Creating Value Unifying Silos into Public Health Business Intelligence

Description
Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers.

EDM Forum
EDM Forum Community
0GE!< (G080;,=482 E?4/08.0 & !0=39/< =9
47:;9?0 :,=408= 9>=.970<)
$>-64<3
3-30-2015
Creating Value: Unifying Silos into Public Health
Business Intelligence
Arthur J. Davidson
Denver Public Health, Denver Health, A;=3>;.D,?4/<98@/33,.9;2
F9669@ =34< ,8/ ,//4=498,6 @9;5< ,=: 3L:://;0:9<4=9;B.,.,/07B30,6=3.9;2/0207<
$,;= 91 =30 H0,6=3 '0;?4.0< &0<0,;.3 C97798<
H4< 0,;8482 H0,6=3 'B<=07 &0?40@ 4< -;9>23= =9 B9> 19; 1;00 ,8/ 9:08 ,..0<< -B =30 =30 $>-64<3 ,= ED! F9;>7 C977>84=B. I= 3,< -008 :00;-
;0?40@0/ ,8/ ,..0:=0/ 19; :>-64.,=498 48 0GE!< (G080;,=482 E?4/08.0 & !0=39/< =9 47:;9?0 :,=408= 9>=.970<).
H0 E60.=;984. D,=, !0=39/< (ED!) F9;>7 4< <>::9;=0/ -B =30 A208.B 19; H0,6=3.,;0 &0<0,;.3 ,8/ %>,64=B (AH&%), G;,8= 1)18H'022789-01.
0GE!< :>-64.,=498< /9 89= ;0J0.= =30 9G.4,6 ?40@< 91 AH&% 9; =30 )84=0/ '=,=0< D0:,;=708= 91 H0,6=3 ,8/ H>7,8 '0;?4.0<.
&0.97708/0/ C4=,=498
D,?4/<98, A;=3>; J. (2014) "C;0,=482 *,6>0: )841B482 '469< 48=9 $>-64. H0,6=3 B><480<< I8=0664208.0," eGEMs (Generating Evidence &
Methods to improve patient outcomes): *96. 2: I<<. 4, A;=4.60 8.
D#I: 3L::///A./94.9;2/10.13063/2327-9214.1172
A?,46,-60 ,=: 3L:://;0:9<4=9;B.,.,/07B30,6=3.9;2/0207</?962/4<<4/8
Creating Value: Unifying Silos into Public Health Business Intelligence
Abstract
Introduction: H;9>23 '0:=07-0; 2014, 10/0;,6 48?0<=708=< 48 30,6=3 4819;7,=498 =0.389692B 3,?0 -008
>8:;0.0/08=0/, @4=3 79;0 =3,8 25 -466498 /966,;< 48 48.08=4?0 1>8/< /4<=;4->=0/ =9 06424-60 39<:4=,6< ,8/
:;9?4/0;<. #?0; 85 :0;.08= 91 06424-60 )84=0/ '=,=0< 39<:4=,6< ,8/ 60 :0;.08= 91 06424-60 :;9?4/0;< 3,?0 ><0/
.0;=4I0/ 060.=;984. 30,6=3 ;0.9;/ (EH&) =0.389692B ,8/ ;0.04?0/ !0,84821>6 )<0 48.08=4?0 1>8/< (HI(ECH
A.=
1
).
Technology: C0;=4I0/ EH& =0.389692B .9>6/ .;0,=0 80@ :>-64. 30,6=3 ($H) ?,6>0 =3;9>23 89?06 ,8/ ;,:4/6B
0?96?482 /,=,-><0 9::9;=>84=40<, 80?0; -019;0 0A:0;408.0/ -B $H. H0 6982-<=,8/482 E<469F ,::;9,.3 =9
1>8/482 3,< 1;,2708=0/ $H :;92;,7< ,8/ /0:,;=708=<,
2
->= =30 .97:9808=< 19; 48=02;,=0/ -><480<<
48=0664208.0 (4.0., =996< ,8/ ,::64.,=498< =9 306: ><0;< 7,50 4819;70/ /0.4<498<) ,8/ 7,A47,66B ;0><0 /,=, ,;0
,?,46,-60 89@.
Systems: C3,660820< 1,.0/ -B $H ,208.40< 98 =30 ;9,/ =9 48=02;,=498 ,;0 :608=41>6, ->= ,8 07:3,<4< 98 $H
<B<=07< ,8/ <0;?4.0< ;0<0,;.3 ($H''&) 7,B 4/08=41B 2,:< ,8/ <96>=498< 19; =30 $H .977>84=B =9 ,//;0<<.
Conclusion: (0.389692B ,8/ <B<=07 ,::;9,.30< =9 60?0;,20 =34< 4819;7,=498 0A:69<498 =9 <>::9;= ,
=;,8<19;70/ 30,6=3 .,;0 <B<=07 ,8/ :9:>6,=498 30,6=3 ,;0 :;9:9<0/. BB 9:=474C482 =34< 4819;7,=498
9::9;=>84=B, $H .,8 :6,B , 2;0,=0; ;960 48 =30 60,;8482 30,6=3 <B<=07.
Acknowledgements
H0 :,:0; @,< 7,/0 :9<<4-60 =3;9>23 =30 1>8/482 91 =30 &9-0;= +99/ J938<98 F9>8/,=498 ,8/ =30
60,/0;<34: 91 A.,/07BH0,6=3 48 9;2,84C482 ,8/ <>::9;=482 =34< ,>=39; ,8/ 9=30;<. H0 ,>=39; @9>6/ 6450 =9
=3,85 =30 7,8B =39>23=1>6 48/4?4/>,6< @39 .97708=0/ 98 ?,;49>< 0,;640; /;,K< 91 =34< :,:0;: ,898B79><
7,8><.;4:= ;0?40@0;<, ,< @066 ,< '.9L A1C,6, 48/, B4630470;, $3D, J,70< +. B>0360;, !D, E64C,-0=3 C960,
E;48 H96?0, $3D, !$H, !$$, B,;-,;, F0;;0;, $3D, !$H, !0/, '0=3 F96/B, !D, !$H, J.$. 04/0;, $3D,
&0760 "0@=98-D,70, !$H, !,;2>0;4=0 J. &9, D;$H, C3,;60< J. &9=3@066, !BA, !', $,>6, '9:0;, !',
!$H, $!$, ,8/ !,L30@ C. '=40106, !$A.
Keywords
$>-64. H0,6=3, $H''&, -><480<< 48=0664208.0
Disciplines
H0,6=3 '0;?4.0< &0<0,;.3 D $>-64. H0,6=3
Creative Commons License
H4< @9;5 4< 64.08<0/ >8/0; , C;0,=4?0 C97798< AL;4->=498-"98.9770;.4,6-"9 D0;4?,=4?0 +9;5< 3.0
4.08<0.
H4< ;0?40@ 4< ,?,46,-60 ,= ED! F9;>7 C977>84=B: 3L:://;0:9<4=9;B.,.,/07B30,6=3.9;2/0207</?962/4<<4/8
Introduction
Te American health care system is a high-cost, low-yield invest-
ment. Despite spending nearly 50 percent more per capita than
most developed countries, the United States ranks 30
th
in many
comparisons of health status.
3
Health care reform seeks improved
population health as an outcome and anticipates greater value
through information technology investments that help transform
health care. In 2004,
4
President Bush declared a national goal
of an electronic medical record (EMR) for every American by
2014. By late 2014, more than 88 percent of all eligible hospitals
and more than 60 percent of Medicare and Medicaid outpatient
providers used an EMR to care for patients.
5
Te next generation
of EMRs, which allow for capture of data that extends to sources
outside of clinical settings, are referred to as electronic health
records (EHR),
4
and use of certifed EHR technology in American
health care has been accelerated by more than 25 billion dollars
in incentive funds distributed to eligible hospitals and providers
through the Health Information Technology for Economic and
Clinical Health (HITECH) Act.
1
Meaningful Use payments en-
courage eligible providers and hospitals to adopt, implement, and
upgrade certifed EHR technology.
Nationally, there is great hope for adapting these technology
investments to a learning health system
6
capable of comparative
efectiveness research
7
and patient-centered oriented research.
8

Given the barriers to interoperability, however, many EMRs are
still in the process to achieve the vision of the EHR. For this rea-
son, “EMR” and “EHR” will be used interchangeably through this
manuscript.
EMR and certifed EHR technology are tools implemented to
qualify for incentive payments and increase opportunities to
access standardized process and outcome measures of patient
care. Local public health (PH) agencies should promote the
concept of a local learning health system that benefts from these
national EHR investments. Among the opportunities presented by
these new sources of data is the ability for well-governed county,
regional, or state jurisdiction data sharing eforts to beneft from
federal investments to drive educated local decision-making.
When combined with routinely collected data (e.g., census, pop-
ulation surveys, socioeconomic and built environment), EHR-
based analyses can inform governmental planning, guide program
i
Denver Public Health
Abstract
Introduction: Through September 2014, federal investments in health information technology have been unprecedented,
with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible
United States hospitals and 60 percent of eligible providers have used certifed electronic health record (EHR) technology and
received Meaningful Use incentive funds (HITECH Act
1
).
Technology: Certifed EHR technology could create new public health (PH) value through novel and rapidly evolving data-
use opportunities, never before experienced by PH. The long-standing “silo” approach to funding has fragmented PH
programs and departments,
2
but the components for integrated business intelligence (i.e., tools and applications to help
users make informed decisions) and maximally reuse data are available now.
Systems: Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and
services research (PHSSR) may identify gaps and solutions for the PH community to address.
Conclusion: Technology and system approaches to leverage this information explosion to support a transformed health
care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the
learning health system.
eGEMs
Creating Value: Unifying Silos into Public Health
Business Intelligence
Arthur J. Davidson, MD, MSPH
i
1
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
development, evaluate policies and programs, support community
health assessment, and identify health disparities.
9
Beyond passive
receipt of data, as community leaders PH agencies should convene
stakeholders and encourage alignment eforts (e.g., nonproft
hospital IRS obligation for community health needs assessments,
10

accountable care organization quality measures,
11
and PH accred-
itation community health assessments
12
) to mutually beneft from
federal investments and potentially improve population health.
13
With local, interoperable data exchange, even more opportuni-
ties emerge for PH agencies to develop new ways of monitoring
essential PH service delivery. Using EMR data, service delivery
systems (e.g., hospitals, integrated networks, and Accountable
Care Organizations) have been able to measure and improve the
quality of care delivered.
14
With EMR-based population moni-
toring, PH agencies will be able to merge social determinant of
health measures
15
to assess subcounty level disparities, target
service coordination for subpopulations, and launch quality and
community health improvement cycles.
12
Te Afordable Care Act
progressively increases health care access; increased care access, in
an EMR-enabled environment, creates additional data to monitor
the impact of coverage on newly insured communities. EMR in-
formation incompletely covers a jurisdiction’s population; unlike
randomly sampled federal population surveys, EMR data may be
biased.
16
However, the sheer magnitude of observations and value
of merging clinical outcomes with insurance coverage or socio-
economic factors makes these PH analyses potentially timelier
and more granular.
Yet PH agency information management
17
readiness factors (i.e.,
workforce, system structure and performance, fnancing and
economics, and information and technology) are a concern. Tis
paper describes a rapidly changing current technology state and
suggests a research agenda through a series of questions using a
PHSSR lens. Two fundamental questions frame this discussion: (1)
what technology approaches (e.g., shared platforms, shared ser-
vices, standards and tools) are available and may be leveraged to
support a transformed health care system and population health,
and (2) what system approaches (e.g., workforce, structures,
fnancing and technology) would optimize this information op-
portunity to fll current gaps in public health data and evidence?
Technology Approaches: New Infrastructure
to Support Public Health Data Access
Federal initiatives seek to dramatically change American health
care; simultaneously, information technology advances have re-
markably enhanced capacity to support that change by leveraging
new data systems to drive improvement. A set of technology ap-
proaches and their current application are briefy described below;
these examples suggest directions, emerging opportunities, and
areas for exploration to harness investments and systems toward
greater population health monitoring capacity in public health.
Cloud-Based Technology Opportunities
Cloud-based computing ofers PH practitioners a highly capable
and cost-efective solution to interface with health care providers,
which is a critical step toward breaking down barriers between
public health and health care, and flling gaps in current surveil-
lance data. However, until recently security concerns have limited
data exchange. Recently, enormous health care innovation has
been seen in cloud-based computing that meets high governmen-
tal security expectations for individual privacy protection.
18
Cloud
computing is a migration of sofware platforms away from local
desktop or server installations to remote hosting, linked by the
Internet for “ubiquitous, convenient, on-demand network access
to a shared pool of confgurable computing resources … rapidly
provisioned and released with minimal management efort”.
19
A
cloud includes hardware and sofware that enable six essential
cloud computing characteristics (Table 1).
Tab|e 1. Essent|a| Character|st|cs of C|oud Comput|ng (mod|ñed
69
)
Characteristic Description
On-demand, resource
outsourcing
Instead of a public health (PH) agency providing hardware, the cloud vendor assumes responsibility for hardware acquisition
and maintenance that the PH agency can unilaterally and automatically provision, as needed.
Rapidly elastic utility
computing
PH agency requests additional resources (e.g., processing time, network storage, management software, or application
services) as needed, and similarly releases these resources when not needed.
Large numbers of
pooled machines
Clouds are typically constructed using large numbers of inexpensive machines so capacity may be added or rapidly replaced
as machines fail. Compared with having machines across multiple PH agencies, machines are more homogeneous regarding
confgurat|on and |ocat|on. PH agency usua||y has ||tt|e contro| over exact |ocat|on of prov|ded resources.
Automated resource
management
var|ous confgurat|on tasks (e.g., automated backup, arch|v|ng, data movement for respons|veness, bandw|dth, act|ve user
accounts, and monitoring for malicious activity) typically handled by a PH agency system administrator are offered by cloud
service providers. Resource usage is monitored, controlled, and transparently reported.
Virtualization O|oud hardware resources are typ|ca||y v|rtua| and shared by mu|t|p|e users to |mprove effc|ency. Phys|ca| and v|rtua|
resources are dynamically assigned and reassigned according to demand. Several lightly utilized logical resources can be
supported by the same physical resource.
Parallel computing Frameworks exist for expressing and easily executing parallel computations using hundreds or thousands of cloud
processors. The system coordinates any necessary interprocess communications and masks any failed processes.
2
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
Te cloud infrastructure contains both a physical and an abstrac-
tion layer. Te physical layer consists of hardware resources neces-
sary to support provision of cloud services, and typically includes
server, storage, and network components. Te abstraction layer
consists of the sofware deployed across the physical layer. Several
service models, defned in Table 2 and modes of deployment
(Table 3) should be considered based on organizational business
needs of public health departments.
19
A simple, centralized cloud-based example of cloud computing
opportunities for public health is the Centers for Disease Control
and Prevention’s (CDC’s) BioSense 2.0, which serves as a national
syndromic surveillance, early warning system.
20
Housed in the
cloud, the Association of State and Territorial Health Ofcers
provides a governance mechanism for local and state jurisdictions
to leverage Stage 2 Meaningful Use-eligible hospital data. Access
to centrally processed data is limited by role and permissions.
Jurisdictions recruiting hospitals to send data to this central site
have made little technology investment, yet now have a new
stream of information for situational awareness. BioSense 2.0, as a
centralized, cloud-based repository, still creates concern, as cloud
storage for PH agencies is new. Tensions exist around who has
the right to access data. PH should proactively promote necessary
local or regional sociotechnical discussions regarding new surveil-
lance opportunities from existing technologies. Once political
barriers to data sharing are addressed, cloud-based technologies
with improved disaster recovery are more cost-efective, rapidly
and competently implemented, easily scaled, rapidly updated and
upgraded, and user friendly.
21
Other federal agencies have also
been attracted to cloud solutions and have explored new applica-
tions and infrastructure design.
Tab|e 2. C|oud-based Serv|ce Mode|s (mod|ñed
19
)
Model Consumer Controlled External to Consumer
Software as a Service (SaaS)
Example: Public health
department Facebook account
• Use provider applications running on a cloud infrastructure.
• Access applications from various client devices through a web
browser (e.g., web-based email), or a program interface.
º Oonfgure app||cat|on sett|ngs (poss|b|y}.
• Manage or control underlying cloud
infrastructure including network, servers,
operating systems, storage, or even
individual application capabilities.
Platform as a Service (PaaS)
Example: BioSense 2.0; ASTHO
hosted web service
• Deploy onto the cloud infrastructure consumer-created or
acquired applications created using programming languages,
libraries, services, and tools supported by the provider.
º Oontro| over dep|oyed app||cat|ons and poss|b|y confgurat|on
settings for the application-hosting environment.
• Manage or control underlying cloud
infrastructure including network, servers,
operating systems, or storage.
Infrastructure as a Service
(IaaS)
Example: Public health
department fully outsources all
information technology (IT)
• Provision processing, storage, networks, and other fundamental
computing resources.
• Deploy and run arbitrary software, which can include operating
systems and applications.
• Control over operating systems, storage, and deployed
applications; and possibly limited control of select networking
components (e.g., host frewa||s}.
• Manage or control underlying cloud
infrastructure.
Tab|e 3. Dep|oyment Mode|s for C|oud-based So|ut|ons (mod|ñed
19
)
Cloud Type Description
Private
Example: Mini-Sentinel project:
FDA automated, postmarket
reporting system
Infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business
units). It may be owned, managed, and operated by the organization, a third party, or some combination of these. And it
may exist on or off premises.
Community
Example: BioSense 2.0: CDC
syndromic surveillance system
lnfrastructure |s prov|s|oned for exc|us|ve use by a spec|fc commun|ty of consumers from organ|zat|ons that have shared
concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be owned, managed, and
operated by one or more of the organizations in the community, a third party, or some combination of these. And it may
exist on or off premises.
Public
Example: HealthData.gov
Infrastructure is provisioned for open use by the general public. It may be owned, managed, and operated by a business,
academic, or government organization, or some combination of these. It exists on the premises of the cloud provider.
Hybrid Infrastructure is composed of two or more distinct cloud infrastructures (private, community, or public) that remain unique
entities, but are bound together by standardized or proprietary technology that enables data and application portability
(e.g., cloud bursting for load balancing between clouds).
3
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
An alternative to the CDC’s centralized approach to data storage
is the distributed data model used by the Food and Drug Ad-
ministration (FDA) to conduct postmarketing surveillance for
approved drugs and devices. Tis infrastructure (Mini-Sentinel
Study
22
) uses a federated query tool (PopMedNet
23
) to identify
risk of adverse events, from a broad network of providers each
of whom have standardized their EMR data into a “virtual data
warehouse”
24
or common data model. Data owners maintain
absolute control of who may query their data and of what results
are returned; and they never release data without prior review.
25

Unlike the relatively limited BioSense 2.0 chief complaint data
model, the more comprehensive Mini-Sentinel clinical data ware-
house is more fexible, extensible, and generally “agnostic” to the
types of questions that may be asked.
Te Patient Centered Outcomes Research Institute (PCORI) now
has also invested heavily in this same distributed technology.
26
PH
agencies with their health care partners should explore local in-
stances of distributed, cloud-based query models;
27
with hundreds
of millions of Americans monitored through FDA and PCORI ini-
tiatives; there is great momentum in this distributed technology.
Potentially even more important for local acceptability and partic-
ipation is that data are not deposited into a central repository.
Interoperability Standards for Data Reuse
Assuming cloud-based architecture becomes a viable platform
for public health systems, the ability to share and efciently reuse
data produced in other contexts requires strong interoperability
standards. Sharing and efcient data reuse require strict adher-
ence to message standards in three key component areas: (1)
structure, (2) content, and (3) transport (see Table 4).
28
Without
all three—format (i.e., syntactic), vocabulary (i.e., semantic) and
transmission standards (i.e., pragmatic)—monitoring systems
will not beneft from automation and technology efciencies.
True interoperability between computers requires all three
components be standardized. For structure, the Meaningful Use
program requires transition of care document exchange using
Health Level 7 (HL7) Version 3 consolidated clinical document
architecture (c-CDA) and for other message types uses a variety of
HL7 Version 2. Many very successful PH messaging systems (i.e.,
immunization registries
29
and electronic laboratory reporting
30
)
have been implemented using these HL7 standards. Without
Table 4. Message Standards Adopted by the Federal Health Architecture
28
Name Full Name Purpose Public Health Example
Structure
HL7
Version 2.x
Health Language 7 H|7 ba||oted structured message spec|fc to
domain need
Immunization reporting, electronic laboratory
reporting, syndromic surveillance.
c-CDA
Version 3.x
Consolidated Clinical Document
Architecture
H|7 ba||oted fex|b|e message w|th temp|ates for
each domain need
Cancer case reporting (proposed)
Content
LOINC |og|ca| Observat|on ldent|fer
and Nomenclature Code
Ün|que |dent|fer for each |aboratory test or
radiologic procedure
Sending a positive gonorrhea result to the state
electronic laboratory reporting system.
SNOMED Systematized Nomenclature for
Medicine
Unique resulted value for many laboratory test
results
Sending a cancer report to a state registry.
ICD9/10 lnternat|ona| O|ass|fcat|on of
Diseases (9
th
or 10
th
edition)
Unique diagnosis code for inpatient and
outpatient administrative purposes
Sending a record of all patients who have a
diagnosis of hypertension (ICD9=401.x) to a registry.
RxNorm RxNorm Normalized names for clinical drugs and links its
names to many of the drug vocabularies
Determine if hypertensive patient or population
has been prescribed and is receiving appropriate
medications.
CVX Vaccine Administered Standard used for reporting to immunization
registry
Determine the up-to-date rate for an individual or
population.
Transport
Direct
SMTP
Direct Messaging Service—
Simple Mail Transport protocol
Method to securely send a health information
message from sender to receiver
Transition of care document after hospitalization or
for e-referral (e.g., specialty services, Quitline).
Direct
XDM
Direct- and Cross-enterprise
Document Media Interchange
Provides document interchange using common
f|e and d|rectory structure over severa| standard
media.
Patient can use physical media (e.g., USB drive or
CD-ROM) to carry medical documents or person-to-
person email to convey medical documents.
Direct
XDR
Direct- and Cross-enterprise
Document Reliable Interchange
Permits direct document interchange between
EHRs, PHRs, and other health care IT systems in
the absence of a document sharing infrastructure
such as XDS Registry and Repositories.
Patients can develop their own personal health
records (PHRs) across multiple providers.
XDS Cross-Enterprise Document
Shar|ng W|th|n an Affn|ty Doma|n
Shares documents to a community enterprise. Community of Care record supported by a regional
health information organization serving all patients in
a given region.
4
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
broad PH enterprise standards, each PH agency would need to
create local standards and then convince health care providers to
adopt them. Tis challenges those health care providers operat-
ing in the adjacent county or state where that health department
encourages a diferent standard. To efectively use newly available
data, PH agencies must gain knowledge, acquire experience, and
contribute to development of messages that adhere to standards
for these three essential components. More recently, an emerging
HL7 standard is the Fast Health care Interoperability Resources
(FHIR, pronounced “fre”), which simplifes implementation of
data exchange between health care applications.
31
Leveraging
the latest web standards and tightly focused on implementation,
FHIR solutions use modular components or resources for easy and
cost-efective assembly into working systems.
Toward System Approaches to Address Key
PH Systems and Services Research (PHSSR)
Questions
Meaningful Use-promoted health information exchange (HIE)
will support improved population health monitoring for specifc
areas (i.e., immunization, laboratory reporting, syndromic sur-
veillance, and cancer registries). To achieve even broader moni-
toring capacity (e.g., New York City
50
) requires potentially more
intensive collaboration from partner health care organizations
and a longer time frame for trust building. Population health for
many health care organizations is narrowly focused on that group
using a specifc clinical entity or service (e.g., patient panel). How-
ever, secondary use of these data by PH agencies permits assess-
ment for all residents in a jurisdiction that can provide a systems
level perspective.
51
Such assessments can help PH agencies, as
they uniquely bridge clinical and community environments and
reinforce and monitor prevention eforts.
52
PH can build on suc-
cessful early models,
53,54
and then identify cost-efective dissemi-
nation strategies to spread these approaches.
Seminal PH systems and services research has identifed
55,56

four distinct domains that infuence collective PH impact on
population health: (1) PH workforce, (2) PH system structure
and performance, (3) PH fnancing and economics, and (4) PH
information and technology. HIE benefts to the last category are
obvious. However, a narrow focus would limit opportunity and
positive impact on developing a competent informatics work-
force,
57
reusing data for quality improvement,
58
and achieving
cost efciencies,
59
across the PH enterprise. Below, each domain
and its associated data and information needs and issues are
described, followed by some potential PHSSR research questions
that will beneft from both a systems approach and ever-growing
technology opportunities.
Public Health Informatics Workforce
To turn volumes of unfamiliar health care provider data into
information, skilled informaticians must transform data into
information tools (e.g., registries) of value to PH ofcials, com-
munities and individuals. Most PH agencies have a workforce
incapable of successful linkage and utilization of new information.
PH is challenged to extract key messages from near-real-time data
streams given inadequate informatics skill and limited knowledge
of standards, within its own workforce.
Tis absence of a robust and savvy informatics workforce is
partially a consequence of competing markets; inequities exist
in pay and benefts between governmental and private sector
informatics positions. Recent clinical and private sector growth
from HITECH incentives have drawn away many skilled person-
nel. Beyond these substantial recruitment hurdles, cost-cutting
measures to restrict staf costs (e.g., hiring caps or freezes, travel
freezes, and furloughs) challenge the capacity to attract, expand,
Table 5. Early Examples of Public Health Data
Aggregation
To inform knowledge-driven PH practice, data must be aggregated into
information that drives decision-making and quality improvement. PH
agencies need to incrementally learn how to curate data and promote
data shar|ng partnersh|ps. To beneft from new|y ava||ab|e data fow,
spec|fc |nteroperab|||ty requ|rements need to be met. Wh||e there may
be technical methods and solutions, (e.g., cloud-based technologies
and messaging standards), there are, fortunately, several exceptional
examples of data successful aggregation for a learning health system.
Among many that exist,
32-37
just a few examp|es are br|efy rev|ewed here.
Example 1: New York
In New York City, the Primary Care Information Project
36,38
seeks to
improve disadvantaged community population health focusing and
reporting
39
in three areas: (1) information systems oriented toward
prevent|on, (2} changes |n care management and pract|ce workfows,
and (3) payment that rewards effective prevention and management
of chronic disease. Results to date provide insight on data value and
PH’s role in HIE.
40
F|rst, systems are not w|thout faws. Parsons et a|.
41

found systems used to measure provider performance and payment
may misclassify and adversely impact EHR use by clinicians. Having the
right input (e.g., leadership from PH, clinicians, and technical resources)
dur|ng requ|rements defn|t|on, des|gn, and system |mp|ementat|on
has s|gn|fcant |mpact on u|t|mate system va|ue, trust, and purpose.
Transparent translation and integration from clinical to engineering
perspectives require innumerable interactions and iterations to test and
ensure the right output.
42
Tracking population health improvements in
delivery of recommended preventive- and health-promoting services
is possible, but interrupted or failed transmissions may occur due to
intermittent technology issues within each practice. Designing for greater
stability and real-time queries has been a recent effort. The next version is
focused on avoiding data transmission errors, greater data validation, and
limiting privacy and security concerns with clinical data extraction.
43
Example 2: Massachusetts
In Massachusetts, another group has been developing a federated query
tool to support PH surveillance.
44
This infrastructure, similar to Mini-
Sentinel, has been developed primarily through collaborative informatics
initiatives with Harvard,
45,46
and has broad application to national
22
and
regional efforts.
35
Spec|fc stud|es have a|gor|thm|ca||y |dent|fed cases
of acute hepatitis B,
47
active tuberculosis,
48
and distinguishing between
type 1 and type 2 diabetes mellitus.
49
This same technology supports
postmarketing adverse event reporting for drugs and devices across more
than 100 million Americans. Further application and dissemination of this
same technology, at community levels, is exciting and promises to be an
area for extensive research and development in the next decade.
5
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
and retain qualifed PH informatics personnel. Te reality for
most health departments is that informatics workforce investment
is generally insufcient; thus, qualifed new trainees ofen land in
private sector jobs.
Given these personnel challenges, a broader, enterprise approach
might help build a knowledgeable workforce corps through col-
laborative projects and a shared PH infrastructure. PH executive
leaders should approach workforce competency development as
a strategic informatics investment. While PH agency assets (e.g.,
systems, knowledge, and personnel) should be extensible and
repurposed across programs, strategic decisions may require an
even larger systems perspective. PH leaders need a cadre of skilled
systems thinkers who critically understand requirements gather-
ing, design, construction, deployment, and system maintenance
for both internal and external exchange opportunities. Multiagen-
cy information exchange should reinforce a broader operational
defnition for the PH system (e.g., health department programs,
health care providers, and accountable care organizations).
Working across these systems, PH may fnd greater workforce
synergies and better return on informatics investment. A savvy,
systems-thinking and cost-conscious PH informatics workforce
would contribute to the strategic multiagency planning, seeking
cost-efective exchange solutions.
Research questions for PH informatics workforce investiga-
tion include the following: What are key governance skills
60
and
methods to support technical solutions? How do managers most
efectively leverage their community engagement experience
toward strategic informatics alliances and investments? How does
a skilled PH workforce help HIE members clearly articulate the
intended usefulness of exchange to their organization? What are
well-defned value propositions and how do they drive constit-
uents to complete required legal, compliance, and governance
documents (e.g., business associates agreement and data use
agreements)? How do health departments achieve (e.g., internally
or externally) subject matter expertise in these technology, legal,
compliance, and privacy aspects?
Public Health (PH) Systems Structure and Performance
To beneft from EHR data exchange and reuse opportunities,
local PH leaders with their communities should mutually develop
a governance structure and resources for data sharing. Beyond
existing mandated reporting, establishing a community structure
and rules for why and how identifed or de-identifed informa-
tion is shared is a nontrivial task. Abiding by federal and state
regulations, the community needs a secure (e.g., authorized,
authenticated, controlled access, and audited) network. Efcient
reuse of health information from health care systems calls for
standardized, minimally burdensome solutions for PH, health
care providers, and EHR vendors. Processes for reporting and
data sharing should be standardized to reduce PH investments to
receive and interpret new EHR data streams. Health care organi-
zations will share information with PH agencies for community
beneft when trust, standard systems, and responsibilities for
both parties have been established. Trust is built on direct local
relationships; participants must mutually do the following: (1)
describe and approve a governance process; (2) build methods
to assure quality, confdentiality and security; and (3) be good
information stewards.
PH agencies need to explore and identify best practice HIE mod-
els from other jurisdictions. Finding the right tool may require
signifcant efort since the modes are neither well developed nor
broadly disseminated. To build broad local interest and for greater
return on investment, a clear requirement should be organizing
systems, knowledge, and data for maximal reuse. Resources are
limited; federation with or replication of existing successful mod-
els is less costly than building de novo. PH leaders should consid-
er regional and even national alliances (e.g., community platforms
hosted at ASTHO
61
) to assure greater investment return using
secure and transferable technologies (e.g., cloud-based solutions,
see Table 2) to accelerate information and knowledge exchange.
Stakeholders (e.g., data partners, data users, and consumers)
should be collectively involved in defning permitted disclosures
and uses (e.g., identifed line lists versus aggregated counts),
through a local governance process.
62,63
A fundamental beneft of
distributed data queries is greater data partner operational control
for when, what, and how data are shared. Yet governance struc-
tures are ofen highly specifc and sensitive to local conditions
(e.g., competitive markets, PH leadership). Engendering trust to
share information may need to organically develop, based on a
local imperative or champions. Alternatively, a fnancial incentive
for health care provider participation in a distributed data net-
work would be achieving a Meaningful Use measure (i.e., special-
ized registry). To ensure and enforce communitywide governance,
external structural elements (e.g., data use and business associate
agreements) build the information trust framework. A principle
that encourages willingness to participate is adherence to fair in-
formation practices—share the minimum necessary information
for a specifed purpose.
64
Beyond governance, data sharing and reuse will operationally be
facilitated when health care providers and the entire PH enter-
prise use a common set of component standards (i.e., security,
data model, defnitions, and query tools). Given its long tradition
of safe and secure protected health information use for commu-
nity beneft around notifable or mandated PH surveillance,
65
PH
has credibility in issues of security. Building on that skill set and
use case, a common security framework and infrastructure should
be established where PH agencies exchange data with health care
partners.
6
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
Te PH enterprise has been slow to embrace structural and se-
mantic rules, unlike many other information rich industries (e.g.,
banking, food chain suppliers, shipping, and inventory control).
PH agencies, as information stewards, need to actively oversee
and protect these rules on the public’s behalf. Stewardship extends
beyond just the knowledge (e.g., rules), information, or data; there
are important community relationships, resources, and services
that are likely also shared. Within a PH agency, each program may
have specifc informatics needs and ideas. However, when consid-
ered as a system, all programs may better beneft from a common
interoperability approach. EHR data will have higher PH value if
multiple program-specifc data streams are collaboratively curat-
ed. Using cost-efective and infrastructure-consolidating solu-
tions, cross-PH agency registry capacity should be coordinated
through a set of shared strategies and business intelligence tools.
Good stewards might focus on achieving greater component
(e.g., security, data models, and query tools) reuse, cost-efective
solutions, dissemination, and transferability especially using cloud
technologies.
Achieving one unifed reporting infrastructure across a range of
PH use cases (e.g., disease reporting, immunization registries, and
syndromic surveillance) and jurisdictions may not be immediate-
ly possible. However, incremental progress toward secure, privacy
protecting, cloud-based services shared across jurisdictions may
rapidly accelerate health agency capacity and increase investment
value. Te current absence of multijurisdictional trust models, in-
tegrated infrastructures, and concordant and reconciled standard
vocabularies limits local, state, and federal system synergies. Lack
of a unifed PH strategy and inadequate PH engagement (both
nationally and regionally) results in dysfunctional standards and
imperfect data sharing.
Research questions for PH systems and performance investi-
gation include the following: What design requirements best
support within- and cross-jurisdictional data sharing, standard-
ization, and knowledge transfer? What has aided jurisdictions
to maximally use HIE and efectively monitor PH intervention
efects? How have jurisdiction- or region-specifc lessons learned
been leveraged for broader and more scalable enterprise solu-
tions? What procurement regulations facilitate or create barriers
for building common solutions? What governance, legal, and
policy issues need to be addressed to build more multipurpose
platforms that store and analyze exchanged data? What role
should PH play in messaging rule adherence, promotion, and
enforcement?
Public Health Financing and Economics
A 2010 National Association of County and City Health Ofcials
(NACCHO) assessment identifed limited local PH agency ability
to access quality, timely, and actionable data for decision- and
policymaking. Less than a third of PH agencies said their staf had
adequate levels of physical infrastructure including information
technology necessary to receive, house, and manage data as part
of their jobs.
66
Local health departments have great challenges in
responding to HIE aforded by the HITECH Meaningful Use pro-
gram. Te NACCHO survey found that 72 percent of respondents
identifed insufcient funding among their top three barriers to
system development.
Budget shortfalls have resulted in extensive stafng shortages at lo-
cal, state, and federal levels. Tese seriously challenge PH agencies’
ability to build the physical infrastructure and staf competencies
to leverage the HIE opportunity. Reaching high information ex-
change functionality requires enormous investments. Te Primary
Care Information Project received nearly $30 million from a com-
bination of sources
36
to achieve the momentum, penetration, and
evaluation capacity it has achieved. Te New York City experience,
with a ready supply of resources and manpower, is unlikely to be
replicated across nearly 3,000 local and state health departments.
To fully maximize HIE opportunities, a PH agency should share
resources between program areas. Archaic funding approaches
and congressional politics have resulted in tremendous inef-
ciencies within health departments. Program-specifc funding
regulations directly inhibit development of program “agnostic,”
multiuse business-intelligence infrastructure. Architects do not
design separate plumbing systems for each room in a house; one
hot water heater serves the entire building. Similarly, a PH agency
should be able to share technologies and gain efciencies across
program areas. Technologies are ever-changing; PH departments
need to strategically manage their technology portfolio
67
to
assure reasonable upfront and depreciated costs and investment
return. By designing and building for aligned, cross-program,
and cross-department functionality, PH agencies can encourage
technology reuse, make more afordable investments, lower total
cost of operations, and improve investment return.
Nationally, research should describe which laws, regulations, and
federal policies inhibit or promote investment synergism and efec-
tive cross-program and jurisdiction collaboration (e.g., BioSense
2.0). Federal funding rules ofen promote silos throughout the PH
sector. Cloud-based “platform as a service” (PaaS)
68
technologies
ofer alternative and potentially less costly approaches. Jurisdic-
tions should review data management alternatives and potential
need for remote hosting policies.
69
Multijurisdictional information
system costs under alternate (e.g., cloud-based) solutions should be
studied; comparison with current methods (e.g., PH agency-based)
should identify which solution yields the best return on investment.
Research questions for public health fnancing include the
following: What drives PH agencies to invest in informatics
initiatives? What are the characteristics of efective crosscutting
systems for regional and internal environments? What are the
7
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
unintended PH costs and risks for maintaining highly separated
(siloed) programs and information systems? How do those silos
create burdens for vendors, eligible hospitals, and eligible pro-
viders who seek common or unifed methods for sharing data
with programs at health agencies, regardless of the jurisdiction?
What standard language might federal program funding an-
nouncements use to hold funded agencies accountable for system
integration and adherence to standards? What guidance strategies
would encourage the following: (1) identifcation of PH agency
commonalities, (2) multijurisdictional collaboration, and (3)
economies of scale?
PH Information and Technology
An emerging strategic plan
58
and the Standard and Interoperabil-
ity (S&I) Framework
70
are federal initiatives focused on greater
HIE through better interoperability (e.g., computers communicat-
ing without human intervention). Current or recent S&I eforts of
interest to PH agencies are summarized in Table 6.
HIE should support essential PH service delivery by making
secondary use of information accessible to monitor health indi-
cators.
71
Despite emerging technical opportunities, there has been
relatively limited local or state PH strategic enterprise planning.
Some approaches might help develop more cost-efective tools
and solutions for indicator measurement. Several multidisci-
plinary groups
72,73
promote joint action planning for better PH
community standards alignment and greater interoperability.
Similarly, key CDC leadership and multiagency agreements (e.g.,
ASTHO hosting BioSense 2.0) create value and begun to fll infra-
structure gaps. Having adopted a common syndromic surveillance
monitoring platform (with relatively little PH agency investment),
state and local PH agencies might look to that shared model and
review opportunities for replication
61
or further dissemination.
To generate meaningful information from new data streams
requires standardized methods for frequent data communication
between clinical environments and PH agencies. Case reports or
observations in a registry (e.g., disease state, behavior, physiologic
condition, or exposure) all need to adhere to structural message
standards (e.g., c-CDA or HL7 2.x). Content, captured during
care, needs to be conceptually organized in a standard manner.
Completeness may be sacrifced as clinical workfows incomplete-
ly collect all required case reporting information. Even having a
partially populated and timely form appear to the clinician who
is using the EMR, permits the clinician to contribute key data in
a structured format (Structured Data Capture
74
). Forms should
be presented to clinicians for completion at the best point in the
workfow to get additional information. At the appropriate time,
clinical decision support (Health eDecisions) should trigger
a reportable (i.e., mandated or voluntary) health observation
prompt to an end user, for sharing with PH agencies for situation-
al awareness and decision-making. Te Data Access Framework
75

proposes queries that happen locally (by providers within an
organization), from one organization to another, and fnally in
a federated manner across organizations for a broad population
view. Te latter approach is a key PH function and reminiscent of
the New York City
37
and Massachusetts
44
examples.
PH distributed queries and responses in PH are possible.
22
Facil-
itated by the S&I Framework components described, those func-
tionalities can be achieved with a common data language adopted
across the ecosystem.
76
Similar to eforts in many state Medicaid
agencies,
77
the PH enterprise needs to adopt a common conceptu-
al and logical data model to limit variation in defnition, meaning,
and value sets across programs and jurisdictions. Tis would avoid
unnecessary confusion, inefciencies, and inability to rapidly re-
use data. As active partners PH has an obligation to help build this
data model and collectively develop enterprise standards.
Table 6. Standards and Interoperability Framework Components of Interest to Public Health
Component Purpose Example
Consolidated CDA Standard message format Cancer case report form completed by a clinician.
Query Health Population based queries Ability to query how many people have hypertension in a jurisdiction.
Public Health Reporting Initiative Harmonized methods for PH reporting Standards and implementation guides support bidirectional
interoperable communication between clinical care and public
health entities.
Structured Data Capture Populate standard forms A pertussis case report form is presented to a health care provider
to collect a few data elements unlikely to be collected during routine
clinical care.
Health eDecisions Clinical decision support (e.g., triggers
for PH screening or collecting data)
EMR presents a query to clinician asking if a newly diagnosed case
of gonorrhea should be reported to the state or local health agency;
or collect more complete data through structured data capture.
Data Access Framework Query data: (1) locally, (2) to targeted
organization, and (3) distributed across
multiple organizations
Ability to conduct population queries (e.g., within a clinic, across an
integrated delivery system or in a jurisdiction) regarding adequate
control of hypertension.
8
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
Greater EHR data access and sharing for PH surveillance purposes
requires a standard data model for optimal reusability. Beyond
data modeling, concepts (e.g., population health indicators) and
knowledge (e.g., rules engine for calculation of immunization up-
to-date status) should be explicitly defned and easily shared across
the entire health and health care enterprise. Once data, concepts,
and knowledge are readily available, disseminated, and imple-
mented in computable format, distributed partner queries are pos-
sible. With proper security, PH (e.g., Massachusetts
44
) should be
capable of submitting queries and receiving responses from pro-
viders to measure population health (e.g., registries) and support
various reporting needs (e.g., nonproft hospital IRS obligations,
10

ACO,
11
and PH agency
12
). For urban areas, with access to routinely
collected data (i.e., resident address), multi-institution registries
could easily represent subcounty (e.g., census tract level) place-
based population health assessments. Tese would blend well with
place-based measures of the social determinants of health.
Poor vocabulary-standards adherence results in errors, incorrect
results, and widespread inefciencies. Meaningful Use incentives
may ofer greater data access, but progress toward standardization
is ofen lacking. PH programs and departments need standard
defnitions, codes, and greater uniformity of workfow (e.g., inputs
and outputs) before we might see benefts from consolidation and
cloud-based solutions. To improve health outcome and health
indicator monitoring,
78
PH should have tools that monitor and
provide feedback on adherence to standard vocabularies. Te goal
may appear clear: consistent, uniform, and reliable population
metrics (e.g., behaviors or outcomes). However, work remains as
PH terms are variably defned, leading to confusion in surveil-
lance measures.
79
To cost-efectively monitor populations and
assess performance, the PH enterprise needs a logical, standard
vocabulary. Tat vocabulary needs to be precise, yet adaptive or
extensible for the advent of new data sources or concepts.
Federated query systems are not without their challenges. Similar
to the internet, an efcient exchange system requires standard
protocols to ensure that computers and systems “talk” to one
another. Across systems, the nonuniformity of data structures,
signifcant quality-control variations, and inconsistent pro-
gramming are nontrivial data and systems management issues.
Modeling data for storage and query needs to be cost-efective to
encourage greater data partner participation. At the same time, it
needs to have sufcient fexibility and extensibility to economical-
ly address new and emerging PH questions. Spending signifcant
time planning for an optimal data model, and defning enterprise
requirements and necessary quality assurance procedures,
80
prior
to building data warehouses, will reduce partner inconsistencies
(i.e., data quality, fle structures, and variable defnitions). Data
partners need to be acknowledged for the public value and signif-
icance of their contribution. Eforts should limit overburdening
these partners, as PH needs to set realistic query expectations.
Research questions for information and technology include the
following: What barriers exist to achieving a comprehensive and
community-engaged information strategy? What role should data
partners play in data validation and interpretation of fndings?
What are (1) the costs for data partner participation, (2) the
comparative data management techniques, and (3) the security
measures across organizations? Formative consultative research
with many data partners,
81
suggests a variety of enhancements for
efective, secure, and efcient data sharing and analysis. What is
needed to establish a PH conceptual and logical data model; how
is that model shared between PH agencies; and how do require-
ments change over time (e.g., incorporating new data types or
elements)? How should a PH common data model leverage health
care coding standards and support standard vocabulary mapping
services? How should query tools work with a data model? How
does the data model help design more transparent, intuitive,
and user-friendly tools? How should knowledge (e.g., rules and
decision support) be managed for efcient deployment, maximal
reach, and proper results interpretation?
Conclusions
Te PH enterprise has learned that collaborative approaches and
greater information fow generally improve the timeliness of our
response. Meaningful Use provides unique opportunities for
quick wins from EHR-enabled HIE using newer and more easily
deployable technologies (e.g., cloud solutions). While eligible hos-
pitals and providers are challenged by near-term regulatory eforts
(e.g., JCAHO, ICD-10 and Meaningful Use), the next three years
of mandated Stage 2 exchange (i.e., immunizations, electronic
laboratory reporting, and syndromic surveillance) and menu ex-
change (i.e., cancer registry and specialty registries) should create
substantial gains in information access for PH.
Adopting consistent standards that vendors, hospitals, and pro-
viders perceive as a reasonable burden has been challenging for
PH. Limiting the variation in interfaces (e.g., building common or
unifed business cases, and more scalable solutions) requires mul-
tiprogram and multijurisdictional PH collaboration. Tis requires
a broad systems approach. PH agencies should actively engage in
information system changes that limit implementation burden on
partners through content, structure, and transport standards. For
decades, immunization programs across the nation have adopted
functional, technical, and semantic standards. Having standards
facilitated the earlier inclusion of immunization data in Meaning-
ful Use exchange, beginning in 2014. Future standards will emerge
as PH creates compelling business cases and the benefts from
health information sharing become more evident and achievable
through uniformly applied interoperability standards.
Moving forward, PHSSR should inform practitioners about ways
to replicate successes through vetting of pressing stakeholder
business cases and consideration of cloud-based solutions. De-
9
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
fning value propositions, which empower and energize commu-
nity stakeholders while cost-efectively supporting multiple PH
programs and jurisdictions, is our current task. Using cloud-based
solutions, a PH informatics infrastructure based on standards can
emerge and be easily disseminated for HIE. PHSSR should study
and then share informatics’ best practice results (e.g., standards
development, program-specifc standards, standards sharing,
knowledge management systems, and common data models) to
achieve the greatest value.
Decades of experience with jurisdiction-specifc initiatives leave
PH agencies weary from failed exchange partnerships, idiosyn-
cratic standards, and stories of poor implementations. Despite
potentially dampened enthusiasm for PH, collaborative tech-
nology and systems-based solutions (e.g., emerging cloud-based
services, adherence to national standards, and shared resources)
ofer enormous opportunities, particularly if PH focuses on im-
proved interoperability. PH, along with community stakeholders
afected by standards adoption, should drive the process. PHSSR
should study these collaborative technology and system eforts in
identifying key attributes of successful collaborators (e.g., end us-
ers, developers, and informatics experts), which may inform what
workforce competencies are required to fully leverage and may
make useful the information explosion. PHSSR should also help
PH practitioners develop, defne, and evaluate a strategic technol-
ogy innovation roadmap. Tat roadmap should acknowledge the
shortcomings of monolithic siloed and infexible PH information
systems.
Recent experiences suggest that the key components likely to max-
imize PH value from recent federal investments are modular sys-
tems, reusable data, shared services, and standards-based business
intelligence design. To accelerate creation of these components
and PH value from certifed electronic health record technology,
a cadre of local and state PH ofcials should collectively focus on
achieving sufcient PH and health care interoperability capable
of truly monitoring population health. PHSSR will be an essential
component of building the evidence base needed to support local
and state PH capacity to participate in the learning health system.
Acknowledgements
Te paper was made possible through the funding of the Robert
Wood Johnson Foundation and the leadership of Academy-
Health in organizing and supporting this author and others. Te
author would like to thank the many thoughtful individuals who
commented on various earlier drafs of this paper: anonymous
manuscript reviewers, as well as Scott Afzal, Linda Bilheimer,
PhD, James W. Buehler, MD, Elizabeth Cole, Erin Holve, PhD,
MPH, MPP, Barbara Ferrer, PhD, MPH, Med, Seth Foldy, MD,
MPH, J.P. Leider, PhD, Remle Newton-Dame, MPH, Marguerite
J. Ro, DrPH, Charles J. Rothwell, MBA, MS, Paula Soper, MS,
MPH, PMP, and Matthew C. Stiefel, MPA.
References
1. US Congress. American Recovery and Reinvestment Act,
Health Information Technology for Economic and Clinical
Health Act’’ or the ‘‘HITECH Act’’. 2009; http://www.healthit.
gov/sites/default/fles/hitech_act_excerpt_from_arra_with_
index.pdf.
2. Institute of Medicine. For the Public’s Health: Investing in a
Healthier Future. Washington, DC: Te National Academies
Press.; 2012.
3. Anderson G, Hussey PS. Comparing Health System Perfor-
mance In OECD Countries. Health Afairs. 2001;20(3):219-
232.
4. Bush GW. Transforming Health Care: Te President’s Health
Information Technology Plan. http://georgewbush-white-
house.archives.gov/infocus/technology/economic_poli-
cy200404/chap3.html. Accessed January 15, 2014.
5. Centers for Medicare and Medicaid Services. Medicare &
Medicaid EHR Incentive Programs, Report to HIT Policy
Committee, February 4, 2014. 2014; http://www.healthit.
gov/FACAS/sites/faca/fles/HITPC_Feb2014_HITPC.pdf.
Accessed March 1, 2014.
6. Institute of Medicine, Grossmann C, Powers B, McGinnis JM,
editors. Digital Infrastructure for the Learning Health System:
Te Foundation for Continuous Improvement in Health and
Health Care: Workshop Series Summary. Washington, DC:
National Academies Press;2011.
7. Agency for Healthcare Research and Quality. What Is Com-
parative Efectiveness Research. http://efectivehealthcare.
ahrq.gov/index.cfm/what-is-comparative-efectiveness-re-
search1/. Accessed December 29, 2014.
8. Patient Centered Outcomes Research Institute. Establishing
the Defnition of Patient-Centered Outcomes Research. http://
www.pcori.org/content/establishing-defnition-patient-cen-
tered-outcomes-research. Accessed December 29, 2014.
9. Friedman DJ, Parrish RG. Te population health record:
concepts, defnition, design, and implementation. J Am Med
Inform Assoc. 2010 17()(4):359-366.
10. Internal Revenue Service. New Requirements for 501(c)(3)
Hospitals Under the Afordable Care Act http://www.irs.gov/
Charities-&-Non-Profts/Charitable-Organizations/New-
Requirements-for-501(c)(3)-Hospitals-Under-the-Aford-
able-Care-Act. Accessed December 29, 2014.
11. Centers for Medicare and Medicaid Services. Quality Mea-
sures and Performance Standards. http://www.cms.gov/Medi-
care/Medicare-Fee-for-Service-Payment/sharedsavingspro-
gram/Quality_Measures_Standards.html. Accessed December
29, 2014.
12. Public Health Accreditation Board. http://www.phaboard.
org/. Accessed March 22, 2014.
13. Friedman DJ, Gibson P, Ross DA. Electronic health records
and US public health: current realities and future promise.
Am J Public Health. 2013;103(9):1560-1567.
10
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
14. National Quality Forum. Measures, Reports & Tools http://
www.qualityforum.org/measures_reports_tools.aspx. Ac-
cessed December 29, 2014.
15. Institute of Medicine, Committee on the Recommended
Social and Behavioral Domains and Measures for Electron-
ic Health Records, Board on Population Health and Public
Health Practice. Capturing Social and Behavioral Domains in
Electronic Health Records. Washington, DC2014
16. Perlman SE, McVeigh KH, Newton-Dame R, et al. Innova-
tions in Population Health Surveillance Using Electronic
Health Record Data. Online Journal of Public Health Informat-
ics. 2014;6(1):e49.
17. Foldy S, Grannis S, Ross D, T. S. A ride in the time machine:
information management capabilities health departments will
need. Am J Public Health. 2014;104()(9):1592-1600.
18. National Institute of Standards and Technology. Inventory of
Standards Relevant to Cloud Computing http://collaborate.
nist.gov/twiki-cloud-computing/bin/view/CloudComputing/
StandardsInventory. Accessed December 29, 2014.
19. Mell P, Grance T. Te NIST Defnition of Cloud Computing:
Recommendations of the National Institute of Standards and
Technology. Gaithersburg, MD: National Institute of Standards
and Technology;2011.
20. Centers for Disease Control and Prevention. BioSense Pro-
gram: Te Community. http://www.cdc.gov/biosense/com-
munity.html. Accessed January 15, 2014.
21. Duan J, Faker P, Fesak A, Stuart T. Benefts and Drawbacks of
Cloud-based Versus Traditional ERP Systems. Paper pre-
sented at: Proceedings of the 2012-13 Course on Advanced
Resource Planning 2013; Tilberg University.
22. Platt R, Carnahan RM, Brown JS, et al. Te U.S. Food and
Drug Administration’s Mini-Sentinel program: status and
direction. Pharmacoepidemiol Drug Saf. 2012;21(S1):1-8.
23. PopMedNet. http://www.popmednet.org/. Accessed March
22, 2014.
24. HMO Research Network. http://www.hmoresearchnetwork.
org/en/. Accessed March 22, 2014.
25. Curtis LH, Weiner MG, Boudreau DM, et al. Design consid-
erations, architecture, and use of the Mini-Sentinel distrib-
uted data system. Pharmacoepidemiology and Drug Safety.
2012;21(S1):23-31.
26. Fleurence RL, Curtis LH, Calif RM, Platt R, Selby JV, Brown
JS. J Am Med Inform Assoc. 21. 2014:578-582.
27. Davidson A, Ames MJ, Bondy J, Johnson SC, Wade TD, Kahn
M. Scaling a Multi-purpose Distributed Registry Network
for Regional Data Sharing. Paper presented at: EDM Forum
Stakeholder Symposium; June 22, 2013, 2014; Baltimore, MD.
28. Ofce of the National Coordinator for Health Information
Technology. Federal Health Architecture: Standards and
Interoperability Framework. http://www.healthit.gov/poli-
cy-researchers-implementers/federal-health-architecture-fa.
Accessed March 22, 2014.
29. Centers for Disease Control and Prevention. HL7 Version
2.5.1 vImplementation Guide for Immunization Messaging.
2012; http://www.cdc.gov/vaccines/programs/iis/techni-
cal-guidance/downloads/hl7guide-1-4-2012-08.pdf. Accessed
December 29, 2014.
30. Health Level 7. HL7 Version 2.5.1 Implementation Guide: Elec-
tronic Laboratory Reporting to Public Health, Release 1 (US
Realm). 2013; http://www.hl7.org/implement/standards/prod-
uct_brief.cfm?product_id=98. Accessed December 29, 2014.
31. Health Level 7. 2014; http://www.hl7.org/implement/stan-
dards/fir/overview.html. Accessed December 10, 2014.
32. Dixon BE, Siegel JA, Oemige TV, Grannis SJ. Challenges and
Interventions to Improve Public Health Surveillance Data and
Practice. Public Health Reports. 2013;128:546-553.
33. Dixon BE, Vreeman DJ, Grannis SJ. Te Long Road to Seman-
tic Interoperability in Support of Public Health: Experiences
from Two States. J Biomed Inform. 2014;49: 3-8.
34. Kho AN, Cashy JP, Hota BN, Sims SA, Malin BA, Galanter
WL. Te Chicago Health Atlas: A Public Resource to Visual-
ize Health Conditions and Resources in Chicago. American
Medical Informatics Association; November 6, 2012; Chicago,
IL.
35. Klompas M, Murphy M, Lankiewicz J, et al. Harnessing
electronic health records for public health surveillance. Online
Journal of Public Health Informatics. 2011;3(3):1-7.
36. Mostashari F, Tripathi M, Kendall M. A Tale Of Two Large
Community Electronic Health Record Extension Projects.
Health Afairs. 2009;28(2):345-356.
37. Klann JG, Buck MD, Brown J, et al. Query Health: stan-
dards-based, cross-platform population health surveillance. J
Am Med Inform Assoc 2014;21:650-656.
38. Frieden TR, Mostashari F. Health Care as If Health Mattered.
Journal of the American Medical Association. 2008;299(8):950-
952.
39. New York City Department of Health and Mental Hygiene.
New York City Macroscope. http://www.nyc.gov/html/doh/
html/data/nycmacroscope.shtml. Accessed March 22, 2014.
40. Kaye K, Singer J, Newton-Dame R, SC. S. Health information
technology and the primary care information project. Am J
Public Health. 2014;104(3):e8-9. [Epub 2014 Jan 2016].
41. Parsons A, McCullough C, Wang J, Shih S. Validity of elec-
tronic health record-derived quality measurement for perfor-
mance monitoring. J Am Med Inform Assoc. 2012;19:604-609.
42. Amirfar S, Taverna J, Anane S, Singer J. Developing public
health clinical decision support systems (CDSS) for the out-
patient community in New York City: our experience. BMC
Public Health. . 2011;11:753.
43. De Leon SF, Shih SC. Tracking the delivery of prevention-ori-
ented care among primary care providers who have adopted
electronic health records. J Am Med Inform Assoc. 2011;18:91-
95.
11
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014
eGEMs
44. Vogel J, Brown JS, Land T, Platt R, Klompas M. MDPHnet:
Secure, Distributed Sharing of Electronic Health Record Data
for Public Health Surveillance, Evaluation, and Planning.
American Journal of Public Health. 2014;104(12):2265-2270.
45. Behrman RE, Benner JS, Brown JS, McClellan M, Wood-
cock J, Platt R. Developing the Sentinel System - A Na-
tional Resource for Evidence Development. N Engl J Med.
2011;364(6):498-499.
46. Maro JC, Platt R, Holmes JH, et al. Design of a National Dis-
tributed Health Data Network. Ann Intern Med. 2009;151:341-
344.
47. Klompas M, Haney G, Church D, Lazarus R, Hou X, Platt R.
Automated identif?cation of acute hepatitis B using electronic
medical record data to facilitate public health surveillance.
PLoS ONE. 2008;3(7):e2626.
48. Calderwood MS, Platt R, Hou X, et al. Real-Time Surveillance
for Tuberculosis Using Electronic Health Record Data from
an Ambulatory Practice in Eastern Massachusetts Public
Health Rep.125(6):843-850.
49. Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R.
Automated detection and classifcation of type 1 versus type
2 diabetes using electronic health record data. Diabetes Care.
2013;36(4)::914-921.
50. McVeigh KH, Newton-Dame R, Perlman S, et al. Developing
an Electronic Health Record-Based Population Health Sur-
veillance System. 2013; http://www.nyc.gov/html/doh/down-
loads/pdf/data/nyc-macro-report.pdf. Accessed December 29,
2014.
51. Kindig D, Stoddart G. What is population health? American
Journal of Public Health. 2003;93(3):380-383.
52. Institute of Medicine. Primary Care and Public Health: Ex-
ploring Integration to Improve Population Health. Washington,
DC: Te National Academies Press;2012.
53. McDonald CJ, Overhage JM, Barnes M, et al. Te Indiana
network for patient care: A working local health information
infrastructure. An example of a working infrastructure collab-
oration that links data from fve health systems and hundreds
of millions of entries. Health Afairs 2005;24(5):1214-1220.
54. Overhage JM, Grannis S, McDonald CJ. A comparison of the
completeness and timeliness of automated electronic labora-
tory reporting and spontaneous reporting of notifable condi-
tions. American Journal of Public Health. 2008;98(2):344-350.
55. Lenaway D, Halverson P, Sotnikov S, Tilson H, Corso L, Mil-
lington W. Public health systems research: setting a national
agenda. Am J Public Health. 2006;96:410 -413.
56. Mays GP, Smith SA, Ingram RC, Racster LJ, Lamberth CD,
Lovely ES. Public Health Delivery Systems Evidence, Un-
certainty, and Emerging Research Needs. Am J Prev Med.
2009;36(3):256-265.
57. Centers for Disease Control and Prevention. Competencies
for Public Health Informaticians. 2009; http://www.cdc.gov/
InformaticsCompetencies.
58. Ofce of the National Coordinator for Health Information
Technology. Te Federal Health IT Strategic Plan 2015-2020.
2014; http://www.healthit.gov/policy-researchers-implement-
ers/health-it-strategic-planning. Accessed December 10, 2014.
59. Samof E, DiBiase L, Fangman MT, Fleischauer AT, Waller
AE, MacDonald PDM. We Can Have It All: Improved Sur-
veillance Outcomes and Decreased Personnel Costs Associ-
ated With Electronic Reportable Disease Surveillance, North
Carolina, 2010. Am J Public Health. 2013;103:2292-2297.
60. Brown J. Public Health and the Learning Health Care System
Lessons from Two Distributed Networks for Public Health.
2013; https://www.networkforphl.org/_asset/lwp1wv/PPT-
PH-and-LHS-Lessons-from-Two-Networks-J-Brown.pdf.
Accessed December 29, 2014.
61. Association of State and Territorial Health Ofcers. Te
Public Health Community Platform. 2014; http://wiki.
siframework.org/fle/view/Public+Health+Community+Plat-
form+Briefng+Document+April+25+2014.pdf. Accessed
December 10, 2014.
62. Elliott TE, Holmes JH, Davidson AJ, La Chance PA, Nel-
son AF, Steiner JF. Data Warehouse Governance Programs
in Healthcare Settings: A Literature Review and a Call to
Action. eGEMs (Generating Evidence & Methods to improve
patient outcomes). 2013;1 (1):Article 15. DOI: http://dx.doi.
org/10.13063/12327-19214.11010 http://repository.academy-
health.org/egems/vol13061/iss13061/13015
63. Holmes JH, Elliott TE, Brown JS, et al. Clinical research data
warehouse governance for distributed research networks
in the USA: a systematic review of the literature. J Am Med
Inform Assoc. 2014 21(4):730-736.
64. Organization for Economic Cooperation and Development.
OECD Guidelines on the Protection of Privacy and Transbor-
der Flows of Personal Data. 1980; http://www.oecd.org/docu-
ment/18/0,2340,en_2649_34255_1815186_1_1_1_1,00.html.
65. Tacker SB, Berkelman RL. Public health surveillance in the
United States. Epidemiol Rev. 1988;10:164-190.
66. National Association of County and City Health Ofcials.
Results from the 2010 NACCHO Informatics Needs Assessment
[ RESEARCH REPORT]. Washington, DC May 2010.
67. Centers for Disease Control and Prevention. Information
Technology Strategic Plan, FY 2012-2016: Strengthening the
Public Health Information Supply Chain to Better Protect the
Health of People Everywhere. 2012. Accessed December 29,
2014.
68. Lenert L, Sundwall DN. Public Health Surveillance and
Meaningful Use Regulations: A Crisis of Opportunity. Am J
Public Health. 2012;102:e1-e7.
69. Rosenthal A, Mork P, Li MH, Stanford J, Koester D, Reynalds
P. Cloud computing:a new business paradigm for biomedical
information sharing. J Biomed Inform. 2010;43(2):342-353.
12
eGEMs (Generating Evidence & Methods to improve patient outcomes), Vol. 2 [2014], Iss. 4, Art. 8
http://repository.academyhealth.org/egems/vol2/iss4/8
DOI: 10.13063/2327-9214.1172
eGEMs
70. Ofce of the National Coordinator for Health Information
Technology, US Department of Health and Human Services.
Standards and Interoperability Framework, Federal Health Ar-
chitecture. http://healthit.gov/policy-researchers-implement-
ers/federal-health-architecture-fa Accessed May 18, 2014.
71. Institute of Medicine. Toward Quality Measures for Population
Health and the Leading Health Indicators. Washington, DC:
Te National Academies Press;2012.
72. Public Health Data Standards Consortium. AHIMA, PHD-
SC Unite to Strengthen Public Health Data Standards. 2014;
http://www.phdsc.org/about/pdfs/AHIMA-PHDSC_%20
Press_Release_10022014.pdf. Accessed December 29, 2014.
73. Joint Public Health Informatics Taskforce. http://jphit.org/.
Accessed December 29, 2014.
74. Integrating the Healthcare Enterprise, QRPH Technical
Committee. Structured Data Capture (SDC), Trial Implemen-
tation. IHE Quality, Research, and Public Health 5 Technical
Framework Supplement 2014; http://ihe.net/uploadedFiles/
Documents/QRPH/IHE_QRPH_Suppl_SDC.pdf. Accessed
December 29, 2014.
75. Bashyam ND, Boone KW. IHE Patient Care Coordination
(PCC) White Paper: A Data Access Framework using IHE
Profles, Draf for Public Comment, Revision 1.0. 2014;
http://www.ihe.net/uploadedFiles/Documents/PCC/IHE_
PCC_White_Paper_DAF_Rev1.0_2014-03-28.pdf. Accessed
December 29, 2014.
76. Sofware Engineering Institute. Ultra-Large-Scale Systems:
Te Sofware Challenge of the Future. Pittsburgh, PA: Carnegie
Mellon;2006.
77. Centers for Medicare and Medicaid Services. Medicaid Infor-
mation Technology Architecture (MITA). 2014; http://www.
medicaid.gov/Medicaid-CHIP-Program-Information/By-Top-
ics/Data-and-Systems/Medicaid-Information-Technology-Ar-
chitecture-MITA.html. Accessed December 29, 2014.
78. Tomines A, Readhead H, Readhead A, Teutsch S. Applica-
tions of Electronic Health Information in Public Health: Uses,
Opportunities and Barriers. eGEMs. 2013;1(2 Article 5).
79. Tacker SB, Qualters JR, Lee LM. Public Health Surveillance
in the United States: Evolution and Challenges*. MMWR.
2012;61(Suppl; July 27, 2012):3-9.
80. Brown JS, Kahn M, Toh S. Data Quality Assessment for Com-
parative Efectiveness Research in Distributed Data Networks.
Med Care 2013;51:S22-S29.
81. Rassen JA, Moran J, Toh D, et al. Statistical Methods: Evaluat-
ing Strategies For Data Sharing And Analyses In Distributed
Data Settings. 2013; http://www.mini-sentinel.org/methods/
methods_development/details.aspx?ID=1041.
13
Davidson: Unifying Silos into PH Business Intelligence
Published by EDM Forum Community, 2014

doc_650676722.pdf