Open source software, the future of medical imaging?

Medical imaging and Picture Archiving and Communication Systems (PACS) in particular, have appeared to be one of the promising areas for Open Source Software (OSS). Open source medical imaging solutions do exist, including PACS, but are not widely deployed at hospitals and health care establishments, which prevents them from achieving their full potential. In most cases where medical OSS systems exist (i.e. not necessarily PACS), it is to a very limited degree, and typically out of sight of the common user. Research we have conducted on medical software companies in North Norway and three hospitals in Europe suggests that if open source medical software is to become a useful alternative to proprietary software, that firstly, the initiative must be taken by the public health services and secondly, that it will require a shift from software companies (from sale-value oriented to service oriented). However, it would be naïve to rely on existing proprietary companies to initiate such a change. Interviews revealed that some companies considered the possibilities of developing using an OSS model, but did not deem it to be profitable, whereas others stated that it was simply out of the question. They are not willing to risk their successful business models, because historically (and perhaps at the cost of quality) it pays to keep the inner workings of their software secret. Other reasons revealed for not using OSS were: poor support, prejudices and the unwillingness of proprietary companies to accept a new business model. We suggest that these problems can be overcome with the emergence of competence centers for OSS, and that if open source medical imaging, PACS projects in particular, are to get started, they are more likely to succeed if a hospital is involved. However, our suggestions can only be tested thoroughly if more implementations are done

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

Søvnforstyrrelser og forskrivning av hypnotika i allmennpraksis – en PraksisNett-studie

Source at https://helse-bergen.no/nasjonal-kompetansetjeneste-for-sovnsykdommer-sovno/tidsskriftet-sovn.Søvnforstyrrelser er svært utbredt i befolkningen. Insomni er den vanligste søvnforstyrrelsen med en forekomst på omkring 10-20 % [1, 2]. Forekomsten ser ut til å være økende i befolkningen [1]. Blant pasienter på venterommet hos norske fastleger er forekomsten så høy som rundt 50 % ifølge to tidligere studier [3, 4]. Insomni utgjør en risikofaktor for utvikling av psykiske lidelser [5] og er identifisert som en mulig kausal årsaksfaktor for en rekke negative helseutfall [6]. Selv om sovemedisiner kun er anbefalt ved akutte søvnplager [7], er forskrivning av sovemedisiner også for langvarige plager svært vanlig [8]. Medikamentgruppen hypnotika inkluderer benzodiazepiner, benzodiazepinlignende sovemidler (z preparater) og melatoninpreparater. I klinisk praksis benyttes av og til også andre preparater enn hypnotika mot søvnproblemer, inkludert antidepressiva, antihistaminer og antipsykotika [9]

The Norwegian PraksisNett: a nationwide practice-based research network with a novel IT infrastructure

Clinical research in primary care is relatively scarce. Practice-based research networks (PBRNs) are research infrastructures to overcome hurdles associated with conducting studies in primary care. In Norway, almost all 5.4 million inhabitants have access to a general practitioner (GP) through a patient-list system. This gives opportunity for a PBRN with reliable information about the general population. The aim of the current paper is to describe the establishment, organization and function of PraksisNett (the Norwegian Primary Care Research Network). Materials and Methods We describe the development, funding and logistics of PraksisNett as a nationwide PBRN. Results PraksisNett received funding from the Research Council of Norway for an establishment period of five years (2018–2022). It is comprised of two parts; a human infrastructure (employees, including academic GPs) organized as four regional nodes and a coordinating node and an IT infrastructure comprised by the Snow system in conjunction with the Medrave M4 system. The core of the infrastructure is the 92 general practices that are contractually linked to PraksisNett. These include 492 GPs, serving almost 520,000 patients. Practices were recruited during 2019–2020 and comprise a representative mix of rural and urban settings spread throughout all regions of Norway. Conclusion Norway has established a nationwide PBRN to reduce hurdles for conducting clinical studies in primary care. Improved infrastructure for clinical studies in primary care is expected to increase the attractiveness for studies on the management of disorders and diseases in primary care and facilitate international research collaboration. This will benefit both patients, GPs and society in terms of improved quality of care.publishedVersio

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Background Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step. Methods We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network. Results The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem. Conclusions The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians

Telementoring as a Service

Abstrac

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Background: Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step. Methods: We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network. Results: The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem. Conclusions: The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians

Causality in Scale Space as an Approach to Change Detection

Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are indicated. Significance regions are established by hypothesis tests for significant gradient at every point in scale space. Causality is imposed onto the space by restricting to kernels with left-bounded or finite support and shifting kernels forward. We show that these adjustments to the methodology enable early detection of changes in time series constituting live surveillance systems of either count data or unevenly sampled measurements. Warning delays are comparable to standard techniques though comparison shows that other techniques may be better suited for single-scale problems. Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. Hence the technique will be applicable for a large variety of sources without tailoring. Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. We apply the technique to disease outbreak detection based on laboratory confirmed cases for pertussis and influenza as well as blood glucose concentration obtained from patients with diabetes type 1

User-centred Design of a Mobile Application for Chronic Pain Management

Chronic pain patients constitute a large and heterogeneous patient group and it is important to build tools and methods that can identify efficient treatment options for each individual patient. It is estimated that 20-30% of the population has suffered from chronic pain and this imposes enormous costs on society and the national welfare programs. The research project Chronic Pain addresses the problem of how to provide patients and physicians with relevant, valid and adapted decision alternatives in a shared decision making tool. This paper presents the results from co-creation workshops early in the user-centred design process of the chronic pain mobile application. The end-users contributed in mapping the user needs and requirements, and made paper prototyping of the user interface. The main contribution lies on how a user-centred design methodology can be applied in a clinical development context

User-centred Design of a Mobile Application for Chronic Pain Management

Chronic pain patients constitute a large and heterogeneous patient group and it is important to build tools and methods that can identify efficient treatment options for each individual patient. It is estimated that 20-30% of the population has suffered from chronic pain and this imposes enormous costs on society and the national welfare programs. The research project Chronic Pain addresses the problem of how to provide patients and physicians with relevant, valid and adapted decision alternatives in a shared decision making tool. This paper presents the results from co-creation workshops early in the user-centred design process of the chronic pain mobile application. The end-users contributed in mapping the user needs and requirements, and made paper prototyping of the user interface. The main contribution lies on how a user-centred design methodology can be applied in a clinical development context