Accuracy Of Referrals Research Articles

Inpatient Dermatology referrals: What is the burden? A retrospective review of 14 years of dermatology inpatient referrals.

The lack of dermatological knowledge by non-dermatologists is exposed by the increasing number of requests made for inpatient dermatological consultations. Patients have been commenced on inappropriate treatment because of poor dermatology training. To determine the burden and accuracy of inpatient dermatology referrals. A retrospective cohort study using paper inpatient dermatology referrals from one Health Board between June 2007 and July 2021. Data analysis included timing of referrals; referring speciality; diagnosis and treatment. Descriptive statistics, using Excel, were used for analyses. The average number of referrals per year was 106 (79-166). The most frequent day of referral was Monday (26%). Most referrals were from medical teams (73%).Differential diagnosis was suggested by the referring team in 59% of referrals. In only 29% of referrals the dermatology team agreed with the differential diagnosis. There was discrepancy in the correctness of diagnosis in all categories, however the paediatricians were most likely to offer a correct differential (44%). In 44% of referrals treatment was commenced by the referring team, most commonly antibiotics. There is an extra burden on dermatology teams to cover inpatients. Our figures highlight two important issues - the need for better dermatological education in medical schools to improve diagnosis accuracy and management of conditions as well as the need to recognise the need for an inpatient dermatology service to review inpatient referrals and advise in diagnosis and management of dermatology cases on the wards, and to protect the service from being uncoupled from the main hospital.

Psychometric Properties of the Generalized Anxiety Disorder Scale (GAD-7) in Older Adults With Advanced Cancer.

Over half of new cancer diagnoses occur in patients aged 65 or older, with up to 40% experiencing anxiety. The American Society of Clinical Oncology recommends using the Generalized Anxiety Disorder Scale (GAD-7) for anxiety screening, but the GAD-7 psychometric properties in this population are unknown. This study examined the GAD-7's reliability, validity, and item parameters, comparing its utility with the GAD-2 in older adults with cancer. This cross-sectional secondary analysis of a nationwide multi-site two-arm cluster randomized trial in older adults (≥70)with advanced cancer. The GAD-7 was administered at baseline. Properties were evaluated with Cronbach's α, Pearson correlation coefficients, and a 2-parameter logistic model. Logistic regression models compared the GAD-2 and GAD-7. The sample included 718 participants (Mean age=77, SD=5) with mild anxiety (M=3.74, SD=4.74). Internal consistency was strong (Cronbach's alpha=0.89) and item-total correlations ranged 0.53 to 0.78. Item 2 (Not being able to stop or control worrying) was the most discriminating and item 5 (Being so restless that it is hard to sit still) was least discriminating. Area Under the Curve (AUC) analyses demonstrated the GAD-2 had a 0.93-0.96 AUC. Establishing the psychometric properties of anxiety screening measures is crucial in the older adults with cancer to maximize referral efficiency and accuracy. This study indicates that the GAD-7 is reliable and valid for older adults with cancer. Analyses suggest the GAD-2 may be as sufficient as the GAD-7 in identifying anxiety in older adults with cancer, thereby reducing assessment burden.

Diagnostic Accuracy of Pediatrician-performed Digital Retinal Imaging with 3nethra neo for ROP Screening.

To evaluate the accuracy of pediatrician-performed wide-field digital retinal imaging (WFDRI) for diagnosing Retinopathy of prematurity (ROP), as compared to binocular indirect ophthalmoscopy (BIO) as the reference standard. Eligible infants undergoing ROP screening were enrolled consecutively. BIO was performed by trained ophthalmologists, followed by WFDRI (using "3nethra neo" camera) by a pediatrician. An expert pediatric ophthalmologist reviewed de-identified images for quality, presence, and severity of ROP. She was masked to the findings of BIO and the pediatrician. Diagnostic accuracy for detecting any ROP, ROP requiring treatment (Type 1), and ROP requiring referral (Type 1 or 2) were calculated for WFDRI, considering BIO as the reference standard. The analysis included 427 eyes. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic accuracy, and diagnostic odds ratio of WFDRI were 0.88 (95% CI: 0.81, 0.93), 0.89 (0.85, 0.92), 7.8 (5.7, 10.9), 0.14 (0.09, 0.21), 0.89 (0.85, 0.91), and 58.3 (31, 110) respectively for detection of 'any ROP'. For detecting ROP requiring treatment (Type 1), the sensitivity, specificity, NLR, and diagnostic accuracy were 0.90 (0.75, 0.97), 1.00 (0.99, 1.00), 0.11 (0.04, 0.27), and 0.99 (0.98, 1.00) respectively. For ROP requiring referral, the sensitivity, specificity, NLR, and diagnostic accuracy of pediatrician-performed WFDRI were 0.92 (0.80, 0.98), 1.00 (0.99, 1.00), 0.08 (0.03, 0.21), and 0.99 (0.98, 1.00) respectively. No serious adverse events were noted. The pediatrician and ophthalmologist had a near-perfect (k-1.00) and strong (k-0.88) agreement for ROP requiring treatment and any ROP, respectively. Pediatrician-performed WFDRI is feasible, safe, and has excellent diagnostic accuracy for identifying ROP requiring treatment.

A comparative performance analysis of live clinical triage using rules-based triage protocols versus artificial intelligence-based automated virtual triage

Objective: Compare the triage care referral accuracy of artificial intelligence (AI) based virtual triage (VT) to rules-based triage protocols (RBTP) live telephonic triage.Methods: Clinical vignettes were selected for a comparison of care referral accuracy of RBTPs with a widely utilized AI-based VT solution. Vignettes (149) included patient complaints, expected triage and urgency assessment. Triage levels were mapped to three triage categories (urgent care, non-emergent care and self-care). Each vignette was evaluated/completed using AI-based VT and RBTP triage modalities by a total of four physicians in series, with independent assessment for errors and inconsistencies. Triage assessment precision was analyzed by matching the expected triage assessment, sensitivity and F1 scores (harmonic mean of precision and recall).Results: Both modalities achieved > 70% triage accuracy, and safety performance was identical at 91%. AI-based VT was more accurate in care referral for emergency and non-emergency care and overtriaged to emergency care 50% less frequently than RBTP, but was less accurate than RBTP in self-care vignettes (neither statistically significant). Both modalities demonstrated decreased sensitivity as care urgency/acuity decreased, more pronounced in AI-based VT than RBTP. AI-based VT captured four times as much information and data as RBTP.Conclusions: AI-based VT and RBTP were comparable in care referral accuracy and disposition safety. While AI-based VT provides accurate and safe triage recommendations at a lower total cost, care organizations should assess how AI-based VT compares to a live clinical triage capability with respect to organizational priorities, budgetary considerations, characteristics of the patient/member population served, and the existing technological environment.

A systematic review of the frequency of features of the seven-point checklist in proven cutaneous melanoma: The importance of change.

Pigmented skin lesions in human adults can present with several different visible features that may indicate signs of malignancy, particularly melanoma. Patient and clinician awareness of these features can aid the early recognition and melanoma diagnosis improving patient outcomes. The seven-point checklist (7PCL) is a clinical prediction rule advocated by the National Institute for Health Care Excellence to aid the assessment of pigmented skin lesions in primary care to indicate referral for specialist opinion. Assess the current evidence to establish which features of the 7PC present more frequently, so public education and clinician assessment can be focused to maximise early diagnosis and minimise referrals of benign lesions. A systematic review of published evidence identified studies that assessed the seven features of the 7PCL in histologically proven melanomas. Two independent reviewers screened eligible studies and independently extracted data and assessed quality. 112 studies were screened, 20 were assessed in full, seven met the inclusion criteria. 1184 histologically diagnosed melanomas were assessed using the 7PCL. Four studies involved patients assessing 335 melanomas, and three involved clinicians who assessed 849 melanomas. The most common feature identified was a change in size of the lesion, and the least common was inflammation. The most frequently occurring features of melanoma involve shape, size and colour, however focussing on changes in features, rather than irregularity, is more likely to identify early melanoma and increase the accuracy of referrals.

Effectiveness and Consequences of Direct Access in Physiotherapy: A Systematic Review.

Background. Direct access in physiotherapy (DAPT) occurs when a patient has the ability to self-refer to physical therapy without physician referral. This model of care in musculoskeletal diseases (MSDs) has shown better outcomes than the traditional-based medical model of care that requires physician referral to access physiotherapist services. This traditional physician referral often results in a delay in care. Unfortunately, DAPT is still not permitted in many countries. Objectives. The primary objective of this systematic review was to compare the effectiveness, safety, and the accuracy of DAPT compared to the physician-led model of care for the management of patients with musculoskeletal disorders. The secondary objective of the present study is to define the physiotherapists' characteristics or qualifications involved in DAPT. Materials and methods. Databases searched included: Medline, Scopus and Web of Science. Databases were searched from their inception to July 2022. Research strings were developed according to the PICO model of clinical questions (patient, intervention, comparison, and outcome). Free terms or synonyms (e.g., physical therapy; primary health care; direct access; musculoskeletal disease; cost-effectiveness) and when possible MeSH (Medical Subject Headings) terms were used and combined with Boolean operators (AND, OR, NOT). Risk of bias assessment was carried out through Version 2 of the Cochrane risk-of-bias tool (ROB-2) for randomized controlled trials (RCTs) and the Newcastle Ottawa Scale (NOS) for observational studies. Authors conducted a qualitative analysis of the results through narrative analysis and narrative synthesis. The narrative analysis was provided for an extraction of the key concepts and common meanings of the different studies, while the summary narrative provided a textual combination of data. In addition, a quantitative analysis was conducted comparing the analysis of the mean and differences between the means. Results. Twenty-eight articles met the inclusion criteria and were analyzed. Results show that DAPT had a high referral accuracy and a reduction in the rate of return visits. The medical model had a higher use of imaging, drugs, and referral to another specialist. DAPT was found to be more cost-effective than the medical model. DAPT resulted in better work-related outcomes and was superior when considering patient satisfaction. There were no adverse events noted in any of the studies. In regard to health outcomes, there was no difference between models. ROB-2 shows an intermediate risk of bias risk for the RCTs with an average of 6/9 points for the NOS scale for observational studies. Conclusion. DAPT is a safe, less expensive, reliable triage and management model of care that results in higher levels of satisfaction for patients compared to the traditional medical model. Prospero Registration Number: CRD42022349261.

Improving descriptive terminology using a descriptive algorithm as an educational intervention to improve referral accuracy.

The accuracy of clinical communication in referrals to dermatology has not been fully explored. Baseline analysis of inpatient ward referral to a dermatology service revealed that only 37% of referrals used accurate descriptive terminology. Theme analysis of individual descriptive terms used was performed to assess knowledge deficits and develop a novel intervention to improve descriptive accuracy. A descriptive algorithm was developed to act as a scaffolding resource for the use of descriptive terms. The Belfast Dermatology Descriptive Algorithm was developed as a tool to determine primary and secondary morphological features of eruptions, and to highlight common clinical pitfalls. It was designed to adhere to principles of adult learning. Assessment was completed following the introduction of the algorithm. The accuracy of descriptions used was increased to 70% with the introduction of the resource. This outcome reflects how educational interventions, in the format of an accessible resource tool, can be used as a unique intervention to improve education in clinical practice.

Referral Patterns to Pediatric Rheumatology From Primary Care Physicians and General Pediatrics at a Single Pediatric Rheumatology Center in Saudi Arabia.

Objective This study aims to investigate referral patterns to pediatric rheumatology and assess the correctness of referrals from primary care physicians and pediatric specialties. Methodology A cross-sectional, retrospective study was conducted on all patients who were referred to the PediatricRheumatology Department since 2015 (N= 282) at King Abdullah Specialized Children's Hospital (KASCH), PediatricRheumatology Clinic. Age, gender, reason for referral, clinical features, referring department, and final diagnosis were taken as variables. Data were collected through the documents and records of the cases (referrals) in the electronic medical records system of the hospital (BestCare).Then Excel was used for data entry, and JMP statistical software, version 14.0.0 (SAS Institute Inc., Cary, NC, USA) was used for data analysis. Results In a totalof 282 patients across the PediatricRheumatology Clinic, KASCH, the most common reason for referral to the clinic was joint pain (112, 43%)and the least common reason was rash (6, 2.3%). The most common diagnosis was juvenile idiopathic arthritis (JIA) (24, 26.6%).The majority of patients referred to the rheumatology department did not have a rheumatological disease (169, 65%). The majority of the referrals were from pediatrics subspecialties (168, 65%). The least referred department was primary care (21, 8%). Conclusions To our knowledge, this is the first study showing the referral pattern, accuracy, and profile of a pediatric rheumatology clinic population in Saudi Arabia. Expectedly, the most common reason for referral was arthralgia. The most common diagnosis was JIA. According to the results, most of the referrals were inaccurate as they did not end up with a rheumatological diagnosis. Pediatricsubspecialties should be more aware of the nature of rheumatological disease to avoid over-referrals. Finding a pattern of referrals to pediatricrheumatology is an excellent modality to accomplish early diagnosis and the best possible prognosis.

Relative Importance of Glaucoma-Referral Indicators in Retinal Images in a Diabetic Retinopathy Screening Programme in Slovenia: A Cross-Sectional Study.

Background and Objectives: Glaucoma is a major cause of irreversible visual impairment and blindness, so its timely detection is crucial. Retinal images from diabetic retinopathy screening programmes (DRSP) provide an opportunity to detect undiagnosed glaucoma. Our aim was to find out which retinal image indicators are most suitable for referring DRSP patients for glaucoma assessment and to determine the glaucoma detection potential of Slovenian DRSP. Materials and Methods: We reviewed retinal images of patients from the DRSP at the University Medical Centre Ljubljana (November 2019-January 2020, May-August 2020). Patients with at least one indicator and some randomly selected patients without indicators were invited for an eye examination. Suspect glaucoma and glaucoma patients were considered accurately referred. Logistic regression (LOGIT) with patients as statistical units and generalised estimating equation with logistic regression (GEE) with eyes as statistical units were used to determine the referral accuracy of indicators. Results: Of the 2230 patients reviewed, 209 patients (10.1%) had at least one indicator on a retinal image of either one eye or both eyes. A total of 149 (129 with at least one indicator and 20 without) attended the eye exam. Seventy-nine (53.0%) were glaucoma negative, 54 (36.2%) suspect glaucoma, and 16 (10.7%) glaucoma positive. Seven glaucoma patients were newly detected. Neuroretinal rim notch predicted glaucoma in all cases. The cup-to-disc ratio was the most important indicator for accurate referral (odds ratio 7.59 (95% CI 3.98-14.47; p < 0.001) and remained statistically significant multivariably. Family history of glaucoma also showed an impact (odds ratio 3.06 (95% CI 1.02-9.19; p = 0.046) but remained statistically significant only in the LOGIT multivariable model. Other indicators and confounders were not statistically significant in the multivariable models. Conclusions: Our results suggest that the neuroretinal rim notch and cup-to-disc ratio are the most important for accurate glaucoma referral from retinal images in DRSP. Approximately half of the glaucoma cases in DRSPs may be undiagnosed.

Assessment of optometrists' referral accuracy and contributing factors: A review.

In the UK, ophthalmology has the highest number of outpatient appointments within the National Health Service. False-positive referrals from primary care are one of the main factors contributing to the oversubscription of hospital eye services (HESs). We reviewed the accuracy of referrals originating from primary care optometrists and contributing factors, such as condition type and years since registration. Of the 31 studies included in the review, 22 were retrospective analyses of referrals and appointments at the HES. Eight were prospective studies, and one used online clinical vignettes. Seven assessed the accuracy of referrals for all ocular conditions. The remaining studies focused on glaucoma (n = 11), cataracts (n = 7), emergency conditions (n = 4), neovascular age-related macular degeneration (n = 1) and paediatric binocular vision (n = 1). The diagnostic agreement for suspected emergency ocular conditions was the lowest, with only 21.1% of referrals considered to require urgent attention in one study. For glaucoma, the first-visit discharge rate was high (16.7%-48%). Optometrist referral accuracy was overall 18.6% higher than General Medical Practitioners'; however, the two mainly referred different ocular conditions. Female optometrists made more false-positive referrals than males (p = 0.008). The proportion of false positives decreased by 6.2% per year since registration (p < 0.001). There was significant variation in referral accuracy across different ocular conditions, partly due to differences when defining accurate referrals. Optometrists working in primary care are generally more limited in their resources than the HES. Thus, choosing the cautious option of referral when they are unsure could be in the patients' best interests. The possible effect of increased use of advanced imaging on referrals requires evaluation. Although interventions such as refinement schemes have been put in place, these vary across regions, and their approaches such as virtual referral triaging may reduce unnecessary HES face-to-face appointments and promote communication between primary and secondary care.

BT09 Clinical performance of an artificial intelligence-based medical device deployed within an urgent suspected skin cancer pathway

Abstract A secondary care trust received over 2800 urgent suspected skin cancer referrals (USCRs) in 2021, 50% more than in 2016 (www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times), &amp;lt; 1 in 10 of which resulted in diagnosis of skin cancer (www.cancerdata.nhs.uk/cwt_conversion_and_detection). Judicious implementation of artificial intelligence (AI) may improve referral accuracy. We conducted a prospective, postdeployment, single-centre clinical performance review of a UK Conformity Assessed Class IIa-approved, AI-based, skin lesion analysis medical device intended for use in the screening, triage and assessment of skin lesions suspicious for skin cancer (AIaMD). The AIaMD is deployed at the trust as a decision-support tool following USCR from primary care, with a second read of all cases by a consultant dermatologist. Patients assessed by the two most recent versions of AIaMD from October 2021 to October 2022 were eligible for inclusion. Outcomes were confirmed from histology reports for cancerous lesions and histology reports or Consultant teledermatology assessment for noncancerous lesions. The AIaMD assessed 1363 lesions with outcomes confirmed, including 51 melanomas, 64 squamous cell carcinomas (SCCs), 96 basal cell carcinomas, 8 rare skin cancers and 244 premalignancies. The AIaMD referral sensitivity for melanoma, all skin cancer and premalignancy was 100% (n = 51/51), 99.5% (n = 218/219) and 96.3% (n = 235/244), respectively. The AIaMD benign lesion specificity was 55.9% (n = 434/777). The negative predictive value (how often AIaMD labelled lesions as premalignant/benign and correctly ruled out cancer) was 99.8% (n = 539/540). The conversion rate (the percentage of lesions classified as high risk by AIaMD that resulted in a diagnosis of melanoma or SCC) was 13.8% (n = 122/886), while the USCR conversion in the local clinical commissioning group was 9.9% in 2020–2021. The service discharged 9.4% (n = 239/2551) of patients during this time without using trust dermatologist capacity, with the potential to have discharged 18.3% (n = 467/2551) without a second read. This postmarket service evaluation reported the clinical outcomes of an AI device used for assessment of skin lesions. The pathway was sensitive, identifying 218 of 219 skin cancers, and specific, correctly identifying over half of benign lesions. Moreover, AIaMD was able to improve the conversion rate of USCRs. The integration of AIaMD into skin cancer diagnostic pathways could significantly improve the accuracy of USCRs. Conflicts of interest: these were commercial deployments of the AI, and three of the authors are employed by the AI provider.

Ear, Nose and Throat (ENT) disease diagnostic error in low-resource health care: Observations from a hospital-based cross-sectional study.

Although the global burden of ear, nose and throat (ENT) diseases is high, data relating to ENT disease epidemiology and diagnostic error in resource-limited settings remain scarce. We conducted a retrospective cross-sectional review of ENT patients' clinical records at a resource-limited tertiary hospital. We determined the diagnostic accuracy and appropriateness of patient referrals for ENT specialist care using descriptive statistics. Cohens kappa coefficient (κ) was calculated to determine the diagnostic agreement between non-ENT clinicians and the ENT specialist, and logistic regression applied to establish the likelihood of patient misdiagnosis by non-ENT clinicians. Of the 1543 patients studied [age 0-87 years, mean age 25(21) years (mean(SD)], non-ENT clinicians misdiagnosed 67.4% and inappropriately referred 50.4%. Compared to those aged 0-5 years, patients aged 51-87 years were 1.77 (95%CI: 1.03-3.04) fold more likely to have a referral misdiagnosis for specialist care. Patients with ear (aOR: 1.63; 95% CI: 1.14-2.33) and those with sinonasal diseases (aOR: 1.80; 95% CI: 1.14-2.45) had greater likelihood of referral misdiagnosis than those with head and neck diseases. Agreement in diagnosis between the ENT specialist and non-ENT clinicians was poor (κ = 0.0001). More effective, accelerated training of clinicians may improve diagnostic accuracy in low-resource settings.

Evaluating patient flow through an emergency ophthalmology consult service in a tertiary care academic centre in Quebec

ObjectiveThis study aims to characterize the epidemiology, common reasons, and diagnostic accuracy of referrals made by emergency departments (EDs) and optometrists to an emergency ophthalmology consultation service as well as to identify opportunities for improvement. DesignRetrospective chart review. ParticipantsA total of 1249 referrals made to the emergency ophthalmology consultation service at a tertiary care centre between July 2018 and June 2019. MethodsPatient charts were examined, and clinical variables were extracted. Statistical significance (p < 0.05) was determined via t tests and χ2 tests for continuous and categorical variables, respectively. Diagnostic accuracy of providers and time delay between referral and ophthalmologic encounter also were assessed. ResultsBoth EDs and optometrists most often referred cases with vitreoretinal (36.48% and 37.19%, respectively) and corneal pathology (21.42% and 20.25%, respectively). Optometrists (n = 240; 52.48%) were significantly more accurate in their diagnoses than EDs (n = 940; 32.45%; p < 0.00001). Specifically, optometrists were significantly more accurate when diagnosing anterior-chamber (n = 29; 58.62%; p = 0.039) and vitreoretinal (n = 89; 60.67%; p < 0.00001) pathology than EDs (anterior chamber, n = 77, 36.36%; vitreoretinal, n = 344, 18.90%). Across all ED referrals (n = 940), 58 (6.17%) had a prolonged delay. Across all optometrist-to-ED referrals (n = 150), 6 (4.00%) had a prolonged delay. Accounting for all cases, the total incidence of prolonged delay was 5.87%. ConclusionsOur results demonstrate the need for improved communication between optometrists and ophthalmologists to reduce the wait-time burden on EDs. Patients may benefit from direct referral by optometrists to ophthalmologists. Education of allied health professionals on ophthalmic disease also may improve diagnostic accuracy.

Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning

Effective and rapid triaging from primary care into secondary care plays a pivotal role in providing patients with timely treatment and managing increasing demands for healthcare resources. Existing triaging methods from primary care to secondary care are labor-intensive processes that involve manually reviewing referral data from multiple sources and can cause long referral to treatment time. There has been no research using machine learning methods that automatically analyzes heterogeneous data including referral letters to recognize regularities to support the primary to secondary care triage. In this paper, we propose a heterogeneous data-driven hybrid machine learning model including Natural Language Processing (NLP) to improve hospital triage efficiency at the point of triage. The proposed model achieved a precision of 0.83, recall of 0.82, F1-Score of 0.83, accuracy of 0.82, AUC of 0.90 in identifying patients with non-inflammatory conditions (NIC) and inflammatory arthritis (IA) at the point of triage with explainable risk stratifications. Our model is piloted in a real-world trial in a large secondary care hospital in the UK to compare referral accuracy and time saved between our model and clinicians, and evaluate its acceptability by users. Our model achieved precision and recall of 0.83 and 0.81, compared with the precision and recall of 0.80 and 0.78 by clinicians. The research also shows that our model enabled decision support can save clinicians 8 h per week in assessing the referral assessment. This paper is the first study to streamline hospital triage from primary care to secondary care using machine learning.

Letter to the Editor: Accuracy of Referrals to Canadian Pediatric Ophthalmology Services.

Letter to the Editor: Accuracy of Referrals to Canadian Pediatric Ophthalmology Services.

MONitoring Knockbacks in EmergencY (MONKEY) - An Audit of Disposition Outcomes in Emergency Patients with Rejected Admission Requests.

BackgroundEmergency Department (ED) clinicians commonly experience difficulties in referring patients to inpatient teams for hospital admission. There is limited literature reporting on patient outcomes following these complicated referrals, where ED requests for inpatient admission are rejected – which study investigators termed a “knockback”.PurposeTo identify disposition outcomes and referral accuracy in ED patients whose admission referral was initially rejected. Secondary objectives were to identify additional patient, clinician and systemic factors associated with knockbacks.Selection and MethodologyEmergency clinicians prospectively nominated a convenience sample of patients identified as having knockbacks over two time periods (Jan–Feb 2020 and Aug 2020 to Jan 2021) at a tertiary Australian ED. Data were analyzed with a mixed-methods approach and subsequent descriptive and thematic analyses were performed.ResultsA total of 109 patients were identified as knockbacks. The referrals were warranted, with 89.0% of cases (n = 97) ultimately requiring a hospital admission. In 60.6% (n = 66) of the admissions, patients were admitted under the inpatient team initially referred to by the ED, suggesting referrals were generally accurate. The number of in-hospital units involved in the admission process and ED length of stay were positively correlated (0.409, p < 0.001). Patient factors associated with knockbacks include pre-existing chronic medical conditions and presenting acutely unwell. Analysis of clinicians’ perspectives yielded recurring themes of disagreements over admission destination and diagnostic uncertainty.ConclusionIn this patient sample, emergency referrals for admission were mostly warranted and accurate. Knockbacks increase ED length of stay and may adversely affect patient care. Further focused discussion and clearer referral guidelines between ED clinicians and their inpatient colleagues are required.

Assessment of Auscultation Skills Among Junior and Senior Healthcare Providers for Pediatric Patients: A Retrospective Study.

BackgroundHeart murmurs are defined as whooshing or swishing sounds, unlike the normal lub-dub sounds detected by physicians while using a stethoscope. They usually develop due to numerous pathologies, with congenital defects accounting for the majority of pediatric murmurs. Few studies have addressed the difference in auscultating skills between senior and junior healthcare providers. Therefore, this study aims to collect local data on this topic as well as identify the gap between experienced and inexperienced providers in their ability to accurately detect heart murmurs.MethodologyThis study utilizes a quantitative retrospective design to collect data from King Abdulaziz Medical City and King Faisal Cardiac Centre, Saudi Arabia, from October 1, 2018, to September 30, 2019. The medical records of 292 pediatric patients, who were 14 years of age or below according to the centers’ aging system, were collected from the Hospital Information System (BESTCare). Subsequently, it was determined whether a senior (R3-R4 residents and above) or a junior (R1-R2 residents and interns) healthcare provider ordered an echocardiogram (ECHO). Finally, using the centers’ imaging system (Xcelera) the exact reason for referral, heart murmurs in the case of this study, was obtained, as well as whether a pathologic cause of a murmur was seen in the ECHO image. By obtaining the aforementioned data, the accuracy of each referral was analyzed using statistical analysis software.ResultsECHO results were categorized into positive and negative outcomes depending on the presence of a structural heart defect, patent foramen ovale (PFO) was considered negative as it causes innocent murmurs. The majority of positive results were atrial septal defects, patent ductus arteriosus, and ventricular septal defects. The majority of negative results were either a structurally normal heart or PFO, which a great number of providers ordered an ECHO for. The Pearson score p-value using the chi-square test was 0.432, leading to the conclusion that junior and senior providers had a similar accuracy of referrals during the study period.ConclusionsJunior healthcare providers display sufficient knowledge of heart murmur auscultation skills similar to senior healthcare providers during the study period. However, because the data only included two local centers with a limited sample and the absence of further local research on this topic, it is necessary to conduct studies of a larger scope on this topic.

Evaluation of a remote telemedicine platform using a novel handheld fundus camera: Physician and patient perceptions from real-world experience.

Although teleophthalmology has gained traction in recent years, it is at the center of the coronavirus disease pandemic. However, most hospitals are not ready owing to a severe lack of real-world experience. Furthermore, a limited number of studies have evaluated telemedicine applications on remote islands. This study aimed to evaluate real-world clinical and referral accuracy, image quality, physician-perceived diagnostic certainty, and patient satisfaction with telemedicine eye screening using a novel handheld fundus camera in a rural and medically underserved population. This prospective study included 176 eyes from a remote island. All participants underwent a comprehensive ophthalmic examination. Nonmydriatic retinal images obtained using a handheld fundus camera were reviewed by two retinal specialists to determine image quality, diagnosis, and need for referrals. The agreement of diagnosis between image-based assessments was compared with that of binocular indirect ophthalmoscopic assessments. Image quality of fundus photographs was considered acceptable or ideal in 97.7% and 95.5% of eyes assessed by two reviewers, respectively. There was considerable agreement in diagnosis between the indirect ophthalmoscopic assessment and image-based assessment by two reviewers (Cohen's kappa = 0.80 and 0.78, respectively). Likewise, substantial agreement was achieved in the referrals. The sensitivity for referable retinopathy from the two reviewers was 78% (95% confidence interval [CI], 57%-91%) and 78% (95% CI, 57%-91%), whereas specificity was 99% (95% CI, 95%-99%] and 98% (95% CI, 93%-99%), respectively. For physicians' perceived certainty of diagnosis, 93.8% and 90.3% were considered either certain or reliable. Overall, 97.4% of participants were satisfied with their experiences and greatly valued the telemedicine services. Novel fundus camera-based telemedicine screening demonstrated high accuracy in detecting clinically significant retinopathy in real-world settings. It achieved high patient satisfaction and physician-perceived certainty in diagnosis with reliable image quality, which may be scaled internationally to overcome geographical barriers under the global pandemic.

Does specificity of electrodiagnostic test referrals predict test outcome in children?

Electrodiagnostic testing (EDX) is important in evaluation of pediatric neuromuscular disease. Non-specific referrals have emerged as a leading reason for EDX in recent years. We examine whether referral-specificity is predictive of test outcomes in children. EDX referrals and outcomes were audited over a 7-year period from 2013 to 2020 at CHI-Crumlin. Pre-test details were coded and compared to EDX outcomes using multinomial logistic regression. EDX studies were performed in 702 children (median age 10.2 years). In 36% of patients, EDX-referrals did not specify any pre-test diagnosis. Mononeuropathy (24%) and polyneuropathy (15%) were the leading pre-specified diagnoses as well as the most common test outcomes. Neurology and orthopedics/plastic surgery contributed the majority of referrals. Metabolic medicine and hematology/oncology were most likely to pre-specify a working diagnosis and were the specialties with both the highest proportion of abnormal outcomes and referral accuracy. EDX abnormality was present in 42% of patients and was predicted by specificity of referral and the absence of pain as a leading symptom. The accuracy of specified pre-test diagnoses was highest for suspected anterior horn cell disorders (67%). Accuracy of referrals, as well as abnormal test outcomes, were negatively predicted by the presence of pain as a leading symptom. EDX is informative in children but the likelihood of abnormal test-outcomes is diminished when a pre-specified working diagnosis is lacking or when the primary reason for referral is pain.

123 Effectiveness of the “Emergency Neurosurgery Referral Card”

Abstract Aim The recording of patient and clinical information is important during the neurosurgery referral process, as time-critical communication with the referring teams and appropriate clinical decisions are dependent on the accuracy and completeness of the information recorded. A ‘Neurosurgery Emergency Referral Card’ was introduced to assist the process for the on-call registrars/consultants. Method Following the first audit cycle was carried out over 52 weeks (1st January 2020 – 31st December 2020), which assessed the percentage of incomplete data entry in the referral database, a ‘Neurosurgery Emergency Referral Card’ was introduced. Data entry was then re-audited (12 weeks; 21st May 2021 – 21st August 2021). Results Referral type (emergency, urgent, semi-urgent, non-urgent) is the most inadequately recorded data field, with a completion rate of 68%. Following the implantation of the card, it improved to 89%. The pre- and post-implementation completion rate for patient age was 82% and 98% respectively, and 85% and 96% respectively for time of referral. The completion rates for the remaining data fields in the 1st cycle (e.g., referring doctor, diagnosis, gender) were generally above 94% but still improved with the usage of the card. The average improvement in referral taking after implementation is 6%. Conclusions Even in the digital age, a ‘low-technology’ yet simple physical card can be an effective adjunct to improve referral accuracy and efficiency.