sherrilynne fuller, phd co-director, center for public health informatics

Decision Support in the Public Health Practice Environment: Opportunities and ChallengesDecision Support for Public Health Practice: Research Findings in the Development of Knowledge Management and Disease Surveillance Tools
University of Ljubljana
July 10, 2007
Sherrilynne Fuller, PhD
Professor Biomedical and Health Informatics,
School of Medicine
Center for Public Health Informatics University of Washington
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NOTE: Map NOT to Scale for Alaska
Seattle, Washington
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University of Washington
Technology & Design
Administrative Core A
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School of Public Health & Community Medicine
Division of Biomedical Informatics, School of Medicine
School of Nursing
Information School
Community Partnerships
Role of Medical Care in 20th Century Public Health Achievements
RA Patrick O’Carroll, MD Region X Health Administrator
PH Achievement
No
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The greatest health gains for populations have derived from initiatives that had little to
do with treatment of illness.
RA Patrick O’Carroll, MD Region 10 Health Administrator
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Clinical (patient) information to support chronic disease interventions in communities: what is the minimum data set?
Situational awareness data exchange – how to do in a focused, timely, comprehensive way?
PH clinical data (e.g. immunization, disease status, relevant community information) to electronic health record?
Timely approaches to people and directory type information interchange?
Research findings – how to extract from the literature and present to practitioners?
Utilization of community health information for decision support for individual patients?
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2.Business process analysis and workflow characterization
Surveillance Data for PH practice
Collection and Analysis
Knowledge in Practice: The Challenge
Neither the creation nor the distribution of information resources* upon which public health practitioners depend is managed or presented in any systematic or comprehensive way at the present time
*data of all types, guidelines, research findings, maps, policies, laws, evaluation metrics, teaching materials, etc.
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Approach
Research workflow and information needs of public health practitioners to support evidence-based practice
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Information Needs of Public Health Practitioners: Observations
Few formal studies of information needs, information-seeking behavior or workflow of PH professionals
Rapidly expanding volume of data and information
Major barriers: time, resource reliability; discerning credibility of information
People are a critical source of information in public health
Silos of surveillance information – lack currency and context
Lack of linkages between individual patient information and public health information systems
Revere D, Turner A, Madhavan A, Rambo N, Bugni PF, Kimball AM, Fuller SS. Understanding the information needs of public health practitioners: A literature review to inform design of an interactive digital knowledge management system. J Biomed. Informatics 2007. Special Issue on Public Health Informatics.
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Information Needs of Public Health Practitioners
Resources used influenced by job function, disciplines & training
Want resources that are easy to access & use, up-to-date, free, pre-digested & stable, focused
One size/approach does not fit all; personal customization is highly desired
Revere D, Turner A, Madhavan A, Rambo N, Bugni PF, Kimball AM, Fuller SS. Understanding the information needs of public health practitioners: A literature review to inform design of an interactive digital knowledge management system. J Biomed. Informatics 2007. Special Issue on Public Health Informatics.
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Information from areas beyond biomedical domains: e.g. social, legal, policy
Systematic reviews & summary information
Evidence-based resources – e.g. full-text research articles; synthesized reviews of clinical topics
Data from community-based and clinical systems with geographic mapping and analysis capability
Information Needs of Public Health Practitioners: Resource Types
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Health information is most useful when it is available at the community level. However, limitations in the underlying data sources, statistical considerations and privacy protection requirements constrain the minimum population size for which data can be made publicly available – that is, how “local” the information can be…
Public health agencies are both sources and users of local health information.
Despite the utility of local health information technical barriers limit its availability and use. Many state and local health departments experience, culturla, organization structure and resource allocations are more strongly oriented to collecting data than to disseminating them.
Population health data sets are usually collected in response to specific legislative mandates or to answer specific research questions.; The structure, design and methods of data collection might make it difficult to make inferences for other purposes.
Why a Content Management System?
CMSs streamline the process of creating, managing
& delivering content, & put processes in place to manage & control information as it is moved & changed
Archive / Retire
Create / Capture
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Rapid Prototyping: Iterative Refinement Process
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Prototyping the Interface
Beta Version 3.0
Advanced retrieval tools
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Promotion began ramp-up - Feb 2007
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Core Workflow Problem
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Faculty from the UW-CPHI are participating in requirements definition projects:
Kitsap County Health District - chronic disease prevention and control (J. Baseman, A. Turner)
Spokane County Health District - public health preparedness (B. Karras)
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Notifiable condition reporting
Electronic forms to support notifiable condition reporting in a health information exchange infrastructure (B. Lober, J. Baseman, B. Karras)
Future: Bayesian networks for case identification
Algorithms for probabilistic reporting criteria
Geospatial mapping of disease incidence
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Outbreak detection, situational awareness, program monitoring & evaluation, community assessment
Multi-attribute Utility Theory
Time tradeoff
Children exposed to a child w/. suspected measles. Low prior probability confirmation needed. Time tradeoff w/ efficacy of vaccination. Utility of a lab result in N hours?
Subjects/Sampling/Instrument
Email request sent to County PH Officers in WA
Interactive Web Survey - Tradeoff questions, rating scales and demographic questions
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Utility as a function of lab delay on time to act - measles outbreak
Data elements have tractable utility representations, and utility tradeoffs.
PH professionals place nonlinear value on time-to-act, preferences
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Utility
-5
-4
-3
-2
-1
All
ID
Area
Position
Years
Age
Gender
Q6
Q7
Q8
Q9
Q10
Q11_1
Q11_2
Q11_3
Q11_4
Q12
Q13
Q14
Q15
Q16
Q17
U6_EU
U7_EU
U8_EU
U9_EU
U10_EU
U6_PU
U7_PU
U8_PU
U9_PU
U10_PU
Lamda_6
Lamda_7
Lamda_8
Lamda_9
Lamda_10
R_6
R_7
R_8
R_9
R_10
Ratio24_12
Ratio24_13
Ratio24_14
Ratio48_15
Ratio48_16
Ratio48_17
Ratio24_12_14
Ratio48_15_17
17
County
4
53
Female
71.5
70
69
60
58
9
8
10
7
12
12
20
24
24
40
-6
-7
-8
-9
-10
10
9
8
7
6
-0.0251
-0.0278
-0.0301
-0.0366
-0.0397
0.5393
0.5172
0.4911
0.4753
0.4413
0.5000
0.5000
0.0833
0.5000
0.5000
0.0833
0.0417
0.0417
30
County
3
65
Female
24
23
22
20
15
9
9
10
10
8
8
23
8
8
24
-6
-7
-8
-9
-10
10
9
8
7
6
-0.0747
-0.0846
-0.0945
-0.1099
-0.1535
0.7245
0.7008
0.6727
0.6496
0.6616
0.6667
0.6667
0.0278
0.8333
0.8333
0.4167
0.0185
0.3472
35
County
1
60
Male
64
56
45.8
36.6
29.2
10
7
9
9
20
23
22
24
44
40
-6
-7
-8
-9
-10
10
9
8
7
6
-0.0280
-0.0347
-0.0454
-0.0600
-0.0789
0.5537
0.5458
0.5437
0.5405
0.5310
0.1667
0.0417
0.0139
0.5000
0.0833
0.0833
0.0023
0.0417
42.2894736842
31.2105263158
26.0736842105
21.1789473684
18.7727777778
Question 2. Which one of the following categories best describes your job position or primary role?
Question 3. For how many years have you worked in your current position?
Question 1. Which one of the following best describes your current area of public health service?
Question 4. What is your age?
Question 5. What is your gender?
Question 6. In the text box, please choose a number* greater than 10 hours and less than 72 hours that makes Lab A and Lab B about equally preferable to you. (* decimal points are allowed.) High volume days Low volume days Lab A 72 hours 2 hours Lab B {ANSWER} hours 10 hours
Question 7. In the text box, please choose a number greater than 10 hours and less than {INSERTANS:1X3X6} hours that makes Lab A and Lab B about equally preferable to you. High volume days Low volume days Lab A {INSERTANS:1X3X6} hours 2 hours Lab B {ANSWER} hours 10 hours
Question 11. Given the measles scenario described previously, please give us a number between 1 and 10 to indicate the importance of the following characteristics of the laboratory results to your investigation: Timeliness - the amount of time (in hours) it takes to obtain data once they have been requested. Relevance - the pertinence of the data to the public health task at hand (in this case the task is outbreak investigation). Accuracy - the proportion of data received that is free from error. Completeness - the proportion of cases for which data values are present (i.e., the proportion of cases for which data values are not missing). [Timeliness]
Question 11. Given the measles scenario described previously, please give us a number between 1 and 10 to indicate the importance of the following characteristics of the laboratory results to your investigation: Timeliness - the amount of time (in hours) it takes to obtain data once they have been requested. Relevance - the pertinence of the data to the public health task at hand (in this case the task is outbreak investigation). Accuracy - the proportion of data received that is free from error. Completeness - the proportion of cases for which data values are present (i.e., the proportion of cases for which data values are not missing). [Relevance]
Question 11. Given the measles scenario described previously, please give us a number between 1 and 10 to indicate the importance of the following characteristics of the laboratory results to your investigation: Timeliness - the amount of time (in hours) it takes to obtain data once they have been requested. Relevance - the pertinence of the data to the public health task at hand (in this case the task is outbreak investigation). Accuracy - the proportion of data received that is free from error. Completeness - the proportion of cases for which data values are present (i.e., the proportion of cases for which data values are not missing). [Accuracy]
Question 11. Given the measles scenario described previously, please give us a number between 1 and 10 to indicate the importance of the following characteristics of the laboratory results to your investigation: Timeliness - the amount of time (in hours) it takes to obtain data once they have been requested. Relevance - the pertinence of the data to the public health task at hand (in this case the task is outbreak investigation). Accuracy - the proportion of data received that is free from error. Completeness - the proportion of cases for which data values are present (i.e., the proportion of cases for which data values are not missing). [Completeness]
Question 12. Below is a table describing the relevance and timeliness of two types of data. Please review the table and answer the question that follows. Data Type* Relevance When available Chief Complaint Relevant Immediately ER Discharge Diagnosis Very relevant Delayed * Both data types have equal numbers of missing or miscoded values Assume that if data arrive after 24 hours then they are of no use to you. Please choose a number of hours (less than 24 hours) that you would be willing to wait for ER discharge diagnosis data to arrive so that you are indifferent to choosing between these very relevant and only relevant data types.
Question 13. Below is a table describing the relevance and timeliness of two types of data. Please review the table and answer the question that follows. Data Type* Relevance When available ER Log Visits Somewhat relevant Immediately ER Discharge Diagnosis Very relevant Delayed * Both data types have equal numbers of missing or miscoded values Assume that if data arrive after 24 hours then they are of no use to you. Please choose a number of hours (less than 24 hours) that you would be willing to wait for ER discharge diagnosis data to arrive so that you are indifferent to choosing between these very relevant and only somewhat relevant data types.
Question 14. Below is a table describing the relevance and timeliness of two types of data. Please review the table and answer the question that follows. Data Type* Relevance When available ER Log Visits Somewhat relevant Immediately Chief Complaint Relevant Delayed * Both data types have equal numbers of missing or miscoded values Assume that if data arrive after 24 hours then they are of no use to you. Please choose a number of hours (less than 24 hours) that you would be willing to wait for chief complaint data to arrive so that you are indifferent to choosing between these relevant and only somewhat relevant data types.
Question 16. Below is a table describing the relevance and timeliness of two types of data. Please review the table and answer the question that follows. Data Type* Relevance When available ER Log Visits Somewhat relevant Immediately ER Discharge Diagnosis Very relevant Delayed * Both data types have equal numbers of missing or miscoded values Assume that if data arrive after 48 hours then they are of no use to you. Please choose a number of hours (less than 48 hours) that you would be willing to wait for ER discharge diagnosis data to arrive so that you are indifferent to choosing between these very relevant and only somewhat relevant data types.
Question 17. Below is a table describing the relevance and timeliness of two types of data. Please review the table and answer the question that follows. Data Type* Relevance When available ER Log Visits Somewhat relevant Immediately Chief Complaint Relevant Delayed * Both data types have equal numbers of missing or miscoded values Assume that if data arrive after 48 hours then they are of no use to you. Please choose a number of hours (less than 48 hours) that you would be willing to wait for chief complaint data to arrive so that you are indifferent to choosing between these relevant and only somewhat relevant data types.
Utility set for Q6 under EU: U(t)=-exp(-λt)
Utility set for Q10 under EU: U(t)= - exp(-λt)
Utility set for Q6 under PU: U(t)=t^r
U(t)=-exp(-λt) λ=-log(-U'(t))/t U'(t) is from U6_EU t is from Q6 (hours)
Ratio=(24-Q13)/24
Ratio=Ratio48_15*((48-Q17)/48)
U(t)=t^r r=ln(U(t))/ln(t) U(t) is from U6_PU t is from Q6 (hours)
Ratio=(24-Q12)/24
Exclusion
Graph1
Disutility
0
0
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Lerping
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Ning:
Delay
Utility
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Delay
Utility
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ID
M
17
0.021
ID
17
18
19
21
22
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26
27
28
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30
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32
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36
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18
0.042
M
0.021
0.042
0.021
0.042
0.021
0.125
0.050
0.028
0.042
0.500
0.021
0.021
0.125
0.083
0.021
0.028
0.042
0.021
0.098
19
0.021
21
0.042
22
0.021
23
0.125
26
0.050
27
0.028
28
0.042
29
0.500
30
0.021
31
0.021
32
0.125
33
0.083
34
0.021
35
0.028
36
0.042
37
0.021
38
0.098
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
M
M
ID
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ID
Q6
Q7
Q8
Q9
Q10
ID
x0
x1
x2
x3
x4
x5
CC_48_delay
M
DD_48
Q15_48
17
24
18
14
12
11
17
0
48
54
58
60
61
36
0.0208333333
48
12
18
48
24
18
12
11
18
0
24
48
54
60
61
30
0.0416666667
48
18
19
24
12
10.5
10.25
19
0
48
60
61.5
61.75
72
24
0.0208333333
48
24
21
48
24
18
12
11
21
0
24
48
54
60
61
44
0.0416666667
48
4
22
24
12
10.1
10.05
10.01
22
0
48
60
61.9
61.95
61.99
24
0.0208333333
48
24
23
64
56
48
40
32
23
0
8
16
24
32
40
24
0.125
48
24
26
60
40
30
24
23
26
0
12
32
42
48
49
24
0.05
48
24
27
36
24
18
12
11
27
0
36
48
54
60
61
1
0.0277777778
48
47
28
48
36
24
16
11
28
0
24
36
48
56
61
24
0.0416666667
48
24
29
71.5
70
69
60
58
29
0
0.5
2
3
12
14
24
0.5
48
24
30
24
23
22
16
15
30
0
48
49
50
56
57
24
0.0208333333
48
24
31
24
23
22
21
20
31
0
48
49
50
51
52
36
0.0208333333
48
12
32
64
56
48
40
32
32
0
8
16
24
32
40
36
0.125
48
12
33
48
36
24
20
16
33
0
24
36
48
52
56
47
0.0833333333
48
1
34
24
23
22
20
15
34
0
48
49
50
52
57
40
0.0208333333
48
8
35
36
24
23
18
12
35
0
36
48
49
54
60
24
0.0277777778
48
24
36
48
24
18
12
10.5
36
0
24
48
54
60
61.5
12
0.0416666667
48
36
37
24
12
11
10.5
10.2
37
0
48
60
61
61.5
61.8
24
0.0208333333
48
24
38
64
56
45.8
36.6
29.2
38
0
8
16
26.2
35.4
42.8
24
0.0980392157
48
24
Question 6. In the text box, please choose a number* greater than 10 hours and less than 72 hours that makes Lab A and Lab B about equally preferable to you. (* decimal points are allowed.) High volume days Low volume days Lab A 72 hours 2 hours Lab B {ANSWER} hours 10 hours
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Form instance and data instance
Demonstration showcased:
Provider engagement
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These are the ideas I want to come back to
Lung Cancer in Washington State
Age Adjusted rates
Mortality: ICD-9 (1990-1998): 162.2-162.9, ICD-10 (1999- ): C34
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Items on this mock-up are approximately what I need. We might tweak some, but I do not see any major changes. There is a chart at the National Cancer Institute that I want to investigate. I suggest you make it easy to move things around. We are likely going to want to change the location of things as we go.
For the tables, it would be useful for the user to select the same selections that are available on the WSCR website but the incidence and mortality would appear in the same table in different columns. The table would appear in another window and be downloadable. Can you allow the table to be visible and at the same time prepare the table in a .DBF format? This would be very useful.
Not the selection rules on the web site. For example selecting a table by single year is not possible for counties, only the state. For race and ethnicity, only state data is available.
I might want to specify an additional table format, a format that will fit into JoinPoint, a popular package from NCI that determines time trends. Someday we will provide that analysis for them.
With regard to the maps, I can generate static images for the time being for each cancer.
If you do find a mapping package, the outline of Adams county is not needed, I lifted these from the pdf I sent you. And the data would be divided up in quantiles. I have 6 here, 5 would do, each representing 20% of the data.
The legend would probably be better in a horizontal format.
Note that the state rate is part of the legend. There should be an addition which indicates that the hatched counties are statistically significantly different than the state. I will have to look and see how the stat significance is noted in the data I sent you. Likely I can give you a rule to use when the map is being constructed. If we use a Java mapping package that allows layers, then I think it would be straight forward. The same sort of info will be needed for the county bar charts.
I’ll come up with an epirical rule that is easy to implement.
Several of the JAVA map packages appear with lots of controls and choices, fill up the entire screen. We want just simple images like this for now.
Trend Charts: Fonts need to be readable, letters large enough to read. When user saves image
It needs to be useable in a report. Or perhaps clicking on it will open a window with a much larger image that can be saved.
County bar charts: These are not lung CA data, both colon ca incidence I happened to have.
One legend, “lower” or “higher” refers to statistical significance. Title of graph needs to have “years 2002-2004”
The display is in decreasing incidence. The numbers at the end of the CIs are not essential. Can be left off.
There may be some charts where CIs of some counties are so wide that it distorts the chart.
You can set up a rule that if the width is beyond a certain percentage of the x axis that the CI is not plotted. Then in the legend one would make some not of that.
Also counties should NOT be on the chart if the total number of cases or deaths in the 2002-2004 period is lt 9. Be sure to code this so this rule is
Easy to fix.
Race distribution. Title need to be properly places, no “*” where they are but put one after “Hispanic” and the legend should be “* all races” (Hispanic incidence/mortality of all races)
Chart3
Mason
12.0440971164
10.3591441104
Clallam
9.3404666003
8.3642486006
AArateT
57.9189545771
56.7058825025
55.8424640556
54.2420179438
54.1168812236
53.2175493344
52.7876294045
52.4515473177
52.2888421288
51.6513696922
51.5808551183
51.1078431421
51.0747054837
48.8784519595
48.4412358596
48.3532795156
48.2825381247
48.2493606746
47.9790577272
47.2112534916
44.7225740122
44.5031723157
43.7520891499
43.0718339282
42.6384966199
42.6246312464
42.4354878377
42.0779617122
41.9101741892
41.679928661
39.9610003675
39.0117418491
35.4048025781
TimeTrendInc
Time Trend Data and Chart
In the white cells below type the values for your chart: In Cell C4 type the title, in Cell C5 type a subtitle if there is one. In Cell C6 type the rate type and year title. In Cell C7 type the Healthy People 2010 goal In Cells B8 to C28 type your data v
Outcome
Colorectal Cancer, Incidence WA State and US WSCR and SEER
WA State and US
WA State and US
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Age/Gender Data and Chart
In the white cells below type the values for your chart: In Cell C4 type the title, in Cell C5 type the rate, year subtitle. In Cells B7 to C17 type your data values for the Male and Female If you have confidence interval data: In Cells D7 to D17 ty
Outcome
Colorectal Cancer Incidence Age and Gender Washington State Cancer Registry, 2002-2004
Data Source & Years
Female
Male
Race & Hispanic Inc
Race/Ethnicity Data and Chart (4 race, 1 hispanic group)
In the white cells below type the values for your chart: In Cell C4 type the title, in Cell C5 type rate and year subtitle. In Cells B7 to B12 type your data values for the rate.
Outcome
Colorectal Cancer Incidence
Colorectal Cancer Incidence Race and Hispanic Origin WA State Cancer Registry 2003-2005
Data Source
Rate
age-adjusted rate per 100,000
Income and Education screening
Annual Household Income/Education Data and Chart
Outcome
Recent Colorectal Screening*
Recent Colorectal Screening* Annual Household Income and Education WA State BRFSS 2004
Data Source
55.6
3.5
3.6
48.7
6
5.9
*Recent colorectal cancer screening: report of FOBT in the past year and/or sigmoidoscopy in the past 5 years or colonoscopy in the past 10 years
% Recently Screened
County Incd
County Data
In cells C4 to C43 you may, but are not required to, enter the counts. In cells D4 to D43 enter the rates to be displayed in the map for your chapter. In cells F3 to F7 enter the information describing your data. Change the (YYYY) to the year or year rang
Outcome
County Code
AArateT
Lung Cancer Incidence County Data
Instructions for putting the bars in correct order. 1) Enter the data into the template 2) Select cells A6 to E45 3) Select "Tools" from the excel menu bar 4) Select "Sort" 5) In the "Sort" dialog box, first drop down list, select "Rate" and the Descending option 6) In the second drop down list select "Name" and the Ascending option 7) The "My data range has" should be set to "Header row" 8) Click OK button.
Instructions for formatting WA State bar and bars significantly higher or lower. To format a bar, 1) Left click on any bar in the chart to select all bars 2) Wait a second or two and then, left click on the specific bar you want to format. 3) Once one bar is selected, right click on the bar and select "format data point" by left clicking on that option. 4) Go to the "Area" portion of the screen and format as follows WA State bar 1) Left Click on the dark gray color square in 8th Column (far right column), 2nd Row. 2) Left click on fill effects 3) Left click on the "Pattern" tab 4) Left click on the pattern in the 1st Column and Last Row. 5) In the foreground dropdown list Left Click on the dark gray color square in 8th Column (far right column), 2nd Row. 6) In the background dropdown list Left Click on the white color square in 8th Column (far right column), 5th Row. 7) Left click on OK 8) Left click on OK Bars that are statistically significantly higher than WA 1) Left Click on the dark gray color square in 8th Column (far right column), 2nd Row. 2) Left click on OK Bars that are statistically significantly lower than WA 1) Left Click on the dark white color square in 8th Column (far right column), 5th Row. 2) Left click on OK
Chart5
Mason
12.0440971164
10.3591441104
Clallam
9.3404666003
8.3642486006
AArateT
57.9189545771
56.7058825025
55.8424640556
54.2420179438
54.1168812236
53.2175493344
52.7876294045
52.4515473177
52.2888421288
51.6513696922
51.5808551183
51.1078431421
51.0747054837
48.8784519595
48.4412358596
48.3532795156
48.2825381247
48.2493606746
47.9790577272
47.2112534916
44.7225740122
44.5031723157
43.7520891499
43.0718339282
42.6384966199
42.6246312464
42.4354878377
42.0779617122
41.9101741892
41.679928661
39.9610003675
39.0117418491
35.4048025781
TimeTrendInc
Outcome
WA State and US
WA State and US
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Outcome
Data Source & Years
Female
Male
Race & Hispanic Inc
Outcome
Colorectal Cancer Incidence
Data Source
Rate
age-adjusted rate per 100,000
Outcome
Data Source
55.6
3.5
3.6
48.7
6
5.9
% Recently Screened
County Incd
County Data
Outcome
County Code
AArateT
Lung Cancer Mortality County Data
Chart6
White*
1.089813825
1.0718890035
Hispanic
6.9534422292
5.7937366949
Black*
8.9868987265
7.8933970504
47.2674222051
31.3015351401
56.7324842996
40.7975166591
58.4
TimeTrendInc
Outcome
WA State and US
WA State and US
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Outcome
Data Source & Years
Female
Male
Race & Hispanic Inc
Outcome
Data Source
Outcome
Data Source
55.6
3.5
3.6
48.7
6
5.9
% Recently Screened
County Incd
County Data
Outcome
County Code
AArateT
Chart7
White*
1.089813825
1.0718890035
Hispanic
6.9534422292
5.7937366949
Black*
8.9868987265
7.8933970504
47.2674222051
31.3015351401
56.7324842996
40.7975166591
58.4
TimeTrendInc
Outcome
WA State and US
WA State and US
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Outcome
Data Source & Years
Female
Male
Race & Hispanic Inc
Outcome
Data Source
Outcome
Data Source
55.6
3.5
3.6
48.7
6
5.9
% Recently Screened
County Incd
County Data
Outcome
County Code
AArateT
Chart8
35-44
35-44
0
0
0
0
45-54
45-54
0
NaN
0
0
55-64
55-64
0.9705005677
0.0366357617
3.0502131088
0.8797863906
65-74
65-74
6.5810282761
3.230545024
4.3134341409
1.6532112551
75-84
75-84
18.6947628501
12.9509942273
15.4571876269
10.2562342772
85+
85+
61.139269822
49.5522195499
47.4444793408
37.3843812802
Female
Male
12.6917603517
11.5481313632
42.2283679782
40.9029791937
85.5388411599
103.0768145491
195.0973550024
239.2085489149
330.3722575211
359.1193142786
366.9857181965
390.9939600952
TimeTrendInc
Outcome
WA State and US
WA State and US
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Outcome
Data Source & Years
Race & Hispanic Inc
Outcome
Data Source
Outcome
Data Source
55.6
3.5
3.6
48.7
6
5.9
% Recently Screened
County Incd
County Data
Outcome
County Code
AArateT
Chart9
35-44
35-44
0
0
0
0
45-54
45-54
0
NaN
0
0
55-64
55-64
0.9705005677
0.0366357617
3.0502131088
0.8797863906
65-74
65-74
6.5810282761
3.230545024
4.3134341409
1.6532112551
75-84
75-84
18.6947628501
12.9509942273
15.4571876269
10.2562342772
85+
85+
61.139269822
49.5522195499
47.4444793408
37.3843812802
Female
Male
12.6917603517
11.5481313632
42.2283679782
40.9029791937
85.5388411599
103.0768145491
195.0973550024
239.2085489149
330.3722575211
359.1193142786
366.9857181965
390.9939600952
TimeTrendInc
Outcome
WA State and US
WA State and US
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Outcome
Data Source & Years
Race & Hispanic Inc
Outcome
Data Source
Outcome
Data Source
55.6
3.5
3.6
48.7
6
5.9
% Recently Screened
County Incd
County Data
Outcome
County Code
AArateT
Conclusion
Integrated PH decision support systems are in their infancy in the U.S.
We can learn from lessons of clinical decision support system design and deployment
It is critical that we figure out how to optimize timely data exchange of critical information between clinical and public health information systems
Much more work is needed to understand information needs and practice in order to optimize the decision support environment for public health practitioners
Centerfor Publ ic Health Informatics University of Washington
Centerfor Publ ic Health Informatics University of Washington
*

Revere D, Madhavan A, Kimball AM, Turner A, Bugni P, Fuller S. myPublicHealth: Research in Public Health Knowledge Management to Support Evidence-Based Practice. In Proceedings of the CDC's Public Health Information Network (PHIN) Conference, Sept 2006. Atlanta GA.
*
Bibliography – Cont.
Revere D, Madhavan A, Kimball AM, Turner A, Bugni P, Fuller S. myPublicHealth: Research in Public Health Knowledge Management to Support Evidence-Based Practice. In Proceedings of the CDC's Public Health Information Network (PHIN) Conference, Sept 2006. Atlanta, GA
Doctor, J. Baseman, JG, Lober, BD, Davies, J., Kobayashi, J. , Karras. B., Fuller, S. Time tradeoff utilities for identifying and evaluating a minimum data set for event detection in time-critical biosurveillance. Submitted: Medical Decision Making.
Hoskins RE, O’Connor C, Johnson C, O’Carroll P, Fuller S. EpiQMS: An Internet Application for Access to Public Health Data for Citizens, Providers, and Public Health Investigators. Journal of Public Health Management and Practice, 8(3):30-36, 2002.
*
*
*
School of Public Health & Community Medicine
Division of Biomedical Informatics, School of Medicine
School of Nursing
Information School
Community Partnerships
Utility

sherrilynne fuller, phd co-director, center for public health informatics

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