Modeling the Application of IHE QRPH Profiles in Data Integration Centers: A Practical Approach with 3LGM2

The Data Integration Centers (DICs), all part of the German Medical Informatics Initiative (MII), prepare routine care data captured in university hospitals to enable its reuse in clinical research. Tackling this challenging task requires them to maintain multiple data stores, implement the necessary transformation processes, and provide the required terminology services, all while also addressing the use case specific needs researchers might have. An MII wide application of the standardized profiles defined in the IHE QRPH domain might therefore be able to drastically reduce the overhead at any one DIC. The MII DIC reference model built in 3LGM2, a method to describe complex information system architectures, serves as a starting point to evaluate whether such an application is possible. We first extend the IHE modeling capabilities of 3LGM2 to also support the five profiles from the QRPH domain that our experts evaluated as relevant in the MII DIC context. We then expand the DIC reference model by some IHE QRPH actors and transactions, showing that their application could be beneficial in the MII DIC context, provided they surpass their trial status.

Survey on Optical Wireless Communications-Based Services Applied to the Tourism Industry: Potentials and Challenges

In this paper, we explore the potential applications of Optical Wireless Communications in the tourism industry, considering both indoor and outdoor scenarios and different transmission speeds. They range from high-speed atmospheric outdoor links (Free-Space Optics (FSO)) to indoor systems based on high-speed lighting networks (known under the trade name LiFi©) or low-speed services support the Internet of Things networks, using visible light (VLC) or IR emitters, with receivers based on either on classical photodiodes or in image sensors, known as Optical Camera Communications. The avant-garde applications of this technology have been studied focusing on three possible use scenarios: the traveler himself, in what we have called TAN (Tourist Area Network); the tourist facility, which includes not only the hotel but also leisure areas (theme parks, museums, natural protected areas) or services (restaurants, shopping areas, etc.); and the entire destination, which can be both the city or the territory where the tourist is received, within the paradigm of the Smart Tourist Destination (STD). In addition to the classic services based on radio frequency and wired broadband networks, these technologies will make it possible to meet the tourist's challenging needs, the establishment, and the destination. Besides, they cover the services imposed by the new marketing services related to location or context and feed the big data systems used to study tourist behavior.

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts

OBJECTIVES

To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic.

METHODS

The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey. A non-probability convenience sampling strategy was employed. Data were analyzed with content analysis.

RESULTS

A total of 65 participants from 16 countries responded to the conducted survey. The four major themes regarding recommended improvements identified from the content analysis included: improved technology availability, improved interoperability, intuitive user interfaces and adoption of standards of care. Respondents also identified several key healthcare information and communication technologies that can help to provide better healthcare to patients during the COVID-19 pandemic, including telehealth, advanced software, electronic health records, remote work technologies (e.g., remote desktop computer access), and clinical decision support tools.

CONCLUSIONS

Our results help to identify several important healthcare information and communication technologies, recommended by health informatics experts, which can help to provide better care to patients during the COVID-19 pandemic. The results also highlight the need for improved interoperability, intuitive user interfaces and advocating the adoption of standards of care.

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature

BACKGROUND

Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic.

METHODS

We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis.

RESULTS

A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers.

CONCLUSION

Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

DimLift: Interactive Hierarchical Data Exploration Through Dimensional Bundling

The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.

Development of safety and usability guideline for clinical information system

Clinical information systems (CISs) that do not consider usability and safety could lead to harmful events. Therefore, we aimed to develop a safety and usability guideline of CISs that is comprehensive for both users and developers. And the guideline was categorized to apply actual clinical workflow and work environment.The guideline components were extracted through a systematic review of the articles published between 2000 and 2015, and existing CIS safety and/or usability design guidelines. The guideline components were categorized according to clinical workflow and types of user interface (UI). The contents of the guideline were evaluated and validated by experts with 3 specialties: medical informatics, patient safety, and human engineering.Total 1276 guideline components were extracted through article and guideline review. Of these, 464 guideline components were categorized according to 5 divisions of the clinical workflow: "Data identification and selection," "Document entry," "Order entry," "Clinical decision support and alert," and "Management". While 521 guideline components were categorized according to 4 divisions of UI: UIs related to information process steps, "Perception," "Recognition," "Control," and "Feedback". We developed a guideline draft with 219 detailed guidance for clinical task and 70 for UI. Overall appropriateness and comprehensiveness were proven to achieve more than 90% in experts' survey. However, there were significant differences among the groups of specialties in the judgment of appropriateness (P < .001) and comprehensiveness (P = .038).We developed and verified a safety and usability guideline for CIS that qualifies the requirements of both clinical workflows and usability issues. The developed guideline can be a practical tool to enhance the usability and safety of CISs. Further validation is required by applying the guideline for designing the actual CIS.

A systematic review of the impact of health information technology on nurses' time

OBJECTIVE

Nursing time represents one of the highest costs for most health services. We conducted a systematic review of the literature on the impact of health information technology on nurses' time.

MATERIALS AND METHODS

We followed PRISMA guidelines and searched 6 large databases for relevant articles published between Jan 2004 and December 2019. Two authors reviewed the titles, abstracts, and full texts. We included articles that included a comparison group in the design, measured the time taken to carry out documentation or medication administration, documented the quantitative estimates of time differences between the 2, had nurses as subjects, and was conducted in either a care home, hospital, or community clinic.

RESULTS

We identified a total of 1647 articles, of which 33 met our inclusion criteria. Twenty-one studies reported the impact of 12 different health information technology (HIT) implementations on nurses' documentation time. Weighted averages were calculated for studies that implemented barcode medication administration (BCMA) and 2 weighted averages for those that implemented EHRs, as these studies used different sampling units; both showed an increase in the time spent in documentation (+22% and +46%). However, the time spent carrying out medication administration following BCMA implementation fell by 33% (P < .05). HIT also caused a redistribution of nurses' time which, in some cases, was spent in more "value-adding" activities, such as delivering direct patient care as well as inter-professional communication.

DISCUSSION AND CONCLUSIONS

Most of the HIT systems increased nursing documentation time, although time fell for medication administration following BCMA. Many HIT systems also resulted in nurses spending more time in direct care and "value-adding" activities.

A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands

INTRODUCTION

Countries need to determine their level of digital health capability maturity to assess and mobilize their knowledge, skills, and resources to systematically develop, implement, evaluate, scale up and maintain large-scale implementations of standards-based interoperable digital health tools.

OBJECTIVE

Develop a Digital Health Profile and Maturity Assessment Toolkit (DHPMAT) to assist Pacific Island Countries (PICs) to harness digital tools to support national health priorities.

MATERIALS AND METHODS

A literature review guided the development of the conceptual framework to underpin the DHPMAT. Key informants collaborated to collect key digital health features and indicators to inform their country's digital health maturity assessment. The DHPMAT was tested with country stakeholders at a Pacific Health Information Network workshop in 2019.

RESULTS

A comprehensive list of indicators to describe country digital health profiles (DHP). A digital health maturity assessment tool that uses criteria codeveloped with country stakeholders to assess essential digital health foundations and quality improvement. DHPs created and maturity assessed and packaged into individualized DHPMATs for 13 PICs. PIC users perceived the DHPMAT as useful, especially the congruence with the 2017 WHO WPRO Regional Strategy but noted a "cognitive overload" from a plethora of complex digital health toolkits.

CONCLUSIONS

The cocreation approach optimized currency, accuracy, and appropriateness of information in the DHP, understanding, and use of the DHPMAT to facilitate informed iterative discussion by PICs on their digital health maturity to harness digital tools to strengthen country health systems. The DHPMAT can rationalize the choice and use of existing tools and reduce cognitive overload.

The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration

OBJECTIVE

Clinical research informatics tools are necessary to support comprehensive studies of infectious diseases. The National Institute of Allergy and Infectious Diseases (NIAID) developed the publicly accessible Tuberculosis Data Exploration Portal (TB DEPOT) to address the complex etiology of tuberculosis (TB).

MATERIALS AND METHODS

TB DEPOT displays deidentified patient case data and facilitates analyses across a wide range of clinical, socioeconomic, genomic, and radiological factors. The solution is built using Amazon Web Services cloud-based infrastructure, .NET Core, Angular, Highcharts, R, PLINK, and other custom-developed services. Structured patient data, pathogen genomic variants, and medical images are integrated into the solution to allow seamless filtering across data domains.

RESULTS

Researchers can use TB DEPOT to query TB patient cases, create and save patient cohorts, and execute comparative statistical analyses on demand. The tool supports user-driven data exploration and fulfills the National Institute of Health's Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

DISCUSSION

TB DEPOT is the first tool of its kind in the field of TB research to integrate multidimensional data from TB patient cases. Its scalable and flexible architectural design has accommodated growth in the data, organizations, types of data, feature requests, and usage. Use of client-side technologies over server-side technologies and prioritizing maintenance have been important lessons learned. Future directions are dynamically prioritized and key functionality is shared through an application programming interface.

CONCLUSION

This paper describes the platform development methodology, resulting functionality, benefits, and technical considerations of a clinical research informatics application to support increased understanding of TB.

[Development of Clinical Information Navigation System Based on 3D Human Model]

A clinical information navigation system based on 3D human body model is designed. The system extracts the key information of diagnosis and treatment of patients by searching the historical medical records, and stores the focus information in a predefined structured patient instance. In addition, the rule mapping is established between the patient instance and the three-dimensional human body model, the focus information is visualized on the three-dimensional human body model, and the trend curve can be drawn according to the change of the focus, meanwhile, the key diagnosis and treatment information and the original report reference function are provided. The system can support the analysis, storage and visualization of various types of reports, improve the efficiency of doctors' retrieval of patient information, and reduce the treatment time.

How hospitals in mainland China responded to the outbreak of COVID-19 using information technology-enabled services: An analysis of hospital news webpages

OBJECTIVE

Many countries have implemented quarantine rules during the global outbreak of coronavirus disease 2019 (COVID-19). Understanding how hospitals can continue providing services in an effective manner under these circumstances is thus important. In this study, we investigate how information technology (IT) helped hospitals in mainland China better respond to the outbreak of the pandemic.

MATERIALS AND METHODS

We conducted a content analysis of pages published on the websites of the top 50 hospitals in mainland China between January 22 and February 21, 2020. In total, we analyzed 368 pages that the hospitals published during the initial days of the COVID-19 pandemic. The purpose was to identify common themes related to the utilization of IT by these hospitals in response to the pandemic's outbreak.

RESULTS

We identified 5 focal themes across the webpages published by the hospitals during our study period, including (1) popular medical science education, (2) digitalized hospital processes, (3) knowledge management for medical professionals, (4) telemedicine, and (5) new IT initiatives for healthcare services. Our analysis revealed that Chinese hospitals spent greater effort in promoting popular medical science education in the initial stages of our study period and more on telemedicine in the latter stages.

DISCUSSION

We propose a configurational approach for hospitals to design response strategies to pandemic outbreaks based on their available resources.

CONCLUSIONS

Our study provides rich insights for hospitals to better utilize their IT resources and some recommendations for policymaker to better support hospitals in the future.

Comparison of Smart Contract Blockchains for Healthcare Applications

Blockchain and smart contracts (i.e., computer code that can be run on blockchain) are increasingly popular for healthcare applications. However, only very few implementations exist because of the complexity of the technologies. Although there are tutorials and reviews to introduce blockchain and smart contracts, a pragmatic comparison of such platforms is needed. In this study, we addressed practical considerations while building a healthcare blockchain and smart contract system, by (1) comparing technical features of platforms, (2) selecting three platforms, (3) constructing blockchain networks, (4) testing the blockchains, and (5) summarizing the experience and time used for implementation by students. We evaluated Ethereum, Hyperledger Fabric, and MultiChain, and confirmed that the selection of a proper platform depends on the requirements of the application. The findings of our study can accelerate the process and reduce the risk of adopting blockchain technology in biomedical and healthcare domain.

ITCBio, a Clinical and Translational Research Platform

An informatics platform has been designed, deployed and validated around the ITCBio initiative to provide support to clinical and translational research in Andalusia. To this end, an infrastructure has been developed which, in a scalable manner, incorporates functionalities aimed to facilitate the consistent definition of information models, the data reusability from electronic health records, as well as the analysis and processing of information. All this with the purpose of providing support to the different clinical and translational research processes associated with clinical trials and research projects. This initiative is based on the creation of a suite of applications that, through using standards, incorporates open-software tools intended to support these research processes. It is currently in widespread and growing use in university hospitals in which the platform is deployed.

Smartphone applications for depression: a systematic literature review and a survey of health care professionals' attitudes towards their use in clinical practice

Smartphone applications ("apps") may contribute to closing the treatment gap for depression by reaching large populations at relatively low costs. The general public seems open towards the use of apps for mental disorders but less is known about the attitudes of health care professionals. Therefore, the aim of this study was to examine the available evidence on the effectiveness of apps for depression and to explore the attitudes of health care professionals towards their use in practice. A systematic literature search was performed aimed at studies utilizing smartphone applications for depression. In addition, a survey was conducted to explore health care professionals' attitudes towards using these treatment apps in clinical practice. Twelve articles were identified in the systematic literature review. All included trials reported a decline in depressive symptoms after the intervention periods. In the survey, 72 health care professionals participated. Significant differences were found between the level of technology experience and how much the health care professional would consider the use of mobile applications in clinical practice. Survey participants reported openness towards therapeutic app use but very little knowledge and experience in the field. Apps appear to be a promising self-management tool for reducing depressive symptoms. Despite some concerns, health care professionals' attitudes towards the use of smartphone applications in clinical practice are quite positive. The provision of information on the potential benefits of e-health interventions as well as the training of professionals in the application of new technologies may increase health care professionals' awareness and knowledge about mobile apps for the treatment of mental disorders.

Digital pills: a scoping review of the empirical literature and analysis of the ethical aspects

BACKGROUND

Digital Pills (DP) are an innovative drug-device technology that permits to combine traditional medications with a monitoring system that automatically records data about medication adherence as well as patients' physiological data. Although DP are a promising innovation in the field of digital medicine, their use has also raised a number of ethical concerns. These ethical concerns, however, have been expressed principally from a theoretical perspective, whereas an ethical analysis with a more empirically oriented approach is lacking. There is also a lack of clarity about the empirical evidence available concerning the application of this innovative digital medicine.

METHODS

To map the studies where DP have been tested on patients and discuss the ethically relevant issues evident therein, we performed a scoping review of the empirical literature concerning DP.

RESULTS

Our search allowed us to identify 18 papers reporting on studies where DP were tested on patients. These included studies with different designs and involving patients with a variety of conditions. In the empirical literature, a number of issues with ethical relevance were evident. At the patient level, the ethical issues include users' interaction with DP, personal sphere, health-related risks and patients' benefits. At the provider level, ethically relevant issues touch upon the doctor-patient relationship and the question of data access. At the societal level, they concern the benefits to society, the quality of evidence and the dichotomy device-medicine.

CONCLUSIONS

We conclude that evidence concerning DP is not robust and that more research should be performed and study results made available to evaluate this digital medicine. Moreover, our analysis of the ethically relevant aspects within empirical literature underscores that there are concrete and specific open questions that should be tackled in the ethical discussion about this new technological solution.

Health information technology:Fallacies and Sober realities - Redux A homage to Bentzi Karsh and Robert Wears

Since the publication of "Health Information Technology: Fallacies and Sober Realities" in 2010, health information technology (HIT) has become nearly ubiquitous in US healthcare facilities. Yet, HIT has yet to achieve its putative benefits of higher quality, safer, and lower cost care. There has been variable but largely marginal progress at addressing the 12 HIT fallacies delineated in the original paper. Here, we revisit several of the original fallacies and add five new ones. These fallacies must be understood and addressed by all stakeholders for HIT to be a positive force in achieving the high value healthcare system the nation deserves. Foundational cognitive and human factors engineering research and development continue to be essential to HIT development, deployment, and use.

The development of a theory-based eHealth app prototype to promote oral health during prenatal care visits

Poor maternal oral health during pregnancy is associated with adverse maternal and child outcomes, including preterm birth and early childhood caries. Subsequently, professional associations have developed prenatal oral health guidelines, but significant gaps exist in implementing guidelines into clinical practice. The purpose of this study was to develop and test the usability of an innovative, theory-driven, eHealth application ("app") to facilitate prenatal providers' (nurse practitioners and midwives) implementation of oral health promotion during prenatal care visits. App development was guided by previous research, an integrated conceptual framework, Scientific Advisory Board input, and consumer-engaged iterative processes utilizing mixed-methods (observations, surveys, in-depth interviews) among providers (n = 4) during 10 unique prenatal care visits at a federally qualified health care center. Triangulation of quantitative and qualitative data analysis produced descriptive frequencies and salient themes. Concepts and principles from the following theoretical frameworks informed intervention development and testing: Consolidated Framework for Implementation Research; Information-Motivation-Behavioral Skills Model; Health Literacy; and Brief Motivational Interviewing. Overall, providers reported the app was effective at providing the information, motivation, and behavioral skills needed to integrate oral health promotion (e.g., easy to use; provided cues to action via scripts and tailored education; and documented findings into the patient's record). Although providers reported high usability, time constraints and detailed patient counseling scripts were identified areas for improvement. Findings suggest that the eHealth app could serve as an innovative mechanism to assist providers in implementing the prenatal oral health guidelines into practice. Future research is needed to continue app development efforts and to determine efficacy and effectiveness in practice settings.

Use of mobile apps and technologies in child and adolescent mental health: a systematic review

QUESTION

This review will aim to critically evaluate the currently available literature concerning the use of online mobile-based applications and interventions in the detection, management and maintenance of children and young people's mental health and well-being.

STUDY SELECTION AND ANALYSIS

A systematic literature search of six electronic databases was conducted for relevant publications until May 2019, with keywords pertaining to mental health, well-being and problems, mobile or internet apps or interventions and age of the study population. The resulting titles were screened and the remaining 92 articles were assessed against the inclusion and exclusion criteria with a total of 4 studies included in the final review.

FINDINGS

In general, young people seem to engage very well with this type of tools, and they demonstrate some positive effects in emotional self-awareness. There have been some studies about this issue and many of the outcomes were notstatistically significant. However, it is still a sparsely documented area, and more research is needed in order to prove these effects.

CONCLUSIONS

Mental health apps directed at young people have the potential to be important assessment, management and treatment tools, therefore creating easier access to health services, helping in the prevention of mental health issues and capacitating to self-help in case of need. However, a limited number of studies are currently available, and further assessments should be made in order to determine the outcomes of this type of interventions.

MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction

Lung cancer postoperative complication prediction (PCP) is significant for decreasing the perioperative mortality rate after lung cancer surgery. In this paper we concentrate on two PCP tasks: (1) the binary classification for predicting whether a patient will have postoperative complications; and (2) the three-class multi-label classification for predicting which postoperative complication a patient will experience. Furthermore, an important clinical requirement of PCP is the extraction of crucial variables from electronic medical records. We propose a novel multi-layer perceptron (MLP) model called medical MLP (MediMLP) together with the gradient-weighted class activation mapping (Grad-CAM) algorithm for lung cancer PCP. The proposed MediMLP, which involves one locally connected layer and fully connected layers with a shortcut connection, simultaneously extracts crucial variables and performs PCP tasks. The experimental results indicated that MediMLP outperformed normal MLP on two PCP tasks and had comparable performance with existing feature selection methods. Using MediMLP and further experimental analysis, we found that the variable of "time of indwelling drainage tube" was very relevant to lung cancer postoperative complications.

Constructing an experiential education model in undergraduate radiology education by the utilization of the picture archiving and communication system (PACS)

BACKGROUND

Medical education in China is in a transitional period, from passive learning models to experiential education. We modified an experiential education method for radiology education. The aim of this study is to evaluate the effect of this method on undergraduate radiology education.

METHOD

With the help of the picture archiving and communication system (PACS) and RadiAnt DICOM Viewer, we modified an experiential education method that simulates similar working conditions for undergraduate medical students to formulate radiology diagnosis similar to clinical radiologists. A total of 101 students were allocated into either the experiential education group or the control group. The final examination scores and a 5-point Likert scale self-assessment questionnaire of radiologic skills were collected from all the students as an objective assessment and a subjective assessment respectively. A questionnaire was also used to assess the satisfaction with the experiential model in the experiential education group. Mann-Whitney U test was used to compare the ranked data, and t-tests were used to compare the numeric data.

RESULTS

The experiential education group demonstrated significantly higher scores (7.4 ± 1.3) compared to the control group (6.7 ± 1.5, p < 0.05) in the question type "description and diagnosis". The self-assessment questionnaire indicated that the experiential education was related to increased familiarity with the diagnosis thinking principle and the sequences and reconstruction methods of computer tomography (CT) imaging, which also strengthen participants' self-confidence to perform future clinical work (p < 0.05). The self-assessment questionnaire in the experiential education group showed that the majority of students were satisfied with the organization (82.5%), interactivity (85%) and quality (85%) of the learning activity. Most students found this model of learning to be helpful for studying radiology (85%) and for understanding anatomy (90%).

CONCLUSION

Compared with the traditional radiology education approach, the experiential education method showed greater efficacy in improving students' analysis and diagnostic skills and their self-confidence.

Case Study: Innovation Procurement for a Digital Services Platform

This case describes a design contest strategy to procure a solution to coordination of care transitions across healthcare programs to strengthen patient outcomes. The fit of the vendors' approach with the organization and the potential for building a strong relationship with the vendor teams were evaluated. A consortium of small Canadian companies was selected to proceed to a proof-of-concept phase and full implementation of the digital solution across the region. This design contest approach resulted in a successful vendor partnership for the organization to co-design, develop, implement and scale an innovative solution to support care transitions across the region.

Six habits of highly successful health information technology: powerful strategies for design and implementation

Healthcare information technologies are now a routine component of patient-clinician interactions. Originally designed for operational functions including billing and regulatory compliance, these systems have had unintended consequences including increased exam room documentation, divided attention during the visit, and use of scribes to alleviate documentation burdens. In an age in which technology is ubiquitous in everyday life, we must re-envision healthcare technology to support both clinical operations and, above all, the patient-clinician relationship. We present 6 habits for designing user-centered health technologies: (1) put patient care first, (2) assemble a team with the right skills, (3) relentlessly ask WHY, (4) keep it simple, (5) be Darwinian, and (6) don't lose the forest for the trees. These habits should open dialogues between developers, implementers, end users, and stakeholders, as well as outline a path for better, more usable technology that puts patients and their clinicians back at the center of care.

Mobile Health, Smartphone/Device, and Apps for Psychiatry and Medicine: Competencies, Training, and Faculty Development Issues

Faculty and trainees need clinical skills, knowledge, and attitudes to ensure quality care using technology. Clinical faculty teach, supervise, and role model skills for trainees and interprofessional team members. Mobile health, smartphone/device, and app competencies may be situated within the graduate medical education milestone domains. This article outlines these competencies and aligns them with clinical care, teaching methods, and evaluation. These competencies have similarities and differences from in-person and telepsychiatric care and additional dimensions like clinical decision support, technology selection, and information flow management across an e-platform. Health systems must integrate in-person and technology-based care, while maintaining the therapeutic relationship.

Testing the Feasibility of Using Ecological Momentary Assessment to Collect Real-Time Behavior and Mood to Predict Technology-Measured HIV Medication Adherence

Identifying distinct patterns of behavior and mood in natural environments that interrupt medication adherence among individuals with HIV will be useful in informing intervention development. This pilot study assessed the initial efficacy of using ecologic momentary assessment to define patterns of alcohol use, mood, and medication adherence. Participants reported intraday alcohol use and mood using app-enabled smartphones and MEMSCap pill bottles to measure medication adherence. There were 34 enrolled participants, 29 of whom completed the 28-day study. Participants drank a mean of 7.75 days of the study period. The positive and negative affect scores ranged from 10 to 50, with a mean of 25.7 and 11.4 for each, respectively. The average medication adherence for the sample was 94.1%. These findings suggest these types of data collection methods are increasingly acceptable in measuring real-time mood and behavior, which may better inform interventions addressed at increasing HIV adherence practices.

Human Factors and Usability for Health Information Technology: Old and New Challenges

OBJECTIVES

Despite national mandates, incentives, and other programs, the design of health information technology (IT) remains problematic and usability problems continue to be reported. This paper reviews recent literature on human factors and usability of health IT, with a specific focus on research aimed at applying human factors methods and principles to improve the actual design of health IT, its use, and associated patient and clinician outcomes.

METHODS

We reviewed recent literature on human factors and usability problems of health IT and research on human-centered design of health IT for clinicians and patients.

RESULTS

Studies continue to show usability problems of health IT experienced by multiple groups of health care professionals (e.g., physicians and nurses) as well as patients. Recent research shows that usability is influenced by both designers (e.g., IT vendors) and implementers in health care organizations, and that the application of human-centered design practices needs to be further improved and extended. We welcome emerging research on the design of health IT for teams as team-based care is increasingly implemented throughout health care.

CONCLUSIONS

Progress in the application of human factors methods and principles to the design of health IT is occurring, with important information provided on their actual impact on care processes and patient outcomes. Future research should examine the work of health IT designers and implementers, which would help to develop strategies for further embedding human factors engineering in IT design processes.

Patient decision aids: a content analysis based on a decision tree structure

INTRODUCTION

This paper presents the preliminary results of a decision-tree analysis of Patient Decision Aids (PDA). PDAs are online or offline tools used to structure health information, elicit relevant values and emphasize the decision as a process, in ways that help patients make more informed health decisions individually or with relevant others.

METHOD

Twenty PDAs are randomly selected from the International Patient Decision Aids Standards (IPDAS) ( https://decisionaid.ohri.ca/AZlist.html ) approved list. An evaluation tool is built bottom-up and top-down and results are described in terms of communicating uncertainty, completeness of the decision tree, ambiguous or misleading phrasing, overall strategies suggested within personal stories.

RESULTS

Twelve of the analyzed PDAs had branches of the decision tree which were not discussed in the tool and 6 had logically ambiguous phrasing. Many tools included dichotomous options, when the option range was wider. Several options were clustered within the "Do not take/Do not do" option and thus the PDA failed to provide all comparisons necessary to make a decision. Some tools employ expressions that do not differentiate between lack of information and known negative effects. Other tools provide unequal amounts or non-comparable bits of information about the options.

CONCLUSION

These results indicate a very loose range of interpretations of what constitutes an option, a treatment, and a treatment option. It thus emphasizes a gap between theory and practice in the evaluation of PDAs. Future developments of PDA evaluation tools should keep track of missing decision tree branches, accurate communication of uncertainty, ambiguity, and lack of knowledge and consider using measures for evaluating the completeness of the option spectrum at an agreed period in time.

Hummingbird Study: a study protocol for a multicentre exploratory trial to assess the acceptance and performance of a digital medicine system in adults with schizophrenia, schizoaffective disorder or first-episode psychosis

INTRODUCTION

In patients with schizophrenia, medication adherence is important for relapse prevention, and effective adherence monitoring is essential for treatment planning. A digital medicine system (DMS) has been developed to objectively monitor patient adherence and support clinical decision making regarding treatment choices. This study assesses the acceptance and performance of the DMS in adults with schizophrenia, schizoaffective disorder or first-episode psychosis and in healthcare professionals (HCPs).

METHODS/ANALYSIS

This is a multicentre, 8-week, single-arm, open-label pragmatic trial designed using coproduction methodology. The study will be conducted at five National Health Service Foundation Trusts in the UK. Patients 18-65 years old with a diagnosis of schizophrenia, schizoaffective disorder or first-episode psychosis will be eligible. HCPs (psychiatrists, care coordinators, nurses, pharmacists), researchers, information governance personnel, clinical commissioning groups and patients participated in the study design and coproduction. Intervention employed will be the DMS, an integrated system comprising an oral sensor tablet coencapsulated with an antipsychotic, non-medicated wearable patch, mobile application (app) and web-based dashboard. The coencapsulation product contains aripiprazole, olanzapine, quetiapine or risperidone, as prescribed by the HCP, with a miniature ingestible event marker (IEM) in tablet. On ingestion, the IEM transmits a signal to the patch, which collects ingestion and physical activity data for processing on the patient's smartphone or tablet before transmission to a cloud-based server for viewing by patients, caregivers and HCPs on secure web portals or mobile apps.

ETHICS AND DISSEMINATION

Approval was granted by London - City and East Research Ethics Committee (REC ref no 18/LO/0128), and clinical trial authorisation was provided by the Medicines and Healthcare products Regulatory Agency. Written informed consent will be obtained from every participant. The trial will be compliant with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines and the Declaration of Helsinki.

TRIAL REGISTRATION NUMBER

NCT03568500; EudraCT2017-004602-17; Pre-results.

A systematic review of the effects of implementing clinical pathways supported by health information technologies

OBJECTIVE

Health information technology (HIT) interventions include electronic patient records, prescribing, and ordering systems. Clinical pathways are multidisciplinary plans of care that enable the delivery of evidence-based healthcare. Our objective was to systematically review the effects of implementing HIT-supported clinical pathways.

MATERIALS AND METHODS

A systematic review protocol was developed including Medline, Embase, and CENTRAL database searches. We recorded data relating to study design, participants, intervention, and outcome characteristics and formally assessed risk of bias.

RESULTS

Forty-four studies involving more than 270 000 patients were included. Investigation methodologies included before-after (n = 16, 36.4%), noncomparative (n = 14, 31.8%), interrupted time series (n = 5, 11.4%), retrospective cohort (n = 4, 9.1%), cluster randomized (n = 2, 4.5%), controlled before-after (n = 1, 2.3%), prospective case-control (n = 1, 2.3%), and prospective cohort (n = 1, 2.3%) study designs. Clinical decision support (n = 25, 56.8%), modified electronic documentation (n = 23, 52.3%), and computerized provider order entry (n = 23, 52.3%) were the most frequently utilized HIT interventions. The majority of studies (n = 38, 86.4%) reported benefits associated with HIT-supported pathways. These included reported improvements in objectively measured patient outcomes (n = 15, 34.1%), quality of care (n = 29, 65.9%), and healthcare resource utilization (n = 10, n = 22.7%).

DISCUSSION

Although most studies reported improvements in outcomes, the strength of evidence was limited by the study designs that were utilized.

CONCLUSIONS

Ongoing evaluations of HIT-supported clinical pathways are justified but would benefit from study designs that report key outcomes (including adverse events) and minimize the risk of bias.

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

IMPORTANCE

Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.

OBJECTIVE

To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.

DESIGN, SETTING, AND PARTICIPANTS

A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.

EXPOSURES

Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.

MAIN OUTCOMES AND MEASURES

Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity.

RESULTS

Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.

CONCLUSIONS AND RELEVANCE

Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.

Exploring the role of competing demands and routines during the implementation of a self-management tool for type 2 diabetes: a theory-based qualitative interview study

BACKGROUND

The implementation of new medical interventions into routine care involves healthcare professionals adopting new clinical behaviours and changing existing ones. Whilst theory-based approaches can help understand healthcare professionals' behaviours, such approaches often focus on a single behaviour and conceptualise its performance in terms of an underlying reflective process. Such approaches fail to consider the impact of non-reflective influences (e.g. habit and automaticity) and how the myriad of competing demands for their time may influence uptake. The current study aimed to apply a dual process theoretical approach to account for reflective and automatic determinants of healthcare professional behaviour while integrating a multiple behaviour approach to understanding the implementation and use of a new self-management tool by healthcare professionals in the context of diabetes care.

METHODS

Following Diabetes UK's national release of the 'Information Prescription' (DUK IP; a self-management tool targeting the management of cholesterol, blood pressure and HbA1c) in January 2015, we conducted semi-structured interviews with 13 healthcare professionals (general practitioners and nurses) who had started to use the DUK IP during consultations to provide self-management advice to people with type 2 diabetes. A theory-based topic guide included pre-specified constructs from a previously developed logic model. We elicited healthcare professionals' views on reflective processes (outcome expectations, self-efficacy, intention, action and coping planning), automatic processes (habit), and multiple behaviour processes (goal priority, goal conflict and goal facilitation). All interviews were audio recorded and transcribed verbatim and all transcripts were independently double coded and analysed using content analysis.

RESULTS

The majority of healthcare professionals interviewed reported strong intentions to use the DUK IP and having formed a habit of using them after a minimum of one month continuous use. Pop-up cues in the electronic patient records were perceived to facilitate the use of the tool. Factors that conflicted with the use of the DUK IP included existing pathways of providing self-management advice.

CONCLUSION

Data suggests that constructs from dual process and multiple behaviour approaches are useful to provide supplemental understanding of the implementation of new self-management tools such as the DUK IP and may help to advance behavioural approaches to implementation science.

Diabetes Technology Society Report on the FDA Digital Health Software Precertification Program Meeting

Diabetes Technology Society (DTS) convened a meeting about the US Food and Drug Administration (FDA) Digital Health Software Precertification Program on August 28, 2018. Forty-eight attendees participated from clinical and academic endocrinology (both adult and pediatric), nursing, behavioral health, engineering, and law, as well as representatives of FDA, National Institutes of Health (NIH), National Telecommunications and Information Administration (NTIA), and industry. The meeting was intended to provide ideas to FDA about their plan to launch a Digital Health Software Precertification Program. Attendees discussed the four components of the plan: (1) excellence appraisal and certification, (2) review pathway determination, (3) streamlined premarket review process, and (4) real-world performance. The format included (1) introductory remarks, (2) a program overview presentation from FDA, (3) roundtable working sessions focused on each of the Software Precertification Program's four components, (4) presentations reflecting the discussions, (5) questions to and answers from FDA, and (6) concluding remarks. The meeting provided useful information to the diabetes technology community and thoughtful feedback to FDA.

Combination of conditional random field with a rule based method in the extraction of PICO elements

BACKGROUND

Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it from unstructured information. The combination of the machine learning methods (MLMs) with rule based methods (RBMs) could facilitate and improve the PICO extraction. This paper studies the PICO elements extraction methods. The goal is to combine the MLMs with the RBMs to extract PICO elements in medical papers to facilitate answering clinical questions formulated with the PICO framework.

METHODS

First, we analyze the aspects of the MLM model that influence the quality of the PICO elements extraction. Secondly, we combine the MLM approach with the RBMs in order to improve the PICO elements retrieval process. To conduct our experiments, we use a corpus of 1000 abstracts.

RESULTS

We obtain an F-score of 80% for P element, 64% for the I element and 92% for the O element. Given the nature of the used training corpus where P and I elements represent respectively only 6.5 and 5.8% of total sentences, the results are competitive with previously published ones.

CONCLUSIONS

Our study of the PICO element extraction shows that the task is very challenging. The MLMs tend to have an acceptable precision rate but they have a low recall rate when the corpus is not representative. The RBMs backed up the MLMs to increase the recall rate and consequently the combination of the two methods gave better results.

Variant information systems for precision oncology

BACKGROUND

The decreasing cost of obtaining high-quality calls of genomic variants and the increasing availability of clinically relevant data on such variants are important drivers for personalized oncology. To allow rational genome-based decisions in diagnosis and treatment, clinicians need intuitive access to up-to-date and comprehensive variant information, encompassing, for instance, prevalence in populations and diseases, functional impact at the molecular level, associations to druggable targets, or results from clinical trials. In practice, collecting such comprehensive information on genomic variants is difficult since the underlying data is dispersed over a multitude of distributed, heterogeneous, sometimes conflicting, and quickly evolving data sources. To work efficiently, clinicians require powerful Variant Information Systems (VIS) which automatically collect and aggregate available evidences from such data sources without suppressing existing uncertainty.

METHODS

We address the most important cornerstones of modeling a VIS: We take from emerging community standards regarding the necessary breadth of variant information and procedures for their clinical assessment, long standing experience in implementing biomedical databases and information systems, our own clinical record of diagnosis and treatment of cancer patients based on molecular profiles, and extensive literature review to derive a set of design principles along which we develop a relational data model for variant level data. In addition, we characterize a number of public variant data sources, and describe a data integration pipeline to integrate their data into a VIS.

RESULTS

We provide a number of contributions that are fundamental to the design and implementation of a comprehensive, operational VIS. In particular, we (a) present a relational data model to accurately reflect data extracted from public databases relevant for clinical variant interpretation, (b) introduce a fault tolerant and performant integration pipeline for public variant data sources, and (c) offer recommendations regarding a number of intricate challenges encountered when integrating variant data for clincal interpretation.

CONCLUSION

The analysis of requirements for representation of variant level data in an operational data model, together with the implementation-ready relational data model presented here, and the instructional description of methods to acquire comprehensive information to fill it, are an important step towards variant information systems for genomic medicine.

Modeling EHR with the openEHR approach: an exploratory study in China

BACKGROUND

The openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling. Developing archetypes for the complete EHR dataset is essential for implementing a large-scale interoperable EHR system with the openEHR approach. Although the openEHR approach has been applied in different domains, the feasibility of archetyping a complete EHR dataset in a hospital has not been reported in academic literature, especially in a country where using openEHR is still in its infancy stage, like China. This paper presents a case study of modeling an EHR in China aiming to investigate the feasibility and challenges of archetyping a complete EHR dataset with the openEHR approach.

METHODS

We proposed an archetype modeling method including an iterative process of collecting requirements, normalizing data elements, organizing concepts, searching corresponding archetypes, editing archetypes and reviewing archetypes. Two representative EHR systems from Chinese vendors and the existing Chinese EHR standards have been used as resources to identify the requirements of EHR in China, and a case study of modeling EHR in China has been conducted. Based on the models developed in this case study, we have implemented a clinical data repository (CDR) to verify the feasibility of modeling EHR with archetypes.

RESULTS

Sixty four archetypes were developed to represent all requirements of a complete EHR dataset. 59 (91%) archetypes could be found in Clinical Knowledge Manager (CKM), of which 35 could be reused directly without change, and 23 required further development including two revisions, two new versions, 18 extensions and one specialization. Meanwhile, 6 (9%) archetypes were newly developed. The legacy data of the EHR system in hospitals could be integrated into the CDR developed with these archetypes successfully.

CONCLUSIONS

The existing archetypes in CKM can faithfully represent most of the EHR requirements in China except customizations for local hospital management. This case study verified the feasibility of modeling EHR with the openEHR approach and identified the fact that the challenges such as localization, tool support, and an agile publishing process still exist for a broader application of the openEHR approach.

Implementation of informatics for integrating biology and the bedside (i2b2) platform as Docker containers

BACKGROUND

Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform. The goal of this work is to simplify the installation and upgrade process for i2b2. Specifically, we have containerized the core components of the platform, and evaluated the containers for ease of installation.

RESULTS

We developed three Docker container images: WildFly, database, and web, to encapsulate the three major deployment components of i2b2. These containers isolate the core functionalities of the i2b2 platform, and work in unison to provide its functionalities. Our evaluations indicate that i2b2 containers function successfully on the Linux platform. Our results demonstrate that the containerized components work out-of-the-box, with minimal configuration.

CONCLUSIONS

Containerization offers the potential to package the i2b2 platform components into standalone executable packages that are agnostic to the underlying host operating system. By releasing i2b2 as a Docker container, we anticipate that users will be able to create a working i2b2 hive installation without the need to download, compile, and configure individual components that constitute the i2b2 cells, thus making this platform accessible to a greater number of institutions.

Current Regulation of Mobile Mental Health Applications

In recent years, the availability of software that is targeted toward the general public and designed to assist in the diagnosis and treatment of mental illness or to promote general mental health has expanded greatly. Regulation of more traditional health care providers and health care-associated devices is well established by statute, regulatory guidelines, and common law precedents. Applications (apps), in contrast, pose a novel regulatory challenge. This review examines the current regulatory guidelines for psychiatric mobile mental health apps, as well as the current state of case law in the psychiatric mobile mental health realm.