Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group

Scoping review: Machine learning interventions in the management of healthcare systems

Background

Healthcare institutions focus on improving the quality of life for end-users, with key performance indicators like access to essential medicines reflecting the effectiveness of management. Effective healthcare management involves planning, organizing, and controlling institutions built on human resources, data systems, service delivery, access to medicines, finance, and leadership. According to the World Health Organization, these elements must be balanced for an optimal healthcare system. Big data generated from healthcare institutions, including health records and genomic data, is crucial for smart staffing, decision-making, risk management, and patient engagement. Properly organizing and analysing this data is essential, and machine learning, a sub-field of artificial intelligence, can optimize these processes, leading to better overall healthcare management.

Objectives

This review examines the major applications of machine learning in healthcare management, the algorithms frequently used in data analysis, their limitations, and the evidence-based benefits of machine learning in healthcare.

Methods

Following PRISMA guidelines, databases such as IEEE Xplore, ScienceDirect, ACM Digital Library, and SCOPUS were searched for eligible articles published between 2011 and 2021. Articles had to be in English, peer-reviewed, and include relevant keywords like healthcare, management, and machine learning.

Results

Out of 51 relevant articles, 6 met the inclusion criteria. Identified algorithms include topic modelling, dynamic clustering, neural networks, decision trees, and ensemble classifiers, applied in areas such as electronic health records, chatbots, and multi-disease prediction.

Conclusion

Machine learning supports healthcare management by aiding decision-making, processing big data, and providing insights for system improvements.

Detection of atypical response trajectories in biomedical longitudinal databases

Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.

Exploring the Evolution of Social Media in Mental Health Interventions: A Mapping Review

BACKGROUND

With the rise of social media, social media use for delivering mental health interventions has become increasingly popular. However, there is no comprehensive overview available on how this field developed over time.

OBJECTIVES

The objective of this paper is to provide an overview over time of the use of social media for delivering mental health interventions. Specifically, we examine which mental health conditions and target groups have been targeted, and which social media channels or tools have been used since this topic first appeared in research.

METHODS

To provide an overview of the use of social media for mental health interventions, we conducted a search for studies in four databases (PubMed; ACM Digital Library; PsycInfo; and CINAHL) and two trial registries (Clinicaltrials.gov; and Cochranelibrary.com). A sample of representative keywords related to mental health and social media was used for that search. Automatic text analysis methods (e.g., BERTopic analysis, word clouds) were applied to identify topics, and to extract target groups and types of social media.

RESULTS

A total of 458 studies were included in this review (n=228 articles, and n=230 registries). Anxiety and depression were the most frequently mentioned conditions in titles of both articles and registries. BERTopic analysis identified depression and anxiety as the main topics, as well as several addictions (including gambling, alcohol, and smoking). Mental health and women's research were highlighted as the main targeted topics of these studies. The most frequently targeted groups were "adults" (39.5%) and "parents" (33.4%). Facebook, WhatsApp, messenger platforms in general, Instagram, and forums were the most frequently mentioned tools in these interventions.

CONCLUSIONS

We learned that research interest in social media-based interventions in mental health is increasing, particularly in the last two years. A variety of tools have been studied, and trends towards forums and Facebook show that tools allowing for more content are preferred for mental health interventions. Future research should assess which social media tools are best suited in terms of clinical outcomes. Additionally, we conclude that natural language processing tools can help in studying trends in research on a particular topic.

Big health data for elderly employees job performance of SOEs: visionary and enticing challenges

The method is providing and overview of the organization in the management perspective, within the health big data analysis, especially for the elderly employees, the organizations could sign the elderly employees within the right tasks, it reducing the costs by increasing the employees' job performance and organization performance. By addressing the importance role of big health data analytics (BDHA) in the healthcare system .moreover BDHA enables a patient's medical records to be searched in a dynamic, interactive manner. One billion records were made in two hours. Current clinical reporting compares large health data profiles and meta-big health data, giving health apps basic interfaces. A combination of Hadoop/MapReduce and HBase was used to generate the necessary hospital-specific large heath data. One billion (10TB) and three billion (30TB) HBase large health data files might be created in a week or a month using the concept. Apache Hadoop technologies tested simulated medical records. Inconsistencies reduced big health data. An encounter-centered big health database was difficult to set up due to the complicated medical system connections between big health data profiles. Associated with job performance such as the gender, current/past job positions and the health conditions are important. For genders the 66.36% of respondents in the experiments are females, 33.64 are males, majority of are healthy which are 66.97%, 30.58% are common geriatric disease, rest 2.45% are suffering from occupational disease; In terms of the current/past job positions, 20% of the respondents are working as accountant, followed by sales and management level. The Diagnostic and Statistical Manual, lists 157 distinct illnesses. Individuals may be diagnosed with one or more illnesses as a consequence of medical health professionals watching and analyzing their symptoms. It has been discovered that mental health issues have a negative impact on employees' job performance. For example, research on individuals with anxiety and depression has a direct impact on concentrations, decision-making process, and risk-taking behavior, which can be determined for job performance. Machine learning focuses on approaches that can be used to create accurate predictions about future characteristics based on previous training and post training. Principles such as job task and computational learning are crucial for machine learning algorithms that use a large amount of big health data.

The impact of commercial health datasets on medical research and health-care algorithms

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology

BACKGROUND

Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries.

OBJECTIVE

To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data.

METHODS

Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots.

RESULTS

A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome.

CONCLUSIONS

Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study

Background

Given the costs of machine learning implementation, a systematic approach to prioritizing which models to implement into clinical practice may be valuable.

Objective

The primary objective was to determine the health care attributes respondents at 2 pediatric institutions rate as important when prioritizing machine learning model implementation. The secondary objective was to describe their perspectives on implementation using a qualitative approach.

Methods

In this mixed methods study, we distributed a survey to health system leaders, physicians, and data scientists at 2 pediatric institutions. We asked respondents to rank the following 5 attributes in terms of implementation usefulness: the clinical problem was common, the clinical problem caused substantial morbidity and mortality, risk stratification led to different actions that could reasonably improve patient outcomes, reducing physician workload, and saving money. Important attributes were those ranked as first or second most important. Individual qualitative interviews were conducted with a subsample of respondents.

Results

Among 613 eligible respondents, 275 (44.9%) responded. Qualitative interviews were conducted with 17 respondents. The most common important attributes were risk stratification leading to different actions (205/275, 74.5%) and clinical problem causing substantial morbidity or mortality (177/275, 64.4%). The attributes considered least important were reducing physician workload and saving money. Qualitative interviews consistently prioritized implementations that improved patient outcomes.

Conclusions

Respondents prioritized machine learning model implementation where risk stratification would lead to different actions and clinical problems that caused substantial morbidity and mortality. Implementations that improved patient outcomes were prioritized. These results can help provide a framework for machine learning model implementation.

Towards the Use of Big Data in Healthcare: A Literature Review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

From Syndemic Lesson after COVID-19 Pandemic to a "Systemic Clinical Risk Management" Proposal in the Perspective of the Ethics of Job Well Done

The syndemic framework proposed by the 2021-2030 World Health Organization (WHO) action plan for patient safety and the introduction of enabling technologies in health services involve a more effective interpretation of the data to understand causation. Based on the Systemic Theory, this communication proposes the "Systemic Clinical Risk Management" (SCRM) to improve the Quality of Care and Patient Safety. This is a new Clinical Risk Management model capable of developing the ability to observe and synthesize different elements in ways that lead to in-depth interventions to achieve solutions aligned with the sustainable development of health services. In order to avoid uncontrolled decision-making related to the use of enabling technologies, we devised an internal Learning Algorithm Risk Management (LARM) level based on a Bayesian approach. Moreover, according to the ethics of Job Well Done, the SCRM, instead of giving an opinion on events that have already occurred, proposes a bioethical co-working because it suggests the best way to act from a scientific point of view.

Nationwide population-based household surveys in health: a narrative review

Household surveys are one of the primary methodologies used in population-based studies. This narrative review of the literature aims to gather and describe the leading national and international household surveys of relevance. In Brazil, the historical role played by the Brazilian Institute of Geography and Statistics (IBGE) in conducting the most relevant research in the production of social data stands out. The Medical-Health Care Survey (AMS) and the National Household Sample Survey (PNAD), with the serial publication of Health Supplements, are the country's primary sources of health information. In 2013, in partnership with the Ministry of Health, IBGE launched the National Health Survey (PNS), the most significant household health survey ever conducted in Brazil. The PNS-2019 received a major thematic and sampling expansion and, for the first time, applied the Primary Care Assessment Tool to assess PHC services in all 27 Brazilian states.

Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments

Background

Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence of neutropenia to identify BSI.

Methods

We included patients 0–18 years of age at cancer diagnosis or HSCT between January 2009 and November 2018. Eligible blood cultures were those with no previous blood culture (regardless of result) within 7 days. The primary outcome was BSI. Four machine learning algorithms were used: elastic net, support vector machine and two implementations of gradient boosting machine (GBM and XGBoost). Model training and evaluation were performed using temporally disjoint training (60%), validation (20%) and test (20%) sets. The best model was compared to neutropenia alone in the test set.

Results

Of 11,183 eligible blood cultures, 624 (5.6%) were positive. The best model in the validation set was GBM, which achieved an area-under-the-receiver-operator-curve (AUROC) of 0.74 in the test set. Among the 2236 in the test set, the number of false positives and specificity of GBM vs. neutropenia were 508 vs. 592 and 0.76 vs. 0.72 respectively. Among 139 test set BSIs, six (4.3%) non-neutropenic patients were identified by GBM. All received antibiotics prior to culture result availability.

Conclusions

We developed a machine learning algorithm to classify BSI. GBM achieved an AUROC of 0.74 and identified 4.3% additional true cases in the test set. The machine learning algorithm did not perform substantially better than using presence of neutropenia alone to predict BSI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07618-2.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Technology Tools: Increasing Our Reach in National Surveillance of Intellectual and Developmental Disabilities

Challenges in collecting comprehensive health surveillance data on people with intellectual and developmental disabilities (IDD) are numerous. A number of important issues and strategies are discussed in the articles contained in this special issue of Intellectual and Developmental Disabilities. In this article, we focus on the advances and tools available in the area of technology. We explore a number of possible sources including accessing big data such as analyzing health information contained in Medicaid and Medicare health databases. We also discuss some of the possibilities afforded to us by complementing Medicaid/Medicare database information with health information available in the myriad of electronic health records. Lastly, we explore other technologies available that might yield valuable health supports and information, including wearable devices, remote supports and other smart home technologies, telehealth and telepsychiatry, as well as looking at ways to access other technologies that collect health information (e.g., glucometer, health apps, connected exercise devices, etc.).

Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging

The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.

Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology

OBJECTIVE

To revise the German guidelines and recommendations for ensuring Good Epidemiological Practice (GEP) that were developed in 1999 by the German Society for Epidemiology (DGEpi), evaluated and revised in 2004, supplemented in 2008, and updated in 2014.

METHODS

The executive board of the DGEpi tasked the third revision of the GEP. The revision was arrived as a result of a consensus-building process by a working group of the DGEpi in collaboration with other working groups of the DGEpi and with the German Association for Medical Informatics, Biometry and Epidemiology, the German Society of Social Medicine and Prevention (DGSMP), the German Region of the International Biometric Society (IBS-DR), the German Technology, Methods and Infrastructure for Networked Medical Research (TMF), and the German Network for Health Services Research (DNVF). The GEP also refers to related German Good Practice documents (e.g. Health Reporting, Cartographical Practice in the Healthcare System, Secondary Data Analysis).

RESULTS

The working group modified the 11 guidelines (after revision: 1 ethics, 2 research question, 3 study protocol and manual of operations, 4 data protection, 5 sample banks, 6 quality assurance, 7 data storage and documentation, 8 analysis of epidemiological data, 9 contractual framework, 10 interpretation and scientific publication, 11 communication and public health) and modified and supplemented the related recommendations. All participating scientific professional associations adopted the revised GEP.

CONCLUSIONS

The revised GEP are addressed to everyone involved in the planning, preparation, execution, analysis, and evaluation of epidemiological research, as well as research institutes and funding bodies.

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

Quantifying the Impact of Trainee Providers on Outpatient Clinic Workflow using Secondary EHR Data

Providers today face productivity challenges including increased patient loads, increased clerical burdens from new government regulations and workflow impacts of electronic health records (EHR). Given these factors, methods to study and improve clinical workflow continue to grow in importance. Despite the ubiquitous presence of trainees in academic outpatient clinics, little is known about the impact of trainees on academic workflow. The purpose of this study is to demonstrate that secondary EHR data can be used to quantify that impact, with potentially important results for clinic efficiency and provider reimbursement models. Key findings from this study are that (1) Secondary EHR data can be used to reflect in clinic trainee activity, (2) presence of trainees, particularly in high-volume clinic sessions, is associated with longer session lengths, and (3) The timing of trainee appointments within clinic sessions impacts the session length.

Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review

Healthcare provides many services such as diagnosing, treatment, prevention of diseases, illnesses, injuries, and other physical and mental disorders. Large-scale distributed data processing applications in healthcare as a basic concept operates on large amounts of data. Therefore, big data application functions are the main part of healthcare operations, but there was not any comprehensive and systematic survey about studying and evaluating the important techniques in this field. Therefore, this paper aims at providing the comprehensive, detailed, and systematic study of the state-of-the-art mechanisms in the big data related to healthcare applications in five categories, including machine learning, cloud-based, heuristic-based, agent-based, and hybrid mechanisms. Also, this paper displayed a systematic literature review (SLR) of the big data applications in the healthcare literature up to the end of 2016. Initially, 205 papers were identified, but a paper selection process reduced the number of papers to 29 important studies.

PERSON-Personalized Expert Recommendation System for Optimized Nutrition

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.

Reproductive management in dairy cows - the future

Background

Drivers of change in dairy herd health management include the significant increase in herd/farm size, quota removal (within Europe) and the increase in technologies to aid in dairy cow reproductive management.

Main body

There are a number of key areas for improving fertility management these include: i) handling of substantial volumes of data, ii) genetic selection (including improved phenotypes for use in breeding programmes), iii) nutritional management (including transition cow management), iv) control of infectious disease, v) reproductive management (and automated systems to improve reproductive management), vi) ovulation / oestrous synchronisation, vii) rapid diagnostics of reproductive status, and viii) management of male fertility. This review covers the current status and future outlook of many of these key factors that contribute to dairy cow herd health and reproductive performance.

Conclusions

In addition to improvements in genetic trends for fertility, numerous other future developments are likely in the near future. These include: i) development of new and novel fertility phenotypes that may be measurable in milk; ii) specific fertility genomic markers; iii) earlier and rapid pregnancy detection; iv) increased use of activity monitors; v) improved breeding protocols; vi) automated inline sensors for relevant phenotypes that become more affordable for farmers; and vii) capturing and mining multiple sources of “Big Data” available to dairy farmers. These should facilitate improved performance, health and fertility of dairy cows in the future.

Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services

Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges of data aggregation, maintenance, integration, translation, analysis, and security/privacy. The study objective to establish an interactive BDA platform with simulated patient data using open-source software technologies was achieved by construction of a platform framework with Hadoop Distributed File System (HDFS) using HBase (key-value NoSQL database). Distributed data structures were generated from benchmarked hospital-specific metadata of nine billion patient records. At optimized iteration, HDFS ingestion of HFiles to HBase store files revealed sustained availability over hundreds of iterations; however, to complete MapReduce to HBase required a week (for 10 TB) and a month for three billion (30 TB) indexed patient records, respectively. Found inconsistencies of MapReduce limited the capacity to generate and replicate data efficiently. Apache Spark and Drill showed high performance with high usability for technical support but poor usability for clinical services. Hospital system based on patient-centric data was challenging in using HBase, whereby not all data profiles were fully integrated with the complex patient-to-hospital relationships. However, we recommend using HBase to achieve secured patient data while querying entire hospital volumes in a simplified clinical event model across clinical services.

Development, Feasibility, and Small-Scale Implementation of a Web-Based Prognostic Tool-Surveillance, Epidemiology, and End Results Cancer Survival Calculator

BACKGROUND

Population datasets and the Internet are playing an ever-growing role in the way cancer information is made available to providers, patients, and their caregivers. The Surveillance, Epidemiology, and End Results Cancer Survival Calculator (SEER*CSC) is a Web-based cancer prognostic tool that uses SEER data, a large population dataset, to provide physicians with highly valid, evidence-based prognostic estimates for increasing shared decision-making and improving patient-provider communication of complex health information.

OBJECTIVE

The aim of this study was to develop, test, and implement SEER*CSC.

METHODS

An iterative approach was used to develop the SEER*CSC. Based on input from cancer patient advocacy groups and physicians, an initial version of the tool was developed. Next, providers from 4 health care delivery systems were recruited to do formal usability testing of SEER*CSC. A revised version of SEER*CSC was then implemented in two health care delivery sites using a real-world clinical implementation approach, and usage data were collected. Post-implementation follow-up interviews were conducted with site champions. Finally, patients from two cancer advocacy groups participated in usability testing.

RESULTS

Overall feedback of SEER*CSC from both providers and patients was positive, with providers noting that the tool was professional and reliable, and patients finding it to be informational and helpful to use when discussing their diagnosis with their provider. However, use during the small-scale implementation was low. Reasons for low usage included time to enter data, not having treatment options in the tool, and the tool not being incorporated into the electronic health record (EHR). Patients found the language in its current version to be too complex.

CONCLUSIONS

The implementation and usability results showed that participants were enthusiastic about the use and features of SEER*CSC, but sustained implementation in a real-world clinical setting faced significant challenges. As a result of these findings, SEER*CSC is being redesigned with more accessible language for a public facing release. Meta-tools, which put different tools in context of each other, are needed to assist in understanding the strengths and limitations of various tools and their place in the clinical decision-making pathway. The continued development and eventual release of prognostic tools should include feedback from multidisciplinary health care teams, various stakeholder groups, patients, and caregivers.

Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data

OBJECTIVE

To detect and visualize salient queries about menopause using Big Data from ChaCha.

METHODS

We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content.

RESULTS

We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment.

CONCLUSIONS

We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.

Clinical Research Informatics for Big Data and Precision Medicine

OBJECTIVES

To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI.

METHODS

We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research.

RESULTS

The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges.

CONCLUSION

The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

Challenges in patient safety improvement research in the era of electronic health records

Electronic health record (EHR) data repositories contain large volumes of aggregated, longitudinal clinical data that could allow patient safety researchers to identify important safety issues and conduct comprehensive evaluations of health care delivery outcomes. However, few health systems have successfully converted this abundance of data into useful information or knowledge for safety improvement. In this paper, we use a case study involving a project on missed/delayed follow-up of test results to discuss real-world challenges in using EHR data for patient safety research. We identify three types of challenges that pose as barriers to advance patient safety improvement research: 1) gaining approval to access/review EHR data; 2) interpreting EHR data; 3) working with local IT/EHR personnel. We discuss the complexity of these challenges, all of which are unlikely to be unique to this project, and outline some key next steps that must be taken to support research that uses EHR data to improve safety. We recognize that all organizations face competing priorities between clinical operations and research. However, to leverage EHRs and their abundant data for patient safety improvement research, many current data access and security policies and procedures must be rewritten and standardized across health care organizations. These efforts are essential to help make EHRs and EHR data useful for progress in our journey to safer health care.

Computational Electrocardiography: Revisiting Holter ECG Monitoring

BACKGROUND

Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed.

OBJECTIVES

After 70 years of technologic developments, we revisit Holter ECG from recording to understanding.

METHODS

A fundamental change is fore-seen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG.

RESULTS

Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction.

CONCLUSIONS

We shall start immediately developing methodology for CECG analysis and understanding.

Health-Enabling and Ambient Assistive Technologies: Past, Present, Future

BACKGROUND

During the last decades, health-enabling and ambient assistive technologies became of considerable relevance for new informatics-based forms of diagnosis, prevention, and therapy.

OBJECTIVES

To describe the state of the art of health-enabling and ambient assistive technologies in 1992 and today, and its evolution over the last 25 years as well as to project where the field is expected to be in the next 25 years. In the context of this review, we define health-enabling and ambient assistive technologies as ambiently used sensor-based information and communication technologies, aiming at contributing to a person's health and health care as well as to her or his quality of life.

METHODS

Systematic review of all original articles with research focus in all volumes of the IMIA Yearbook of Medical Informatics. Surveying authors independently on key projects and visions as well as on their lessons learned in the context of health-enabling and ambient assistive technologies and summarizing their answers. Surveying authors independently on their expectations for the future and summarizing their answers.

RESULTS

IMIA Yearbook papers containing statements on health-enabling and ambient assistive technologies appear first in 2002. These papers form a minor part of published research articles in medical informatics. However, during recent years the number of articles published has increased significantly. Key projects were identified. There was a clear progress on the use of technologies. However proof of diagnostic relevance and therapeutic efficacy remains still limited. Reforming health care processes and focussing more on patient needs are required.

CONCLUSIONS

Health-enabling and ambient assistive technologies remain an important field for future health care and for interdisciplinary research. More and more publications assume that a person's home and their interaction therein, are becoming important components in health care provision, assessment, and management.

A patient-centered system in a provider-centered world: challenges of incorporating post-discharge wound data into practice

OBJECTIVE

The proposed Meaningful Use Stage 3 recommendations require healthcare providers to accept patient-generated health data (PGHD) by 2017. Yet, we know little about the tensions that arise in supporting the needs of both patients and providers in this context. We sought to examine these tensions when designing a novel, patient-centered technology - mobile Post-Operative Wound Evaluator (mPOWEr) - that uses PGHD for post-discharge surgical wound monitoring.

MATERIALS AND METHODS

As part of the iterative design process of mPOWEr, we conducted semistructured interviews and think-aloud sessions using mockups with surgical patients and providers. We asked participants how mPOWEr could enhance the current post-discharge process for surgical patients, then used grounded theory to develop themes related to conflicts and agreements between patients and providers.

RESULTS

We identified four areas of agreement: providing contextual metadata, accessible and actionable data presentation, building on existing sociotechnical systems, and process transparency. We identified six areas of conflict, with patients preferring: more flexibility in data input, frequent data transfer, text-based communication, patient input in provider response prioritization, timely and reliable provider responses, and definitive diagnoses.

DISCUSSION

We present design implications and potential solutions to the identified conflicts for each theme, illustrated using our work on mPOWEr. Our experience highlights the importance of bringing a variety of stakeholders, including patients, into the design process for PGHD applications.

CONCLUSION

We have identified critical barriers to integrating PGHD into clinical care and describe design implications to help address these barriers. Our work informs future efforts to ensure the smooth integration of essential PGHD into clinical practice.

Towards exergaming commons: composing the exergame ontology for publishing open game data

BACKGROUND

It has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits.

METHODS

We have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web.

RESULTS

Exergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data.

CONCLUSIONS

We model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

Leveraging cues from person-generated health data for peer matching in online communities

OBJECTIVE

Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor.

MATERIALS AND METHODS

Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants' ratings with linear regression and qualitatively analyzed participants' feedback for emerging themes about choosing mentors and improving profile design.

RESULTS

Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor.

CONCLUSION

Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities.

Real-world data in the molecular era-finding the reality in the real world

Real-world data (RWD) promises to provide a pivotal element to the understanding of personalized medicine. However, without true representation (or the reality) of the patient-disease biosystem and its molecular contributors, RWD may hamper rather than help this advancement. In this review article, we discuss RWD vs. clinical reality and the disconnects that exist currently (emphasizing molecular medicine), and methods of closing the gaps between RWD and reality.

How Big Data Informs Us About Cataract Surgery: The LXXII Edward Jackson Memorial Lecture

PURPOSE

To characterize the role of Big Data in evaluating quality of care in ophthalmology, to highlight opportunities for studying quality improvement using data available in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry, and to show how Big Data informs us about rare events such as endophthalmitis after cataract surgery.

DESIGN

Review of published studies, analysis of public-use Medicare claims files from 2010 to 2013, and analysis of IRIS Registry from 2013 to 2014.

METHODS

Statistical analysis of observational data.

RESULTS

The overall rate of endophthalmitis after cataract surgery was 0.14% in 216 703 individuals in the Medicare database. In the IRIS Registry the endophthalmitis rate after cataract surgery was 0.08% among 511 182 individuals. Endophthalmitis rates tended to be higher in eyes with combined cataract surgery and anterior vitrectomy (P = .051), although only 0.08% of eyes had this combined procedure. Visual acuity (VA) in the IRIS Registry in eyes with and without postoperative endophthalmitis measured 1-7 days postoperatively were logMAR 0.58 (standard deviation [SD]: 0.84) (approximately Snellen acuity of 20/80) and logMAR 0.31 (SD: 0.34) (approximately Snellen acuity of 20/40), respectively. In 33 547 eyes with postoperative VA after cataract surgery, 18.3% had 1-month-postoperative VA worse than 20/40.

CONCLUSIONS

Big Data drawing on Medicare claims and IRIS Registry records can help identify additional areas for quality improvement, such as in the 18.3% of eyes in the IRIS Registry having 1-month-postoperative VA worse than 20/40. The ability to track patient outcomes in Big Data sets provides opportunities for further research on rare complications such as postoperative endophthalmitis and outcomes from uncommon procedures such as cataract surgery combined with anterior vitrectomy. But privacy and data-security concerns associated with Big Data should not be taken lightly.

Big Data Technologies: New Opportunities for Diabetes Management

The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.

Demographic and health related data of users of a mobile application to support drug adherence is associated with usage duration and intensity

PURPOSE

A wealth of mobile applications are designed to support users in their drug intake. When developing software for patients, it is important to understand the differences between individuals who have, who will or who might never adopt mobile interventions. This study analyzes demographic and health-related factors associated with real-life "longer usage" and the "usage-intensity per day" of the mobile application "Medication Plan".

METHODS

Between 2010-2012, the mobile application "Medication Plan" could be downloaded free of charge from the Apple-App-Store. It was aimed at supporting the regular and correct intake of medication. Demographic and health-related data were collected via an online questionnaire. This study analyzed captured data.

RESULTS

App-related activities of 1799 users (1708 complete data sets) were recorded. 69% (1183/1708) applied "Medication Plan" for more than a day. 74% were male (872/1183), the median age 45 years. Variance analysis showed a significant effect of the users' age with respect to duration of usage (p = 0.025). While the mean duration of use was only 23.3 days for users younger than 21 years, for older users, there was a substantial increase over all age cohorts up to users of 60 years and above (103.9 days). Sex and educational status had no effect. "Daily usage intensity" was directly associated with an increasing number of prescribed medications and increased from an average of 1.87 uses per day and 1 drug per day to on average 3.71 uses per day for users stating to be taking more than 7 different drugs a day (p<0.001). Demographic predictors (sex, age and educational attainment) did not affect usage intensity.

CONCLUSION

Users aged 60+ as well as those with complicated therapeutic drug regimens relied on the service we provided for more than three months on average. Mobile applications may be a promising approach to support the treatment of patients with chronic conditions.