scholarly journals Impact of the COVID-19 pandemic on the symptomatic diagnosis of cancer: the view from primary care

2020 ◽  
Vol 21 (6) ◽  
pp. 748-750 ◽  
Author(s):  
Daniel Jones ◽  
Richard D Neal ◽  
Sean R G Duffy ◽  
Suzanne E Scott ◽  
Katriina L Whitaker ◽  
...  
Keyword(s):  
Primary Care ◽  
Diagnosis Of Cancer ◽  
Symptomatic Diagnosis

Understanding variation in the timeliness of diagnosis of cancer in symptomatic patients.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 301-301
Author(s):  
Georgios Lyratzopoulos
Keyword(s):  
Primary Care ◽  
Family Doctor ◽  
Patient Characteristics ◽  
Care Physician ◽  
Future Research ◽  
Time Interval ◽  
Stage At Diagnosis ◽  
Specialist Referral ◽  
Diagnosis Of Cancer ◽  
Diagnostic Intervals

301 Background: Diagnosing cancer promptly in symptomatic patients is a priority for healthcare systems worldwide, but little is known about how initiatives can be targeted to patients at greater risk. Methods: UK data on the number of consultations with a family doctor before specialist referral (‘pre-referral consultations’); the time interval from presentation to referral (‘primary care interval’); and stage at diagnosis, were analysed using multivariable regression models. Results: Both patient experience (41,299 patients, 24 cancers) and clinical audit (13,031 patients, 18 cancers) data indicated wide variation in two correlated measures* of the difficulty of suspecting the diagnosis of cancer once the patient had presented to their family doctor. For example, >30% of patients with multiple myeloma, pancreatic and lung cancer experienced three or more pre-referral consultations; in contrast, this was true for <10% of patients with breast cancer and melanoma (p<0.001). Adjusting for diagnostic case-mix, younger and ethnic minority patients, and women, were more likely to experience three or more pre-referral consultations. Data from 88,657 patients (10 cancers) suggested socio-demographic disparities in stage at diagnosis for some only cancers: For patients with melanoma, breast and endometrial cancer, lower socioeconomic status was associated with higher risk of advanced stage at diagnosis and for, these three cancers, the same was true for older age. Conclusions: Different diagnostic intervals vary widely by cancer diagnosis and patient characteristics. Notable disparities in stage at diagnosis are apparent for ‘easy-to-suspect’ cancers (which are associated with minimal delay post-presentation), strongly implicating psychosocial patient factors as the source of these disparities. These findings can help to appropriately target early diagnosis policy initiatives and future research to patients at greater risk of prolonged diagnostic intervals. *Number of pre-referral consultations with a primary care physician and length of primary care interval (Spearman’s r=0.70).

scholarly journals Auditing the diagnosis of cancer in primary care: the experience in Scotland

2009 ◽  
Vol 101 (S2) ◽  
pp. S87-S91 ◽  
Author(s):  
P Baughan ◽  
B O'Neill ◽  
E Fletcher
Keyword(s):  
Primary Care ◽  
Diagnosis Of Cancer

Missed diagnosis of cancer in primary care: Insights from malpractice claims data

2019 ◽  
Vol 39 (2) ◽  
pp. 19-29 ◽  
Author(s):  
Emily L. Aaronson ◽  
Gene R. Quinn ◽  
Chris I. Wong ◽  
Ann Marie Murray ◽  
Carter R. Petty ◽  
...  
Keyword(s):  
Primary Care ◽  
Claims Data ◽  
Malpractice Claims ◽  
Diagnosis Of Cancer ◽  
Missed Diagnosis

National Cancer Diagnosis Audit: initial results on avoidable delays

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696677
Author(s):  
Ruth Swann ◽  
ÒJana Witt ◽  
Brian Shand ◽  
Georgios Lyratzopoulos ◽  
Sara Hiom ◽  
...  
Keyword(s):  
Primary Care ◽  
Quality Improvement ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Tertiary Care ◽  
Patient Characteristics ◽  
Cancer Type ◽  
Breast Cancer Patients ◽  
Primary Care Data ◽  
Diagnosis Of Cancer

BackgroundAn earlier diagnosis of cancer can increase cancer survival and quality of life. Characterising avoidable delays to a patient’s diagnosis of cancer can help to direct quality improvement initiatives.AimTo evaluate avoidable delays to cancer diagnoses and the variation by cancer type and patient characteristics using primary care data collected as part of the National Cancer Diagnosis Audit (NCDA).MethodEnglish general practices participating in the NCDA (439) entered primary care data on patients (17,042) diagnosed with cancer in 2014. Using a taxonomy developed from the National Audit of Cancer Diagnosis in Primary Care (2011), GPs reported delays to the diagnosis that in their judgement were avoidable.ResultsIn 22% of NCDA patient records (n = 3380), the GP considered there to be an avoidable delay to the patient receiving their cancer diagnosis. There was variation by cancer type; 7% of breast cancer patients experienced delays compared to 34% of stomach cancer patients. 49% of avoidable delays occurred in primary care and 38% in secondary or tertiary care. Of all delays, 28% were attributed to clinician factors and 34% to health care system factors. Results will be presented by patient characteristics.ConclusionPrimary care data from the NCDA can be used to better understand potentially avoidable delays to diagnosis and identify possible solutions for improving the diagnostic pathway. Avoidable delays during cancer diagnosis occur for many reasons. These insights can inform quality improvement initiatives, which should be directed at both clinical and organisational factors in primary care and hospital settings.

scholarly journals Application of artificial intelligence to electronic health records for early detection and diagnosis of cancer in primary care: a Systematic Review (Preprint)

2020 ◽  
Author(s):  
Owain Tudor Jones ◽  
Natalia Calanzani ◽  
Smiji Saji ◽  
Stephen W Duffy ◽  
Jon Emery ◽  
...  
Keyword(s):  
Primary Care ◽  
Early Detection ◽  
Electronic Health Records ◽  
Inclusion Criteria ◽  
Implementation Barriers ◽  
Health Records ◽  
Primary Care Settings ◽  
Primary Care Data ◽  
Diagnosis Of Cancer ◽  
Electronic Health

BACKGROUND More than 17 million people worldwide, including 360,000 people in the UK, were diagnosed with cancer in 2018. Cancer prognosis and disease burden is highly dependent on disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection, and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of healthcare. OBJECTIVE We aimed to systematically review AI technologies based on electronic health record (EHR) data that may facilitate the earlier diagnosis of cancer in primary care settings. We evaluated the quality of the evidence, the phase of development the AI technologies have reached, the gaps that exist in the evidence, and the potential for use in primary care. METHODS We searched Medline, Embase, SCOPUS, and Web of Science databases from 1st January 2000 to 11th June 2019 (PROSPERO ID CRD42020176674), and included all studies providing evidence for accuracy or effectiveness of applying AI technologies to early detection of cancer using electronic health records. We included all study designs, in all settings and all languages. We extended these searches through a scoping review of commercial AI technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS We identified 10,456 studies: 16 met the inclusion criteria, representing the data of 3,862,910 patients. 13 studies described the initial development and testing of AI algorithms and three studies described the validation of an AI technology in independent datasets. One study was based on prospectively collected data; only three studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk-of-bias assessment highlighted a wide range in study quality. The additional scoping review of commercial AI tools identified 21 technologies, only one meeting our inclusion criteria. Meta-analysis was not undertaken due to heterogeneity of AI modalities, dataset characteristics and outcome measures. CONCLUSIONS Applying AI technologies to electronic health records for early detection of cancer in primary care is at an early stage of maturity. Further evidence is needed on performance using primary care data, implementation barriers and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended. This study was supported by funding from the NIHR Cancer Policy Research Programme and Cancer Research UK.

scholarly journals Patient involvement in diagnosing cancer in primary care: a systematic review of current interventions

2018 ◽  
Vol 68 (668) ◽  
pp. e211-e224 ◽  
Author(s):  
Jane Heyhoe ◽  
Caroline Reynolds ◽  
Alice Dunning ◽  
Olivia Johnson ◽  
Alex Howat ◽  
...  
Keyword(s):  
Primary Care ◽  
Patient Involvement ◽  
Empirical Studies ◽  
Diagnostic Process ◽  
Future Research ◽  
Qualitative Synthesis ◽  
Potential Barriers ◽  
Ovid Medline ◽  
Diagnosis Of Cancer ◽  
Author Publication

BackgroundPatients can play a role in achieving an earlier diagnosis of cancer by monitoring and re-appraising symptoms after initially presenting to primary care. It is not clear what interventions exist, or what the components of an intervention to engage patients at this diagnostic stage are.AimThe review had two aims: 1) to identify interventions that involve patients, and 2) to establish key components for engaging patients in the diagnosis of cancer in primary care at the post-presentation stage.Design and settingEmpirical studies and non-empirical articles were identified searching Ovid MEDLINE, PsycINFO, and Embase databases, relevant journals, and available key author publication lists.MethodAbstracts and titles were screened against inclusion and exclusion criteria. Qualitative synthesis of empirical research and current opinion from across all articles was used to select, organise, and interpret findings.ResultsNo interventions were found. Sixteen articles provided suggestions for potential interventions and components important at the post-presentation stage. Factors contributing to patients not always being engaged in assisting with diagnosis, strategies to foster patient involvement, and moderators and benefits to patients and health services (proximal and distal outcomes) were captured in a logic model.ConclusionThere is an absence of interventions involving patients during the post-presentation stage of the diagnostic process. Limited literature was drawn upon to identify potential barriers and facilitators for engaging patients at this diagnostic stage, and to establish possible mechanisms of change and measurable outcomes. Findings can direct future research and the development of interventions.

Sign in / Sign up

Export Citation Format

Share Document