Personal Health Records (PHRs) have the potential to give patients fine-grained, personalized and secure access to their own medical data and to enable self-management of care. Emergent trends around ...the use of Blockchain, or Distributed Ledger Technology, seem to offer solutions to some of the problems faced in enabling these technologies, especially to support issues consent, data exchange, and data access. We present an analysis of existing blockchain-based health record solutions and a reference architecture for a "Ledger of Me" system that extends PHR to create a new platform combining the collection and access of medical data and digital interventions with smart contracts. Our intention is to enable patient use of the data in order to support their care and to provide a strong consent mechanisms for sharing of data between different organizations and apps. Ledger of Me is based on around the principle that this combination of event-driven smart contracts, medical record data, and patient control is important for the adoption of blockchain-based solutions for the PHR. The reference architecture we present can serve as the basis of a range of future blockchain-based medical application architectures.
In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of ...research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining 'proof' of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Our study examines if SARS-CoV-2 infections varied by vaccination status, if an individual had previously tested positive and by neighbourhood socioeconomic deprivation across the Delta and Omicron ...epidemic waves of SARS-CoV-2.
Population cohort study using electronic health records for 2.7 M residents in Cheshire and Merseyside, England (3rd June 2021 to 1st March 2022). Our outcome variable was registered positive test for SARS-CoV-2. Explanatory variables were vaccination status, previous registered positive test and neighbourhood socioeconomic deprivation. Cox regression models were used to analyse associations.
Originally higher SARS-CoV-2 rates in the most socioeconomically deprived neighbourhoods changed to being higher in the least deprived neighbourhoods from the 1st September 2021, and were inconsistent during the Omicron wave. Individuals who were fully vaccinated (two doses) were associated with fewer registered positive tests (e.g., individuals engaged in testing between 1st September and 27th November 2021-Hazards Ratio (HR) = 0.48, 95% Confidence Intervals (CIs) = 0.47-0.50. Individuals with a previous registered positive test were also less likely to have a registered positive test (e.g., individuals engaged in testing between 1st September and 27th November 2021-HR = 0.16, 95% CIs = 0.15-0.18. However, the Omicron period saw smaller effect sizes for both vaccination status and previous registered positive test.
Changing patterns of SARS-CoV-2 infections during the Delta and Omicron waves reveals a dynamic pandemic that continues to affect diverse communities in sometimes unexpected ways.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
4.
Blockchain Native Data Linkage Cunningham, James; Davidge, Gail; Davies, Nigel ...
Frontiers in blockchain,
10/2021, Letnik:
4
Journal Article
Recenzirano
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Data providers holding sensitive medical data often need to exchange data pertaining to patients for whom they hold particular data. This involves requesting information from other providers to ...augment the data they hold. However, revealing the superset of identifiers for which a provider requires information can, in itself, leak sensitive private data. Data linkage services exist to facilitate the exchange of anonymized identifiers between data providers. Reliance on third parties to provide these services still raises issues around the trust, privacy and security of such implementations. The rise and use of blockchain and distributed ledger technologies over the last decade has, alongside innovation and disruption in the financial sphere, also brought to the fore and refined the use of associated privacy-preserving cryptographic protocols and techniques. These techniques are now being adopted and used in fields removed from the original financial use cases. In this paper we present a combination of a blockchain-native auditing and trust-enabling environment alongside a query exchange protocol. This allows the exchange of sets of patient identifiers between data providers in such a way that only identifiers lying in the intersection of sets of identifiers are revealed and shared, allowing further secure and privacy-preserving exchange of medical information to be carried out between the two parties. We present the design and implementation of a system demonstrating the effectiveness of these exchange protocols giving a reference architecture for the implementation of such a system.
It is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and ...emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions.
We did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use.
Between March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1–30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0–14·8), with the number increasing to 35·6% (34·9–36·3) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 55·9%); minimal admissions (n=12 057 35·9%); recently high admissions (n=1931 5·8%), and persistently high admissions (n=820 2·4%). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 95% CI 2·35–2·81; p<0·0001) and the risk of readmission was highest in the persistently high admissions group (3·23 2·89–3·61; p<0·0001).
Long-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support.
Chief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation.
With investment from large multinational technology companies and venture capital, the use of blockchain technology is growing in markets beyond finance such as the pharmaceutical and health-care ...industries.2 In Estonia, every citizen's health record is secured with blockchain technology, which allows them to express consent to different uses of their data and to be assured as to who has viewed their records.3 Companies and researchers have also begun development of blockchain use for managing access to health data. For these reasons, private data, especially large files such as multimedia, are often stored outside of blockchain.8 Finally, data that are stored in the blockchain ledger cannot be deleted, which could conflict with the EU General Data Protection Regulation, whereby patients must be able to opt out of the storage and use of their data.9 Blockchain is an immature technology that might not be sustainable, and individual platforms can be exposed to security flaws, such as the attack on Ethereum Classic in January, 2019, with the theft of almost US$500 000,10 especially with the waxing and waning in popularity of various blockchain platforms and when security is based on no single provider holding a majority of the ability to compute tokens. ...use of blockchain might provide evidence to support initiatives such as the UK's National Institute for Health and Care Excellence (NICE) and National Health Service (NHS) digital frameworks for evaluation.14 This transparency might act as a catalyst for the trust and subsequent adoption of some AI-based tools in health care.
AbstractObjectiveTo develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).DesignProspective observational cohort ...study.SettingInternational Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020.ParticipantsAdults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction.Main outcome measureIn-hospital mortality.Results35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73).ConclusionsAn easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations.Study registrationISRCTN66726260
Sharing personal health data for direct care, health improvement, planning and research is recognised as important to improving the quality and safety of care. However, the complexities of sharing ...data, including technology, information governance and consent issues, means that many projects have difficulty communicating with the public about why they wish to share data, or what the benefits might be. Great Manchester Academic Health Science Network has established a Public Experience Group to help co-design the requirements for a health information exchange, called DataWell, across over 30 health and care organisations in Greater Manchester. This group has allowed the programme to uniquely respond to questions of how consent and data sharing should work with DataWell for direct care, as well as exploring other uses of the data, including planning and research.
Several high profile, problematic UK health data-sharing projects have shaped NHS professionals' and the public's opinion about NHS data-sharing projects, and there is a substantial body of evidence ...identifying barriers to Health Information Exchange (HIE) adoption. Socio-technical factors are a significant consideration and this paper describes the approach taken to address these concerns in the design and implementation of a HIE for Greater Manchester.
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