Incomplete patient information exchange and unnecessary repeat diagnostics during oncological referrals in the Netherlands: exploring the role of information exchange.

Data management in transmural care is complex. Without digital innovations like Health Information Exchange (HIE), patient information is often dispersed and inaccessible across health information systems between hospitals. The extent of information loss and consequences remain unclear. We aimed to quantify patient information availability of referred oncological patients and to assess its impact on unnecessary repeat diagnostics by observing all oncological multidisciplinary team meetings (MDTs) in a tertiary hospital. During 84 multidisciplinary team meetings, 165 patients were included. Complete patient information was provided in 17.6% (29/165, CI = 12.3-24.4) of patients. Diagnostic imaging was shared completely in 52.5% (74/141, CI = 43.9-60.9), imaging reports in 77.5% (100/129, CI = 69.2-84.2), laboratory results in 55.2% (91/165, CI = 47.2-62.8), ancillary test reports in 58.0% (29/50, CI = 43.3-71.5), and pathology reports in 60.0% (57/95, CI = 49.4-69.8). A total of 266 tests were performed additionally, with the main motivation not previously performed followed by inconclusive or insufficient quality of previous tests. Diagnostics were repeated unnecessarily in 15.8% (26/165, CI = 10.7-22.4) of patients. In conclusion, patient information was provided incompletely in majority of referrals discussed in oncological multidisciplinary team meetings and led to unnecessary repeat diagnostics in a small number of patients. Additional research is needed to determine the benefit of Health Information Exchange to improve data transfer in oncological care.

The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors.

More evidence is needed on technology implementation for remote monitoring and self-management across the various settings relevant to chronic conditions. This paper describes the findings of a survey designed to explore the relevance of socio-demographic factors to attitudes towards connected health technologies in a community of patients. Stroke survivors living in the UK were invited to answer questions about themselves and about their attitudes to a prototype remote monitoring and self-management app developed around their preferences. Eighty (80) responses were received and analysed, with limitations and results presented in full. Socio-demographic factors were not found to be associated with variations in participants' willingness to use the system and attitudes to data sharing. Individuals' levels of interest in relevant technology was suggested as a more important determinant of attitudes. These observations run against the grain of most relevant literature to date, and tend to underline the importance of prioritising patient-centred participatory research in efforts to advance connected health technologies.

Overall health information exposure, its barriers and impacts on attitude toward healthcare among cancer patients. The long-term aftermath of the 2011 triple disaster in Fukushima, Japan: A single institution cross-sectional study.

Disasters can hinder access to health information among cancer patients. However, little is known regarding overall health information exposure (HIE), its barriers and its impacts on attitudes toward healthcare among cancer patients in the long-term aftermath of disasters. The aims of this study were threefold: assess the extent of HIE; identify associations between family composition and a non-engagement with HIE; and examine the effects of HIE on attitudes toward healthcare among local cancer patients-5 years after the 2011 triple disaster (earthquake, tsunami, and nuclear disaster) in Fukushima, Japan. We conducted self-administered surveys with all cancer and non-cancer surgery department outpatients at Minamisoma Municipal General Hospital (MMGH), Minamisoma City, from October 2016 to January 2017. In total, 404 patients (263 cancer patients and 141 non-cancer patients) voluntarily participated in the study. The results revealed that a regular level of HIE occurred among 90.5% of the cancer patients. In cancer patients, family composition was not significantly associated with HIE, and HIE was not associated with attitude toward healthcare. In conclusion, most cancer patients visiting the MMGH surgical department were regularly engaged in HIE.

Medication reconciliation process: Assessing value, adoption, and the potential of information technology from pharmacists' perspective.

The Medication Reconciliation (MedRec) process aims to improve patient safety through safe prescription and medication administration. A validated survey was carried out to address aspects related to MedRec process, its obstacles, the role of information technology, and the required functionalities for optimizing the MedRec process. A total of 81% of the survey's respondents acknowledged the roles of EHR (62% of respondents), PHR (41%), and electronic medication registration list (33%) as necessary technology tools for MedRec. Most respondents emphasized the need to compile multiple medications' entries of information technology systems into one application (96.4%), allowing the entries from community pharmacies (90.6%). Further, incorporating information technology into the MedRec process presents a challenge in terms of legal responsibility (92 %) and the ability to integrate medications with other hospitals and community medications (78.6%). Findings affirm the need for a well-designed MedRec process aided with information technology solutions. The external data and user preferences should be considered when redesigning the MedRec process. The study also suggests initiating a policy that mandates sharing data necessary for creating a compiled medication list for each patient. MedRec is an indispensable tool for building a fruitful medication management system in a healthcare organization.

Using knowledge discovery through data mining to gain intelligence from routinely collected incident reporting in an acute English hospital.

PURPOSE: Incident reporting systems are commonly deployed in healthcare but resulting datasets are largely warehoused. This study explores if intelligence from such datasets could be used to improve quality, efficiency, and safety. DESIGN/METHODOLOGY/APPROACH: Incident reporting data recorded in one NHS acute Trust was mined for insight (n = 133,893 April 2005-July 2016 across 201 fields, 26,912,493 items). An a priori dataset was overlaid consisting of staffing, vital signs, and national safety indicators such as falls. Analysis was primarily nonlinear statistical approaches using Mathematica V11. FINDINGS: The organization developed a deeper understanding of the use of incident reporting systems both in terms of usability and possible reflection of culture. Signals emerged which focused areas of improvement or risk. An example of this is a deeper understanding of the timing and staffing levels associated with falls. Insight into the nature and grading of reporting was also gained. PRACTICAL IMPLICATIONS: Healthcare incident reporting data is underused and with a small amount of analysis can provide real insight and application to patient safety. ORIGINALITY/VALUE: This study shows that insight can be gained by mining incident reporting datasets, particularly when integrated with other routinely collected data.

The unmet needs of carers of stroke survivors: An evaluation of Google search results.

Google is the most used search engine in the world, and likely to be used by caregivers of stroke survivors to find online forums and online communities to connect with other caregivers. This study aims to identify the types of websites accessed by caregivers of stroke survivors to connect with other caregivers, and analyse the online content produced by caregivers to identify their unmet needs. The first 20 websites from eight search strings entered into Google were systematically reviewed. Unmet needs on included websites were identified using a pre-determined coding schedule. Six websites were analysed. Most were discussion boards (n = 5, 83%) developed by organisations in the United States (n = 4, 66.6%). Overall, 2124 unmet needs appeared in 896 posts from caregivers. 'Emotional and psychological' were the most reported needs across posts (n = 765, 36%). Content produced on websites may address social isolation and provide insight into delivering and developing services to meet the needs of caregivers of stroke survivors.

Current challenges in health information technology-related patient safety.

We identify and describe nine key, short-term, challenges to help healthcare organizations, health information technology developers, researchers, policymakers, and funders focus their efforts on health information technology-related patient safety. Categorized according to the stage of the health information technology lifecycle where they appear, these challenges relate to (1) developing models, methods, and tools to enable risk assessment; (2) developing standard user interface design features and functions; (3) ensuring the safety of software in an interfaced, network-enabled clinical environment; (4) implementing a method for unambiguous patient identification (1-4 Design and Development stage); (5) developing and implementing decision support which improves safety; (6) identifying practices to safely manage information technology system transitions (5 and 6 Implementation and Use stage); (7) developing real-time methods to enable automated surveillance and monitoring of system performance and safety; (8) establishing the cultural and legal framework/safe harbor to allow sharing information about hazards and adverse events; and (9) developing models and methods for consumers/patients to improve health information technology safety (7-9 Monitoring, Evaluation, and Optimization stage). These challenges represent key "to-do's" that must be completed before we can expect to have safe, reliable, and efficient health information technology-based systems required to care for patients.

Impact of an educational intervention on eye gaze behaviour in retinal image interpretation by consultant and trainee ophthalmologists.

This study uses eye-tracking technology to assess the differences in gaze behaviours between ophthalmologists of different experience levels while interpreting retinal images of diabetic retinopathy. The differences in gaze behaviours before and after a teaching intervention which introduced a suggested search strategy is also investigated. A total of 9 trainees and 10 consultant ophthalmologists interpreted six retinal images. They were then shown a 5-min tutorial that demonstrated a search strategy. This was followed by six further retinal image interpretations. Participants completed questionnaires indicating clinical signs seen, appropriate retinopathy grade, and confidence. Eye movements were tracked during each interpretation.Overall, trainees compared to consultants demonstrated more uncertain and unstructured gaze behaviours. Trainee eye gaze metrics included: longer interpretation time, 36.5 s (SD = 6.2 vs. 31.4 s) (SD = 4.2) (p = 0.024), higher visit count, 17.38 visits (SD = 5.13) versus 12.18 visits(SD = 2.64) (p = 0.01), higher proportion of fixation, 57.0 per cent (SD = 5) versus 50.5 per cent (SD = 5) (p = 0.05) and shorter time to first fixation, 0.232 s (SD = 0.10) versus 0.821 s (SD = 0.77) (p = 0.001), respectively. The teaching intervention resulted in more focused gaze patterns in both groups. Pre-intervention and post-intervention mean proportion fixation on areas of interest were 38.6 per cent (SD = 6.8) and 51.8 per cent (SD = 13.9) for the trainee group, respectively, and 39.9 per cent (SD = 4.1) and 50.9 per cent (SD = 9.3) for the consultant group (p = 0.01).Consultants used more systematic and efficient approaches than trainees during interpretation. After the introduction of a suggested search strategy, trainees showed trends towards consultant eye gaze behaviours. Eye tracking gives an interesting insight into the thought processes of physicians carrying out complex tasks. The implication is that eye tracking may have future use in teaching and assessment. Its use in objectively assessing different teaching strategies could be a valuable tool for medical education.

A machine learning-based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome.

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning-based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005-30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning-based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning-based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.

Blockchain in Healthcare: A Patient-Centered Model.

A blockchain is a system for storing and sharing information that is secure because of its transparency. Each block in the chain is both its own independent unit containing its own information, and a dependent link in the collective chain, and this duality creates a network regulated by participants who store and share the information, rather than a third party. Blockchain has many applications in healthcare, and can improve mobile health applications, monitoring devices, sharing and storing of electronic medical records, clinical trial data, and insurance information storage. Research about blockchain and healthcare is currently limited, but blockchain is on the brink of transforming the healthcare system; through its decentralized principles, blockchain can improve accessibility and security of patient information, and can therefore overturn the healthcare hierarchy and build a new system in which patients manage their own care.

The qualculative dimension of healthcare data interoperability.

Research on interoperability and information exchange between information technology systems touts the use of secondary data for a variety of purposes, including research, management, quality improvement, and accountability. However, many studies have pointed out that this is difficult to achieve in practice. Hence, this article aims to examine the causes for this by reporting an ethnographic study of the data work performed by medical records coders and birth certificate clerks working in a hospital system to uncover the practices of creating administrative data (e.g. secondary data). The article illustrates that clerks and coders use situated qualitative judgments of the accuracy and authority of different primary medical accounts. Coders and clerks also employ their understandings of the importance of different future uses of data as they make crucial decisions about how much discretion to exercise in producing accurate data and how much effort to put toward clarifying problematic medical data. These findings suggest that information technology systems designed for interoperability and secondary data also need to be designed in ways that support the qualculative practices of data workers in order to succeed, including making future uses of data clear to data workers and finding ways to minimize conflicting data before data workers encounter it.

Test results management and distributed cognition in electronic health record-enabled primary care.

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.

Predictors in starting and stopping Internet use between 2002 and 2012 by Dutch adults 65 years and older.

Research has indicated the need to consider the ageing process with technology adoption by older adults. This study examined psychological, health, social and demographic predictors with starting and stopping Internet use by older adults (2002-2012). Data were used from the Longitudinal Aging Study Amsterdam, and Cox regression analyses were done to test predictors over time with starting or stopping Internet use. The results indicated that older adults starting to use the Internet (11.6%) outnumbered those who stopped (3.1%). Psychological, health, social and demographic predictors separately predicted starting and stopping Internet use. Starting use was predicted by lower age, higher education, normal cognition and living alone. The predictors in stopping use were being younger, having a high sense of mastery and being higher educated. The results need to be interpreted as indicative due to the small number of stoppers. Suggestions are made on how to improve usability.

Knowledge patterns for online health portal development.

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.

Legal and ethical issues surrounding the use of crowdsourcing among healthcare providers.

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.

Systemic Design Approach to a Real-Time Healthcare Monitoring System: Reducing Unplanned Hospital Readmissions.

Following hospital discharge, millions of patients continue to recover outside formal healthcare organizations (HCOs) in designated transitional care periods (TCPs). Unplanned hospital readmissions of patients during TCPs adversely affects the quality and cost of care. In order to reduce the rates of unplanned hospital readmissions, we propose a real-time patient-centric system, built around applications, to assist discharged patients in remaining at home or in the workplace while being supported by care providers. Discrete-event system modeling techniques and supervisory control theory play fundamental roles in the system's design. Simulation results and analysis show that the proposed system can be effective in documenting a patient's condition and health-related behaviors. Most importantly, the system tackles the problem of unplanned hospital readmissions by supporting discharged patients at a lower cost via home/workplace monitoring without sacrificing the quality of care.

Infrastructures for healthcare: From synergy to reverse synergy.

The Danish General Practitioners Database has over more than a decade developed into a large-scale successful information infrastructure supporting medical research in Denmark. Danish general practitioners produce the data, by coding all patient consultations according to a certain set of classifications, on the entire Danish population. However, in the Autumn of 2014, the system was temporarily shut down due to a lawsuit filed by two general practitioners. In this article, we ask why and identify a political struggle concerning authority, control, and autonomy related to a transformation of the fundamental ontology of the information infrastructure. We explore how the transformed ontology created cracks in the inertia of the information infrastructure damaging the long-term sustainability. We propose the concept of reverse synergy as the awareness of negative impacts occurring when uncritically adding new actors or purposes to a system without due consideration to the nature of the infrastructure. We argue that while long-term information infrastructures are dynamic by nature and constantly impacted by actors joining or leaving the project, each activity of adding new actors must take reverse synergy into account, if not to risk breaking down the fragile nature of otherwise successful information infrastructures supporting research on healthcare.

Data linkage in medical science using the resource description framework: the AVERT model.

There is an ongoing challenge as to how best manage and understand 'big data' in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This "AVERT model" provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.

eHealth and the use of individually tailored information: A systematic review.

Tailored messages are those that specifically target individuals following an assessment of their unique characteristics. This systematic review assesses the evidence regarding the effectiveness of tailoring within eHealth interventions aimed at chronic disease management. OVID Medline/Embase databases were searched for randomised control trials, controlled clinical, trials, before -after studies, and time series analyses from inception - May 2014. Objectively measured clinical processes/outcomes were considered. Twenty-two papers were eligible for inclusion: 6/22 used fully tailored messaging and 16/22 used partially tailored messages. Two studies isolated tailoring as the active component. The remainder compared intervention with standard care. In all, 12/16 studies measuring clinical processes and 2/6 studies reporting clinical outcomes showed improvements, regardless of target group. Study quality was low and design did not allow for identification of interventions' active component. Heterogeneity precluded meta-analysis. This review has demonstrated that there is a lack of evidence to suggest that tailoring within an eHealth context confers benefit over non-tailored eHealth interventions.

Improving National Trauma Data Bank® coding data reliability for traumatic injury using a prospective systems approach.

Trauma centers manage an active Trauma Registry from which research, quality improvement, and epidemiologic information are extracted to ensure optimal care of the trauma patient. We evaluated coding procedures using the Relational Trauma Scoring System™ to determine the relative accuracy of the Relational Trauma Scoring System for coding diagnoses in comparison to the standard retrospective chart-based format. Charts from 150 patients admitted to a level I trauma service were abstracted using standard methods. These charts were then randomized and abstracted by trauma nurse clinicians with coding software aide. For charts scored pre-training, percent correct for the trauma nurse clinicians ranged from 52 to 64 percent, while the registrars scored 51 percent correct. After training, percentage correct for the trauma nurse clinicians increased to a range of 80-86 percent. Our research has demonstrated implementable changes that can significantly increase the accuracy of data from trauma centers.