Innovative uses of electronic health records and social media for public health surveillance

Public Health Surveillance in Electronic Health Records: Lessons From PCORnet

Introduction

PCORnet, the National Patient-Centered Clinical Research Network, is a large research network of health systems that map clinical data to a standardized data model. In 2018, we expanded existing infrastructure to facilitate use for public health surveillance. We describe benefits and challenges of using PCORnet for surveillance and describe case studies.

Methods

In 2018, infrastructure enhancements included addition of a table to store patients' residential zip codes and expansion of a modular program to generate population health statistics across conditions. Chronic disease surveillance case studies conducted in 2019 assessed atrial fibrillation (AF) and cirrhosis. In April 2020, PCORnet established an infrastructure to support COVID-19 surveillance with institutions frequently updating their electronic health record data.

Results

By August 2023, 53 PCORnet sites (84%) had a 5-digit zip code available on at least 95% of their patient populations. Among 148,223 newly diagnosed AF patients eligible for oral anticoagulant (OAC) therapy, 43.3% were on any OAC (17.8% warfarin, 28.5% any novel oral anticoagulant) within a year of the AF diagnosis. Among 60,268 patients with cirrhosis (2015-2019), common documented etiologies included unknown (48%), hepatitis C infection (23%), and alcohol use (22%). During October 2022 through December 2023, across 34 institutions, the proportion of COVID-19 patients who were cared for in the inpatient setting was 9.1% among 887,051 adults aged 20 years or older and 6.0% among 139,148 children younger than 20 years.

Conclusions

PCORnet provides important data that may augment traditional public health surveillance programs across diverse conditions. PCORnet affords longitudinal population health assessments among large catchments of the population with clinical, treatment, and geographic information, with capabilities to deliver rapid information needed during public health emergencies.

Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Background

Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions.

Objective

We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data.

Methods

Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data.

Results

Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001). In regression analyses adjusted for age, sex, study site, and Physician Global Assessment score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18).

Conclusions

High percentages of youth with rheumatic diseases used and were willing to share their social media data for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media-derived findings.

Factors Influencing the Effectiveness of Adopting Electronic Medical Record-Based Reporting Systems for Notifiable Disease Surveillance: A Quantitative Analysis

The coronavirus disease 2019 (COVID-19) pandemic has led to greater attention being given to infectious disease surveillance systems and their notification functionalities. Although numerous studies have explored the benefits of integrating functionalities with electronic medical record (EMR) systems, empirical studies on the topic are rare. The current study assessed which factors influence the effectiveness of EMR-based reporting systems (EMR-RSs) for notifiable disease surveillance. This study interviewed staff from hospitals with a coverage that represented 51.39% of the notifiable disease reporting volume in Taiwan. Exact logistic regression was employed to determine which factors influenced the effectiveness of Taiwan's EMR-RS. The results revealed that the influential factors included hospitals' early participation in the EMR-RS project, frequent consultation with the information technology (IT) provider of the Taiwan Centers for Disease Control (TWCDC), and retrieval of data from at least one internal database. They also revealed that using an EMR-RS resulted in more timely, accurate, and convenient reporting in hospitals. In addition, developing by an internal IT unit instead of outsourcing EMR-RS development led to more accurate and convenient reporting. Automatically loading the required data enhanced the convenience, and designing input fields that may be unavailable in current databases to enable physicians to add data to legacy databases also boosted effectiveness of the reporting system.

#FOODPORN KAVRAMI VE SOSYAL MEDYA ILE İLIŞKISI

Yemeğin, beslenme ve açlığı giderme amacı dışında imaj ve izlenim oluşturma, cinsiyet rollerini sergileme, yeni kimlikler yaratma gibi amaçları da bulunmaktadır. Paylaşım ekonomisinin aracıları olan sosyal medya platformlarında yiyecek-içecek görüntülerinin artması yemeğin anlamı dışına taşan bir forma dönüşmesine neden olmaktadır. Sosyal medyanın gıda ile olan yakın ilişkisi de bu yeni anlamlara hizmet etmekte ve gıdanın sosyal medyadaki yeni formunu güçlendirmektedir. Yiyeceklerin tedariği, üretimi, hazırlanması, sunumu ve tüketimine dair görüntülerin parlak, sanatsal ve kültürel özelliklerde sunulması, yemek yapmanın veya tüketmenin göz alıcı ve kusursuz bir görsel şölene dönüşmesi, kavramı pornografik bir seviyeye taşımıştır. Henüz Türkçe yazında ele alınmamış olan #foodporn kavramını açıklamak, ne olduğunu ortaya koymak ve beraberinde hangi akımların gıda ve sosyal medya arasındaki ilişkide rol oynadığını göstermek bu çalışmanın temel amaçlarındandır. #Foodporn kavramını açıklayan ilk Türkçe kaynak olma özelliğini taşıması ve gelecekte bu konuda yapılacak araştırmalara rehberlik etmesi bu çalışmayı önemli kılmaktadır.

Diabetic patients’ behavior observation on social media using active surveillance

Diabetes is a complex disease that can only be controlled and prevented by a healthy lifestyle. We have selected the investigation of diabetes for this research as a substantially large fragment of our society is suffering from diabetes. It has been observed that diabetic patients are more expressive on social media as compared to real-life interactions. Furthermore, online communities are playing a significant role in providing social support and knowledge to patients through their experiences. Diabetes has only been monitored through wearable (sensor-based) and glucose meters. However, the problem arises when the patients become reluctant about giving the required information themselves. For this purpose, a taxonomic system based on business process models has been developed which uses the textual data from the patients in which they express their emotions regarding Diabetes. Social media support groups related to Diabetes are used to gather data. Diabetic patients tend to share their emotions and feelings with people who are face a similar situation. However, there is no established measure to calculate the behavioral impact of diabetes on diabetic patients. In our research, we have studied how diabetic patients collaborate with each other to help others through social media and the impact of social communities on diabetic patient’s lifestyles. The results show the extent to which diabetic people follow a healthy lifestyle.

Using inpatient electronic medical records to study influenza for pandemic preparedness

BACKGROUND

We assessed the ability to identify key data relevant to influenza and other respiratory virus surveillance in a large-scale US-based hospital electronic medical record (EMR) dataset using seasonal influenza as a use case. We describe characteristics and outcomes of hospitalized influenza cases across three seasons.

METHODS

We identified patients with an influenza diagnosis between March 2017 and March 2020 in 140 US hospitals as part of the US FDA's Sentinel System. We calculated descriptive statistics on the presence of high-risk conditions, influenza antiviral administrations, and severity endpoints.

RESULTS

Among 5.1 million hospitalizations, we identified 29,520 hospitalizations with an influenza diagnosis; 64% were treated with an influenza antiviral within 2 days of admission, and 25% were treated >2 days after admission. Patients treated >2 days after admission had more comorbidities than patients treated within 2 days of admission. Patients never treated during hospitalization had more documentation of cardiovascular and other diseases than treated patients. We observed more severe endpoints in patients never treated (death = 3%, mechanical ventilation [MV] = 9%, intensive care unit [ICU] = 26%) or patients treated >2 days after admission (death = 2%, MV = 14%, ICU = 32%) than in patients treated earlier (treated on admission: death = 1%, MV = 5%, ICU = 23%, treated within 2 days of admission: death = 1%, MV = 7%, ICU = 27%).

CONCLUSIONS

We identified important trends in influenza severity related to treatment timing in a large inpatient dataset, laying the groundwork for the use of this and other inpatient EMR data for influenza and other respiratory virus surveillance.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

Identifying non-traditional electronic datasets for population-level surveillance and prevention of cardiometabolic diseases: a scoping review protocol

INTRODUCTION

Cardiometabolic diseases, including cardiovascular disease, obesity and diabetes, are leading causes of death and disability worldwide. Modern advances in population-level disease surveillance are necessary and may inform novel opportunities for precision public health approaches to disease prevention. Electronic data sources, such as social media and consumer rewards points systems, have expanded dramatically in recent decades. These non-traditional datasets may enhance traditional clinical and public health datasets and inform cardiometabolic disease surveillance and population health interventions. However, the scope of non-traditional electronic datasets and their use for cardiometabolic disease surveillance and population health interventions has not been previously reviewed. The primary objective of this review is to describe the scope of non-traditional electronic datasets, and how they are being used for cardiometabolic disease surveillance and to inform interventions. The secondary objective is to describe the methods, such as machine learning and natural language processing, that have been applied to leverage these datasets.

METHODS AND ANALYSIS

We will conduct a scoping review following recommended methodology. Search terms will be based on the three central concepts of non-traditional electronic datasets, cardiometabolic diseases and population health. We will search EMBASE, MEDLINE, CINAHL, Scopus, Web of Science and Cochrane Library peer-reviewed databases and will also conduct a grey literature search. Articles published from 2000 to present will be independently screened by two reviewers for inclusion at abstract and full-text stages, and conflicts will be resolved by a separate reviewer. We will report this data as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

ETHICS AND DISSEMINATION

No ethics approval is required for this protocol and scoping review, as data will be used only from published studies with appropriate ethics approval. Results will be disseminated in a peer-reviewed publication.

Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics

OBJECTIVE

There was a significant delay in compiling a complete list of the symptoms of COVID-19 during the 2020 outbreak of the disease. When there is little information about the symptoms of a novel disease, interventions to contain the spread of the disease would be suboptimal because people experiencing symptoms that are not yet known to be related to the disease may not limit their social activities. Our goal was to understand whether users' social media postings about the symptoms of novel diseases could be used to develop a complete list of the disease symptoms in a shorter time.

MATERIALS AND METHODS

We used the Twitter API to download tweets that contained 'coronavirus', 'COVID-19', and 'symptom'. After data cleaning, the resulting dataset consisted of over 95,000 unique, English tweets posted between January 17, 2020 and March 15, 2020 that contained references to the symptoms of COVID-19. We analyzed this data using network and time series methods.

RESULTS

We found that a complete list of the symptoms of COVID-19 could have been compiled by mid-March 2020, before most states in the U.S. announced a lockdown and about 75 days earlier than the list was completed on CDC's website.

DISCUSSION & CONCLUSION

We conclude that national and international health agencies should use the crowd-sourced intelligence obtained from social media to develop effective symptom surveillance systems in the early stages of pandemics. We propose a high-level framework that facilitates the collection, analysis, and dissemination of information that are posted in various languages and on different social media platforms about the symptoms of novel diseases.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

The anatomy of electronic patient record ethics: a framework to guide design, development, implementation, and use

BACKGROUND

This manuscript presents a framework to guide the identification and assessment of ethical opportunities and challenges associated with electronic patient records (EPR). The framework is intended to support designers, software engineers, health service managers, and end-users to realise a responsible, robust and reliable EPR-enabled healthcare system that delivers safe, quality assured, value conscious care.

METHODS

Development of the EPR applied ethics framework was preceded by a scoping review which mapped the literature related to the ethics of EPR technology. The underlying assumption behind the framework presented in this manuscript is that ethical values can inform all stages of the EPR-lifecycle from design, through development, implementation, and practical application.

RESULTS

The framework is divided into two parts: context and core functions. The first part 'context' entails clarifying: the purpose(s) within which the EPR exists or will exist; the interested parties and their relationships; and the regulatory, codes of professional conduct and organisational policy frame of reference. Understanding the context is required before addressing the second part of the framework which focuses on EPR 'core functions' of data collection, data access, and digitally-enabled healthcare.

CONCLUSIONS

The primary objective of the EPR Applied Ethics Framework is to help identify and create value and benefits rather than to merely prevent risks. It should therefore be used to steer an EPR project to success rather than be seen as a set of inhibitory rules. The framework is adaptable to a wide range of EPR categories and can cater for new and evolving EPR-enabled healthcare priorities. It is therefore an iterative tool that should be revisited as new EPR-related state-of-affairs, capabilities or activities emerge.

Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy

The digitalization of health and medicine and the growing availability of electronic health records (EHRs) has encouraged healthcare professionals and clinical researchers to adopt cutting-edge methodologies in the realms of artificial intelligence (AI) and big data analytics to exploit existing large medical databases. In Hospital and Health System pharmacies, the application of natural language processing (NLP) and machine learning to access and analyze the unstructured, free-text information captured in millions of EHRs (e.g., medication safety, patients’ medication history, adverse drug reactions, interactions, medication errors, therapeutic outcomes, and pharmacokinetic consultations) may become an essential tool to improve patient care and perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs. This approach has an enormous potential to support share-risk agreements and guide decision-making in pharmacy and therapeutics (P&T) Committees.

Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review

Background

Electronic patient record (EPR) technology is a key enabler for improvements to healthcare service and management. To ensure these improvements and the means to achieve them are socially and ethically desirable, careful consideration of the ethical implications of EPRs is indicated. The purpose of this scoping review was to map the literature related to the ethics of EPR technology. The literature review was conducted to catalogue the prevalent ethical terms, to describe the associated ethical challenges and opportunities, and to identify the actors involved. By doing so, it aimed to support the future development of ethics guidance in the EPR domain.

Methods

To identify journal articles debating the ethics of EPRs, Scopus, Web of Science, and PubMed academic databases were queried and yielded 123 eligible articles. The following inclusion criteria were applied: articles need to be in the English language; present normative arguments and not solely empirical research; include an abstract for software analysis; and discuss EPR technology.

Results

The medical specialty, type of information captured and stored in EPRs, their use and functionality varied widely across the included articles. Ethical terms extracted were categorised into clusters ‘privacy’, ‘autonomy’, ‘risk/benefit’, ‘human relationships’, and ‘responsibility’. The literature shows that EPR-related ethical concerns can have both positive and negative implications, and that a wide variety of actors with rights and/or responsibilities regarding the safe and ethical adoption of the technology are involved.

Conclusions

While there is considerable consensus in the literature regarding EPR-related ethical principles, some of the associated challenges and opportunities remain underdiscussed. For example, much of the debate is presented in a manner more in keeping with a traditional model of healthcare and fails to take account of the multidimensional ensemble of factors at play in the EPR era and the consequent need to redefine/modify ethical norms to align with a digitally-enabled health service. Similarly, the academic discussion focuses predominantly on bioethical values. However, approaches from digital ethics may also be helpful to identify and deliberate about current and emerging EPR-related ethical concerns.

Social media based surveillance systems for healthcare using machine learning: A systematic review

BACKGROUND

Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain.

METHODS

To study the landscape of research in health informatics performing surveillance of the various health-related data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms.

FINDINGS

Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification.

CONCLUSIONS

The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper.

Evaluating Mobile Apps and Biosensing Devices to Monitor Physical Activity and Respiratory Function in Smokers With and Without Respiratory Symptoms or Chronic Obstructive Pulmonary Disease: Protocol for a Proof-of-Concept, Open-Label, Feasibility Study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a global public health problem, and continuous monitoring is essential for both its management as well as the management of other chronic diseases. Telemonitoring using mobile health (mHealth) devices has the potential to promote self-management, improve control, increase quality of life, and prevent hospital admissions.

OBJECTIVE

This study aims to demonstrate whether a large-scale study assessing the use of mHealth devices to improve the treatment, assessment, compliance, and outcomes of chronic diseases, particularly COPD and cardio-metabolic syndrome, is feasible. This will allow our team to select the appropriate design and characteristics for our large-scale study.

METHODS

A total of 3 cohorts, with 9 participants in each, will use mHealth devices for 90 days while undergoing the current standard of care. These groups are: 9 "non-COPD," otherwise healthy, smokers; 9 "grey zone" smokers (forced expiratory volume in 1 second/ forced vital capacity ≥0.70 after bronchodilator treatment; COPD Assessment Test ≥10); and 9 smokers diagnosed with Stage 1-3 COPD. Rates of recruitment, retention, and adherence will be measured. Overall, two mHealth devices will be utilized in the study: the AnaMed Original Equipment Manufacturer device (measures distance, energy expenditure, heart rate, and heart rate variability) and the Air Next mobile spirometry device. The mHealth devices will be compared against industry standards. Additionally, a questionnaire will be administered to assess the participants' perceptions of the mHealth technologies used.

RESULTS

The inclusion of participants started in June 2019. Study results will be published in peer-reviewed scientific journals.

CONCLUSIONS

This study will demonstrate whether a large-scale study to assess the use of mHealth devices to improve the treatment, assessment, compliance, and outcomes of chronic diseases, particularly COPD and cardio-metabolic syndrome, is feasible. It will also allow the research team to select the appropriate design and characteristics for the large-scale study.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04081961; https://clinicaltrials.gov/ct2/show/NCT04081961.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16461.

Utilizing Standard Data Transactions and Public-Private Partnerships to Support Healthy Weight Within the Community

CONTEXT

Obesity is a significant health issue in the United States that both clinical and public health systems struggle to address. Electronic health record data could help support multi-sectoral interventions to address obesity. Standards have been identified and created to support the electronic exchange of weight-related data across many stakeholder groups.

CASE DESCRIPTION

The Centers for Disease Control and Prevention initiated a public-private partnership including government, industry, and academic technology partners to develop workflow scenarios and supporting systems to exchange weight-related data through standard transactions. This partnership tested the transmission of data using this newly-defined Healthy Weight (HW) profile at multiple health data interoperability demonstration events.

FINDINGS

Five transaction types were tested by 12 partners who demonstrated how the standards and related systems support end-to-end workflows around managing weight-related issues in the community. The standard transactions were successfully tested at two Integrating the Healthcare Enterprise (IHE) Connectathon events through 86 validated tests encompassing 38 multi-partner transactions.

DISCUSSION

We have successfully demonstrated the transactions defined in the HW profile with a public-private partnership. These tested IT products and HW standards could be used to support a continuum of care around health related issues encompassing both health care and public health functions.

CONCLUSION

The use of the HW profile, including a set of transactions and identified standards to implement those transactions, in IT products is a helpful first step in leveraging health information technology to address weight-related issues in the United States. Future work is needed to expand the use of these standards and to assess their use in real world settings.

Tweet Can Be Fit

Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness--related attributes, such as body mass index (BMI) category or disease tendency, as well as understanding of global dependencies between wellness attributes and users’ behavior, is of crucial importance to various applications in personal and public wellness domains. At the same time, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse. This study represents one of the first attempts in this direction. Specifically, we infer personal wellness attributes by utilizing our proposed multisource multitask wellness profile learning framework—WellMTL—which can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. To gain insights into the data at a global level, we also examine correlations between first-order data representations and personal wellness attributes. Our experimental results show that the integration of sensor data and multiple social media sources can substantially boost the performance of individual wellness profiling.

Academic and Government Partnerships to Address Diabetes in the USA: a Narrative Review

PURPOSE OF REVIEW

Multi-sector partnerships are broadly considered to be of value for diabetes prevention and management. The purpose of this article is to summarize academic and government collaborations focused on diabetes prevention and management.

RECENT FINDINGS

Using a narrative review approach, we identified 17 articles describing 10 academic and government partnerships for diabetes management and surveillance. Challenges and gaps in the literature include complexity of diabetes management vis a vis current healthcare infrastructure; a paucity of racial/ethnic diversity in translational efforts; and the time/effort needed to maintain strong relationships across partner institutions. Academic and government partnerships are of value for diabetes prevention and management activities. Acknowledgment that the key priorities of government programming are often costs and feasibility is critical for collaborations to be successful. Future translational efforts of diabetes prevention and management programs should focus on the following: (1) expansion of partnerships between academia and local health departments; (2) increased utilization of implementation science for enhanced and efficient implementation and dissemination; and (3) harnessing of technological advances for data analysis, patient communication, and report generation.

State and Local Chronic Disease Surveillance Using Electronic Health Record Systems

OBJECTIVES

To assess the feasibility of chronic disease surveillance using distributed analysis of electronic health records and to compare results with Behavioral Risk Factor Surveillance System (BRFSS) state and small-area estimates.

METHODS

We queried the electronic health records of 3 independent Massachusetts-based practice groups using a distributed analysis tool called MDPHnet to measure the prevalence of diabetes, asthma, smoking, hypertension, and obesity in adults for the state and 13 cities. We adjusted observed rates for age, gender, and race/ethnicity relative to census data and compared them with BRFSS state and small-area estimates.

RESULTS

The MDPHnet population under surveillance included 1 073 545 adults (21.8% of the state adult population). MDPHnet and BRFSS state-level estimates were similar: 9.4% versus 9.7% for diabetes, 10.0% versus 12.0% for asthma, 13.5% versus 14.7% for smoking, 26.3% versus 29.6% for hypertension, and 22.8% versus 23.8% for obesity. Correlation coefficients for MDPHnet versus BRFSS small-area estimates ranged from 0.890 for diabetes to 0.646 for obesity.

CONCLUSIONS

Chronic disease surveillance using electronic health record data is feasible and generates estimates comparable with BRFSS state and small-area estimates.

Innovations in health information technologies for chronic pulmonary diseases

Asthma and chronic obstructive pulmonary disease (COPD) are common chronic obstructive lung disorders in the US that affect over 49 million people. There is no cure for asthma or COPD, but clinical guidelines exist for controlling symptoms that are successful in most patients that adhere to their treatment plan. Health information technologies (HITs) are revolutionizing healthcare by becoming mainstream tools to assist patients in self-monitoring and decision-making, and subsequently, driving a shift toward a care model increasingly centered on personal adoption and use of digital and web-based tools. While the number of chronic pulmonary disease HITs is rapidly increasing, most have not been validated as clinically effective tools for the management of disease. Online communities for asthma and COPD patients are becoming sources of empowerment and support, as well as facilitators of patient-centered research efforts. In addition to empowering patients and facilitating disease self-management, HITs offer promise to aid researchers in identifying chronic pulmonary disease endotypes and personalized treatments based on patient-specific profiles that integrate symptom occurrence and medication usage with environmental and genomic data.

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics

Background

The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact.

Methods

In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks.

Results

We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences.

Conclusions

These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

Electronic health records and community health surveillance of childhood obesity

BACKGROUND

Childhood obesity remains a public health concern, and tracking local progress may require local surveillance systems. Electronic health record data may provide a cost-effective solution.

PURPOSE

To demonstrate the feasibility of estimating childhood obesity rates using de-identified electronic health records for the purpose of public health surveillance and health promotion.

METHODS

Data were extracted from the Public Health Information Exchange (PHINEX) database. PHINEX contains de-identified electronic health records from patients primarily in south central Wisconsin. Data on children and adolescents (aged 2-19 years, 2011-2012, n=93,130) were transformed in a two-step procedure that adjusted for missing data and weighted for a national population distribution. Weighted and adjusted obesity rates were compared to the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in 2014.

RESULTS

The weighted and adjusted obesity rate was 16.1% (95% CI=15.8, 16.4). Non-Hispanic white children and adolescents (11.8%, 95% CI=11.5, 12.1) had lower obesity rates compared to non-Hispanic black (22.0%, 95% CI=20.7, 23.2) and Hispanic (23.8%, 95% CI=22.4, 25.1) patients. Overall, electronic health record-derived point estimates were comparable to NHANES, revealing disparities from preschool onward.

CONCLUSIONS

Electronic health records that are weighted and adjusted to account for intrinsic bias may create an opportunity for comparing regional disparities with precision. In PHINEX patients, childhood obesity disparities were measurable from a young age, highlighting the need for early intervention for at-risk children. The electronic health record is a cost-effective, promising tool for local obesity prevention efforts.

Clinical research informatics and electronic health record data

OBJECTIVES

The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics.

RESULTS

Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data.

CONCLUSIONS

The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.