Examining patient benefit.

Healthcare policy, clinical practice and clinical research all declare patient benefit as their avowed aim. Yet, the conceptual question of what exactly constitutes patient benefit has received much less attention than the practical means of realising it. Currently, three key areas of conceptual unclarity make the achieved, real-world impact hard to quantify and disconnect it from the magnitude of the practical endeavour: (1) the distinction between objective and subjective benefit, (2) the relation between individual and population measures of benefit, and (3) the optimal measurement of benefit in research studies. A philosophical understanding of wellbeing is required to clarify these problems. Adopting a rigorous philosophical framework makes apparent that the differing goals of clinicians, researchers and research funders may make differing conceptions of patient benefit appropriate. A framework is proposed for developing rigour in methods for specifying and measuring patient benefit, and for matching benefit measures to different contexts.

Advancing the digital and computational capabilities of healthcare providers: A qualitative study of a hospital organisation in the NHS.

Objective: Healthcare systems require transformation to meet societal challenges and projected health demands. Digital and computational tools and approaches are fundamental to this transformation, and hospitals have a key role to play in their development and implementation. This paper reports on a study with the objective of exploring the challenges encountered by hospital leaders and innovators as they implement a strategy to become a data-driven hospital organisation. In doing so, this paper provides guidance to future leaders and innovators seeking to build computational and digital capabilities in complex clinical settings. Methods: Interviews were undertaken with 42 participants associated with a large public hospital organisation within England's National Health Service. Using the concept of institutional readiness as an analytical framework, the paper explores participants' perspectives on the organisation's capacity to support the development of, and benefit from, digital and computational approaches. Results: Participants' accounts reveal a range of specific institutional readiness criteria relating to organisational vision, technical capability, organisational agility, and talent and skills that, when met, enhance the organisations' capacity to support the development and implementation of digital and computational tools. Participant accounts also reveal challenges relating to these criteria, such as unrealistic expectations and the necessary prioritisation of clinical work in resource-constrained settings. Conclusions: The paper identifies a general set of institutional readiness criteria that can guide future hospital leaders and innovators aiming to improve their organisation's digital and computational capability. The paper also illustrates the challenges of pursuing digital and computational innovation in resource-constrained hospital environments.

Deep forecasting of translational impact in medical research.

The value of biomedical research-a $1.7 trillion annual investment-is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation-as indexed by inclusion in patents, guidelines, or policy documents-from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990-2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC] > 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential.

Boundary work during COVID-19: The transformation of research review and set-up.

BACKGROUND AND AIMS: The rapid setting up of research during the COVID-19 pandemic led to changes in ways of working within research organisations. The aim of this study was to examine the experiences of staff involved in the research review and set-up system at a large NHS and university partnership in the UK through the lens of boundary theory. METHODS: We carried out a rapid qualitative appraisal based on telephone interviews (n=25) to explore how staff experienced the research review and set-up system during the pandemic. RESULTS: In light of the pressures created by the pandemic, the boundaries established to set up distinct groups and responsibilities were modified to allow for different ways of working. Some of the new structures and processes were seen positively and brought groups that previously worked at a distance closer together. CONCLUSIONS: The reconceptualisation of relations within the research system during the pandemic added more fluidity to ways of working within the research office and contributed to closer working interactions and an expanded team ethos.

The Value of Data: Applying a Public Value Model to the English National Health Service.

Research and innovation in biomedicine and health care increasingly depend on electronic data. The emergence of data-driven technologies and associated digital transformations has focused attention on the value of such data. Despite the broad consensus of the value of health data, there is less consensus on the basis for that value; thus, the nature and extent of health data value remain unclear. Much of the existing literature presupposes that the value of data is to be understood primarily in financial terms, and assumes that a single financial value can be assigned. We here argue that the value of a dataset is instead relational; that is, the value depends on who wants to use it and for what purposes. Moreover, data are valued for both nonfinancial and financial reasons. Thus, it may be more accurate to discuss the values (plural) of a dataset rather than the singular value. This plurality of values opens up an important set of questions about how health data should be valued for the purposes of public policy. We argue that public value models provide a useful approach in this regard. According to public value theory, public value is created, or captured, to the extent that public sector institutions further their democratically established goals, and their impact on improving the lives of citizens. This article outlines how adopting such an approach might be operationalized within existing health care systems such as the English National Health Service, with particular focus on actionable conclusions.

Quality research in healthcare: are researchers getting enough statistical support?

BACKGROUND: Reviews of peer-reviewed health studies have highlighted problems with their methodological quality. As published health studies form the basis of many clinical decisions including evaluation and provisions of health services, this has scientific and ethical implications. The lack of involvement of methodologists (defined as statisticians or quantitative epidemiologists) has been suggested as one key reason for this problem and this has been linked to the lack of access to methodologists. This issue was highlighted several years ago and it was suggested that more investments were needed from health care organisations and Universities to alleviate this problem. METHODS: To assess the current level of methodological support available for health researchers in England, we surveyed the 25 National Health Services Trusts in England, that are the major recipients of the Department of Health's research and development (R&D) support funding. RESULTS AND DISCUSSION: The survey shows that the earmarking of resources to provide appropriate methodological support to health researchers in these organisations is not widespread. Neither the level of R&D support funding received nor the volume of research undertaken by these organisations showed any association with the amount they spent in providing a central resource for methodological support for their researchers. CONCLUSION: The promotion and delivery of high quality health research requires that organisations hosting health research and their academic partners put in place funding and systems to provide appropriate methodological support to ensure valid research findings. If resources are limited, health researchers may have to rely on short courses and/or a limited number of advisory sessions which may not always produce satisfactory results.

Data protection. Get in on the act.

Informed consent for any use of data beyond immediate care and treatment is fundamental to meeting the requirements of the Data Protection Act. It is important to identify data owners and controllers and to ensure they are aware of their responsibilities to ensure that data is held securely. No data should be passed to third parties without written agreements and unless EU equivalent data protection can be ensured. NHS organisations should ensure they have good policies and training in place to ensure compliance.