Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

Progress and challenges in the implementation of Electronic Medical Records in Saudi Arabia : a systematic review

Electronic Medical Record (EMR) systems are being implemented increasingly worldwide. Saudi Arabia is one of the developing countries that commenced implementing such systems in 1988. Whilst EMR uptake has been low in Saudi Arabia until now, a number of hospitals have implemented EMR systems successfully. This paper analyses available studies (n = 28) in the literature regarding EMR implementation in Saudi Arabia to identify the progress of EMR implementation to date and to identify the facilitators and barriers to implementation.

Understanding Continuance Intentions of Physicians with Electronic Medical Records (EMRS): An Expectancy-Confirmation Perspective

This thesis examines physicians’ satisfaction with electronic medical records (EMRs) in the post-adoption phase. More specifically, the study examines how physicians’ satisfaction with EMRs impacts on their intention to continue using as well as extend their adoption of additional functions of EMRs. Expectation-confirmation theory is used with the incorporation of perceived risk as the theoretical framework. The extended theoretical model is used to formulate eight hypotheses to aid in the understanding of physicians’ continuance intentions. A field survey of 135 Canadian physicians that utilize EMRs was performed to test the model empirically. The study found that physicians are willing to continue using and adopting additional components of EMRs. In addition, the empirical results suggest that physicians’ perceived usefulness and perceived risk impacts satisfaction, which in turn influences physicians’ continuance intentions. As well, perceived risk has an influence on physicians’ continuance intentions directly.

Extending feature usage: a study of the post-adoption of electronic medical records

Technology Acceptance Model (TAM) posits that Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) influence the ‘intention to use’. The Post-Acceptance Model (PAM) posits that continued use is influenced by prior experience. In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical Bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the Corr-wddCRF also shows considerable accuracy.

Using technology to teach technology: design and evaluation of bilingual online physician education about electronic medical records.

The "EMR Tutorial" is designed to be a bilingual online physician education environment about electronic medical records. After iterative assessment and redesign, the tutorial was tested in two groups: U.S. physicians and Mexican medical students. Split-plot ANOVA revealed significantly different pre-test scores in the two groups, significant cognitive gains for the two groups overall, and no significant difference in the gains made by the two groups. Users rated the module positively on a satisfaction questionnaire.

Timely follow-up of abnormal diagnostic imaging test results in an outpatient setting: are electronic medical records achieving their potential?

BACKGROUND: Given the fragmentation of outpatient care, timely follow-up of abnormal diagnostic imaging results remains a challenge. We hypothesized that an electronic medical record (EMR) that facilitates the transmission and availability of critical imaging results through either automated notification (alerting) or direct access to the primary report would eliminate this problem. METHODS: We studied critical imaging alert notifications in the outpatient setting of a tertiary care Department of Veterans Affairs facility from November 2007 to June 2008. Tracking software determined whether the alert was acknowledged (ie, health care practitioner/provider [HCP] opened the message for viewing) within 2 weeks of transmission; acknowledged alerts were considered read. We reviewed medical records and contacted HCPs to determine timely follow-up actions (eg, ordering a follow-up test or consultation) within 4 weeks of transmission. Multivariable logistic regression models accounting for clustering effect by HCPs analyzed predictors for 2 outcomes: lack of acknowledgment and lack of timely follow-up. RESULTS: Of 123 638 studies (including radiographs, computed tomographic scans, ultrasonograms, magnetic resonance images, and mammograms), 1196 images (0.97%) generated alerts; 217 (18.1%) of these were unacknowledged. Alerts had a higher risk of being unacknowledged when the ordering HCPs were trainees (odds ratio [OR], 5.58; 95% confidence interval [CI], 2.86-10.89) and when dual-alert (>1 HCP alerted) as opposed to single-alert communication was used (OR, 2.02; 95% CI, 1.22-3.36). Timely follow-up was lacking in 92 (7.7% of all alerts) and was similar for acknowledged and unacknowledged alerts (7.3% vs 9.7%; P = .22). Risk for lack of timely follow-up was higher with dual-alert communication (OR, 1.99; 95% CI, 1.06-3.48) but lower when additional verbal communication was used by the radiologist (OR, 0.12; 95% CI, 0.04-0.38). Nearly all abnormal results lacking timely follow-up at 4 weeks were eventually found to have measurable clinical impact in terms of further diagnostic testing or treatment. CONCLUSIONS: Critical imaging results may not receive timely follow-up actions even when HCPs receive and read results in an advanced, integrated electronic medical record system. A multidisciplinary approach is needed to improve patient safety in this area.

Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices

SUMMARY There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.