Biology subjects

Calder, S.

Publications and source records attributed to Calder, S..

3 recordsLinked to original sources

bioRxiv2021

Validation of non-invasive body-surface gastric mapping for detecting electrophysiological biomarkers by simultaneous high-resolution serosal mapping in a porcine model

Gastric disorders are increasingly prevalent, but reliable clinical tools to objectively assess gastric function are lacking. Body-surface gastric mapping (BSGM) is a non-invasive method for the detection of gastric electrophysiological biomarkers including slow wave direction, which have correlated with symptoms in patients with gastroparesis and functional dyspepsia. However, no studies have validated the relationship between gastric slow waves and body surface activation profiles. This study aimed to comprehensively evaluate the relationship between gastric slow waves and body-surface recordings. High-resolution electrode arrays were placed to simultaneously capture slow waves from the gastric serosa (32x6 electrodes at 4 mm resolution) and abdominal surface (8x8 at 20 mm inter-electrode spacing) in a porcine model. BSGM signals were extracted based on a combination of wavelet and phase information analyses. A total of 1185 individual cycles of slow waves assessed, out of which 897 (76%) were normal antegrade waves, occurring in 10/14 (71%) subjects studied. BSGM accurately detected the underlying slow wave in terms of frequency (r = 0.99, p = 0.43) as well as the direction of propagation (p = 0.41, F-measure: 0.92). In addition, the cycle-by-cycle match between BSGM and transitions of gastric slow waves in terms either or both temporal and spatial abnormalities was demonstrated. These results validate BSGM as a suitable method for non-invasively and accurately detecting gastric slow wave activation profiles from the body surface. Single sentence summarySimultaneous recordings of the stomach using serosal and body-surface electrode arrays demonstrated reliable detection of frequency and classification of propagation.

physiology↗

bioRxiv2019

Reducing Unintentional Duplicate Orders using Passive Inline Just-in-Time Decision Support

ImportanceElectronic health records (EHRs) allow teams of clinicians to simultaneously care for patients but an unintended consequence could result in duplicate ordering of tests and medications.\n\nObjectiveWe asked if a simple visual aid would reduce duplicate ordering of tests and medications for busy teams of clinicians in our emergency department by placing a red highlight around the checkbox of a computer-based order if previously ordered.\n\nDesignWe performed an interrupted time series to analyze all patient visits 1 year before and 1 year after the intervention. Significance testing was performed using a negative binomial regression with Newey-West standard errors, correcting for patient level variables and environmental variables that might be associated with duplicate orders.\n\nSettingThe emergency department of an academic hospital in Boston, MA with 55,000 visits annually.\n\nParticipants184,722 consecutive emergency department patients.\n\nExposureIf an order had previously been placed during that ED visit, we cue the user by showing a red highlight around the checkbox of that order.\n\nMain OutcomeNumber of unintentional duplicate orders.\n\nResultsAfter deployment of the non-interrupting nudge, the rate of unintentional duplicates for laboratory orders decreased 49% (incidence rate ratio 0.51, 95% CI 0.45-0.59) and for radiology orders decreased with an incidence rate ratio of 0.60 (0.44-0.82). There was no change in unintentional medication duplicate orders. We estimated that the nudge eliminated 17,936 clicks in our EHR.\n\nConclusions and RelevancePassive visual queues that provide just-in-time decision support are effective, not disruptive of workflow, and may decrease alert fatigue in busy clinical environments.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSCan a simple visual aid reduce duplicate ordering in an electronic health record?\n\nFindingsIn this interrupted time series, the rate of unintentional duplicates for laboratory orders decreased 49% and for radiology orders decreased 40%. There was no change in unintentional medication duplicate orders. We estimated that the nudge eliminated 17,936 clicks in our EHR.\n\nMeaningQuality improvement often relies on changing clinician behavior. We believe guiding clinicians to a right action is better than telling the clinician they have already made an error. Our approach will help reduce alert fatigue and lessen clinician complaints about EHRs.

bioinformatics↗

bioRxiv2017

Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department

ObjectiveTo determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).\n\nMaterials and MethodsWe used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patients reason for visiting the ED. 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 prospective before-and-after study design.\n\nResultsA total of 279,231 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), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). 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.\n\nDiscussionWe have 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 hours to 4.8 hours.\n\nConclusionImplementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.

bioinformatics↗