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Background: Public Health Informatics (PHI) has taken on new importance in recent years as health and well-being face a number of challenges, including environmental disasters, emerging infectious diseases, such as Zika, Ebola and SARS-CoV-2, the growing impact of the Influenza virus, the opioid epidemic, and social determinants of health. Understanding the relationship between climate change and the health of populations adds further complexity to global health issues. Objectives: To describe four examples of curricula that exist in U.S. based graduate-level public and population health informatics training programs. Methods: Biomedical informatics educators are challenged to provide learners with relevant, interesting, and meaningful educational experiences in working with and learning from the many data sources that comprise the domain of PHI. Programs at four institutions were reviewed to examine common teaching practices that stimulate learners to explore the field of public health informatics. Results: Four case studies represent a range of pedagogical approaches to meeting the requirements of three established accreditation/certification agencies relevant to PHI education. Despite their differences, each program achieved the established learning objectives along with a substantive record of student learning achievements. Conclusion: The overarching goal of empowering learners to serve an active and dynamic role in enhancing preventive measures, informing policy, improving personal health behaviors, and clarifying issues such as quality, cost of care, and the social determinants of health, are essential components of PHI education and training, and must receive additional consideration now and in the future by educators, policy makers, administrators, and government officials.

Objective To enhance the process by which outpatient providers report surveillance case information to public health authorities following a laboratory-confirmed diagnosis of a reportable disease. Introduction Traditionally, public health agencies (PHAs) wait for hospital, laboratory or clinic staff to initiate case reports. However, this passive approach is burdensome for reporters and produces incomplete and delayed reports, which can hinder assessment of disease in the community and potentially delay recognition of patterns and outbreaks (1). Modern surveillance practice is shifting toward greater use of electronically transmitted disease information. The adoption of electronic health record (EHR) systems and health information exchange (HIE) among clinical organizations and systems, driven by policies such as the ‘meaningful use’ program, is creating an information infrastructure that public health organizations can take advantage of to improve surveillance practice (2). Methods Using an existing HIE infrastructure, we developed, tested and implemented an intervention that pre-populates an electronic version of the official Indiana State Department of Health communicable disease reporting form following detection of a notifiable disease in an electronic laboratory message. Pre-populated form fields included patient demographics, notifiable disease confirmatory test results, and provider information. The intervention was delivered electronically to the provider using the HIE network. Seven representative, high priority diseases were included: chlamydia, gonorrhea, syphilis, salmonella, hepatitis B, hepatitis C, and histoplasmosis. The intervention was implemented in seven diverse primary care clinics in central Indiana. Control clinics were all other outpatient settings connected to the HIE that reported at least one communicable disease case to the county PHA during the same time period. The primary outcome measure was reporting rate (provider reports received as a percent of all case reports received by the PHA for individuals with a notifiable disease). Clinic-submitted case reports were collected between 9/9/2013 – 3/15/2014 and 9/15/2014 – 6/12/2016 from all clinics (intervention and non-intervention) that reported to the county PHA before and during the intervention period. We grouped case reports into non-intervention and intervention groups based on whether or not the laboratory test occurred at an intervention clinic during the clinic's intervention period. Reports received from intervention clinics outside of the clinics’ intervention period were classified as non-intervention reports. To evaluate the impact of the intervention on the outcome measures, we employed a difference-in-difference approach in which the change in reporting rates among intervention clinics before and after implementation was compared to the change in reporting rates among non-intervention clinics during the same timeframes. The analysis utilized generalized linear models with logistic regression using the logit link function for reporting rate. All statistical tests were performed using the NLestimate macro within Enterprise SAS Version 9.4. Results Of 16,172 unique cases observed across all time periods, 1,453 (9%) were reported by intervention clinics. During intervention time periods, provider reporting rates significantly increased from 20% to 50% in intervention clinics while falling from 12% to 10% in the control clinics (p

Objective The purpose of this study is to review the extant literature for evidence on the validity of ICD-9-CM and -10-CM codes for the purpose of identifying cases of chlamydia, gonorrhea, and syphilis. Introduction Administrative data refers to data generated during the processes of health care. These data are a rich source of patient health information, including diagnoses and problem lists, laboratory and diagnostic tests, and medications. Established standards are used to code each data into the appropriate coding systems. The International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) codes are the coding standard for diagnoses and have been frequently used to identify cases for the creation of cohorts in examining care delivery, screening, prevalence, and risk factors 1,2 . However, while some studies have assessed the validity and reliability of ICD-CM codes to identify various conditions such as cerebral palsy and rheumatoid arthritis 3,4 , the evidence for using ICD codes to accurately identify sexually transmitted infection (STI) cases is largely unexamined. The purpose of this study is to review the extant literature for evidence on the validity of ICD codes for identifying cases of chlamydia, gonorrhea, and syphilis. Our findings will inform efforts to improve the use of administrative data for STI-related health service and surveillance researches. Methods Our systematic review followed a protocol consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). We comprehensively searched PubMed and Scopus databases for peer-reviewed articles published before February 2018. Articles were identified with search terms related to our STIs of interest (chlamydia, syphilis or gonorrhea), pelvic inflammatory disease (PID), administrative codes, and validation studies. PID was included as 33%-50% of PID cases are due to chlamydia or gonorrhea 5 . Only empirical publications appearing in peer-reviewed English language journals were included. Further, we excluded articles classified as letters to the editor, policy briefs, perspectives, commentaries, summaries of future research plans, and grey literature. Additionally, articles without abstracts were also excluded. The screening process used by our review is outlined in Figure 1. Briefly, all articles were subjected to a two-step screening process. First, we reviewed articles based on title and abstract. We eliminated studies that did not focus on STIs or on validation in the context of STIs. Articles were included if they focused on any combination of the STIs of interest, or on PID, and were validation studies on diagnostic testing or administrative codes. Second, selected articles were then reviewed in full to identify studies which included the STIs of interest, assessed and listed ICD-9-CM or -10-CM codes, and measured validity. The snowball technique was used on included articles, whereby we reviewed all references found in the references of the included articles. Results Our search strategy identified 1,754 articles to be screened by title and abstract. Of these, only five (0.29%) articles met the initial inclusion criteria. After full text review, only two articles 6,7 met the final inclusion criteria to be included in the systematic review. Both articles focused on PID with no assessment of syphilis. They utilized ICD-9-CM codes to identify cases with PID and performed chart reviews to determine true PID status. Results of both articles found positive predictive value (PPV) of PID to be between 18%–79%. Only one article 7 examined the PPV of chlamydia (56%; 5/9 cases) and gonorrhea (100%; 4/4 cases) separately. Conclusions We identified just two studies that evaluated the validity of ICD codes in identifying the STIs of interest. Both studies focused on PID cases in which chlamydia and gonorrhea diagnoses and tests might be documented. Additionally, since both studies were published before 2015, neither evaluates the use of administrative data following the U.S. transition to ICD-10 codes. Given these findings, further studies are required to examine the predictive value of ICD-9 and -10 codes for all three diseases in the general population. References 1. Tao G, Zhang CX. HIV Testing of Commercially Insured Patients Diagnosed With Sexually Transmitted Diseases. Sexually Transmitted Diseases. 2008; 35 (1):43-6. 2. Evans HE, Mercer CH, Rait G, et al. Trends in HIV testing and recording of HIV status in the UK primary care setting: a retrospective cohort study 1995-2005. Sexually Transmitted Infections. 2009. 3. Oskoui M, Ng P, Dorais M, et al. Accuracy of administrative claims data for cerebral palsy diagnosis: a retrospective cohort study. CMAJ open. 2017 Jul 18; 5 (3):E570-e5. 4. Sauer BC, Teng C-C, Accortt NA, et al. Models solely using claims-based administrative data are poor predictors of rheumatoid arthritis disease activity. Arthritis Research & Therapy. 2017 2017/05/08; 19 (1):86. 5. Haggerty CL, Ness RB. Epidemiology, pathogenesis and treatment of pelvic inflammatory disease. Expert review of anti-infective therapy. 2006 Apr; 4 (2):235-47. 6. Satterwhite CL, Yu O, Raebel MA, et al. Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data. Infect Dis Obstet Gynecol. 2011; 2011 :7. 7. Ratelle S, Yokoe D, Blejan C, et al. Predictive value of clinical diagnostic codes for the CDC case definition of pelvic inflammatory disease (PID): implications for surveillance. Sex Transm Dis. 2003 Nov; 30 (11):866-70.

Introduction: Timeliness of data availability is a key performance measure in cancer reporting. Previous studies evaluated timeliness of cancer reporting using a single metric, yet this metric obscures the details within each step of the reporting process. To enhance understanding of cancer reporting processes, we measured the timeliness of discrete cancer reporting steps and examined changes in timeliness across a decade.Methods: We analyzed 76,259 cases of breast, colorectal and lung cancer reported to the Indiana State Cancer Registry between 2001 and 2011. We measured timeliness for three fundamental reporting steps: report completion time, report submission time, and report processing time. Timeliness was measured as the difference, in days, between timestamps recorded in the cancer registry at each step. We further examined the reporting pattern among facilities within each step.Results: Identifying and gathering details about cases (report completion) accounts for the largest proportion of time during the cancer reporting process. Although submission time accounts for a lesser proportion of time, there is wide variation among facilities. One-seventh (7 out of 49) facilities accounted for 28.4% of the total cases reported, all of which took more than 100 days to submit the completed cases to the registry.Conclusions: Measuring timeliness of the individual steps in reporting processes can enable cancer registry programs to target individual facilities as well as tasks that could be improved to reduce overall case reporting times. Process improvement could strengthen cancer control programs and enable more rapid discovery in cancer research.

Objective To assess the equivalence of hypertension prevalence estimates between longitudinal electronic health record (EHR) data from a community-based health information exchange (HIE) and the Behavioral Risk Factor Surveillance System (BRFSS). Introduction Hypertension (HTN) is a highly prevalent chronic condition and strongly associated with morbidity and mortality. HTN is amenable to prevention and control through public and population health programs and policies. Therefore, public and population health programs require accurate, stable estimates of disease prevalence, and estimating HTN prevalence at the community-level is acutely important for timely detection, intervention, and effective evaluation. Current surveillance methods for HTN rely upon community-based surveys, such as the BRFSS. While BRFSS is the standard at the state- and national-level, they are expensive to collect, released once per year, and their confidence intervals are too wide for precise estimates at the local level. More timely, frequently updated, and locally precise prevalence estimates could greatly improve the timeliness and precision of public health interventions. The current study evaluated EHR data from a large, mature HIE as an alternative to community-based surveys for timely, accurate, and precise HTN prevalence estimation. Methods Two years (2014-2015) of EHR data were obtained from the Indiana Network for Patient Care for two major health systems in Marion County, Indiana, representing approximately 75% of the total county population (n=530,244). These data were linked and evaluated for prevalent HTN. Six HTN phenotypes were defined using structured data variables including clinical diagnoses (ICD9/10 codes), blood pressure (BP) measurements (HTN = ≥140mmhg systolic or ≥90mmHg diastolic), and dispensed HTN medications (Table 1). Phenotypes were validated using a random sample of 600 records, comparing EHR phenotype HTN to HTN as determined through manual chart review by a Registered Nurse. Each phenotype was further evaluated against BRFSS estimates for Marion County, and stratified by sex, race, and age to compare EHR-generated HTN prevalence measures to those known and in current use for chronic disease surveillance. Comparisons were made using the two one-sided statistical test (TOST) of equivalence, wherein the null hypothesis is the BRFSS and EHR prevalence estimates are different by +/-5% and the alternative is estimates differ by less than +/-5%. Rejection of the null resulted in the conclusion of equivalence of the estimates for use in population/public health. Results In general, the performance of the EHR phenotypes was characterized by high specificity (>87%) and low to moderate sensitivity (range 25.4%-95.3%). The false positive rate was lowest among the phenotype defining HTN by both clinical diagnosis and BP measurements (0.3%), and sensitivity was greatest for the phenotype combining all three structured data elements (95.2%). The prevalence of HTN in Marion County, Indiana (2014-2015) for the EHR sample (n=530,244) ranged between 13.7% and 36.2%, compared to 28.4% in the BRFSS sample (Table 1). Only one EHR phenotype (≥1 HTN BP measurement) demonstrated equivalence with BRFSS prevalence at the county level (difference 0.9%, 90% CI for difference -2.3%-4.0%). HTN prevalence by sex, race, age, sex and age, and sex and race (n=120 comparisons) failed to demonstrate equivalence between EHR and BRFSS measures in all but two comparisons, both among females aged 18-39 years. Differences between EHR and BRFSS HTN prevalence at the subgroup level varied but were particularly pronounced among older adults. As suspected, HTN prevalence precision was improved in the EHR sample with the largest subgroup 95% CI width of 0.7% for male African Americans compared to the BRFSS sample 95% CI width of 29.6%. Conclusions The applicability of the tested HTN phenotypes will vary based upon which EHR structured data elements are available to public health (i.e., ICD10, vitals, medications). We found that HTN surveillance using a community-based HIE was not a valid replacement for the BRFSS, although the HIE-based estimates could be readily generated and had much narrower confidence intervals. References Mozaffarian D, et al. Heart Disease and Stroke Statistics — 2016 Update. Circulation. 2016; 133: e38-e360. Yoon S, Fryar C, Carroll M. HTN Prevalence and Control Among Adults: United States, 2011–2014. NCHS Data Brief No. 220. 2015; Hyattsville, MD: National Center for Health Statistics, Centers for Disease Control and Prevention, US Dept of Health and Human Services.

Objective: To measure stillbirth delivery rates and syphilis screening rates among women with a stillbirth delivery using electronic health record data available in a health information exchange. Introduction: Reports of infants born with congenital syphilis have increased in the United States every year since 2012. Prevention depends on high performing surveillance systems and compliance with the U.S. Centers for Disease Control and Prevention (CDC) recommendations to perform syphilis testing early in pregnancy, in the third trimester and at delivery if a woman is at high risk, and following a stillbirth delivery. These guidelines exist, because untreated syphilis is associated with adverse fetal outcomes including central nervous system infection and death. Surveillance of congenital syphilis and stillbirth is challenging because available data sources are limited. Assessment of compliance with testing guidelines is particularly challenging, since public health agencies often lack access to comprehensive cohorts of tested individuals as most public health laws only require reporting of positive disease case information. Methods: Using integrated electronic health records available in a community-based health information exchange, we examined syphilis testing patterns for women with a stillbirth delivery in Indiana between 2010-2016. The cohort was examined to determine whether the women received syphilis testing in accordance with the CDC recommendations. During this time period, Indiana recorded around 84,000 live births per year. Data were extracted from electronic health records, including encounter data, laboratory test results and procedure data, captured by the Indiana Network for Patient Care (INPC), one of the largest community-based HIE networks in the United States. The INPC connects over 90 health care facilities, including hospitals, physicians’ practices, pharmacy networks, long-term post-acute care facilities, laboratories, and radiology centers. In addition to clinical care, the INPC supports surveillance of STIs 1 . Women with a stillbirth delivery were identified using International Classification of Disease (ICD) Clinical Modification (CM) codes from the 9 th and 10 th editions (ICD-CM-9 and ICD-CM-10). Inclusion codes: ICD-CM-9 codes 656.4, 779.9, V27.1, V27.3, V27.4, V27.6, V27.7, V32.01, V32.1, V32.2, V36.1; and ICD-CM-10 codes P95, P96.9, O36.4, Z37.1, Z37.3, Z37.4, Z37.9. Using the master person index for the INPC, we linked stillbirth deliveries with pregnancy encounters and laboratory testing data. We analyzed documentation of syphilis testing during the pregnancy (up to 270 days prior to the stillbirth delivery) as well as after the stillbirth delivery (up to 30 days). Broad time ranges were utilized to account for potential delays in reporting of either the stillbirth delivery or the syphilis test results. Documentation could include either presence of a result from a laboratory test for syphilis or a CPT code (80055, 86780, 86781, 86592, 86593) indicating performance of a syphilis test. Results: A total of 4,361 stillbirth deliveries attributable to 4,265 unique women were identified in the INPC between 2010-2016; representing a rate of 7.44 stillbirths per 1,000 live births during the same time period. Of the stillbirth deliveries, syphilis testing occurred within 270 days prior to or 30 days after delivery for 2,763 (63.4%) cases. Figure 1 displays the number of stillbirth cases observed each year and the number of cases in which syphilis testing occurred during the pregnancy or after delivery. Conclusions: Using integrated electronic health records data, we discovered that fetal deaths occurred more frequently (7.44 versus 4.09 per 1,000) than previously estimated 2 through fetal death reporting mechanisms in Indiana. Furthermore, we observed increasing rates of stillbirth within Indiana in recent years. Integrated data further enabled measurement of syphilis testing rates for stillbirth cases, which were similar to those reported by Patel et al. 3 using a large, national administrative data set. Testing rates in Indiana are well below the targets set by national and international public health organizations. Accessing more complete data on populations using a health information exchange is valuable, although doing so may uncover a more negative picture of health in one’s community. Deeper analysis of these trends is warranted to explore factors related to increasing rates as well as limited testing in this population.

Objective To extend an open source platform for measuring the quality of electronic health data by adding functions useful for syndromic surveillance. Introduction Nearly all of the myriad activities (or use cases) in clinical and public health (e.g., patient care, surveillance, community health assessment, policy) involve generating, collecting, storing, analyzing, or sharing data about individual patients or populations. Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems, including but not limited to electronic health records. However, the quality of data in electronic health record systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers. Methods Using the Health Data Stewardship Framework 1 , we will extend Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES), a software package published open-source by the Observational Health Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org) to measure the quality of data electronically reported from disparate information systems. Our extensions will focus on analysis of data reported electronically to public health agencies for disease surveillance. Next we will apply the ACHILLES extensions to explore the quality of data captured from multiple real-world health systems, hospitals, laboratories, and clinics. We will further demonstrate the extended software to public health professionals, gathering feedback on the ability of the methods and software tool to support public health agencies’ efforts to routinely monitor the quality of data received for surveillance of disease prevalence and burden. Results To date we have mapped key surveillance data fields into the OHDSI common data model, and we have transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients for importation into ACHILLES. Using these data, we are exploring the existing 167 metrics across 16 categories available within ACHILLES, including a person (e.g., number of unique persons); and observation period (e.g., Distribution of age at first observation period). Syndromic surveillance (SS), however, is driven largely by monitoring patient stated chief complaints (non-standard free text clinical data) in addition to coded diagnoses. Consequently, ACHILLES must be extended to maximally support use in analyzing SS datasets. Conclusions This work remains a work-in-progress. Over the coming year, we will not only explore existing ACHILLES constructs using real-world public health data but also introduce new functionality to explore 1) patient demographics; 2) facility and location (e.g., emergency department where care was delivered); and 3) clinical observations (e.g., chief complaint). The design and methods for examining these aspects of surveillance data will be included on the poster, and they will be made freely available for distribution with a future instance of the ACHILLES software. We ultimately envision these tools being available for use on platforms such as the CDC’s Biosense – open to all local and state health agencies as a one-stop portal for surveillance data analysis – or research environments where they can be used to examine and improve the quality of data output from informatics systems.

The International Society for Disease Surveillance (ISDS) held its fifteenth annual conference in Atlanta, GA, from December 6-8, 2016. Since 2001, individuals interested in sharing and learning emerging trends in surveillance research and practice have found the ISDS Annual Conference a unique forum to advance their knowledge in the discipline of disease surveillance. The 15th ISDS conference received a total of 233 abstracts from 23 countries. From the submissions, 189 (81%) were accepted for presentation at the conference as an oral presentation (N=96) or poster (N=93). The theme for the 15th annual conference was New Frontiers in Surveillance: Data Science and Health Security. The theme united two dominant trends in public health surveillance: 1) a growing desire to extract knowledge from increasing volumes of structured and unstructured data available from health information systems; and 2) increased pressure on nations to strengthen their capacity for disease surveillance and response to outbreaks when and where they occur across the globe. In addition to the major themes of the conference, abstracts were accepted in additional tracks that remain important to the practice of public health around the world: One Health uniting animal and human health; Methodological advances in applied epidemiology; Public health informatics; Public health policy; and Biosurveillance practice. As usual, accepted abstracts for the 2016 ISDS Conference span the breadth of surveillance practice around the globe. There are timely abstracts on the detection and response to vector-borne diseases such as Zika virus and chikungunya across the Americas, as well as abstracts on the surveillance of opioid abuse observed in many parts of the U.S. Other abstracts cover the surveillance of non-communicable diseases that are now the leading causes of death globally. Additionally, some abstracts focus on capacity building within low resource settings on multiple continents to enhance global health security. While other abstracts describe the impact of health information technology (or eHealth) policies on surveillance practice at local, national, or regional levels. And still other abstracts contain emerging, novel methods that advance our understanding of how to analyze “Big” data or reduce the messiness associated with realworld surveillance data. Together these abstracts represent the broad, diverse and interesting nature of surveillance practice. Furthermore, the abstracts represent important work being done in high income countries like the U.S., Canada and the U.K. as well as critical work being done in low-and-middle income nations such as Nigeria, Pakistan, and Sierra Leone. I wish to thank the dedicated members of the Scientific Programming Committee (SPC) and ISDS staff who helped to manage the process of selecting this year’s abstracts for presentation. These individuals are domain experts across the spectrum of tracks and themes represented in the program, and their service is much appreciated. The SPC helped to recruit dozens of public health researchers and practitioners who also spent time reviewing abstracts. I also thank these volunteers for contributing to the richness and diversity of this year’s program. Finally, I wish to thank the Track Chairs who reviewed abstracts and recruited peers to perform reviews, and whom helped me organize presentations into meaningful sessions for the final conference program. Their names are listed in the proceedings to recognize their selfless service to ISDS and the field of public health surveillance. I hope that these proceedings help to advance scientific understanding and the practice of surveillance in public health. Please use the knowledge herein to improve how you practice or evaluate surveillance in your jurisdiction. Or you may find ways to apply the knowledge elsewhere in population health. However you use it, I ask that you document your lessons or findings and submit to ISDS in the future to share the outcomes with others. Together we can reduce the burden of disease and improve health outcomes for populations globally.

Objective: To categorize and describe the public health informatics (PHI) and global health informatics (GHI) literature between 2012 and 2014.Methods: We conducted a semi-systematic review of articles published between January 2012 and September 2014 where information and communications technologies (ICT) was a primary subject of the study or a main component of the study methodology. Additional inclusion and exclusion criteria were used to filter PHI and GHI articles from the larger biomedical informatics domain. Articles were identified using MEDLINE as well as personal bibliographies from members of the American Medical Informatics Association PHI and GHI working groups.Results: A total of 85 PHI articles and 282 GHI articles were identified. While systems in PHI continue to support surveillance activities, we identified a shift towards support for prevention, environmental health, and public health care services. Furthermore, articles from the U.S. reveal a shift towards PHI applications at state and local levels. GHI articles focused on telemedicine, mHealth and eHealth applications. The development of adequate infrastructure to support ICT remains a challenge, although we observed a small but growing set of articles that measure the impact of ICT on clinical outcomes.Discussion: There is evidence of growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. Yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles.Conclusion: While the body of knowledge around PHI and GHI continues to mature, additional studies of higher quality are needed to generate the robust evidence base needed to support continued investment in eHealth by governmental health agencies.

Communicable disease reporting remains a challenging task for public health. To understand modern reporting behaviors and challenges, we investigated a sample of chlamydia laboratory and provider communicable disease documentation sent to a local health department. We measured the delay between a chlamydia positive test result and when reports were sent to public health in addition to comparing the results to state law. Laboratory reporting has improved given the adoption of electronic lab reporting. However, provider reports still lag and tend to be fax-based. Given the movement towards electronic health record (EHR) systems, health IT platforms may further improve the timeliness of public health reporting.

Objective To support health department estimation of future electronic laboratory report volumes from hospitals that achieve Stage 2 meaningful use. Introduction The American Recovery and Reinvestment Act of 2009 authorized the Centers for Medicare and Medicaid Services (CMS) to incentivize hospitals and physicians to become meaningful users of electronic health record (EHR) systems. In a final rule issued August 2012, CMS outlined the requirements for Stage 2 meaningful use to be effective in 2014 (1). The Stage 2 criteria require eligible hospitals to submit electronic laboratory reports to health departments. While many state health departments receive some portion of notifiable disease reports electronically, the final Stage 2 rule is likely to increase the volume of incoming electronic reports. The Centers for Disease Control and Prevention are urging health departments to prepare for the sharp increase in electronic laboratory reporting (ELR). Crucial to preparedness is estimation of how many ELR reports can be expected. However, few health departments have experience with high volume ELR, making estimation difficult. The Indiana Network for Patient Care (INPC), a regional health information exchange, has been processing high volumes of ELR for over a decade (2). To support health departments estimate potential ELR increases, the INPC examined its current volumes from hospitals with advanced EHR capabilities. Methods The INPC uses an automated case-detection system called the Notifiable Condition Detector (NCD) developed by investigators at the Regenstrief Institute (3). The NCD uses a standards-based messaging and vocabulary infrastructure to process more than 350,000 clinical transactions daily, including laboratory studies, diagnoses, and transcriptions from more than 40 hospitals, national labs and local ancillary service organizations. Data processed between January 1, 2010 and December 15, 2011 were extracted from the NCD. Validated cases of notifiable conditions of interest to the Indiana State Department of Health were filtered out for use in this analysis. We further eliminated duplicate cases of the same reportable record for the same individual. Unique notifiable disease cases were divided by the population of the Indianapolis Metropolitan Statistical Area (MSA) to obtain a ratio for estimation of future volume. Results We identified a total of 77,199 unique notifiable disease cases. According to 2010 census data, the population of the Indianapolis MSA is 1,834,672. This produces a ratio of 2,104 ELR cases per 100,000 population per year. Conclusions Roughly 2% of the population had an unique notifiable disease case reported, more than double current rates (4). Actual rates could be higher given this analysis eliminated duplicate reports for chronic diseases, such as tuberculosis, hepatitis B and C, and sickle cell disease. The impact on local and state health departments is likely to be significant given scarce resources. Although the calculated ratio may stimulate conversations within health departments, it represents an approximate estimator. Future work will seek to refine estimation techniques by accounting for acute versus chronic notifiable disease as well as additional factors, such as the notifiable condition and/or the relative size of the hospital sending lab data to the health department. These refined estimators will enable improved planning efforts within state and local health departments.

One of the factors affecting the quality of health data is the variation in public health practitioners' perceived value of the information. To better understand this variation, we asked four local public health practitioners' to evaluate the fields they use for four communicable diseases. We assessed their perceived value of these fields to initiate or complete communicable disease case reports and the variability in their responses.

Objective To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance. Introduction The use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary. Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test. Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice. Methods To characterize the use of standardized vocabularies in electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, we analyzed ELR messages from two states: Indiana and Wisconsin. We examined the data in the ELR messages where tests and results are reported (3). For each field, the proportion of field values that used either LOINC or SNOMED CT codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields. Results Results are summarized in Table-1. In Indiana, less than 17% of incoming ELR messages contained a standardized code for identifying the test performed by the laboratory, and none of the test result fields contained a standardized vocabulary concept. For Wisconsin, none of the incoming ELR messages contained a standardized code for identifying the test performed, and less than 13% of the test result fields contained a SNOMED CT concept. Conclusions Although Wisconsin and Indiana both have high adoption of advanced health information systems with many hospitals and laboratories using commercial systems which claim to support interoperability, very few ELR messages emanate from real-world systems with interoperable codes to identify tests and clinical results. To effectively use the arriving ELR messages, Indiana and Wisconsin health departments employ software and people workarounds to translate the incoming data into standardized concepts that can be utilized by the states’ surveillance systems. These workarounds present challenges for budget constrained public health departments seeking to leverage Meaningful Use Certified technologies to improve notifiable disease surveillance.

As we enter the 'big medical data' era, a new core competency is to continuously monitor quality of data collected from electronic sources, including population surveillance data sources. We describe how entropy, a fundamental information measure, can help monitor the characteristics of chief complaints in an operational surveillance system.