SpineData – A Danish clinical registry of people with chronic back pain

Background: Large-scale clinical registries are increasingly recognized as important resources for quality assurance and research to inform clinical decision-making and health policy. We established a clinical registry (SpineData) in a conservative care setting where more than 10,000 new cases of spinal pain are assessed each year. This paper describes the SpineData registry, summarizes the characteristics of its clinical population and data, and signals the availability of these data as a resource for collaborative research projects. Methods: The SpineData registry is an Internet-based system that captures patient data electronically at the point of clinical contact. The setting is the government-funded Medical Department of the Spine Centre of Southern Denmark, Hospital Lillebaelt, where patients receive a multidisciplinary assessment of their chronic spinal pain. Results: Started in 2011, the database by early 2015 contained information on more than 36,300 baseline episodes of patient care, plus the available 6-month and 12-month follow-up data for these episodes. The baseline questionnaire completion rate has been 93%; 79% of people were presenting with low back pain as their main complaint, 6% with mid-back pain, and 15% with neck pain. Collectively, across the body regions and measurement time points, there are approximately 1,980 patient-related variables in the database across a broad range of biopsychosocial factors. To date, 36 research projects have used data from the SpineData registry, including collaborations with researchers from Denmark, Australia, the United Kingdom, and Brazil. Conclusion: We described the aims, development, structure, and content of the SpineData registry, and what is known about any attrition bias and cluster effects in the data. For epidemiology research, these data can be linked, at an individual patient level, to the Danish population-based registries and the national spinal surgery registry. SpineData also has potential for the conduct of cohort multiple randomized controlled trials. Collaborations with other researchers are welcome

Perinatal mental health: how nordic data sources have contributed to existing evidence and future avenues to explore

Purpose Perinatal mental health disorders affect a significant number of women with debilitating and potentially life-threatening consequences. Researchers in Nordic countries have access to high quality, population-based data sources and the possibility to link data, and are thus uniquely positioned to fill current evidence gaps. We aimed to review how Nordic studies have contributed to existing evidence on perinatal mental health. Methods We summarized examples of published evidence on perinatal mental health derived from large population-based longitudinal and register-based data from Denmark, Finland, Iceland, Norway and Sweden. Results Nordic datasets, such as the Danish National Birth Cohort, the FinnBrain Birth Cohort Study, the Icelandic SAGA cohort, the Norwegian MoBa and ABC studies, as well as the Swedish BASIC and Mom2B studies facilitate the study of prevalence of perinatal mental disorders, and further provide opportunity to prospectively test etiological hypotheses, yielding comprehensive suggestions about the underlying causal mechanisms. The large sample size, extensive follow-up, multiple measurement points, large geographic coverage, biological sampling and the possibility to link data to national registries renders them unique. The use of novel approaches, such as the digital phenotyping data in the novel application-based Mom2B cohort recording even voice qualities and digital phenotyping, or the Danish study design paralleling a natural experiment are considered strengths of such research. Conclusions Nordic data sources have contributed substantially to the existing evidence, and can guide future work focused on the study of background, genetic and environmental factors to ultimately define vulnerable groups at risk for psychiatric disorders following childbirth

Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencie

Clinflow:an interactive application for processing and exploring clinical data

Abstract. Clinical data is the most valuable resource in healthcare development, but it also comes with many challenges. When clinical researchers are required to combine medical expertise with statistical and programming knowledge, the need for data analysis tools arises. The aim of this thesis was to design ClinFlow, an application for clinical data processing and visualization based on user needs. R language and Shiny framework were selected for creating this tool. The goal was to give the means for the clinical researcher to conduct data analysis in an interactive environment, with no need for statistical programming knowledge. A case study using data from The Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study was conducted to demonstrate the feasibility of this application. The initial results achieved in this case study support the previous research of the DIPP study. ClinFlow shows potential for becoming a useful data analysis tool for clinical research

Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment

BACKGROUND: Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries. METHODS: We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report. RESULTS: Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure. CONCLUSION: In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better

Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions

The use of administrative health data for research, monitoring, and quality improvement has proliferated in recent decades, leading to improvements in health across many disease areas and across the life course. However, not all populations are equally visible in administrative health data, and those that are less visible may be excluded from the benefits of associated research. Socially excluded populations -- including the homeless, people with substance dependence, people involved in sex work, migrants or asylum seekers, and people with a history of incarceration -- are typically characterised by health inequity. Yet people who experience social exclusion are often invisible within routinely collected administrative health data because information on their markers of social exclusion are not routinely recorded by healthcare providers. These circumstances make it difficult to understand the often complex health needs of socially excluded populations, evaluate and improve the quality of health services that they interact with, provide more accessible and appropriate health services, and develop effective and integrated responses to reduce health inequity. In this commentary we discuss how linking data from multiple sectors with administrative health data, often called cross-sectoral data linkage, is a key method for systematically identifying socially excluded populations in administrative health data and addressing other issues related to data quality and representativeness. We discuss how cross-sectoral data linkage can improve the representation of socially excluded populations in research, monitoring, and quality improvement initiatives, which can in turn inform coordinated responses across multiple sectors of service delivery. Finally, we articulate key challenges and potential solutions for advancing the use of cross-sectoral data linkage to improve the health of socially excluded populations, using international examples

Tiered Provider Networks in Health insurance

Health insurers are increasingly using plan designs that incentivize consumers to shop for health care based on price. This dissertation studies the effects of one such plan design on demand and equilibrium prices. Tiered hospital networks group hospitals by price ranking and vary consumers\u27 out-of-pocket prices to reflect the price variation faced by the insurer. Proponents argue that tiered networks reduce health care spending by steering consumers toward lower-priced hospitals, and by giving insurers an additional bargaining lever in price negotiations with hospitals. To evaluate these claims, I estimate a structural model of health care demand and insurer-hospital bargaining over prices in the Massachusetts private health insurance market. The model extends the standard Nash bargaining framework to explicitly account for the multiplicity of possible tier outcomes. I find that the effects of tiered networks on demand alone can lead to moderate or sizable reductions in hospital spending, ranging from 1% to 8% depending on the consumer population and the concentration of the hospital market. The effects on negotiated hospital prices add an additional 2% to 4% savings, for a total savings from tiered networks of up to 12% under favorable market conditions. I conclude that insurance plan designs with demand-side incentives can have large health care spending reduction effects