This document discusses public health surveillance systems and their importance. It defines public health surveillance as the ongoing collection and analysis of health data to plan, implement and evaluate public health practices. Emergency departments are seen as ideal locations for collecting surveillance data due to the large number of visits. The benefits of surveillance include improving communication between health departments and EDs, improving response to public health emergencies, and influencing policy through data. Key stakeholders in developing surveillance systems include health care facilities, public health agencies, and information technology experts.
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Pg2 Beginning in 1991, the IOM (which stands for the Institute o.docxrandymartin91030
Pg2 Beginning in 1991, the IOM (which stands for the Institute of Medicine of the National Academies) sponsored studies and created reports that led the way toward the concepts we have in place today for electronic health records. Originally, the IOM called them computer-based patient records.1 During their evolution, the EHR have had many other names, including electronic medical records, computerized medical records, longitudinal patient records, and electronic charts. All of these names referred to essentially the same thing, which in 2003, the IOM renamed as the electronic health records, or EHR.
Note: EHR
The acronym EHR is commonly used as shorthand for Electronic Health Records, and will be used in the remainder of this book.
Institute of Medicine (IOM)
The IOM report2 put forth a set of eight core functions that an EHR should be capable of performing:
Health information and data
This function provides a defined data set that includes such items as medical and nursing diagnoses, a medication list, allergies, demographics, clinical narratives, and laboratory test results. Further, it provides improved access to information needed by care providers when they need it.
Result management
Computerized results can be accessed more easily (than paper reports) by the provider at the time and place they are needed.
· Reduced lag time allows for quicker recognition and treatment of medical problems.
· The automated display of previous test results makes it possible to reduce redundant and additional testing.
· Having electronic results can allow for better interpretation and for easier detection of abnormalities, thereby ensuring appropriate follow-up.
· Access to electronic consults and patient consents can establish critical links and improve care coordination among multiple providers, as well as between provider and patient
Order management
Computerized provider order entry (CPOE) systems can improve workflow processes by eliminating lost orders and ambiguities caused by illegible handwriting, generating related orders automatically, monitoring for duplicate orders, and reducing the time required to fill orders.
· CPOE systems for medications reduce the number of errors in medication dose and frequency, drug allergies, and drug–drug interactions.
· The use of CPOE, in conjunction with an EHR, also improves clinician productivity.
Decision Support
Computerized decision support systems include prevention, prescribing of drugs, diagnosis and management, and detection of adverse events and disease outbreaks.
· Computer reminders and prompts improve preventive practices in areas such as vaccinations, breast cancer screening, colorectal screening, and cardiovascular risk reduction.
Electronic communication and connectivity
Electronic communication among care partners can enhance patient safety and quality of care, especially for patients who have multiple providers in multiple settings that must coordinate care plans.
· Electronic co.
Strengthening Public Health and Primary CareCollaboration Th.docxflorriezhamphrey3065
Strengthening Public Health and Primary Care
Collaboration Through Electronic Health Records
Electronic health records
(EHRs) have great potential
to serve as a catalyst for
more effective coordina-
tion between public health
departments and primary
care providers (PCP) in
maintaining healthy com-
munities.
As a system for docu-
menting patient health data,
EHRs can be harnessed to
improve public health sur-
veillance for communica-
ble and chronic illnesses.
EHRs facilitate clinical alerts
informed by public health
goals that guide primary
care physicians in real time
in their diagnosis and treat-
ment of patients.
As health departments
reassess their public health
agendas, the use of EHRs to
facilitate this agenda in pri-
mary care settings should
be considered. PCPs and
EHR vendors, in turn, will
need to configure their EHR
systems and practice work-
flows to align with public
health priorities as these
agendas include increased
involvement of primary
care providers in addressing
public health concerns. (Am
J Public Health. 2012;102:
e13–e18. doi:10.2105/AJPH.
2012.301000)
Neil Calman, MD, Diane Hauser, MPA, Joseph Lurio, MD, Winfred Y. Wu, MD, MPH, and Michelle Pichardo, MPH
ELECTRONIC HEALTH RECORDS
(EHRs) have great potential to
serve as a catalyst for more effec-
tive coordination between public
health departments and primary
care providers in maintaining
healthy communities. As promi-
nent health risks to the community
continue their shift from conta-
gious diseases to chronic illnesses,
public health departments are in-
creasingly focused on conditions
such as diabetes and obesity. At
the same time, serious threats
persist from traditional public
health concerns, such as commu-
nicable disease outbreaks.
Primary care providers, and
particularly community health
centers (CHCs), that provide care
for low-income populations are
on the front lines in treating and
containing both communicable
diseases and chronic illnesses that
are more prevalent in these com-
munities. Traditional models of
primary care are also evolving, with
increased focus on community-
based approaches in response to
changing financial incentives and
formal recognition programs, such
as the Patient-Centered Medical
Home certification offered by the
National Committee for Quality
Assurance and the Joint Commis-
sion.1,2 Use of these models is
facilitated by the parallel increase
in adoption of EHRs.
Federal incentive programs
have been a proponent of EHR
implementation and “meaningful
use” of EHRs among primary
care providers, with targeted
funding to support their adoption
among CHCs.3 The promotion
of health information technology
to improve the public’s health is
1 of 5 focus areas for meaningful
use of EHRs. Finally, 1 of the
3-part aims of the Centers for
Medicare and Medicaid Services
(CMMS) is the improvement of
population health—a goal that
will only be met through im-
proved coordination of primary
care and public .
المركز الرابع لمشاريع تحدي الامراض المزمنة
في مبادرة التحول الرقمي fekra_tech
وهو عبارة عن توضيف مكائن الفحص الذاتي للكشف عن المرضي المعرضين للاصابة بالسكري
fekratech.gov.sa
@NDU_KSA
This document discusses using real world data from healthcare databases to support adaptive biomedical innovation. It outlines four key principles - meaningful, valid, expedited, and transparent evidence (MVET) - that are necessary to generate evidence from healthcare databases that is fit for decision making. Meaningful evidence requires using relevant and high quality data sources to answer the research question. Evidence should be generated and shared in a transparent manner while protecting patient privacy. Following MVET principles can help produce rigorous evidence from real world data to support faster access to new medications through adaptive pathways, while maintaining evidentiary standards.
This paper discusses the evolution of health care information systems and how they affect the day to day operations in hospitals today compared to years ago. It discusses the effect it has on patient care and reimbursement. It compares the collection of data today, using technology, and how data was collected years ago.
Health care information systems have evolved significantly over the past 20 years. In the past, medical documentation was entirely handwritten using different colored inks. Now, electronic health records allow for digital documentation and sharing of patient information across providers. This evolution has improved patient care through faster access to test results, coordinated care, and disease surveillance. Major technological advances like health information exchanges and widespread adoption of health IT have enabled the electronic sharing of health data, leading to more efficient care and better patient outcomes. As technology continues to advance, health care information systems will likewise continue to evolve to make better use of available data.
Here are some thought-provoking questions about using public health informatics and data to address community health issues:
- What public health data would have been used to determine the need for a mass inoculation program against a new strain of influenza? Data on previous flu seasons like hospitalizations and deaths, current flu activity in the population, characteristics of the new strain, and susceptibility in the community based on previous vaccination coverage could all factor into determining if a mass program is needed.
- What data will be collected to determine the success of such a program? Data that could be collected includes numbers of individuals vaccinated, demographic information on who was vaccinated, monitoring disease surveillance systems for cases and outbreaks associated with the new strain, tracking severe
In the realm of healthcare, data is a critical asset that holds the potential to revolutionise patient care, enhance treatment outcomes, and streamline healthcare operations. One of the most valuable resources in this data-driven landscape is healthcare datasets. These datasets encompass a wide range of information, from patient medical records and clinical trial data to health insurance claims and public health statistics.
Healthcare datasets serve as the foundation for evidence-based medicine, enabling researchers and healthcare professionals to analyse trends, identify patterns, and make informed decisions. By delving into these datasets, medical researchers can uncover new insights into disease progression, treatment efficacy, and patient outcomes. This knowledge is crucial for developing more effective therapies, improving diagnostic accuracy, and tailoring treatment plans to individual patients' needs.
Moreover, healthcare datasets play a pivotal role in public health initiatives. By examining data on disease incidence, vaccination rates, and health behaviours, public health officials can design targeted interventions, allocate resources more efficiently, and monitor the impact of public health policies. This data-driven approach helps in controlling the spread of infectious diseases, promoting healthy lifestyles, and ultimately reducing the burden of illness on society.
The integration of healthcare datasets with advanced analytics and machine learning technologies opens up even more possibilities. Predictive models built on these datasets can forecast disease outbreaks, identify high-risk patient populations, and optimise resource allocation in healthcare facilities. These predictive insights are invaluable for proactive healthcare management and ensuring that patients receive timely and appropriate care.
However, the effective use of healthcare datasets is not without challenges. Issues related to data privacy, security, and interoperability need to be addressed to ensure that sensitive patient information is protected and that data from different sources can be integrated seamlessly. Additionally, the quality and completeness of data are crucial for drawing accurate conclusions, necessitating rigorous data management and validation practices.
In conclusion, healthcare datasets are a vital resource that holds immense potential for advancing medical research, improving patient care, and enhancing public health outcomes. As technology continues to evolve, the ability to harness the power of these datasets will become increasingly important in shaping the future of healthcare.
Framework for Data Warehousing and Mining Clinical Records of Patients: A ReviewBRNSSPublicationHubI
This document discusses a framework for data warehousing and mining clinical records of patients. It begins with an abstract that describes how a clinical data warehouse can provide access to clinical data for healthcare providers and support areas like research and management. The rest of the document reviews the background and need for integrating disparate clinical data sources, describes challenges in current fragmented systems, and discusses the significance of developing a clinical data warehousing and mining framework to organize and extract medical records from different systems.
Building a Citywide, All-Payer, Hospital Claims Databaseto I.docxjasoninnes20
Building a Citywide, All-Payer, Hospital Claims Database
to Improve Health Care Delivery
in a Low-Income, Urban Community
Kennen Gross, PhD, MPH,
1
Jeffrey C. Brenner, MD,
1
Aaron Truchil, MS,
1
Ernest M. Post, MD,
2
and Amy Henderson Riley, MA, CHES
1
Abstract
Developing data-driven local solutions to address rising health care costs requires valid and reliable local data.
Traditionally, local public health agencies have relied on birth, death, and specific disease registry data to guide
health care planning, but these data sets provide neither health information across the lifespan nor information
on local health care utilization patterns and costs. Insurance claims data collected by local hospitals for ad-
ministrative purposes can be used to create valuable population health data sets. The Camden Coalition of
Healthcare Providers partnered with the 3 health systems providing emergency and inpatient care within
Camden, New Jersey, to create a local population all-payer hospital claims data set. The combined claims data
provide unique insights into the health status, health care utilization patterns, and hospital costs on the pop-
ulation level. The cross-systems data set allows for a better understanding of the impact of high utilizers on a
community-level health care system. This article presents an introduction to the methods used to develop
Camden’s hospital claims data set, as well as results showing the population health insights obtained from this
unique data set. (Population Health Management 2013;16:S-20–S-25)
Surveillance is an integral part of public healthpractice. The ability to obtain accurate, timely information
about the patterns of disease incidence and mortality has
traditionally been a central goal of public health. With the
growing concerns over the inefficient uses of health care ser-
vices and rising health care costs, public health must add cost
and utilization to its surveillance practice. A key component
of any surveillance system is that data are gathered at the
geographic level at which the interventions will be delivered.
For example, interventions that aim to reduce preterm deliv-
ery must be delivered to those at risk for preterm delivery,
creating a need for preterm delivery surveillance data at the
city or neighborhood level in order to understand the size,
scale, and geographic concentration of the problem.
Birth outcome surveillance data, such as prevalence of
preterm births, have been available through birth certificate
registry data sets. These data sets are compiled from birth
certificate forms that states require hospitals to complete and
send to state health departments. State health departments
process the data, creating local population data sets for city
and county health departments. Death certificate data are
compiled via a similar procedure.1,2 Birth and death data sets
allow for local surveillance information about what condi-
tions residents are born with and die from, but do not pro ...
This document discusses surveillance in healthcare. It defines surveillance as the ongoing collection and analysis of health-related data for public health purposes. The document outlines different types of surveillance including passive, active, and sentinel surveillance. Passive surveillance relies on voluntary reporting while active surveillance stimulates more regular reporting. Sentinel surveillance monitors specific sites. The advantages and disadvantages of each type are provided. The document also discusses important qualities of an effective surveillance system such as simplicity, flexibility, acceptability, sensitivity, predictive value, representativeness, and timeliness.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Big healthcare data from electronic medical records can be used to understand drug effectiveness and safety. It is most useful when combined with experimental research throughout the drug lifecycle. Some uses of big data include exploring patterns to identify populations, conducting association studies by linking to genomics data, predicting treatment responses, and assessing causal relationships between drugs and health outcomes. However, healthcare data was primarily collected for administrative purposes, not research, so it has limitations that require careful analysis to draw valid conclusions and produce meaningful evidence on therapeutic safety and effectiveness.
Early diagnosis and prevention enabled by big data geneva conference finale-Marefa
The presentation provides an overview of how digital health or use of data processing and telecommunication infrastructure can contribute to the early diagnosis and prevention of diseases.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about transformational advances in patient care, research, and healthcare management. United States is the focus due fact that many academic and research institutions in the country are at the forefront of healthcare data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect, process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more educated decisions, forecast health outcomes, manage population health, customize treatment, optimize workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data intelligence applications raises issues and concerns about data privacy, fairness, transparency, data quality, accountability, fair data access, regulatory compliance, and the balance between automation and human judgment. Emerging themes include AI and machine learning domination, stronger ethical and regulatory frameworks, edge and quantum computing, data democratization, sustainability applications, and developing human-machine collaboration. Data intelligence has an impact that goes beyond healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth. Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
Healthcare data comes from various sources like electronic health records, personal health records, and health apps. Data analytics in healthcare represents automating the collection, processing, and analysis of complex healthcare data to provide insights and help practitioners make informed decisions. There are different types of analytics used in healthcare to address challenges like ever-changing medicine, mixed data sources, and emerging regulations. Examples include using analytics to speed up insurance claims submission by preparing them faster with fewer errors and predicting suicide attempts by classifying patients into risk groups.
Team Sol2 01 Health Care Informatics Power PointMessner Angie
The document discusses clinical information systems and their components. It provides an overview of electronic health records and describes key parts of a clinical information system including health information, order entry, decision support, and clinical documentation. It also discusses clinical decision making systems and their importance in reducing variation, costs, and improving diagnosis. Safety, education and costs related to clinical information systems are also evaluated.
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Still I Rise by Maya Angelou
-Table of Contents
● Questions to be Addressed
● Introduction
● About the Author
● Analysis
● Key Literary Devices Used in the Poem
1. Simile
2. Metaphor
3. Repetition
4. Rhetorical Question
5. Structure and Form
6. Imagery
7. Symbolism
● Conclusion
● References
-Questions to be Addressed
1. How does the meaning of the poem evolve as we progress through each stanza?
2. How do similes and metaphors enhance the imagery in "Still I Rise"?
3. What effect does the repetition of certain phrases have on the overall tone of the poem?
4. How does Maya Angelou use symbolism to convey her message of resilience and empowerment?
Slide Presentation from a Doctoral Virtual Open House presented on June 30, 2024 by staff and faculty of Capitol Technology University
Covers degrees offered, program details, tuition, financial aid and the application process.
Righteous among Nations - eTwinning e-book (1).pdf
Week 10 Managing the Public Health Surveillance and.docx
1. (Mt) – Week 10 – Managing the Public Health Datastream: Surveillance
and
Emerg Med Clin N Am 24 (2006) 1035–1052 Update on Public Health Surveillance in
Emergency Departments Shawn M. Varney, Lt Col, USAF, MCa,*, Jon Mark Hirshon, MD,
MPHb,c a 59 MDW/MCED, 2200 Bergquist Drive, Suite 1, Lackland AFB, TX 78236-5500,
USA b Division of Emergency Medicine, Department of Emergency Medicine and
Department of Epidemiology and Preventive Medicine, University of Maryland School of
Medicine, Baltimore, MD, USA c The Charles McC. Mathias, Jr. National Study Center for
Trauma and EMS, University of Maryland School of Medicine, 701 West Pratt Street, Fifth
Floor, Baltimore, MD 21201, USA The systematic collection and analysis of health data are
important actions required to help understand the health needs of a population. When it is
done to investigate a problem to contribute to generalizable knowledge, it is defined as
research [1]. If these activities are done through the collection of health data in an ongoing
manner to influence the health of the public, it can be considered public health surveillance.
Considering that in 2003 there were an estimated 113.9 million emergency department
(ED) visits nationwide [2], EDs are an ideal location to collect de-identified information on
the acute health needs and patterns of the population of the United States. The systematic
collection of data from multiple EDs can also serve as a barometer of the overall status of
the US health system. While there are a number of logistical and infrastructural barriers
that can impede the development of surveillance systems, the potential benefits from these
systems are significant. The ability to analyze data; distribute results; and influence policy,
funding, and patients’ behavior are important outgrowths of public health surveillance in
emergency departments. * Corresponding author. Department of Emergency Medicine, 59
MDW/MCED, 2200 Bergquist Drive, Suite 1, Lackland AFB, TX 78236-5500. E-mail address:
shawn.varney@lackland.af.mil (S.M. Varney). 0733-8627/06/$ – see front matter ! 2006
Elsevier Inc. All rights reserved. doi:10.1016/j.emc.2006.06.004 emed.theclinics.com 1036
VARNEY & HIRSHON What is public health surveillance? Definition of surveillance The
Centers for Disease Control and Prevention (CDC) has defined public health surveillance as
‘‘the ongoing systematic collection, analysis and interpretation of health data essential to
the planning, implementation and evaluation of public health practices, closely integrated
with the timely dissemination of these data to those who need to know. The final link in the
surveillance chain is the application of these data to prevention and control’’ [3].
Surveillance systems are used to prepare, execute, and assess public health intervention
2. programs and relay the acquired information to decision makers. In the present age of
heightened security awareness and threats of bioterrorism, surveillance systems play an
additional role in the early detection of health use anomalies. Through the rapid recognition
of multiple patients with similar symptoms suggestive of an atypical or biologic agent, alerts
are triggered so that public health professionals are notified of a potential threat.
Surveillance system components Surveillance systems may range from rudimentary to
complexdie, from manual collection and documentation on sheets of paper to automated
realtime data delivery. The steps required for a public health surveillance system include
data acquisition on a periodic and ongoing basis, timely data collation and analysis, and the
application of these data by the proper public health professionals. The basic components of
a surveillance system include equipment, personnel, and the required resources for the
personnel to analyze the data, communicate promptly and effectively, and maintain the
system adequately. The ability to amass and analyze large amounts of information has
markedly improved with the advent of current computer technology. Therefore, essential
equipment for an ED-based public health surveillance system now includes a robust
computerized database system with appropriate Internet and networking capabilities,
along with sophisticated software to analyze data for areas of interest. The potential
applications of data and the requirements for interoperability with collaborators, such as
regional, state, or national systems, dictate the necessary degree of complexity.
Fundamental personnel consist of individuals responsible for (1) data collection, (2)
information analysis, and (3) timely response to material collected. Thus, many partners are
involved, including health care providers in physicians’ offices and EDs and public health
professionals in local, state, and federal agencies, as well as laboratory workers,
researchers, academicians, and information technology (IT) experts. The ability to maintain
multidirectional communication flow among these team members is critical for a functional
system. UPDATE ON PUBLIC HEALTH SURVEILLANCE IN THE ED 1037 Required additional
resources include financial, institutional, and IT (encompassing communication, data
management, and data analysis). To be effective, surveillance system development requires
full endorsement and involvement from interested public health, political, and private
leaders in many fields. Data sources may include standardized clinical databases from
hospitals, doctors’ offices, EDs, pharmacies, telephone health lines, and others. The
integration of these databases into a cohesive system requires significant time and effort to
garner support of critical partners and to make the system fully operational. Definition of
syndromic surveillance Syndromic surveillance describes a dynamic process of collecting
real-time or near real-time data on symptom clusters suggestive of a biological disease
outbreak. Ideally, these diseases will be detected early in the processdbefore the definitive
diagnosisdto enable a rapid response and mitigate adverse outcomes [4,5]. Syndromic
surveillance systems have secondary objectives including determining the size, spread, and
tempo of an outbreak, or even providing reassurance that an outbreak has not occurred [4].
Initially, syndromic surveillance systems were designed for the early detection of biological
terrorism agents. The focus has evolved subsequent to the 9/11 World Trade Center and
anthrax terrorist attacks of 2001. Present emphasis lies on the timely collection,
assimilation, and analysis of health care data gathered from existing community systems to
3. provide immediate feedback to decision makers about unexpected disease clusters or
sentinel cases [4]. In contrast to the standard diagnosis-based disease surveillance (labs and
cultures), syndromic surveillance is prediagnosticdie, it recognizes a cluster of symptoms,
or the onset of a disease, before full-blown illness manifestation. Identifying a peak of
unusual symptoms above the background/steady state may allow a few extra days for
further observation, evaluation, and treatment before the severe illness becomes apparent
by conventional diagnostic methods. Theoretically, early detection equates to earlier
treatment and decreased morbidity and mortality. Syndromic surveillance systems tend to
derive their data from two sources: (1) clinical data from health care services (ED visits,
clinic visits, or Emergency Medical Services [EMS] records), and (2) alternative sources
(work or school absentee rates, pharmaceutical sales, calls to emergency or information
hotlines, Internet-based illness reporting systems) [6]. Each data source has advantages.
For example, clinical data sources provide the ability to follow patients and, in the case of a
public health emergency, to contact infected individuals. These actions, however, would
require significant efforts and high-level approvals to override existing privacy and
confidentiality safeguards. In addition, clinical data encourages bidirectional
communication and fosters improved relationships between community providers 1038
VARNEY & HIRSHON and public health staff, which is an important step in a functional
public health system. Alternative data sources, such as pharmacy sales including over-the-
counter products, may signal the occurrence of events before people seek formal health
care and may represent a broader sample of the population at risk. In one study of 3919 ED
visits, Begier and colleagues [5] found good overall agreement (kappa ¼ 0.639) between
chief complaint and discharge diagnosis, but substantial variability by specific syndromes.
All ED patient encounters were coded via a mutually exclusive algorithm into one of eight
syndromes: death, sepsis, rash, respiratory illness, gastrointestinal illness, unspecified
infection, neurologic illness, and other. They observed lower agreement among sepsis,
neurologic, and unspecified infection. Begier and colleagues concluded that although there
is good agreement for most syndromes, the chief complaint better identifies illnesses with
nonspecific symptoms (ie, fever), while discharge diagnoses detect illnesses requiring
clinical evaluation (ie, sepsis and meningitis). Another form of syndromic surveillance is
‘‘event-based’’ or ‘‘drop-in’’ surveillance, which lasts for a finite period or event. It relies on
health care providers in EDs and large clinics to collect nonroutine data. Such a system was
implemented and proved useful during the 2000 Democratic National Convention in
California and the 2002 Winter Olympic Games in Utah [7,8]. Although syndromic
surveillance may be able to play a key role in early recognition of disease outbreaks, it
neither replaces traditional public health surveillance nor supplants the critical role of an
astute physician reporting atypical diseases and events. Why is surveillance important?
General rationales for ED-based public health surveillance There are a number of rationales
for the development of public health surveillance based on ED visits [9]. These include: 1.
Improved communication between health departments and emergency departments for
addressing ongoing local, regional, and state-level problems. 2. Improved public health
response to rapidly developing public health emergencies. 3. Improved ability to correlate
environmental events and visits. 4. Improved information on the scope and nature of ED
4. visits for injuries (both minor and major). 5. Improved documentation and evaluation of ED
visits for infectious diseases. 6. Improved hospital-based patient record systems. UPDATE
ON PUBLIC HEALTH SURVEILLANCE IN THE ED 1039 7. Influence policy discussions and
decisions through improved data. These rationales can be conceptually divided into those
designed to improve the health of the public and those designed to improve the security of
the population. Improving public health Surveillance is an outcome-oriented science that
provides information for action. Public health surveillance focuses on health-related issues
or their preceding events. It plays a key role in protecting the public by devising ways to
improve health and to mitigate morbidity and mortality. In the context of public health,
Teutsch and Churchill [10] described multiple ways that surveillance data are useful: to
estimate the magnitude of a health problem; to understand the natural history of a disease
or injury; to detect outbreaks or epidemics; to document the distribution and spread of a
health event; to test hypotheses about etiology; to evaluate control strategies; to monitor
changes in infectious agents; to monitor isolation activities; to detect changes in health
practice; to identify research needs and facilitate epidemiologic and laboratory research;
and to facilitate planning. Surveillance allows for the monitoring and evaluation of the
health of the public. However, it is critical that appropriate public health professionals then
translate the information garnered from these efforts into action. A feedback loop is thus
developed to produce positive effects within the monitored population. Timely and accurate
health-related data, properly collected and analyzed, allow public health leaders, politicians,
and others to act appropriately to mitigate disasters or epidemics through judicious
allocation of suitable resources. A current example of ongoing surveillance of a potential
public health threat is the actions by national governments in Asia, the CDC, and the World
Health Organization (WHO) to monitor the current status of avian influenza (bird flu),
especially the influenza A (H5N1) virus [11]. While H5N1 primarily affects fowl, there is
concern for the potential personto-person transmission of the virus leading to a pandemic.
Thus the CDC has recommended enhanced surveillance for this disease in the United States
to promote its rapid diagnosis and to prevent its dissemination. If bird flu were discovered
in a patient in the United States, the CDC could rapidly mobilize resources to limit the
spread of infection and panic among the population. Terrorism response/homeland
security According to the Advisory Panel to Assess Domestic Response Capabilities for
Terrorism Involving Weapons of Mass Destruction, ‘‘a robust public health system is
fundamental to a long-term solution for a variety of health issues, including terrorism’’ [12].
Public health surveillance 1040 VARNEY & HIRSHON systems, such as those based on ED
visits, are part of this solution. While it is difficult to assess the magnitude of the threat,
there is no question that all societies are at risk from conventional explosives and,
potentially, from weapons of mass destruction. Within this global context of increased
insecurity, it is important to be able to detect unusual diseases and events. The ongoing,
systematic collection of ED data to identify unusual diseases and patterns may help shorten
the time required to respond to biological or chemical attacks and thus decrease the
morbidity and mortality from these weapons. Recognition on the national level can be seen
by the increased federal dollars allocated to public health, much of which has been used for
increased disease surveillance and response. Additionally, a number of projects focusing on
5. syndromic surveillance, such as the Electronic Surveillance System for the Early Notification
of Community-Based Epidemics (ESSENCE), were developed or tested through funding
from the Defense Advanced Research Project Agency (DARPA) and the Department of
Defense [13]. Stakeholders in developing surveillance systems Health care facilities The ED
plays a key role in the development and use of a public health surveillance system. Patients
come into EDs 24 hours a day, 7 days a week, every day of the year, making it an
appropriate place for data gathering and collation. Health care providers in the ED
simultaneously see multiple patients and often have high daily patient volumes. This
enables the derivation of the relative prevalence of symptom clusters that may represent
worrisome syndromes or epidemics. Outlying clinics frequently refer sicker patients to local
EDs, facilitating collection of information on more cases. Emergency physicians are taught
to have a high index of suspicion for uncommon diseases, leading to broad differential
diagnoses and clinical acumen. They are the first physician contacts for patients in many
situations and may detect aberrations in the usual incidence of disease. From these frontline
positions, they need to be able to transmit their findings and concerns in a timely and
accurate manner to the appropriate public health authority. As a primary participant in the
disease recognition process, emergency physicians and other ED staff must be involved in
surveillance system development. The information collecting process should be simple,
quick, and easy to implement with minimal or no impact on health care practitioners.
Automatic classification of broad symptom categories for chief complaints can be included
as a part of triage. Alternatively, a computer can be placed in a kiosk by the registration
desk in the ED. Simple questions may identify symptom clusters that the computer can
analyze at regular intervals and produce warnings or alerts to hospital personnel or public
health agencies. UPDATE ON PUBLIC HEALTH SURVEILLANCE IN THE ED 1041 ED
personnel end up participating in the surveillance process to some degree whether they
realize it or not. Simply observing patients and assimilating and documenting information
(gathering chief complaints, identifying trends, and so forth) contributes. Passing the data
to the public health sector may mitigate morbidity and mortality. Automatic data entry from
multiple hospitals into a centralized repository may facilitate disease recognition and
coordinate findings citywide, similar to a well-run emergency medical services system.
Ideally, a large funding source, such as state and federal governments, should support this
initiative in the interest of the public’s health. Public health agencies Public health agencies
and their staff play a pivotal role in monitoring and managing the public’s health, from
scrutinizing for disease outbreaks to implementing quarantine measures. They function as
the keystone of a public health surveillance system and their involvement in system
development and use is crucial. While EDs and other data sources, such as laboratory
personnel and pharmacists, supply the input, public health professionals must accept the
collected data, analyze it, and then return recommendations and policy actions to
appropriate officials. Timely reporting is critical to allow public health professionals to
perform their jobs. As part of this involvement, bidirectional communication is vital
between frontline providers, such as emergency physicians, and public health experts.
While it is important that accurate information be sent to the health department in a timely
manner, it is of equal significance that informed and authoritative health messages be
6. disseminated to both health care professionals and to the public. The information received
by emergency physicians and other practitioners influences the evaluation and treatment of
patients. Public health messages can assist in the effective management of the behavior and
responses of the community at large, especially in times of crisis. Of additional consequence
in this partnership between health care and public health is the understanding that system
development requires the support, financial and otherwise, of health departments and
public health professionals. An individual ED is not a surveillance system, although it may
function as a monitoring station within one. A public health surveillance system based on
ED visits, as well as other potential data sources, requires significant infrastructural support
to receive large amounts of health-related data and then to rapidly analyze it for unusual
patterns or increased disease frequency. Information technology With the increased ability
to rapidly collect and analyze data from multiple sources, the involvement and support of
experts in information 1042 VARNEY & HIRSHON technology are important aspects of the
team effort to develop a functional public health surveillance system. In general, data are
not transmitted as a continuous stream, but rather at periodic intervals (eg, hourly, daily,
weekly). Data can be collected and analyzed manually, but the greater the automation, the
more rapid and accurate the results are likely to be. Automation can enhance the data
collection and analysis process, minimizing delays and decreasing inaccuracy caused by the
need to depend on human interactions. Through the use of software that automatically
collects the number of visits (or other data parameter) by category, the amount of effort
required by health care providers in data input can be significantly decreased. Advanced
logic algorithms can help look for unusual trends through analysis of the data from multiple
sources and can be instructed to alert when specific patterns are noted. While these
processes can decrease the daily effort required of health care and public health
professionals, individuals knowledgeable about the appropriate software and hardware are
required for a smoothly functioning, integrated system. Surveillance implementation Health
data standards and timeliness An ideal public health surveillance system would be
interoperable, universal, automated, real-time, economical, secure, sensitive, and specific.
To date, information technology (IT) developers have not created a product to satisfy these
parameters. To enhance interoperability between different current systems and between
existing and future systems, certain information system standards have been identified. In
addition, information systems supported by government funds must comply with federally
mandated standards. Broome and Loonsk [14] discussed three vital justifications for
standards-based system development: (1) electronic messaging (ie, Standard Health Level
7, or HL7, interface) provides the most effective and efficient way to collect real-time data
from multiple sources; (2) specified standards provide public health departments greater
control over previous investments in their IT infrastructures; and (3) standard formats and
electronic data delivery reduce the burden on individual providers’ reporting practices.
Multiple government agencies have identified important standards integral to improved
information exchange between clinicians and health departments [14]. The CDC and its
state and local delegates formed the Public Health Information Network that identified
standards for data, technology, terminology, and confidentiality. This network named five
major functional areas (detection and monitoring, data analysis, knowledge management,
7. alerting, and response) and itemized specifications UPDATE ON PUBLIC HEALTH
SURVEILLANCE IN THE ED 1043 for nine IT functions that form the basis for interoperable
standardsbased systems: 1. 2. 3. 4. 5. 6. 7. 8. 9. automated data exchange between public
health partners; use of electronic clinical data for event detection; manual data entry for
event detection and management; specimen and laboratory result information management
and exchange; management of possible case, contacts, and threat data; analysis and
visualization; directories of public health and clinical personnel; public health information
dissemination and alerting; and IT security and critical infrastructure protection [15]. At the
request of the CDC Information Council, the Gartner Group, an independent IT consulting
firm, reviewed the Public Health Information Network’s specifications and functions and
endorsed them as the ‘‘foundational road map’’ for systems integration in public health [16].
Timeliness related to surveillance systems impacts all aspects of the process from data
collection, through data transfer and analysis, to returning treatment and policy
recommendations. These criteria are ranked among the most important and most often
described in published reports [17,18]. The ability to react quickly to public health
emergencies depends on rapid recognition and response to possible or actual threats, which
is the core issue of timeliness. Data collection Two prevailing data-gathering principles in
public health surveillance are (1) collect information judiciously, and (2) gather and retain
information as locally as possible [6]. Both principles facilitate compliance with the Health
Insurance Portability and Accountability Act (HIPAA) of 1996 and also help limit the
amount of labor involved in the data input phase. From a pragmatic perspective, it is
important to limit the amount of data collection effort required by frontline providers, to
achieve high levels of compliance and data fidelity without impacting providers’ ability to
care for patients. While most hospitals do not have real-time or near-real-time surveillance
systems, some have adapted current systems to achieve this objective. For example, in Hong
Kong the hospital authority developed an ED computer system used across the region. For 2
years (1999–2000) they gathered data on common diseases, namely upper respiratory
infections and gastrointestinal illnesses, and followed trends and seasonal peaks. They
tracked diagnoses, prescriptions, specialty information, and patient demographics monthly.
When peaks exceeded two standard deviations of variance, a computer-generated report
was sent to the ED director and hospital authority officials for appropriate intervention
[19]. Noting the unexpected infectious 1044 VARNEY & HIRSHON disease surges, the health
authorities alerted the media and educated the public. This may have helped curtail disease
transmission. The simple step of compiling computerized ED records of patient volume,
chief complaints, and diagnoses, along with applying a standard statistical program, forms
the first step in disease surveillance. Providers, in general, do not prioritize nonclinical
responsibilities during clinical hours. In a busy ED, an emergency physician will need to see
direct patient care benefit from data collection, otherwise data acquisition will be
inconsistent. Much of the data collation can occur in an environment away from the clinical
area thereby limiting the impact on the health care providers. Data transfer As discussed
above, standards are important to ensure timely and accurate transfer of data. Considering
the current state of computer technology, electronic data transfer best meets these needs. In
addition, data security, such as encryption, is of fundamental importance, especially when
8. considering the increased responsibility of covered entities to securely protect the
confidentiality of personal health information. While HIPAA allows for exemptions
concerning the use of data for public health purposes, surveillance systems and related
stakeholders would have difficulty withstanding public scrutiny if data were mishandled or
inappropriately released. Data analysis Rapid, accurate analysis of the data is important to
develop appropriate and timely policy recommendation. It is the critical step in turning
large amounts of seemingly unrelated data into coherent information, and subsequently
into action. Individuals may analyze data manually with statistical programs or using
automated algorithmic processes. In most instances it will be a combination of both
modalities. Automatic algorithms can greatly enhance the speed of analysis and produce
predefined alerts, but will still require interpretation and monitoring by those with an in-
depth knowledge of the surveillance system. While there are many similarities between
systems designed to collect health data, sources of information vary. Data analysis solutions
require modifications for specific circumstances. For example, the detection of an abnormal
increase in a disease is dependent on the definition of the baseline incidence of that disease,
as seen by syndromic surveillance for flu-like illnesses. The number of cases that would be
considered abnormal will be very different in the winter months during the ‘‘flu season,’’ as
opposed to the summer months when influenza is unlikely. One solution to this problem is
to use a progressive baseline derived from the number of flu-like cases in the previous 2
weeks. Thus, when influenza spreads through the community in late fall/early winter, the
system would initially produce alerts based on UPDATE ON PUBLIC HEALTH
SURVEILLANCE IN THE ED 1045 a predetermined variance from baseline but would quickly
develop a modified baseline that would be appropriate for a season of increased cases. This
baseline would then decrease as the number of flu-like illnesses drop in late winter/early
spring leading to an appropriate baseline for a low-incidence season. Another aspect of data
analysis involves investigating specific alerts. For example, signal investigation plays a key
role in outbreak detection. SteinerSichel and colleagues [20] described their experience
with the New York City Department of Health and Mental Hygiene (DOHMH), which has
operated a syndromic surveillance system based on ED chief complaints since November
2001. The DOHMH conducted field investigations of suspected outbreaks when the
surveillance systems signaled an unexpected increase/ excess above the expected rates for
respiratory, fever, diarrhea, and vomiting syndromes. They sought to determine if the
signals correlated with clinically significant disease outbreaks. In more than 40 signal
investigations, none definitively detected an infectious disease outbreak. They also found
that none of the localized outbreaks investigated by the traditional methods revealed any
syndromic surveillance signal. Steiner-Sichel and colleagues attributed this to the difficulty
of proving causality and using a sensitive, but not specific, detection system. The advantage
of early detection may be offset by the complexities of field investigation and epidemiologic
data acquisition. At the present time a number of issues need to be addressed to improve
data analysis, particularly as it relates to syndromic surveillance. These issues include how
to best analyze data from multiple data streams [21,22], improve the linkage of data from
different data sources [23], and create flexible space-time shapes in the analysis of disease
clusters [24]. While a great deal of energy and resources have been spent to improve public
9. health surveillance, especially as it relates to syndromic surveillance, further work is clearly
necessary. Use and misuse of data Although there are clear public health and public safety
aspects to the use of aggregated health-related data, the potential misuse of data is of
significant concern. Misuse and abuse may come in many forms. The ability to contact trace
individuals in case of a highly transmissible and deadly infectious disease or a bioterrorism
event is critical to decreasing the potential morbidity and mortality. On the other hand,
sufficient safeguards must be in place to prevent the inadvertent or malicious release of
personal information. HIPAA attempts to address many of the issues related to the use and
sharing of individual health records, especially as it is collected from clinical encounters,
and mandates the appropriate handling of this personal information. One way to address
the conflict between public health and patient privacy is by releasing only de-identified data
to the public health agency collecting 1046 VARNEY & HIRSHON the data, thus making
inadvertent tracing much less likely. If specific information is needed to prevent a public
health emergency, then the appropriate individuals with the proper legal authority could
request the specific identifiable information from the data-collecting site, such as the
hospital. This multiple-step process may slow the evaluation and possible response by
public health officials, but this must be balanced with the need to protect individuals’
privacy. The complex interplay between the health needs of the general public and an
individual’s rights and privacy is placed within an intricate legal setting and leads to one of
the great dynamics of public health, namely balancing human rights and public safety.
Barriers to surveillance systems development General barriers to ED-based public health
surveillance There are a number of barriers to the development of public health
surveillance based on ED visits [9]. These include the following: 1. 2. 3. 4. 5. Costs for public
health agencies Costs for emergency departments and hospitals Need to improve and
standardize data collection Security and confidentiality issues Obtaining acceptance and
support from emergency medicine leadership and practitioners These can be conceptually
divided into funding issues, data-related issues, and the need to obtain acceptance and
support from key partners. Funding Development of the public health surveillance
infrastructure requires significant financial investment, especially by public health
authorities. Whereas governments can mandate certain actions (especially on the part of
large entities such as hospitals), institutions will resist actions that adversely impact their
financial status. Since the end users of these systems are public health authorities and
ultimately the public, it is the responsibility of the government to bear a significant burden
of the cost. Since the terrorist attacks of September 11, 2001, and the subsequent anthrax
letters, a significant amount of federal dollars has gone to public health agencies, especially
at the local jurisdiction. Much of this money was designated for terrorism response
activities, including improving surveillance and communications. Despite these large sums
of money, there is considerable variance in current public health surveillance infrastructure
throughout the United States. Additional funding is needed to continue to improve and
standardize public health surveillance activitiesdespecially syndromic surveillance. These
resources will need to be shared between UPDATE ON PUBLIC HEALTH SURVEILLANCE IN
THE ED 1047 the data collection entities and those entities analyzing the data and
producing public health responses. Sharing of data The data that routine surveillance
10. systems collect differ from syndromic surveillance data in that the former are based on
diagnostic or culturepositive diseases, whereas the latter are founded in prediagnostic, or
clusters of symptoms suggesting potentially infectious disease outbreaks. Health
information privacy rules such as HIPAA may apply differently to routine and syndromic
surveillance data. The prevailing feeling among some physicians is that reporting and
investigating patients with culture-positive diseases do not violate patient privacy, whereas
inconclusive disease processes are not certain enough to warrant full disclosure of patient
privacy information for further contact [25]. Data collection for syndromic surveillance
requires the ability to identify and contact individual patients when a surge in unusual
symptoms (signal) occurs. In a survey sent to state epidemiologists and terrorism
preparedness coordinators regarding the effects of HIPAA Privacy Rule requirements on
syndromic surveillance system implementation, Drociuk and colleagues [25] found that
more than half reported ‘‘some’’ or ‘‘substantial’’ problems. HIPAA’s ‘‘minimum necessary’’
stipulation thwarted disease surveillance activities. The ‘‘minimum necessary’’ standard
states that health care providers must take reasonable steps to limit the use or disclosure of
protected health information (PHI) to the minimum necessary to accomplish the intended
purpose [26]. Covered entities (ie, all health care organizations) have the flexibility to make
their own assessment of what PHI is reasonably necessary for a particular purpose.
Unfortunately, there is no broadly accepted definition for ‘‘minimum necessary’’ in either
routine or syndromic surveillance systems. As noted above, a proper balance must exist
between protecting personal health information and the need to protect the general public
health. The HIPAA Privacy Rule permits PHI disclosures without individual authorization to
public health agents and designees when intended to prevent or control disease, injury, or
disability, including public health surveillance, investigation, and intervention [27]. One
solution to satisfy patient confidentiality concerns is collecting limited data sets, ie,
information that is not directly identifiable. Specific data use agreements must establish
who is permitted to use the data. The benefit is fewer problems with HIPAA and potentially
better participation from surveillance institutions, but the drawback includes delayed signal
investigations. The delays may significantly counter the potential theoretical advantage of
early outbreak detection by syndromic surveillance. Regarding data transfer, 27/32 (87%)
respondents reported no security concerns because of the secure transmission measures
and off-system 1048 VARNEY & HIRSHON data-archiving protocols [25]. Despite the
problems with patient confidentiality and data transfer, physicians felt more secure and
ready for potential terrorist attacks. Furthermore, the mere fact that surveillance systems
exist may serve as deterrence against terrorist strikes since the community may appear
poised and ready to act. Buy-in from collaborators There are two main groups that are
important for a functional public health surveillance system based on ED data: the hospitals
(ie, the data sources) and the public health departments (ie, data analyzers and users) [9].
Both groups should be actively involved in the creation and deployment of the final system,
since ongoing bidirectional communication and interagency cooperation are critical. The
development of the relationship between the hospitals and the health department are as
important as the final system structure, since public health professionals need the data to
help make informed policy decisions and action recommendations, and medical
11. professionals, such as emergency physicians, may need to implement these
recommendations. Obtaining this buy-in requires a commitment from both sides.
Cooperation is developed through working together and developing a shared vision. There
needs to be mutual understanding of the goals and expectations for the system and the roles
that each participating organization will play. There are a number of potential barriers to
the creation of a relationship, including the costs involved for both the data collection and
the data analysis. However, a clearer understanding of the importance of the public health–
medical collaborations has grown over the past 5 years with the increased awareness of the
risks of disease spread, such as from avian influenza, and the potential for bioterrorism.
Criteria for evaluating a surveillance system Buehler [28], from the CDC 2003 Working
Group on Public Health Surveillance Systems, described a comprehensive framework of four
categories for evaluating all public health surveillance systems: system description,
outbreak detection, experience, and conclusions and recommendations. In summary, the
system description should clearly state the system’s purpose, including indications for its
use, duration, area of emphasis, and the desired sensitivity and specificity. It should identify
the stakeholders, meaning those supplying the data and applying the information. Finally, it
should provide a detailed description of all operational aspects including data flow, data
sources, data processing before analysis, statistical analysis, and epidemiological analysis
and interpretation [25]. The second category is outbreak detection and discusses factors
affecting timely data gathering and processing, data validity, and data UPDATE ON PUBLIC
HEALTH SURVEILLANCE IN THE ED 1049 aberrancy–detection methods. Timeliness
describes a continuum from the onset of symptoms to public health intervention.
Establishing the validity of a surveillance system to detect an outbreak requires
epidemiological tools like outbreak and case definitions, statistical analysis, and assessment
of the data quality. The third area for system evaluation is documented experience with the
system. Important factors for system experience include the following: usefulness (impact
of its application), flexibility (ability to adapt easily to changing needs and new technology),
acceptability (willingness of parties to submit timely and complete data, widespread use),
portability (ease of reproduction in other centers), stability (minimal downtime and
maximal consistency), and cost (for software and support as well as for false alarms and
failed detection). The final system evaluation category is a summary of the conclusions and
recommendations of the advantages and disadvantages of each system. A useful approach
would include possible modifications of present systems to meet the increasing needs in
public health surveillance. Bravata and colleagues performed a systematic review on
surveillance systems for the early detection of bioterrorism-related diseases [15,29]. After
reviewing more than 17,000 article citations and 8000 web sites, they found 192 reports on
115 systems that gathered surveillance data, including nine syndromic surveillance
systems. Bravata and colleagues evaluated the systems for reports on nine qualities the CDC
had defined previously: usefulness, importance, timeliness, flexibility, sensitivity,
representativeness, simplicity, acceptability, and specificity [30–32]. Only one article
addressed all nine criteria [16]. Usefulness, importance, and timeliness were most
commonly described, whereas only three reports of three systems provided actual values
for sensitivity and specificity [15]. Clearly, there is little scientific evidence supporting the
12. use of surveillance systems. Future studies are needed to evaluate present and new systems
for these nine characteristics of effective surveillance systems. Three of the most important
criteria appear to be sensitivity and specificity, timeliness, and the ability of the system to
impact decision making. Sosin and DeThomasis [33], members of the CDC 2003 Working
Group on Public Health Surveillance Systems, summarized the group’s findings by
developing a task list of specific, goal-directed questions for early outbreak detection. Sosin
and DeThomasis reviewed 99 abstracts presented at the 2003 National Syndromic
Surveillance conference and found limited information on system evaluation. Because a
detailed analysis of systems would likely be laborious and expensive, Sosin and DeThomasis
proposed emphasizing timeliness, validity, and usefulness to measure the success of
detection methods. Criteria for evaluating surveillance systems are more complex and
difficult to assess than originally conceived. Despite rigorous descriptions and defined
criteria, few researchers have produced data following the CDC’s 1050 VARNEY & HIRSHON
recommended framework. Perhaps a simplified approach reviewing only timeliness,
validity, and usefulness may show the impact and cost of detection methods. Summary The
development of public health surveillance systems based on ED visits, in conjunction with
other health and nonhealth-related data, is an important step to better understanding the
health needs of the US population. There are multiple steps required to develop a functional
organization, and these actions require the support and involvement of many different
partners. In any given jurisdiction a number of obstacles to structure development may
exist and will require teamwork to overcome. Yet, the information derived from these
systems on the acute health needs and health care usage patterns of the US population can
help both to improve the health of the public and to serve as an early warning system for a
possible bioterrorism event. Whereas surveillance systems can serve many important
functions, it is also critical to maintain the privacy and confidentiality of protected health
information while these systems are created and used. Through the establishment of public
health surveillance systems, bidirectional communication is developed, strengthening the
relationship between clinical and public health practitioners. The ability to (1) analyze data;
(2) distribute results; and (3) influence policy, funding, and patients’ behavior are
important outgrowths of emergency department–based public health surveillance systems.
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