Perioperative care has a direct and crucial impact on patient safety and patient outcomes, as well as the financial viability of the healthcare facility. The time pressure and workload of caring patients facing surgery are heavier than caring inpatients of other departments. This workload raises requirements for PreOp nurses, the primary PreOp caregiver, to complete information gathering, screening, and verification tasks accurately and efficiently. EHRs (Electronic Health Record System) have evolved continuously with increasing features to meet newly raised needs and expectations. Many healthcare institutions have undergone EHR conversion since the introduction of first-generation EHRs. Thus, the need for a systematic evaluation of changed information system workflow following conversion is becoming more and more manifest. There are a growing number of methods for analyzing health information technology use. However, few studies provide and apply a standard method to understand the impact of EHR transition and inspire opportunities for improvement. This dissertation focuses on PreOp nurse’s EHR use in PreOp settings. The goals of this dissertation are to: (a) introduce a systematic framework to evaluate EHR-mediated workflow and the impact of the EHR transition; (b) understand the impact of different EHR systems on PreOp nurse’s workflow and preoperative care efficiency; (c) transform the evaluation results into practical user-centered EHR designs. This research draws on computational ethnography, cognitive engineering process and user-centered design concepts to build a practical approach for EHR transition-related workflow evaluation and optimization. Observational data were collected before and after a large-scale EHR conversion throughout Mayo Clinic’s different regional health systems. For a structured computational evaluation framework, the time-efficiency of PreOp nurses’ work were compared quantitatively by means of coding and segmenting nurses’ tasks. Interview data provided contextual information, reflecting practical challenges and opportunities before and after the EHR transition. The total case time, the time spent on EHR, and the task fragmentation were improved after converting to the new EHR system. A trend of standardization of information-related workflow and EHR transition was observed. Notably, the approach helped to identify current new system challenges and pointed out potential optimization solutions.

This longitudinal research study addresses the correlations between infant feeding status in relation to infant temperament, maternal postpartum depression, and weight changes over time in a lower income, diverse population sample. The majority of participants were enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in Mesa, Arizona. Each participant was recruited in their third trimester and followed for the infants' first six months of life. A series of four home visits were made in which demographic surveys, temperament questionnaires, postpartum depression scales, weight changes over time, fecal, vaginal, saliva, and breast milk samples were taken. Due to the extensive process of extracting and sequencing of the microbiome samples, data has yet to be collected from the fecal, vaginal, saliva, and breast milk samples and were not included in this study. Correlational and descriptive statistics were run on the qualitative data including various demographic questions, total temperament, depression, and healthy eating index scores. It was found that lower socioeconomic factors contributed to more difficult infant temperaments, mothers who breastfed had higher post-delivery postpartum depression scores, and mothers with healthier eating indexes formula or mixed fed their infants. The findings from this data will be compared to the incoming microbiome data to examine the role of the microbiome of mothers and infants and its effects on infant temperament, postpartum depression, and weight changes over time.

The purpose of this secondary data-analysis was to explore the factors that predict a healthy birth outcome and a healthful postpartum weight for Mexican American mothers. There is a need to educate women who are overweight and obese that in order to have a healthier pregnancy and delivery they need to reduce their BMI. Demographic factors, the frequency of pregnancy complications, the weight and BMI of the mother, and Gestational Weight Gain were all factors investigated. These factors were investigated to determine parameters to obtain healthy birth weights in Mexican American infants.