Using Primary Care Data to Report Real-World Pancreatic Cancer Survival and Symptomatology.

Pancreatic cancer is the 10th most common cancer diagnosed; despite recent advances in many areas of oncology, survival remains poor, in part owing to late diagnosis. Whilst primary care data are used widely for epidemiology and pharmacovigilance, they are less used for observing survival. In this study we extracted a pancreatic cancer cohort from a nationally representative English primary care database of electronic health records (EHRs) and reported on their symptom and mortality data. A total of 11, 649 cases were identified within the Oxford Royal College of General Practitioners (RCGP) Clinical Informatics Digital Hub network. All-cause mortality data was recorded for 4623 (39.69%). Mean age at recording of cancer diagnosis was 71.4 years (SD 12.0 years). 1-year and 5-year survival was 22.06% and 3.27% respectively. Within a multivariate model, age had a significant impact on survival; those diagnosed under the age of 60 had the longest survival, as compared to those age 60 - 79 (HR: 1.36, 95% CI: 1.20 - 1.54, p < 0.001) and 80+ (HR: 2.13, 95% CI: 1.86 - 2.44, p < 0.01). Symptomatology was examined; at any time point abdominal pain was the most commonly reported symptom present in 5271 cases (45.2%), but within the 12 months preceding diagnosis jaundice was the most common feature, present in 2587 patients (22.2%). Future studies clarifying other contributing factors on survival outcomes and patterns of symptomatology are needed; primary care EHRs provide an opportunity to evaluate real-world cancer patient cohort data.

Primary Care Informatics Response to Covid-19 Pandemic: Adaptation, Progress, and Lessons from Four Countries with High ICT Development.

OBJECTIVE: Internationally, primary care practice had to transform in response to the COVID pandemic. Informatics issues included access, privacy, and security, as well as patient concerns of equity, safety, quality, and trust. This paper describes progress and lessons learned. METHODS: IMIA Primary Care Informatics Working Group members from Australia, Canada, United Kingdom and United States developed a standardised template for collection of information. The template guided a rapid literature review. We also included experiential learning from primary care and public health perspectives. RESULTS: All countries responded rapidly. Common themes included rapid reductions then transformation to virtual visits, pausing of non-COVID related informatics projects, all against a background of non-standardized digital development and disparate territory or state regulations and guidance. Common barriers in these four and in less-resourced countries included disparities in internet access and availability including bandwidth limitations when internet access was available, initial lack of coding standards, and fears of primary care clinicians that patients were delaying care despite the availability of televisits. CONCLUSIONS: Primary care clinicians were able to respond to the COVID crisis through telehealth and electronic record enabled change. However, the lack of coordinated national strategies and regulation, assurance of financial viability, and working in silos remained limitations. The potential for primary care informatics to transform current practice was highlighted. More research is needed to confirm preliminary observations and trends noted.

Quality assessment of real-world data repositories across the data life cycle: A literature review.

OBJECTIVE: Data quality (DQ) must be consistently defined in context. The attributes, metadata, and context of longitudinal real-world data (RWD) have not been formalized for quality improvement across the data production and curation life cycle. We sought to complete a literature review on DQ assessment frameworks, indicators and tools for research, public health, service, and quality improvement across the data life cycle. MATERIALS AND METHODS: The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases from health, physical and social sciences were used: Cinahl, Embase, Scopus, ProQuest, Emcare, PsycINFO, Compendex, and Inspec. Embase was used instead of PubMed (an interface to search MEDLINE) because it includes all MeSH (Medical Subject Headings) terms used and journals in MEDLINE as well as additional unique journals and conference abstracts. A combined data life cycle and quality framework guided the search of published and gray literature for DQ frameworks, indicators, and tools. At least 2 authors independently identified articles for inclusion and extracted and categorized DQ concepts and constructs. All authors discussed findings iteratively until consensus was reached. RESULTS: The 120 included articles yielded concepts related to contextual (data source, custodian, and user) and technical (interoperability) factors across the data life cycle. Contextual DQ subcategories included relevance, usability, accessibility, timeliness, and trust. Well-tested computable DQ indicators and assessment tools were also found. CONCLUSIONS: A DQ assessment framework that covers intrinsic, technical, and contextual categories across the data life cycle enables assessment and management of RWD repositories to ensure fitness for purpose. Balancing security, privacy, and FAIR principles requires trust and reciprocity, transparent governance, and organizational cultures that value good documentation.

Association Between Blood Pressure Control and Coronavirus Disease 2019 Outcomes in 45 418 Symptomatic Patients With Hypertension: An Observational Cohort Study.

Hypertension has been identified as a risk factor for coronavirus disease 2019 (COVID-19) and associated adverse outcomes. This study examined the association between preinfection blood pressure (BP) control and COVID-19 outcomes using data from 460 general practices in England. Eligible patients were adults with hypertension who were tested or diagnosed with COVID-19. BP control was defined by the most recent BP reading within 24 months of the index date (January 1, 2020). BP was defined as controlled (<130/80 mm Hg), raised (130/80-139/89 mm Hg), stage 1 uncontrolled (140/90-159/99 mm Hg), or stage 2 uncontrolled (≥160/100 mm Hg). The primary outcome was death within 28 days of COVID-19 diagnosis. Secondary outcomes were COVID-19 diagnosis and COVID-19-related hospital admission. Multivariable logistic regression was used to examine the association between BP control and outcomes. Of the 45 418 patients (mean age, 67 years; 44.7% male) included, 11 950 (26.3%) had controlled BP. These patients were older, had more comorbidities, and had been diagnosed with hypertension for longer. A total of 4277 patients (9.4%) were diagnosed with COVID-19 and 877 died within 28 days. Individuals with stage 1 uncontrolled BP had lower odds of COVID-19 death (odds ratio, 0.76 [95% CI, 0.62-0.92]) compared with patients with well-controlled BP. There was no association between BP control and COVID-19 diagnosis or hospitalization. These findings suggest BP control may be associated with worse COVID-19 outcomes, possibly due to these patients having more advanced atherosclerosis and target organ damage. Such patients may need to consider adhering to stricter social distancing, to limit the impact of COVID-19 as future waves of the pandemic occur.

Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study.

BACKGROUND: National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to overcome this problem by recommending 3 standardized primary care codes, these codes do not capture the social prescribing activity to a level of granularity that would allow for fair attribution of outcomes to social prescribing. OBJECTIVE: In this study, we explored whether an alternative approach to coding social prescribing activity, specifically through a social prescribing ontology, can be used to capture the social prescriptions used in primary care in greater detail. METHODS: The social prescribing ontology, implemented according to the Web Ontology Language, was designed to cover several key concepts encompassing social determinants of health. Readv2 and Clinical Terms Version 3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre, a sentinel network of over 1000 primary care practices across England covering a population of more than 4,000,000 registered patients, was used for data analyses for a defined period (ie, January 2011 to December 2019). RESULTS: In all, 668 codes capturing social prescriptions addressing different social determinants of health were identified for the social prescribing ontology. For the study period, social prescribing ontology codes were used 5,504,037 times by primary care practices of the Royal College of General Practitioners Research Surveillance Centre as compared to 29,606 instances of use of social prescribing codes, including NHS England's recommended codes. CONCLUSIONS: A social prescribing ontology provides a powerful alternative to the codes currently recommended by NHS England to capture detailed social prescribing activity in England. The more detailed information thus obtained will allow for explorations about whether outputs or outcomes of care delivery can be attributed to social prescriptions, which is essential for demonstrating the overall value that social prescribing can deliver to the NHS and health care systems.

Use and impact of social prescribing: a mixed-methods feasibility study protocol.

INTRODUCTION: Social prescribing aims to address social determinants of health, which account for 80%-90% of health outcomes, but the evidence base behind it is limited due to a lack of data linkingsocial prescribing activity and outcomes. METHODS AND ANALYSIS: The objective of the quantitative component of this feasibility studyisto identify the characteristics of individuals who receive social prescriptions and describe the use and estimate the impact of social prescribing; the latter will be done on a homeless subgroup. We will use the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network, whose general practicescover a population of over 4 000 000 patients. Social prescribing data will be extracted onall recorded patients for 5 years up to 31 January 2020. The objective for the qualitative component of the study isto explore approaches to understand the contextual factors that will have influenced our quantitative findings to identify mechanisms to encourage adoption of social prescribing in primary care while improving data quality. Itwill comprise up to three 90-120 minute advisory group meetings for six to eight participants. Participants will be recruited based on their experience of delivering primary care within Oxfordshire and Surrey. The advisory group outputs will be analysed using framework analysis and will be used to create a survey instrument consisting of statements that surveyees, who will consist of primary care practitioners within the RCGP RSC, can agree or disagree with. ETHICS AND DISSEMINATION: All RCGP RSC data are pseudonymised at the point of data extraction. No personally identifiable data are required for this investigation. This protocol follows the Good Reporting of a Mixed Methods Study checklist. The study results will be published in a peer-reviewed journal and the dataset will be available to other researchers.

Computerised medical record systems that guide and protect - reflections on the Bawa-Garba case.

Lawrence Weed proposed we develop computerised, problem orientated medical records that guide and teach.  The Bawa-Garba case outcomes might have been different if care had been supported by computerised medical record (CMR) systems. CMR systems can reduce prescribing errors and could be develop to flag gaps in supervision.   However, CMR systems are not a panacea and need to be fit for purpose.  Our informatics perspective on this case is to call for widespread use of CMR systems - designed to guide and protect.

An integrated organisation-wide data quality management and information governance framework: theoretical underpinnings.

INTRODUCTION: Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical governance. We aimed to extend and update the review with a theoretical framework. METHODS: We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, information ecosystem, data quality management (DQM), data governance, information governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. FINDINGS: There are good theoretical reasons for integrated governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health information ecosystems to process data in ways that are aligned with improving health and system efficiency and ensuring patient safety. Despite an increasingly 'big-data' environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. CONCLUSIONS: The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise.

Accelerating the development of an information ecosystem in health care, by stimulating the growth of safe intermediate processing of health information (IPHI).

Health care, in common with many other industries, is generating large amounts of routine data, data that are challenging to process, analyse or curate, so-called 'big data'. A challenge for health informatics is to make sense of these data. Part of the answer will come from the development of ontologies that support the use of heterogeneous data sources and the development of intermediate processors of health information (IPHI). IPHI will sit between the generators of health data and information, often the providers of health care, and the managers, commissioners, policy makers, researchers, and the pharmaceutical and other healthcare industries. They will create a health ecosystem by processing data in a way that stimulates improved data quality and potentially healthcare delivery by providers of health care, and by providing greater insights to legitimate users of data. Exemplars are provided of how a health ecosystem might be encouraged and developed to promote patient safety and more efficient health care. These are in the areas of how to integrate data around the unsafe use of alcohol and to explore vaccine safety. A challenge for IPHI is how to ensure that their processing of data is valid, safe and maintains privacy. Development of the healthcare ecosystem and IPHI should be actively encouraged internationally. Governments, regulators and providers of health care should facilitate access to health data and the use of national and international comparisons to monitor standards. However, most importantly, they should pilot new methods of improving quality and safety through the intermediate processing of health data.