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BackgroundJanus kinase inhibitors drugs (JAKi) are novel small molecule medications known to cause abnormalities such as elevations in hepatic transaminases, decreases in neutrophil and lymphocyte counts and elevations in cholesterol and creatinine kinase. Blood monitoring is recommended and dose adjustments are advised if abnormalities arise. Recent warnings by the EMA and MHRA have highlighted the importance of monitoring these medications.Timely review and management of patients on JAKi drugs is difficult to maintain with increasing workload amongst the rheumatology team. A baseline audit (2020) demonstrated that hospital blood monitoring guidelines for JAKi drugs were not being followed. The rheumatology multidisciplinary team met and utilised Quality Improvement methodology including fish and driver diagrams to address this. This led to the creation of a pharmacist-led JAKi blood monitoring clinic.ObjectivesTo establish a pharmacist-led rheumatology blood monitoring clinic for the JAKi drug class in order to: increase patient safety with increased compliance to blood monitoring, save consultant/nurse time, improve communication with primary care on the frequency of blood testing required, increase patient understanding of the importance of blood monitoring with JAKi drugs, reinforce counselling advice such as risk of infections, shingles and thrombosis and promote medication adherence.MethodsThe clinic was established in March 2021. Patients commencing JAKi drugs are referred to the pharmacist-led clinic by the medical team. The pharmacist contacts the patient by phone following delivery of their medication. The patient is counselled on their new medication and dates for blood checks are agreed. A letter is sent to the patient and their GP providing this information. The patient is booked into virtual telephone appointments and bloods are monitored every month for the first 3 months and every 3 months thereafter. Any change or abnormality in blood results are flagged early in the patient's treatment and if necessary, discussed with the consultant. Adjustments are made to the patient's dose if appropriate.ResultsIn order to evaluate the benefit of the pharmacist clinic a re-audit of compliance with blood monitoring (March 2021- September 2022) was carried out alongside a patient satisfaction postal survey (August 2022).A total of 58 patients were sampled in the re-audit. The re-audit found an increase in compliance in blood monitoring since the introduction of the pharmacist clinic. 98% of patients had their full blood count performed at 3 months compared to 56% in audit 1 and 95% of patients had their lipid profile completed at 3 months compared to 15% in audit 1 (Table 1).A patient satisfaction survey (N=62, response rate 48%) found that 28 (93%) patients either agreed or strongly agreed that they were more aware of the importance of attending for regular blood monitoring when prescribed JAKi therapy as a result of the clinic.The pharmacy team made several significant interventions (self-graded Eadon grade 4 and 5). For example by improving medication adherence, detecting haematological abnormalities that required JAKi dose reduction, identifying patients suffering from infection requiring intervention including shingles and Covid-19.Table 1.Comparison of audit results pre (Audit 1) and post (Audit 2) clinic establishmentAudit 1 (N=48)Audit 2 (N=58)Number of patients with full blood count completed at weeks 4, 8 & 1227 (56%)57 (98%)Number of patients with lipid profile completed at week 127 (15%)55 (95%)Number of patients LFTs completed at weeks 4, 8 & 1226 (54%)54 (93%)ConclusionIntroduction of the pharmacist-led clinic has increased patient safety by ensuring compliance with blood monitoring as per hospital guidelines. The clinic has paved the way for improved communication with primary care teams and has provided patients with extra support during their first months on treatment with their JAKi. It has also expanded the role of the rheumatology pharmacy team and saved nursing and medical time.Acknowled ementsI wish to thank the SHSCT Rheumatology team for all their help, support and guidance with this project.Disclosure of InterestsNone Declared.
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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Objective: To evaluate the efficacy of hematological parameters to predict severity of COVID-19 patients. Method: This was a cross-sectional comparative study conducted at Central Park Teaching Hospital, Lahore in COVID ward and COVID ICU between April 23, 2021 to June 23, 2021. Patients of all ages and both genders with positive PCR admitted in the COVID ward and ICU during this time span of two months were included in the study. Data was collected retrospectively. Results: This study included 50 patients with male to female ratio of 1.38:1. Though males are more affected by COVID-19 but the difference is not statistically significant. The mean age of the study population was 56.21 and the patients in the severe disease group have higher age. It was observed that in severe/critical group the mean values of total leukocyte count 21.76×103 µI (p-value= 0.002), absolute neutrophil count 71.37% (p-value=0.045), neutrophil lymphocyte ratio (NLR) 12.80 (p-value=0.00) and PT 11.9 seconds (p-value=0.034) and the difference was statistically significant. While in severe/critical group, the mean values of hemoglobin 12.03g/dl (p-value=0.075), lymphocyte count 28.41% (p-value=0.8), platelet count 226×103 µI (p-value=0.67) and APTT 30.7 (p-value=0.081) and the difference was not significantly different between groups. Conclusion: It can be concluded from the study that total leucocyte count, absolute neutrophil count and neutrophil lymphocyte ratio can predict in-hospital mortality and morbidity in COVID-19 patients.
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Although mRNA vaccine responses following previous coronavirus disease 2019 (COVID-19) infection have not been assessed in trials, it has been shown that serological evidence of previous COVID-19 generates strong humoral and cellular responses to one dose of mRNA vaccine. We describe a patient with prior COVID-19 infection who developed acute transient encephalopathy with elevated inflammatory markers within 24 h of her first injection of Moderna COVID-19 vaccine. A 69-year-old cognitively normal woman presented with intermittent inattention, disorientation, left/right confusion, weakness, gait instability, and decreased speech. Head CT, brain MRI and MRA, complete blood count, liver enzymes, hepatitis B serology, ammonia, thyroid function, vitamin B12, and pulse oximetry were normal. Electroencephalography performed 48 h after symptom onset showed diffuse triphasic waves, diffuse theta and delta slowing, and no posterior dominant rhythm. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG was positive and inflammatory markers were elevated. On day 5 post-vaccine, she returned to her baseline, without neurological sequelae. The reported patient likely developed a transient inflammatory encephalopathy associated with an abnormal immunologic reaction to one dose of COVID-19 vaccine, in the setting of remote COVID-19 infection (1 year prior), SARS-CoV-2 IgG-positivity, and multiple comorbidities. Physicians should be alert to possible postvaccination reactogenicity in individuals with SARS-CoV-2 IgG-positivity, including risk of neuro-inflammation.
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This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.
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IntroductionThe aim of this study was to determine the impact of the COVID-19 pandemic on the provision of clinical services (perioperative clinical outcomes and productivity) of the department of endocrine and general surgery at a teaching hospital in the UK.MethodsA retrospective chart review was conducted of all patients who were operated in our department during two periods: 1 April to 31 October 2019 (pre-COVID-19 period) and 1 April to 31 October 2020 (COVID-19 period). The perioperative clinical outcomes and productivity of our department for the two time periods were compared.ResultsIn the pre-COVID-19 period, 130 operations were carried out, whereas in the COVID-19 group, this reduced to 89. The baseline characteristics between the two groups did not significantly differ. Parathyroid operations decreased significantly by 68% between the two study periods. Overall, during the COVID-19 phase, the department maintained 68% of its operating workload compared with the respective 2019 time period. The clinical outcomes for the patients who had a thyroid/parathyroid/adrenal operation were not statistically different for the two study periods. There were no COVID-19 related perioperative complications for any of the operated patients and no patient tested positive for COVID-19 while an inpatient. For the COVID-19 group, the department maintained 67% of its outpatient appointments for endocrine surgery and 26% for general surgery pathologies.ConclusionsThe COVID-19 pandemic significantly reduced the clinical activity of our department. However, it is possible to continue providing clinical services for urgent/cancer cases with the appropriate safety measures in place.
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The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
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This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License
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In this retrospective cohort study, we investigated the formation of individual classes of antibodies to SARS-CoV-2 in archived serial sera from hospitalized patients with the medium-severe (n = 17) and severe COVID-19 (n = 11). The serum/plasma samples were studied for the presence of IgG, IgM and IgA antibodies to the recombinant S- and N-proteins of SARS-CoV-2. By the 7th day of hospitalization, an IgG increase was observed in patients both with a positive PCR test and without PCR confirmation of SARS-CoV-2 infection. Significant increases in the anti-spike IgG levels were noted only in moderate COVID-19. The four-fold increase of IgM to N-protein was obtained more often in the groups with mild and moderate infections. The IgA levels decreased during the infection to both the S- and N-proteins, and the most pronounced decrease was in the severe COVID-19 patients. The serum IgG to S-protein one week after hospitalization demonstrated a high-power relationship (rs = 0.75) with the level of RBD antibodies. There was a medium strength relationship between the levels of CRP and IgG (rs = 0.43). Thus, in patients with acute COVID-19, an increase in antibodies can develop as early as 1 week of hospital stay. The SARS-CoV-2 antibody conversions may confirm SARS-CoV-2 infection in PCR-negative patients.
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Background: In the era of coronavirus disease 2019 (COVID-19), it is mandatory to identify vulnerable people with cancers as they have impaired immune system that can lead to high mortality. This study analyzes the complete blood count (CBC) derived inflammatory biomarkers and the level of anti-SARS-CoV-2 neutralizing antibody (NAb) and spike protein's receptor-binding domain immunoglobulin G (S-RBD IgG) among cancer survivors. Methods: A cross-sectional study was conducted in patients with either solid or hematological cancers who had received two-doses of COVID-19 vaccinations within six months. Results: From 119 subjects, the COVID-19 vaccines demonstrated laboratory efficacy (median NAb=129.03 AU/mL;median S-RBD IgG=270.53 AU/mL). The seropositive conversion of NAb reached 94.1% and S-RBD IgG reached 93.3%. Additionally, the S-RBD IgG had very weak correlation with absolute monocyte count (R=-0.185;p-value=0.044). The NAb also had very weak correlation with leukocyte (Kendall's tau-b (τb)=-0.147;p-value=0.019), absolute neutrophil count (τb=-0.126;p-value=0.044), absolute eosinophil count (τb=-0.132;p-value=0.034). Conclusion: The seropositivity rate of anti-SARS-CoV-2 NAb and S-RBD IgG were significantly high. However, the CBC derived inflammatory biomarkers had poor correlation with anti-SARS-CoV-2 NAb and S-RBD IgG. Thus, anti-SARS-CoV-2 NAb and S-RBD IgG are currently the only reliable markers for measuring the COVID-19 vaccine efficacy which should be widely accessible.
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Aim: COVID-19 is a cause of high-mortality pandemic with the RNA virus in its etiology and has an effect all over the world. In the present study, the relationship between in-hospital prognosis and mortality was investigated by comparing neutrophil-to-lymphocyte ratio (NLR), platelet –to-lymphocyte ratio (PLR) values with C-reactive protein (CRP) and with a detailed analysis of complete blood count and biochemical parameters in mild and severe COVID-19 cases.Method: A total of 271 patients who were diagnosed with pneumonia because of COVID-19 and 278 healthy control groups were included in the study. In our study, COVID-19 cases were divided into 2 groups as mild and severe, and the data were compared with healthy people without COVID-19. Lung tomography results of the cases that were diagnosed with COVID-19 were examined. Those with positive RT-PCR (Real-Time Polymerized Chain Reaction) test results were recorded from the system. Biochemical tests and complete blood count parameters of the patients, NLR/ lymphocyte- to- monocyte ratio (LMR)/PLR N/L, and CRP levels were compared with the control group. The results were evaluated and analyzed in statistical terms.Results:When all the data were analyzed, NLR/PLR and CRP levels were found to be higher at statistically significant levels in the severe patient group than in the control group, and LMR was lower (p<0.01). In ROC analysis, NLR/PLR and CRP had a high AUC (area under the curve) (0.844/0.719/0.501) and LMR had a low AUC (0.225).Conclusion: NLR and PLR might be useful in demonstrating the prognosis in severe COVID-19 cases.
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Machine learning for Covid-19 diagnosis from blood tests is a topical problem. Many studies of this problem are mainly devoted to comparing various algorithms' efficiency. However, the first and often the most critical part of machine learning is the preparation of a relevant and correct dataset of the required size for developing the generalization models. This study demonstrates the lack of the models' generalization performance based on some publicly available datasets. That leads to the futility of such models in practice even if they were developed using the best algorithms and achieved high metrics. Therefore, another dataset is proposed. Its features are discussed. This dataset splits into training and testing sets by stratification due to an imbalanced data structure. Machine learning models of the problem by various algorithms are developed based on the proposed dataset. The modelling results on the testing set have demonstrated that the best models - Gradient Boosting Classifier with fixing imbalance methods SMOTE and ADASYN, TensorFlow and Gene Expression Programming - handle negative Covid-19 diagnosis well enough since they have high precision and high recall. However, mixed signals have been obtained for a positive Covid-19 diagnosis. TensorFlow and Gene Expression Programming models have high precision and relatively low recall for positive Covid-19 diagnosing. It means these models can't detect Covid-19 well enough but are highly reliable when they do. Gradient Boosting Classifier models do not have enough high precision and recall for positive Covid-19 diagnosing. New challenges of machine learning for Covid-19 diagnosis based on blood tests are found for future work. © 2022 IEEE.
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When the patients were assessment of according to clinical features, an important distinction was found between the two groups according to age, gender, cardiovascular disease, chronic renal failure, neurolvascular disease, and diabetes mellitus. METHODS Study participants and design Patient selection 315 patients with PCR positive and chest tomography results appropriate for hospitalized with the identification of COVID-19 pneumonia were retrospectively registered in this study between 1 April 2021 and 30 June 2021. Cardiovascular comorbidities contained arterial hypertension (n = 51), systolic heart failure (n = 7), coronary artery disease (n = 19), diabetes mellitus (n = 52), renal failure (n=10), and cerebrovascular disease (n=4), (Table I). Based on assessment of patients clinical features, an important distinction was found between the two groups with respect to age, gender, ischemic heart disease, chronic kidney disease, neurologic disease and diabetes mellitus.
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The lymphocyte count, platelet count, mean platelet volume (MPV), plateletcrit (PCT), C-reactive protein (CRP), neutrophillymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) obtained from the patients' complete blood count were evaluated. Children are less affected by COVID-19 than adults (1). Since the first case was reported by Chan et al. on January 20, 2020, the number of cases has been gradually increasing (2). According to the inclusion criteria, patients under 18 years of age with positive PCR test were included in the study. Electronic impedance + optical scatter and blood gas tests were performed on an ABL80 FLEX BASIC analyzer using the electrochemical biosensor method in the Diyarbakır Pediatric Disease Hospital laboratory.
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SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.
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The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
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P80 Figure 1This ongoing project will enable our team to identify those requiring blood tests and potential treatment. As we continue we expect an increase in patient numbers to our CNS clinics. One major limitation is limited access to regional hospital laboratory results. Another limitation is staff being available to review files, responses to letters and chasing blood tests or results.Public Health England (2017). Hepatitis C in the UK – 2017 Report.Working to Eliminate Hepatitis C as a Major Public Health Threat. London: PHE.Thursz M. (2017). The fight against hepatitis C has not yet been won: here’s what we have to do. Huffington Post;10 August 2017.Vine LJ et al. Diagnosis and management of hepatitis C. British Journal of Hospital Medicine;2015;76:11, 625–630.World Health Organization (2016). Combating Hepatitis B and C to Reach Elimination by 2030. Geneva: WHO.
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Chronic hepatitis B (CHB) patients require long term medical care, with regular monitoring blood tests and ultrasound scans. The COVID-19 pandemic disrupted outpatient services and many patients fell ‘out of sync’ with their routine care;missing scans, blood tests and running out of medication as a result. Telephone appointments are now routine in outpatient care, and fewer patients attend for F2F appointments than pre-pandemic. We developed a virtual patient forum for patient engagement and service evaluation.Patients were invited to take part in a virtual patient group during routine telephone appointments. Further information was sent by email with a link to virtual meeting. Meetings were scheduled in weekday evenings to allow those who are currently working to attend, with a duration of 1–1.5 hours. Feedback surveys were sent out via email, and notes from the meeting were sent to patient participants for approval.Results3 sessions were held virtually between November 2021 – June 2022. A total of 14 patient interactions across 3 sessions (Male n=5, Female n= 3). A doctor chaired the sessions and a nurse specialist was also in attendance. Topics raised varied but there was repeated discussion regarding treatment, patient support and disease information. Diagnosis was highlighted as particularly difficult;patients suggested increasing available resources. Patients on treatment reported difficulties obtaining repeat prescriptions, uncertainty about long-term implications of taking medication and requested more information on treatment and new therapies.Post feedback surveys were distributed within 1 week of the sessions and had a 78% completion rate. All respondents reported sessions were useful to them. Additional comments mentioned the utility of speaking to other CHB patients (n=5,) the value of being able to “contribute in a way which helps services develop/improve” (n=3,) having an avenue to “express concerns” (n=2) and opportunity to hear about treatment developments (n=2).Patient expectations of the sessions were as follows;wanting to engage with other patients (n=8,) engaging with the clinical team (n=5,) raising concerns/issues (n=4) and a desire for more information about CHB (n=4.) All patients stated the sessions met their expectations, and that they would be interested in attending similar sessions in future.ConclusionsVirtual patient groups were effective in our patient cohort for gathering feedback on service delivery and formulating goals for future work and service improvement. Patients respond positively to the opportunity to share their opinions, and this enables effective collaboration necessary to drive change.
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Aims• Initially to review the use of the Children’s Day Services at the Lister Hospital1. Reviewing the first of a three-part quality improvement project to review and improve the booking system for Children’s Day Services• To compare the expected demands of the day services unit with daily realities in services provided.• To assess how this interplays with staffing levels and safe, appropriate usage.• Eventually, to provide an overview of what services are required for the local population and to assess what changes might be required to deliver this safely.• Finally, to review changes implemented to improve day services especially the use of the booking system.Methods• Quantitative data:1. 90 days of appointment data was reviewed retrospectively between April-June 2021. A total of 842 appointments were reviewed. This was categorised into type of day service appointment e.g. blood test, jaundice clinic, allergy etc.2. Using the data from a patient and staff survey and previous phlebotomy audit, completed by the Children’s day services team.• Qualitative data: 2 weeks’ worth of qualitative data was collected. This included a written account of all informal or verbal requests including additional ‘walk-in’ patients. The qualitative data also included written accounts of staff that reflected on patient safety.ResultsOver the data collection period, blood tests accounted for 41.6% of workload, despite only 12.5% of appointments being for phlebotomy. This was reached largely through the use of designated ‘ward attender’ slots for blood tests. 20.2% of day service appointments were used to provide a prolonged jaundice clinic, which is a foundation doctor led clinic. An average of 9.3% of day services consisted of allergy clinic, while registrar reviews were 9.3% of encounters.Quantitative results overall showed a disproportionate and inappropriate number of appointments booked as blood tests, and more jaundice clinic slots than required.The qualitative data displayed a broad range of scenarios varying in complexity. Some showed foundation doctors being required to oversee difficult procedures alongside running clinic. Other scenarios included poor referrals with missing or inadequate information with demands on day services that were inappropriate and potentially unsafe.ConclusionThe day service has changed over the COVID pandemic and, with that, the demands on its staff have also changed. One key finding is that there is a high phlebotomy service demand which is currently disproportionate to expectations. The demand on prolonged jaundice clinic is lower than expected, and there is huge variety between the complexity of tasks that are indistinguishable in the current booking system. There may be opportunities to outsource phlebotomy to better use resources, while staffing should better match real demands. Patient booking must find a balance between flexibility and rigidity to ensure an efficient and safe system. Lastly, the day services unit is a bridge between the hospital service and community paediatrics and could be utilised better with the knowledge this review has shown. The next stage will be to review the changes made to the service to complete the cycle of the quality improvement project.