Pre-hospital and Hospital Based Interventions

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Pre-hospital and Hospital Based Interventions

Pre-hospital and Hospital Based Interventions

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Analytics

As with all quality improvement initiatives such as the sepsis example above, measurement is critical to defining success. This would include demonstration of clinical, operational, and financial measures (including process and outcome measures for those domains) for quantifying value in clinical standards work. The science of informatics (data plus meaning), as it relates to pediatrics, must target population health (including ED care within a continuum) It must simultaneously address the rising costs associated with implementation and maintenance of computerized systems of care coordination, while at the same time contribute towards excellence in patient care.45 Analytics (data plus information) plays a key role in predictive assessment, clinical decision support, and various patient throughput measures.46 To illustrate this, an initiative to create and implement clinical standards for asthma included EMSC related activities comprised of prehospital and hospital based interventions that included early steroid delivery; standardization of scoring scales and pathways linking protocols for care; standardization of first line, adjunct, and second line therapies; standardization of asthma action plans; and control medication for persistent asthma from any acute venue of care including emergent care settings. One component of the bundle driven by the ED is illustrated in Figure 2A, where efforts to decrease orders for unnecessary chest radiographs was targeted. Comprehensive guidelines for care were implemented with education, CDS, dashboard dissemination, and enhanced communication strategies that included components pertinent to the ED. As this bundle of activities aligned with the asthma guideline also included inpatient, critical care, and outpatient activities, the resultant decreases in length of stay, reductions in unnecessary test ordering, reductions in readmissions, and other improvements in clinically relevant quality metrics led to a decrease in cost of care for the population of thousands of children with asthma treated in our enterprise and is demonstrated in Figure 2B.

In order to support the analytics capabilities necessary to demonstrate improvements from clinical standards, health care systems must drive increasing . To meet this demand, data systems must move from simple data gathering and reporting, as can be done from a patient EMR report at the bedside, to aggregating and analyzing data in populations or themes (data analytics), to predicting patients at risk (predictive analytics), or linking health observation with health knowledge to influence clinical decisions (prescriptive analytics or clinical decision support).47 (See Figure 3).

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