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From Simple to Sophisticated:Using Event and Claims
Data to Drive Action
Tim C. Over
Senior Vice President, Specialty Operations
Ann D. Gaffey, RN, MSN, CPHRM, DFASHRMSVP, Healthcare Risk Management and Patient Safety
Sedgwick
Too much?Too little?No idea what to do with it?Waste of time?
SIX CORE DATA BUILDING BLOCKS TO CONSIDER…
…as you contemplate use of data• Data governance• Data acquisition• Data sharing• Integration• Standardization• Analytics
CSC White Paper: Transforming Healthcare Through Better Use of Data (2012)
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DATA – WHERE DO YOU EVEN BEGIN?
• Event Data• Claims Data• Patient Satisfaction Data• Complaint Data• Billing Data
• Industry Benchmarks
• Newer Sources of Datao Medicare Payment Data
o Sunshine Act Data
CAN YOU “MINE” YOUR DATA?
• Event data – Medication Event
• Improper Order (Patient Allergic)– Metadata in EHR
» Provider ignored an alert about a drug allergy
• Claims data– Frequency of claims related to patient falls
• Facility with highest frequency– Event location
» ??????
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ONE FACILITY’S JOURNEY – CLAIMS DATA
Facility Frequency Severity
A 25 $834,455B 28 $809,688C 26 $585,951D 16 $501,939E 35 $326,077
TOTAL 130 $3,058,110
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Data Analytics Summary - Loss Control Characteristics
• The loss leader in both claim frequency and severity (indemnity/expense dollars paid) are events related to patient falls.
• Dollars paid on patient fall Claims account for 47% of the total severity.
• Most patient falls occurred at Facility E, however the cost-driver patient fall Claims originated from Facility A.
A B C D E$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
$900,000
0
5
10
15
20
25
30
35
40
Frequency and Severity of Patient Fall Claims by Facility
Tota
l Ind
emni
ty a
nd E
xpen
se $
Pai
d
ONE FACILITY’S JOURNEY – CLAIMS DATA
Injury Type Frequency SeverityFracture(s) 252 $6,468,661Hematoma 28 $2,166,128Laceration 25 $276,954Abrasion 15 $103,926
Contusion 12 $16,000TOTAL 332 $9,031,669
Data Analytics Summary - Loss Control CharacteristicsThe top injuries related to patient and visitor falls are:
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Fracture(s) Hematoma Laceration Abrasion Contusion0
50
100
150
200
250
300
$0
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
$7,000,000
252
28 2515 12
Top injuries related to patient and visitor falls by Frequency and Severity
Frequency Severity
DOES THIS DATA HELP ME?
…OR DOES THIS HELP ME MORE?
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Patient 3 16 35 71 69 78 62 87 57 21
Visitor 0 4 27 44 57 69 55 60 83 57
10
30
50
70
90
110
130
150
Breakdown of Fall Events by Category and Policy Period
Num
ber
of F
alls
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HOUSTON, WE HAVE A PROBLEM!
A B C D E F G H I J K
Patient 25 13 12 26 10 35 28 9 10 28 8
Visitor 31 40 36 20 29 4 7 22 20 1 21
2.5
7.5
12.5
17.5
22.5
27.5
32.5
37.5
42.5
Facilities with the highest frequency of patient/visitor fall events 2003-2013 Policy Periods
Num
ber o
f fal
ls
HOUSTON, WE HAVE A PROBLEM!
WHAT IS THIS TELLING US?
• CLAIMS DATA SHOWING– Significant frequency with falls– Spending lots of $$ on something that shouldn’t happen
so often– It’s not just patients
BUT WE HAVE AN EXCELLENT FALL PREVENTION PROGRAM!– Tons of data– Lots of people looking at it
– What are we missing?
AN OUTSIDE SET OF EYES
The “best” facilities The “worst” facilities Is it REALLY best practices? If yes, then what continues to be the
problem? Great data available Right idea, wrong approach?
A DREAM COME TRUE!
SLICING AND DICING
– Data collection was detailed– Data was centralized– Data was analyzed…monthly– Date was NOT analyzed with a
longitudinal view of event experience over time
– New analysis drove more meaningful change
TAKING ADVANTAGE OF VOLUMES OF DATA
TAKING ADVANTAGE OF VOLUMES OF DATA
CAN DATA BE USED TO DRIVE CHANGE?
• Consider the urgency of your “problem”– Is there a high potential for patient harm,
sooner rather than later?– Is the intervention proposed expensive?– Can it be tested easily?– Can you develop metrics to measure
outcomes?
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IS IT POSSIBLE TO DEMONSTRATE SUCCESS?
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DEMONSTRATING THE INTERVENTIONS WERE SUCCESSFUL
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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013*
Actual 21 23 3 17 26 37 64 51 31 52 26 20 18
Forecast 21 23 3 17 26 37 64 51 31 52 26 20 24
5
15
25
35
45
55
65
Frequency of Pressure UlcersComparison of Actual Frequency vs. Forecast
# of
Pre
ssur
e U
lcer
s
ANOTHER SAMPLE METRIC
AAZYX
WVUTSRPONMLKLI
HGFEDCBA
0 50 100 150 200 250 300 350324
272227
172146
139137
131128
121117
115115
109102
8375
6866
6150
4441
342828
Incident Date to Report to Risk Management Date
Number of Days
WHAT ABOUT BENCHMARKING?
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Anupam, B., Seabury, S., Lakdawall, D.,and Chandra, A. (2011). Malpractice Risk According to Medical Specialty. New England Journal of Medicine, 365(7), 629-636
USING DATA TO DRIVE ACTION
• Evaluate the availability, access and integrity of your data• Consider what you ultimately want to measure when setting
up systems• Use a system that allows for ease of data input and robust
analysis• Ensure you have the right team in place to analyze the data• Recognize there is more than one way to look at what’s in
front of you• Be open to implementing small changes, and using rapid cycle
improvement opportunities to test and revise
Questions and CommentsThank you!
Tim C. OverSVP, Specialty [email protected]
Ann D. GaffeySVP, Healthcare Risk Management and Patient Safety
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