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Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.
JMIR Medical Informatics (JMI, ISSN 2291-9694) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2017: 4.671), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.
JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).
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Background: Digital transformation in healthcare is being driven by the need to improve quality, reduce costs and enhance the patient experience of healthcare delivery. It does this through both the...
Background: Digital transformation in healthcare is being driven by the need to improve quality, reduce costs and enhance the patient experience of healthcare delivery. It does this through both the direct intervention of technology to create new diagnostic and treatment opportunities, but also through the improved use of information to create more engaging and efficient care processes. Objective: In a modern digital hospital, improved clinical and business processes are often driven through enhancing the information flows which support them. To understand an organization’s ability to transform their information flows requires a clear understanding of the capabilities of an organization’s information technology infrastructure. To date, hospital facilities have been challenged by the absence of uniform ways of describing this infrastructure that would enable them to benchmark where they are and create a vision of where they would like to be. Whilst there an industry assessment measure for electronic medical record (EMR) adoption using the HIMSS Analytics EMR Adoption Model (EMRAM), there is no equivalent for assessing the infrastructure and associated technology capabilities for digital hospitals. It is important to fill this gap as hospital administrators and clinicians need to know how and why to invest in supporting information infrastructure to realize the benefits in patient safety and patient care. Methods: Using an Operational Framework for Capability Maturity Modelling, devised specifically for healthcare, information use characteristics are used to define eight Information Systems maturity levels and associated technology infrastructure capabilities. These levels are mapped to user experiences to create a linkage between technology infrastructure and experience outcomes. Subsequently, specific technology capabilities are deconstructed to identify the technology features required to meet each maturity level. Results: The resulting assessment framework clearly defines 164 individual capabilities across the five technology domains and eight maturity levels in the infrastructure continuum. These level-dependent capabilities characterize the ability of the hospital’s information infrastructure to support the business of digital hospitals including clinical and administrative requirements. Further, it allows the addition of a scoring calculation for each capability, domain and the overall infrastructure, and identifies critical requirements to meet each of the eight maturity levels. Conclusions: This new Infrastructure Maturity Assessment framework will allow digital hospitals to assess the maturity of their infrastructure in terms of their digital transformation aligning to business outcomes and to meet the desired level of clinical and operational competency. It provides the ability to establish an international benchmark of hospital infrastructure performance, whilst identifying weaknesses in current infrastructure capability. Further, it provides a business case justification and roadmap for subsequent digital transformation and demonstrates the derived value of moving from one maturity level to the next. As such, this framework will lead the information driven, digital transformation in healthcare.
Background: Word embedding technologies are now used in a wide range of applications. However, no formal evaluation and comparison have been made on models produced by the three most famous implementa...
Background: Word embedding technologies are now used in a wide range of applications. However, no formal evaluation and comparison have been made on models produced by the three most famous implementations (Word2Vec, GloVe and FastText). Objective: The goal of this study is to compare embedding implementations on a corpus of documents produced in a working context, by health professionals. Methods: Models have been trained on documents coming from the Rouen university hospital. This data is not structured and cover a wide range of documents produced in a clinic (discharge summary, prescriptions ...). Four evaluation tasks have been defined (cosine similarity, odd one, mathematical operations and human formal evaluation) and applied on each model. Results: Word2Vec had the highest score for three of the four tasks (mathematical operations, odd one similarity and human validation), particularly regarding the Skip-Gram architecture. Conclusions: Even if this implementation had the best rate, each model has its own qualities and defects, like the training time which is very short for GloVe or morphosyntaxic similarity conservation observed with FastText. Models and test sets produced by this study will be the first publicly available through a graphical interface to help advance French biomedical research.
Background: Monitoring the effectiveness of the influenza vaccination programme within the UK is necessary in order to assess its clinical impact. Data are collected from general practice sentinel net...
Background: Monitoring the effectiveness of the influenza vaccination programme within the UK is necessary in order to assess its clinical impact. Data are collected from general practice sentinel network computerised medical record (CMR) systems on patients from whom virology specimens have been taken for influenza. The data collected includes demographics, comorbidities, vaccine exposure and if patients have had a virology specimen taken. Unfortunately not all virology specimens collected can be used in the vaccine effectiveness (VE) studies conducted. Objective: To describe the proportion, reasons and any trends in virology specimen data collected but not used in influenza VE analyses, with the goal of defining strategies to reduce collection of specimens ineligible for use in VE studies. Methods: We examined UK influenza VE studies from the past 10 years and identified incidences where data were labelled unusable. We categorised reasons for not using data as: (1) Vaccination history: Missing or Uncertain categories (including patient not registered with the practice at the start of the season); (2) Swab timing: Not recorded; More than 7 days (historically over 29 days) after symptom onset or within 14 days of vaccination; (3) Laboratory: Not sufficient data for processing (e.g. no age), CT values; (4) Flu or vaccination type of no interest (including pandemic years). The proportion, reasons and trends for data loss were identified through descriptive statistics and graphical representations. We included an analysis of where other data had been available at the point of analysis but not used. Results: Over 30% (13292/41337) of virology specimen data was not used across all seasons. Data loss gradually began to decrease from 2014/15 onwards. Data loss were highest for flu or vaccination type of no interest and swab timing. Retrospective and prospective actions were identified to reduce data loss in future. Around 60% of samples could have been included if identifiable data were better shared between records. Conclusions: The reasons for excluding samples and missing data varied, particularly prior to 2014; consistent categorisation was in place from 2014 onwards. Leaving aside the different issues around pandemic years, many of the virology swabs not included were due to suboptimal case selection by practices, but over half (58%) could have been included if identifiable data were better shared between data sources. Clinical Trial: N/A
Background: Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies, such as decision support systems, have the potential...
Background: Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies, such as decision support systems, have the potential to advance the standard of perioperative patient care. Failure to evaluate the usability of these technologies and barriers to their implementation can limit their acceptance within health systems. Objective: This manuscript describes the usability and acceptability of and systematic process for developing and adapting an innovative telemedicine based clinical support system, the Anesthesiology Control Tower. It also reports stakeholders’ perceptions of the barriers and facilitators the implementation of the intervention. Methods: Three phases of testing were conducted in an iterative manner in order to evaluate both the individual components of the Anesthesiology Control Tower and their integration as a whole. Phase 1 testing employed a “think-aloud” protocol analysis to identify surface level usability problems with individual software components of the ACT, in addition to the entirety of the structure. Phase 2 testing involved an extended qualitative and quantitative in-situ usability analysis. Phase 3 sought to identify major barriers and facilitators to implementation of the ACT through semi-structured interviews with key stakeholders. Results: Numerous usability problems with the software components of the ACT were identified in the Phase 1 and Phase 2 usability testing sessions. In response to these problems, seven iterations of the ACT software platform were developed. Initial satisfaction with the ACT, as measured by standardized measures, was below commonly accepted cutoffs for these measures. Satisfaction improved to acceptable levels over the course of revision and testing. A number of barriers to implementation were identified and addressed during the refinement of the ACT intervention. Conclusions: The Anesthesiology Control Tower system has the potential to improve the standard of perioperative anesthesia care. Through our thorough and iterative usability testing process and stakeholder assessment of barriers and facilitators, we were able to maximize the acceptability of this novel technology, thus improving our ability to implement this innovation into the model of care for perioperative medicine.
Background: Telemonitoring (TM) of heart failure patients in a clinic setting has been shown to be effective if properly implemented, but little is known about the feasibility and impact of implementi...
Background: Telemonitoring (TM) of heart failure patients in a clinic setting has been shown to be effective if properly implemented, but little is known about the feasibility and impact of implementing TM through a home care nursing agency. Objective: The goal of this study was to determine the feasibility of implementing a smartphone-based TM system through a home care nursing agency, and to explore the feasibility of conducting a future effectiveness trial. Methods: A feasibility study was conducted, aiming to recruit 10-15 heart failure patients who would use the TM system for 4 months by taking daily measurements of weight and blood pressure, and recording symptoms. Home care nurses responded to alerts generated by the TM system either through a phone call and/or home visit. Results: Only six patients were recruited over a six-month period due to lack of referrals from physicians. Potential benefits of TM through a home care nursing agency were indicated, including through improved patient education, providing nurses with a better understanding of the patient’s health status, and reductions in home visits. Barriers to implementation included challenges in nurses contacting patients and physicians, retention issues, and integrating the TM system into a complex home care nursing workflow. Conclusions: Lessons learned included the need to incentivize physicians, to ensure streamlined processes for recruitment and communication, to target appropriate patient populations, and to create a core clinical group. Barriers encountered in this feasibility trial should be considered to determine their applicability when deploying innovations into different service delivery models.
Background: Telehealth has been shown to improve access to healthcare and to reduce costs to the patient and healthcare system, especially for patients living in rural settings. However, unique challe...
Background: Telehealth has been shown to improve access to healthcare and to reduce costs to the patient and healthcare system, especially for patients living in rural settings. However, unique challenges arise when implementing telehealth in remote communities. Objective: The objectives of this evaluation were to understand the current use, challenges, and opportunities of the Yukon Telehealth System. The lessons learned from this case study were used to determine important factors to consider when attempting to advance and expand telehealth programs in remote communities. Methods: A mixed-methods approach was used to evaluate the Yukon Telehealth System and to determine possible future advances. Quantitative data were obtained through usage logs. In addition, online questionnaires were administered to nurses in each of the 14 Yukon community health centres outside of Whitehorse, and patients who had used telehealth were also asked to complete a questionnaire. Qualitative data included focus groups and semi-structured interviews with a total of 36 telehealth stakeholders. Results: Since 2008, there have been a consistent total number of telehealth sessions of about 1000 per year, with the main use being for clinical care (70% of all sessions in 2015). From the questionnaire data (11 community nurses, 10 patients) and interview data, there was a consensus among the clinicians and patients that the System provided timely access and cost savings from reduced travel. However, they believed that it was underutilized and the equipment was outdated. The challenges and opportunities discovered led to the identification of four factors that should be considered when trying to advance and expand a telehealth program. 1) Patient and clinician buy-in: Past telehealth experiences should be considered when advancing the system, such as negative clinician experiences with outdated technology. Expansion of services in orthopaedics, dermatology, and psychiatry were found to have particular benefit in Yukon by clinicians specializing in these areas. 2) Workflow: The use and scheduling of telehealth should be streamlined and automated as much as possible to reduce dependencies on the single Yukon Telehealth Coordinator. 3) Access to telehealth technology: Clinicians and patients should have easy access to telehealth technology, whether it is telehealth units or alternative desktop applications. The use of consumer products, such as mobile technology, should be leveraged as appropriate. 4) Infrastructure: The required human resources and technology need to be established when expanding and advancing telehealth. Conclusions: While clinicians and patients have generally positive perceptions of the Yukon Telehealth System, there was consensus that it was underutilized. Many opportunities exist to significantly expand the types of telehealth services and the number of telehealth sessions. The lessons learned from this evaluation can be applied to other remote communities to realize telehealth’s potential as a means for efficient, safe, convenient, and cost-effective care delivery.