Search

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

BackgroundHospitals worldwide are developing ambitious digital transformation programs as part of broader efforts to create digitally advanced health care systems. However, there is as yet no consensus on how best to characterize and assess digital excellence in hospitals. ObjectiveOur aim was to develop an international agreement on a defined set of technological capabilities to assess digital excellence in hospitals. MethodsWe conducted a two-stage international modified electronic Delphi (eDelphi) consensus-building exercise, which included a qualitative analysis of free-text responses. In total, 31 international health informatics experts participated, representing clinical, academic, public, and vendor organizations. ResultsWe identified 35 technological capabilities that indicate digital excellence in hospitals. These are divided into two categories: (a) capabilities within a hospital (n=20) and (b) capabilities enabling communication with other parts of the health and social care system, and with patients and carers (n=15). The analysis of free-text responses pointed to the importance of nontechnological aspects of digitally enabled change, including social and organizational factors. Examples included an institutional culture characterized by a willingness to transform established ways of working and openness to risk-taking. The availability of a range of skills within digitization teams, including technological, project management and business expertise, and availability of resources to support hospital staff, were also highlighted. ConclusionsWe have identified a set of criteria for assessing digital excellence in hospitals. Our findings highlight the need to broaden the focus from technical functionalities to wider digital transformation capabilities.

Background UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational “Big Data”. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depend upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope. Issues In the UK, the separate worlds of health data science (bioinformatics, “Big Data”) and effective healthcare system design and implementation (clinical informatics, “Digital Health”) have operated as ‘two cultures’. Much NHS and social care data is of unusably poor quality. Substantial research funding is wasted on ‘data cleansing’ or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. Recommendation The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline’s evidence base and mitigate the absence of regulation.Independent evaluation of digital health interventions should be the norm, not the exception. Conclusions Policy makers and research funders need to acknowledge the existing gap between the ‘two cultures’ and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.

This article reflects on the changing nature of health information access and the transition of focus from electronic health records (EHRs) to personal health records (PHRs) along with the challenges and need for alignment of national initiatives for EHR and PHR in the National Health Service (NHS) of the UK. The importance of implementing integrated EHRs as a route to enhance the quality of health delivery has been increasingly understood. EHRs, however, carry several limitations that include major fragmentation through multiple providers and protocols throughout the NHS. Questions over ownership and control of data further complicate the potential for fully utilising records. Analysing the previous initiatives and the current landscape, we identify that adopting a patient health record system can empower patients and allow better harmonisation of clinical data at a national level. We propose regional PHR ‘hubs’ to provide a universal interface that integrates digital health data at a regional level with further integration at a national level. We propose that these PHR hubs will reduce the complexity of connections, decrease governance challenges and interoperability issues while also providing a safe platform for high-quality scalable and sustainable digital solutions, including artificial intelligence across the UK NHS, serving as an exemplar for other countries which wish to realise the full value of healthcare records.

Radiation treatment (RT) for cancer is a critical medical procedure that occurs in a complex environment that is subject to uncertainties and errors. We employed a simulation (a variant of Monte Carlo) model that followed a cohort of hypothetical breast cancer patients to estimate the probability of incorrect staging and treatment decisions. As inputs, we used a combination of literature information and expert judgement. Input variables were defined as probability distributions within the model. Uncertainties were propagated via simulation. Sensitivity and value-of-information analyses were then conducted to quantify the effect of variable uncertainty on the model outputs. We found a small but non-trivial probability that patients would be incorrectly staged and thus be subjected to inappropriate treatment. Some routinely used tests for staging and metastasis detection have very limited informational value. This work has implications for the methods used in cancer staging and subsequent risk assessment of treatment errors. doi: 10.1057/palgrave.jors.2602269 Keywords: decision analysis; medicine; probability; risk; Simulation

Collembola are well known to occur in soils and litters, as well as inter-tidal habitats, caves and tree canopies, but are not normally thought of as members of the groundwater community. Here we report on studies of the colonisation of gravels in the hyporheic zone under the river Skirfare, a karstic river in Yorkshire. Unexpectedly, flooding during the second experimental run allowed a comparison between low- and high-flow conditions. Five species of Collembola were found in permanently saturated habitats 30 cm below a river bed. One of these species, Hymenaphorura nova, is the first record of its genus in the UK, and appears to be a groundwater specialist. The other named species (Anurida granaria and Mucrosomia (= Cryptopygus) garretti), Deuteraphorura cebennaria (= D. inermis) have wider distributions but are often associated with mines and caves; D. cebennaria is known to occur in the Scoska cave, whose waters feed into the Skirfare shortly upstream of the experimental location. There was some evidence of differences between dates (more first instars and fewer adults after the flood) and between sediments (adults found mainly in coarser materials, first instars in finer sediments), but the statistical significance was weak (0.1 > p > 0.05). The distribution of animals was not clumped (as is typical for Collembola) but followed a Poisson distribution, suggesting it to be primarily random. These results agree with previous authors who suggested that several species of Collembola may live for prolonged periods wholly submerged. The random distribution and their water-repellent cuticles suggest a model of eggs or early instars being washed underground following groundwater, surviving underwater for prolonged periods. We do not have evidence that these Collembola are able to complete their life cycle underwater.

It is often desirable to collect Collembola from remote locations where the usual extraction systems based on thermal gradients are simply impossible, e.g. for lack of adequate energy supplies. Winkler funnels are often used in such situations as zero-energy extractors of soil arthropods, but have not been calibrated for the collection of Collembola. Here we report a series of trials comparing densities and species-richness of Collembola in leaf moulds using parallel Tullgren and Winkler extractions. Each litter was tested separately with the Winkler funnel environment at low and at high humidity. Our results showed that Winkler funnels are a poor substitute for Tullgrens, under-recording species by 30% and densities by 78%, suggesting that the basic Winkler funnel method is not suitable for objective quantitative Collembola population studies.