Developing Pediatric Data Standards

Abstract

A major goal of Electronic Health Record (EHR) systems is to collect, store, and make available high-quality clinical information to healthcare providers whenever and wherever it is needed.1 This clinical information can help inform decision making, as well as supply data for research and drive quality assessment. To support clinical decision making, EHR systems typically make available many types of clinical information, including lists of a patient's allergies, medications, or diagnoses, the results from laboratory testing and narrative documents expressing healthcare providers' clinical observations and impressions.2–5 The methods used by EHR systems to aggregate and display clinical information can impact healthcare providers' workflow and decisions related to patient care.2,5,6 For example, a single medical center may use more than one laboratory for testing blood, including a central laboratory and one in the emergency department. In the case that the child with abdominal pain has her blood tested for inflammation in the emergency department, and then has the same test in clinic the next day as follow-up with her physician, the physician would likely expect that the results are aggregated together in the EHR system. If the results from the emergency department are not easily seen alongside the results from the follow-up testing, the physician may not see them and may request additional subsequent testing and withhold any change in therapy while awaiting those results. A physician concerned that the abdominal pain may be due to a serious illness, such as appendicitis, may obtain a CT scan or ultrasound rather than waiting for a third blood test result. If, by contrast, the results are all displayed together in the EHR system, the physician will be able to make decisions and prescribe the necessary therapies more efficiently.KeywordsElectronic Health RecordHealth Information TechnologyGrowth ChartElectronic Medical Record SystemSemantic StandardThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.