Optimal data systems: the future of clinical predictions and decision support

Purpose of reviewThe purpose of the review is to describe the evolving concept and role of data as it relates to clinical predictions and decision-making. Recent findingsCritical care medicine is, as an especially data-rich specialty, becoming acutely cognizant not only of its historic deficits in data utilization but also of its enormous potential for capturing, mining, and leveraging such data into well-designed decision support modalities as well as the formulation of robust best practices. SummaryModern electronic medical records create an opportunity to design complete and functional data systems that can support clinical care to a degree never seen before. Such systems are often referred to as ‘data-driven,’ but a better term is ‘optimal data systems’ (ODS). Here we discuss basic features of an ODS and its benefits, including the potential to transform clinical prediction and decision support.

[1]  Marzyeh Ghassemi,et al.  Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. , 2014, Chest.

[2]  João Miguel da Costa Sousa,et al.  Reducing unnecessary lab testing in the ICU with artificial intelligence , 2013, Int. J. Medical Informatics.

[3]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[4]  L. Tarassenko,et al.  Dynamic Data During Hypotensive Episode Improves Mortality Predictions Among Patients With Sepsis and Hypotension* , 2013, Critical care medicine.

[5]  R. Steinbrook.  Improving clinical practice guidelines. , 2014, JAMA internal medicine.

[6]  David A Cook,et al.  Barriers and decisions when answering clinical questions at the point of care: a grounded theory study. , 2013, JAMA internal medicine.

[7]  J. Vincent.  Give your patient a fast hug (at least) once a day* , 2005, Critical care medicine.

[8]  M. Westphal.  Get to the point in intensive care medicine - the sooner the better? , 2013, Critical Care.

[9]  J. Doyle,et al.  Bow Ties, Metabolism and Disease , 2022 .

[10]  Thanh N. Huynh,et al.  The frequency and cost of treatment perceived to be futile in critical care. , 2013, JAMA internal medicine.

[11]  Michael J. Breslow,et al.  Readmissions and Death after ICU Discharge: Development and Validation of Two Predictive Models , 2012, PloS one.

[12]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[13]  P. Pronovost,et al.  Improving communication in the ICU using daily goals. , 2003, Journal of critical care.

[14]  Leo Anthony Celi,et al.  Dynamic Clinical Data Mining: Search Engine-Based Decision Support , 2014, JMIR medical informatics.

[15]  David J. Stone,et al.  "Big data" in the intensive care unit. Closing the data loop. , 2013, American journal of respiratory and critical care medicine.