Diagnostic Doubt and Artificial Intelligence: An Inductive Field Study of Radiology Work

Technological developments in emerging AI technologies are assumed to further routinize and improve the efficiency of decision making tasks, even in professional contexts such as medical diagnosis, human resource management, and criminal justice. We have little research on how AI technologies are actually used and adopted in practice. Prior research on technology in organizations documents a gap between the expectations for new technology and its actual use in practice. We conducted a comparative field study of three sections in a Department of Radiology in a major US hospital, whereby new and existing AI tools were being used and experimented with. In contrast to expectations about AI tools, our study reveals how such tools can lead routine professional decision making tasks to become nonroutine, as they increased ambiguity and decision makers had to work to reduce it. This is particularly challenging since the costs of dealing with ambiguity – increased time to diagnose – were often weighed against the benefits of such ambiguity (potentially more accurate diagnoses). This study contributes to literatures related to technology, work, and organizations, as well as the role of ambiguity in professionals’ knowledge work

Good Interdepartmental Relationships: The Foundations of a Solid Emergency Department

“No man is an island” said the English poet, John Donne, and nowhere can that statement be better appreciated than in a modern emergency department (ED). As emergency physicians, we work in the setting of a close knit team involving nurses, technicians, consultants, clerks, security guards and many more. On a macroscopic level as well, the ED itself needs productive relationships with every other department in the hospital. Back when the ED was staffed by physicians-in-training, general practitioners and moonlighting specialists, the care of patients was jealously divided between the long-entrenched traditional specialties. Anesthesiologists handled difficult airways; Surgeons took care of trauma; Radiologists did the ultrasounds and read all the films, and so forth. Emergency medicine—a specialty that encompassed parts of many disciplines—was initially met with skepticism and resistance from the traditional fields.   I have been in practice long enough to remember when anesthesiologists fought against emergency physicians doing RSI and how they tried to stop us from using propofol or ketamine for procedural sedation. Orthopedists wanted to be consulted before we reduced a shoulder. Surgeons got angry if you gave morphine to a belly pain patient. In the early 1990’s at the University of Rochester, my colleague, Dr. Steve White, had to sneak into the ED with his own portable ultrasound device (with its postage stamp sized screen), because to have done so openly would have brought down the wrath of radiologists who believed that ultrasonography belonged to their department alone. These turf battles are mostly a thing of the past, thanks to clinical studies conducted by our specialty that proved what we can and should do. But challenges regarding interdepartmental relationships still remain. In the following discussion we will look at current friction points between the ED and other departments, including radiology, anesthesia, surgery, obstetrics/gynecology, cardiology, and the internal medicine admitting services

Performance and Evaluation in Computed Tomographic Colonography Screening for Colorectal Cancer

Each year over 20,000 people die from colorectal cancer (CRC). However, despite causing the second highest number of cancer deaths, CRC is not only curable if detected early but can be prevented by population screening. The detection and removal of pre-malignant polyps in the colon prevents cancer from ever developing. As such, screening of the at-risk population (those over 45-50 years) confers protection against CRC incidence and mortality. Although the principles and benefit of screening are well established, the adequate provision of screening is a complex process requiring robust healthcare infrastructure, evidence-based quality assurance and resources. The success of any screening programme is dependent on the accuracy of the screening investigations deployed and sufficiently high uptake by the target population. In England, the Bowel Cancer Screening Programme (BCSP) delivers screening via initial stool testing to triage patients for the endoscopic procedure, colonoscopy, or the radiological investigation CT colonography (CTC) in some patients. There has been considerable investment in colonoscopy accreditation processes which contribute to high quality services, suitable access for patients and a competent endoscopy workforce. The performance of colonoscopists in the BCSP is tightly monitored and regulated; however, the same is not true for CTC. Comparatively, there has been little investment in CTC services, and in fact there is no mandatory accreditation or centralised training. Instead, CTC reporting radiologists must learn ad hoc on the job, or at self-funded commercial workshops. This inevitably leads to variability in quality and expertise, inequity in service provision, and could negatively impact patient outcomes. To address this disparity and develop evidence-based training, one must determine what factors affect the performance of CTC reporting radiologists, what CTC training is necessary, and what training works. This thesis investigates these topics and is structured as follows: Section A reviews the background literature, describing the public health burden of CRC and the role of screening. Aspects of CTC screening and its role in the BCSP are explored. The importance of performance monitoring and value of accreditation are examined and the disparity between CTC, colonoscopy and other imaging-based screening programmes is discussed. Section B expands on radiologist performance by determining the post-imaging CRC (or interval cancer) rate through systematic review and meta-analysis. Factors contributing to the interval cancer rate are evaluated, and an observational study assessing factors affecting CTC accuracy is presented. The impact of CTC training is assessed via a structured review and best principles for training delivery are discussed. Section C presents a multicentre, cluster-randomised control trial developed from the data and understanding described in Sections A and B. Section D summarises the thesis and discusses future recommendations and research

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

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

Hospital-Based Decision Support

book chapterBiomedical Informatic

Clinical Decision Support at Intermountain Healthcare

book chapterBiomedical Informatic

Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations

Abstract: Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far

Regulation 61-16 minimum standards for licensing hospitals and institutional general infirmaries

The regulations clearly define important terms, licensing procedures, and general management information for hospitals and institutional general infirmaries in South Carolina. These facilities are required to follow licensure laws as outlined in Section 44-7-260 of the Code of Laws of South Carolina (1976)