Health Informatics Journal

Providing evidence-based information at the point of care for time-poor nurses may lead to better clinical care and patient outcomes. Smartphone applications (apps) have the advantage of providing immediate access to information potentially increasing time spent with patients. This small-scale pre-post survey study explored the impact a smartphone app had on the distance nurses walked and their perceived clinical decision-making ability. A total of 20 nurses working in a rural hospital medical/surgical unit participated. The findings suggest that the use of the smartphone app did not decrease nurses’ walking distance. Nor did using the app enhances nurses’ perception of their clinical decision-making ability. However, there was a statistically significant increase in confidence in the app over time (F(1,16) = 5.416, p = 0.033, partial η² = 0.253), suggesting that providing training opportunities including time to learn how to use smartphone applications has the potential to enhance nurses work.

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

People with multiple chronic conditions often struggle with managing their health. The purpose of this research was to identify specific challenges of patients with multiple chronic conditions and to use the findings to form design principles for a telemonitoring system tailored for these patients. Semi-structured interviews with 15 patients with multiple chronic conditions and 10 clinicians were conducted to gain an understanding of their needs and preferences for a smartphone-based telemonitoring system. The interviews were analyzed using a conventional content analysis technique, resulting in six themes. Design principles developed from the themes included that the system must be modular to accommodate various combinations of conditions, reinforce a routine, consolidate record keeping, as well as provide actionable feedback to the patients. Designing an application for multiple chronic conditions is complex due to variability in patient conditions, and therefore, design principles developed in this study can help with future innovations aimed to help manage this population.

Crohn’s disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn’s disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn’s disease patients.

Today, social media is increasingly used by patients to openly discuss their health. Mining automatically such data is a challenging task because of the non-structured nature of the text and the use of many abbreviations and the slang terms. Our goal is to use Patient Authored Text to build a French Consumer Health Vocabulary on breast cancer field, by collecting various kinds of non-experts’ expressions that are related to their diseases and then compare them to biomedical terms used by health care professionals. We combine several methods of the literature based on linguistic and statistical approaches to extract candidate terms used by non-experts and to link them to expert terms. We use messages extracted from the forum on ‘cancerdusein.org’ and a vocabulary dedicated to breast cancer elaborated by the Institut National Du Cancer. We have built an efficient vocabulary composed of 192 validated relationships and formalized in Simple Knowledge Organization System ontology.

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record’s ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

This study describes a unique approach to information transfer affecting the perceived value of this information and related impact on smoker behavior. Data were collected via survey, sampling approximately 120 participants. An online survey tool was used for the survey creation, data collection and monitoring. Another online tool was used by participants to create short animation videos as a means of increasing their engagement with information in an experiential fashion. Study findings included that the process experienced by the test group was influential and facilitated participants’ change of mind regarding enrollment in a smoking cessation workshop. This was partly attributable to the IKEA effect. The study provides evidence that a change in habits crucial to improve health and enhance positive lifestyle choices can be stimulated through active engagement with artifact creation in a technology-mediated environment.

The study aim was to identify and evaluate the reliability and quality of online resources for parents of preterm infants seeking health information about their infant using the DISCERN tool and Health on Net code. An Internet search (www.google.com) was used to identify websites for parents of preterm infants on their infants’ health and health issues. For each search, the top 100 “hits” were downloaded, yielding 1200 websites. After reviewing websites for exclusion criteria and duplicates, 197 websites remained and were analyzed. According to the DISCERN tool, the websites had a moderate reliability score (mean = 29.88, standard deviation = 4.88, range: 18–40), moderate treatment score (mean = 24.15, standard deviation = 5.79, range: 10–35), and moderate overall quality score (mean = 3.41, standard deviation = 0.89, range: 1–5). Only 24 (12.2%) websites had current Health on Net code approval and no other websites met full eligibility for certification. Overall, the reliability and quality of information available online to parents of preterm infants is lacking.

The presence and increase of challenges to eHealth in today’s society have begun to generate doubts about the capability of technology in patient empowerment, especially within the frameworks supporting empowerment. Through the review of existing frameworks and articulation of patient demands, weaknesses in the current application of technology to support empowerment are explored, and key constituents of a technology-driven framework for patient empowerment are determined. This article argues that existing usage of technology in the design, development and implementation of patient empowerment in the healthcare system, although well intentioned, is insufficiently constituted, primarily as a result of fragmentation. Systems theory concepts such as holism and iteration are considered vital in improving the role of technology in enabling patient empowerment.

Veracity, one of the five V’s used to describe big data, has received attention when it comes to using electronic medical record data for research purposes. In this perspective article, we discuss the idea of data veracity and associated concepts as it relates to the use of electronic medical record data and administrative data in research. We discuss the idea that electronic medical record data are “good enough” for clinical practice and, as such, are “good enough” for certain applications. We then propose three primary issues to attend to when establishing data veracity: data provenance, cross validation, and context.

Prenatal clinical notes in electronic medical records contain a wealth of information on pregnancy complications and outcomes. Extracting this critical information provides a unique opportunity for risk assessment to identify at-risk patients who may benefit from early monitoring and intervention. We developed and validated a rule-based computerized algorithm called PregHisEx to characterize past obstetrical history (preeclampsia/eclampsia) by mining prenatal clinical notes for women delivered in 2012 within a large healthcare maintenance organization. The algorithm successfully identified cases with past history of preeclampsia/eclampsia: 2984 definite and 479 probable cases at sentence level; 2419 definite and 348 probable cases at note level; and 762 definite and 82 probable cases at pregnancy episode level. Validation conducted on a random sample of 200 notes using PregHisEx yielded 88.0 percent sensitivity, 98.9 percent specificity, 91.7 percent positive predictive value, 98.3 percent negative predictive value, and F-score of 0.90. The high-performing PregHisEx can be applied for other prenatal conditions.

Waterpipe tobacco smoking has grown in popularity among US college students and is associated with serious health risks. Much of the waterpipe tobacco smoking takes place in establishments such as “hookah bars” or in lounge settings. Web-based data platforms such as Yelp have demonstrated utility in locating these establishments but are prone to over- and underestimation. The purpose of this study was to optimize strategies for algorithmically estimating the prevalence of waterpipe tobacco smoking establishments. We conducted searches for potential waterpipe tobacco smoking establishments near highly residential US universities (N = 41). Of 521 potential establishments, independent coders confirmed 257 as permitting waterpipe tobacco smoking. We compared four strategies for using Yelp metadata to estimate the number of confirmed waterpipe tobacco smoking establishments by location. An accuracy-weighted approach generated estimates that closely matched confirmed data without significant over- or underestimation. The use of algorithms such as these may dramatically improve the feasibility and efficacy of future research linking environmental data and health outcomes.

The widespread use of technology in hospitals and the difficulty of sterilising computer controls has increased opportunities for the spread of pathogens. This leads to an interest in touchless user interfaces for computer systems. We present a review of touchless interaction with computer equipment in the hospital environment, based on a systematic search of the literature. Sterility provides an implied theme and motivation for the field as a whole, but other advantages, such as hands-busy settings, are also proposed. Overcoming hardware restrictions has been a major theme, but in recent research, technical difficulties have receded. Image navigation is the most frequently considered task and the operating room the most frequently considered environment. Gestures have been implemented for input, system and content control. Most of the studies found have small sample sizes and focus on feasibility, acceptability or gesture-recognition accuracy. We conclude this article with an agenda for future work.

This study aimed to explore experiences with online information regarding food, weight management, and health in a group of adolescents in treatment for obesity. Individual semi-structured interviews with 20 adolescents were conducted. Participants used a screen-recorded laptop to demonstrate their search procedures and online information sources. The transcribed interviews were categorized using qualitative content analysis. The adolescents described both encouraging and discouraging experiences. On one hand, they said that online forums could provide nutritious meal ideas and inspiration as well as social support for behavior change. On the other hand, they mentioned that there was a confusing amount of misleading commercial content online and also experiences of peer-facilitated food marketing in online networks. An overarching theme was generated: social media might be a resource for health inspiration, health information, and social support, but requires awareness and competencies. Implications for clinical practice are discussed in light of these findings.

The objective of this study is to identify factors influencing unsafe use of hospital information systems in Malaysian government hospitals. Semi-structured interviews with 31 medical doctors in three Malaysian government hospitals implementing total hospital information systems were conducted between March and May 2015. A thematic qualitative analysis was performed on the resultant data to deduce the relevant themes. A total of five themes emerged as the factors influencing unsafe use of a hospital information system: (1) knowledge, (2) system quality, (3) task stressor, (4) organization resources, and (5) teamwork. These qualitative findings highlight that factors influencing unsafe use of a hospital information system originate from multidimensional sociotechnical aspects. Unsafe use of a hospital information system could possibly lead to the incidence of errors and thus raises safety risks to the patients. Hence, multiple interventions (e.g. technology systems and teamwork) are required in shaping high-quality hospital information system use.

The objective of this study was to conduct the first patient usability testing of a mobile health (mHealth) system for in-home swallowing therapy. Five participants with a history of head and neck cancer evaluated the mHealth system. After completing an in-application (app) tutorial with the clinician, participants were asked to independently complete five tasks: pair the device to the smartphone, place the device correctly, exercise, interpret progress displays, and close the system. Quantitative and qualitative methods were used to evaluate the effectiveness, efficiency, and satisfaction with the system. Critical changes to the app were found in three of the tasks, resulting in recommendations for the next iteration. These issues were related to ease of Bluetooth pairing, placement of device, and interpretation of statistics. Usability testing with patients identified issues that were essential to address prior to implementing the mHealth system in subsequent clinical trials. Of the usability methods used, video observation (synced screen capture with videoed gestures) revealed the most information.

Human immunodeficiency virus vaccination and pre-exposure prophylaxis represent two different emerging preventive tools. Google Trends was used to assess the public interest toward these tools in terms of digital activities. Worldwide web searches concerning the human immunodeficiency virus vaccine represented 0.34 percent, 0.03 percent, and 46.97 percent of human immunodeficiency virus, acquired immune deficiency syndrome, and human immunodeficiency virus/acquired immune deficiency syndrome treatment–related Google Trends queries, respectively. Concerning temporal trends, digital activities were shown to increase from 0 percent as of 1 January 2004 percent to 46 percent as of 8 October 2017 with two spikes observed in May and July 2012, coinciding with the US Food and Drug Administration approval. Bursts in search number and volume were recorded as human immunodeficiency virus vaccine trials emerged. This search topic has decreased in the past decade in parallel to the increase in Truvada-related topics. Concentrated searches were noticed among African countries with high human immunodeficiency virus/acquired immune deficiency syndrome prevalence. Stakeholders should take advantage of public interest especially in preventive medicine in high disease burden countries.

The electronic sharing of medical imaging data is an important element of modern healthcare systems, but current infrastructure for cross-site image transfer depends on trust in third-party intermediaries. In this work, we examine the blockchain concept, which enables parties to establish consensus without relying on a central authority. We develop a framework for cross-domain image sharing that uses a blockchain as a distributed data store to establish a ledger of radiological studies and patient-defined access permissions. The blockchain framework is shown to eliminate third-party access to protected health information, satisfy many criteria of an interoperable health system, and readily generalize to domains beyond medical imaging. Relative drawbacks of the framework include the complexity of the privacy and security models and an unclear regulatory environment. Ultimately, the large-scale feasibility of such an approach remains to be demonstrated and will depend on a number of factors which we discuss in detail.

The purpose of this article is to identify and assess service delivery issues within a hospital emergency department and propose an improved model to address them. Possible solutions and options to these issues are explored to determine the one that best fits the context. In this article, we have analysed the emergency department’s organizational models through i* strategic dependency and rational modelling technique before proposing updated models that could potentially drive business process efficiencies. The results produced by the models, framework and improved patient journey in the emergency department were evaluated against the statistical data revealed from a reputed government organization related to health, to ensure that the key elements of the issues such as wait time, stay time/throughput, workload and human resource are resolved. The result of the evaluation was taken as a basis to determine the success of the project. Based on these results, the article recommends implementing the concept on actual scenario, where a positive result is achievable.

This study presents the results of experimental evaluations of an ontology-based frequently asked question retrieval system in the domain of HIV and AIDS. The main purpose of the system is to provide answers to questions on HIV/AIDS using ontology. To evaluate the effectiveness of the frequently asked question retrieval system, we conducted two experiments. The first experiment focused on the evaluation of the quality of the ontology we developed using the OQuaRE evaluation framework which is based on software quality metrics and metrics designed for ontology quality evaluation. The second experiment focused on evaluating the effectiveness of the ontology in retrieving relevant answers. For this we used an open-source information retrieval platform, Terrier, with retrieval models BM25 and PL2. For the measurement of performance, we used the measures mean average precision, mean reciprocal rank, and precision at 5. The results suggest that frequently asked question retrieval with ontology is more effective than frequently asked question retrieval without ontology in the domain of HIV/AIDS.

Persuasive technologies are tools for motivating behaviour change using persuasive strategies. socially-driven persuasive technologies employ three common socially-oriented persuasive strategies in many health domains: competition, social comparison, and cooperation. Research has shown the possibilities for socially-driven persuasive interventions to backfire by demotivating behaviour, but we lack knowledge about how the interventions could motivate or demotivate behaviours. To close this gap, we studied 1898 participants, specifically Socially-oriented strategies and their comparative effectiveness in socially-driven persuasive health interventions that motivate healthy behaviour change. The results of a thematic analysis of 278 pages of qualitative data reveal important strengths and weaknesses of the individual socially-oriented strategies that could facilitate or hinder their effectiveness at motivating behaviour change. These include their tendency to simplify behaviours and make them fun, challenge people and make them accountable, give a sense of accomplishment and their tendency to jeopardize user’s privacy and relationships, creates unnecessary tension, and reduce self-confidence and self-esteem, and provoke a health disorder and body shaming, respectively. We contribute to the health informatics community by developing 15 design guidelines for operationalizing the strategies in persuasive health intervention to amplify their strengths and overcome their weaknesses.

Twitter has the potential to optimize research conduct, but more research is needed around the nature of study-related tweets and strategies for optimizing reach. In the context of our caregiving study, we aimed to describe the nature and extent of study-related tweets, the extent to which they were shared by others, and their potential reach. To do so, we conducted a secondary analysis of our Twitter recruitment. We aggregated and categorized study-related tweets and analyzed the reach of the 10 most retweeted tweets. Results indicated that of 71 caregivers, 27 were recruited via Twitter. General recruitment tweets were most-shared by users. Tweet reach ranged from 5273 to 62,144 users. Twitter caregivers were demographically comparable to non-Twitter caregivers but had higher Internet proficiency and fewer children. Overall, using a personal Twitter account can expand the reach of study recruitment. Future research should compare different recruitment strategies and explore characteristics that may challenge the heterogeneity of Twitter samples.

Implementation of patient’s decision aids in routine clinical care is generally low. This study evaluated uptake and usage of a novel Dutch web-based prostate cancer treatment decision aid within the Prostate Cancer Patient Centered Care trial. From an estimated total patient sample of 1006 patients, 351 received a decision aid (35% implementation rate; hospital ranges 16%–84%). After receipt of the decision aid, most patients accessed the decision aid, utilized most functions, although not completely, and discussed the decision aid summary in a subsequent consultation with their care provider. Including nurses for dissemination of decision aids seemed to positively affect decision aid uptake. Once received, patients seemed able to use the decision aid and engage in shared decision-making as intended; however, decision aid uptake and complete usage of all decision aid components should be further improved. Prior to the diagnosis consultation, handing out of the decision aid should be prepared.

The main objective of this study was to preliminary explore the effects of DARWeb on different outcomes. A Quasi-experimental, one-group, pretest–posttest design was used. Parents and children were asked to complete questionnaires and questions (separately) about quality of life, abdominal pain severity, and satisfaction. Semi-structured interviews with families were also performed. This study focuses on 17 families. Results showed that parent’s ratings of children’s abdominal pain severity were significantly lower after finishing the intervention and at the 3-month follow-up, and quality of life scores had increased significantly after 3 months. From children’s ratings, mean abdominal pain severity scores were significantly lower after the intervention compared to the preintervention assessment. Both parents and children were quite satisfied with the intervention. In qualitative interviews, families suggested that DARWeb helped them to give less importance to pain and to learn coping strategies. In conclusion, this study showed the potential usefulness of DARWeb for children with functional abdominal pain and for their parents.

Health informatics applications reduce time intervals in acute coronary syndromes, but their impact on guideline adherence is unknown. This pre–post intervention study compared guideline adherence between telemedically supported (n = 101, April 2014–July 2015) and conventional on-scene care (n = 120, January 2014–March 2014) in acute coronary syndrome. A multivariate logistic regression was performed for dependent variables: adverse events 0 versus 0, p = NA; electrocardiogram 101 versus 120, p = NA; acetylic salicylic acid 91 versus 102, p = 0.21; heparin 92 versus 112, p = 0.99; morphine 96 versus 107, p = 0.33; oxygen 83 versus 102, p = 0.92; glyceroltrinitrate 55 versus 90, p = 0.038; correct destination: 100 versus 119, p = 1.0. The time from ambulance arrival to hospital arrival was prolonged with telemedicine: 48.7 ± 11 min versus 35.5 ± 8.1 min, p < 0.001. Guideline adherence showed no differences except for glyceroltrinitrate. Prolonged time requirements are critical, though explainable. However, this approach enables a timely and high-quality backup strategy if only paramedics are on-scene.

In this study, we explore how healthcare professionals in primary care and outpatient clinics perceive the outcomes of giving patients online access to their electronic health records. The study was carried out as a case study and included a workshop, six interviews and a survey that was answered by 146 healthcare professionals. The results indicate that professionals working in primary care perceive that an increase in information-sharing with patients can increase adherence, clarify important information to the patient and allow the patient to quality-control documented information. Professionals at outpatient clinics seem less convinced about the benefits of patient accessible electronic health records and have concerns about how patients manage the information that they are given access to. However, the patient accessible electronic health record has not led to a change in documentation procedures among the majority of the professionals. While the findings can be connected to the context of outpatient clinics and primary care units, other contextual factors might influence the results and more in-depth studies are therefore needed to clarify the concerns.

Managing abnormal test results in primary care involves coordination across various settings. This study identifies how primary care teams manage test results in a large, computerized healthcare system in order to inform health information technology requirements for test results management and other distributed healthcare services. At five US Veterans Health Administration facilities, we interviewed 37 primary care team members, including 16 primary care providers, 12 registered nurses, and 9 licensed practical nurses. We performed content analysis using a distributed cognition approach, identifying patterns of information transmission across people and artifacts (e.g. electronic health records). Results illustrate challenges (e.g. information overload) as well as strategies used to overcome challenges. Various communication paths were used. Some team members served as intermediaries, processing information before relaying it. Artifacts were used as memory aids. Health information technology should address the risks of distributed work by supporting awareness of team and task status for reliable management of results.

Clinical pathways are used to support the management of patients in emergency departments. An existing document-based clinical pathway was used as the foundation on which to design and build a digital clinical pathway for acute chest pain, with the aim of improving clinical calculations, clinician decision-making, documentation, and data collection. Established principles of decision support system design were used to build an application within the existing electronic health record, before testing with a multidisciplinary team of doctors using a think-aloud protocol. Technical authoring was successful, however, usability testing revealed that the user experience and the flexibility of workflow within the application were critical barriers to implementation. Emergency medicine and acute care decision support systems face particular challenges to existing models of linear workflow that should be deliberately addressed in digital pathway design. We make key recommendations regarding digital pathway design in emergency medicine.

HIV medical care providers need a wide range of evidence-based clinical information resources to manage their patients’ health. We determined whether providers’ choice of information sources for HIV care and treatment are associated with their demographic and medical practice characteristics. Data used for this study were obtained from a probability sample of HIV medical care providers in 13 outpatient HIV facilities in Houston/Harris County, Texas, surveyed between June and September 2009. The mean number of information sources used by HIV medical care providers for HIV care and treatment was 5.83 (95% confidence interval: 4.90–6.75). Antiretroviral therapy guidelines (95.6%), medical journals and textbooks (82.6%), and Internet sources (69.5%) were ranked first, second, and third as sources of clinical information. At least one of the providers’ demographic or medical practice characteristics was significantly (p ⩽ 0.05) associated with six of the clinical information sources. Integration of these information resources into clinicians’ workflow may enhance efficiency of HIV care and treatment and facilitate improved patients’ care and health outcomes.

Mobile phones can produce detailed and personalized data to help diagnose, treat, prevent or rehabilitate women who are victims of gender violence. We sent automatic text messages, based on cognitive-behavioural therapy, to female victims of gender violence to assess the feasibility of the medium for mental health intervention. During the 28-day intervention period, each woman received four automatic text messages every day on a pre-established schedule. We evaluated symptoms of depression using the Personal Health Questionnaire-9. The Personal Health Questionnaire-9 decreased from an average of 14.8 at the beginning of the intervention, to 6.1 at the end of the text messaging period. All of the women reported that they liked receiving the messages, they found them useful and they felt that the messages had improved their health. Text messages are acceptable to women users who are recipients of public health and social services. Future research should focus more on the evaluation of the results and on how the specific content and aspects of text messaging improve health outcomes.

The objective of this study was to test the feasibility of video discharge education to improve self-efficacy in dealing with medication barriers around hospital discharge. We conducted a single-arm intervention feasibility trial to evaluate the use of video education in participants who were being discharged home from the hospital. The scores of pre- and post-intervention self-efficacy involving medication barriers were measured. We also assessed knowledge retention, patient and nursing feedback, follow-up barrier assessments, and hospital revisits. A total of 40 patients participated in this study. Self-efficacy scores ranged from 5 to 25. Median pre- and post-intervention scores were 21.5 and 23.5, respectively. We observed a median increase of 2.0 points from before to after the intervention (p = 0.046). In total, 95 percent of participants reported knowledge retention and 90 percent found the intervention to be helpful. Video discharge education improved patient self-efficacy surrounding discharge medication challenges among general medicine inpatients. Patients and nurses reported satisfaction with the video discharge education.

This retrospective study investigates the potential benefits from the introduction of point-of-care tests for rapid diagnosis of infectious diseases. We analysed a sample of 441 hospitalized patients who had received a final diagnosis related to 18 pathogenic agents. These pathogens were mostly detected by standard tests but were also detectable by point-of-care testing. The length of hospital stay was partitioned into pre- and post-laboratory diagnosis stages. Regression analysis and elementary queueing theory were applied to estimate the impact of quick diagnosis on the mean length of stay and the utilization of healthcare resources. The analysis suggests that eliminating the pre-diagnosis times through point-of-care testing could shorten the mean length of hospital stay for infectious diseases by up to 34 per cent and result in an equal reduction in bed occupancy and other resources. Regression and other more sophisticated models can aid the financing decision-making of pilot point-of-care laboratories in healthcare systems.

As the pace of medical discovery widens the knowledge-to-practice gap, technologies that enable peer-to-peer crowdsourcing have become increasingly common. Crowdsourcing has the potential to help medical providers collaborate to solve patient-specific problems in real time. We recently conducted the first trial of a mobile, medical crowdsourcing application among healthcare providers in a university hospital setting. In addition to acknowledging the benefits, our participants also raised concerns regarding the potential negative consequences of this emerging technology. In this commentary, we consider the legal and ethical implications of the major findings identified in our previous trial including compliance with the Health Insurance Portability and Accountability Act, patient protections, healthcare provider liability, data collection, data retention, distracted doctoring, and multi-directional anonymous posting. We believe the commentary and recommendations raised here will provide a frame of reference for individual providers, provider groups, and institutions to explore the salient legal and ethical issues before they implement these systems into their workflow.

The development of eHealth is dramatically changing the way healthcare is provided and organized. eHealth applications are used not only by healthcare professionals but also by patients specifically to self-manage their health condition. The development of eHealth applications requires a new methodological approach, departing from the more conventional methods dedicated to designing health information systems. There is a gap between theories to design persuasive eHealth applications and practices. In the Netherlands, eHealth innovation emerges from three areas. In research, the development of eHealth application often remains in a pilot phase. Healthcare organizations are also keen to innovate but do not always have the know-how. We further witness technology push from business and industry, undermining the co-creation process of the innovation. We consequently advocate an integrated, systematic and practical but scientifically based methodology to design effective persuasive eHealth applications. This approach is being successfully embedded in our educational health informatics program.

With the development of Web 2.0 technologies, an increasing number of websites are providing online healthcare services, and they have potential to alleviate problems of overloaded medical resources in China. However, some patients are reluctant to trust and continue using online healthcare services, partly due to the immature development of healthcare websites. Previous research has argued that online trust is significantly associated with the risk or benefit perceived by users. This study aims to extend prior research and examine how perceptual factors influence patients’ online trust and intention to continue using online healthcare services. We developed a model with the moderating role of purpose of use and tested it with data collected from 283 participants. The results support the validity of the model and most hypotheses. The moderating role of purpose of use between the perceived benefits/risks and patients’ online trust is also highlighted. Theoretical and practical implications are also discussed.

Consumers are increasingly using social media to interact with other consumers about health conditions and treatment options. This study aimed to investigate the advantages and disadvantages of using social media for health-related purposes from the consumers’ perspectives. Five focus groups with 36 Australian adults with a chronic condition and on medication were conducted, audio-recorded, transcribed verbatim, and thematically analysed. Consumers reported that social media was very convenient, for accessing health-related information and for peer engagement; user-friendly; improved their health knowledge; empowered them; and provided social and emotional support. The disadvantages included information overload, wasting time; negative feelings; doubts about online information credibility; and issues related to online interactions. Despite some disadvantages, health-related use of social media led consumers to feel supported, knowledgeable, and empowered. Consumers’ motivation to keep accessing social media for health-related purposes opens up avenues for the delivery of services via social media.

Wearable technologies have created fascinating opportunities for patients to treat chronic pain in a discreet, mobile fashion. However, many of these health wearables require patients to disclose sensitive information, including health information (e.g., heart rate, glucose levels) and personal information (location, email, name, etc.). Individuals using wearables for treatment of chronic pain may sacrifice social health elements, including their privacy, in exchange for better physical and mental health. Utilizing communication privacy management, a popular disclosure theory, this article explores the policy and ethical ramifications of patients disclosing sensitive health information in exchange for better health treatment and relief of chronic pain. The article identifies scenarios where a user must disclose information, and what factors motivate or dissuade disclosure, and ultimately the use of a health wearable. Practical implications of this conceptual article include an improved understanding of how and why consumers may disclose personal data to health wearables, and potential impacts for public policy and ethics regarding how wearables and their manufacturers entice disclosure of private health information.

Project Emerge took a systems engineering approach to reduce avoidable harm in the intensive care unit. We developed a socio-technology solution to aggregate and display information relevant to preventable patient harm. We compared providers’ efficiency and ability to assess and assimilate data associated with patient-safety practice compliance using the existing electronic health record to Emerge, and evaluated for speed, accuracy, and the number of mouse clicks required. When compared to the standard electronic health record, clinicians were faster (529 ± 210 s vs 1132 ± 344 s), required fewer mouse clicks (42.3 ± 15.3 vs 101.3 ± 33.9), and were more accurate (24.8 ± 2.7 of 28 correct vs 21.2 ± 2.9 of 28 correct) when using Emerge. All results were statistically significant at a p-value < 0.05 using Wilcoxon signed-rank test (n = 18). Emerge has the potential to make clinicians more productive and patients safer by reducing the time and errors when obtaining information to reduce preventable harm.

We investigated the reasons why the transition from paper to electronically formatted records during patient handover between ambulance crews and emergency department staff in a North East England Emergency Department has not always been viewed positively. Interviews with seven paramedics and three emergency department staff were conducted in addition to observations of 74 ambulance staff during 37 handovers in the emergency department. In just over half of the handovers (20), paramedics found it necessary to provide written information to aid emergency department staff, in addition to that recorded electronically. There were a number of issues that impeded the ready utilisation of electronic records in this context. The major factors identified as contributing to this were the choice of system architecture, the design of user interfaces, and the procurement process used by the National Health Service. We have made some suggestions about how the system could evolve from one focused on providing management information to one that also supports operational needs.

This work presents an analysis of 3.5 million calls made to a mental health and well-being helpline, seeking to answer the question, what different groups of callers can be characterised by specific usage patterns? Calls were extracted from a telephony informatics system. Each call was logged with a date, time, duration and a unique identifier allowing for repeat caller analysis. We utilized data mining techniques to reveal new insights into help-seeking behaviours. Analysis was carried out using unsupervised machine learning (K-means clustering) to discover the types of callers, and Fourier transform was used to ascertain periodicity in calls. Callers can be clustered into five or six caller groups that offer a meaningful interpretation. Cluster groups are stable and re-emerge regardless of which year is considered. The volume of calls exhibits strong repetitive intra-day and intra-week patterns. Intra-month repetitions are absent. This work provides new data-driven findings to model the type and behaviour of callers seeking mental health support. It offers insights for computer-mediated and telephony-based helpline management.

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic procedures are time-consuming and not cost-effective. Overall, the economic impact of autism and the increase in the number of autism spectrum disorder cases across the world reveal an urgent need for the development of easily implemented and effective screening methods. This article proposes a new mobile application to overcome the problem by offering users and the health community a friendly, time-efficient and accessible mobile-based autism spectrum disorder screening tool called ASDTests. The proposed ASDTests app can be used by health professionals to assist their practice or to inform individuals whether they should pursue formal clinical diagnosis. Unlike existing autism screening apps being tested, the proposed app covers a larger audience since it contains four different tests, one each for toddlers, children, adolescents and adults as well as being available in 11 different languages. More importantly, the proposed app is a vital tool for data collection related to autism spectrum disorder for toddlers, children, adolescent and adults since initially over 1400 instances of cases and controls have been collected. Feature and predictive analyses demonstrate small groups of autistic traits improving the efficiency and accuracy of screening processes. In addition, classifiers derived using machine learning algorithms report promising results with respect to sensitivity, specificity and accuracy rates.

Binge drinking is a severe health problem faced by many US colleges and universities. College students often post drinking-related text and images on social media, portraying their alcohol use as socially desirable. In this project, we investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents. We manually annotated 4266 posts during 21 October 2011 and 3 November 2014 from “I’m Shmacked” group on Facebook, where 511 posts were drinking-related. Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81. Prediction models built on text data were more reliable compared to those built on image and video data for predicting drinking-related contents. As the first step of our efforts in this direction, this feasibility study showed promise toward unleashing the potential of mining social media to identify students who binge drink.

This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health records with an imbalance ratio of 1:222, this means that for each drug–disease pair that is an adverse drug reaction, there are approximately 222 that are not adverse drug reactions. The application of a sampling technique before using cost-sensitive learning offered the best result. On the test set, the f-measure was 0.121 for the minority class and 0.996 for the majority class.

This article describes the development and evaluation of a set of knowledge patterns that provide guidelines and implications of design for developers of mental health portals. The knowledge patterns were based on three foundations: (1) knowledge integration of language technology approaches; (2) experiments with language technology applications and (3) user studies of portal interaction. A mixed-methods approach was employed for the evaluation of the knowledge patterns: formative workshops with knowledge pattern experts and summative surveys with experts in specific domains. The formative evaluation improved the cohesion of the patterns. The results of the summative evaluation showed that the problems discussed in the patterns were relevant for the domain, and that the knowledge embedded was useful to solve them. Ten patterns out of thirteen achieved an average score above 4.0, which is a positive result that leads us to conclude that they can be used as guidelines for developing health portals.

Telemonitoring leverages technology for the follow-up of patients with heart failure. Limited evidence exists on how telemonitoring influences senior patients’ attitudes and self-care practices. This study examines telemonitoring impacts on patient empowerment and self-care, and explores adoption factors among senior patients. A longitudinal study design was used, involving three surveys of elderly with chronic heart failure (n = 23) 1 week, 3 months, and 6 months after beginning telemonitoring use. Self-care, patient empowerment, and adoption factors were assessed using existing scales. The patients involved in this study perceived value of using telemonitoring, did not expect it to be difficult to use, and did not encounter adoption barriers. There was a significant improvement in patients’ confidence in their ability to evaluate their symptoms, address them, and evaluate the effectiveness of the measures taken to address these symptoms. Yet, patients performed less self-care maintenance activities, and the capability of involvement in the decision-making related to their condition decreased. Telemonitoring can improve seniors’ confidence in evaluating and addressing their symptoms in relation to heart failure. This patient management approach should be coupled with targeted education geared toward self-maintenance and self-management practices.

Health-based electronic games present an innovative tool to deal with mental and behavioral disorders as well as physical. An appropriately designed game can optimize health care. The aim of this study was to establish a design for health-based electronic games model and to develop a sample game in diabetes. This study was carried out in two phases: first, the design of a health-based electronic game framework and second, the design and production of sweet care game. There were two intended information prerequisites: determination of effective components in models of behavior change and validation of these components on health topics and structural formation of a game. The game framework design was presented. In the second phase, a sample of the game was developed in the field of diabetes. Important components to empower people were ranked using the Friedman test in the following order: self-learning, treatment, mental empowerment, condition management, healthy eating and physical activity. The axis of self-learning received the highest rating from the perspective of the subjects with an average of 3.93 percent. Another part was the game framework with components as follows: application areas, educational content for change behavior, resources, game structure and game production. Finally, the sweet care game was designed on diabetes with educational, technological and clinical content. The health-based electronic game designed on a systematic framework could be applied as a tool to expanding the knowledge base on how to create effective health-based digital games that entertain while promoting behavior change.

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between suicide and stressors. Very few studies have investigated stressor data in electronic health records, mostly due to the data being recorded in narrative text. This study takes the initiative to automatically extract and classify psychiatric stressors from clinical text using natural language processing–based methods. Suicidal behaviors were also identified by keywords. Then, a statistical association analysis between suicide ideations/attempts and stressors extracted from a clinical corpus is conducted. Experimental results show that our natural language processing method could recognize stressor entities with an F-measure of 89.01 percent. Mentions of suicidal behaviors were identified with an F-measure of 97.3 percent. The top three significant stressors associated with suicide are health, pressure, and death, which are similar to previous studies. This study demonstrates the feasibility of using natural language processing approaches to unlock information from psychiatric notes in electronic health record, to facilitate large-scale studies about associations between suicide and psychiatric stressors.

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases. We found that Google search volume is relatively strongly correlated with the burdens for 39 of 1633 diseases, including viral hepatitis, diabetes mellitus, multiple sclerosis, and hemorrhoids. Wikipedia and Twitter data strongly correlated with the burdens of 15 and 7 diseases, respectively. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and the presence of stigma.

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models.