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- All Subjects: Computer Science
- Resource Type: Text
- Status: Published
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Hospital Emergency Departments (EDs) are frequently crowded. The Center for
Medicare and Medicaid Services (CMS) collects performance measurements from EDs
such as that of the door to clinician time. The door to clinician time is the time at which a
patient is first seen by a clinician. Current methods for documenting the door to clinician
time are in written form and may contain inaccuracies. The goal of this thesis is to
provide a method for automatic and accurate retrieval and documentation of the door to
clinician time. To automatically collect door to clinician times, single board computers
were installed in patient rooms that logged the time whenever they saw a specific
Bluetooth emission from a device that the clinician carried. The Bluetooth signal is used
to calculate the distance of the clinician from the single board computer. The logged time
and distance calculation is then sent to the server where it is determined if the clinician
was in the room seeing the patient at the time logged. The times automatically collected
were compared with the handwritten times recorded by clinicians and have shown that
they are justifiably accurate to the minute.
Medicare and Medicaid Services (CMS) collects performance measurements from EDs
such as that of the door to clinician time. The door to clinician time is the time at which a
patient is first seen by a clinician. Current methods for documenting the door to clinician
time are in written form and may contain inaccuracies. The goal of this thesis is to
provide a method for automatic and accurate retrieval and documentation of the door to
clinician time. To automatically collect door to clinician times, single board computers
were installed in patient rooms that logged the time whenever they saw a specific
Bluetooth emission from a device that the clinician carried. The Bluetooth signal is used
to calculate the distance of the clinician from the single board computer. The logged time
and distance calculation is then sent to the server where it is determined if the clinician
was in the room seeing the patient at the time logged. The times automatically collected
were compared with the handwritten times recorded by clinicians and have shown that
they are justifiably accurate to the minute.
ContributorsFrisby, Joshua (Author) / Nelson, Brian C (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Smith, Vernon (Committee member) / Kaufman, David R. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The American Heart Association recommended in 1997 the data elements that should be collected from resuscitations in hospitals. (15) Currently, data documentation from resuscitation events in hospitals, termed ‘code blue’ events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of dynamic documentation of patient, event and outcome variables as the code blue event unfolds.
This thesis is based on the hypothesis that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. The primary goal of this thesis is to create an iOS based application, primarily designed for iPads, for code blue events at the Mayo Clinic Hospital. The secondary goal is to build an open-source software development framework for converting paper-based hospital protocols into digital format.
The tool created in this study enabled data documentation to be completed electronically rather than on paper for resuscitation outcomes. The tool was evaluated for usability with twenty nurses, the end-users, at Mayo Clinic in Phoenix, Arizona. The results showed the preference of users for the iPad application. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure.
This thesis is based on the hypothesis that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. The primary goal of this thesis is to create an iOS based application, primarily designed for iPads, for code blue events at the Mayo Clinic Hospital. The secondary goal is to build an open-source software development framework for converting paper-based hospital protocols into digital format.
The tool created in this study enabled data documentation to be completed electronically rather than on paper for resuscitation outcomes. The tool was evaluated for usability with twenty nurses, the end-users, at Mayo Clinic in Phoenix, Arizona. The results showed the preference of users for the iPad application. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure.
ContributorsBokhari, Wasif (Author) / Patel, Vimla L. (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Nelson, Brian (Committee member) / Sen, Ayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.
ContributorsSubbiah, Naveen Kumar (Author) / Patel, Vimla L. (Thesis advisor) / Hsiao, Sharon (Thesis advisor) / Sen, Ayan (Committee member) / Atkinson, Robert K (Committee member) / Arizona State University (Publisher)
Created2018