Brian Hazlehurst

MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical Record

MediClass is a knowledge-based system that processes both free-text and coded data to automatically detect clinical events in electronic medical records (EMRs). This technology aims to optimize both clinical practice and process control by automatically coding EMR contents regardless of data input method (e.g., dictation, structured templates, typed narrative). We report on the design goals, implemented functionality, generalizability, and current status of the system. MediClass could aid both clinical operations and health services research through enhancing care quality assessment, disease surveillance, and adverse event detection.

Distributed cognition: An alternative model of cognition for medical informatics

BACKGROUND Medical informatics has been guided by an individual-centered model of human cognition, inherited from classical theory of mind, in which knowledge, problem-solving, and information-processing responsible for intelligent behavior all derive from the inner workings of an individual agent. OBJECTIVES AND RESULTS In this paper we argue that medical informatics commitment to the classical model of cognition conflates the processing performed by the minds of individual agents with the processing performed by the larger distributed activity systems within which individuals operate. We review trends in cognitive science that seek to close the gap between general-purpose models of cognition and applied considerations of real-world human performance. One outcome is the theory of distributed cognition, in which the unit of analysis for understanding performance is the activity system which comprises a group of human actors, their tools and environment, and is organized by a particular history of goal-directed action and interaction. CONCLUSION We describe and argue for the relevance of distributed cognition to medical informatics, both for the study of human performance in healthcare and for the design of technologies meant to enhance this performance.

Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME-DM Project

Introduction Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research. Methods We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment. Results The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases. Conclusion The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.

A Survey of Informatics Platforms That Enable Distributed Comparative Effectiveness Research Using Multi-institutional Heterogenous Clinical Data

Comparative effectiveness research (CER) has the potential to transform the current health care delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods, and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for interinstitutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast 6 large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, sociotechnical model of health information technology to help guide our work. We identified 6 generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.

Detecting possible vaccine adverse events in clinical notes of the electronic medical record

The Vaccine Safety Datalink (VSD) is a collaboration between the CDC and eight large HMOs to investigate adverse events following immunization through analyses of clinical data. We modified an existing system, called MediClass, that uses natural language processing to identify clinical events recorded in electronic medical records (EMRs). We customized MediClass so it could detect possible vaccine adverse events (VAEs) generally, and gastrointestinal-related VAEs in particular, in the text clinical notes of encounters recorded in the EMR of a large HMO. Compared to methods that use diagnosis and utilization codes assigned to encounters by clinicians and administrators, the MediClass system can both find more adverse events and improve the positive predictive value for detecting possible VAEs.

Auto-organizaiton and emergence of shared language structure

The principal goal of attempts to construct computational models of the emergence of language is to shed light on the kinds of processes that may have led to the development of such phenomena as shared lexicons and grammars in the history of the human species. Researchers who attempt to model the emergence of lexicons make a set of shared assumptions about the nature of the problem to be solved. First, there are constraints on what counts as a shared lexicon. A lexicon is a systematic set of associations (a mapping) between forms and meanings. Forms are patterns. Tokens of a form are physical structures that bear the pattern of a particular form. For example, words are forms in this sense. Each instance of a particular word is a token of that word because it bears the pattern (sequence of sounds or letters) of that word. Forms must be discriminable from one another. Meanings are generally taken to be mental structures which, on the one hand, shape agents’ interactions with a world of objects and, on the other hand, also shape agents’ interactions with forms.

Getting the right tools for the job: distributed planning in cardiac surgery

Successful cardiac surgery requires having the right tools for the job, in the right place, at the right time, even in the face of unforeseen circumstances. We describe how cognitive and material resources in the activity system of the operating room enable well-defined courses of action (through preparatory configuration) while dynamically accommodating unlikely events (through replanning). Using ethnographic data from observations and video recordings in the operating room, we describe the nature of distributed planning in a bounded activity system with defined cognitive and physical resources. We describe the role of preparatory configuration for accomplishing expected courses of action, and the role of active replanning to achieve goals in the face of unexpected circumstances or events, using a specific case study to illustrate these phenomena. We discuss these findings, and their relevance for reconsidering the concept of error, from a systems perspective.

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data ☆

OBJECTIVES Comparative effectiveness research (CER) requires the capture and analysis of data from disparate sources, often from a variety of institutions with diverse electronic health record (EHR) implementations. In this paper we describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations. METHODS The CER Hub platform implements a data processing pipeline that employs informatics standards for data representation and web-based tools for developing study-specific data processing applications, providing standardized access to the patient-centric electronic health record (EHR) across organizations. RESULTS The CER Hub is being used to conduct two CER studies utilizing data from six geographically distributed and demographically diverse health systems. These foundational studies address the effectiveness of medications for controlling asthma and the effectiveness of smoking cessation services delivered in primary care. DISCUSSION The CER Hub includes four key capabilities: the ability to process and analyze both free-text and coded clinical data in the EHR; a data processing environment supported by distributed data and study governance processes; a clinical data-interchange format for facilitating standardized extraction of clinical data from EHRs; and a library of shareable clinical data processing applications. CONCLUSION CER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data. By offering a range of informatics tools integrated into a framework for conducting studies using EHR data, the CER Hub provides a solution to the challenges of multi-institutional research using electronic medical record data.

Getting the right tools for the job: preparatory system configuration and active replanning in cardiac surgery

Successful cardiac surgery requires having the right tools for the job in the right place and at the right time, even in the face of unforeseen circumstances. We describe how cognitive and material resources in the activity system of the OR enable well-defined courses of action (through preparatory configuration) while dynamically accommodating unlikely events (through replanning). Using ethnographic data from observations and video recordings in the operating room, we describe the nature of distributed planning in a bounded activity system with defined cognitive and physical resources. We describe the role of preparatory configuration for accomplishing expected courses of action, and the role of active replanning to achieve goals in the face of unexpected circumstances or events. Using a specific case study to illustrate these phenomena, we discuss these findings and their relevance to patient safety.

Assessing Trends in Tobacco Cessation in Diverse Patient Populations.

INTRODUCTION This study examined change in tobacco use over 4 years among the general population of patients in six diverse health care organizations using electronic medical record data. METHODS The study cohort (N = 34 393) included all patients age 18 years or older who were identified as smokers in 2007, and who then had at least one primary care visit in each of the following 4 years. RESULTS In the 4 years following 2007, this patient cohort had a median of 13 primary care visits, and 38.6% of the patients quit smoking at least once. At the end of the fourth follow-up year, 15.4% had stopped smoking for 1 year or more. Smokers were more likely to become long-term quitters if they were 65 or older (OR = 1.32, 95% CI = [1.16, 1.49]), or had a diagnoses of cancer (1.26 [1.12, 1.41]), cardiovascular disease (1.22 [1.09, 1.37]), asthma (1.15 [1.06, 1.25]), or diabetes (1.17 [1.09, 1.27]). Characteristics associated with lower likelihood of becoming a long-term quitter were female gender (0.90 [0.84, 0.95]), black race (0.84 [0.75, 0.94]) and those identified as non-Hispanic (0.50 [0.43, 0.59]). CONCLUSIONS Among smokers who regularly used these care systems, one in seven had achieved long-term cessation after 4 years. This study shows the practicality of using electronic medical records for monitoring patient smoking status over time. Similar methods could be used to assess tobacco use in any health care organization to evaluate the impact of environmental and organizational programs.