DOI: 10.5061/dryad.d51c5b02g
title: Data from: Research and exploratory analysis driven - time-data visualization (read-tv) software

description: read-tv The main paper is about, read-tv, open-source software for
 longitudinal data visualization. We uploaded sample use case surgical flow
 disruption data to highlight read-tv's capabilities. We scrubbed the
 data of protected health information, and uploaded it as a single CSV
 file. A description of the original data is described below. Data source
 Surgical workflow disruptions, defined as “deviations from the natural
 progression of an operation thereby potentially compromising the
 efficiency or safety of care”, provide a window on the systems of work
 through which it is possible to analyze mismatches between the work
 demands and the ability of the people to deliver the work. They have been
 shown to be sensitive to different intraoperative technologies, surgical
 errors, surgical experience, room layout, checklist implementation and the
 effectiveness of the supporting team. The significance of flow disruptions
 lies in their ability to provide a hitherto unavailable perspective on the
 quality and efficiency of the system. This allows for a systematic,
 quantitative and replicable assessment of risks in surgical systems,
 evaluation of interventions to address them, and assessment of the role
 that technology plays in exacerbation or mitigation. In 2014, Drs
 Catchpole and Anger were awarded NIBIB R03 EB017447 to investigate flow
 disruptions in Robotic Surgery which has resulted in the detailed,
 multi-level analysis of over 4,000 flow disruptions. Direct observation of
 89 RAS (robitic assisted surgery) cases, found a mean of 9.62 flow
 disruptions per hour, which varies across different surgical phases,
 predominantly caused by coordination, communication, equipment, and
 training problems.
description: This section does not describe the methods of
 <em>read-tv</em> software development, which can be found in
 the associated manuscript from JAMIA Open (JAMIO-2020-0121.R1). This
 section describes the methods involved in the surgical work flow
 disruption data collection. A curated, PHI-free (protected health
 information) version of this dataset was used as a use case for this
 manuscript. <strong><i>Observer
 training</i></strong> Trained human factors
 researchers conducted each observation following the completion of
 observer training. The researchers were two full-time research assistants
 based in the department of surgery at site 3 who visited the other two
 sites to collect data. Human Factors experts guided and trained each
 observer in the identification and standardized collection of FDs. The
 observers were also trained in the basic components of robotic surgery in
 order to be able to tangibly isolate and describe such disruptive
 events. Comprehensive observer training was ensured
 with both classroom and floor training. Observers were required to review
 relevant literature, understand general practice guidelines for observing
 in the OR (e.g., where to stand, what to avoid, who to speak to), and
 conduct practice observations. The practice observations were broken down
 into three phases, all performed under the direct supervision of an
 experienced observer. During phase one, the trainees oriented themselves
 to the real-time events of both the OR and the general steps in RAS. The
 trainee was also introduced to the OR staff and any other involved key
 personnel. During phase two, the trainer and trainee observed three RAS
 procedures together to practice collecting FDs and become familiar with
 the data collection tool. Phase three was dedicated to determining
 inter-rater reliability by having the trainer and trainee simultaneously,
 yet independently, conduct observations for at least three full RAS
 procedures. Observers were considered fully trained if, after three full
 case observations, intra-class correlation coefficients (based on number
 of observed disruptions per phase) were greater than 0.80, indicating good
 reliability. <strong><i>Data
 collection</i></strong> Following the
 completion of training, observers individually conducted observations in
 the OR. All relevant RAS cases were pre-identified on a monthly basis by
 scanning the surgical schedule and recording a list of procedures. All
 procedures observed were conducted with the Da Vinci Xi surgical robot,
 with the exception of one procedure at Site 2, which was performed with
 the Si robot. Observers attended those cases that fit within their
 allotted work hours and schedule. Observers used Microsoft Surface Pro
 tablets configured with a customized data collection tool developed using
 Microsoft Excel to collect data. The data collection tool divided
 procedures into five phases, as opposed to the four phases previously used
 in similar research, to more clearly distinguish between task demands
 throughout the procedure. Phases consisted of phase 1 - patient in the
 room to insufflation, phase 2 -insufflation to surgeon on console
 (including docking), phase 3 - surgeon on console to surgeon off console,
 phase 4 - surgeon off console to patient closure, and phase 5 - patient
 closure to patient leaves the operating room. During each procedure, FDs
 were recorded into the appropriate phase, and a narrative, time-stamp, and
 classification (based off of a robot-specific FD taxonomy) were also
 recorded. Each FD was categorized into one of ten
 categories: communication, coordination, environment, equipment, external
 factors, other, patient factors, surgical task considerations, training,
 or unsure. The categorization system is modeled after previous studies, as
 well as the examples provided for each FD category.
 Once in the OR, observers remained as unobtrusive as possible.
 They stood at an appropriate vantage point in the room without getting in
 the way of team members. Once an appropriate time presented itself,
 observers introduced themselves to the circulating nurse and informed them
 of the reason for their presence. Observers did not directly engage in
 conversations with operating room staff, however, if a staff member
 approached them with any questions/comments they would respond.
 <strong><em>Data Reduction and PHI (Protected Health
 Information) Removal</em></strong> This
 dataset uses 41 of the aforementioned surgeries. All columns have been
 removed except disruption type, a numeric timestamp for number of minutes
 into the day, and surgical phase. In addition, each surgical case had
 it's initial disruption set to 12 noon, (720 minutes).
description: The scrubbed, PHI-free version of the data can be found
 at: https://github.com/JDMusc/surgery-analysis With a specific link to the CSV file: https://github.com/JDMusc/surgery-analysis/blob/master/data/events_phi_free.csv <em>read-tv</em> software was used to (1) visually analyze the data, and (2) calculate change points in disruption rate from the data. It can be found below: https://github.com/JDMusc/READ-TV