Correction to: Refining Boolean queries to identify relevant studies for systematic review updates.
Author(s):
DOI: 10.1093/jamia/ocac249
Author(s):
DOI: 10.1093/jamia/ocac249
Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using [...]
Author(s): Lou, Sunny S, Liu, Hanyang, Harford, Derek, Lu, Chenyang, Kannampallil, Thomas
DOI: 10.1093/jamia/ocac239
Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development [...]
Author(s): Zhang, Meina, Zhu, Linzee, Lin, Shih-Yin, Herr, Keela, Chi, Chih-Lin, Demir, Ibrahim, Dunn Lopez, Karen, Chi, Nai-Ching
DOI: 10.1093/jamia/ocac231
While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.
Author(s): Singh, Neetu, Varshney, Upkar
DOI: 10.1093/jamia/ocac253
Electronic health records (EHRs) offer decision support in the form of alerts, which are often though not always interruptive. These alerts, though sometimes effective, can come at the cost of high cognitive burden and workflow disruption. Less well studied is the design of the EHR itself-the ordering provider's "choice architecture"-which "nudges" users toward alternatives, sometimes unintentionally toward waste and misuse, but ideally intentionally toward better practice. We studied 3 different [...]
Author(s): Grouse, Carrie K, Waung, Maggie W, Holmgren, A Jay, Mongan, John, Neinstein, Aaron, Josephson, S Andrew, Khanna, Raman R
DOI: 10.1093/jamia/ocac238
Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation.
Author(s): van de Burgt, Britt W M, Wasylewicz, Arthur T M, Dullemond, Bjorn, Grouls, Rene J E, Egberts, Toine C G, Bouwman, Arthur, Korsten, Erik M M
DOI: 10.1093/jamia/ocac240
Author(s):
DOI: 10.1093/jamia/ocac243
Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications.
Author(s): Dolin, Robert H, Heale, Bret S E, Alterovitz, Gil, Gupta, Rohan, Aronson, Justin, Boxwala, Aziz, Gothi, Shaileshbhai R, Haines, David, Hermann, Arthur, Hongsermeier, Tonya, Husami, Ammar, Jones, James, Naeymi-Rad, Frank, Rapchak, Barbara, Ravishankar, Chandan, Shalaby, James, Terry, May, Xie, Ning, Zhang, Powell, Chamala, Srikar
DOI: 10.1093/jamia/ocac246
Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations.
Author(s): Iott, Bradley E, Adler-Milstein, Julia, Gottlieb, Laura M, Pantell, Matthew S
DOI: 10.1093/jamia/ocac251
In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an [...]
Author(s): Hacking, Coen, Verbeek, Hilde, Hamers, Jan P H, Aarts, Sil
DOI: 10.1093/jamia/ocac241