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Medical encounters require an efficient and focused history of present illness (HPI) to create differential diagnoses and guide diagnostic testing and treatment. Our aim was to compare the HPI of notes created by an automated digital intake tool versus standard medical notes created by clinicians.Prospective trial in a quaternary academic Emergency Department (ED). Notes were compared using the 5-point Physician Documentation Quality Instrument (PDQI-9) scale and the Centers for MedicareMedicaid Services (CMS) level of complexity index. Reviewers were board certified emergency medicine physicians blinded to note origin. Reviewers received training and calibration prior to note assessments. A difference of 1 point was considered clinically significant. Analysis included McNemar's (binary), Wilcoxon-rank (Likert), and agreement with Cohen's Kappa.A total of 148 ED medical encounters were charted by both digital note and standard clinical note. The ability to capture patient information was assessed through comparison of note content across paired charts (digital-standard note on the same patient), as well as scores given by the reviewers. Reviewer agreement was kappa 0.56 (CI 0.49-0.64), indicating moderate level of agreement between reviewers scoring the same patient chart. Considering all 18 questions across PDQI-9 and CMS scales, the average agreement between standard clinical note and digital note was 54.3% (IQR 44.4-66.7%). There was a moderate level of agreement between content of standard and digital notes (kappa 0.54, 95%CI 0.49-0.60). The quality of the digital note was within the 1 point clinically significant difference for all of the attributes, except for conciseness. Digital notes had a higher frequency of CMS severity elements identified.Digitally generated clinical notes had moderate agreement compared to standard clinical notes and within the one point clinically significant difference except for the conciseness attribute. Digital notes more reliably documented billing components of severity. The use of automated notes should be further explored to evaluate its utility in facilitating documentation of patient encounters.

To test the equivalence of two fetal weight estimation formulas generated by Hadlock, a formula that includes head circumference parameter (H1), and another (H2) which excludes this parameter. A secondary aim was to identify the patients in which H2 formula is less reliable to use.This retrospective cohort study included a total of 1220 sonographic fetal weight estimations performed within seven days of delivery and recorded at a single medical center from January 2014 to December 2016. Estimated fetal weight was calculated using H1 and H2 formulas. Their accuracies were compared using percentage error, the proportion of weight estimations falling within ±15% error interval and by Bland-Altman analysis. Multivariate regression was performed to evaluate factors affecting weight estimation by H2 formula.The mean birth weight was 3288.92 ± 641.27gr. The H2 formula presented with statistically significant higher value of systemic mean percent error comparing to H1 (3.19% vs. 1.87%, p 0.001 respectively). H2 formula had a lower accuracy compared to H1 in predicting fetal weight within ±15% of birth weight (90.49% vs. 93.44%, p 0.01 respectively). Using Bland-Altman analysis, the 95% limits of agreement between both formulas were (-142.03) to 231.79gr with a mean of 44.88gr. Factors found to influence significantly on H2 formula were long femur length (OR 1.144, p 0.0001) and low maternal age (OR 0.947, p 0.01).H1formula was more accurate than H2 formula in predicting fetal weight at term. However, the accuracy difference was found to be small. Therefore, if ultrasonographic evaluation of HC is technically difficult, Hadlock formula that excludes head circumference can be used with confidence. Caution should be paid with higher values of femur length and lower maternal age.