Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department

Improving tabular data extraction in scanned laboratory reports using deep learning models

OBJECTIVE

Medical laboratory testing is essential in healthcare, providing crucial data for diagnosis and treatment. Nevertheless, patients' lab testing results are often transferred via fax across healthcare organizations and are not immediately available for timely clinical decision making. Thus, it is important to develop new technologies to accurately extract lab testing information from scanned laboratory reports. This study aims to develop an advanced deep learning-based Optical Character Recognition (OCR) method to identify tables containing lab testing results in scanned laboratory reports.

METHODS

Extracting tabular data from scanned lab reports involves two stages: table detection (i.e., identifying the area of a table object) and table recognition (i.e., identifying and extracting tabular structures and contents). DETR R18 algorithm as well as YOLOv8s were involved for table detection, and we compared the performance of PaddleOCR and the encoder-dual-decoder (EDD) model for table recognition. 650 tables from 632 randomly selected laboratory test reports were annotated and used to train and evaluate those models. For table detection evaluation, we used metrics such as Average Precision (AP), Average Recall (AR), AP50, and AP75. For table recognition evaluation, we employed Tree-Edit Distance (TEDS).

RESULTS

For table detection, fine-tuned DETR R18 demonstrated superior performance (AP50: 0.774; AP75: 0.644; AP: 0.601; AR: 0.766). In terms of table recognition, fine-tuned EDD outperformed other models with a TEDS score of 0.815. The proposed OCR pipeline (fine-tuned DETR R18 and fine-tuned EDD), demonstrated impressive results, achieving a TEDS score of 0.699 and a TEDS structure score of 0.764.

CONCLUSIONS

Our study presents a dedicated OCR pipeline for scanned clinical documents, utilizing state-of-the-art deep learning models for region-of-interest detection and table recognition. The high TEDS scores demonstrate the effectiveness of our approach, which has significant implications for clinical data analysis and decision-making.

Electronic Case Reporting Development, Implementation, and Expansion in the United States

INTRODUCTION

The COVID-19 pandemic highlighted the need for a nationwide health information technology solution that could improve upon manual case reporting and decrease the clinical and administrative burden on the US health care system. We describe the development, implementation, and nationwide expansion of electronic case reporting (eCR), including its effect on public health surveillance and pandemic readiness.

METHODS

Multidisciplinary teams developed and implemented a standards-based, shared, scalable, and interoperable eCR infrastructure during 2014-2020. From January 27, 2020, to January 7, 2023, the team conducted a nationwide scale-up effort and determined the number of eCR-capable electronic health record (EHR) products, the number of reportable conditions available within the infrastructure, and technical connections of health care organizations (HCOs) and jurisdictional public health agencies (PHAs) to the eCR infrastructure. The team also conducted data quality studies to determine whether HCOs were discontinuing manual case reporting and early results of eCR timeliness.

RESULTS

During the study period, the number of eCR-capable EHR products developed or in development increased 11-fold (from 3 to 33), the number of reportable conditions available increased 28-fold (from 6 to 173), the number of HCOs connected to the eCR infrastructure increased 143-fold (from 153 to 22 000), and the number of jurisdictional PHAs connected to the eCR infrastructure increased 2.75-fold (from 24 to 66). Data quality reviews with PHAs resulted in select HCOs discontinuing manual case reporting and using eCR-exclusive case reporting in 13 PHA jurisdictions. The timeliness of eCR was <1 minute.

PRACTICE IMPLICATIONS

The growth of eCR can revolutionize public health case surveillance by producing data that are more timely and complete than manual case reporting while reducing reporting burden.

Google trends as an early indicator of African swine fever outbreaks in Southeast Asia

African Swine Fever (ASF) is a reportable disease of swine that causes far-reaching losses to affected countries and regions. Early detection is critically important to contain and mitigate the impact of ASF outbreaks, for which timely available data is essential. This research examines the potential use of Google Trends data as an early indicator of ASF outbreaks in Southeast Asia, focusing on the three largest swine producing countries, namely, Vietnam, the Philippines, and Thailand. Cross-correlation and Kullback-Leibler (KL) divergence indicators were used to evaluate the association between Google search trends and the number of ASF outbreaks reported. Our analysis indicate strong and moderate correlations between Google search trends and number of ASF outbreaks reported in Vietnam and the Philippines, respectively. In contrast, Thailand, the country of this group in which outbreaks were reported last, exhibits the weakest correlation (KL = 2.64), highlighting variations in public awareness and disease dynamics. These findings suggest that Google search trends are valuable for early detection of ASF. As the disease becomes endemic, integrating trends with other epidemiological data may support the design and implementation of surveillance strategies for transboundary animal diseases in Southeast Asia.

Development and implementation of an interoperability tool across state public health agency's disease surveillance and immunization information systems

Public health information systems have historically been siloed with limited interoperability. The State of Minnesota's disease surveillance system (Minnesota Electronic Disease Surveillance System: MEDSS, ∼12 million total reportable events) and immunization information system (Minnesota Immunization Information Connection: MIIC, ∼130 million total immunizations) lacked interoperability between them and data exchange was fully manual. An interoperability tool based on national standards (HL7 and SOAP/web services) for query and response was developed for electronic vaccination data exchange from MIIC into MEDSS by soliciting stakeholder requirements (n = 39) and mapping MIIC vaccine codes (n = 294) to corresponding MEDSS product codes (n = 48). The tool was implemented in March 2022 and incorporates MIIC data into a new vaccination form in MEDSS with mapping of 30 data elements including MIIC demographics, vaccination history, and vaccine forecast. The tool was evaluated using mixed methods (quantitative analysis of user time, clicks, queries; qualitative review with users). Comparison of key tasks demonstrated efficiencies including vaccination data access (before: 50 clicks, >2 min; after: 4 clicks, 8 s) which translated directly to staff effort (before: 5 h/week; after: ∼17 min/week). This case study demonstrates the contribution of improving public health systems interoperability, ultimately with the goal of enhanced data-driven decision-making and public health surveillance.

Timeliness and completeness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020-2021

INTRODUCTION

Disease surveillance provides vital data for disease prevention and control programs. Incomplete and untimely data are common challenges in planning, monitoring, and evaluation of health sector performance, and health service delivery. Weekly surveillance data are sent from health facilities using mobile tracking (mTRAC) program, and synchronized into the District Health Information Software version 2 (DHIS2). The data are then merged into district, regional, and national level datasets. We described the completeness and timeliness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020-2021.

METHODS

We abstracted data on completeness and timeliness of weekly reporting of epidemic-prone diseases from 146 districts of Uganda from the DHIS2.Timeliness is the proportion of all expected weekly reports that were submitted to DHIS2 by 12:00pm Monday of the following week. Completeness is the proportion of all expected weekly reports that were completely filled and submitted to DHIS2 by 12:00pm Wednesday of the following week. We determined the proportions and trends of completeness and timeliness of reporting at national level by year, health region, district, health facility level, and facility ownership.

RESULTS

National average reporting timeliness and completeness was 44% and 70% in 2020, and 49% and 75% in 2021. Eight of the 15 health regions achieved the target for completeness of ≥ 80%; Lango attained the highest (93%) in 2020, and Karamoja attained 96% in 2021. None of the regions achieved the timeliness target of ≥ 80% in either 2020 or 2021. Kampala District had the lowest completeness (38% and 32% in 2020 and 2021, respectively) and the lowest timeliness (19% in both 2020 and 2021). Referral hospitals and private owned health facilities did not attain any of the targets, and had the poorest reporting rates throughout 2020 and 2021.

CONCLUSION

Weekly surveillance reporting on epidemic prone diseases improved modestly over time, but timeliness of reporting was poor. Further investigations to identify barriers to reporting timeliness for surveillance data are needed to address the variations in reporting.

Evaluation of malaria surveillance system in Benue State, Nigeria

BACKGROUND

Malaria is a priority global health disease with high morbidity and mortality especially among children under-five and pregnant women. Malaria elimination requires an effective surveillance system. The malaria surveillance system in Benue State was evaluated to assess its attributes and performance in line with set objectives.

METHODS

The updated United States Centers for Disease Control and Prevention guideline for evaluating surveillance systems was used. The surveillance system's key attributes was quantitatively and qualitatively assessed. Semi-structured questionnaires were administered to all Local Government Area (LGA) Roll Back Malaria (RBM) focal persons and five key informants were interviewed at the State level. The Benue State District Health Information System-2 (DHIS-2) malaria data and monthly summary forms were reviewed from January 2015 to December 2019.

RESULTS

A total of 46 RBM focal persons and 5 key-informants participated. About 56.9% were males, the mean-age 43.8 (SD ± 9.3) years and 32 (62.8%) had ≥ 20-year experience on malaria surveillance with mean-year-experience 20.8 (SD ± 7.8) years. All 46 (100%) RBMs understood the case definition; 43 (93.5%) found it easy-to-fill the standardized data tools and understood the data flow channels. The malaria surveillance system in Benue is simple, acceptable and useful to all stakeholders, 36 (70.6%) found switching from the paper-based to the electronic-data tools with ease and 45 (88.2%) stated that analysed data were used for decision-making. Data flow from LGA to State is clearly defined, however majority of the data is collected from public health facilities through the DHIS-2 Platform. The overall timeliness and completeness of reporting was 76.5% and 95.7%, respectively, which were below the ≥ 80% and 100% targets, respectively.

CONCLUSIONS

The malaria surveillance system in Benue State is simple, useful, acceptable, and flexible, but it is not representative and timely. Public-private and public-public-partnerships should be strengthened to encourage reporting from both private and tertiary health facilities and improve representativeness, and frequent feedback to improve reporting timeliness.

Electronic case reporting (eCR) of COVID-19 to public health: implementation perspectives from the Minnesota Department of Health

Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites), and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20 000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the 6 reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues, and expanding to other reportable conditions.

Baseline assessment of staff perception of critical value practices in government hospitals in Kuwait

Background

Notification of laboratory-determined critical values is key for effective clinical decision making and is thus a consequential step in a patient’s health care and safety. This study presents an overview of staff reporting policies and procedures concerning critical values in Kuwaiti governmental hospitals.

Methods

A cross-sectional descriptive study design was adopted. Study subjects were affiliated with laboratories from five government hospitals (four general and one sub-specialty hospital). All laboratory staff in every hospital were included. The Statistical Package for the Social Sciences (version 23) was used to analyse the collected data at a significance level of ≤ 0.05. Quantitative data analysis included univariate descriptive (means, medians, standard deviations, frequencies, percentages) and bivariate (chi-squared, ANOVA and Kruskal–Wallis tests) analyses. These analyses provided associations between participating hospitals and staff perceptions towards the policies and procedures surrounding critical values.

Results

559 questionnaires were returned, a total response of 30.5% after those of 79 phlebotomists were excluded (eligible sample size n = 1833). The notification of critical values differs between participated laboratories in delivering protocol and time duration. Linked protocols between laboratories did not exist regarding policies and guidelines for applying the same procedures for critical value notification. There are differences in critical value limits among the participating laboratories.

Conclusion

This study is the first to survey laboratory staff perceptions of critical value practices in Kuwaiti government hospitals. Enhancing critical value reporting and policy is crucial for improving patient safety and to develop high-quality health services. The findings of this study can help policy makers implement future intervention studies to enhance laboratory practices in the area of critical values and improve patient safety and the quality of government hospital systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08329-z.

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Objective

We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors.

Materials and Methods

We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities.

Results

Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media.

Discussion

Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19.

Conclusions

The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Clinical data sharing improves quality measurement and patient safety

OBJECTIVE

Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement.

MATERIALS AND METHODS

Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.

RESULTS

A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.

DISCUSSION

These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.

CONCLUSIONS

Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic

BACKGROUND

Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.

MATERIALS AND METHODS

First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.

RESULTS

A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.

CONCLUSION

The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

Gonorrhea testing, morbidity, and reporting using an integrated sexually transmitted disease registry in Indiana: 2004-2016

Surveillance of gonorrhea (GC), the second most common notifiable disease in the United States, depends on case reports. Population-level data that contain the number of individuals tested in addition to morbidity are lacking. We performed a cross-sectional analysis of data obtained from individuals tested for GC recorded in a sexually transmitted disease (STD) registry in the state of Indiana. Descriptive statistics were performed, and a Poisson generalized linear model was used to evaluate the number of individuals tested for GC and the positivity rate. GC cases from a subset of the registry were compared to CDC counts to determine the completeness of the registry. A total of 1,870,811 GC tests were linked to 627,870 unique individuals. Individuals tested for GC increased from 54,334 in 2004 to 269,701 in 2016; likewise, GC cases increased from 2,039 to 5,997. However, positivity rate decreased from 3.75% in 2004 to 2.22% in 2016. The difference in the number of GC cases captured by the registry and those reported to the CDC was not statistically significant (P = 0.0665). Population-level data from an STD registry combining electronic medical records and public health case data may inform STD control efforts. In Indiana, increased testing rates appeared to correlate with increased GC morbidity.

Evaluation of a health information exchange system for microcephaly case-finding - New York City, 2013-2015

Background

Birth defects surveillance in the United States is conducted principally by review of routine but lagged reporting to statewide congenital malformations registries of diagnoses by hospitals or other health care providers, a process that is not designed to rapidly detect changes in prevalence. Health information exchange (HIE) systems are well suited for rapid surveillance, but information is limited about their effectiveness at detecting birth defects. We evaluated HIE data to detect microcephaly diagnosed at birth during January 1, 2013–December 31, 2015 before known introduction of Zika virus in North America.

Methods

Data from an HIE system were queried for microcephaly diagnostic codes on day of birth or during the first two days after birth at three Bronx hospitals for births to New York City resident mothers. Suspected cases identified by HIE data were compared with microcephaly cases that had been identified through direct inquiry of hospital records and confirmed by chart abstraction in a previous study of the same cohort.

Results

Of 16,910 live births, 43 suspected microcephaly cases were identified through an HIE system compared to 67 confirmed cases that had been identified as part of the prior study. A total of 39 confirmed cases were found by both studies (sensitivity = 58.21%, 95% CI: 45.52–70.15%; positive predictive value = 90.70%, 95% CI: 77.86–97.41%; negative predictive value = 99.83%, 95% CI: 99.76–99.89% for HIE data).

Conclusion

Despite limitations, HIE systems could be used for rapid newborn microcephaly surveillance, especially in the many jurisdictions where more labor-intensive approaches are not feasible. Future work is needed to improve electronic medical record documentation quality to improve sensitivity and reduce misclassification.

Improving Notifiable Disease Case Reporting Through Electronic Information Exchange-Facilitated Decision Support: A Controlled Before-and-After Trial

OBJECTIVE

Outbreak detection and disease control may be improved by simplified, semi-automated reporting of notifiable diseases to public health authorities. The objective of this study was to determine the effect of an electronic, prepopulated notifiable disease report form on case reporting rates by ambulatory care clinics to public health authorities.

METHODS

We conducted a 2-year (2012-2014) controlled before-and-after trial of a health information exchange (HIE) intervention in Indiana designed to prepopulate notifiable disease reporting forms to providers. We analyzed data collected from electronic prepopulated reports and "usual care" (paper, fax) reports submitted to a local health department for 7 conditions by using a difference-in-differences model. Primary outcomes were changes in reporting rates, completeness, and timeliness between intervention and control clinics.

RESULTS

Provider reporting rates for chlamydia and gonorrhea in intervention clinics increased significantly from 56.9% and 55.6%, respectively, during the baseline period (2012) to 66.4% and 58.3%, respectively, during the intervention period (2013-2014); they decreased from 28.8% and 27.5%, respectively, to 21.7% and 20.6%, respectively, in control clinics (P < .001). Completeness improved from baseline to intervention for 4 of 15 fields in reports from intervention clinics (P < .001), although mean completeness improved for 11 fields in both intervention and control clinics. Timeliness improved for both intervention and control clinics; however, reports from control clinics were timelier (mean, 7.9 days) than reports from intervention clinics (mean, 9.7 days).

CONCLUSIONS

Electronic, prepopulated case reporting forms integrated into providers' workflow, enabled by an HIE network, can be effective in increasing notifiable disease reporting rates and completeness of information. However, it was difficult to assess the effect of using the forms for diseases with low prevalence (eg, salmonellosis, histoplasmosis).

Reliability of administrative data to identify sexually transmitted infections for population health: a systematic review

Introduction

International Classification of Diseases (ICD) codes in administrative health data are used to identify cases of disease, including sexually transmitted infections (STIs), for population health research. The purpose of this review is to examine the extant literature on the reliability of ICD codes to correctly identify STIs.

Methods

We conducted a systematic review of empirical articles in which ICD codes were validated with respect to their ability to identify cases of chlamydia, gonorrhoea, syphilis or pelvic inflammatory disease (PID). Articles that included sensitivity, specificity and positive predictive value of ICD codes were the target. In addition to keyword searches in PubMed and Scopus databases, we further examined bibliographies of articles selected for full review to maximise yield.

Results

From a total of 1779 articles identified, only two studies measured the reliability of ICD codes to identify cases of STIs. Both articles targeted PID, a serious complication of chlamydia and gonorrhoea. Neither article directly assessed the validity of ICD codes to identify cases of chlamydia, gonorrhoea or syphilis independent of PID. Using ICD codes alone, the positive predictive value for PID was mixed (range: 18%–79%).

Discussion and conclusion

While existing studies have used ICD codes to identify STI cases, their reliability is unclear. Further, available evidence from studies of PID suggests potentially large variation in the accuracy of ICD codes indicating the need for primary studies to evaluate ICD codes for use in STI-related public health research.

Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency

Background

Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.

Objectives

The study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.

Methods

The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.

Results

All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals.

Conclusion

With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

The benefits of health information exchange: an updated systematic review

Objective

Widespread health information exchange (HIE) is a national objective motivated by the promise of improved care and a reduction in costs. Previous reviews have found little rigorous evidence that HIE positively affects these anticipated benefits. However, early studies of HIE were methodologically limited. The purpose of the current study is to review the recent literature on the impact of HIE.

Methods

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct our systematic review. PubMed and Scopus databases were used to identify empirical articles that evaluated HIE in the context of a health care outcome.

Results

Our search strategy identified 24 articles that included 63 individual analyses. The majority of the studies were from the United States representing 9 states; and about 40% of the included analyses occurred in a handful of HIEs from the state of New York. Seven of the 24 studies used designs suitable for causal inference and all reported some beneficial effect from HIE; none reported adverse effects.

Conclusions

The current systematic review found that studies with more rigorous designs all reported benefits from HIE. Such benefits include fewer duplicated procedures, reduced imaging, lower costs, and improved patient safety. We also found that studies evaluating community HIEs were more likely to find benefits than studies that evaluated enterprise HIEs or vendor-mediated exchanges. Overall, these finding bode well for the HIEs ability to deliver on anticipated improvements in care delivery and reduction in costs.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.