Collaborative Care Model were used as QI methods. Encouraging trends in realizing improved outcomes through QI initiatives were observed, ranging from improving teamwork, implementation of clinical guidelines, implementation of physician alerts and development of a decision support system. Facilitators for implementing QI initiatives included a high quality database, audits, frequent reporting and feedback, patient involvement, communication, standardization, engagement, and leadership., Conclusion: This review suggests that outcomes collected in clinical registries are supportive to realize QI initiatives. Organizational readiness and an active approach are key in achieving improved outcomes.

Background: Uniform criteria for performing hip replacement surgery in hip osteoarthritis patients are currently lacking. As a result, variation in surgery and inappropriateness of care may occur. The aim of this study was to develop a consensus-based decision tool to support the decision-making process for hip replacement surgery.Methods: Patients with a diagnosis of unilateral or bilateral osteoarthritis were included. Consensus rounds with orthopedic surgeons were organized to blindly reassess medical files and to decide whether surgery is indicated or not, based on all available pre-treatment information. We compared the outcomes obtained from the blind reassessment by the consensus group with the actual treatment. Furthermore, prediction models were fitted on the reassessment outcome to identify which set of clinical parameters would be most predictive and uniformly shared in the decision to operate.Two prediction models were fitted, one model without radiologic outcomes and one model where radiologic outcomes were included.Results: In total, 364 medical files of osteoarthritis patients were included and reassessed in the analyses. Key predictors in the prediction model without radiology were age, flexion, internal rotation and the Hip disability and Osteoarthritis Outcome Score-quality of life. The discriminative power was high (Area Under Receiver Operating Curve (AUC) = 0.86). Key predictors in the prediction model with radiology were age, internal rotation and Kellgren and Lawrence severity score (AUC = 0.94).Conclusion: The study yielded a decision tool with uniform criteria for hip replacement surgery in osteoarthritis patients. The tool will guide the clinical decision-making process of physicians on whether to perform hip surgery and should be used together with information about patient preferences and social context. [ABSTRACT FROM AUTHOR]