Information Technologies for the Healthcare Delivery System

That modern healthcare requires information technology to be efficient and fully effective is evident if one spends any time observing the delivery of institutional health care. Consider the observation of a practitioner of the discipline, David M. Eddy, MD, PhD, voiced in Clinical Decision Making, JAMA 263:1265-75, 1990, . . .All confirm what would be expected from common sense: The complexity of modern medicine exceeds the inherent limitations of the unaided human mind. The goal of this thesis is to identify the technological factors that are required to enable a fully sufficient application of information technology (IT) to the modern institutional practice of medicine. Perhaps the epitome of healthcare IT is the fully integrated, fully electronic patient medical record. Although, in 1991 the Institute of Medicine called for such a record to be standard technology by 2001, it has still not materialized. The author will argue that some of the technology and standards that are pre-requisite for this achievement have now arrived, while others are still evolving to fully sufficient levels. The paper will concentrate primarily on the health care system in the United States, although much of what is contained is applicable to a large degree, around the world. The paper will illustrate certain of these pre-requisite IT factors by discussing the actual installation of a major health care computer system at the University of Rochester Medical Center (URMC) in Rochester, New York. This system is a Picture Archiving and Communications System (PACS). As the name implies, PACS is a system of capturing health care images in digital format, storing them and communicating them to users throughout the enterprise

Focal Spot, Spring 2003

https://digitalcommons.wustl.edu/focal_spot_archives/1093/thumbnail.jp

The effect of the introduction of picture archive and communication systems (PACS) on patient radiation doses and patient management

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2000.This thesis considers the effects of Picture Archive and Communications Systems (PACS), on both patient radiation doses and patient management. PACS is a relatively new technology which acquires, transmits, and stores radiological images digitally. This thesis investigates the doses which are required to produce radiographic images which are acceptable to radiologists and referring clinicians, and compares these doses with those required for the film/screen systems which they are replacing. A review of the literature shows that despite claims of dose reductions, very little good evidence exists about dose changes with the introduction of PACS. A comparison of images of test objects indicates that the images are comparable under limited conditions, that PACS has a much wider latitude than film (>250 mAs), and that contrast detail improves with increase in exposure. Two original observational studies are described in which PACS and film doses are compared for examinations of two groups of adult patients. The results indicate that the doses for PACS equate to those used with a 300 speed film/screen system thus necessitating dose increases of around 30% for the majority of adult patients in the UK. The issue of whether the number of images which are repeated, with additional patient doses, due to unsatisfactory images (rejected images), or unavailability of the images when clinically required (lost images), is addressed and indicates that PACS may allow a dose saving of 1.1 % and 1.4% respectively. The overall result of these studies indicates that the widespread introduction of PACS is likely to increase population doses. Two original studies which consider patients within the Accident and Emergency department are described. These studies aim to produce evidence to justify the introduction of the new technology, despite higher radiation doses, by identifying improvements in patient management which might improve patient outcomes. The results of these studies provide little evidence of such benefits to patients. This thesis concludes that the use of current PAC systems produces an increase in the radiation dose to the adult population in the UK, without demonstrable improvements in patient management

METADATA-BASED IMAGE COLLECTING AND DATABASING FOR SHARING AND ANALYSIS

Data collecting and preparing is generally considered a crucial process in data science projects. Especially for image data, adding semantic attributes when preparing image data provides much more insights for data scientists. In this project, we aim to implement a general-purpose central image data repository that allows image researchers to collect data with semantic properties as well as data query. One of our researchers has come up with the specific challenge of collecting images with weight data of infants in least developed countries with limited internet access. The rationale is to predict infant weights based on image data by applying Machine Learning techniques. To address the data collecting issue, I implemented a mobile application which features online and offline image and annotation upload and a web application which features image query functionality. This work is derived and partly decoupled from the previous project – ImageSfERe (Image Sharing for Epilepsy Research), which is a web-based platform to collect and share epilepsy patient imaging

Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.Comment: preprint versio

Understanding Complex Coordination Processes in Health Care

This paper identifies and analyses complex coordination processes at radiology departments in Austria1, Denmark, and Sweden. The understanding of coordination work is emphasised by focusing on different interdependencies between work activities. It illustrates that various interdependencies have different properties, which in turn have derived different coordination dimensions. We refer to these dimensions as predefined and situated coordination. This paper points to the needs for designing coordination tools inscribed with properties that fit the properties of various kinds of coordination work. Finally, ways of integrating these tools are discussed

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster