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A real-time electronic CDS for pneumonia (ePNa) identifies possible pneumonia patients, measures severity and antimicrobial resistance risk, and then recommends disposition, antibiotics, and microbiology studies. Use is voluntary, and clinicians may modify treatment recommendations. ePNa was associated with lower mortality in emergency department (ED) patients versus usual care (Annals EM 66:511). We adapted ePNa for the Cerner EHR, and implemented it across Intermountain Healthcare EDs (Utah, USA) throughout 2018. We introduced ePNa through didactic, interactive presentations to ED clinicians; follow-up visits identified barriers and facilitators to use. Email reminded clinicians and answered questions. Hospital admitting clinicians encouraged ePNa use to smooth care transitions. Audit-and-feedback measured utilization, showing variations from best practice when ePNa and associated electronic order sets were not used. Use was initially low, but gradually increased especially at larger hospitals. A user-friendly interface, frequent reminders, audit-and- feedback, a user survey, a nurse educator, and local physician champions are additive towards implementation success., (©2019 AMIA - All rights reserved.)

Intermountain Healthcare has designed and implemented a publish-subscribe (PubSub) infrastructure to support essential event processing workflows across our organization. A recent implementation of a commercial EMR highlighted the need to provide this capability on top of the EMR to support external applications and services that require access to triggering events within the EMR. A description of the PubSub architecture is presented. Use cases for health information exchange, public health reporting, and pulmonary embolism diagnosis that utilize PubSub are described, along with benefits of using the paradigm. Besides providing support for these external applications, the PubSub infrastructure allows additional event handling functionality not available in the commercial EMR. The open, standards-based nature of the design should allow other organizations to implement the system in their information systems environment.

During the last decade, software supporting healthcare delivery has proliferated. This software can be divided into electronic medical record (EHR) systems and applications that treat EHRs as platforms. These collect, manage, and interpret medical data, thereby adding value to associated EHRs. To reduce the burden of developing for multiple EHR platforms, a group of standards has evolved that allow software written for one vendor's EHR to be introduced into settings supported by other vendors. The Health Services Platform Consortium (HSPC) is a collaborative effort to advocate for standards that will make healthcare applications truly interoperable. In this document, we discuss the approach adopted by the consortium and the standards central to this approach. We discriminate between interoperability standards that support the plug-and-play transfer of applications from one vendor's EHR to another and knowledge portability standards that allow knowledge artifacts used in one software environment to be introduced effectively in others.

Natural language processing (NLP) technologies provide an opportunity to extract key patient data from free text documents within the electronic health record (EHR). We are developing a series of components from which to construct NLP pipelines. These pipelines typically begin with a component whose goal is to label sections within medical documents with codes indicating the anticipated semantics of their content. This Clinical Section Labeler prepares the document for further, focused information extraction. Below we describe the evaluation of six algorithms designed for use in a Clinical Section Labeler. These algorithms are trained with N-gram-based feature sets extracted from document sections and the document types. In the evaluation, 6 different Bayesian models were trained and used to assign one of 27 different topics to each section. A tree-augmented Bayesian network using the document type and N-grams derived from section headers proved most accurate in assigning individual sections appropriate section topics.

A semantic lexicon which associates words and phrases in text to concepts is critical for extracting and encoding clinical information in free text and therefore achieving semantic interoperability between structured and unstructured data in Electronic Health Records (EHRs). Directly using existing standard terminologies may have limited coverage with respect to concepts and their corresponding mentions in text. In this paper, we analyze how tokens and phrases in a large corpus distribute and how well the UMLS captures the semantics. A corpus-driven semantic lexicon, MedLex, has been constructed where the semantics is based on the UMLS assisted with variants mined and usage information gathered from clinical text. The detailed corpus analysis of tokens, chunks, and concept mentions shows the UMLS is an invaluable source for natural language processing. Increasing the semantic coverage of tokens provides a good foundation in capturing clinical information comprehensively. The study also yields some insights in developing practical NLP systems.

Sepsis is a systemic inflammatory state due to an infection, and is associated with very high mortality and morbidity. Early diagnosis and prompt antibiotic and supportive therapy is associated with improved outcomes. Our objective was to detect the presence of sepsis soon after the patient visits the emergency department. We used Dynamic Bayesian Networks, a temporal probabilistic technique to model a system whose state changes over time. We built, trained and tested the model using data of 3,100 patients admitted to the emergency department, and measured the accuracy of detecting sepsis using data collected within the first 3 hours, 6 hours, 12 hours and 24 hours after admission. The area under the curve was 0.911, 0.915, 0.937 and 0.944 respectively. We describe the data, data preparation techniques, model, results, various statistical measures and the limitations of our experiments. We also briefly discuss techniques to improve accuracy, and the generalizability of our methods to other diseases.

A standards-based, service-oriented architecture for clinical decision support (CDS) has the potential to significantly enhance CDS scalability and robustness. To enable such a CDS architecture, the Health Level 7 CDS Work Group reviewed the literature, hosted multi-stakeholder discussions, and consulted domain experts to identify and prioritize the services and capabilities required from clinical information systems (CISs) to enable service-oriented CDS. In addition, relevant available standards were identified. Through this process, ten CIS services and eight CIS capabilities were identified as being important for enabling scalable, service-oriented CDS. In particular, through a survey of 46 domain experts, five services and capabilities were identified as being especially critical: 1) the use of standard information models and terminologies; 2) the ability to leverage a Decision Support Service (DSS); 3) support for a clinical data query service; 4) support for an event subscription and notification service; and 5) support for a user communication service.

Personalized medicine will require detailed clinical patient profiles, and a particular focus on capturing data that is useful in forecasting risk. A detailed family health history is considered a critical component of these profiles, insomuch that it has been coined as 'the best genetic test available'. Despite this, tools aimed at capturing this information for use in electronic health records have been characterized as inadequate. In this manuscript we detail the creation of a patient-facing family health history tool known as OurFamilyHealth, whose long-term emphasis is to facilitate risk assessment and clinical decision support. We present the rationale for such a tool, describe its development and release as a component of Intermountain Healthcare's patient portal, and detail early usage statistics surrounding the application. Data derived from the tool since its release are also compared against family history charting patterns in Intermountain's electronic health records, revealing differences in data availability.

Adequate control of serum glucose in critically ill patients is a complex problem requiring continuous monitoring and intervention, which have a direct effect on clinical outcomes. Understanding temporal relationships can help to improve our knowledge of complex disease processes and their response to treatment. We discuss a Dynamic Bayesian Network (DBN) model that we created using the open-source Projeny toolkit to represent various clinical variables and the temporal and atemporal relationships underlying insulin and glucose homeostasis. We evaluated this model by comparing the DBN model's insulin dose predictions against those of a rule-based protocol (eProtocol-insulin) currently used in the ICU. The results suggest that the DBN model's predictions are as effective as or better than those of the rule-based protocol. The limitations of our methods are discussed, with a brief note on their generalizability.

Infobuttons have been proven as an effective means for providing quick, context-specific links to pertinent information resources at the point of care. Current infobutton manager implementations, however, lack the ability to exchange metadata, are limited to a relatively small set of information providers, and are targeted primarily for a clinician audience. As part of a local effort to implement infobuttons for patient use via a tethered personal health record, we present a series of metadata extraction routines. These routines were constructed to extract key pieces of information from health information providers on the Internet, including content coverage, language availability, and readability scores. The extraction routines were tested using thirty different disease conditions against eight different providers. The routines yielded 183 potential infobutton targets and associated metadata for each. The capabilities of the extraction routines will be expanded to cover new types of metadata in the future.

Natural language processing applications that extract information from text rely on semantic representations. The objective of this paper is to describe a methodology for creating a semantic representation for information that will be automatically extracted from textual clinical records. We illustrate two of the four steps of the methodology in this paper using the case study of encoding information from dictated dental exams: (1) develop an initial representation from a set of training documents and (2) iteratively evaluate and evolve the representation while developing annotation guidelines. Our approach for developing and evaluating a semantic representation is based on standard principles and approaches that are not dependent on any particular domain or type of semantic representation.

Family history (FH) is an important risk factor for many diseases and its assessment can be a powerful tool for identifying and stratifying patients at risk. As part of an initiative to improve FH collection and decision support at Intermountain Healthcare we conducted a literature review on FH data collection and reviewed a number of current Web-based tools. An ideal list of features was produced to help implement goals of the initiative.

In critically ill patients control of blood sugar levels with IV insulin has been shown to improve clinical outcomes in the intensive care units. We have developed an analytical framework with which to evaluate and compare IV insulin-treatment models and protocols. Performance of the analytical framework is demonstrated using protocols published by others and new protocols under development by our group.

Current trends have brought resurgent interest in developing consumer-oriented tools that gather patient-entered clinical data. Family health history data has long been recognized as valuable for risk assessment in primary care, but has gained renewed attention recently as part of IT-oriented efforts in personalized medicine. In order to better understand the breadth of data collected in consumer-oriented web applications, we evaluated their collection patterns using the recommendations issued by the American Health Information Community (AHIC).

Constantly changing diabetes care standards makes it challenging to deliver care adapted to the unique condition of the individual patient. The availability of large amounts of data from patient's electronic medical records makes it possible to individualize diabetes management. Initial design of a "patient-specific" hybrid system (physiological-causal probabilistic) of adaptive diabetes models and insulin treatment algorithms will be presented. The system is uniquely derived and tested using a diabetes data mart of about 33,000 patients.

PTXT Finder was developed to reduce the manual efforts necessary to map from clinical variables in a decision support system to data elements in an EHR. The descriptions in a data dictionary may be inadequate for pinpointing data elements that represent a clinical variable. Semantics implied in taxonomy and real usage of the element are two important supporting information sources. PTXT Finder provides description, hierarchy, and statistics displays so users can cross-reference among these views.

Unlabelled: "Infobuttons" are information retrieval tools that predict the questions and the on-line information resources that a clinician may need in a particular context. The goal of this study was to employ infobutton usage data to produce classification models that predict the information resource that is most likely to be selected by a user in a given context., Methods: Data mining techniques were applied to a dataset with 13 attributes and 7,968 infobutton sessions conducted in a six-month period. Five classification models were generated and compared in terms of prediction performance., Results: All classification models performed statistically better than the implementation currently in use at our institution. Two to five attributes were sufficient for the models to achieve their best performance., Conclusion: The application of data mining tools over infobutton usage data is a promising strategy to further improve the prediction capability of infobuttons.

Electronic health records are designed to provide online transactional data recording and reporting services that support the health care process. The characteristics of clinical data as it originates during the process of clinical documentation, including issues of data availability and complex representation models can make data mining applications challenging. Data preprocessing and transformation are required before one can apply data mining to clinical data. In this article, an approach to data preparation that utilizes information from the data, metadata and sources of medical knowledge is described. Heuristic rules and policies are defined for these three types of supporting information. Compared with an entirely manual process for data preparation, this approach can potentially reduce manual work by achieving a degree of automation in the rule creation and execution. A pilot experiment demonstrates that data sets created through this approach lead to a better model learning results than a fully manual process.

To help maintain a complete, accurate and timely Problem List, we are developing a system to automatically retrieve medical problems from free-text documents. This system uses Natural Language Processing to analyze all electronic narrative text documents in a patient's record. Here we evaluate and compare 3 different applications of NLP technology in our system: the first using MMTx (MetaMap Transfer) with a negation detection algorithm (NegEx), the second using an alpha version of a locally developed NLP application called MPLUS2, and the third using keyword searching. They were adapted and trained to extract medical problems from a set of 80 problems of diagnosis type. The version using MMTx and NegEx was improved by adding some disambiguation and modifying the negation detection algorithm, and these modifications significantly improved recall and precision. The different versions of the NLP module were compared, and showed the following recall / precision results: standard MMTx with NegEx version 0.775 / 0.398; improved MMTx with NegEx version 0.892 / 0.753; MPLUS2 version 0.693 / 0.402; and keyword searching version 0.575 / 0.807. Average results for the reviewers were a recall of 0.788 and a precision of 0.912.

We are developing tools to help maintain a complete, accurate and timely problem list within a general purpose Electronic Medical Record system. As a part of this project, we have designed a system to automatically retrieve medical problems from free-text documents. Here we describe an information model based on XML (eXtensible Markup Language) and compliant with the CDA (Clinical Document Architecture). This model is used to ease the exchange of clinical data between the Natural Language Understanding application that retrieves potential problems from narrative document, and the problem list management application.