A Business Analytics Framework for Primary Care.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England.

Learning to Discover Explainable Clinical Features With Minimum Supervision.

Purpose: To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain.

Methods: Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used.

Automated feature-based grading and progression analysis of diabetic retinopathy.

Background: In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade.

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective.

In the Internet of Things (IoT) + Fog + Cloud architecture, with the unprecedented growth of IoT devices, one of the challenging issues that needs to be tackled is to allocate Fog service providers (FSPs) to IoT devices, especially in a game-theoretic environment. Here, the issue of allocation of FSPs to the IoT devices is sifted with game-theoretic idea so that utility maximizing agents may be benign. In this scenario, we have multiple IoT devices and multiple FSPs, and the IoT devices give preference ordering over the subset of FSPs.

Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations.

Purpose: The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography.

Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software.

Information Theory and Medical Decision Making.

Information theory has gained application in a wide range of disciplines, including statistical inference, natural language processing, cryptography and molecular biology. However, its usage is less pronounced in medical science. In this chapter, we illustrate a number of approaches that have been taken to applying concepts from information theory to enhance medical decision making.

An instrument to identify computerised primary care research networks, genetic and disease registries prepared to conduct linked research: TRANSFoRm International Research Readiness (TIRRE) survey.

Purpose: The Translational Research and Patients safety in Europe (TRANSFoRm) project aims to integrate primary care with clinical research whilst improving patient safety. The TRANSFoRm International Research Readiness survey (TIRRE) aims to demonstrate data use through two linked data studies and by identifying clinical data repositories and genetic databases or disease registries prepared to participate in linked research.

Method: The TIRRE survey collects data at micro-, meso- and macro-levels of granularity; to fulfil data, study specific, business, geographical and readiness requirements of potential data providers for the TRANSFoRm demonstration studies.

Accessible Modelling of Complexity in Health (AMoCH) and associated data flows: asthma as an exemplar.

Background Modelling is an important part of information science. Models are abstractions of reality. We use models in the following contexts: (1) to describe the data and information flows in clinical practice to information scientists, (2) to compare health systems and care pathways, (3) to understand how clinical cases are recorded in record systems and (4) to model health care business models.

Smartphone-Based Real-Time Indoor Location Tracking With 1-m Precision.

Monitoring the activities of daily living of the elderly at home is widely recognized as useful for the detection of new or deteriorating health conditions. However, the accuracy of existing indoor location tracking systems remains unsatisfactory. The aim of this study was, therefore, to develop a localization system that can identify a patient's real-time location in a home environment with maximum estimation error of 2 m at a 95% confidence level.

Using ontologies to improve semantic interoperability in health data.

The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text.

Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

Background: A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.

Conducting requirements analyses for research using routinely collected health data: a model driven approach.

Background: Medical research increasingly requires the linkage of data from different sources. Conducting a requirements analysis for a new application is an established part of software engineering, but rarely reported in the biomedical literature; and no generic approaches have been published as to how to link heterogeneous health data.

Methods: Literature review, followed by a consensus process to define how requirements for research, using, multiple data sources might be modeled.

Developing a survey instrument to assess the readiness of primary care data, genetic and disease registries to conduct linked research: TRANSFoRm International Research Readiness (TIRRE) survey instrument.

Background: Clinical data are collected for routine care in family practice; there are also a growing number of genetic and cancer registry data repositories. The Translational Research and Patient Safety in Europe (TRANSFoRm) project seeks to facilitate research using linked data from more than one source. We performed a requirements analysis which identified a wide range of data and business process requirements that need to be met before linking primary care and either genetic or disease registry data.

The Hayes principles: learning from the national pilot of information technology and core generalisable theory in informatics.

Background: There has been much criticism of the NHS national programme for information technology (IT); it has been an expensive programme and some elements appear to have achieved little. The Hayes report was written as an independent review of health and social care IT in England.

Objective: To identify key principles for health IT implementation which may have relevance beyond the critique of NHS IT.

A system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data for research and quality improvement.

Background: We have used routinely collected clinical data in epidemiological and quality improvement research for over 10 years. We extract, pseudonymise and link data from heterogeneous distributed databases; inevitably encountering errors and problems.

Objective: To develop a solution-orientated system of error reporting which enables appropriate corrective action.

Procuring interoperability at the expense of usability: a case study of UK National Programme for IT assurance process.

Background: The allure of interoperable systems is that they should improve patient safety and make health services more efficient. The UK's National Programme for IT has made great strides in achieving interoperability; through linkage to a national electronic spine. However, there has been criticism of the usability of the applications in the clinical environment.

Dimensional reduction for a Bayesian filter.

An adaptive strategy is proposed for reducing the number of unknowns in the calculation of a proposal distribution in a sequential Monte Carlo implementation of a Bayesian filter for nonlinear dynamics. The idea is to solve only in directions in which the dynamics is expanding, found adaptively; this strategy is suggested by earlier work on optimal prediction. The construction should be of value in data assimilation, for example, in geophysical fluid dynamics.