AI and Big Data: A New Paradigm for Decision Making in Healthcare

The latest developments in artificial intelligence (AI) - a general-purpose technology impacting many industries - have been based on advancements in machine learning, which is recast as a quality-adjusted decline in forecasting ratio. The influence of Policy on AI and big data has impacted two key magnitudes which are known as diffusion and consequences. And these must be focused primarily on the context of AI and big data. First, in addition to the policies on subsidies and intellectual property (IP) that will affect the propagation of AI in ways close to their effect on other technologies, three policy categories - privacy, exchange, and liability - may have a specific impact on the diffusion of AI. The first step in the prohibition process is to identify the shortcomings of current hospital procedures, why we need acute care AI, and eventually how the direction of patient decision-making will shift with the introduction of AI-based research. The second step is to establish a plan to shift the direction of medical education in order to enable physicians to retain control of AI. Medical research would need to rely less on threshold decision-making and more on the prediction, interpretation, and pathophysiological context of contextual time cycles. This should be an early part of a medical student's education, and this is what their hospital aid (AI) ought to do. Effective contact between human and artificial intelligence includes a shared pattern of focused knowledge base. Human-to-human contact protection in hospitals should lead this professional transformation process

From the biomedical to the biopsychosocial model: the implementation of a stepped and collaborative care model in Swiss general hospitals

Background and objectives International and national initiatives like the Sustainable Development Goals and the National Strategy on the Prevention of non-communicable diseases aim to reduce the burden of mental health. Early detection of mental health conditions is thus, a major priority of public health. Stepped and Collaborative Care Models (SCCMs) offer an opportunity to early detect and appropriately treat mental health conditions in vulnerable populations, fostering integrated care. This thesis focuses on a SCCM that aims to implement a routine psychosocial distress assessment and offers appropriate treatment to distressed hospital patients. However, integration of mental health services into somatic settings was seen to be challenging in other settings, e.g., primary care. Evidence for patients with mental–somatic multimorbidities in hospital settings is scarce. Thus, the main objectives of this thesis were to assess the integration of mental health services and to assess implementation of a SCCM into general hospitals in Basel-Stadt, Switzerland (Objectives 1 and 2). The unforeseen coronavirus disease 2019 (COVID-19) pandemic additionally triggered further research questions. We investigated the association between COVID-19 restrictions and mental health of non-COVID-19 hospital patients (Objective 3). Additionally, we explored an alternative method to monitor mental health consequences of the COVID-19 pandemic, the use of Big Data (Objective 4). Methods This thesis focuses on a SCCM implemented in four hospitals, three of which were included in the studies presented here: the University Hospital Basel, the University Department of Geriatric Medicine FELIX PLATTER, and the Bethesda Hospital. Including three hospitals differing in structure and focus allowed us to get a broader view of possible facilitators and barriers to the integration of mental health and the implementation of the SCCM. We conducted qualitative interviews with physicians and nurses operating the SCCM at the hospital before (N = 18) and after (N = 18) the implementation of the SCCM. Additionally, we used quantitative data of 873 patients on COVID-19 distress, mental health consequences, and social support collected during periods with different COVID-19 restriction levels, using multiple regression models. The last objective was presented as an opinion paper, highlighting advantages and disadvantages of Big Data based on literature. Results Before the SCCM was implemented in hospital settings in Basel, Switzerland, healthcare professionals perceived mental–somatic multimorbidities to be relevant due to their high perceived frequency (Objective 1). Mental health dimensions had, however, a low priority due to suboptimal environments, suboptimal interprofessional collaboration, existing stigma among healthcare professionals and patients, lack of mental health knowledge, and the strong emphasis on somatic diseases. Particularly physicians reported the low priority of mental health, also due to historical views focusing on biomedical aspects and time constraints. Afterwards, we assessed facilitators and barriers of implementing the first step of the SCCM (Objective 2). The first step of the SCCM is a psychosocial distress assessment of patients through healthcare professionals. Healthcare professionals highlighted the importance of integrating the assessment into preexisting hospital workflows and IT systems. Being able to adapt certain workflows to the needs of the different wards and hospitals was key to adherence and thus, to the sustainability of the SCCM. Still, structural and social barriers to the implementation of the psychosocial distress assessment were emphasized. Hospitals are characterized by a strong focus on somatic diseases with tight working routines. Adding additional tasks like the mental health assessment constituted a challenge. Besides the strong emphasis on somatic diseases and the time constraints, lack of knowledge, awareness, and familiarity and subjectivity of the mental health assessment were impeding the efforts towards integrated care. This, partially, is also caused by the high turnover rate of physicians. The implementation of the SCCM described herewas accompanied by the COVID-19 pandemic. The Swiss government set different COVID-19 restrictions depending on COVID-19 case numbers, hospitalizations, and deaths. Thus, we investigated the association between the COVID-19 restrictions and the COVID-19-related distress, mental health consequences, and social support (Objective 3). Multiple regression analyses of non-COVID-19 patients during different levels of COVID-19 restrictions indicated that hospital patients were more distressed related to leisure time and loneliness when stronger COVID-19 restrictions were in place. Surprisingly, this did not result in increased mental health consequences or changes in social support. Another approach to monitor mental health of the general population or subgroups like hospital patients could be Big Data, such as social media or routine hospital data (Objective 4). These may help to tailor appropriate interventions to populations at risk of mental health consequences. Applying Big Data should always consider ethical and legal concerns to protect privacy and data. Particularly, transparency regarding data analysis may prevent these concerns. Conclusion This thesis adds evidence to the integration of mental health and implementation of a SCCM to hospital settings in Switzerland. Structural and social challenges, such as missing knowledge and awareness, strong emphasis on somatic diseases, time constraints, suboptimal environment, suboptimal interprofessional collaboration, and stigma were emphasized by healthcare professionals. To overcome these challenges, hospitals and policy makers need to think about changes in the healthcare system. For instance, task shifts, new roles, and new processes are needed in the hospital setting to better achieve integrated care. Hospitals are built to care for patients in acute medical situations. Patients with mental–somatic multimorbidities, however, need continuous and long-term care. Certain patient groups (e.g., cancer patients, transplantation patients) receive this care within hospitals. Other patient groups rely on treatment outside hospital. Strong networks between services within and outside hospitals are, thus, essential to guarantee continuity of care. Overall, the current healthcare system with its strong biomedical focus needs to adapt to the increasing number of patients with chronic diseases, including mental–somatic multimorbidities. This system change could be achieved through learning health systems, where interprofessional and interdisciplinary work is a high priority. Continuously collected data supports the adaptation of the healthcare system to the current needs and evidence base. Thus, the change from the biomedical to the biopsychosocial model may be strengthened

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

How immunological profle drives clinical phenotype of primary Sjögren’s syndrome at diagnosis: analysis of 10,500 patients (Sjögren Big Data Project)

To evaluate the influence of the main immunological markers on the disease phenotype at diagnosis in a large international cohort of patients with primary Sjögren´s syndrome (SjS).METHODS:The Big Data Sjögren Project Consortium is an international, multicentre registry created in 2014. As a first step, baseline clinical information from leading centres on clinical research in SjS of the 5 continents was collected. The centres shared a harmonised data architecture and conducted cooperative online efforts in order to refine collected data under the coordination of a big data statistical team. Inclusion criteria were the fulfillment of the 2002 classification criteria. Immunological tests were carried out using standard commercial assays.RESULTS:By January 2018, the participant centres had included 10,500 valid patients from 22 countries. The cohort included 9,806 (93%) women and 694 (7%) men, with a mean age at diagnosis of primary SjS of 53 years, mainly White (78%) and included from European countries (71%). The frequency of positive immunological markers at diagnosis was 79.3% for ANA, 73.2% for anti-Ro, 48.6% for RF, 45.1% for anti- La, 13.4% for low C3 levels, 14.5% for low C4 levels and 7.3% for cryoglobulins. Positive autoantibodies (ANA, Ro, La) correlated with a positive result in salivary gland biopsy, while hypocomplementaemia and especially cryoglo-bulinaemia correlated with systemic activity (mean ESSDAI score of 17.7 for cryoglobulins, 11.3 for low C3 and 9.2 for low C4, in comparison with 3.8 for negative markers). The immunological markers with a great number of statistically-significant associations (p<0.001) in the organ-by-organ ESS- DAI evaluation were cryoglobulins (9 domains), low C3 (8 domains), anti-La (7 domains) and low C4 (6 domains).CONCLUSIONS:We confirm the strong influence of immunological markers on the phenotype of primary SjS at diagnosis in the largest multi-ethnic international cohort ever analysed, with a greater influence for cryoglobulinaemic-related markers in comparison with Ro/La autoantibodies and ANA. Immunological patterns play a central role in the phenotypic expression of the disease already at the time of diagnosis, and may guide physicians to design a specific personalised management during the follow-up of patients with primary SjS.Fil: Brito Zerón, Pilar. Hospital Sanitas CIMA; España. Universidad de Barcelona; EspañaFil: Acar Denizli, Nihan. Mimar Sinan Fine Arts University; TurquíaFil: Ng, Wan Fai. University of Newcastle; Reino UnidoFil: Zeher, Margit. University of Debrecen; HungríaFil: Rasmussen, Astrid. Oklahoma Medical Research Foundation; Estados UnidosFil: Mandl, Thomas. Lund University; SueciaFil: Seror, Raphaele. Université Paris Sud; FranciaFil: Xiaolin, Li. Anhui Provincial Hospital; ChinaFil: Baldini, Chiara. Università degli Studi di Pisa; ItaliaFil: Gottenberg, Jaques. Université de Strasbourg; Francia. Centre National de la Recherche Scientifique; FranciaFil: Danda, Debashish. Christian Medical College & Hospital; IndiaFil: Quartuccio, Luca. University Hospital “Santa María della Misericordia”; ItaliaFil: Priori, Roberta. Università degli Studi di Roma "La Sapienza"; ItaliaFil: Hernandez Molina, Gabriela. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán; MéxicoFil: Armagan, Berkan. Hacettepe University. Faculty of Medicine.Department of Internal Medicine; TurquíaFil: Kruize, Aike. University Medical Center Utrecht; Países BajosFil: Kwok, Seung Ki. The Catholic University of Korea; Corea del SurFil: Kvarnström, Marika. Karolinska University Hospital.Department of Medicine.Unit of Rheumatology. Karolinska Institutet ; SueciaFil: Praprotnik, Sonja. University Medical Centre; EsloveniaFil: Sene, Damien. Université Paris Diderot - Paris 7; FranciaFil: Bartoloni, Elena. Università di Perugia; ItaliaFil: Solans, R.. Hospital Vall d’Hebron; ItaliaFil: Rischmueller, M.. University of Western Australia; AustraliaFil: Suzuki, Y.. Kanazawa University Hospital; JapónFil: Isenberg, D. A.. University College London; Estados UnidosFil: Valim, V.. Federal University of Espírito Santo; BrasilFil: Wiland, P.. Wroclaw Medical Hospital; PoloniaFil: Nordmark, G.. Uppsala Universitet; SueciaFil: Fraile, G.. Hospital Ramón y Cajal; EspañaFil: Retamozo, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; Argentina. Hospital Privado Centro Medico de Córdoba; Argentina; Argentina. Instituto Universitario de Ciencias Biomédicas de Córdoba; Argentin

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

The Serums Tool-Chain:Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems

Digital technology is permeating all aspects of human society and life. This leads to humans becoming highly dependent on digital devices, including upon digital: assistance, intelligence, and decisions. A major concern of this digital dependence is the lack of human oversight or intervention in many of the ways humans use this technology. This dependence and reliance on digital technology raises concerns in how humans trust such systems, and how to ensure digital technology behaves appropriately. This works considers recent developments and projects that combine digital technology and artificial intelligence with human society. The focus is on critical scenarios where failure of digital technology can lead to significant harm or even death. We explore how to build trust for users of digital technology in such scenarios and considering many different challenges for digital technology. The approaches applied and proposed here address user trust along many dimensions and aim to build collaborative and empowering use of digital technologies in critical aspects of human society