AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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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.
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AMIA Annu Symp Proc · Jan 2014
Development, Implementation and Use of Electronic Surveillance for Ventilator-Associated Events (VAE) in Adults.
Mechanical ventilation provides an important, life-saving therapy for severely ill patients, but ventilated patients are at an increased risk for complications, poor outcomes, and death during hospitalization.1 The timely measurement of negative outcomes is important in order to identify potential issues and to minimize the risk to patients. The Centers for Disease Control and Prevention (CDC) created an algorithm for identifying Ventilator-Associated Events (VAE) in adult patients for reporting to the National Healthcare Safety Network (NHSN). Currently, the primarily manual surveillance tools require a significant amount of time from hospital infection prevention (IP) staff to apply and interpret. This paper describes the implementation of an electronic VAE tool using an internal clinical data repository and an internally developed electronic surveillance system that resulted in a reduction of labor efforts involved in identifying VAE at Barnes Jewish Hospital (BJH).
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AMIA Annu Symp Proc · Jan 2014
Sharing my health data: a survey of data sharing preferences of healthy individuals.
We interviewed 70 healthy volunteers to understand their choices about how the information in their health record should be shared for research. Twenty-eight survey questions captured individual preferences of healthy volunteers. ⋯ Respondents indicated a strong preference towards controlling access to specific data (83%), and a large proportion (68%) indicated concern about the possibility of their data being used by for-profit entities. The results suggest that transparency in the process of sharing is an important factor in the decision to share clinical data for research.
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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.
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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.