Objective To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30‐day readmissions. Study Setting A multihospital academic health system in southeastern Massachusetts. Study Design An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics. Data Collection/Extraction Methods All‐payer claims, EHR data, and physician notes extracted from a centralized clinical registry. Principal Findings All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD‐9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk‐adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01). Conclusions The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

The LOINC (Logical Observation Identifier Names and Codes) vocabulary is a set of more than 10,000 names and codes developed for use as observation identifiers in standardized messages exchanged between clinical computer systems. The goal of the study was to create universal names and codes for clinical observations that could be used by all clinical information systems. The LOINC names are structured to facilitate rapid matching, either automated or manual, between local vocabularies and the universal LOINC codes. If LOINC codes are used in clinical messages, each system participating in data exchange needs to match its local vocabulary to the standard vocabulary only once. This will reduce both the time and cost of implementing standardized interfaces. The history of the development of the LOINC vocabulary and the methodology used in its creation are described.

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Provision of query systems which are intuitive for non-experts has been recognized as an important informatics challenge. We developed a prototype of a flowchart-based analytical framework called RetroGuide that enables non-experts to formulate query tasks using a step-based, patient-centered paradigm inspired by workflow technology. We present results of the evaluation of RetroGuide in comparison to Structured Query Language (SQL) in laboratory settings using a mixed method design. We asked 18 human subjects with limited database experience to solve query tasks in RetroGuide and SQL, and quantitatively compared their test scores. A follow-up questionnaire was designed to compare both technologies qualitatively and investigate RetroGuide technology acceptance. The quantitative comparison of test scores showed that the study subjects achieved significantly higher scores using the RetroGuide technology. Qualitative study results indicated that 94% of subjects preferred RetroGuide to SQL because RetroGuide was easier to learn, it better supported temporal tasks, and it seemed to be a more logical modeling paradigm. Additional qualitative evaluation results, based on a technology acceptance model, suggested that a fully developed RetroGuide-like technology would be well accepted by users. Our study is an example of a structure validation study of a prototype query system, results of which provided significant guidance in further development of a novel query paradigm for EHR data. We discuss the strengths and weakness of our study design and results, and their implication for future evaluations of query systems in general.

Objective: Develop a model for structured and encoded representation of medical information that supports human review, decision support applications, ad hoc queries, statistical analysis, and natural-language processing.Design: A medical information representation model was developed from manual and semiautomated analysis of patient data. The key assumption of the model is that medical information can be represented as a series of linked events. The event representationhas two main components. The first component is a frame or template definition that specifies the attributes of the event. The second component is a structured vocabulary, the terms of which are taken as the values of the slots in the event template structure. Individual event instances are linked by specific named relationships.Results: The proposed model was used to represent a chest-radiograph report.Conclusions: The event model of medical information representation provides a mechanism for formal definition of the logical structure of medical data and allows explicit time-oriented and associative relationships between event instances. recently presented specific models for medical-concept representation. '-' In conjunction with these efforts, we have been working on an event-based model of medical information representation, which is described in this article. Before we describe the model, we describe the context in which the model was developed and give a brief summary of our expectations and requirements for the model.

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The challenges and gaps in mapping MDD to RxNorm are mainly due to unique user or application requirements for representing drug concepts and the different modeling approaches inherent in the two terminologies. An automated approach based on NLP followed by human expert review is an efficient and feasible way for conducting dynamic mapping.