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The Role of Public Health Informatics in Enhancing Public Health Surveillance
Corresponding author: Thomas G. Savel, MD, Public Health Informatics and Technology Program Office, CDC, Office of Surveillance, Epidemiology, and Laboratory Services, 2500 Century Center, MS E55, Atlanta, GA 30329. Telephone 404-498-3081; E-mail: firstname.lastname@example.org.
Public health surveillance has benefitted from, and has often pioneered, informatics analyses and solutions. However, the field of informatics also serves other facets of public health including emergency response, environmental health, nursing, and administration. Public health informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning (1). It is an interdisciplinary profession that applies mathematics, engineering, information science, and related social sciences (e.g., decision analysis) to important public health problems and processes. Public health informatics is a subdomain of the larger field known as biomedical or health informatics. Health informatics is not synonymous with the term health information technology (IT). Although the concept of health IT encompasses the use of technology in the field of health care, one can think of health informatics as defining the science, the how and why, behind health IT. For example, health IT professionals should be able to resolve infrastructure problems with a network connection, whereas trained public health informaticians should be able to support public health decisions by facilitating the availability of timely, relevant, and high-quality information. In other words, they should always be able to provide advice on methods for achieving a public health goal faster, better, or at a lower cost by leveraging computer science, information science, or technology.
This report proposes a vision for informatics in enhancing public health surveillance, identifies challenges and opportunities, and suggests approaches to attain the vision. This topic was identified by CDC leadership as one of six major concerns that must be addressed by the public health community to advance public health surveillance in the 21st century. The six topics were discussed by CDC workgroups that were convened as part of the 2009 Surveillance Consultation to advance public health surveillance to meet continuing and new challenges (2). Although this report is not based on workgroup discussions, it is intended to continue the conversations with the public health community for a shared vision for public health surveillance in the 21st century.
The work of public health informatics can be divided into three categories. First is the study and description of complex systems (e.g., models of disease transmission or public health nursing work flow). Second is the identification of opportunities to improve the efficiency and effectiveness of public health systems through innovative data collection or use of information. Third is the implementation and maintenance of processes and systems to achieve such improvements.
The informatics perspective can provide insights and opportunities to improve each of the seven ongoing elements of any public health surveillance system (3). Examples include the following:
- Planning and system design – Identifying information and sources that best address a surveillance goal; identifying who will access information, by what methods and under what conditions; and improving analysis or action by improving the surveillance system interaction with other information systems.
- Data collection – Identifying potential bias associated with different collection methods (e.g., telephone use or cultural attitudes toward technology); identifying appropriate use of structured data compared with free text, most useful vocabulary, and data standards; and recommending technologies (e.g., global positioning systems and radio-frequency identification) to support easier, faster, and higher-quality data entry in the field.
- Data management and collation – Identifying ways to share data across different computing/technology platforms; linking new data with data from legacy systems; and identifying and remedying data-quality problems while ensuring data privacy and security.
- Analysis – Identifying appropriate statistical and visualization applications; generating algorithms to alert users to aberrations in health events; and leveraging high-performance computational resources for large data sets or complex analyses.
- Interpretation – Determining usefulness of comparing information from one surveillance program with other data sets (related by time, place, person, or condition) for new perspectives and combining data of other sources and quality to provide a context for interpretation.
- Dissemination – Recommending appropriate displays of information for users and the best methods to reach the intended audience; facilitating information finding; and identifying benefits for data providers.
- Application to public health programs – Assessing the utility of having surveillance data directly flow into information systems that support public health interventions and information elements or standards that facilitate this linkage of surveillance to action and improving access to and use of information produced by a surveillance system for workers in the field and health-care providers.
The evolving field of surveillance informatics presents both challenges and opportunities. The challenges include finding efficient and effective ways of combining multiple sources of complex data and information into meaningful and actionable knowledge (e.g., for situational awareness). As these challenges are met, opportunities will arise for faster, better, and lower cost surveillance and interpretation of health events and trends. The domain of public health informatics designs and evaluates methods appropriate for this complex environment.
High-value data, information, and knowledge are exchanged in a secure and timely manner for use in public health surveillance tools that are powerful and sophisticated but user friendly to accomplish the work of surveillance and response.
Realizing this vision for 21st century public health surveillance requires attention to technology and process and to the specific needs (i.e., requirements) of the public health community. The technology challenges for public health surveillance are daunting. Public health surveillance systems manage data that are high volume, heterogeneous, and distributed widely. In addition, data-quality concerns also might exist, occurring in both new and older legacy systems. Data from many information systems might not be shared easily or exchanged, as that might not have been a requirement of the system at the time of its development. Changing these systems in an environment of limited funding and time presents barriers that are at least as substantial as those for technologic and scientific concerns. Impediments include laws and regulations that preserve different data collection and sharing rules, privacy and security concerns, and academic and economic disincentives to sharing and collaboration.
Technology that seems the most innovative often relies on adopting and leveraging technology standards. Systems must have the ability not only to talk and listen, but also to understand each other. Unfortunately, adopting only certain standards is insufficient. Both semantic (vocabulary) and syntactic (sentence structure) standards must be implemented and tested to ensure a system's validity. Certain types of errors are associated with data manipulation. Even highly structured data-collection techniques do not completely eliminate data errors. For example, providing data elements that can be selected from a drop-down list cannot prevent the entry of a male who is documented as receiving a Papanicolaou test. However, structured data collection techniques can simplify minimizing or identifying many such data-quality problems.
The standardization process that facilitates computer-readable forms of data, by its very nature, risks losing the richness of information found within unstructured documents (i.e., clinicians' notes or field observations). Accessing and integrating both structured and unstructured data is a major focus in health informatics. As public health surveillance systems collect more and more structured data directly from clinical information systems, this capacity for structured and unstructured data access is increasingly important.
Economic pressures on health care and public health are diminishing the practicality of conducting active surveillance techniques (e.g., using detailed patient interviews, manual chart reviews, or manual data entry). In addition, the need for speed in the face of rapid global pandemics and bioterrorism makes the often incomplete ascertainment from passive reporting processes a substantial challenge. The application of informatics science can help ensure that 21st century systems are as valid as current methods while providing improved efficiency.
The process of change is difficult, and transforming information systems and work flows is no exception. Initial investments of time, human resources, and capital are difficult to assemble. Transitioning to interconnected (i.e., interoperable) public health surveillance information systems from multiple, stand-alone siloed systems involves unique challenges. For example, setting up automated data-collection streams from electronic health record (EHR) data sources is different from manual data abstraction from health-care records. Concerns related to data quality, data standardization, process automation, work flow design, and system validation all need to be addressed. The need to use new and legacy systems in parallel for a period must be considered and planned for, including the challenging process of transitioning users off legacy systems. Challenges and resistance to change must be balanced by clearly defined desirable goals and objectives associated with the new surveillance system and informed by strong, systematic informatics analyses.
Leadership and Workforce
Because 21st century surveillance crosses the lines of complex social and political systems, it can no longer rely solely on creative innovation among field personnel, but requires senior leaders who can see the opportunities and have the resources to address the challenges. Optimistic and strong leadership for public health informatics is critical to augment public health surveillance sufficiently in the 21st century. Public health leaders have the responsibility of examining their workforce and making the conscious decisions to augment it with public health informatics expertise. Leadership also requires the ability to assemble the appropriate set of stakeholders when addressing 21st century public health surveillance challenges. New challenges will, for example, require input and guidance from legal and privacy subject-matter specialists. Leadership is needed to devote adequate funding to implement short-term improvements and long-term visions of informatics-augmented public health surveillance. The leadership challenge is complex considering the need to integrate siloed systems, which are often governed and funded independently (i.e., HIV, TB, lead poisoning). All members of the team, from senior management to the end user, need to be invested in creating the most usable, goal-oriented system possible, identifying the ways electronic information can be managed and used for the maximum benefit.
Numerous opportunities are available to facilitate public health informatics' impact on public health surveillance. An important opportunity is the increasing adoption of EHRs and health information exchange (HIE) systems. The demonstration of meaningful use of EHRs, as articulated in the Centers for Medicare and Medicaid Services (CMS) final rule, and described in detail in the next section, includes three public health requirements: electronic submission to public health agencies of immunization registry data, reportable lab results data, and syndromic surveillance data.
Electronic Health Records
EHR and HIE systems collate information about individual patients from different information systems (e.g., registration, clinical record, laboratory, and imaging) and through information exchange or aggregation from across different provider entities. Adoption of the systems is being incentivized and facilitated by the Health Information Technology for Economic and Clinical Health (HITECH) Act in the United States. Enacted as part of the American Recovery and Reinvestment Act (ARRA) of 2009 (4), the HITECH act authorized Medicaid and Medicare financial incentives for providers to adopt and use EHRs and authorized funding for the Office of the National Coordinator for Health Information Technology (ONC) to encourage health IT adoption, aid in standard-setting, build work force, and support state- and regional-level development of HIE.
The goal of this funding has been to modernize the health system by promoting and expanding the adoption of health information technology by 2014. Consequently, opportunities are available to facilitate public health informatics' impact on public health surveillance. For example, hospitals now have an economic incentive to electronically transmit reportable laboratory results to public health agencies (electronic laboratory reporting). This can improve the speed and ascertainment completeness of reporting and also can affect the surveillance work flow and work load. As the semantics and the syntax of such electronic reports become more widely adopted (a process also accelerated by the HITECH Act), such information can flow more easily between computer applications and systems (i.e., interoperability). This interoperability creates the potential to eliminate data-reentry into case management applications, which can improve efficiency while reducing resource requirements and data-entry errors. As clinicians and public health workers increasingly work in electronic environments using the same types of interoperable data, the opportunity for bidirectional communication around cases or clusters of conditions also can increase.
Meaningful Use of EHRs
In the summer of 2010, the CMS issued a final rule on the Electronic Health Record Incentive Program (42 CFR Parts 412, 413, 422, and 495). To be eligible to receive CMS incentive payments for the use of electronic health record technology, participants must implement certified technology and also must demonstrate meaningful use of that technology. To receive incentive payments in 2011 and 2012, eligible providers must perform one of three forms of reporting to public health agencies: submitting electronic data to immunization registries, submitting reportable lab results to public health agencies, and submitting electronic syndromic surveillance data to public health agencies. EHRs also must record demographic and other data of interest for surveillance systems (e.g., racial, ethnic, and language ). The requirements for meaningful use incentives will change and evolve over the next few years. In fact, though incentives are currently in place, financial penalties are scheduled to take effect by 2015 (5).
Several other programs provide additional funds to support the development of health IT solutions. One is the Strategic Health IT Advanced Research Projects (SHARP) program, which is funded by ARRA through the ONC. SHARP awards have funded research to identify technology solutions to address well-documented problems impeding adoption of health information technology (health IT). CDC is on the federal steering committee overseeing the SHARP program and is providing input to ensure that the public health perspective is considered. Another series of grants support HIE systems in states and advanced demonstrations for the use of exchange systems to improve care quality and public health outcomes in local areas (BEACON grants). Another program, the Program of Assistance for University-Based Training, is prepared to produce trained public health informaticians in universities during the next few years (6).
Electronic real-time data regarding the environment (e.g., water-quality data from supervisory control and data-acquisition systems) and remote sensing systems (continuous and/or automated collection and transmission) combined with the global positioning system and geographic information system revolutions also facilitate the overlay of environmental and person-centric information by time and place. As public participation in submitting information into the World Wide Web increases (often labeled Web 2.0 and accelerated by the widespread adoption of smart phones and other wireless devices), the possibility exists to tap into information directly supplied by large numbers of persons (crowdsourcing) (7) or derived from near-real time information-seeking behaviors (8). Several of these types of data have been used to derive signals of important health trends faster and more broadly than more traditional case reporting systems (e.g., outbreak detection or monitoring by syndromic surveillance systems).
Public Health Informaticians
One of the most valuable resources to be tapped is the diverse population of public health professionals (formally trained or not) who have already made informatics a priority in their work. These include staff at CDC and other federal agencies; state and local health departments, members of the Public Health Data Standards Consortium and informatics leaders in several public health associations, workers from all walks of public health life who attend Public Health Information Network meetings, university scholars of public health informatics, and staffs of nonprofit organizations like the Public Health Informatics Institute. Representatives of these groups come together to harmonize an ongoing agenda for public health informatics at the Joint Public Health Informatics Taskforce, a coordinating body of several associations (9). By educating leaders and peers, testing innovations, and disseminating lessons learned, these persons and agencies are improving public health surveillance (and ultimately health outcomes) by reducing costs, bridging silos, and improving access to timely, quality information.
These opportunities also represent a crisis: the move from manual reporting from traditional data sources to automated data collection from novel data sources has suddenly begun in earnest, and public health agencies will need to keep pace or risk gradually losing old systems of health event ascertainment and failing to achieve the benefits of new electronic reporting. Several steps can help public health agencies. ONC-specified standards to accept surveillance information from health-care providers should be adopted but will require changes to established surveillance and other information management systems. Public health agencies with limited informatics support might find it valuable to work with academic centers or other agencies to facilitate their transition to the use of more standardized electronic data. Using this form of data should, in time, enable them to reduce labor while increasing the sophistication of their analyses in both surveillance systems and response systems. Active collaboration on new information system and data collection initiatives can reap substantial benefits.
To achieve the vision, certain key points must be addressed. Stand-alone systems should be considered only when no other options are available. Existing systems (including commercial off-the-shelf solutions) should be used or modified wherever possible and existing data streams should be leveraged for multiple purposes. In the search for change, the Pareto principle is instructive — that there exists a 20% change that has the ability to solve 80% of the problem (10). Rather than delaying work by striving to develop an ideal system, small, incremental steps should be considered rather than immediate wholesale changes. Although time consuming, planning for evaluations of surveillance systems can affect both time and resource savings. Combining disparate sources and forms of information can provide a richer picture of disease burden than individual data streams. Whenever possible, support staff should be enabled to sharpen their skills in fundamentals of public health informatics using local resources, online training, or national conferences. Even with the best planning, problems will occur; detecting them as early as possible and addressing them immediately is essential. Active participation in EHR/HIE initiatives will help ensure that public health is represented in planning as the overall health-care system continues to change and evolve.
- O'Carroll PW, et al. Public health informatics and information systems, Springer, 2002.
- CDC. Introduction. In: Challenges and opportunities in public health surveillance: a CDC perspective. MMWR 2012;61(Suppl; July 27, 2012):1-2.
- Lee LM, Teutsch SM, Thacker SB, St. Louis ME. Principles & Practice of Public Health Surveillance. New York, NY: Oxford University Press; 2010.
- US. Congress (2009). American Recovery and Reinvestment Act of 2009.
- Centers for Medicare and Medicaid Services. Meaningful use resources. Available at http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov_meaningful_use_announcement/2996. Accessed March 20, 2012.
- U.S. Department of Health and Human Services. Program of assistance for university-based training. Accessed March 20, 2012. Available at http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov_university-based_training_program/1808.
- Howe J. Crowdsourcing: Why the power of the crowd is driving the future of business. Crown Business; 2008.
- Freifeld CC, Chunara R, Mekaru SR, Chan EH, Kass-Hout T, et al. Participatory epidemiology: Use of mobile phones for community-based health reporting. PLoS Med 7(12): e1000376. doi:10.1371/journal.pmed.1000376.
- Association of Public Health Laboratories: Joint Public Health Informatics Taskforce. Available at http://www.aphl.org/aphlprograms/informatics/collaborations/jointpublic/pages/default.aspx. Accessed March 20, 2012.
- Pinnicle Management. How the 80/20 rule helps us be more effective. Available at http://www.pinnicle.com/Articles/Pareto_Principle/pareto_principle.html. Accessed March 20, 2012.
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