Understanding safety-critical interactions with a home medical device through Distributed Cognition

As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients’ interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients’ interaction strategies, and pointed to design opportunities that could improve patients’ experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a DCog approach effectively helps to understand how safety is achieved or compromised in such a system

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Augmenting Distributed Cognition analysis for home haemodialysis: from a system of representations to systems of activity-centric interactions

This thesis investigates the application of Distributed Cognition (DCog) to understand patients’ situated interactions with Home Haemodialysis Technology (HHT). With the anticipated increase in home healthcare, there is a need to understand how Home Medical Devices (HMDs) should be designed so that they are patient-friendly and can be safely used in the home. This implies studying situated interactions with current HMDs and identifying the issues that patients face. Taking HHT as an example of a HMD, this thesis focuses on understanding the contexts in which renal patients interact with HHT, and their interaction strategies and issues, from a DCog perspective. DCog has been a useful theoretical framework for understanding work in clinical settings, but has not previously been applied to the study of interactions with HMDs. Data was gathered during visits to 19 patients through ethnographic observations and semi-structured interviews. 3 renal nurses, 3 renal technicians, and 1 nephrologist were also interviewed. Data was analysed by constructing the representational models of the Distributed Cognition for Teamwork framework (DiCoT) to understand the context of interactions, focusing on system activities, information flows, physical layouts, artefacts, social structures, and system evolution, and by applying the principles associated with these models to identify patients’ interaction strategies and issues. This thesis brings five contributions to the study of situated interactions with HHT. Firstly, it provides an account of patients’ experiences of interacting with HHT. Secondly, it demonstrates the utility of DCog as a theoretical framework for understanding interactions with a HMD such as HHT. Thirdly, it develops new theoretical principles that help to understand how people distribute cognitive processes through time. Fourthly, it develops a Contextual Factors Analysis that facilitates the analysis of complex interaction strategies. Finally, it develops an overarching approach that augments DCog analysis from considering a system of representations to considering systems of activity-centric interactions

Strategies for conducting situated studies of technology use in hospitals

Ethnographic methods are widely used for understanding situated practices with technology. When authors present their data gathering methods, they almost invariably focus on the bare essentials. These enable the reader to comprehend what was done, but leave the impression that setting up and conducting the study was straightforward. Text books present generic advice, but rarely focus on specific study contexts. In this paper, we focus on lessons learnt by non-clinical researchers studying technology use in hospitals: gaining access; developing good relations with clinicians and patients; being outsiders in healthcare settings; and managing the cultural divide between technology human factors and clinical practice. Drawing on case studies across various hospital settings, we present a repertoire of ways of working with people and technologies in these settings. These include engaging clinicians and patients effectively, taking an iterative approach to data gathering and being responsive to the demands and opportunities provided by the situation. The main contribution of this paper is to make visible many of the lessons we have learnt in conducting technology studies in healthcare, using these lessons to present strategies that other researchers can take up

Patients' and carers' experiences of interacting with home haemodialysis technology: implications for quality and safety

© 2014 Rajkomar et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.(http://creativecommons.org/licenses/by/4.0).BACKGROUND: Little is known about patients' and carers' experiences of interacting with home haemodialysis (HHD) technology, in terms of user experience, how the design of the technology supports safety and fits with home use, and how the broader context of service provision impacts on patients' use of the technology. METHODS: Data were gathered through ethnographic observations and interviews with 19 patients and their carers associated with four different hospitals in the UK, using five different HHD machines. All patients were managing their condition successfully on HHD. Data were analysed qualitatively, focusing on themes of how individuals used the machines and how they managed their own safety. RESULTS: Findings are organised by three themes: learning to use the technology, usability of the technology, and managing safety during dialysis. Home patients want to live their lives fully, and value the freedom and autonomy that HHD gives them; they adapt use of the technology to their lives and their home context. They also consider the machines to be safe; nevertheless, most participants reported feeling scared and having to learn through mistakes in the early months of dialysing at home. Home care nurses and technicians provide invaluable support. Although participants reported on strategies for anticipating problems and keeping safe, perceived limitations of the technology and of the broader system of care led some to trade off safety against immediate quality of life. CONCLUSIONS: Enhancing the quality and safety of the patient experience in HHD involves designing technology and the broader system of care to take account of how individuals manage their dialysis in the home. Possible design improvements to enhance the quality and safety of the patient experience include features to help patients manage their dialysis (e.g. providing timely reminders of next steps) and features to support communication between families and professionals (e.g. through remote monitoring).Peer reviewedFinal Published versio

Distributed cognition for evaluating healthcare technology

Distributed Cognition (DCog) has been proposed as being a better approach to analyzing healthcare work than traditional cognitive approaches, due to the collaborative nature of healthcare work. This study sought to explore this by applying two DCog frameworks, DiCoT and the Resources Model, to the analysis of infusion pump use in an Intensive Care Unit. Data was gathered through observations and interviews, and then analysed using DiCoT and the Resources Model to construct models representing the social structures, information flows, physical layouts and artefact use involved in infusion administration in the ICU. The findings of the study confirm that DCog can be a methodology of choice for studying healthcare work: nurses collaborated significantly, artefacts played a major role in coordinating activity, and the physical environment influenced activity - properties which DCog effectively supports reasoning about

Understanding Infusion Administration in the ICU through Distributed Cognition

To understand how healthcare technologies are used in practice and evaluate them, researchers have argued for adopting the theoretical framework of Distributed Cognition (DC). This paper describes the methods and results of a study in which a DC methodology, Distributed Cognition for Teamwork (DiCoT), was applied to study the use of infusion pumps by nurses in an Intensive Care Unit (ICU). Data was gathered through ethnographic observations and interviews. Data analysis consisted of constructing the representational models of DiCoT, focusing on information flows, physical layouts, social structures and artefacts. The findings show that there is significant distribution of cognition in the ICU: socially, among nurses; physically, through the material environment; and through technological artefacts. The DiCoT methodology facilitated the identification of potential improvements that could increase the safety and efficiency of nurses’ interactions with infusion technology