Named Entity Recognition in Electronic Health Records: A Methodological Review

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. Methods We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. Results Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. Conclusions EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Positive health: The passport approach to improving continuity of care for low income South African chronic disease sufferers

Research Problem: The South African health system faces numerous challenges associated with its status as a middle-income developing nation. Wasteful expenditure and poor clinical outcomes arise from inefficient inter-organizational communication of patient information and the lack of a centralized health database. Research question: How does the experience of chronic disease patients with their health information inform the development of future health records in low income population groups? Proposition: Exploration of patient and health care workers experiences of medical records can inform their future development to enhance continuity of care. Objectives, methodology, procedures and outcome: Identification of an appropriate format, technological basis and functional design of a prototype medical record system by means of a phenomenological study conducted through in-depth interviews of patients and doctors in order to improve clinical care. Left and right hermeneutics were used to analyse the data and develop themes. Findings: Health records play a critical role in the clinics workflow processes, document the patients' management and clinical progress. They are an important intermediary in the relationship between the patient and the facility. Inefficiencies in the paper-based system lead to ineffective consultations, loss of continuity of care and discord between practitioners and patients. Improvement of the records format is required to provide ubiquitous access to health and improve patient health literacy

Use of workers' compensation data for occupational safety and health: proceedings from June 2012 workshop

The purpose for the June 2012 Use of Workers' Compensation Data for Occupational Safety and Health Workshop was to explore ways in which workers' compensation information can be used for public health research and surveillance. Thirty-five poster and platform presentations described studies that utilized workers' compensation information while exploring limitations of these resources. The workshop proceedings contain summary articles for the presentations plus notes from the discussion groups for the 6 white papers that were drafted for the workshop. The workshop was co-sponsored by the Bureau of Labor Statistics (BLS), Council of State and Territorial Epidemiologists (CSTE), International Association of Industrial Accident Boards and Commissions (IAIABC), National Council on Compensation Insurance (NCCI), National Institute for Occupational Safety and Health (NIOSH), Occupational Safety and Health Administration (OSHA), and the Washington State Department of Labor and Industries, Safety and Health Assessment for Research and Prevention (SHARP) program.Introduction -- Acknowledgements -- Use of workers' compensation for occupational safety and health: opening remarks -- The advantages of combining workers' compensation data with other employee databases for surveillance of occupational injuries and illnesses in hospital workers -- Safe lifting in long-term care facilities, workers' compensation savings and resident well-being -- Workers' compensation versus safety data use at the veterans health administration: uses and weaknesses -- Linking workers' compensation data and earnings data to estimate the economic consequences of workplace injuries -- Workers' compensation costs in wholesale and retail trade sectors -- Linking workers' compensation and group health insurance data to examine the impact of occupational injury on workers' and their family members' health care use and costs: two case studies -- Occupational amputations in illinois: data linkage to target interventions -- The role of professional employer organizations in workers compensation: evidence of workplace safety and reporting -- Using workers' compensation data to conduct OHS surveillance of temporary workers in Washington state -- How WorkSafeBC uses workers' compensation data for loss prevention -- Hitting the mark: improving effectiveness of high hazard industry interventions by modifying identification and targeting methodology -- Injury trends in the Ohio Workers' Compensation System -- Randomized government safety inspections reduce worker injuries with no detectable job loss -- Comparison of data sources for the surveillance of work injury -- OSHA recordkeeping practices and workers compensation claims in Washington; results from a survey of Washington BLS respondents -- Completeness of workers' compensation data in identifying work-related injuries -- Another method for comparing injury data from workers compensation and survey sources -- Using O*Net to study the relationship between psychosocial characteristics of the job and workers' compensation claims outcomes -- Impact of differential injury reporting on the estimation of the total number of work-related amputation injuries -- Exploring New Hampshire workers' compensation data for its utility in enhancing the state's occupational health surveillance system -- Using workers' compensation data for surveillance of occupational injuries and illnesses-Ohio, 2005-2009 -- Using an administrative workers' compensation claims database for occupational health surveillance in California: validation of a case classification scheme for amputations -- Describing agricultural occupational injury in Ohio using Bureau of Workers' Compensation claims -- Use of multiple data sources to enumerate work-related amputations in Massachusetts: the contribution of workers' compensation records -- Workers' compensation-related CSTE occupational health indicators -- The effectiveness of the Safety and Health Achievement Recognition Program (SHARP) in reducing the frequency and cost of workers' compensation claims -- Comparison of cost valuation methods for workers compensation data -- Development and evaluation of an auto-coding model for coding unstructured text data among workers' compensation claims -- Patterns in employees' compensation appeals board decisions: exploratory text mining and information extraction -- Identifying workers' compensation as the expected payer in emergency department medical records -- Utilizing workers' compensation data to evaluate interventions and develop business cases -- gender, age, and risk of injury in the workplace -- The mystery of more Monday soft-tissue injury claims -- Is occupational injury risk higher at new firms? -- Discussion of: Successes using workers' compensation data for health care injury prevention: surveillance, design, costs, and accuracy -- Discussion of: The total burden of work-related injuries and illnesses: a draft white paper developed for the workshop on the use of workers' compensation data -- Discussion of: Workers' compensation loss prevention: a white paper for discussion -- Discussion of: Contingent workers: data analysis limitations and strategies -- Discussion of: Using workers' compensation administrative data to analyze injury rates: a sample study with the Wisconsin Workers' Compensation Division -- Discussion of: The Role of leading indicators in the surveillance of occupational health and safety -- Final workshop discussion group -- State health agencies' access to state workers' compensation data: results of an assessment conducted by the Council of State and Territorial Epidemiologists, 2012 -- Workshop participants -- Workshop agenda -- Poster presentations.David F. Utterback and Teresa M. Schnorr, editors.May 2013.Also available via the World Wide Web as an Acrobat .pdf file (11.9 MB, 232 p.).Includes bibliographical references

Transactions of 2019 International Conference on Health Information Technology Advancement Vol. 4 No. 1

The Fourth International Conference on Health Information Technology Advancement Kalamazoo, Michigan, October 31 - Nov. 1, 2019. Conference Co-Chairs Bernard T. Han and Muhammad Razi, Department of Business Information Systems, Haworth College of Business, Western Michigan University Kalamazoo, MI 49008 Transaction Editor Dr. Huei Lee, Professor, Department of Computer Information Systems, Eastern Michigan University Ypsilanti, MI 48197 Volume 4, No. 1 Hosted by The Center for Health Information Technology Advancement, WM

Impact of implementing a computerised quality improvement intervention in primary healthcare

Health systems worldwide experience large evidence practice gaps with underuse of proven therapies, overuse of inappropriate treatments and misuse of treatments due to medical error. Quality improvement (QI) initiatives have been shown to overcome some of these gaps. Computerised interventions, in particular, are potential enablers to improving system performance. However, implementation of these interventions into routine practice has resulted in mixed outcomes and those that have been successfully integrated into routine practice are difficult to sustain. The objective of this thesis is to understand how a multifaceted, computerised QI intervention for cardiovascular disease (CVD) prevention and management was implemented in Australian general practices and Aboriginal Community Controlled Health Services and assess the implications for scale-up of the intervention. The intervention was implemented as part of a large cluster-randomised controlled trial, the TORPEDO (Treatment of Cardiovascular Risk using Electronic Decision Support) study. The intervention was associated with improved guideline recommended cardiovascular risk factor screening rates but had mixed impact on improving medication prescribing rates. In this thesis, I designed a multimethod process and economic evaluation of the TORPEDO trial. The aims were to: i. Develop a theory-informed logic model to assist in the design of the overall evaluation to address study aims (Chapter 3). ii. Conduct a post-trial audit to quantify changes in cardiovascular risk factor screening and prescribing to high risk patients over an 18-month post-trial period and understand the impact of the intervention outside of a research trial setting (Chapter 4). vi iii. Use normalisation process theory to identify the underlying mechanisms by which the intervention did and did not have an impact on trial outcomes (Chapter 5). iv. Use video ethnography to explore how the intervention was used and cardiovascular risk communicated between patients and healthcare providers (Chapter 6). v. Conduct an economic evaluation to inform policy makers for delivering the intervention at scale through Primary Health Networks in New South Wales (Chapter 7). vi. Use a new theory to explain the factors that drove adoption and non-adoption of the intervention and assess what modifications may be needed to promote spread and scale-up (Chapter 8). I found variable outcomes during the post-trial period with a plateauing of improvements in guideline recommended screening practices but an ongoing improvement in prescribing to high risk patients. The group that continued to have the most benefit was patients at high CVD risk who were not receiving recommended medications at baseline. The delay in prescribing recommended medication suggests healthcare providers adopt a cautious approach when introducing new treatments. Six intervention primary healthcare services participated as case studies for the process evaluation. Qualitative and quantitative data sources were combined at each primary healthcare service to enable a detailed examination of intervention implementation from multiple perspectives. The process evaluation identified the complex interaction between several underlying mechanisms that influenced the implementation processes and explained the mixed trial outcomes: (1) organisational mission; (2) leadership; (3) the role of teams; (4) technical competence and dependability of the software tools. Further, there were different ‘active ingredients’ vii necessary during the initial implementation compared to those needed to sustain use of the intervention. In the video ethnography and post-consultation patient interviews, important insights were gained into how the intervention was used, and its interpretation by the doctor and patient. Through ethnographic accounts, the doctor’s communication of cardiovascular risk was not sufficient in engaging patients and having them act upon their high-risk status; effective communication required interactions be assessed, discussed and negotiated. The economic evaluation identified the cost implications of implementing the intervention as part of a Primary Health Network program in the state of New South Wales, Australia; and modelled data looked at the impact of small but statistically significant reductions in clinical risk factors based on the trial data. When scaled to a larger population the intervention has potential to prevent major CVD events at under AU$50,000 per CVD event averted largely due to the low costs of implementing the intervention. However, the clinical risk factor reductions were small and a stronger case for investment would be made if the effects sizes could be enhanced and sustained over time. The findings from chapters 4-6 provide insight into the intricacy of the barriers influencing implementation processes and adoption of the intervention. Taken together, these studies provide a detailed explanation of the processes that may be required to implement such an intervention at scale and the factors that might influence its impact and sustainability. The findings are expected to assist policy makers, administrators and health professionals in developing multiple interdependent QI strategies at the organisational, provider and consumer levels to improve primary healthcare system performance for cardiovascular disease management and prevention