POPULATION HEALTH MANAGEMENT Volume 18, Number 3, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/pop.2014.0093

The State of Population Health Surveillance Using Electronic Health Records: A Narrative Review Margaret M. Paul, MS,1 Carolyn M. Greene, MD,2 Remle Newton-Dame, MPH,3 Lorna E. Thorpe, PhD, MPH,4 Sharon E. Perlman, MPH,2 Katherine H. McVeigh, PhD,5 Marc N. Gourevitch, MD, MPH1

Abstract

Electronic health records (EHRs) are transforming the practice of clinical medicine, but the extent to which they are being harnessed to advance public health goals remains uncertain. Data extracted from integrated EHR networks offer the potential for almost real-time determination of the health status of populations in care, for targeting interventions to vulnerable populations, and for monitoring the impact of such initiatives over time. This is especially true in ambulatory care settings, which are uniquely suited for monitoring population health indicators including risk factors and disease management indicators associated with chronic diseases. As efforts gather steam to integrate health data across delivery systems, large networks of electronic patient information are increasingly emerging. Few of the national population health surveillance systems that rely on EHR data have progressed beyond laying groundwork to launch and maintain EHR-based surveillance, but a limited number of more focused or local efforts have demonstrated innovation in population health surveillance. Common challenges include incompleteness of population coverage, lack of interoperability across data systems, and variable data quality. This review defines progress, opportunities, and challenges in using EHR data for population health surveillance. (Population Health Management 2015;18:209–216)

Background and Purpose

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lectronic health records (EHRs) are beginning to transform the clinical practice of medicine, but the extent to which they are being harnessed to advance the goals of public health is unclear. As efforts gather steam to integrate health data across delivery systems, large networks of electronic patient information are increasingly emerging. These platforms securely transfer patient information between providers, health plans, and delivery systems, and have been used to support quality improvement and clinical research studies. However, the potential of these platforms to provide population health data is only beginning to be realized, and public health agencies continue to monitor population health using primarily traditional sources of data such as community surveys, vital records, and mandatory laboratory and physi-

cian reporting. Simultaneously, chronic disease burden is increasing in the United States and globally, expanding the traditional areas of public health focus for many jurisdictions. As the uptake of EHRs grows and surveillance needs expand beyond prevalence to condition management, the question emerges: To what extent can EHR data support population health surveillance and provide reliable data to set priorities, plan interventions, and monitor progress toward goals? The Health Information Technology for Economic and Clinical Health (HITECH) Act, authorized in 2009 as a part of the federal stimulus bill, established financial incentives for providers to implement and meaningfully use EHRs. These ‘‘Meaningful Use’’ criteria require compliant systems to be able to submit electronic syndromic surveillance reports to public health agencies.1–3 Recent analyses have suggested that such incentives are indeed hastening EHR

1

Department of Population Health, New York University School of Medicine, New York, New York. Division of Epidemiology, New York City Department of Health and Mental Hygiene, Long Island City, New York. Division of Health Care Access and Improvement, New York City Department of Health and Mental Hygiene, Primary Care Information Project, Long Island City, New York. 4 Epidemiology and Biostatistics Program, CUNY School of Public Health at Hunter College, New York, New York. 5 Special Projects Unit, Bureau of Epidemiology Services, New York City Department of Health and Mental Hygiene, Long Island City, New York. 2 3

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adoption in spite of the high cost and steep learning curve associated with implementing these systems; over 70% of office-based practices reported use of some type of EHR system in 2012.4 EHRs have transformed clinical practice in many ways, including providing automated alerts and reminders to encourage adherence to best practices and guidelines. Although these innovations have the potential to improve individual and population-level health, efforts to harness EHRs for large-scale active population health surveillance remain sporadic. As specific initiatives in this area have begun to appear in the literature, it is timely to review them and to define key challenges and promising approaches that can help guide future efforts in this rapidly evolving field. In particular, this article focuses on ambulatory care EHRs, which are particularly suited to capture measures of population health. This article presents findings from a narrative literature review to define progress, innovations, and challenges in harnessing EHR data for population health surveillance.

nals.7 Four databases (PubMed, Scopus, Web of Knowledge, and Embase) were searched for peer-reviewed articles and 24 Web sites were accessed for grey literature published in English between July 2003 and July 2013. Titles and abstracts of articles were screened and the full texts of those meeting the criteria for content focus were reviewed. Bibliographies of included articles were reviewed to identify additional sources. In addition to published literature and reports, personal communications were used to acquire current descriptions of national and regional initiatives not found in the literature. Articles that described the challenges associated with using EHR systems for surveillance were included, as were articles describing EHR-based systems monitoring outcomes such as birth defects that typically are not captured in public health surveillance systems. Studies that described the use of EHRs for research (eg, clinical trials) for institution-specific quality improvement projects or for other purposes distinct from monitoring population health were excluded.

Methods

Synthesis

Definitional considerations

A total of 42 peer-reviewed publications and 23 articles of grey literature constituted the basis of this narrative review. The research team elected to perform a narrative review instead of a systematic review because this methodology was best suited to the goal of providing an overview of how EHRs are being used for population health surveillance. This method also allowed for real-time input from experts working on relevant population health surveillance efforts and provided a framework that allowed inclusion of recommendations for further research that reflect the authors’ experience in the field. In contrast to a narrative review, systematic reviews focus on research studies addressing specific questions in a particular topic area and do not allow for expert input. Narrative synthesis occurred between July 2013 and January 2014. Findings are organized according to themes that emerged upon close review of the evidence. Major national and regional efforts of broad scope and scale were reviewed and characterized, as were local and other more focused initiatives, and common challenges that emerged across initiatives were explored.

Population health surveillance seeks to monitor a range of indicators that together embody key health characteristics of a population. Surveillance activities in the United States, historically focused on infectious diseases, have expanded to other conditions including injuries, birth defects, chronic medical conditions, mental illness, illicit drug use, health behaviors, and environmental exposures.5 Although syndromic surveillance, in which selected electronically transmitted health care data are aggregated to detect potential outbreaks, is not a primary focus of this review, accessing EHR-derived data for such purposes is discussed briefly. The term population can apply to many groupings of individuals and is used here to refer to persons in a community or geographic region, rather than to persons receiving medical care from a specific delivery system. For purposes of population health surveillance, EHR data can be transmitted and reviewed at the individual level (‘‘line listed’’) or in aggregate reports.6 Line listed data sets are useful because individual cases of disease can be investigated and associated risk factors explored. Aggregate reports avoid the patient privacy issues associated with identifiable data, but lack granularity. There are differences between EHRs across settings. Although hospital EHRs can include outpatient records, they tend to be self-contained and episodic, with deep, detailed data regarding episodes of acute illness. Ambulatory care EHRs typically reflect care that is longitudinal and are more likely to contain the clinical, laboratory, and medication data from which indicators of general health status can be constructed. Strategy

The search strategy employed combinations of specific search terms to identify references in peer-reviewed publications and grey literature that met the inclusion criterion of describing efforts to develop or report on large-scale active surveillance systems. Grey literature includes reports produced by governments, academia, or businesses that are not published by commercial publishers or in peer-review jour-

Results Major national and regional initiatives

The greatest promise of EHR-based population health surveillance lies in implementing strategies to leverage the full potential of health indicators captured in EHR systems to actively monitor population health across multiple health conditions.8 Several countries, including the United States, United Kingdom, Australia, Canada, France, and Norway, have developed such strategies at the national level; however, the extent to which these initiatives have been implemented and are sustained varies substantially. Few nations have progressed beyond laying groundwork to successfully launch and maintain a broad platform of EHR-based surveillance. Some of these systems use line-level data from longitudinal registries, but most are distributed systems that collect only data essential to answer a given question. Globally, the National Health Service (NHS) in the United Kingdom has developed the most comprehensive national

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EHR-based population health initiative to date; however, the initiative was not fully implemented as planned. Entitled Connecting for Health, it represented a potential investment of up to £12 billion over the period 2002–2013.9–11 In the primary care domain, the program delivered comprehensive digitization of health records, introduced electronic prescribing, and implemented systems that allow patients to choose the time, date, and location of their appointments (Alex Abbot, e-mail communication, March 14, 2014). In secondary care, a national picture archiving and communication system was deployed and a number of hospitals received EHRs although the delivery never achieved the pace and scale that was originally envisaged (Alex Abbot, e-mail communication, March 14, 2014). Nationally, a private wide-area network delivered connectivity to NHS organizations, and a secure messaging infrastructure enabled the safe transmission of clinical information between organizations (Alex Abbot, e-mail communication, March 14, 2014). More than 30 million extracts from the primary care record have been made available to emergency clinicians. A demographics database containing more than 70 million records provides an authoritative source of NHS number, which acts as a unique identifier for all NHS patients and allows the safe integration of information across organizations. Activity data are collected from hospitals and provide both a means of calculating hospital payments and a valuable public health resource (Alex Abbot, e-mail communication, March 14, 2014). The National Programme for IT and Connecting for Health was closed in 2013, although many of the services delivered by it continue to be in use in the NHS today and their successors form part of the national strategy for the future.12 The UK Department of Health is no longer pursuing a centrally procured nationwide Health Information Exchange (HIE) system; however the Department, together with NHS England, is overseeing the formation of local and regional HIEs linked by national infrastructure (Alex Abbot, e-mail communication, March 14, 2014).12 Though much of this infrastructure has been established, the system is not yet being used to its fullest extent for population health surveillance.11 In Norway, researchers at the Norwegian Center for Integrated Care and Telemedicine have developed Snow Agent, a distributed, bidirectional, query-based population health monitoring system that connects EHRs from disparate general practitioners in the same geographic area.13 The system was designed for the purpose of relaying data back to providers on the front line of primary care on relevant issues such as the daily incidence of influenza-like illness at clinical sites in their geographic area. Snow Agent was pilot tested between 2007 and 2012 and, although it is not yet being used by governmental public health authorities, it is capable of conducting automated epidemiologic surveillance by aggregating data on clinical diagnoses and symptoms.14 HealthConnect was Australia’s strategy for establishing a national health information infrastructure to facilitate EHR data exchange.15 Pilot sites were launched in 2004 and a series of evaluations demonstrated success in implementing the information technology (IT) infrastructure.16 HealthConnect was completed in 2009, at which point responsibility to develop HIEs was passed to individual jurisdictions, with program standards set by the National e-Health Transition Authority (NeHTA) (Alison Sweeney, e-mail communication, January 21, 2014). Australians have been able

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to register for an eHealth record since July 2012.17 These records are secure summaries of an individual’s personal health information accessible to patients and to providers to whom they grant access.18 NeHTA anticipates that by 2020, 60%–75% of general practitioners, 70%–80% of citizens, and 40%–60% of hospitals will be using the system.19 Although immediate goals are focused on increasing health care quality, NeHTA intends to develop population health surveillance efforts as well (Alison Sweeney, e-mail communication, January 21, 2014). In the shorter term, NeHTA is building capacity to report notifiable diseases recorded in eHealth records to local health authorities and to release deidentified data sets to public health agencies.20 The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) was established in 2008 by the Standing Committee of the Board of The College of Family Physicians of Canada to track 5 chronic diseases (diabetes mellitus, hypertension, depression, osteoarthritis, and chronic obstructive pulmonary disease) and, more recently, 3 neurologic conditions (Alzheimer’s and dementia, epilepsy, and Parkinson’s disease) through an HIE system (Anita Lambert Lanning, e-mail communication, January 27, 2014). As of September 30, 2013, CPCSSN had enrolled 476 providers caring for more than 580,000 patients across the country (Anita Lambert Lanning, e-mail communication, January 27, 2014).21–23 CPCSSN collects data from 10 different primary care research networks distributed across 8 provinces and using 9 different EHR systems (Anita Lambert Lanning, e-mail communication, January 27, 2014). Expansion plans include enrolling between 600–1000 providers across all provinces and territories by March 2015 in order to establish a representative sample of the population (Anita Lambert Lanning, e-mail communication, January 27, 2014).22 Data validation for the 8 chronic illnesses has been completed and a pan-Canadian architecture has been successfully developed for EHR-based chronic disease surveillance using sentinel providers.21–24 In the Rhoˆne-Alpes region of France, a pilot program assessing the utility of regional platforms for managing EHR data to monitor public health indicators is under way.25 In this effort, inpatient and ambulatory EHR data from diverse systems are converted to a shared platform supporting access to de-identified data from both structured and unstructured fields.25 To guide the evolution of EHR systems in France, a Steering Committee developed a list of 7 metrics most relevant to public health surveillance, ranging from traditional measures, such as adult body mass index (BMI), to more global indicators, such as life expectancy by sociodemographic risk factors.25 Although these metrics have not yet been integrated into broad-scale EHR-based surveillance efforts, pilot programs such as this show promise for such initiatives. The US federal government is laying the groundwork for monitoring many domains of population health through the Query Health project, developed by the Standards and Interoperability Framework and the Office of the National Coordinator for Health Information Technology (HIT).26,27 Query Health’s goal is to standardize and pilot a common strategy to enable uniform clinical quality measurement gathering, allowing public health or clinical users to synthesize population-level health information gathered from multiple systems. Five local pilot projects were launched in 2012 to validate this approach. New York City will be

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testing a prototype of the system in 2014 with the statewide HIE entity, the New York eHealth Collaborative, providing aggregate count results from providers transmitting continuity of care document information from major hospitals and ambulatory practices. Final standards from the Query Health project are expected to be distributed nationally through the Meaningful Use framework.27 The need for standards to guide preparation and use of EHR-based data for population health surveillance has stimulated several efforts. In 2012, the International Society for Disease Surveillance released Electronic Syndromic Surveillance Using Hospital Inpatient and Ambulatory Clinical Care Electronic Health Record Data: Recommendations from the ISDS Meaningful Use Workgroup and the Patient-Centered Outcomes Research Institute released the Standards in the Use of Collaborative or Distributed Data Networks in Patient Centered Outcomes Research to establish a set of standards of use for distributed data networks that rely on clinical information from EHRs.28,29 The guidelines are aligned with Meaningful Use objectives and are intended to harmonize associated incentives and other federal HIT investments with state and local public health department IT goals and capabilities.29 Recent evidence suggests that data quality improves substantially for fields associated with financial incentives.30 In addition to these efforts, the National Institutes of Health has launched the Patient Reported Outcomes Measurement Information System (PROMIS), designed to facilitate standardized collection and reporting of summary measures of patient self-reported health and well-being, predominantly to standardize primary or secondary end points in clinical studies of treatment effectiveness.31 Local and outcome-specific initiatives

Although the national and regional initiatives described hold great promise for broad-scale EHR-based population health surveillance, a limited number of more narrowly focused or local efforts have been at the forefront of innovation in this area. The following sections illustrate specific challenges and opportunities to fully harness the power of EHRs to conduct population health surveillance. EHR-based surveillance to support outbreak detection and preparedness. EHRs have the potential to facilitate the

early detection of outbreaks and use of chemical, biological, or radiological (CBR) weapons. National-level innovation in this area in the United States has been limited to emergency department (ED) based surveillance of non-chronic conditions, including illness and symptoms consistent with outbreaks and CBR weapons. In 2010, the Centers for Disease Control and Prevention (CDC) launched Biosense 2.0, a redesign of the program first mandated in the Public Health Security and Bioterrorism Preparedness and Response Act of 2002.32 Biosense 2.0 incorporates ED data contributed by local and state public health agencies, as well as hospital admissions data from more than 100 hospitals and the Veterans Affairs system.32 With a focus on syndromic surveillance and preparedness, Biosense 2.0 can identify and describe public health events at the national level in real time. The system also allows public health agencies to organize and store EHR syndromic surveillance data sent from physicians and institutions complying with Meaningful Use requirements.32

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Beyond Biosense, a number of noteworthy local EHRbased surveillance systems have been developed in the United States, typically using line listed patient data from hospital EDs to track non-chronic conditions.33–35 For example, using surveillance of EHR and related laboratory data, Kaiser Permanente identified outbreaks of infectious gastrointestinal disease in northern California.34 Systems also have been established for the purpose of short-term monitoring of the incidence and prevalence of influenza-like illness in a specific geographic area during flu season. Most of these systems rely on daily reports sent by EDs to local public health agencies.36–39 Such activities are also initiated on an as-needed basis; for example, to cover large public events (eg, the Olympics33). Spatial mapping technology is increasingly used to complement EHR-based data in defining outbreaks.35 EHR-based surveillance of injuries, chronic conditions, and risk factors. EHR data are increasingly being used to

assess the epidemiology of chronic conditions as well as of injuries and birth defects. Internationally, several studies have reported on localized applications of EHR data for such purposes. Practitioners in Mosoriot, Kenya, implemented the first EHR system in a rural Kenyan primary care clinic in 2001, and used geospatial mapping techniques to monitor the epidemiology of injuries to inform local community intervention efforts.40 In Lombardy, Italy, local health authorities used all available health records, including hospital-based EHRs, hospital discharge files, death certificates, and pathology reports, to monitor the incidence and geographic distribution of birth defects, a leading cause of neonatal and infant mortality in the region.41 Data were integrated using Open Registry software, designed to extract, standardize, and link disparate electronic files, and merged records were analyzed and used to alert public health officials to outcomes warranting further investigation or intervention.41,42 EHRs also have been used to advance chronic illness and risk factor surveillance and to provide more effective management for patients with comorbid chronic conditions. Investigators in London integrated geospatial techniques with EHR data obtained from the NHS to map geographic variation in the 10-year risk of developing diabetes in Tower Hamlets, an inner-city, high-prevalence neighborhood.43 In the United States, a study conducted within the Geisinger Health System (central and northeastern Pennsylvania) linked geocoded EHR data from over 47,000 children and adolescents aged 5–18 years with environmental measures from Census files to demonstrate that the association between BMI and environmental risk factors differed by age.44 The Marshfield Epidemiologic Study Area study in Ladysmith, Wisconsin, used EHR data to obtain the local incidence of dental disease45,46 and, similarly, the Heart of New Ulm project in rural Minnesota applied advanced analytics to the large Allina Health System EHR database to monitor cardiovascular risk factors.47 Beginning in 2011, the New York City Department of Health and Mental Hygiene (NYC DOHMH) Primary Care Information Project (PCIP) developed the Hub Population Health System (‘‘the Hub’’) to retrieve aggregate data from participating outpatient systems across the city.48 As of 2013, the Hub includes more than 650 practices serving more than 1.6 million patients in the past year and 4 million

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patients since 2009.48 Routine as well as customized requests for aggregate data are submitted by NYC DOHMH to the Hub, which applies the query against local practicebased databases before returning results centrally within 24 hours.49 Using this architecture, PCIP has developed the capacity to track indicators such as BMI or blood pressure across a large number of NYC residents receiving care.48 In 2014, NYC DOHMH and collaborating partners will derive standardized population prevalence estimates from PCIP data.48 These estimates will be compared with ‘‘gold standard’’ measures obtained from the 2013–2014 New York City Health and Nutrition Examination Survey.48 Findings from this comparison will inform interpretation of EHRbased estimates and advance refinements to their use in conducting population surveillance. Challenges

Several important challenges must be overcome before the prospects of harnessing EHR-derived data for population health surveillance can be fully realized. Completeness of sampling. Fundamentally, EHR data represent populations that have interacted with a health care provider whose office is equipped with an EHR. Underrepresented subgroups, such as healthy adults, are likely to differ from their care-seeking counterparts.50 This selection bias must be taken into account when considering how to interpret EHR-based population health surveillance reports, as it will affect the generalizability of estimates. Although a number of studies have reported prevalence estimates based on EHR-derived data, the true prevalence in the population of individuals in the community from among whose ranks these patients were drawn generally is not validated.51–54 A critical step in examining and quantifying potential biases in EHR-based population prevalence estimates will be to conduct careful comparisons of EHR-derived data against data derived from ‘‘gold standard’’ surveillance approaches such as household surveys.48 Although solo practitioners, small group practices, nonteaching hospitals, and rural practices tend to adopt EHRs more slowly, the proportion of all persons with health data in EHRs continues to increase.55 The promise of EHR-based population health surveillance will rely significantly on efforts under way by providers and health care facilities to join their EHR systems in networks capable of sharing data for clinical and administrative purposes, provided that such networks also enter into agreements with organizations seeking to monitor population health, such as health departments. Core goals of the organizations generating EHR data (eg, health care delivery systems, HIEs) differ from those of target users for population health surveillance (eg, public health agencies), raising challenges in uniting stakeholders.56 Multiple data sources. Although several distributed query-based architectures have been developed to access EHRs for epidemiologic surveillance, many public health departments are grappling with issues of interoperability. By 2010, 94% of states had an operational communicable disease electronic surveillance system, yet only 15% were technologically capable of receiving EHR data.57 Developing this capacity poses a variety of challenges for health departments,

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including limited informatics knowledge among staff, insufficient and inconsistently adopted data standards, inadequate computer infrastructure, poor data quality, and insufficient manpower.58 In 2010, nearly 25% of local health departments reported having no knowledge about the state of the field of surveillance IT, and the majority of public health agencies cite insufficient funding as a major impediment to information system development.58,59 Interoperability challenges will be mitigated as emerging regional health information organizations make it possible for state and local authorities to obtain integrated data from participating health care systems. Data quality and availability. A chief concern regarding the use of EHR-derived data for surveillance purposes is the quality of the data being aggregated. EHR data are entered by clinical staff in clinical settings for clinical purposes, and missing elements and inconsistencies are common. Data quality in areas targeted by Meaningful Use (complete demographics, vital signs, electronic medication ordering, and up-to-date problem lists of diagnoses in Stage 1; smoking status, lab results, preventive care, and medication reconciliation in Stage 2) are showing improvement and may improve more as incentives are disbursed.60 The reliability of EHRbased surveillance also may improve as metrics supporting assessment of population health become integrated into existing EHR-based quality measurement frameworks such as the Healthcare Effectiveness Data and Information Set (HEDIS).61 Further innovation is needed to better integrate physical and mental health care and to document social factors that may contribute to overall health. Many data elements of potential public health value are unevenly collected in EHRs, especially behavioral variables such as smoking, alcohol and drug use; demographic variables such as race and ethnicity; and contextual factors such as employment history, marital status, housing status, literacy, and income level. Some behaviors are missing altogether, such as diet and physical activity.30,62,63 Recent research by the Distributed Ambulatory Research in Therapeutics Network showed that incorporating the Patient Health Questionnaire into EHRs improved the diagnosis and management of depression; however, indicators of mental health, including measures to properly screen for depression and suicide ideation, are not readily available in ambulatory care EHR systems.64 Recognizing these limitations, the Institute of Medicine established a committee to recommend social and behavioral domains and measures to be incorporated into EHRs.65 Although most efforts to harness EHRs for population health surveillance have focused on establishing and extracting structured data elements, such as laboratory results and International Classification of Diseases, Ninth Revision codes, strategies are being developed for more complex data as well. For example, natural language processing applied to providers’ narrative notes in the EHR can generate reliable data on the presence of specific risk factors and conditions.66,67 Additional research is needed to optimize the sensitivity and specificity of this approach, and safeguards will be needed to protect against inadvertent extraction of protected health information from free text.67 Privacy. The Health Insurance Portability and Accountability Act of 1996 includes several mechanisms that allow EHR data to be used for public health surveillance,

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including in response to mandatory reporting laws and regulations, when de-identification is employed, and in the context of limited data sets.68 A CDC initiative to assess the potential contribution of HIEs to improving preventive care identified legal concerns on the part of HIEs regarding data sharing with public health authorities as a significant impediment to using EHRs for this purpose on a national scale.56 Summary measures of health. Population health indicators such as life expectancy and measures of function pose another challenge to EHRs. With few exceptions (eg, Framingham Risk Score), the data elements required for many such indicators (eg, self-reported mobility, days of health in the prior month) are not captured by EHR systems.69–71 The lack of a validated EHR-derived metric for global health status is a barrier to realizing the full potential of EHR-based population health surveillance, particularly in the context of aging populations and the increasing frequency with which patients live with multiple chronic conditions.72 Progress has been made toward EHR field and metric standardization through Meaningful Use and PROMIS, developed by the National Institutes of Health; however, more work is needed to create EHR-aligned, high-quality global measures of health and to validate them against standard measures. Conclusions

Information derived from EHRs has the potential to benefit individual health, health care delivery and, ultimately, the health of populations.73 The increasingly widespread adoption of EHRs holds great promise for population health surveillance strategies. Data extracted from integrated EHR networks offer the potential for rapid ascertainment of the health status of populations in care, for targeting interventions to vulnerable populations, and for monitoring the impact of such initiatives over time. Challenges remain, including issues of sampling, data quality, interoperability, and privacy. More broadly, the stalling or termination of several major national and regional efforts highlights the challenge of securing and maintaining sufficient resources to establish and maintain such systems. Nevertheless, new incentives associated with health reform offer financial support for repurposing clinical data for public health use. Innovative initiatives under way domestically and internationally will clarify and extend the potential of EHR-based strategies for characterizing the health status of populations. Author Disclosure Statement

Drs. Greene, Thorpe, McVeigh, and Gourevitch, Ms. Paul, Ms. Newton-Dame, and Ms. Perlman, declared no conflicts of interest with respect to the research, authorship, and/or publication of this article. Support for this work was provided by the New York State Health Foundation (NYSHealth). The mission of NYSHealth is to expand health insurance coverage, increase access to high-quality health care services, and improve public and community health. The views presented herein are those of the authors and not necessarily those of the NYSHealth or its directors, officers, or staff. The authors also acknowledge the support of the de Beaumont Foundation and the Robert Wood Johnson Foundation for this work.

PAUL ET AL. Acknowledgments

The authors wish to acknowledge Michael D. Buck, PhD, Melissa Chew, MPH, and Elisabeth F. Snell, MPH, all from the New York City Department of Health and Mental Hygiene, and Claudia R. Chernov, MPH, from the CUNY School of Public Health at Hunter College, for their contributions to this review. References

1. Syndromic Surveillance: Submission of electronic syndromic surveillance data to public health agencies. Meaningful Use Fact Sheet. http://www.cdc.gov/phin/library/PHIN_Fact_ Sheets/FS_MU_SS.pdf. Accessed July 22, 2013. 2. Blumenthal D, Tavenner M. The ‘‘meaningful use’’ regulation for electronic health records. N Engl J Med. 2010; 363(6):501–504. 3. Lenert L, Sundwall DN. Public health surveillance and meaningful use regulations: a crisis of opportunity. Am J Public Health. 2012;102(3):e1–7. 4. Hsiao CJ, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001–2012. Hyattsville, MD: The National Center for Health Statistics; 2012. 5. Thacker SB, Qualters JR, Lee LM; Centers for Disease Control and Prevention. Public health surveillance in the United States: evolution and challenges. MMWR Surveill Summ. 2012;61 suppl:3–9. 6. Platt R. Opportunity knocks: the electronic (public health) medical record. Epidemiology. 2009;20:662–663. 7. GL ‘99 Conference Program. Fourth International Conference on Grey Literature: New Frontiers in Grey Literature. Washington, DC: GrayNet, Gray Literature Network Service: October 1999. 8. Tomines A, Readhead H, Readhead A, TeuTsch S. Applications of electronic health information in public health: uses, opportunities and barriers. http://repository.academyhealth .org/egems/vol1/iss2/5/. Accessed September 10, 2014. 9. Murray E, Burns J, May C, et al. Why is it difficult to implement e-health initiatives? A qualitative study. Implementation Science. 2011;6:6. 10. Cross M. Will connecting for health deliver its promises? BMJ. 2006;332(7541):599–601. 11. The National Programme for IT in the NHS: an update on the delivery of detailed care records systems: Forty-fifth Report of Session 2010–12. 2011. http://www.nao.org.uk/ report/the-national-programme-for-it-in-the-nhs-an-updateon-the-delivery-of-detailed-care-records-systems/. Accessed July 22, 2013. 12. The Dismantled National Program for IT in the NHS. London: House of Commons Committee of Public Accounts, July 15, 2013. Nineteenth Report of Session 2013– 14. http://www.publications.parliament.uk/pa/cm201314/ cmselect/cmpubacc/294/294.pdf. Accessed July 22, 2013. 13. Bellika JG, Hasvold T, Hartvigsen G. Propagation of program control: a tool for distributed disease surveillance. Int J Med Inform. 2007;76:313–329. 14. Bellika JG, Sue H, Bird L, Goodchild A, Hasvold T, Hartvigsen G. Properties of a federated epidemiology query system. Int J Med Inform. 2007;76:664–676. 15. Health Connect Australian Government: Department of Health and Ageing. Updated March 7, 2011. http://www .health.gov.au/internet/main/publishing.nsf/Content/EHeath + Healthconnect. Accessed July 1, 2012.

HEALTH SURVEILLANCE USING ELECTRONIC HEALTH RECORDS

16. Communio. Health Connect Evaluation. Australian Government: Department of Health and Ageing, 2009. http:// www.health.gov.au/internet/main/publishing.nsf/Content/ A3E1F0F02186B88FCA257BF0001A4D9F/$File/BFHP .pdf. Accessed July 22, 2013. 17. National e-Health Transition Authority. < file:///C:yUsersy mp2579yDownloadsyNEHTA%20Annual%20Report% 202012-13%2029%20Oct%20Final%20(1).pdf > . Accessed December 16, 2013. 18. Australian Government Department of Health. Personally Controlled Electronic Health Records for All Australians. Updated September 14, 2012. http://www.health.gov.au/ internet/budget/publishing.nsf/Content/budget2010-hmedia09 .htm. Accessed July 1, 2012. 19. Lim KS. The use of innovative ideas such as e-health and PCEHR to improve patient outcomes. NSW Symposium Presentation: The National E-Health Transition Authority, June 23, 2012. < file:///C:yUsersymp2579yDownloadsy NSW%20Symposium%2023%20June%20(1).pdf > . Accessed July 22, 2013. 20. Schaper L. The impact that the personally controlled electronic health record will have on clinical research in Australia. 2012. < file:///C:yUsersymp2579yDownloadsy NEHTA_Annual_Report_07-08.pdf > . Accessed July 23, 2013. 21. Birtwhistle R, Keshavjee K, Lambert-Lanning A, et al. Building a pan-Canadian primary care sentinel surveillance network: initial development and moving forward. J Am Board Fam Med. 2009;22:412–422. 22. Birtwhistle RV. Canadian Primary Care Sentinel Surveillance Network: a developing resource for family medicine and public health. Can Fam Physician. 2011;57:1219–1220. 23. Williamson T L-LA, Martin K, Leggett J, Morkam R, Khan S, Birtwhistle R. Primary Health Care Intelligence: the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Annual Report: 2013 October 2013. http:// cpcssn.ca/wp-content/uploads/2014/04/CPCSSN_2012_13_ reportEN.pdf. Accessed December 16, 2013. 24. Keshavjee K, Chevendra V, Martin K, et al. Design and testing of an architecture for a national primary care chronic disease surveillance network in Canada. Stud Health Technol Informat. 2011;164:341–345. 25. Metzger MH, Durand T, Lallich S, Salamon R, Castets P. The use of regional platforms for managing electronic health records for the production of regional public health indicators in France. BMC Medical Informatics and Decision Making. 2012;12:28. 26. Blumenthal D. Launching HITECH. N Engl J Med. 2010; 362(5):382–385. 27. Query Health Wiki The Office of the National Coordinator for Health Information Technology 2012. http://wiki.siframework .org/Query + Health + - + Project + Charter. Accessed July 22, 2013. 28. Patient-Centered Outcomes Research Institute. Technical Report: Standards in the Use of Collaborative or Distributed Data Networks in Patient Centered Outcomes Research. 2012. http://www.pcori.org/assets/pdfs/Standards%20Outcomes% 20Research.pdf. Accessed July 22, 2013. 29. Draft Guidelines for Syndromic Surveillance Using Inpatient and Ambulatory Clinical Care EHR Data International Society for Disease Surveillance, 2012 March. http:// www.syndromic.org/storage/ISDS_2012-MUse-Recommend ations.pdf. Accessed July 22, 2013. 30. Ancker JS, Shih S, Singh MP, Snyder A, Edwards A, Kaushal R; HITEC Investigators. Root causes underlying

31.

32.

33.

34.

35.

36. 37. 38.

39.

40.

41. 42.

43.

44.

45.

46. 47.

215

challenges to secondary use of data. AMIA Annu Symp Proc. 2011;2011:57–62. Fries JF, Bruce V, Cella D. The promise of PROMIS: using item response theory to improve assessment of patientreported outcomes. Clin Exp Rheumatol. 2005;23(5 suppl 39):S53–S57. Centers for Disease Control and Prevention. Biosense 2.0. http://www.cdc.gov/biosense/files/DHIS_BioSense%20 Overview_244951_12_3_2013.pdf. Accessed July 22, 2013. Gundlapalli AV, Olson J, Smith SP, et al. Hospital electronic medical record-based public health surveillance system deployed during the 2002 Winter Olympic Games. Am J Infect Control. 2007;35:163–171. Greene SK, Huang J, Abrams AM, et al. Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records. Foodborne Pathog Dis. 2012;9: 431–441. Horst MA, Coco AS. Observing the spread of common illnesses through a community: using geographic information systems (GIS) for surveillance. J Am Board Fam Med. 2010;23:32–41. Bellazzini MA, Minor KD. Emergency department syndromic surveillance for novel H1N1 spring 2009. Am J Emerg Med. 2011;29:70–74. Gangnon R, Bellazzini M, Minor K, Johnson M. Syndromic surveillance: early results from the MARISSA project. WMJ. 2009;108:256–258. Olson DR, Heffernan RT, Paladini M, Konty K, Weiss D, Mostashari F. Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City. PLoS Med. 2007;4(8):e247. Plagianos MG, Wu WY, McCullough C, et al. Syndromic surveillance during pandemic (H1N1) 2009 outbreak, New York, New York, USA. Emerg Infect Dis. 2011;17:1724– 1726. Odero W, Rotich J, Yiannoutsos CT, Ouna T, Tierney WM. Innovative approaches to application of information technology in disease surveillance and prevention in Western Kenya. J Biomed Inform. 2007;40:390–397. Tagliabue G, Tessandori R, Caramaschi F, et al. Descriptive epidemiology of selected birth defects, areas of Lombardy, Italy, 1999. Popul Health Metr. 2007;5:4. Tagliabue G, Maghini A, Fabiano S, et al. Consistency and accuracy of diagnostic cancer codes generated by automated registration: comparison with manual registration. Popul Health Metr. 2006;4:10. Noble D, Smith D, Mathur R, Robson J, Greenhalgh T. Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning. BMJ Open. 2012;2(1):e000711. Schwartz BS, Stewart WF, Godby S, et al. Body mass index and the built and social environments in children and adolescents using electronic health records. Am J Prev Med. 2011;41(4):e17–28. Acharya A, VanWormer JJ, Waring SC, Miller AW, Fuehrer JT, Nycz GR. Regional epidemiologic assessment of prevalent periodontitis using an electronic health record system. Am J Epidemiol. 2013;177:700–707. Wilke RA, Berg RL, Peissig P, et al. Use of an electronic medical record for the identification of research subjects with diabetes mellitus. Clin Med Res. 2007;5(1):1–7. VanWormer JJ, Johnson PJ, Pereira RF, et al. The Heart of New Ulm Project: using community-based cardiometabolic

216

48.

49.

50. 51.

52.

53.

54.

55.

56.

57. 58.

59.

60.

PAUL ET AL.

risk factor screenings in a rural population health improvement initiative. Popul Health Manag. 2012;15:135–143. New York City Department of Health and Mental Hygiene. Developing an Electronic Health Record-Based Population Health Surveillance System. New York: NYCDOHMH; 2013. Buck MD, Anane S, Taverna J, Amirfar S, Stubbs-Dame R, Singer J. The Hub population health system: distributed ad hoc queries and alerts. J Am Med Inform Assoc. 2012;19(e1): e46–e50. Calman NS, Hauser D, Chokshi DA. ‘‘Lost to follow-up’’: the public health goals of accountable care. Arch Intern Med. 2012;172:584–586. Solberg LI, Flottemesch TJ, Foldes SS, Molitor BA, Walker PF, Crain AL. Tobacco-use prevalence in special populations taking advantage of electronic medical records. Am J Prev Med. 2008;35(6 suppl):S501–S507. Esteban-Vasallo MD, Dominguez-Berjon MF, AstrayMochales J, et al. Epidemiological usefulness of populationbased electronic clinical records in primary care: estimation of the prevalence of chronic diseases. Fam Pract. 2009; 26:445–454. de Wilde JA, van Dommelen P, Middelkoop BJ, Verkerk PH. Trends in overweight and obesity prevalence in Dutch, Turkish, Moroccan and Surinamese South Asian children in the Netherlands. Arch Dis Child. 2009;94:795–800. Pedersen ML, Jacobsen JL. Improvement of diabetes care in a small but geographically widely spread population in Greenland. Effects of a national diabetes care programme. Diabet Med. 2011;28:1425–1432. DesRoches CM, Worzala C, Joshi MS, Kralovec PD, Jha AK. Small, nonteaching, and rural hospitals continue to be slow in adopting electronic health record systems. Health Aff (Millwood). 2012;31:1092–1099. Office of the National Coordinator for Health Information Technology (ONC). Federal Health Information Technology Strategic Plan: 2011–2015. http://www.healthit.gov/ sites/default/files/utility/final-federal-health-it-strategic-plan0911.pdf. Accessed October 7, 2014. Centers for Disease Control and Prevention. State electronic disease surveillance systems—United States, 2007 and 2010. MMWR. 2011;60(41):1421–1423. The National Association of County and City Health Officials. 2010 National Profile of Local Health Departments. http://www.naccho.org/topics/infrastructure/profile/resources/ 2010report/upload/2010_profile_main_report-web.pdf. Accessed July 22, 2013. Livingood WC, Coughlin S, Remo R. Public Health & Electronic Health Information Exchange: A Guide to Local Agency Leadership. http://www.phii.org/sites/default/files/resource/ pdfs/Public%20Health%20%26%20EHIE%20(DCHD) %20FINAL.pdf. Accessed July 22, 2013. Newton-Dame R, Schreibstein L, Maa P, Cheung F, Gottlieb D, Jacobson L. Who Uses What: Investigating EHR Data Documentation in New York City Practices. Boston, MA: American Public Health Association; November 4, 2013.

61. Institute of Medicine. Population Health Implications of the Affordable Care Act: Workshop Summary. Washington, DC: National Academies Press; 2014. 62. Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. Summit Translat Bioinforma. 2010;2010:1–5. 63. Glasgow RE, Kaplan RM, Ockene JK, Fisher EB, Emmons KM. Patient-reported measures of psychosocial issues and health behavior should be added to electronic health records. Health Aff (Millwood). 2012;31:497–504. 64. Valuck RJ, Anderson HO, Libby AM, et al. Enhancing electronic health record measurement of depression severity and suicide ideation: a Distributed Ambulatory Research in Therapeutics Network (DARTNet) study. J Am Board Fam Med. 2012;25:582–593. 65. Institute of Medicine. Recommended social and behavioral domains and measures for electronic health records 2013. http://www.iom.edu/Activities/PublicHealth/SocialDeterminants EHR.aspx. Accessed July 22, 2013. 66. Yli-Hietanen J, Niiranen S, Aswell M, Nathanson L. Domain-specific analytical language modeling—the chief complaint as a case study. Int J Med Inform. 2009;78(12): e27–e30. 67. Morrison FP, Li L, Lai AM, Hripcsak G. Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes? J Am Med Inform Assoc. 2009;16:37–39. 68. Sengupta S, Calman NS, Hripcsak G. A model for expanded public health reporting in the context of HIPAA. J Am Med Inform Assoc. 2008;15:569–574. 69. Behavioral Risk Factor Surveillance System Survey Questionnaire. 2013. http://www.cdc.gov/brfss/questionnaires/pdfques/2013%20BRFSS_English.pdf. Accessed July 22, 2013. 70. Sun S, Irestig R, Burstrom B, Beijer U, Burstrom K. Healthrelated quality of life (EQ-5D) among homeless persons compared to a general population sample in Stockholm County, 2006. Scand J Public Health. 2012;40:115–125. 71. Kukafka R, Ancker JS, Chan C, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40:398–409. 72. Teutsch S, Chokshi D, Stine N, Fielding J. HALE— Unification Theory for Clinical Medicine and Population Health. https://www.iom.edu/*/media/Files/PerspectivesFiles/2013/Discussion-Papers/BPH-HALE.pdf. Accessed December 16, 2013. 73. Friedman DJ, Parrish RG, Ross DA. Electronic health records and US public health: current realities and future promise. Am J Public Health. 2013;103:1560–1567.

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