Economic evaluations of big data analytics for clinical decision-making

__Objective:__ Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. __Materials and Methods:__ We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term. __Results:__ The search yielded 12 133 papers but only 71 studies fulfilled all eligibi

Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis

Background: Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective: We aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment. Methods: A scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed. Results: Among 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies. Conclusions: Current studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology

Bridging the gap between evidence and what people value from osteoarthritis care in New Zealand using multi-criteria decision analysis (MCDA)

International clinical practice guidelines (CPG) for osteoarthritis (OA) consistently recommend core management strategies of exercise, weight-loss and self-management education. However, these interventions are not routinely delivered or taken up by people experiencing OA, resulting in a tendency to deliver low-value care. Tailoring the delivery of recommended OA care to the preferences of stakeholders in a health system may help support greater implementation of high-value OA care. However, little is known about the preferences of stakeholders for evidence and recommendations for OA care in a CPG. The aim of this research was to establish a framework to prioritise knee interventions for managing OA and evaluate the relative importance of these interventions across the healthcare sector in New Zealand (NZ) using multi-criteria decision analysis (MCDA). This research used a mixed-methods approach to develop the MCDA framework. The first stage involved focus group discussions to generate a thematic framework of what OA consumers, health care providers, policy-makers, Māori representatives and OA experts consider in their choice of knee OA interventions. A Delphi survey was used to verify the thematic analysis and rank the most important criteria concerning the characteristics of interventions; these findings informed the criteria selected in the second stage. In the second stage, interventions were indirectly prioritised by systematically combining preference data with intervention performance data on the criteria. First, a survey involving trade-offs was used to measure stakeholders’ preferences, represented as ‘weights’, within and between the criteria. Evidence for the performance of 15 recommended knee OA interventions were then extracted from a CPG for hip and knee OA, and rated on the criteria according to their level of achievement (e.g. high, medium, low). To prioritise the interventions, a total score for each intervention was calculated by summing the weights associated with the intervention ratings, after which they were ranked by importance. Associations between the weights and stakeholder groups were explored using regression analysis. Thematic analysis of data from six focus groups produced a framework comprising three overarching categories, consisting of characteristics of the: (i) intervention, (ii) consumer and (iii) health system. Participants identified and ranked nine characteristics of interventions; the most important eight were included in the MCDA framework. The choice-based survey revealed that stakeholders valued the intervention characteristics, in decreasing order of importance (weight): Recommendation (19.0%), Quality of evidence (17.7%), Effectiveness (15.0%), Duration of effect (13.2%), Risk of serious harm (12.8%), Risk of mild side-effects (9.4%), Cost (6.6%) and Accessibility (6.3%). Total scores for the 15 guideline-recommended interventions revealed that for first-, second- and third-line OA care respectively, all land-based exercise (total score= 71.7%), NSAIDs (topical) (74.2%) and total joint replacement (74.3%) were ranked first. For first-, second- and third-line OA care, the recommended core interventions of weight management and self-management education ranked between 11th and 15th (48.0% to 56.0%). Regression analysis identified only small differences in weights (≤5.7%; p<0.01) between stakeholder groups. These findings suggest that stakeholders’ preferences for the core interventions of weight management and self-management education represent a system-wide barrier to their implementation. This research addresses an important knowledge gap concerning cross-sectoral stakeholders’ preferences for knee OA interventions in a CPG. By inclusively, systematically and transparently incorporating what matters to people with evidence and recommendations in a CPG, the MCDA framework developed in this thesis can help support the design of patient-centred, high-value healthcare for people experiencing OA

Quality Indicators for Hospital Care

Abstract Hospital quality indicators are widely implemented for purposes such as accountability, transparency and the overarching aim of quality improvement. However, it is not clear whether currently used hospital quality indicators actually reflect quality of care. The aim of this thesis was to expand our knowledge on how to measure quality of hospital care with the focus on external comparison. I specifically investigated reliability and validity as these are key aspects of quality indicators. For reliability we focused on data quality, indicator definitions and statistical uncertainty. For the validity we focused on case-mix correction