Principal Findings. The three patients groups chose health care providers on a different basis. The most valued attributes were effectiveness and safety (knee arthrosis); continuity of care and relationship with the therapist (chronic depression); and expertise (Alzheimer's disease). Preferences differed between subgroups, mainly in relation to patients' choice profiles, severity of disease, and some background characteristics.Conclusions. This study showed that there is substantial room for (quality) information about health care providers in patients' decision processes. This information should be tailor-made, targeting specific patient segments, because different actors and factors play a part in their search and selection process.

SummaryEuropean countries have enhanced the scope of private provision within their health care systems. Privatizing services have been suggested as a means to improve access, quality, and efficiency in health care. This raises questions about the relative performance of private hospitals compared with public hospitals. Most systematic reviews that scrutinize the performance of the private hospitals originate from the United States. A systematic overview for Europe is nonexisting. We fill this gap with a systematic realist review comparing the performance of public hospitals to private hospitals on efficiency, accessibility, and quality of care in the European Union. This review synthesizes evidence from Italy, Germany, the United Kingdom, France, Greece, Austria, Spain, and Portugal. Most evidence suggests that public hospitals are at least as efficient as or are more efficient than private hospitals. Accessibility to broader populations is often a matter of concern in private provision: Patients with higher social‐economic backgrounds hold better access to private hospital provision, especially in private parallel systems such as the United Kingdom and Greece. The existing evidence on quality of care is often too diverse to make a conclusive statement. In conclusion, the growth in private hospital provision seems not related to improvements in performance in Europe. Our evidence further suggests that the private (for‐profit) hospital sector seems to react more strongly to (financial) incentives than other provider types. In such cases, policymakers either should very carefully develop adequate incentive structures or be hesitant to accommodate the growth of the private hospital sector.

Background Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records. Methods We approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which uses features extracted from the free texts of the medical records (keyword model), and to doctors’ performance on a similar task as described in scientific literature. Results Both doctors and the baseline model were correct in 20% of the cases, taking a margin of 33% around the actual life expectancy as the target. The keyword model, in comparison, attained an accuracy of 29% with its prognoses. While doctors overestimated life expectancy in 63% of the incorrect prognoses, which harms anticipation to appropriate end-of-life care, the keyword model overestimated life expectancy in only 31% of the incorrect prognoses. Conclusions Prognostication of life expectancy is difficult for humans. Our research shows that machine learning and natural language processing techniques offer a feasible and promising approach to predicting life expectancy. The research has potential for real-life applications, such as supporting timely recognition of the right moment to start Advance Care Planning.

From previous work, we know that medical practice varies widely, and that unwarranted variation signals low value for patients and society. We also know that public reporting helps to create awareness of the need for quality improvement. Despite the availability of rich data, most Western countries have no routine surveillance of the geographic distribution of utilization, costs, and outcomes of healthcare, including trends in variation over time. This paper highlights the role of transparent public reporting as a necessary first step to spark change and reduce unwarranted variation. Two recent examples of public reporting are presented to illustrate possible ways to reduce unwarranted variation and improve care. We conclude by introducing the Value Improvement Cycle, which underscores that reporting is only a necessary first step, and suggests a path toward developing a multi-stakeholder approach to change.