From Smart Health to Smart Hospitals

Hospital space interpreted according to Heidegger's concepts of care and dwelling

Modern hospitals have succeeded in saving humans from numerous diseases owing to the rapid development of medical technology. However, modern medical science, combined with advanced technology, has developed a strong tendency to view human beings as mere targets of restoration and repair, with modern hospitals characterised as spaces centred on technology-focused treatment. This results in a situation where human beings are reduced to objects and alienated. This study, integrating Heidegger's concepts of dwelling and care, contends that 'care' is a vital concept in terms of the fundamental spatiality of hospitals and needs to be restored as the key guiding principle affecting hospital space. The loss of the caring spirit in the development of modern hospitals affects how hospitals are conceived, built and managed, as well as how human experiences within hospitals are dealt with or allowed for appropriately. This study offers critical reflection on how future planning of hospital spaces can be better conducted to ensure that human experiences, and the care needed to appropriately value such experiences, are adequately expressed, and the complexity of human existence is suitably considered.

Smart hospital definition: Academic and industrial perspective

BACKGROUNDS

Healthcare is a social and economic challenge in many countries, exacerbated by today's increasing demand. Many studies demonstrate that hospitals that move towards smartness, and some of their processes are smart, can provide more appropriate treatments and deal with problems more flexibly. It is axiomatic that implementing smart hospitals and healthcare tools requires a clear objective. However, the concept of a smart hospital lacks a comprehensive and broadly accepted definition, leading to varied interpretations and misconceptions. Many developments touted as 'smart' merely digitize existing hospital environments without truly embracing the full potential of smart technology. Furthermore, research studies have neglected to consider industrial perspectives, which will soon cause a gap between industry and academics in this concept.

OBJECTIVES

This research aims to explore the attributes of a smart hospital and use them to propose a definition for it, considering both scholarly and industrial viewpoints.

METHOD AND RESULTS

The PRISMA method is employed to select academic and practical papers providing definitions and insights into smart hospitals or healthcare. 17 studies are analyzed, and a total, 83 characteristics are identified to describe the smart hospital. These features are categorized into three primary categories: "technologies", "services", and "goals". The most important features are determined by analyzing the frequencies of these characteristics across all the studies. In the results section, these data are presented in graphical form, highlighting both academic and industrial perspectives separately, as well as a combined analysis. Furthermore, an attempt is made to uncover trends in smart hospitals from 2015 to 2023.

CONCLUSION

A comprehensive definition of the smart hospital, encompassing both academic and industrial perspectives, is proposed using the investigated characteristics. This study also presents research opportunities and discusses the existing gap between academia and industry concerning smart hospitals.

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

Pressure sensors integrated in surfaces, such as the floor, can enable movement, event, and object detection with relatively little effort and without raising privacy concerns, such as video surveillance. Usually, this requires a distributed array of sensor pixels, whose design must be optimized according to the expected use case to reduce implementation costs while providing sufficient sensitivity. In this work, we present an unobtrusive smart floor concept based on floor tiles equipped with a printed piezoelectric sensor matrix. The sensor element adds less than 130 µm in thickness to the floor tile and offers a pressure sensitivity of 36 pC/N for a 1 cm² pixel size. A floor model was established to simulate how the localized pressure excitation acting on the floor spreads into the sensor layer, where the error is only 1.5%. The model is valuable for optimizing the pixel density and arrangement for event and object detection while considering the smart floor implementation in buildings. Finally, a demonstration, including wireless connection to the computer, is presented, showing the viability of the tile to detect finger touch or movement of a metallic rod.

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.

Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes

Smart health is considered to be a new phase in the application of information and communication technologies (ICT) in healthcare that can improve its efficiency and sustainability. However, based on our literature review on the concept of smart health, there is a lack of a comprehensive perspective on the concept of smart health and a framework for how to link the drivers and outcomes of smart health. This paper aims to interweave the drivers and outcomes in a multi-dimensional framework under the input-process-output (IPO) logic of the "system view" so as to promote a deeper understanding of the model of smart health. In addition to the collection of studies, we used the modified Delphi method (MDM) to invite 10 experts from different fields, and the views of the panelists were analyzed and integrated through a three-round iterative process to reach a consensus on the elements included in the conceptual framework. The study revealed that smart health contains five drivers (community, technology, policy, service, and management) and eight outcomes (efficient, smart, sustainable, planned, trustworthy, safe, equitable, health-beneficial, and economic). They all represent a unique aspect of smart health. This paper expands the research horizon of smart health, shifting from a single technology to multiple perspectives, such as community and management, to guide the development of policies and plans in order to promote smart health.

Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

BACKGROUND

With the internet's penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma-a common respiratory disease-is present.

OBJECTIVE

This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data.

METHODS

Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term "asthma," forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined.

RESULTS

Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years' queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=-.82, P=.001) and current asthma (r=-.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=-.78, P=.003) and current asthma (r=-.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available.

CONCLUSIONS

Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma.

Machine Learning and Decision Support in Critical Care

Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding "secondary use of medical records" and "Big Data" analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of "precision medicine." This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; on-line patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.