AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
-
AMIA Annu Symp Proc · Jan 2014
An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes.
Emergency department (ED) visits due to allergic reactions are common. Allergy information is often recorded in free-text provider notes; however, this domain has not yet been widely studied by the natural language processing (NLP) community. We developed an allergy module built on the MTERMS NLP system to identify and encode food, drug, and environmental allergies and allergic reactions. ⋯ We developed an annotation schema and annotated 400 ED notes that served as a gold standard for comparison to MTERMS output. MTERMS achieved an F-measure of 87.6% for the detection of allergen names and no known allergies, 90% for identifying true reactions in each allergy statement where true allergens were also identified, and 69% for linking reactions to their allergen. These preliminary results demonstrate the feasibility using NLP to extract and encode allergy information from clinical notes.
-
Increasing regulatory incentives to computerize provider order entry (CPOE) and connect stores of unvalidated allergy information with the electronic health record (EHR) has created a perfect storm to overwhelm clinicians with high volumes of low or no value drug allergy alerts. Data sources include the patient and family, non-clinical staff, nurses, physicians and medical record sources. There has been little written on how to collect hypersensitivity information suited for drug allergy alerting. Opiates in particular are a frequently ordered class of drugs that have one of the highest rates of allergy alert override and are often a component of pre-populated Computerized Provider Order Entry (CPOE) order sets. Targeted research is needed to reduce alert volume, increase clinician acceptance, and improve patient safety and comfort. ⋯ With an increasingly complex, information dependent healthcare culture, clinicians do not have unlimited time and cognitive capacity to interpret and effectively act on high volumes of low value alerts. Drug allergy alerting was one of the earliest and supposedly simplest forms of CPOE clinical decision support (CDS), yet still has unacceptably high override rates. Targeted strategies to exclude GI non-allergic type hypersensitivities, mild overdose, or adverse effects could yield large reductions in overall drug overrides rates. Explicit allergy and severity definitions, staff training, and improved clinical decision support at the point of allergy data input are needed to inform how we process new and re-process historical allergy data.
-
AMIA Annu Symp Proc · Jan 2014
Medical alert management: a real-time adaptive decision support tool to reduce alert fatigue.
With the adoption of electronic medical records (EMRs), drug safety alerts are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, even with proper tuning of the EMR alert parameters, the volume of unfiltered alerts can be overwhelming to users. In this paper, we design an adaptive decision support tool in which past cognitive overriding decisions of users are learned, adapted and used for filtering actions to be performed on current alerts. ⋯ The decision support system facilitates filtering of non-essential alerts and adaptively learns critical alerts and highlights them prominently to catch providers' attention. The tool can be plugged into an existing EMR system as an add-on, allowing real-time decision support to users without interfering with existing EMR functionalities. By automatically filtering the alerts, the decision support tool mitigates alert fatigue and allows users to focus resources on potentially vital alerts, thus reducing the occurrence of adverse drug events.
-
AMIA Annu Symp Proc · Jan 2014
Identification and management of information problems by emergency department staff.
Patient-care teams frequently encounter information problems during their daily activities. These information problems include wrong, outdated, conflicting, incomplete, or missing information. Information problems can negatively impact the patient-care workflow, lead to misunderstandings about patient information, and potentially lead to medical errors. ⋯ However, there is limited research on how patient-care teams currently identify and manage information problems that they encounter during their work. Through qualitative observations and interviews in an emergency department (ED), we identified the types of information problems encountered by ED staff, and examined how they identified and managed the information problems. We also discuss the impact that these information problems can have on the patient-care teams, including the cascading effects of information problems on workflow and the ambiguous accountability for fixing information problems within collaborative teams.
-
Mobile Health (mHealth) applications lie outside of regulatory protection such as HIPAA, which requires a baseline of privacy and security protections appropriate to sensitive medical data. However, mHealth apps, particularly those in the app stores for iOS and Android, are increasingly handling sensitive data for both professionals and patients. This paper presents a series of three studies of the mHealth apps in Google Play that show that mHealth apps make widespread use of unsecured Internet communications and third party servers. Both of these practices would be considered problematic under HIPAA, suggesting that increased use of mHealth apps could lead to less secure treatment of health data unless mHealth vendors make improvements in the way they communicate and store data.