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.

A Practitioner-Driven Research Agenda for Syndromic Surveillance.

Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.

A practical approach for incorporating dependence among fields in probabilistic record linkage.

BACKGROUND: Methods for linking real-world healthcare data often use a latent class model, where the latent, or unknown, class is the true match status of candidate record-pairs. This commonly used model assumes that agreement patterns among multiple fields within a latent class are independent. When this assumption is violated, various approaches, including the most commonly proposed loglinear models, have been suggested to account for conditional dependence. METHODS: We present a step-by-step guide to identify important dependencies between fields through a correlation residual plot and demonstrate how they can be incorporated into loglinear models for record linkage. This method is applied to healthcare data from the patient registry for a large county health department. RESULTS: Our method could be readily implemented using standard software (with code supplied) to produce an overall better model fit as measured by BIC and deviance. Finding the most parsimonious model is known to reduce bias in parameter estimates. CONCLUSIONS: This novel approach identifies and accommodates conditional dependence in the context of record linkage. The conditional dependence model is recommended for routine use due to its flexibility for incorporating conditional dependence and easy implementation using existing software.

Towards public health decision support: a systematic review of bidirectional communication approaches.

OBJECTIVE: To summarize the literature describing computer-based interventions aimed at improving bidirectional communication between clinical and public health. MATERIALS AND METHODS: A systematic review of English articles using MEDLINE and Google Scholar. Search terms included public health, epidemiology, electronic health records, decision support, expert systems, and decision-making. Only articles that described the communication of information regarding emerging health threats from public health agencies to clinicians or provider organizations were included. Each article was independently reviewed by two authors. RESULTS: Ten peer-reviewed articles highlight a nascent but promising area of research and practice related to alerting clinicians about emerging threats. Current literature suggests that additional research and development in bidirectional communication infrastructure should focus on defining a coherent architecture, improving interoperability, establishing clear governance, and creating usable systems that will effectively deliver targeted, specific information to clinicians in support of patient and population decision-making. CONCLUSIONS: Increasingly available clinical information systems make it possible to deliver timely, relevant knowledge to frontline clinicians in support of population health. Future work should focus on developing a flexible, interoperable infrastructure for bidirectional communications capable of integrating public health knowledge into clinical systems and workflows.

Impact of selective mapping strategies on automated laboratory result notification to public health authorities.

Automated electronic laboratory reporting (ELR) for public health has many potential advantages, but requires mapping local laboratory test codes to a standard vocabulary such as LOINC. Mapping only the most frequently reported tests provides one way to prioritize the effort and mitigate the resource burden. We evaluated the implications of selective mapping on ELR for public health by comparing reportable conditions from an operational ELR system with the codes in the LOINC Top 2000. Laboratory result codes in the LOINC Top 2000 accounted for 65.3% of the reportable condition volume. However, by also including the 129 most frequent LOINC codes that identified reportable conditions in our system but were not present in the LOINC Top 2000, this set would cover 98% of the reportable condition volume. Our study highlights the ways that our approach to implementing vocabulary standards impacts secondary data uses such as public health reporting.

An evaluation of the rates of repeat notifiable disease reporting and patient crossover using a health information exchange-based automated electronic laboratory reporting system.

Patients move across healthcare organizations and utilize services with great frequency and variety. This fact impacts both health information technology policy and patient care. To understand the challenges faced when developing strategies for effective health information exchange, it is important to understand patterns of patient movement and utilization for many healthcare contexts, including managing public-health notifiable conditions. We studied over 10 years of public-health notifiable diseases using the nation's most comprehensive operational automatic electronic laboratory reporting system to characterize patient utilization patterns. Our cohort included 412,699 patients and 833,710 reportable cases. 11.3% of patients had multiple notifiable case reports, and 19.5% had notifiable disease data distributed across 2 or more institutions. This evidence adds to the growing body of evidence that patient data resides in many organizations and suggests that to fully realize the value of HIT in public health, cross-organizational data sharing must be meaningfully incentivized.

Development and Assessment of a Public Health Alert Delivered through a Community Health Information Exchange.

Timely communication of information to health care providers during a public health event can improve overall response to such events. However, current methods for sending information to providers are inefficient and costly. Local health departments have traditionally used labor-intensive, mail-based processes to send public health alerts to the provider community. This article describes a novel approach for delivering public health alerts to providers by leveraging an electronic clinical messaging system within the context of a health information exchange. Alerts included notifications related to the 2009 H1N1 flu epidemic, a syphilis outbreak, and local rabies exposure. We describe the process for sending electronic public health alerts and the estimated impact on efficiency and cost effectiveness.

Reliability of Indiana birth certificate data compared to medical records.

PURPOSE: The purpose of this study was to measure the reliability of data reported on Indiana electronic birth certificates. Knowing the accuracy of birth certificate data is crucial when identifying community health needs and evaluating birth outcomes interventions. METHODS: This study compared 1996 electronic birth certificate data on a random sample of 1050 Indiana hospital births to data abstracted from the hospital medical records for the same patients. Kappa scores, Pearson r correlation values, sensitivity, specificity, and positive predictive values of the birth certificate data were used to measure agreement. RESULTS: Parents' demographic variables had the best agreement, followed by birth outcome variables. Delivery type, cesarean indications, pregnancy history, prenatal care and mother's risk variables were found to have moderate agreement. Agreement was poor for variables measuring labor and delivery complications, obstetric procedures, concurrent illnesses, pregnancy complications, congenital anomalies, and abnormal conditions. CONCLUSIONS: The results of this study clearly show that some important descriptive and outcome data are reliable while infrequent events are generally not. The results indicate a need to improve the quality of data reported on birth certificates.