Assessing Hospital Information Systems Processes: A Validation of PRISE Information Systems Success Model in Healthcare

Although there is limited research and evidence base, it is reasonable to expect that high quality information technology is an integral factor in the success of today’s health care sector. However, the health care sector is considered to be low level investor in Information Technology (IT) when compared to other sectors. There are studies that look at the sums spent on health IT as a basis for determining how effective the IT systems are. We support the idea that the effectiveness of IT systems, is not an exact measure and a more systematic approach needs to be taken when evaluating success of an IT system. In this study, we have evaluated an assessment method, which is, “Process Based Information Systems (IS) Effectiveness (PRISE)” based on a novel model of IS effectiveness in the health care sector. The results of our case series provide specific implications concerning the applicability of a general “IS assessment” approach, in the medical context

Robust and fully automated segmentation of mandible from CT scans

Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical Imaging (ISBI) 201

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

Kanıta Dayalı Tıp ve Tıp Kütüphaneciliği

Medical profession is in a transition as it has never been since its foundation thousands of years ago. We are experiencing a world where paternalistic medicine is being given up by most practitioners as hundreds of articles are published daily on scientific journals and patients can acquire the most contemporary scientific information through internet instantaneously. Hence this concept renders a shift from Experience Based Medicine towards Evidence Based Medicine (EBM). EBM requires a team effort in which clinicians, medical informatics specialists and medical librarians need to work together thus providing patients -the most reliable and contemporary scientific evidence- with best informed choices for their diagnosis and treatment process. EBM is a team effort in which clinicians, medical informatics specialists and medical librarians should work together to provide the patients -using the best evidence from contemporary and reliable scientific evidence– with best informed choices in their diagnosis and treatment process. This study is intended to discuss Evidence Based Medicine and its relation with to the profession of medical librarianship

Kanıta Dayalı Tıp ve Tıp Kütüphaneciliği / Evidence Based Medicine and Medical Librarianship

Medical profession is in a transition as it has never been since its foundation thousands of years ago. We are experiencing a world where paternalistic medicine is being given up by most practitioners as hundreds of articles are published daily on scientific journals and patients can acquire the most contemporary scientific information through internet instantaneously. Hence this concept renders a shift from Experience Based Medicine towards Evidence Based Medicine (EBM). EBM requires a team effort in which clinicians, medical informatics specialists and medical librarians need to work together thus providing patients - the most reliable and contemporary scientific evidence - with best informed choices for their diagnosis and treatment process. EBM is a team effort in which clinicians, medical informatics specialists and medical librarians should work together to provide the patients-using the best evidence from contemporary and reliable scientific evidence – with best informed choices in their diagnosis and treatment process. This study is intended to discuss Evidence Based Medicine and its relation with to the profession of medical librarianship

Session 2 - Opportunities for Improving Health through Big Data and Data Science: \u3cem\u3eData Science Infrastructure in the Cloud for Sanford Imagenetics, a Population Scale Genetic Medicine Initiative\u3c/em\u3e

The sequencing of the human genome has facilitated a significant increase in our understanding of disease. By using individual genetic information to prevent, diagnose, and treat disease with better precision, genomics-enabled medicine promises health care that is personalized, predictive, proactive, and preventive rather than reactive (Roundtable on Translating Genomic-Bas...). Realizing this goal requires integration and joint analyses of large genomic and clinical datasets which demand computational storage and processing capacities which are not found in a typical healthcare provider organization. To facilitate this type of analyses that are at the care of precision healthcare, Sanford Imagenetics implemented a cloud based platform utilizing commercial Infrastructure as a Service (IasS) with Amazon Web Services and Platform as a Service (PaaS) with Databricks. Databricks Unified Analytics platform (Databricks Delta: Unified Data Manage...) allows Sanford Imagenetics to store and analyze population scale genomic and clinical data in a HIPAA compliant platform that can dynamically scale according to our needs, and with a cost structure proportional to active usage

Evaluation Of A Hospital Information System In An International Context: Towards Implementing Pb-Ism In Turkey

High quality information technology (IT) support is an integral factor in the success of today's health sector. However, the medical profession is considered to be low level investors in IT when compared to other sectors. Many international comparisons look at the expenditure spent on health IT as a basis for determining how effective the systems are. This prescriptive mainly cost/benefit approach is believed to be deficient as a sole factor in evaluation procedures. The research in this study supports the notion that more systematic attention needs to be taken into evaluating the success of an IT system. A perspective noted through a process based assessment for information systems effectiveness assessment (PB-ISAM) is therefore proposed. The method was developed and subsequently evaluated through three case organizations in the medical sector. Specific implications are drawn concerning the applicability of PB-ISAM, conclusions are drawn from the insights gained and suggestions for further research reported.Publisher's Versio