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research-article

Contextual Computing

Published: 01 January 2017 Publication History

Abstract

Display Omitted The door to doctor time plays a large role in Emergency Department crowding.This time is also one of the criteria for assessment of Meaningful Use (MU).Current manual documentation methods for assessing time are imprecise.Sensor-based methods offer a more precise and efficient measure of this time.Automatic collection of time data lessens cognitive load of physicians. Hospital Emergency Departments (EDs) frequently experience crowding. One of the factors that contributes to this crowding is the door to doctor time, which is the time from a patients registration to when the patient is first seen by a physician. This is also one of the Meaningful Use (MU) performance measures that emergency departments report to the Center for Medicare and Medicaid Services (CMS). Current documentation methods for this measure are inaccurate due to the imprecision in manual data collection. We describe a method for automatically (in real time) and more accurately documenting the door to physician time. Using sensor-based technology, the distance between the physician and the computer is calculated by using the single board computers installed in patient rooms that log each time a Bluetooth signal is seen from a device that the physicians carry. This distance is compared automatically with the accepted room radius to determine if the physicians are present in the room at the time logged to provide greater precision. The logged times, accurate to the second, were compared with physicians handwritten times, showing automatic recordings to be more precise. This real time automatic method will free the physician from extra cognitive load of manually recording data. This method for evaluation of performance is generic and can be used in any other setting outside the ED, and for purposes other than measuring physician time.

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  • (2023)Contact Tracing for Healthcare Workers in an Intensive Care UnitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109247:3(1-23)Online publication date: 27-Sep-2023
  • (2020)From indoor paths to gender prediction with soft clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18911639:5(6529-6538)Online publication date: 1-Jan-2020
  • (2020)Segmentation of indoor customer paths using intuitionistic fuzzy clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17944038:1(675-684)Online publication date: 1-Jan-2020
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Information & Contributors

Information

Published In

cover image Journal of Biomedical Informatics
Journal of Biomedical Informatics  Volume 65, Issue C
January 2017
133 pages

Publisher

Elsevier Science

San Diego, CA, United States

Publication History

Published: 01 January 2017

Author Tags

  1. Clinical workflow
  2. Contextual Computing
  3. Meaningful Use
  4. Patient safety
  5. Sensor-based tracking

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Cited By

View all
  • (2023)Contact Tracing for Healthcare Workers in an Intensive Care UnitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109247:3(1-23)Online publication date: 27-Sep-2023
  • (2020)From indoor paths to gender prediction with soft clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18911639:5(6529-6538)Online publication date: 1-Jan-2020
  • (2020)Segmentation of indoor customer paths using intuitionistic fuzzy clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17944038:1(675-684)Online publication date: 1-Jan-2020
  • (2019)Tracking a moving user in indoor environments using Bluetooth low energy beaconsJournal of Biomedical Informatics10.1016/j.jbi.2019.10328898:COnline publication date: 1-Oct-2019
  • (2018)Pattern Matching Based Sensor Identification Layer for an Android PlatformWireless Communications & Mobile Computing10.1155/2018/47345272018Online publication date: 10-Oct-2018
  • (2018)A Survey of Sensors in Healthcare Workflow MonitoringACM Computing Surveys10.1145/317785251:2(1-37)Online publication date: 17-Apr-2018
  • (2018)A method for the analysis and visualization of clinical workflow in dynamic environmentsJournal of Biomedical Informatics10.1016/j.jbi.2018.01.00779:C(20-31)Online publication date: 1-Mar-2018

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