Assessment of actionable findings in radiology reports

Purpose: The American College of Radiology (ACR) Actionable Reporting Work Group defined three categories of imaging findings that require additional, nonroutine communication wit

The Promise of Health Information Technology: Ensuring that Florida's Children Benefit

Substantial policy interest in supporting the adoption of Health Information Technology (HIT) by the public and private sectors over the last 5 -- 7 years, was spurred in particular by the release of multiple Institute of Medicine reports documenting the widespread occurrence of medical errors and poor quality of care (Institute of Medicine, 1999 & 2001). However, efforts to focus on issues unique to children's health have been left out of many of initiatives. The purpose of this report is to identify strategies that can be taken by public and private entities to promote the use of HIT among providers who serve children in Florida

Actionable Patient Safety Solutions (APSS) #6: Hand-off Communications

Hand-off communications, or hand-off processes, involve the transition of care as well as the transfer of patient-specific information by one healthcare professional to another with the purpose of providing a patient with safe, continuous care. A successful hand-off can only be achieved by effective communication

Potential Impacts of Artificial Intelligence on Spine Imaging Interpretation and Diagnosis

Spine and related disorders represent one of the most common causes of pain and disability in the United States. Imaging represents an important diagnostic procedure in spine care. Imaging studies contain actionable data and insights undetectable through routine visual analysis. Convergent advances in imaging, artificial intelligence (AI), and radiomic methods has revealed the potential of multiscale in vivo interrogation to improve the assessment and monitoring of pathology. AI offers various types of decision support through the analysis of structured and unstructured data. The primary purpose of this qualitative exploratory case study was to identify the potential impacts of AI solutions on spine imaging interpretation and diagnosis. Selected constructs from the diffusion of innovations theory and the technology acceptance model provided the conceptual framework. Data were acquired from 4 consensus-based white papers, researcher reflective journaling, and 2 homogenous focus group sessions comprising radiologists and AI experts. Content and thematic analyses of acquired data were performed with ATLAS.ti. Three primary themes emerged from qualitative analysis: patient-based decision support, population-based decision support, and application-based decision support. Subthemes include multiscale in vivo analysis, naturally language processing, change analysis, prioritization, and ground truth. The results suggest how further development of AI could fundamentally alter how spine pathology is detected, characterized, and classified. The study also addresses the potential impact of AI on in vivo tissue analysis, the differential diagnosis, and imaging workflow. This includes introducing the concept of the virtual biopsy and its use in spine imaging

Modelling of Internet of Things (IoT) for Healthcare

Purpose: Information technology benefits the world, and it’s required for health care system, such as electronic medical records (EMR). We have proposed systematic model to study hoe IoT with 5g network has potential to benefit various healthcare services. For example, telemedicine may have some usage restrictions in rural areas and physicians may find it difficult to provide continuous monitoring to patients from such area. There are higher chances that the calls or video conferences getting significantly affected by poor networks and signals as well as non-compatible devices and patient may not get the treatment on time. 5G networking with IoT devices are believed to be the game changer for communication technology. The IoT model assists in attaining information by measuring its benefits through criteria which include 5G and IoT features along with a healthcare service requirement. Purpose of this paper is to present a model using Internet of Things (IoT) and 5G technology which will help to understand improved efficiency and efficacy of healthcare services. Our main research methodologies are literature review and modeling. The obtained results can be used for information technology applications in healthcare for various healthcare services and assist in increasing health quality in the healthcare industry.Method: Created a model to set the standard for incorporating 5G IoT devices health related technology and services. Reviewed through several models that serve as potential model to involve key factors that are unique certain healthcare services. We picked one model that can be easily incorporated in the system and can be revised to fit within the requirements using 5G IoT devices. Gathering of related literature served as a foundation in understanding the benefits of 5G IoT in the healthcare systems and parameters were pooled from it to revise the IoT model. Results: Incorporating 5G IoT features into a chosen model gave an overview of various determinants that can help understanding how IoT can influence any healthcare service and improve the quality of health. There are no rules and restrictions for use and utilization of this technology for health management yet in developing stage however, healthcare systems can rely on the 5G IoT devices for quality betterment. Conclusion: IoT with 5G has potential to improve healthcare management. The 5G world with an IoT will allow us to enter an era where real-time health services will become the part of the daily routine rather than the exception. However, further research needs to be done about its usage within any kind of specific health technology. Future research directions can utilize our model for other lesser known healthcare services

Marshfield Clinic: Health Information Technology Paves the Way for Population Health Management

Highlights Fund-defined attributes of an ideal care delivery system and best practices, including an internal electronic health record, primary care teams, physician quality metrics and mentors, and standardized care processes for chronic care management

DICOM metadata: a useful resource for big data analytics

Postprint (published version

From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider