Catálogo de publicaciones - libros

Prediction models have the potential to positively influence clinical decision-making and thus the overall quality of healthcare. The translational gap needs to be bridged between development of complex statistical models requiring multiple predictors and widespread usage in clinical consultation. A recent review found that inadequate quality of reporting of prediction modelling studies could be a contributing factor in slow transition to the clinic. This chapter emphasises the importance of high-quality reporting of modelling studies and the need for critical appraisal to understand the potential issues limiting generalizability of published models. Evidence synthesis (such as systematic reviews and pooled analysis of disparate models) are relatively under-represented in literature, though methodological studies and guidelines are now starting to appear.

Clinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.

This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.

Mobile apps are an important source of data, but also an important tool for applying models. The goal of this chapter is to provide a short overview of relevant app development background including data collection tools, as well as provide a literature review on mobile clinical decision support systems. Regulatory issues will be touched upon to create awareness for this important topic.

Healthcare transformation is necessary to address rising costs of care. Operational Excellence can optimize care processes and create more value for each patient with the same resources. Operational Excellence uses different optimization tools in a continuous improvement (DMAIC-)cycle to optimize processes. In this regard, process mining can play an important role in discovering, analyzing and controlling care processes by making use of available data. Additionally, understanding the type of care process and care organization is important in order to choose the right Operational Excellence approach. Operational Excellence also includes a combination of leadership and work design (sociotechnical systems) which improves the success of process optimizations.

The involves measuring outcomes that matter and costs of care to optimize patient outcomes per dollar spent. Outcome and cost measurement in the value-based health care framework, centered around a patient condition or segment of the population, depends on data in every step towards healthcare system redesign. Technological and service delivery innovations are key components of driving transformation towards high-value health care. The learning health system and network-based thinking are complementary frameworks to the value agenda. Health care and medicine exist in a data-rich environment, and learning about how data can be used to measure and improve value of care for patients is and increasingly essential skill for current and future clinicians.