The Future of the Radiology Information System

Nance, John W.; Meenan, Christopher; Nagy, Paul

doi:10.2214/ajr.12.10326

Cited by 45 publications

(22 citation statements)

References 61 publications

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“…Description HIS Hospital information system; the system provides quality community for historical data resource, information, and knowledge in healthcare for hospital administration and patient health care (Bagayoko et al, 2010;Kanagaraj & Sumathi, 2011;Sirintrapun & Artz, 2016;Tsumoto, Hirano, & Iwata, 2013) LIS Laboratory information system; often used to collect, restore, archive, process, extract, and analyze data in laboratory; this system aims to improve efficiency of turn-around-times (TAT) of records, quality of resource utilization, and public health supporting (Blaya et al, 2007;Sepulveda & Young, 2013) RIS Radiology information system; it is used to capture and store data including images, demographic and clinical information, and so on, also assisting in patient registration, report repository, and physician directory with advanced technology (Nance, Meenan, & Nagy, 2013) PACS (super sound PACS, endoscope PACS) Picture archiving and communication systems; it is a common HIS for storage and transferring of digital images (Joshi & Yesha, 2012) EMR EMR system is used to maintain medical records and store, process, and retrieve information. It also ensures accuracy of information.…”

Section: Systemmentioning

confidence: 99%

Big Data in Health Care: Applications and Challenges

Hong

Luo

Wang

et al. 2018

Data and Information Management

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The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote healthrelated research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care. Abstract:In the concept of the aesthetic formation of knowledge and its as soon as possible and success-oriented application, insights and profits without the reference to the arguments developed around 1900. The main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique. Studies and InvestigationsThe main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique.Abstract: In the concept of the aesthetic formation of knowledge and its as soon as possible and success-oriented application, insights and profits without the reference to the arguments developed around 1900. The main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique. Studies and InvestigationsThe main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique.

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Section: Systemmentioning

confidence: 99%

Big Data in Health Care: Applications and Challenges

Hong

Luo

Wang

et al. 2018

Data and Information Management

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“…During run time, the semantic vector representation module is applied to the reason for exam and the corresponding vector is used to retrieve the relevant radiology exam reports in the form of vectors. The next step is to utilize the semantic vectors to compute the similarity between the reason for exam q and each prior report p based on cosine similarity as defined below in Equation 1.…”

Section: B Relevant Report Retrievalmentioning

confidence: 99%

“…There is however, at least one major barrier that stands in the way of this transformation: radiologists are frequently presented with images for interpretation without the supporting clinical data needed [1] as a result of fragmented data sources. Without easy access to clinical history, non-radiology reports, or other brief unstructured documents [1] forming a comprehensive clinical review of an imaging study is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Without easy access to clinical history, non-radiology reports, or other brief unstructured documents [1] forming a comprehensive clinical review of an imaging study is difficult. Additionally, in those scenarios where the data mentioned above is accessible, navigating it to find information that is relevant to the study at hand is time-consuming and burdensome.…”

Section: Introductionmentioning

confidence: 99%

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Feedback-Driven Radiology Exam Report Retrieval with Semantics

Lalithsena

Tari

Reden

et al. 2015

2015 International Conference on Healthcare Informatics

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Abstract-Clinical documents are vital resources for radiologists to have a better understanding of patient history. The use of clinical documents can complement the often brief reasons for exams that are provided by physicians in order to perform more informed diagnoses. With the large number of study exams that radiologists have to perform on a daily basis, it becomes too time-consuming for radiologists to sift through each patient's clinical documents. It is therefore important to provide a capability that can present contextually relevant clinical documents, and at the same time satisfy the diverse information needs among radiologists from different specialties. In this work, we propose a knowledge-based semantic similarity approach that uses domain-specific relationships such as part-of along with taxonomic relationships such as is-a to identify relevant radiology exam records. Our approach also incorporates explicit relevance feedback to personalize radiologists information needs. We evaluated our approach on a corpus of 6,265 radiology exam reports through study sessions with radiologists and demonstrated that the retrieval performance of our approach yields an improvement of 5% over the baseline. We further performed intra-class and inter-class similarities using a subset of 2,384 reports spanning across 10 exam codes. Our result shows that intra-class similarities are always higher than the inter-class similarities and our approach was able to obtain 6% percent improvement in intra-class similarities against the baseline. Our results suggest that the use of domain-specific relationships together with relevance feedback provides a significant value to improve the accuracy of the retrieval of radiology exam reports.

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“…Traditionally, the electronic medical record (EMR) contains registration data, insurance and billing data, as well as nursing data, and result data from lab systems. The radiology information system (RIS) contains scheduling information, as well as radiology reports, and many timestamps that can be useful to evaluate workflow [1]. The EMR and RIS both use HL7 as the main standard for communication which comes with a significant limitation-there is no mechanism to request a piece of information via HL7.…”

Section: Healthcare Standards and Interfacesmentioning

confidence: 99%

Building Blocks for a Clinical Imaging Informatics Environment

et al. 2013

Self Cite

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Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

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The Future of the Radiology Information System

Cited by 45 publications

References 61 publications

Big Data in Health Care: Applications and Challenges

Big Data in Health Care: Applications and Challenges

Feedback-Driven Radiology Exam Report Retrieval with Semantics

Building Blocks for a Clinical Imaging Informatics Environment

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