Harnessing Electronic Health Records for Public Health Surveillance

Electronic medical record (EMR) systems are a rich potential source for detailed, timely, and efficient surveillance of large populations. We created the Electronic medical record Support for Public Health (ESP) system to facilitate and demonstrate the potential advantages of harnessing EMRs for public health surveillance. ESP organizes and analyzes EMR data for events of public health interest and transmits electronic case reports or aggregate population summaries to public health agencies as appropriate. It is designed to be compatible with any EMR system and can be customized to different states’ messaging requirements. All ESP code is open source and freely available. ESP currently has modules for notifiable disease, influenza-like illness syndrome, and diabetes surveillance.An intelligent presentation system for ESP called the RiskScape is under development. The RiskScape displays surveillance data in an accessible and intelligible format by automatically mapping results by zip code, stratifying outcomes by demographic and clinical parameters, and enabling users to specify custom queries and stratifications. The goal of RiskScape is to provide public health practitioners with rich, up-to-date views of health measures that facilitate timely identification of health disparities and opportunities for targeted interventions. ESP installations are currently operational in Massachusetts and Ohio, providing live, automated surveillance on over 1 million patients. Additional installations are underway at two more large practices in Massachusetts.     

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 coordination between public health departments and primary care providers (PCP) in maintaining healthy communities.As a system for documenting patient health data, EHRs can be harnessed to improve public health surveillance for communicable and chronic illnesses. EHRs facilitate clinical alerts informed by public health goals that guide primary care physicians in real time in their diagnosis and treatment of patients.As health departments reassess their public health agendas, the use of EHRs to facilitate this agenda in primary care settings should be considered. PCPs and EHR vendors, in turn, will need to configure their EHR systems and practice workflows to align with public health priorities as these agendas include increased involvement of primary care providers in addressing public health concerns.Electronic health records (EHRs) have great potential to serve as a catalyst for more effective coordination between public health departments and primary care providers in maintaining healthy communities. As prominent health risks to the community continue their shift from contagious diseases to chronic illnesses, public health departments are increasingly focused on conditions such as diabetes and obesity. At the same time, serious threats persist from traditional public health concerns, such as communicable 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 communities. 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 Commission.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 improved coordination of primary care and public health.4,5In 2003, the potential for addressing community health needs with the aid of EHR data exchange initiated a partnership between The New York City Department of Health and Mental Hygiene (NYC DOHMH) and The Institute for Family Health. Together, these organizations have developed, tested, implemented, and monitored the use of an EHR in meeting public health and primary care goals. NYC DOHMH is one of the world’s largest public health agencies, operating programs in disease control, environmental health, epidemiology, health care access, health promotion and disease prevention, and mental hygiene. It also makes public health-enabled EHRs available to over 2500 primary care providers throughout New York City as part of its Primary Care Information Project (PCIP).The Institute for Family Health is a nonprofit organization that provides care to more than 80 000 patients in 26 federally qualified health center sites in New York City and New York State’s Mid-Hudson Valley. The Institute’s goal in establishing an EHR system was not only to enhance the quality of patient care in its own practices, but also to improve the health of the communities it serves. Recognizing that the 2 organizations had parallel missions to maintain healthy communities, the Institute and NYC DOHMH partnered in EHR data exchange initiatives to meet the shared goals of improving the surveillance and management of both communicable disease and chronic disease. Projects addressing these goals are described below.     

Electronic Health Records and US Public Health: Current Realities and Future Promise

Electronic health records (EHRs) could contribute to improving population health in the United States. Realizing this potential will require understanding what EHRs can realistically offer to efforts to improve population health, the requirements for obtaining useful information from EHRs, and a plan for addressing these requirements. Potential contributions of EHRs to improving population health include better understanding of the level and distribution of disease, function, and well-being within populations. Requirements are improved population coverage of EHRs, standardized EHR content and reporting methods, and adequate legal authority for using EHRs, particularly for population health. A collaborative national effort to address the most pressing prerequisites for and barriers to the use of EHRs for improving population health is needed to realize the EHR’s potential.The potential contributions of electronic health records (EHRs) to clinical care, on the one hand, and to population health and public health on the other, were delineated in the United States in the 1990s and early 2000s.1–4 (Population health is defined as “The health outcomes of a group of individuals, including the distribution of such outcomes within the group”5^(p381); public health is “The practices, procedures, institutions, and disciplines required to achieve the desired state of population health.”6^(p138) For other definitions used in this article, see appendix, available as a supplement to the online version of this article at http://www.ajph.org.) Data flows would be simplified and streamlined, and burdens on data providers and data collectors reduced; EHRs would enable collection of data just once, which then could be repurposed for multiple uses.The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Standards for Privacy of Individually Identifiable Health Information (Privacy Rule) established national legal authority permitting, though not requiring, “covered entities” to transmit individually identifiable health information from EHRs and health care transactions to public health authorities. The Privacy Rule authorizes public health authorities to receive such information for the purpose of preventing or controlling disease, injury, or disability and for specified uses including “reporting of disease, injury, and vital events” and “conducting public health surveillance, investigations, and interventions.”7^(p2) Covered entities can also disclose deidentified data and limited data sets, as defined in the Privacy Rule.7Whereas the Privacy Rule established national legal authority for sharing EHR data for specific public health purposes, the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 established funding for sharing specified EHR data with public health authorities. The EHR Incentive Program mandated under HITECH provides Medicare and Medicaid incentive payments and penalties for specified “meaningful uses” of EHRs.8 For the first stage of meaningful use implementation, EHR systems certified under HITECH must be able to perform 3 functions for public health population-based programs: interfacing with immunization registries to transmit electronic data as directed by public health agencies; electronically recording, modifying, retrieving, and submitting syndromic surveillance data; and electronically recording, modifying, retrieving, and submitting reportable clinical laboratory results using Health Level Seven standards.9 The second stage of meaningful use adds 2 more functions: identifying and reporting cancer cases to a state cancer registry, and identifying and reporting specific cases to a specialized registry (other than a cancer registry).10 For those public health purposes currently included within HITECH’s meaningful use provisions, initial data and transmission standards are specified.9 Through January 2011, the Office of the National Coordinator issued $548 million in grants to help states develop health information exchanges for transmitting electronic health data among health care providers, and with Medicare, Medicaid, and public health agencies.11In addition to the HIPAA Privacy Rule and the HITECH meaningful use provisions, long-standing state statutes and regulations require transmission of health care provider data for specified surveillance and civil registration purposes, such as reportable diseases and conditions, vital records, and cancer registries. State legal authority typically also enables collection of data in response to threats to public health.Our purpose in this article is fourfold: (1) to describe briefly current US efforts to use EHRs for population and public health; (2) to identify potential contributions of EHRs to population and public health; (3) to delineate barriers and prerequisites to achieving those potential contributions; and (4) to suggest next steps for realizing this potential.     

Estimating Health Conditions for Small Areas: Asthma Symptom Prevalence for State Legislative Districts

Research Objective. To create prevalence estimates of asthma symptoms for California legislative districts.
Data Sources. Three main data sources were used for this study: 2001 California Health Interview Survey, 2000 Census, and 2000–2002 March Current Population Surveys.
Study Design. Secondary data analyses were conducted from cross-sectional data to distribute the joint probability of ever having an asthma diagnosis and symptoms in the last 12 months within an Assembly district. We applied hierarchical logistic regressions to estimate the parameters for selected survey and census data that predicted the probabilities of diagnosed asthmatics with asthma symptoms. Predictors included individual-level variables and contextual variables at zip code levels.
Principal Findings. Asthma symptom prevalence geographically varied by age within and across Assembly districts throughout California.
Conclusions. With modest investments in establishing analytic data files and estimating regression parameters for target conditions, small area estimation (SAE) procedures can create health data estimates not otherwise available at the sub-county level. Applying SAE procedures to asthma symptom prevalence suggest that these data can become essential reference tools for advocates and policy makers currently addressing this and other public health concerns in the state.     

The Feasibility of Using Electronic Health Records to Describe Demographic and Clinical Indicators of Migrant and Seasonal Farmworkers

Few extensive, national clinical databases exist on the health of migrant and seasonal farmworkers (MSFWs). Electronic health records (EHRs) are increasingly utilized by Federally Qualified Health Centers (FQHCs) and have the potential to improve clinical care and complement current surveillance and epidemiologic studies of underserved working populations, such as MSFWs. The aim of this feasibility study was to describe the demographics and baseline clinical indicators of patients at an FQHC by MSFW status. The authors described 2012 patient demographics, social history, medical indicators, and diagnoses by MSFW status from the de-identified EHR database of a large, multisite Colorado Migrant Health Center (MHC). Included in the study were 41,817 patients from 2012: 553 (1.3%) MSFWs, 20,665 (49.4%) non-MSFWs, and 20,599 (49.3%) who had no information in the MSFW field. MSFWs were more often male, married, employed, Hispanic, and Spanish-speaking compared with non-MSFWs. The most frequent diagnoses for all patients were hypertension, overweight/obesity, lipid disorder, type 2 diabetes, or a back disorder. Although there were significant missing values, this feasibility study was able to analyze medical data in a timely manner and show that Meaningful Use requirements can improve the usability of EHR data for epidemiologic research of MSFWs and other patients at FQHCs. The results of this study were consistent with current literature available for MSFWs. By reaching this vulnerable working population, EHRs may be a key data source for occupational injury and illness surveillance and research.     

电子健康档案标准符合性测试研究

电子健康档案标准符合性测试是为了推动卫生信息标准化,加快卫生信息标准的宣贯和落地实施。全文分析了电子健康档案信息标准符合性测试的研究背景,确定了标准符合性测试研究的目的、策略和基本原则。通过标准符合性测试方法研究,提出了标准符合性测试技术主要是人工评审和自动化测试两类;测试流程包括测试申请、测试准备、测试实施和测试评级四个阶段。通过标准符合性测试研究和预测试,了解卫生信息标准的应用状况,推进卫生信息标准的落地实施,完善卫生信息标准体系,为实现卫生信息的互联互通和规范化健康档案建设提供支持和保障。     

Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records

This paper describes a probabilistic case detection system (CDS) that uses a Bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. The diagnostic accuracy of CDS for these conditions, as measured by the area under the ROC curve, was 0.97, and the overall accuracy for NLP employed in CDS was 0.91.     

Electronic Health Information Quality Challenges and Interventions to Improve Public Health Surveillance Data and Practice

Objective

We examined completeness, an attribute of data quality, in the context of electronic laboratory reporting (ELR) of notifiable disease information to public health agencies.

Methods

We extracted more than seven million ELR messages from multiple clinical information systems in two states. We calculated and compared the completeness of various data fields within the messages that were identified to be important to public health reporting processes. We compared unaltered, original messages from source systems with similar messages from another state as well as messages enriched by a health information exchange (HIE). Our analysis focused on calculating completeness (i.e., the number of nonmissing values) for fields deemed important for inclusion in notifiable disease case reports.

Results

The completeness of data fields for laboratory transactions varied across clinical information systems and jurisdictions. Fields identifying the patient and test results were usually complete (97%–100%). Fields containing patient demographics, patient contact information, and provider contact information were suboptimal (6%–89%). Transactions enhanced by the HIE were found to be more complete (increases ranged from 2% to 25%) than the original messages.

Conclusion

ELR data from clinical information systems can be of suboptimal quality. Public health monitoring of data sources and augmentation of ELR message content using HIE services can improve data quality.Electronic laboratory reporting (ELR), the electronic submission of laboratory data following the confirmation of an infectious disease, was demonstrated more than a decade ago to be an effective method to increase the timeliness of notifiable (communicable) disease reporting as well as the number of notifiable disease case reports submitted to public health agencies.¹ With pervasive, increasingly sophisticated information technology and the rise of interconnected systems, the medical community recognizes the need for proven methods and best practices for managing electronic health information. This recognition has led to investments from the U.S. government, states, and a number of private foundations totaling billions of dollars for the development, implementation, and adoption of electronic health record (EHR) systems, which support laboratory, surveillance, and other information systems.^2–6 Such initiatives seek to improve the timeliness, accuracy, and completeness of data needed to support a variety of services, including surveillance activities.The number of state health agencies receiving ELR data has increased during the past decade. Currently, more than 40 states in the U.S. have the capacity to receive electronic reports from laboratories,⁷ and the number of electronic reports submitted to state agencies is expected to increase given Stage 2 “meaningful use” program incentives (i.e., increased reimbursement for the adoption and use of EHR systems) from the U.S. Centers for Medicare & Medicaid Services that require eligible hospitals and encourage eligible providers to submit notifiable disease laboratory results to public health agencies using ELR.⁸Simply reporting laboratory data electronically instead of using paper, however, does not solve the fundamental challenge of receiving high-quality, reliable data in support of public health functions. Several studies suggest that ELR may not improve data completeness.^9–11 These studies indicate that important challenges persist beyond adoption of ELR for the public health surveillance and informatics communities to collaboratively address. One of those challenges is improving poor data quality.Poor data quality is a pervasive issue affecting all industries and organizations using information systems.¹² Typical data quality issues encountered include inaccurate data, inconsistencies across data sources, and incomplete (or unavailable) data necessary for operations or decisions.¹³ In health care, the completeness of data in EHR systems has been found to vary from 30.7% to 100.0%.¹⁴Despite the pervasive nature of this problem, there is little evidence characterizing the impact of poor data quality on health-care delivery processes or population outcomes. General estimates of impacts include increased costs, with up to 40%–60% of a service organization''s expenses consumed as a result of poor data; poorer decisions that take longer to make; lower consumer satisfaction with information systems; and increased difficulty in reengineering work and information flows to improve service delivery.¹³ Impacts on health care include less informed decisions when humans or machines use poor quality data inputs from EHR systems.^15,16 For example, a study comparing electronic pharmacy data with the medications actually taken by patients found that only 5% of patients had perfect agreement between their computerized medication profile and the medications actually consumed.¹⁷ Clinical queries of such pharmacy databases could lead to errors of omission and comission when making prescribing decisions.The importance of data quality is increasing as the nation develops an infrastructure to collect, store, manage, and exchange large amounts of health-care information. Policies that are encouraging ELR, including the Health Information Technology for Economic and Clinical Health (HITECH) Act provisions of the American Recovery and Reinvestment Act of 2009, also incentivize hospitals and physician practices to use technology to better coordinate patient care as well as the information about care delivery processes.^18,19 Better management and coordination of information across the nation''s fragmented health delivery system require health information exchange (HIE), which is defined as the electronic transfer of clinical and administrative information across diverse and often competing health-care organizations.^20,21Federal policies and programs to encourage HIE have given rise to a number of HIE organizations in nearly every state and territory.²² These organizations primarily focus on improving individual patient care processes and outcomes by leveraging large volumes of clinical and administrative data from payers, hospitals, outpatient clinics, and pharmacies. In addition to patient care, several HIEs are supporting population health functions, including ELR to public health agencies.^23–26 Because HIEs are patient-centric, they have the capacity to provide public health programs with comprehensive medical records. In the case of ELR, an HIE may be able to deliver liver enzymes in parallel with the results of a positive laboratory test for hepatitis C. Yet, little evidence remains demonstrating how and to what effect such enhanced ELR would have on public health surveillance programs.Given a paucity of evidence that ELR does or does not impact the quality of data received by public health, we examined the completeness of ELR data from clinical information systems in two states. In addition to characterizing the completeness of ELR data, we further compared raw data directly sent from clinical information systems with data enhanced by an HIE. If an HIE can improve the completeness of ELR data submitted to public health, enhancement methods would represent a valuable, effective method for improving notifiable disease data quality. Improving data quality will likely translate into improvements in disease surveillance processes, impacting both clinicians and public health professionals.