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How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs., Materials and Methods: In this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa., Results: The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa., Conclusions: A deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety., Target Audience: This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards., Scope: This tutorial covers the following standards: Arden Syntax (using FHIR as the data model), Clinical Quality Language, FHIR Clinical Reasoning, SMART on FHIR, and CDS Hooks. Detailed descriptions and selected examples are provided., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.)

Study Objective: Despite evidence that guideline adherence improves clinical outcomes, management of pneumonia patients varies in emergency departments (EDs). We study the effect of a real-time, ED, electronic clinical decision support tool that provides clinicians with guideline-recommended decision support for diagnosis, severity assessment, disposition, and antibiotic selection., Methods: This was a prospective, controlled, quasi-experimental trial in 7 Intermountain Healthcare hospital EDs in Utah's urban corridor. We studied adults with International Classification of Diseases, Ninth Revision codes and radiographic evidence for pneumonia during 2 periods: baseline (December 2009 through November 2010) and post-tool deployment (December 2011 through November 2012). The tool was deployed at 4 intervention EDs in May 2011, leaving 3 as usual care controls. We compared 30-day, all-cause mortality adjusted for illness severity, using a mixed-effect, logistic regression model., Results: The study population comprised 4,758 ED pneumonia patients; 14% had health care-associated pneumonia. Median age was 58 years, 53% were female patients, and 59% were admitted to the hospital. Physicians applied the tool for 62.6% of intervention ED study patients. There was no difference overall in severity-adjusted mortality between intervention and usual care EDs post-tool deployment (odds ratio [OR]=0.69; 95% confidence interval [CI] 0.41 to 1.16). Post hoc analysis showed that patients with community-acquired pneumonia experienced significantly lower mortality (OR=0.53; 95% CI 0.28 to 0.99), whereas mortality was unchanged among patients with health care-associated pneumonia (OR=1.12; 95% CI 0.45 to 2.8). Patient disposition from the ED postdeployment adhered more to tool recommendations., Conclusion: This study demonstrates the feasibility and potential benefit of real-time electronic clinical decision support for ED pneumonia patients., (Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Objectives: To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia., Materials and Methods: We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse., Results: The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool., Discussion: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement., Conclusions: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

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.

Objective: Clinicians face numerous information needs during patient care activities and most of these needs are not met. Infobuttons are information retrieval tools that help clinicians to fulfill their information needs by providing links to on-line health information resources from within an electronic medical record (EMR) system. The aim of this study was to produce classification models based on medication infobutton usage data to predict the medication-related content topics (e.g., dose, adverse effects, drug interactions, patient education) that a clinician is most likely to choose while entering medication orders in a particular clinical context., Design: We prepared a dataset with 3078 infobutton sessions and 26 attributes describing characteristics of the user, the medication, and the patient. In these sessions, users selected one out of eight content topics. Automatic attribute selection methods were then applied to the dataset to eliminate redundant and useless attributes. The reduced dataset was used to produce nine classification models from a set of state-of-the-art machine learning algorithms. Finally, the performance of the models was measured and compared., Measurements: Area under the ROC curve (AUC) and agreement (kappa) between the content topics predicted by the models and those chosen by clinicians in each infobutton session., Results: The performance of the models ranged from 0.49 to 0.56 (kappa). The AUC of the best model ranged from 0.73 to 0.99. The best performance was achieved when predicting choice of the adult dose, pediatric dose, patient education, and pregnancy category content topics., Conclusion: The results suggest that classification models based on infobutton usage data are a promising method for the prediction of content topics that a clinician would choose to answer patient care questions while using an EMR system.

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.

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.

Purpose: A new model expressing Arden Syntax with the eXtensible Markup Language (XML) was developed to increase its portability., Methods: Every example was manually parsed and reviewed until the schema and the style sheet were considered to be optimized. When the first schema was finished, several MLMs in Arden Syntax Markup Language (ArdenML) were validated against the schema. They were then transformed to HTML formats with the style sheet, during which they were compared to the original text version of their own MLM. When faults were found in the transformed MLM, the schema and/or style sheet was fixed. This cycle continued until all the examples were encoded into XML documents. The original MLMs were encoded in XML according to the proposed XML schema and reverse-parsed MLMs in ArdenML were checked using a public domain Arden Syntax checker., Results: Two hundred seventy seven examples of MLMs were successfully transformed into XML documents using the model, and the reverse-parse yielded the original text version of MLMs. Two hundred sixty five of the 277 MLMs showed the same error patterns before and after transformation, and all 11 errors related to statement structure were resolved in XML version. The model uses two syntax checking mechanisms, first an XML validation process, and second, a syntax check using an XSL style sheet., Conclusion: Now that we have a schema for ArdenML, we can also begin the development of style sheets for transformation ArdenML into other languages.

When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the "missingness" of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or "missingness" of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in "missingness" itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.

In the third stage of electronic medical record development, key features of EMR systems have converged, making them more difficult to differentiate from a cost/benefit standpoint. Fourth stage EMR systems are likely to introduce new, more effective decision support capabilities, offering a competitive advantage to hospitals that use them. Intermountain Health Care's HELP systems, which developed along different paths than other early EMR systems, offer unique decision-support capabilities that provide significant additional benefits. This paper quantifies and compare the benefits of Third Stage EMR systems, and the possible Fourth Stage capabilities of the HELP.

Purpose: This report describes an ongoing transition from the HELP Hospital Information System to HELP II, a replacement Health Information System built to manage clinical information captured in a variety of medical settings. The focus of the article is on the medical decision support provided by this system and studied by researchers at the University of Utah and Intermountain Health Care (IHC), a large health care organization in Utah, for many years., Methods: Select success features of the original HELP system's decision support environment are identified and lessons learned are related. Plans for transferring these features to HELP II are discussed., Results: The article focuses on four features: (1) the importance of easy access to patient data essential for decision support, (2) the commitment to continued measurement and revision of both the logic and the interventional strategy in a decision support application, (3) experience with data mining as a tool for developing decision support tools, and (4) the role of clinical reports in supporting the decision making process.

Planning the clinical evaluation of a computerized decision support system requires a strategy that encompasses the different aspects of the clinical problem, the technical difficulties of software and hardware integration and implementation, the behavioral aspects of the targeted users, and the discipline of study design. Although clinical information systems are becoming more widely available, only a few decision support systems have been formally evaluated in clinical environments. Published accounts of difficulties associated with the clinical evaluation of decision support systems remain scarce. The authors report on a variety of behavioral, logistical, technical, clinical, cost, and work flow issues that they had to address when choosing a study design for a clinical trial for the evaluation of an integrated, real-time decision support system for the automatic identification of patients likely to have pneumonia in an emergency department. In the absence of a true gold standard, they show how they created a credible, clinically acceptable, and economical reference standard for the diagnosis of pneumonia, to determine the overall accuracy of the system. For the creation of a reference standard, they describe the importance of recognizing verification bias and avoiding it. Finally, advantages and disadvantages of different study designs are explored with respect to the targeted users and the clinical setting.

Objective: To assess the ability of an integrated, real-time diagnostic system (Bayesian network) to identify patients with community-acquired pneumonia who are eligible for a computerized pneumonia guideline without requiring clinicians to enter additional data., Design: Prospective validation study., Patients: All patients 18 years and older who presented to the emergency department of a tertiary care hospital., Methods: The diagnostic system computed a probability of pneumonia for every patient. The final diagnosis was established using ICD-9 discharge diagnoses. Outcome measures were sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, and test effectiveness., Results: During the 9-week study period there were 4,361 patients (112 pneumonia patients). The area under the receiver operating characteristic curve was 0.930 (CI: 0.907, 0.948). At a fixed sensitivity of 95%, the specificity was 68.5%, the positive predictive value 7.3%, the negative predictive value 99.8%, the positive likelihood ratio 3.0, the negative likelihood ratio 0.08, and the test effectiveness 2.05., Conclusion: The diagnostic system was able to detect patients who are eligible for a pneumonia guideline. The detection of eligible patients can be applied to automatically initiate and evaluate computerized guidelines.

Objective: This study examined whether clinical data routinely available in a computerized patient record (CPR) can be used to drive a complex guideline that supports physicians in real time and at the point of care in assessing the risk of mortality for patients with community-acquired pneumonia., Setting: Emergency department of a tertiary-care hospital., Design: Retrospective analysis with medical chart review., Patients: All 241 inpatients during a 17-month period (Jun 1995 to Nov 1996) who presented to the emergency department and had a primary discharge diagnosis of community-acquired pneumonia. METHODS/MAIN OUTCOME MEASURES: The 20 guideline variables were extracted from the CPR (HELP System) and the paper chart. The risk score and the risk class of the Pneumonia Severity Index were computed using data from the CPR alone and from a reference standard of all data available in the paper chart and the CPR at the time of the emergency department encounters. Availability and concordance were quantified to determine data quality. The type and cause of errors were analyzed depending on the source and format of the clinical variables., Results: Of the 20 guideline variables, 12 variables were required to be present for every computer-charted emergency department patient, seven variables were required for selected patients only, and one variable was not typically available in the HELP System during a patient's encounter. The risk class was identical for 86.7 percent of the patients. The majority of patients with different risk classes were assigned too low a risk class. The risk scores were identical for 72.1 percent of the patients. The average availability was 0.99 for the data elements that were required to be present and 0.79 for the data elements that were not required to be present. The average concordance was 0.98 when all a patient's variables were taken into account. The cause of error was attributed to the nurse charting in 77 percent of the cases and to the computerized evaluation in 23 percent. The type of error originated from the free-text fields in 64 percent, from coded fields in 21 percent, from vital signs in 14 percent, and from laboratory results in 1 percent., Conclusion: From a clinical perspective, the current level of data quality in the HELP System supports the automation and the prospective evaluation of the Pneumonia Severity Index as a computerized decision support tool.

Decision support systems that integrate guidelines have become popular applications to reduce variation and deliver cost-effective care. However, adverse characteristics of decision support systems, such as additional and time-consuming data entry or manually identifying eligible patients, result in a "behavioral bottleneck" that prevents decision support systems to become part of the clinical routine. This paper describes the design and the implementation of an integrated decision support system that explores a novel approach for bypassing the behavioral bottleneck. The real-time decision support system does not require health care providers to enter additional data and consists of a diagnostic and a management component.