visualization to support coordination objectives: understand –basic principles of visualizing...
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Visualization to Support Coordination
• Objectives: understand– Basic principles of visualizing uncertainty
information– Examples of visualization in managing
operating room
Coordination & IT Support• Collaborative work is the joint performance
of people and surrounding artifacts
Schedule surgeryRe-schedule surgeryCoordinate staffingCoordinate room assignmentCoordinate equipmentCoordinate patient preparedness
purpose
Pies show percents
2.55%3.54%
19.01%
11.41%
37.14%
26.35%
Frequencies of communication episodes by purpose of communication
J Nursing Admin: 34(2):93-100, 2004
OR Coordination: Labor intensive, little automation, and error prone
Information Needs in Coordinationof Operating Rooms
• Naïve assumption: only about OR schedules (posters)– Rooms– Cases– Procedures– Instruments– Surgeons– Nurses– Anesthesiologists
Commercial Vendors
DocuSys DocuView® Board
•Tracking of patients and staff
• “..permits remote collection, management and viewing of pre-procedure information “
Other Techniques Used by Researchers & Commercial Vendors (cont’d)
Picis CareSuite SmarTrack• Tracks patient flow & resources• Notification of late arrivals
Technology for Coordination
P. St. Jacques, N. Patel, M. Higgins, JCA, 2004
Other Techniques Used by Researchers & Commercial Vendors (cont’d)
StatCom O.R.• Communicates patient location and case status• Location of equipment
Changes DeletionsUnchangedLEGEND:OR Time Patient
Surg &Anes
Procedure(Diagnosis)
Extra Resources,Comments
07
Apr19---------7:158:45
Peter WilliamAge: 55Room: SDSDest: SDS
SparksColeKhan
GLAUCOMA WITH OCCULAR INFLAMMATION66170 LEFT EYE TRABECULECTOMY
07
Apr19---------8:45
10:15
Evan MendelAge: 60Room: SDSDest: SDS
SparksColeKhan
PRIMARY OPEN ANGLE GLAUCOMA66710 RIGHT EYE DIODE CYCLOABLATION
07
Apr19---------11:0013:00
Sarah GeorgeAge: 34Room: SDSDest: SDS
VickColeKhan
LEMPROMANDIBULAR JOINT DISORDER29804 LEFT ARTHROPLASTY, TMJ
PATIENT HAS HEART MURMUR1 HR PRE-MEDNOT AVAILABLE 11.00PM
08
Apr19---------7:15
11:45
Victor MonroeAge: 53Room: AMADMIDest: SICU
DasColeSaddler
M-NEOPLASM RENAL PELVIS50545 LAPROSCOPY, SURGICAL RADICAL NEPHRECTOMY, LEFT
STANDARD LAPROSCOPY TRAY
08
Apr19---------11:4517:15
Payal DesaiAge: 42Room: AMADMIDest: SICU
DasCole Saddler
CALCULUS OF URETERM-NEOPLASM RENAL PELVIS52353 CYSTOURETHROSCOPY WITH UERETERO AND PYELOSCOPY WITH LITHOTRIPSY50545 LEFT LAPROSCOPY, SURGICALRADICAL NEPHRECTOMY
STANDARD LAPROSCOPY TRAYNEPHRO/URETEROSCOPES, HOLMUMLASERBLOOD: 2
Master Schedule
Changes from Friday, April 14 2006 06:00 AM – Wednesday, April 19 2006 06:00 AM
Changes DeletionsUnchangedLEGEND:OR Time Patient
Surg &Anes
Procedure(Diagnosis)
Extra Resources,Comments
07
Apr19---------7:158:45
Peter WilliamAge: 55Room: SDSDest: SDS
SparksColeKhan
GLAUCOMA WITH OCCULAR INFLAMMATION66170 LEFT EYE TRABECULECTOMY
07
Apr19---------8:45
10:15
Evan MendelAge: 60Room: SDSDest: SDS
SparksColeKhan
PRIMARY OPEN ANGLE GLAUCOMA66710 RIGHT EYE DIODE CYCLOABLATION
07
Apr19---------11:0013:00
Sarah GeorgeAge: 34Room: SDSDest: SDS
VickColeKhan
LEMPROMANDIBULAR JOINT DISORDER29804 LEFT ARTHROPLASTY, TMJ
PATIENT HAS HEART MURMUR1 HR PRE-MEDNOT AVAILABLE 11.00PM
08
Apr19---------7:15
11:45
Victor MonroeAge: 53Room: AMADMIDest: SICU
DasColeSaddler
M-NEOPLASM RENAL PELVIS50545 LAPROSCOPY, SURGICAL RADICAL NEPHRECTOMY, LEFT
STANDARD LAPROSCOPY TRAY
08
Apr19---------11:4517:15
Payal DesaiAge: 42Room: AMADMIDest: SICU
DasCole Saddler
CALCULUS OF URETERM-NEOPLASM RENAL PELVIS52353 CYSTOURETHROSCOPY WITH UERETERO AND PYELOSCOPY WITH LITHOTRIPSY50545 LEFT LAPROSCOPY, SURGICALRADICAL NEPHRECTOMY
STANDARD LAPROSCOPY TRAYNEPHRO/URETEROSCOPES, HOLMUMLASERBLOOD: 2
Master Schedule
Changes from Friday, April 14 2006 06:00 AM – Wednesday, April 19 2006 06:00 AM
7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
OR 1
OR 2
OR 3
OR 4
OR 5
OR 6
OR 7
OR 8
Sandberg, MGH/CIMIT
Decision Support Through Display of “Right” Information
Uncertainty
• How long a case will last?
• Does the surgeon know?
• Can history be a good predictor?
• Will prediction also be stochastic?
Uncertainty in Case Durations
Actual Time
285 -
300
270 -
285
255 -
270
240 -
255
225 -
240
210 -
225
195 -
210
180 -
195
165 -
180
150 -
165
135 -
150
120 -
135
105 -
120
90 -
105
75 -
90
60 -
75
45 -
60
30 -
45
15 -
30
Distribution of Actual Time
Fre
quency
30
20
10
0
Std. Dev = 47.05
Mean = 67
N = 111.00
Date: April 2005
All cases were scheduled for 60 minutes
Uncertainty in Case Durations
0%
5%
10%
15%
20%
25%
30%
0 5 10 15 20 25 30 35 40 45 50 55 60
Lead time (min) for case durations <= 2.5 hrs
Fre
qu
en
cy
Lead time=finishing time – precursor event time
Operating Room Management
• Operating room: the core of many large hospitals and about 40% of cost and revenue
• Strategic management: services, capacity
• Tactic management: staffing, assignment
• Day of surgery management: pre-planning and ad hoc adjustment
Day of Surgery Management
• Surgical schedules: A list of cases to be carried out– By surgeon, in a certain room– With expectation of case durations– Equipment request
• Staff: number of hours of operating rooms staffed by nurses, technicians, and anesthesia care providers
Disruptions and Uncertainties
• Patients may be late or not ready (e.g., needing additional tests)
• Surgeons may be late or not available (e.g., fulfilling other responsibilities)
• Equipment may not be available (e.g., breakdowns)
• Case durations may be much longer • Emergency cases have priorities
Sources of interruptions
EquipmentOperating rooms
Patients
StaffOrganizational
Examples
Autoclave breaks down overnightCeiling tile falls onto operating room floor
Day patient has not had physical exam or history taken•Patient withdraws consent for procedure•Patient is very large requiring multiple people to move, but body weight is not communicated •Emergency patient undergoing angiography
Surgeon called in to assess emergency patientSurgeons negotiation whether emergency procedure should be done in STC OR or Main OR
AMIA, pp. 524-8, 2003
Many sources of interruptionsrequire coordination
OR Management: How Much Is There to Manage?
• 17 (+8, 6-34) daily changes– Canceling– Adding– Holding– Changing orders– Changing rooms)
• Highest changes – on Monday– Before 9AM
• Emergency cases (N=41) did not correlate with # of changes.
ASA 2005 Poster Presentation
Percentages of total changes (N=510 for 30 days)
0%
2%
4%
6%
8%
10%
12%
14%
7A 8A 9A 10A 11A 12P 1P 2P 3P 4P 5P 6P
Time of the Day
Change room
Change case order
Hold
Add
Cancel
Context Rich Information to Support Management of
Uncertainty
Video can be part of a command and control system for safe and efficient ORs
• Better information and situation awareness:– “When did the case start?”– “Is the case head of mine finished?”– “Should I check if the case is about to finish?”– “Did they change my cases?”– “Is there a chance to avoid overtime today?”
• Outcomes– Staff satisfaction (better informed, less waiting, more
appreciation of problems, reduced communication workload)
– Turn-over time (better anticipation)– Safety perception (less interruptions, more pro-active
measures)
IEEE SMC pp. 4141-6, 2003Anesthesiology, 1444-53,2004
Adjustable levels of details tomatch staff needs
Automatic identification of OR Occupancy
Video used for coordination: VideoBoard System
Adjustable levels of details to match staff needs
Degraded Blurred “Abstracted”
Anesthesia & Analgesia (2005)
Can network monitors be used to remotely identify patient in/out times?
• In-times: within 4.9 min (CI 4.2-5.7)• Out-times: within 2.8 min (CI 2.3-3.5)
Integrated Video & Occupancy Status Display
Pt. in timePt. out time Pt. connected in OR
Too much transparency?• Malone & Crowston’s definition (1990):
“coordination is managing dependencies between activities.”
• Bannon & Schmidt (1991): “A worker engaged in cooperative decision making must be able to control the dissemination of information pertaining to his or her work: what is to be revealed, when, to whom, in which form?”
• OR Nurse: “I am totally against the idea [displaying video images on the OR board]. It does not matter what resolution or quality the images are. I mean, this is big brother watching.”
Video: Is it acceptable?
• MD (n=24) and others (N=39)
• 80% thought useful, 50% no concerns
• 36% had less concern and 50% found more useful after 2 mo of use
ASA 2004 Poster-Discussion
Coordination and Negotation: The power of information
• Fairness– Equity, – Priorities, rules– Scores, cheating, gaming
• Negotiation– Give and take, favors– Pacifying, placating,
appeasement
• Commitment– Expectation, trust– Entitlement– Precedents
“Secondary” Impact of Coordination Technology
• Implications for information and communication technology (ICT): coordination at the “sharp end”– Controlling transparency (margin for negotiation)?– Controlling audience (local documents)?– Controlling perception of commitment (ad hoc, transient
documents)?– Anticipating perceived gains/loss due to ICT deployment (trust
building/adjustment period)?
Video: Who Uses It?
• Setting: 19 ORs, 131 consecutive weekdays• 3 peak access periods: 7-8AM, 1-2PM, and 4-5PM• Most (77%) by anesthesiologists
ASA 2005 Poster Presentation
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine Learning Event Identification
SimulationData Mining
ORViS (Operating Room Visualization)A Research Paradigm
Business Process Management Real-Time Decision Support Human
Decision Makers
Peri-operative processes
Real-Time Data
Historical Data Projection
ModelsDisplay
Synthesis
Machine Learning Event Identification
SimulationData Mining
Real-Time Decision Support for Coordination
CIS: clinical information system. SIS: Surgical information system
RFID
Video images
Bio sensors
Event sensors
CIS
SIS
Human steering
Auto-selection of models
Hybrid interface
Wearable interface
Real-Time Data: “Cheaper by the day”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
RFID
Sandberg, MGH/CIMIT
VideoXiao, Maryland
Jacques, Vanderbilt
Machine Learning: Fast CPU cycles
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
Xie, ColumbiaActivity modeling for video
Event Identification: “Only the Essence, Please!”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
OR Occupancy by Vital SignsAutomatic identification of OR
Occupancy
Pt. in timePt. out time Pt. connected in OR
Anesthesia & Analgesia (In press). 2005
Synthesis: “What it means to me?”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
Aiming at decisions
Sandberg, MGH/CIMIT
Simulation: Crystal ball made of models
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
Scheduled duration (min)
Absolute Errors
“Error” = Actual - scheduled duration (min)
Error (min)
Frequency
400
300
200
100
0
Std. Dev = 61.18
Mean = 53.1
N = 916.00
Error (min)
Fre
quency
50
40
30
20
10
0
Std. Dev = 32.54
Mean = 34.5
N = 111.00
y = 0.139x + 25.23
0
30
60
90
120
150
180
210
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600Sta
nda
rd D
evia
tion
(min
) of
Abs
olu
te E
rror
s
Display: “Silicon Meets Carbon”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
DAVIS DL, BARNES JH, JACKSON WM. COMPUTERS & OPERATIONS RESEARCH 20 (2): 215-225 1993
Display: “Silicon Meets Carbon”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
Trajectory Management in Surgical Operating Rooms- Visualization
• What has happened?– How many cases have been completed for an OR?
• What is the current status?– How many rooms are running right now?
• What will happen next?– When will a case be finished in an OR?
Visualization of Projected Trajectory
• Color coding to alert collaborating workers of pending events and trigger preparatory activities.
• How likely is a case ending within the next hour?
Uncertainty in Case Durations
Actual Time
285 -
300
270 -
285
255 -
270
240 -
255
225 -
240
210 -
225
195 -
210
180 -
195
165 -
180
150 -
165
135 -
150
120 -
135
105 -
120
90 -
105
75 -
90
60 -
75
45 -
60
30 -
45
15 -
30
Distribution of Actual Time
Fre
quency
30
20
10
0
Std. Dev = 47.05
Mean = 67
N = 111.00
Date: April 2005
All cases were scheduled for 60 minutes
Using Projected Finishing Times to Facilitate Waitlist Case Scheduling
•Triangles indicate the potential of a case lasting into the next scheduled case
•Concern for the decision maker: underestimating the duration of a waitlist case
•Visualizing may help to realize the potential of a waitlist case lasting too long
Visualizing Staffing Vs. Case Load
• Especially a concern at the end of the day.
• Statistically possible to project number of rooms running at a specified future time
• Visualizing planned utilization, projected rooms running, and staff schedules can help determine if there’s sufficient staff to handle the case load.
Display: “Silicon Meets Carbon”
Real-Time Data
Historical Data Projection
Models Display
Synthesis
Machine LearningEvent Identification
SimulationData Mining
Uncertainty: how to tell thy users?
Conceptual work in progress, Maryland
Uncertainty: how to tell thy users?
Visualizing projected case duration
• Case end time can be predicted statistically based on historic data
• Visualization options:– Show the probability
distribution– Show the likelihood of
a case ending within the hour (low, medium, high)
Tasks depend on event trajectories
Preparing an OR for next patient
Cleaning and setting up equipment for an OR
Caring for patient once he/she leaves OR
Planning and adjusting staffing levels
Visualizations for a hurried user
• “One-glance” recognition of current status
• Draw attention to events of importance– Room finishing
soon– Room running
overtime
Supporting coordination through computing
• Can we support the coordination of “expectations,” “intentions,” and “beliefs”?
Range(High)
NumericExpression
LinguisticExpression
Colored Icon
ArrowIcon
.90-1 95%Almost Certain
.81-.90 86%Highly
Probable
.72-.81 77%QuiteLikely
.63-.72 68%Rather Likely
.54-.63 59%Better
Than Even
0-.9 5% Rarely
.9-.18 14%Very
Unlikely
.18-.27 23%Fairly
Unlikely
.27-.36 32%Somewhat
Unlikely
.36-.45 41% Uncertain
.45-.54 50% Tossup
1.0 100%Absolutely
Certain
0 0%Absolutely Impossible
**
**
**
**
*
*
***
***
***
Range(High)
NumericExpression
LinguisticExpression
Colored Icon
ArrowIcon
.90-1 95%Almost Certain
.90-1 95%Almost Certain
.81-.90 86%Highly
Probable.81-.90 86%
Highly Probable
.72-.81 77%QuiteLikely
.72-.81 77%QuiteLikely
.63-.72 68%Rather Likely
.63-.72 68%Rather Likely
.54-.63 59%Better
Than Even.54-.63 59%
Better Than Even
0-.9 5% Rarely0-.9 5% Rarely
.9-.18 14%Very
Unlikely.9-.18 14%
Very Unlikely
.18-.27 23%Fairly
Unlikely.18-.27 23%
Fairly Unlikely
.27-.36 32%Somewhat
Unlikely.27-.36 32%
SomewhatUnlikely
.36-.45 41% Uncertain.36-.45 41% Uncertain
.45-.54 50% Tossup.45-.54 50% Tossup
1.0 100%Absolutely
Certain1.0 100%
Absolutely Certain
0 0%Absolutely Impossible
0 0%Absolutely Impossible
**
**
**
**
*
*
******
******
******
Schinzer et al: Investment Decisions
Best Practices Under Time Stress
A B vs. A B A .95B .70
.95 .70
3MEarly
3MLate0
.05 M .95
2. Visually link uncertainty representation to uncertain element (Proximity compatibility principle): Why visual display is good.
3. Express uncertainty in the “language of action” for: DIAGNOSIS PREDICTION
Spatial occupancy contours time windows
4. Need for standardization of contour level (95%?)
1. Eliminate redundant extra information (declutter)
.05 M .95Just as good?
Probabilistic Display
Visualizing NPO times and case start time uncertainties
• “No food or drink after midnight” often isn’t necessary; but it’s easier to remember
• Can visualizing probability of case start time allow more flexible NPO times without risking misinformation?
Visualizing NPO times and case start time uncertainties
• “No food or drink after midnight” often isn’t necessary; but it’s easier to remember
• Can visualizing probability of case start time allow more flexible NPO times without risking misinformation?
Summary
• Collaborative coordination efforts may be supported by computational power
• But:– Principles of displaying uncertainty
information are not established
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