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BackgroundMore than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the 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 health care. ObjectiveThis study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. MethodsWe searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. ResultsWe identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 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 of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. ConclusionsAI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.

Objective: The CombiConsultation is an innovative concise clinical pharmacy service by the community pharmacist for patients with a chronic condition. We aimed to identify relevant factors influencing the implementation of the CombiConsultation in Dutch clinical practice. Methods: A mixed-methods study involving interviews and a questionnaire. Content analysis topics within TDF domains were derived from the interview data and were related to the COM-B-model (capability–opportunity–motivation–Behaviour). The relevance of the resulting topics was explored using a questionnaire with 19 statements administered to all 27 pharmacists who performed CombiConsultations. Key findings: Eighteen topics emerged from the interviews. The questionnaire was completed by 23 of the 27 pharmacists. In the domain 'capability', a small number of participants indicated that they need more expertise in pharmacotherapy (13%) and training in consultation skills (35%). In the domain 'opportunity', all participants indicated that an existing good collaboration with the general practitioner/practice nurse and access to all relevant medical data were necessary to implement the CombiConsultation. In terms of motivation, job satisfaction was most important to all participants, followed by adequate reimbursement (83%) and improving collaboration with other healthcare providers and the relationship with patients (78%). Conclusions: Capability, opportunity, and motivation were all considered relevant for the implementation of the CombiConsultation. There were crucial factors on the level of the individual pharmacist, on the level of the local collaboration and organization, and on the health system level. [ABSTRACT FROM AUTHOR]

BackgroundC-reactive protein (CRP) is increasingly being included in the diagnostic work-up for community-acquired pneumonia in primary care. Its added diagnostic value beyond signs and symptoms, however, remains unclear. We conducted a meta-analysis of individual patient data to quantify the added value of CRP measurement.MethodsWe included studies of the diagnostic accuracy of CRP in adult outpatients with suspected lower respiratory tract infection. We contacted authors of eligible studies for inclusion of data and for additional data as needed. The value of adding CRP measurement to a basic signs-and-symptoms prediction model was assessed. Outcome measures were improvement in discrimination between patients with and without pneumonia in primary care and improvement in risk classification, both within the individual studies and across studies.ResultsAuthors of 8 eligible studies (n = 5308) provided their data sets. In all of the data sets, discrimination between patients with and without pneumonia improved after CRP measurement was added to the prediction model (extended model), with a mean improvement in the area under the curve of 0.075 (range 0.02-0.18). In a hypothetical cohort of 1000 patients, the proportion of patients without pneumonia correctly classified at low risk increased from 28% to 36% in the extended model, and the proportion with pneumonia correctly classified at high risk increased from 63% to 70%. The number of patients with pneumonia classified at low risk did not change (n = 4). Overall, the proportion of patients assigned to the intermediate-risk category decreased from 56% to 51%.InterpretationAdding CRP measurement to the diagnostic work-up for suspected pneumonia in primary care improved the discrimination and risk classification of patients. However, it still left a substantial group of patients classified at intermediate risk, in which clinical decision-making remains challenging.

BackgroundPneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.Methods and findingsS&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74-0.85) and 0.72 (0.68-0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61-0.68), 0.64 (0.61-0.67), 0.56 (0.49-0.63) and 0.53 (0.5-0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.ConclusionsThe model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.

PURPOSE An optimal diagnostic process in primary care is pivotal for reducing cancer-related disease burden. This study aims to explore reasons for long times to referral for Dutch colorectal cancer (CRC) patients in primary care. METHODS A retrospective cohort study of anonymized free-text primary care records from the Julius General Practitioners' Network database, linked to the Netherlands Cancer Registry. Patients with a confirmed CRC diagnosis from 2007 through 2011 that symptomatically presented in primary care were included. Median time and interquartile ranges from presentation in primary care to referral were calculated for multiple patient and presentation characteristics. Associations of these characteristics with long time to referral (75th percentile was [greater than or equal to]59 days) were examined with log-binomial regression analyses. Routes to referral of patients with the longest times to referral were explored using thematic free-text analyses (90th percentile at [greater than or equal to]219 days). RESULTS Among the 309 people with CRC, patients who were female, did not have a registered family history, had a history of malignancy, lacked alarm symptoms at presentation, or had hemorrhoids at physical examination were at risk for longer time to referral in univariable analyses (longer median durations and/ or univariable association with the 75th percentile). Only presentation without alarm symptoms showed a statistically significant association with long duration (75th percentile) in multivariable analysis (relative risk = 1.7; 95% CI, 1.1-2.6). Thematic exploration of the diagnostic routes to referral of patients with the longest durations (90th percentile) showed 2 dominating themes: 'alternative working diagnosis' and 'suboptimal diagnostic strategies,' and included the sub-themes 'omitting to reconsider an initial diagnosis' and 'lacking follow-up.' CONCLUSIONS Long time to referral for CRC in primary care is mainly related to low cancer suspicion. There is potential for reducing the longest times to referral for patients with CRC in primary care, with earlier reconsideration of the initial hypothesis and implementation of strict follow-up consultations. Key words: colorectal cancer; delayed diagnosis; early diagnosis; general practice; primary health care, INTRODUCTION Colorectal cancer (CRC) is the second most common cancer and the second most common cause of cancer-related death in Europe, with approximately 450,000 new patients with CRC and 215,000 [...]