INFORMATION VISUALIZATION
for healthcare
Krist wongsuphasawat
@kristwongz
Department of Computer Science & Human-Computer Interaction Lab
University of Maryland
INFORMATION VISUALIZATION
INFO. VIS.
A picture is worth a thousand words.
INFORMATION VISUALIZATION
INFO. VIS.

      “   Using visual representations and interaction techniques,
          which take advantage of the human eye’s
          broad bandwidth pathway into the mind,
          to allow users to see, explore, and understand
          large amounts of information at once.”

                                                        [Wikipedia]!
                                                        	
  
Anscombe’s quartet

       #1            #2            #3             #4
     X
     Y
      X
     Y
     X
     Y
      X
     Y

    10.0   8.04    10.0   9.14   10.0   7.46    8.0    6.58

    8.0    6.95    8.0    8.14   8.0    6.77    8.0    5.76

    13.0   7.58    13.0   8.74   13.0   12.74   8.0    7.71

    9.0    8.81    9.0    8.77   9.0    7.11    8.0    8.84

    11.0   8.33    11.0   9.26   11.0   7.81    8.0    8.47

    14.0   9.96    14.0   8.10   14.0   8.84    8.0    7.04

    6.0    7.24    6.0    6.13   6.0    6.08    8.0    5.25

    4.0    4.26    4.0    3.10   4.0    5.39    19.0   12.50

    12.0   10.84   12.0   9.13   12.0   8.15    8.0    5.56

    7.0    4.82    7.0    7.26   7.0    6.42    8.0    7.91

    5.0    5.68    5.0    4.74   5.0    5.73    8.0    6.89
Anscombe’s quartet

     #1                #2               #3              #4

                   Property
               Value

                  Mean of X                 11.0

                 Variance of X              10.0

                  Mean of Y                 7.5

                 Variance of Y              3.75

          Correlation between X and Y      0.816

               Linear regression        y = 3.0 +0.5x




            Identical statistics
Anscombe’s quartet

                 #1                      #2                          #3                          #4
 12!                          10!                         14!                         14!
                               9!
 10!                                                      12!                         12!
                               8!
                               7!                         10!                         10!
  8!
                               6!                          8!                          8!
  6!                           5!
                               4!                          6!                          6!
  4!                           3!                          4!                          4!
  2!                           2!
                                                           2!                          2!
                               1!
  0!                           0!                          0!                          0!
       0!   5!    10!   15!         0!   5!   10!   15!         0!   5!   10!   15!         0!    10!   20!




                              But very different
INFORMATION VISUALIZATION
    Visual representation
                     +
      User interactions
        click, drag, zoom, select, etc.!
                       	
  
Healthcare
Electronic medical records (EMRs)

     “   To improve the quality of our health care while lowering its cost,
         we will make the immediate investments necessary to ensure that,
         within five years, all of America's medical records are computerized.
         This will cut waste, eliminate red tape and reduce the need
         to repeat expensive medical tests.
         But it just won't save billions of dollars and thousands of jobs;
         it will save lives by reducing the deadly but preventable medical errors
         that pervade our health-care system.”

                                                [President Barack Obama – Jan 2009]!
                                                	
  
EMRs + INFO. VIS.
A lot of data!   Help understand data!
      	
                  	
  
One patient




  x
Example of EMRs System
Lifelines
  One patient




          x
    [Plaisant et al. 1998]!
http://www.cs.umd.edu/hcil/lifelines!
LifeLines
LifeLines
LifeLines
LifeLines
LifeLines
Lifelines user study




         Faster decision
          Better recall
Lifelines
              One patient

Demographic
-  Gender
-  Age
-  …
                      x
                [Plaisant et al. 1998]!
                                         Medical Events*
                                         -  Emergency room on Jan 15
                                         -  Surgery on Oct 1
                                         -  …




            http://www.cs.umd.edu/hcil/lifelines!
Lifelines 2
Multiple patients




xxxxx
    [Wang et al. 2008, 2009]!
  http://www.cs.umd.edu/hcil/lifelines2!
Case study

       Contrast-induced nephropathy

  Radiographic Examination
  (Medical Imaging)
  e.g. X-ray
  using a contrast agent
  e.g. Iodine, Barium        x
                             Creatinine
                             -  Amino Acid
                                            Damage to the kidney




                             -  Levels in blood reflect kidney function
Data : contrast & creatinine

      CREAT-    Normal level of Creatinine
      RADIOLOGY Radiographic exam (with contrast)
      CREAT-H   High level of Creatinine (bad)




    x
                         Time
          Jan      Feb          Mar   Apr
LifeLines2
Video demo
Data Analysis with Lifelines2
Lifelines 2
Multiple patients




xxxxx
    [Wang et al. 2008, 2009]!
  http://www.cs.umd.edu/hcil/lifelines2!
Lifelines 2
search from medical events




 xxxxx  [Wang et al. 2008, 2009]!
      http://www.cs.umd.edu/hcil/lifelines2!
Data : patients transfer


      ARRIVAL      Arrive the hospital
      EMERGENCY    Emergency room
      ICU          Intensive Care Unit
      FLOOR        Normal room
      EXIT-ALIVE   Leave the hospital alive
      EXIT-DEAD    Leave the hospital dead
Improve the Quality of Care	



  Pa$ent	
  ID:	
  45851733 	
  	
  
    Pa$ent	
  ID:	
  45851732 	
  	
  
12/02/2008	
  14:26 	
  Arrival	
  
12/02/2008	
  14:26I	
  D:	
  45851731
       Pa$ent	
   Emergency	
          	
  	
  
                                                   Emergency Department
  12/02/2008	
  14:26 	
  Arrival	
  



                                                      6,000+
12/02/2008	
  22:44 	
  ICU	
  mergency	
  
     12/02/2008	
  14:26 	
  E 	
  
12/05/2008	
  05:071Floor	
  Arrival	
  
          12/02/2008	
   	
   4:26
     12/02/2008	
  22:44 	
  ICU	
  mergency	
  
          12/02/2008	
  14:26 	
  E
12/08/2008	
  10:02 	
  Floor	
  
     12/05/2008	
  05:07 	
  Floor	
  
          12/02/2008	
  22:44 	
  ICU	
  
12/14/2008	
  06:19 	
  Discharge	
  
     12/08/2008	
  10:02 	
  Floor	
  
          12/05/2008	
  05:07 	
  Floor	
  
	
   12/14/2008	
  06:19 	
  Discharge	
  
          12/08/2008	
  10:02 	
  Floor	
  
     	
   12/14/2008	
  06:19 	
  Discharge	
          patients per month
       	
  
task
           Find “Bounce backs”
            ICU         Floor        ICU



                                within 2 days
       Limitations

         High-level questions
                     Arrival
                                         ?
                      ICU

          ?                              ?
patients


x x x xx x x x xx
 x xx x x x
x x x x x x x
LifeFlow
     overview


    xx
     x x
   xx x
  x x xx
[Wongsuphasawat et al. 2011]!
 http://www.cs.umd.edu/hcil/lifeflow!
Video demo
 Creating LifeFlow
LifeFlow
Video demo
Data Analysis with LifeFlow
collected                               visual representation
             large                  eye                 interactions

 rich

        EMRs + INFO. VIS.
            A lot of data!                  Help understand data!
                  	
                                 	
                  and more…
                                                Lifelines           LifeFlow

        Save more lives
                                                                  Lifelines 2

                    Patientslikeme / i2b2 / BTRIS
                      Many case studies / etc.
                                                     Krist wongsuphasawat
                                                                              @kristwongz
                                                                              kristw@cs.umd.edu!
                                                            http://www.cs.umd.edu/hcil/temporalviz!
                                                                                                  	
  
Other examples
Visualizations in the Medical Domain
MIDGAARD
hFp://www.infovis-­‐wiki.net/index.php?$tle=MIDGAARD	
  
HemoVis
hFp://people.seas.harvard.edu/~borkin/HemoVis/	
  

Information Visualization for Health Care

  • 1.
    INFORMATION VISUALIZATION for healthcare Kristwongsuphasawat @kristwongz Department of Computer Science & Human-Computer Interaction Lab University of Maryland
  • 2.
  • 3.
    A picture isworth a thousand words.
  • 4.
    INFORMATION VISUALIZATION INFO. VIS. “ Using visual representations and interaction techniques, which take advantage of the human eye’s broad bandwidth pathway into the mind, to allow users to see, explore, and understand large amounts of information at once.” [Wikipedia]!  
  • 5.
    Anscombe’s quartet #1 #2 #3 #4 X Y X Y X Y X Y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
  • 6.
    Anscombe’s quartet #1 #2 #3 #4 Property Value Mean of X 11.0 Variance of X 10.0 Mean of Y 7.5 Variance of Y 3.75 Correlation between X and Y 0.816 Linear regression y = 3.0 +0.5x Identical statistics
  • 7.
    Anscombe’s quartet #1 #2 #3 #4 12! 10! 14! 14! 9! 10! 12! 12! 8! 7! 10! 10! 8! 6! 8! 8! 6! 5! 4! 6! 6! 4! 3! 4! 4! 2! 2! 2! 2! 1! 0! 0! 0! 0! 0! 5! 10! 15! 0! 5! 10! 15! 0! 5! 10! 15! 0! 10! 20! But very different
  • 8.
    INFORMATION VISUALIZATION Visual representation + User interactions click, drag, zoom, select, etc.!  
  • 9.
    Healthcare Electronic medical records(EMRs) “ To improve the quality of our health care while lowering its cost, we will make the immediate investments necessary to ensure that, within five years, all of America's medical records are computerized. This will cut waste, eliminate red tape and reduce the need to repeat expensive medical tests. But it just won't save billions of dollars and thousands of jobs; it will save lives by reducing the deadly but preventable medical errors that pervade our health-care system.” [President Barack Obama – Jan 2009]!  
  • 10.
    EMRs + INFO.VIS. A lot of data! Help understand data!    
  • 11.
  • 12.
  • 13.
    Lifelines Onepatient x [Plaisant et al. 1998]! http://www.cs.umd.edu/hcil/lifelines!
  • 14.
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  • 19.
    Lifelines user study Faster decision Better recall
  • 20.
    Lifelines One patient Demographic -  Gender -  Age -  … x [Plaisant et al. 1998]! Medical Events* -  Emergency room on Jan 15 -  Surgery on Oct 1 -  … http://www.cs.umd.edu/hcil/lifelines!
  • 21.
    Lifelines 2 Multiple patients xxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 22.
    Case study Contrast-induced nephropathy Radiographic Examination (Medical Imaging) e.g. X-ray using a contrast agent e.g. Iodine, Barium x Creatinine -  Amino Acid Damage to the kidney -  Levels in blood reflect kidney function
  • 23.
    Data : contrast& creatinine CREAT- Normal level of Creatinine RADIOLOGY Radiographic exam (with contrast) CREAT-H High level of Creatinine (bad) x Time Jan Feb Mar Apr
  • 24.
  • 27.
    Video demo Data Analysiswith Lifelines2
  • 28.
    Lifelines 2 Multiple patients xxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 29.
    Lifelines 2 search frommedical events xxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 30.
    Data : patientstransfer ARRIVAL Arrive the hospital EMERGENCY Emergency room ICU Intensive Care Unit FLOOR Normal room EXIT-ALIVE Leave the hospital alive EXIT-DEAD Leave the hospital dead
  • 31.
    Improve the Qualityof Care Pa$ent  ID:  45851733     Pa$ent  ID:  45851732     12/02/2008  14:26  Arrival   12/02/2008  14:26I  D:  45851731 Pa$ent   Emergency       Emergency Department 12/02/2008  14:26  Arrival   6,000+ 12/02/2008  22:44  ICU  mergency   12/02/2008  14:26  E   12/05/2008  05:071Floor  Arrival   12/02/2008     4:26 12/02/2008  22:44  ICU  mergency   12/02/2008  14:26  E 12/08/2008  10:02  Floor   12/05/2008  05:07  Floor   12/02/2008  22:44  ICU   12/14/2008  06:19  Discharge   12/08/2008  10:02  Floor   12/05/2008  05:07  Floor     12/14/2008  06:19  Discharge   12/08/2008  10:02  Floor     12/14/2008  06:19  Discharge   patients per month  
  • 32.
    task Find “Bounce backs” ICU Floor ICU within 2 days Limitations High-level questions Arrival ? ICU ? ?
  • 33.
    patients x x xxx x x x xx x xx x x x x x x x x x x
  • 34.
    LifeFlow overview xx x x xx x x x xx [Wongsuphasawat et al. 2011]! http://www.cs.umd.edu/hcil/lifeflow!
  • 35.
  • 38.
  • 39.
  • 40.
    collected visual representation large eye interactions rich EMRs + INFO. VIS. A lot of data! Help understand data!     and more… Lifelines LifeFlow Save more lives Lifelines 2 Patientslikeme / i2b2 / BTRIS Many case studies / etc. Krist wongsuphasawat @kristwongz kristw@cs.umd.edu! http://www.cs.umd.edu/hcil/temporalviz!  
  • 41.
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  • 43.