Nonconvulsive seizures after subarachnoid hemorrhage: Multimodal detection and outcomes.

Objective: Seizures have been implicated as a cause of secondary brain injury, but the systemic and cerebral physiologic effects of seizures after acute brain injury are poorly understood.

Methods: We analyzed intracortical electroencephalographic (EEG) and multimodality physiological recordings in 48 comatose subarachnoid hemorrhage patients to better characterize the physiological response to seizures after acute brain injury.

Results: Intracortical seizures were seen in 38% of patients, and 8% had surface seizures.

Caveats for the use of operational electronic health record data in comparative effectiveness research.

The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols.

An Integrated Model for Patient Care and Clinical Trials (IMPACT) to support clinical research visit scheduling workflow for future learning health systems.

We describe a clinical research visit scheduling system that can potentially coordinate clinical research visits with patient care visits and increase efficiency at clinical sites where clinical and research activities occur simultaneously. Participatory Design methods were applied to support requirements engineering and to create this software called Integrated Model for Patient Care and Clinical Trials (IMPACT). Using a multi-user constraint satisfaction and resource optimization algorithm, IMPACT automatically synthesizes temporal availability of various research resources and recommends the optimal dates and times for pending research visits.

Discovering medical conditions associated with periodontitis using linked electronic health records.

Aim: To use linked electronic medical and dental records to discover associations between periodontitis and medical conditions independent of a priori hypotheses.

Materials And Methods: This case-control study included 2475 patients who underwent dental treatment at the College of Dental Medicine at Columbia University and medical treatment at NewYork-Presbyterian Hospital. Our cases are patients who received periodontal treatment and our controls are patients who received dental maintenance but no periodontal treatment.

Recommendations for the use of operational electronic health record data in comparative effectiveness research.

There is an increasing amount of clinical data in operational electronic health record (EHR) systems. Such data provide substantial opportunities for their re-use for many purposes, including comparative effectiveness research (CER). In a previous paper, we identified a number of caveats related to the use of such data, noting that they may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, or incompatible with research protocols.

Population physiology: leveraging electronic health record data to understand human endocrine dynamics.

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use.

Publication bias in clinical trials of electronic health records.

Objective: To measure the rate of non-publication and assess possible publication bias in clinical trials of electronic health records.

Methods: We searched ClinicalTrials.gov to identify registered clinical trials of electronic health records and searched the biomedical literature and contacted trial investigators to determine whether the results of the trials were published.

The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting.

Much of what is currently documented in the electronic health record is in response toincreasingly complex and prescriptive medicolegal, reimbursement, and regulatory requirements. These requirements often result in redundant data capture and cumbersome documentation processes. AMIA's 2011 Health Policy Meeting examined key issues in this arena and envisioned changes to help move toward an ideal future state of clinical data capture and documentation.

Next-generation phenotyping of electronic health records.

The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.

Clinical documentation: composition or synthesis?

Objective: To understand the nature of emerging electronic documentation practices, disconnects between documentation workflows and computing systems designed to support them, and ways to improve the design of electronic documentation systems.

Materials And Methods: Time-and-motion study of resident physicians' note-writing practices using a commercial electronic health record system that includes an electronic documentation module. The study was conducted in the general medicine unit of a large academic hospital.

A study of terminology auditors' performance for UMLS semantic type assignments.

Auditing healthcare terminologies for errors requires human experts. In this paper, we present a study of the performance of auditors looking for errors in the semantic type assignments of complex UMLS concepts. In this study, concepts are considered complex whenever they are assigned combinations of semantic types.

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series.

A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals.

Impact of integrating public health clinical decision support alerts into electronic health records on testing for gastrointestinal illness.

Laboratory testing by clinicians is essential to outbreak investigations. Electronic health records may increase testing through clinical decision support that alerts providers about existing outbreaks and facilitates laboratory ordering. The impact on laboratory testing was evaluated for foodborne disease outbreaks between 2006 and 2009.

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations.

This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted.

Proof of concept for the role of glycemic control in the early detection of infections in diabetics.

The relationship of infections and glycemic control in diabetes has been previously investigated but no solid findings have been described. Meanwhile, the detection of any infection at the early stages of disease progression, i.e.

Visualizing the operating range of a classification system.

The performance of a classification system depends on the context in which it will be used, including the prevalence of the classes and the relative costs of different types of errors. Metrics such as accuracy are limited to the context in which the experiment was originally carried out, and metrics such as sensitivity, specificity, and receiver operating characteristic area--while independent of prevalence--do not provide a clear picture of the performance characteristics of the system over different contexts. Graphing a prevalence-specific metric such as F-measure or the relative cost of errors over a wide range of prevalence allows a visualization of the performance of the system and a comparison of systems in different contexts.

Improving access to longitudinal patient health information within an emergency department.

We designed and implemented an electronic patient tracking system with improved user authentication and patient selection. We then measured access to clinical information from previous clinical encounters before and after implementation of the system. Clinicians accessed longitudinal information for 16% of patient encounters before, and 40% of patient encounters after the intervention, indicating such a system can improve clinician access to information.

Auditing hierarchical cycles to locate other inconsistencies in the UMLS.

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships-irrespective of the correctness of the deleted relationships.

Exploiting time in electronic health record correlations.

Objective: To demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations; to illustrate several types of associations; and to ascertain the value of exploiting time.

Materials And Methods: Lagged linear correlation was calculated between seven clinical laboratory values and 30 clinical concepts extracted from resident signout notes from a 22-year, 3-million-patient database of electronic health records. Time points were interpolated, and patients were normalized to reduce inter-patient effects.

Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.

Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category.

A review of causal inference for biomedical informatics.

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data.

Bias associated with mining electronic health records.

Large-scale electronic health record research introduces biases compared to traditional manually curated retrospective research. We used data from a community-acquired pneumonia study for which we had a gold standard to illustrate such biases. The challenges include data inaccuracy, incompleteness, and complexity, and they can produce in distorted results.

Minimizing electronic health record patient-note mismatches.

We measured the prevalence (or rate) of patient-note mismatches (clinical notes judged to pertain to another patient) in the electronic medical record. The rate ranged from 0.5% (95% CI 0.

Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge.

Knowledge of medical entities, such as drug-related information is critical for many automated biomedical applications, such as decision support and pharmacovigilance. In this work, heterogeneous information sources were integrated automatically to obtain drug-related knowledge. We focus on one type of knowledge, drug-treats-condition, in the study and propose a framework for integrating disparate knowledge sources.