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  1. Article ; Online: Impact of primary to secondary care data sharing on care quality in NHS England hospitals.

    Zhang, Joe / Ashrafian, Hutan / Delaney, Brendan / Darzi, Ara

    NPJ digital medicine

    2023  Volume 6, Issue 1, Page(s) 144

    Abstract: Health information exchange (HIE) is seen as a key component of effective care but remains poorly evidenced at a health system level. In the UK National Health Service (NHS), the ability to share primary care data with secondary care clinicians is a ... ...

    Abstract Health information exchange (HIE) is seen as a key component of effective care but remains poorly evidenced at a health system level. In the UK National Health Service (NHS), the ability to share primary care data with secondary care clinicians is a focus of continued digital investment. In this study, we report the evolution of interoperable technology across a period of rapid digital transformation in NHS England from 2015 to 2019, and test association of primary to secondary care data-sharing capabilities with clinical care quality indicators across all acute secondary care providers (n = 135 NHS Trusts). In multivariable analyses, data-sharing capabilities are associated with reduction in patients breaching an Accident & Emergency (A&E) 4-h decision time threshold, and better patient-reported experience of acute hospital care quality. Using synthetic control analyses, we estimate mean 2.271% (STD+/-3.371) absolute reduction in A&E 4-h decision time breach, 12 months following introduction of data-sharing capabilities. Our findings support current digital transformation programmes for developing regional HIE networks but highlight the need to focus on implementation factors in addition to technological procurement.
    Language English
    Publishing date 2023-08-14
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-023-00891-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Are vaccines a potential treatment for long covid?

    Sivan, Manoj / Greenhalgh, Trisha / Milne, Ruairidh / Delaney, Brendan

    BMJ (Clinical research ed.)

    2022  Volume 377, Page(s) o988

    MeSH term(s) COVID-19/complications ; COVID-19/prevention & control ; COVID-19 Vaccines ; Humans ; SARS-CoV-2 ; Vaccines
    Chemical Substances COVID-19 Vaccines ; Vaccines
    Language English
    Publishing date 2022-05-18
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.o988
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  3. Article ; Online: Authors' reply to Ward.

    Greenhalgh, Trisha / Sivan, Manoj / Delaney, Brendan / Evans, Rachael / Milne, Ruairidh

    BMJ (Clinical research ed.)

    2022  Volume 379, Page(s) o2506

    Language English
    Publishing date 2022-10-20
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.o2506
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  4. Article ; Online: RECAP-KG: Mining Knowledge Graphs from Raw Primary Care Physician Notes for Remote COVID-19 Assessment in Primary Care.

    Mekhtieva, Rachel Lee / Forbes, Brandon / Alrajeh, Dalal / Delaney, Brendan / Russo, Alessandra

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2024  Volume 2023, Page(s) 1145–1154

    Abstract: Building Clinical Decision Support Systems, whether from regression models or machine learning requires clinical data either in standard terminology or as text for Natural Language Processing (NLP). Unfortunately, many clinical notes are written quickly ... ...

    Abstract Building Clinical Decision Support Systems, whether from regression models or machine learning requires clinical data either in standard terminology or as text for Natural Language Processing (NLP). Unfortunately, many clinical notes are written quickly during the consultation and contain many abbreviations, typographical errors, and a lack of grammar and punctuation Processing these highly unstructured clinical notes is an open challenge for NLP that we address in this paper. We present RECAP-KG - a knowledge graph construction frame workfrom primary care clinical notes. Our framework extracts structured knowledge graphs from the clinical record by utilising the SNOMED-CT ontology both the entire finding hierarchy and a COVID-relevant curated subset. We apply our framework to consultation notes in the UK COVID-19 Clinical Assessment Service (CCAS) dataset and provide a quantitative evaluation of our framework demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients.
    MeSH term(s) Humans ; Algorithms ; Pattern Recognition, Automated ; Physicians, Primary Care ; Electronic Health Records ; COVID-19 ; Natural Language Processing ; Primary Health Care
    Language English
    Publishing date 2024-01-11
    Publishing country United States
    Document type Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
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  5. Article ; Online: Long covid-an update for primary care.

    Greenhalgh, Trisha / Sivan, Manoj / Delaney, Brendan / Evans, Rachael / Milne, Ruairidh

    BMJ (Clinical research ed.)

    2022  Volume 378, Page(s) e072117

    MeSH term(s) COVID-19/complications ; Humans ; Primary Health Care ; SARS-CoV-2
    Language English
    Publishing date 2022-09-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj-2022-072117
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  6. Article ; Online: Influences of early diagnostic suggestions on clinical reasoning.

    Kourtidis, Ploutarchos / Nurek, Martine / Delaney, Brendan / Kostopoulou, Olga

    Cognitive research: principles and implications

    2022  Volume 7, Issue 1, Page(s) 103

    Abstract: Previous research has highlighted the importance of physicians' early hypotheses for their subsequent diagnostic decisions. It has also been shown that diagnostic accuracy improves when physicians are presented with a list of diagnostic suggestions to ... ...

    Abstract Previous research has highlighted the importance of physicians' early hypotheses for their subsequent diagnostic decisions. It has also been shown that diagnostic accuracy improves when physicians are presented with a list of diagnostic suggestions to consider at the start of the clinical encounter. The psychological mechanisms underlying this improvement in accuracy are hypothesised. It is possible that the provision of diagnostic suggestions disrupts physicians' intuitive thinking and reduces their certainty in their initial diagnostic hypotheses. This may encourage them to seek more information before reaching a diagnostic conclusion, evaluate this information more objectively, and be more open to changing their initial hypotheses. Three online experiments explored the effects of early diagnostic suggestions, provided by a hypothetical decision aid, on different aspects of the diagnostic reasoning process. Family physicians assessed up to two patient scenarios with and without suggestions. We measured effects on certainty about the initial diagnosis, information search and evaluation, and frequency of diagnostic changes. We did not find a clear and consistent effect of suggestions and detected mainly non-significant trends, some in the expected direction. We also detected a potential biasing effect: when the most likely diagnosis was included in the list of suggestions (vs. not included), physicians who gave that diagnosis initially, tended to request less information, evaluate it as more supportive of their diagnosis, become more certain about it, and change it less frequently when encountering new but ambiguous information; in other words, they seemed to validate rather than question their initial hypothesis. We conclude that further research using different methodologies and more realistic experimental situations is required to uncover both the beneficial and biasing effects of early diagnostic suggestions.
    MeSH term(s) Humans ; Physicians, Family/psychology ; Clinical Reasoning
    Language English
    Publishing date 2022-12-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2365-7464
    ISSN (online) 2365-7464
    DOI 10.1186/s41235-022-00453-y
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  7. Article ; Online: Fresh evidence of the scale and scope of long covid.

    Sivan, Manoj / Rayner, Clare / Delaney, Brendan

    BMJ (Clinical research ed.)

    2021  Volume 373, Page(s) n853

    MeSH term(s) COVID-19 ; Hospitalization ; Hospitals ; Humans ; Retrospective Studies ; SARS-CoV-2
    Language English
    Publishing date 2021-04-01
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.n853
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  8. Article ; Online: Formalising triage in general practice towards a more equitable, safe, and efficient allocation of resources.

    Rodrigues, Daniela / Kreif, Noemi / Saravanakumar, Kavitha / Delaney, Brendan / Barahona, Mauricio / Mayer, Erik

    BMJ (Clinical research ed.)

    2022  Volume 377, Page(s) e070757

    MeSH term(s) Critical Care ; General Practice ; Health Care Rationing ; Humans ; Resource Allocation ; Triage
    Language English
    Publishing date 2022-05-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj-2022-070757
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  9. Article ; Online: Pathophysiological Mechanisms in Long COVID: A Mixed Method Systematic Review.

    Diar Bakerly, Nawar / Smith, Nikki / Darbyshire, Julie L / Kwon, Joseph / Bullock, Emily / Baley, Sareeta / Sivan, Manoj / Delaney, Brendan

    International journal of environmental research and public health

    2024  Volume 21, Issue 4

    Abstract: Introduction: Long COVID (LC) is a global public health crisis affecting more than 70 million people. There is emerging evidence of different pathophysiological mechanisms driving the wide array of symptoms in LC. Understanding the relationships between ...

    Abstract Introduction: Long COVID (LC) is a global public health crisis affecting more than 70 million people. There is emerging evidence of different pathophysiological mechanisms driving the wide array of symptoms in LC. Understanding the relationships between mechanisms and symptoms helps in guiding clinical management and identifying potential treatment targets.
    Methods: This was a mixed-methods systematic review with two stages: Stage one (Review 1) included only existing systematic reviews (meta-review) and Stage two (Review 2) was a review of all primary studies. The search strategy involved Medline, Embase, Emcare, and CINAHL databases to identify studies that described symptoms and pathophysiological mechanisms with statistical analysis and/or discussion of plausible causal relationships between mechanisms and symptoms. Only studies that included a control arm for comparison were included. Studies were assessed for quality using the National Heart, Lung, and Blood Institute quality assessment tools.
    Results: 19 systematic reviews were included in Review 1 and 46 primary studies in Review 2. Overall, the quality of reporting across the studies included in this second review was moderate to poor. The pathophysiological mechanisms with strong evidence were immune system dysregulation, cerebral hypoperfusion, and impaired gas transfer in the lungs. Other mechanisms with moderate to weak evidence were endothelial damage and hypercoagulation, mast cell activation, and auto-immunity to vascular receptors.
    Conclusions: LC is a complex condition affecting multiple organs with diverse clinical presentations (or traits) underpinned by multiple pathophysiological mechanisms. A 'treatable trait' approach may help identify certain groups and target specific interventions. Future research must include understanding the response to intervention based on these mechanism-based traits.
    MeSH term(s) Humans ; COVID-19/physiopathology ; SARS-CoV-2 ; Post-Acute COVID-19 Syndrome
    Language English
    Publishing date 2024-04-12
    Publishing country Switzerland
    Document type Journal Article ; Systematic Review ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph21040473
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  10. Article ; Online: Can decision support combat incompleteness and bias in routine primary care data?

    Kostopoulou, Olga / Tracey, Christopher / Delaney, Brendan C

    Journal of the American Medical Informatics Association : JAMIA

    2021  Volume 28, Issue 7, Page(s) 1461–1467

    Abstract: Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.: Materials and methods: ... ...

    Abstract Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.
    Materials and methods: We used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician's final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.
    Results: Supported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = -0.08 [-0.11, -0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.
    Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians' cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.
    MeSH term(s) Bias ; Documentation ; Electronic Health Records ; Humans ; Primary Health Care ; Referral and Consultation
    Language English
    Publishing date 2021-03-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocab025
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