Publications

OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).

METHODS: As part of a quality improvement project, we simultaneously implemented three interventions: a domain-specific ontology, contextual autocomplete, and top five suggestions. Contextual autocomplete is a user interface that ranks concepts by their predicted probability which helps nurses enter data about a patient's presenting problems. Nurses were also given a list of top five suggestions to choose from. These presenting problems were represented using a consensus ontology mapped to SNOMED CT. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a mixed methods retrospective before-and-after study design.

RESULTS: A total of 279,231 consecutive patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (p < 0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; p = 0.0004) and higher in overall quality (3.38 vs. 3.72; p = 0.0002), but showed no difference in precision (3.59 vs. 3.74; p = 0.1). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p < 0.0001), a 95% improvement.

DISCUSSION: We demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 h to 4.8 h.

CONCLUSION: Implementation of a domain-specific ontology and machine learning-driven user interfaces resulted in improved structured data capture, ontology usage compliance, and data quality.

IMPORTANCE: Electronic health records allow teams of clinicians to simultaneously care for patients, but an unintended consequence is the potential for duplicate orders of tests and medications.

OBJECTIVE: To determine whether a simple visual aid is associated with a reduction in duplicate ordering of tests and medications.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used an interrupted time series model to analyze 184 694 consecutive patients who visited the emergency department (ED) of an academic hospital with 55 000 ED visits annually. Patient visits occurred 1 year before and after each intervention, as follows: for laboratory orders, from August 13, 2012, to August 13, 2014; for medication orders, from February 3, 2013, to February 3, 2015; and for radiology orders, from December 12, 2013, to December 12, 2015. Data were analyzed from April to September 2019.

EXPOSURE: If an order had previously been placed during the ED visit, a red highlight appeared around the checkbox of that order in the computerized provider order entry system.

MAIN OUTCOMES AND MEASURES: Number of unintentional duplicate laboratory, medication, and radiology orders.

RESULTS: A total of 184 694 patients (mean [SD] age, 51.6 [20.8] years; age range, 0-113.0 years; 99 735 [54.0%] women) who visited the ED were analyzed over the 3 overlapping study periods. After deployment of a noninterruptive nudge in electronic health records, there was an associated 49% decrease in the rate of unintentional duplicate orders for laboratory tests (incidence rate ratio, 0.51; 95% CI, 0.45-0.59), from 4485 to 2731 orders, and an associated 40% decrease in unintentional duplicate orders of radiology tests (incidence rate ratio, 0.60; 95% CI, 0.44-0.82), from 956 to 782 orders. There was not a statistically significant change in unintentional duplicate orders of medications (incidence rate ratio, 1.17; 95% CI, 0.52-2.61), which increased from 225 to 287 orders. The nudge eliminated an estimated 17 936 clicks in our electronic health record.

CONCLUSIONS AND RELEVANCE: In this interrupted time series cohort study, passive visual cues that provided just-in-time decision support were associated with reductions in unintentional duplicate orders for laboratory and radiology tests but not in unintentional duplicate medication orders.

OBJECTIVE: Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges.

MATERIALS AND METHODS: We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT).

RESULTS: Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set.

DISCUSSION AND CONCLUSION: We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

INTRODUCTION: Opioid abuse has reached epidemic proportions in the United States. Patients often present to the emergency department (ED) with painful conditions seeking analgesic relief. While there is known variability in the prescribing behaviors of emergency physicians, it is unknown if there are differences in these behaviors based on training level or by resident specialty.

METHODS: This is a retrospective chart review of ED visits from a single, tertiary-care academic hospital over a single academic year (2014-2015), examining the amount of opioid pain medication prescribed. We compared morphine milligram equivalents (MME) between provider specialty and level of training (emergency medicine [EM] attending physicians, EM residents in training, and non-EM residents in training).

RESULTS: We reviewed 55,999 total ED visits, of which 4,431 (7.9%) resulted in discharge with a prescription opioid medication. Residents in a non-EM training program prescribed higher amounts of opioid medication (108 MME, interquartile ratio [IQR] 75-150) than EM attendings (90 MME, lQR 75-120), who prescribed more than residents in an EM training program (75 MME, IQR 60-113) (p<0.01).

CONCLUSION: In an ED setting, variability exists in prescribing patterns with non-EM residents prescribing larger amounts of opioids in the acute setting. EM attendings should closely monitor for both over- and under-prescribing of analgesic medications.

BACKGROUND: The landscape of mobile devices is changing and their present use by patients for healthcare purposes is unknown. An understanding of current attitudes and usage may help increase patient engagement through mobile applications. This study sought to determine characteristics of mobile device ownership among Emergency Department patients, patients' feelings regarding their use in healthcare, and desired functionality in mobile applications.

METHODS: A cross-sectional survey was undertaken at a single urban tertiary care academic center. A convenience sample of adult English-speaking patients in the Emergency Department were surveyed from June 21 st, 2017 to December 30th, 2017. A secondary analysis of the data was performed based on demographic and socioeconomic factors.

RESULTS: 260 patients were approached for participation, 11 patients declined, and one patient was excluded. The 248 participants had a median age of 49 (interquartile range 28-62) and 54% were female. 91% of those surveyed own smartphones, 58% owned tablets, and 77% of these patients were comfortable using mobile devices. Those without mobile devices were older (p < 0.001) and held less commercial insurance (p = 0.01). A majority of patients were interested in using applications to enter information, track their visit, view results, and communicate with providers during their visit. Following care, there is interest in viewing information about their visit and receiving reminders for appointments and medications. Patients are also interested in using applications for learning about medical conditions and managing medications. Though there are mixed feelings regarding the protection of privacy by apps, they are felt to be safe, effective, useful, and not difficult to use.

CONCLUSION: Ownership of smartphones is high across the Emergency Department population and patients are enthusiastic about using mobile devices as part of their care. Further study can elucidate opportunities to further integrate mobile device applications into patient care.

OBJECTIVES: To characterize emergency department sedation practices in mechanically ventilated patients, and test the hypothesis that deep sedation in the emergency department is associated with worse outcomes.

DESIGN: Multicenter, prospective cohort study.

SETTING: The emergency department and ICUs of 15 medical centers.

PATIENTS: Mechanically ventilated adult emergency department patients.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: All data involving sedation (medications, monitoring) were recorded. Deep sedation was defined as Richmond Agitation-Sedation Scale of -3 to -5 or Sedation-Agitation Scale of 2 or 1. A total of 324 patients were studied. Emergency department deep sedation was observed in 171 patients (52.8%), and was associated with a higher frequency of deep sedation in the ICU on day 1 (53.8% vs 20.3%; p < 0.001) and day 2 (33.3% vs 16.9%; p = 0.001), when compared to light sedation. Mean (SD) ventilator-free days were 18.1 (10.8) in the emergency department deep sedation group compared to 20.0 (9.8) in the light sedation group (mean difference, 1.9; 95% CI, -0.40 to 4.13). Similar results according to emergency department sedation depth existed for ICU-free days (mean difference, 1.6; 95% CI, -0.54 to 3.83) and hospital-free days (mean difference, 2.3; 95% CI, 0.26-4.32). Mortality was 21.1% in the deep sedation group and 17.0% in the light sedation group (between-group difference, 4.1%; odds ratio, 1.30; 0.74-2.28). The occurrence rate of acute brain dysfunction (delirium and coma) was 68.4% in the deep sedation group and 55.6% in the light sedation group (between-group difference, 12.8%; odds ratio, 1.73; 1.10-2.73).

CONCLUSIONS: Early deep sedation in the emergency department is common, carries over into the ICU, and may be associated with worse outcomes. Sedation practice in the emergency department and its association with clinical outcomes is in need of further investigation.