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Journal of the American Medical Informatics Association: JAMIA

Emily M Powers, Richard N Shiffman, Edward R Melnick, Andrew Hickner, Mona Sharifi
Objective: Clinical decision support (CDS) hard-stop alerts-those in which the user is either prevented from taking an action altogether or allowed to proceed only with the external override of a third party-are increasingly common but can be problematic. To understand their appropriate application, we asked 3 key questions: (1) To what extent are hard-stop alerts effective in improving patient health and healthcare delivery outcomes? (2) What are the adverse events and unintended consequences of hard-stop alerts? (3) How do hard-stop alerts compare to soft-stop alerts? Methods and Materials: Studies evaluating computerized hard-stop alerts in healthcare settings were identified from biomedical and computer science databases, gray literature sites, reference lists, and reviews...
September 18, 2018: Journal of the American Medical Informatics Association: JAMIA
Robert M Cronin, Douglas Conway, David Condon, Rebecca N Jerome, Daniel W Byrne, Paul A Harris
Background: Over the past decade, public interest in managing health-related information for personal understanding and self-improvement has rapidly expanded. This study explored aspects of how patient-provided health information could be obtained through an electronic portal and presented to inform and engage patients while also providing information for healthcare providers. Methods: We invited participants using ResearchMatch from 2 cohorts: (1) self-reported healthy volunteers (no medical conditions) and (2) individuals with a self-reported diagnosis of anxiety and/or depression...
September 18, 2018: Journal of the American Medical Informatics Association: JAMIA
Jennifer E Prey, Fernanda Polubriaginof, Lisa V Grossman, Ruth Masterson Creber, Demetra Tsapepas, Rimma Perotte, Min Qian, Susan Restaino, Suzanne Bakken, George Hripcsak, Leigh Efird, Joseph Underwood, David K Vawdrey
Objective: Unintentional medication discrepancies contribute to preventable adverse drug events in patients. Patient engagement in medication safety beyond verbal participation in medication reconciliation is limited. We conducted a pilot study to determine whether patients' use of an electronic home medication review tool could improve medication safety during hospitalization. Materials and Methods: Patients were randomized to use a tool before or after hospital admission medication reconciliation to review and modify their home medication list...
September 4, 2018: Journal of the American Medical Informatics Association: JAMIA
Karol M Pencina, Ralph B D'Agostino, Ramachandran S Vasan, Michael J Pencina
It is unclear to what extent simulated versions of real data can be used to assess potential value of new biomarkers added to prognostic risk models. Using data on 4522 women and 3969 men who contributed information to the Framingham CVD risk prediction tool, we develop a simulation model that allows assessment of the added contribution of new biomarkers. The simulated model matches closely the one obtained using real data: discrimination area under the curve (AUC) on simulated vs actual data is 0.800 vs 0...
August 28, 2018: Journal of the American Medical Informatics Association: JAMIA
Kerry A McBrien, Sepideh Souri, Nicola E Symonds, Azin Rouhi, Brendan C Lethebe, Tyler S Williamson, Stephanie Garies, Richard Birtwhistle, Hude Quan, Gabriel E Fabreau, Paul E Ronksley
Objectives: Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data. Materials and Methods: We searched MEDLINE and Embase (from inception to June 2016) to identify studies that describe case definitions for clinical conditions in EMR data and report on the performance metrics of these definitions...
August 22, 2018: Journal of the American Medical Informatics Association: JAMIA
Ross Gruetzemacher, Ashish Gupta, David Paradice
Objective: To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. Materials and Methods: The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results: Results for candidate generation on the test data indicated a detection rate of 94...
August 22, 2018: Journal of the American Medical Informatics Association: JAMIA
Evan W Orenstein, Irit R Rasooly, Mark V Mai, Adam C Dziorny, Wanczyk Phillips, Levon Utidjian, Anthony Luberti, Jill Posner, Rebecca Tenney-Soeiro, Chris P Bonafide
Objective: Electronic health record (EHR) simulation with realistic test patients has improved recognition of safety concerns in test environments. We assessed if simulation affects EHR use patterns in real clinical settings. Materials and Methods: We created a 1-hour educational intervention of a simulated admission for pediatric interns. Data visualization and information retrieval tools were introduced to facilitate recognition of the patient's clinical status...
August 21, 2018: Journal of the American Medical Informatics Association: JAMIA
Sara Belle Donevant, Robin Dawson Estrada, Joan Marie Culley, Brian Habing, Swann Arp Adams
Objectives: Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features. Materials and Methods: This search included primary articles focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension...
August 17, 2018: Journal of the American Medical Informatics Association: JAMIA
Sharidan K Parr, Matthew S Shotwell, Alvin D Jeffery, Thomas A Lasko, Michael E Matheny
Objective: Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes. Materials and Methods: Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016...
August 17, 2018: Journal of the American Medical Informatics Association: JAMIA
Sophie Marien, Delphine Legrand, Ravi Ramdoyal, Jimmy Nsenga, Gustavo Ospina, Valéry Ramon, Benoit Boland, Anne Spinewine
Objective: Medication reconciliation (MedRec) can improve patient safety by resolving medication discrepancies. Because information technology (IT) and patient engagement are promising approaches to optimizing MedRec, the SEAMPAT project aims to develop a MedRec IT platform based on two applications: the "patient app" and the "MedRec app." This study evaluates three dimensions of the usability (efficiency, satisfaction, and effectiveness) and usefulness of the patient app...
August 17, 2018: Journal of the American Medical Informatics Association: JAMIA
Jay K Nathan, Jenevra Foley, Tiffany Hoang, Jim Hiner, Stephanie Brooks, Julian L Gendreau, William J Meurer, Aditya S Pandey, Eric E Adelman
To facilitate high-quality inpatient care for stroke patients, we built a system within our electronic health record (EHR) to identify stroke patients while they are in the hospital; capture necessary data in the EHR to minimize the burden of manual abstraction for stroke performance measures, decreasing daily time requirement from 2 hours to 15 minutes; generate reports using an automated process; and electronically transmit data to third parties. Provider champions and support from the EHR development team ensured that we balanced the needs of the hospital with those of frontline providers...
August 16, 2018: Journal of the American Medical Informatics Association: JAMIA
Jennifer A Pacheco, Luke V Rasmussen, Richard C Kiefer, Thomas R Campion, Peter Speltz, Robert J Carroll, Sarah C Stallings, Huan Mo, Monika Ahuja, Guoqian Jiang, Eric R LaRose, Peggy L Peissig, Ning Shang, Barbara Benoit, Vivian S Gainer, Kenneth Borthwick, Kathryn L Jackson, Ambrish Sharma, Andy Yizhou Wu, Abel N Kho, Dan M Roden, Jyotishman Pathak, Joshua C Denny, William K Thompson
Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format...
August 16, 2018: Journal of the American Medical Informatics Association: JAMIA
Laila Cochon, Ronilda Lacson, Aijia Wang, Neena Kapoor, Ivan K Ip, Sonali Desai, Allen Kachalia, Jack Dennerlein, James Benneyan, Ramin Khorasani
Objective: To assess information sources that may elucidate errors related to radiologic diagnostic imaging, quantify the incidence of potential safety events from each source, and quantify the number of steps involved from diagnostic imaging chain and socio-technical factors. Materials and Methods: This retrospective, Institutional Review Board-approved study was conducted at the ambulatory healthcare facilities associated with a large academic hospital. Five information sources were evaluated: an electronic safety reporting system (ESRS), alert notification for critical result (ANCR) system, picture archive and communication system (PACS)-based quality assurance (QA) tool, imaging peer-review system, and an imaging computerized physician order entry (CPOE) and scheduling system...
August 16, 2018: Journal of the American Medical Informatics Association: JAMIA
Jason L Salemi, Rachel E Rutkowski, Jean Paul Tanner, Jennifer Matas, Russell S Kirby
Objective: Public health surveillance programs worldwide implement a variety of case-finding strategies, and many rely at least in part on International Classification of Diseases (ICD)-based diagnostic codes in administrative and clinical databases. Over time, state- and national-level hospital discharge databases have been expanding the number of reported diagnosis code fields. This study aimed to evaluate the impact of these expansions on frequencies and rates of major birth defects, and the classification of birth defects as isolated vs multiple...
August 16, 2018: Journal of the American Medical Informatics Association: JAMIA
Yuze Yang, Stacy Ward-Charlerie, Nitu Kashyap, Richelle DeMayo, Thomas Agresta, James Green
Objective: To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies. Materials and Methods: A retrospective analysis on a nationally representative random sample of 1 400 000 NewRx messages transmitted over 7 days to identify e-prescriptions containing medication discontinuation instructions in NewRx text fields...
August 7, 2018: Journal of the American Medical Informatics Association: JAMIA
Yiye Zhang, Richard Trepp, Weiguang Wang, Jorge Luna, David K Vawdrey, Victoria Tiase
Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population statistics, 2) individualized prediction using machine learning, and 3) clinical knowledge...
August 7, 2018: Journal of the American Medical Informatics Association: JAMIA
Megan Hoopes, Heather Angier, Lewis A Raynor, Andrew Suchocki, John Muench, Miguel Marino, Pedro Rivera, Nathalie Huguet
Objective: Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications. Materials and Methods: We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states...
August 3, 2018: Journal of the American Medical Informatics Association: JAMIA
Behrooz Davazdahemami, Dursun Delen
Objectives: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs. Materials and methods: large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins...
July 30, 2018: Journal of the American Medical Informatics Association: JAMIA
Jeffrey G Klann, Lori C Phillips, Christopher Herrick, Matthew A H Joss, Kavishwar B Wagholikar, Shawn N Murphy
Objective: Healthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support diverse patient-centric data models...
July 30, 2018: Journal of the American Medical Informatics Association: JAMIA
Adam Wright, Aileen P Wright, Skye Aaron, Dean F Sittig
Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships - specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor and descendant relationships) differently...
July 26, 2018: Journal of the American Medical Informatics Association: JAMIA
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