An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases.

This study presents a method laying the groundwork for systematically monitoring food quality and the healthfulness of consumers' point-of-sale grocery purchases. The method automates the process of identifying United States Department of Agriculture (USDA) Food Patterns Equivalent Database (FPED) components of grocery food items. The input to the process is the compact abbreviated descriptions of food items that are similar to those appearing on the point-of-sale sales receipts of most food retailers. The FPED components of grocery food items are identified using Natural Language Processing techniques combined with a collection of food concept maps and relationships that are manually built using the USDA Food and Nutrient Database for Dietary Studies, the USDA National Nutrient Database for Standard Reference, the What We Eat In America food categories, and the hierarchical organization of food items used by many grocery stores. We have established the construct validity of the method using data from the National Health and Nutrition Examination Survey, but further evaluation of validity and reliability will require a large-scale reference standard with known grocery food quality measures. Here we evaluate the method's utility in identifying the FPED components of grocery food items available in a large sample of retail grocery sales data (~190 million transaction records).

Why aren't they happy? An analysis of end-user satisfaction with Electronic health records.

Introduction. Implementations of electronic health records (EHR) have been met with mixed outcome reviews. Complaints about these systems have led to many attempts to have useful measures of end-user satisfaction. However, most user satisfaction assessments do not focus on high-level reasoning, despite the complaints of many physicians. Our study attempts to identify some of these determinants. Method. We developed a user satisfaction survey instrument, based on pre-identified and important clinical and non-clinical clinician tasks. We surveyed a sample of in-patient physicians and focused on using exploratory factor analyses to identify underlying high-level cognitive tasks. We used the results to create unique, orthogonal variables representative of latent structure predictive of user satisfaction. Results. Our findings identified 3 latent high-level tasks that were associated with end-user satisfaction: a) High- level clinical reasoning b) Communicate/coordinate care and c) Follow the rules/compliance. Conclusion: We were able to successfully identify latent variables associated with satisfaction. Identification of communicability and high-level clinical reasoning as important factors determining user satisfaction can lead to development and design of more usable electronic health records with higher user satisfaction.

Towards Measuring the Food Quality of Grocery Purchases: an Estimation Model of the Healthy Eating Index-2010 Using only Food Item Counts.

Measuring the quality of food consumed by individuals or groups in the U.S. is essential to informed public health surveillance efforts and sound nutrition policymaking. For example, the Healthy Eating Index-2010 (HEI) is an ideal metric to assess the food quality of households, but the traditional methods of collecting the data required to calculate the HEI are expensive and burdensome. We evaluated an alternative source: rather than measuring the quality of the foods consumers eat, we want to estimate the quality of the foods consumers buy. To accomplish that we need a way of estimating the HEI based solely on the count of food items. We developed an estimation model of the HEI, using an augmented set of the What We Eat In America (WWEIA) food categories. Then we mapped ~92,000 grocery food items to it. The model uses an inverse Cumulative Distribution Function sampling technique. Here we describe the model and report reliability metrics based on NHANES data from 2003-2010.

qDIET: toward an automated, self-sustaining knowledge base to facilitate linking point-of-sale grocery items to nutritional content.

The United States, indeed the world, struggles with a serious obesity epidemic. The costs of this epidemic in terms of healthcare dollar expenditures and human morbidity/mortality are staggering. Surprisingly, clinicians are ill-equipped in general to advise patients on effective, longitudinal weight loss strategies. We argue that one factor hindering clinicians and patients in effective shared decision-making about weight loss is the absence of a metric that can be reasoned about and monitored over time, as clinicians do routinely with, say, serum lipid levels or HgA1C. We propose that a dietary quality measure championed by the USDA and NCI, the HEI-2005/2010, is an ideal metric for this purpose. We describe a new tool, the quality Dietary Information Extraction Tool (qDIET), which is a step toward an automated, self-sustaining process that can link retail grocery purchase data to the appropriate USDA databases to permit the calculation of the HEI-2005/2010.