Mary is a 73-year-old female who presents to her primary care physician with weakness and reduced oral intake for the past 3 months, Mary is admitted to the medical inpatient unt. Due to her recent admission, there is ímited information in the medical record. Anthropometric Data: Biochemical Data Clinical Data Height 160 cm (63) Sodium 119(135-145 mEq L) Past Medical History: Weight : 65kg (143 lbs) Potassium 34(0.6-5.0mEqL) Hypertension, osteoporosis BMI: 25.4 kg/m2 Blood urea nitrogen 28 (6.24 Medications: Lisinopril , Weight history medl) alendronate 66.5kg (145 lbs) 1 month ago Creatinme 0.5(0.4-1.3 mg/dL) Vital Signs: Blood pressure 70 kg (154 lbs) 3 months ago Glucose 105 (70-99 mg d.) 100/70 mm Hg . Temperature 77 kg (169 lbs) 6 months ago Albumin 2.903.5-5.0 g/dL) 99 F, Heart rate 85 beats/min (usual body weight Total cholesterol 150mg d. (Desirable 200mg d.) Questions 1. To determine whether Mary warrants immediate nutrition assessment , what additional information from the patient or the medical record would you like to consider? 2. What validated screening tool(s) would be appropriate to use to determine Mary's nutrition risk? 3. What specific information do you need to determine whether Mary has malnutrition?
Answers
Answer:
Background: Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However the use of EMRs in research has been impeded by lack of standardization of EMRs systems, data access and concerns about the quality of the data.
Objectives: The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes.
Methods: A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Records Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the WHO Growth Standards and Reference.
Results: A total of 54,964 children were identified from EMRALD. Overall, 93% had at least 1 complete set of growth measurements to calculate a BMI z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.
Conclusions: Data completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care.
Keywords: Body Mass Index; Child; Data Accuracy; Electronic Health Records; Obesity
Answer:
Data: Biochemical Data Clinical Data Height 160 cm (63) Sodium 119(135-145 mEq L) Past Medical History: Weight : 65kg (143 lbs) Potassium 34(0.6-5.0mEqL) Hypertension, osteoporosis BMI: 25.4 kg/m2 Blood urea nitrogen 28 (6.24 Medications: Lisinopril , Weight history medl) alendronate 66.5kg (145 lbs) 1 month ago Creatinme 0.5(0.4-1.3 mg/dL) Vital Signs: Blood pressure 70 kg (154 lbs) 3 months ago Glucose 105 (70-99 mg d.) 100/70 mm Hg