Developing evaluation capacities in integrated care projects: Lessons from a scientific support mission implemented in Belgium

The capacity of self-assessment, to learn from experience, to make information-based decisions, and to adapt over time are essential drivers of success for any project aiming at healthcare system change. Yet, many of those projects are managed by healthcare providers' teams with little evaluation capacity. In this article, we describe the support mission delivered by an interdisciplinary scientific team to 12 integrated care pilot projects in Belgium, mobilizing a set of tools and methods: a dashboard gathering population health indicators, a significant event reporting method, an annual report, and the development of a sustainable "learning community." The article provides a reflexive return on the design and implementation of such interventions aimed at building organizational evaluation capacity. Some lessons were drawn from our experience, in comparison with the broader evaluation literature: The provided support should be adapted to the various needs and contexts of the beneficiary organizations, and it has to foster experience-based learning and requires all stakeholders to adopt a learning posture. A long-time, secure perspective should be provided for organizations, and the availability of data and other resources is an essential precondition for successful work.

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

The evidence-based medicine allows the physician to evaluate the risk-benefit ratio of a treatment through setting and data. Risk-based choices can be done by the doctor using different information. With the emergence of new technologies, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. Machine learning is an ensemble of methods that process data to model a learning problem. Supervised machine learning algorithms consist in using annotated data to construct the model. This category allows to solve prediction data analytics problems. In this paper, we detail the use of supervised machine learning algorithms for predictive data analytics problems in medicine. In the medical field, data can be split into two categories: medical images and other data. For brevity, our review deals with any kind of medical data excluding images. In this article, we offer a discussion around four supervised machine learning approaches: information-based, similarity-based, probability-based and error-based approaches. Each method is illustrated with detailed cardiovascular and nuclear medicine examples. Our review shows that model ensemble (ME) and support vector machine (SVM) methods are the most popular. SVM, ME and artificial neural networks often lead to better results than those given by other algorithms. In the coming years, more studies, more data, more tools and more methods will, for sure, be proposed.