Visualization to Support Coordination Objectives: understand –Basic principles of visualizing uncertainty information –Examples of visualization in managing

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


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


ModelsDisplay

Synthesis

Machine Learning Event Identification


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


Models Display

Synthesis

Machine LearningEvent Identification


RFID

Sandberg, MGH/CIMIT

VideoXiao, Maryland

Jacques, Vanderbilt

Machine Learning: Fast CPU cycles

Real-Time Data


Models Display

Synthesis



Xie, ColumbiaActivity modeling for video

Event Identification: “Only the Essence, Please!”

Real-Time Data


Models Display

Synthesis



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


Models Display

Synthesis



Aiming at decisions

Sandberg, MGH/CIMIT

Simulation: Crystal ball made of models

Real-Time Data


Models Display

Synthesis



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


Models Display

Synthesis



DAVIS DL, BARNES JH, JACKSON WM. COMPUTERS & OPERATIONS RESEARCH 20 (2): 215-225 1993


Real-Time Data


Models Display

Synthesis



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?


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.


Real-Time Data


Models Display

Synthesis



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



.81-.90 86%Highly

Probable.81-.90 86%

Highly Probable





.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



**

**

**

**

*

*

******

******

******

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

Documents

Visualization to Support Coordination Objectives: understand –Basic principles of visualizing uncertainty information –Examples of visualization in managing