Radiology Referrals Research Articles

Systematic review of interventions aimed at improving the quality of referrals to radiology.

Despite ubiquitous use of medical imaging in daily medical practice, the quality of referrals varies significantly across a variety of practice types and locations. This systematic review summarises studies in the literature that have employed interventions aimed at improving radiology referrals, excluding clinical decision support software. A systematic review of literature was conducted in PubMed, EMBASE, Scopus, and Cochrane. Two reviewers independently identified studies for inclusion. All studies that included interventions with any outcome measure were included. Any irrelevant studies, non-English studies or not retrievable studies were excluded. Studies were grouped into Education, Feedback, Rationing, Penalties, and Other. The outcomes of the studies were summarised and qualitatively analysed due to anticipated heterogeneity. Four thousand six hundred and forty-two studies were identified throughout PubMed, EMBASE, Scopus, and Cochrane. One hundred and eighty-seven duplicates were removed and 4436 abstracts were screened. Two hundred and forty were identified on the first phase of the screening with 167 then excluded for non-relevancy. Seventy-five full studies were included in the final analysis following the addition of 2 additional studies. Fifty-seven studies were grouped into Education, 10 into Feedback, 4 into Rationing, 8 into Penalties, 9 into Other and 11 containing multiple. Eighty-four percent of the studies reported an improvement in the quality of the referrals. Despite a variable rate of quality referrals, there are many interventions that radiology departments across the world can utilise to improve the referral process.

Machine learning and deep learning for classifying the justification of brain CT referrals.

To train the machine and deeplearning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts. Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deeplearning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set. 42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons. Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals. Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources. Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditingfor better implementation of imaging referral guidelines.

ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and Imaging Selection

PurposeThe quality of radiology referrals influences patient management and imaging interpretation by radiologists. The aim of this study was to evaluate ChatGPT-4 as a decision support tool for selecting imaging examinations and generating radiology referrals in the emergency department (ED). MethodsFive consecutive clinical notes from the ED were retrospectively extracted, for each of the following pathologies: pulmonary embolism, obstructing kidney stones, acute appendicitis, diverticulitis, small bowel obstruction, acute cholecystitis, acute hip fracture, and testicular torsion. A total of 40 cases were included. These notes were entered into ChatGPT-4, requesting recommendations on the most appropriate imaging examinations and protocols. The chatbot was also asked to generate radiology referrals. Two independent radiologists graded the referral on a scale ranging from 1 to 5 for clarity, clinical relevance, and differential diagnosis. The chatbot’s imaging recommendations were compared with the ACR Appropriateness Criteria (AC) and with the examinations performed in the ED. Agreement between readers was assessed using linear weighted Cohen’s κ coefficient. ResultsChatGPT-4’s imaging recommendations aligned with the ACR AC and ED examinations in all cases. Protocol discrepancies between ChatGPT and the ACR AC were observed in two cases (5%). ChatGPT-4-generated referrals received mean scores of 4.6 and 4.8 for clarity, 4.5 and 4.4 for clinical relevance, and 4.9 from both reviewers for differential diagnosis. Agreement between readers was moderate for clinical relevance and clarity and substantial for differential diagnosis grading. ConclusionsChatGPT-4 has shown potential in aiding imaging study selection for select clinical cases. As a complementary tool, large language models may improve radiology referral quality. Radiologists should stay informed about this technology and be mindful of potential challenges and risks.

Patient initiated radiology requests: proof of wellness through images.

Traditionally, general practitioners (GPs) have initiated the need for, and ordered, radiological tests. With the emergence of consumer-centred care, patients have started to request scans from doctors on their own initiative. Consumeristic health care has shifted the patient-doctor dyadic relationship, with GPs trending towards accommodating patients' requests. A mixed method analysis was conducted using a survey instrument with open ended questions and concurrent interviews to explore participants' responses from their requests for radiological studies from GPs. Themes emerging from both qualitative and quantitative methodologies were mapped onto the Andersen Newman Model (ANM). Data were analysed for 'predisposing,' 'need' and 'enabling' elements of the ANM model and were correspondingly mapped to patient's requests for radiological referrals according to the elements of the ANM. Participants expressed anxiety about their health, were confident in the types of radiological scans they desired and typically indicated the need for evidence of good health. Their desire for such requested scans was often enabled through prior exposure to health information and the experience of specific symptoms. Requests came with the expectation of validation, and if these requests were denied, participants indicated that they would seek another doctor who would oblige. In our modest study of Australian patients, participants were well informed about their health. Exposure to information seems to create a sense of anxiousness prior to visiting the doctor. Individuals sought visual proof of wellness through imaging, and doctors in return often accommodated patient requests for radiological studies to appease patients' needs and to maintain workflow.

Patient requests for radiological services: An Australian study of patient agency and the impact of online health information.

The Internet has been instrumental in patients' knowledge about health and medicine through increasing consultation of online sources that advocate self-management. For example, those patients who request referrals from their doctors for tests and procedures in radiology. Such patient-initiated referral requests can devolutionise the traditional model of health care. This study aimed to understand individuals who sought online health information (OHI) and whether requests for radiological referrals were the result of OHI seeking. The individuals targeted were those who have had a radiological procedure in the past 5 years. Using an online survey tool, individuals completed a 20-min anonymous survey. Included in the survey was a validated digital health literacy measurement scale, eHEALS. Those who scored higher on the eHEALS measure were likely to be under 55 years of age and were more inclined to request radiological referrals. Though they were not concerned with the credibility of sourced websites, most secured the desired outcome from their requests. Overall, this study indicates that patients are consulting online sites for health information, and individuals with higher digital literacy scores are asking doctors for referrals for radiological tests and procedures. These findings confirm our anecdotal experience in radiology departments. So what?: In conducting this study, we hope to better inform radiology and other health practitioners of the way OHI is impacting clinical practice.

Automated vetting of radiology referrals: exploring natural language processing and traditional machine learning approaches

BackgroundWith a significant increase in utilisation of computed tomography (CT), inappropriate imaging is a significant concern. Manual justification audits of radiology referrals are time-consuming and require financial resources. We aimed to retrospectively audit justification of brain CT referrals by applying natural language processing and traditional machine learning (ML) techniques to predict their justification based on the audit outcomes.MethodsTwo human experts retrospectively analysed justification of 375 adult brain CT referrals performed in a tertiary referral hospital during the 2019 calendar year, using a cloud-based platform for structured referring. Cohen’s kappa was computed to measure inter-rater reliability. Referrals were represented as bag-of-words (BOW) and term frequency-inverse document frequency models. Text preprocessing techniques, including custom stop words (CSW) and spell correction (SC), were applied to the referral text. Logistic regression, random forest, and support vector machines (SVM) were used to predict the justification of referrals. A test set (300/75) was used to compute weighted accuracy, sensitivity, specificity, and the area under the curve (AUC).ResultsIn total, 253 (67.5%) examinations were deemed justified, 75 (20.0%) as unjustified, and 47 (12.5%) as maybe justified. The agreement between the annotators was strong (κ = 0.835). The BOW + CSW + SC + SVM outperformed other binary models with a weighted accuracy of 92%, a sensitivity of 91%, a specificity of 93%, and an AUC of 0.948.ConclusionsTraditional ML models can accurately predict justification of unstructured brain CT referrals. This offers potential for automated justification analysis of CT referrals in clinical departments.

Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: Comparison models vs healthcare professionals

IntroductionReferrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the vetting process and to compare their performance with healthcare professionals. Methods1020 lumbar spine MRI referrals were collected retrospectively from two Irish hospitals. Three expert MRI radiographers classified the referrals into indicated or not indicated for scanning based on iRefer guidelines. The reference label for each referral was assigned based on the majority voting. The corpus was divided into two datasets, one for the models' development with 920 referrals, and one included 100 referrals used as a held-out for the final comparison of the AI models versus national and international MRI radiographers. Three traditional models were developed: SVM, LR, RF, and two deep neural models, including CNN and Bi-LSTM. For the traditional models, four vectorisation techniques applied: BoW, bigrams, trigrams, and TF-IDF. A textual data augmentation technique was applied to investigate the influence of data augmentation on the models' performances. ResultsRF with BoW achieved the highest AUC reaching 0.99. CNN model outperformed Bi-LSTM with AUC = 0.98. With the augmented dataset, the performance significantly improved with an increase in F1 scores ranging from 1% to 7%. All models outperformed the national and international radiographers when compared on the hold-out dataset. ConclusionThe models assigned the referrals' appropriateness with higher accuracies than the national and international radiographers. Applying data augmentation significantly improved the models' performances. Implications for practiceThe outcomes suggest that the use of AI for checking referrals' eligibility could serve as a supporting tool to improve the referrals' management in radiology departments.

Impact of a radiological protection campaign in emergency paediatric radiology: a multicentric observational study in Brazil

PurposeTo evaluate the impact of a paediatric radiological protection campaign, implemented in the emergency units of a healthcare provider network in Brazil. This campaign aimed to promote awareness among emergency department physicians, regarding justification of paediatric X-ray referrals for paranasal sinus, chest and CT exams, as a strategy to reduce exposure to ionising radiation.MethodFrequency analysis of common paediatric imaging referrals from 19 emergency departments was performed for a 3-year period (2015–2018) to coincide with before, during and after the implementation of the radiation protection campaign. The campaign was multifaceted and involved dissemination of educational materials and imaging referral guidelines along with quarterly meetings with participating centres' leaderships. Additionally, patient dose cards were distributed to patients/carers. The Chi-Square test was used to examine the association between the type of examination and the patient's age group. Exact-Fisher test was performed to check for an association between participant engagement and the existence of the radiation protection committee.ResultsReferrals reduced by 25% following the campaign with no reports of misdiagnosis. Many referrals in the youngest age groups. In 15 units, a radiological protection committee was created to raise awareness and to create a multi professional team to communicate the risks and benefits of radiological procedure in children.ConclusionThe campaign resulted in a substantial reduction in radiological referrals while promoting a radiation protection culture. Simple education initiatives can contribute to savings in both finances and radiation doses, particularly important in radiosensitive cohorts.

Radiographers' actions and challenges when confronted with inappropriate radiology referrals.

ObjectivesTo explore radiographers’ actions toward inappropriate referrals and hindrances to assessing referrals.MethodsAn online survey was distributed to radiographers via the International Society of Radiographers and Radiological Technologists (ISRRT) networks. The questionnaire consisted of 5-point Likert scale questions on radiographers’ actions to supplement referral information, actions for unjustified referrals and hindrances to referral assessment. The questionnaire was validated using a test–retest reliability analysis. Kappa values ≥ 0.6 were accepted. SPSS software was used for data analysis and chi-square tests to compare subgroups.ResultsTotal responses received were 279. The most reported actions to supplement missing referral information were to ask the patient or relative, examine the body region of concern and check medical records (73%, 70%, 67%, responded often/always, respectively). The actions when confronted with unjustified referrals were reported equally to consult the radiologist, referring clinician and radiographer (69–68% often/always responses). The hindering factors ranked high (agreed/strongly agreed responses) pertained to inadequate information in referral forms (83%), ineffective communication among healthcare professionals (79%), lack of training (70%) and allocated time (61%). Statistically significant associations were observed for a few actions and hindrances with education level, modality of practice and responsibility to screen imaging referrals.ConclusionRadiographers consult colleagues about suspected unjustified referrals. Effective communication pathways, training and time allocation to improve radiographers’ skills to assess referrals may enhance appropriate imaging and delivery of quality patient care.Key Points • Radiographers’ actions of supplementing missing information in radiology referrals facilitate provision of high-quality health services. • Radiographers’ strategy when confronted with inappropriate referrals is to consult radiologists and referring clinicians. • Better inter-professional communication and organisation of tasks can facilitate radiographers’ participation in referral assessment to ensure appropriate imaging. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-08470-z.

Machine learning and deep learning-based Natural Language Processing for auto-vetting the appropriateness of Lumbar Spine Magnetic Resonance Imaging Referrals

Manual vetting of radiology referrals is an essential daily task to ensure the appropriateness of the received referrals. Such tasks require sufficient clinical experience and may challenge the radiology staff. With the emerging of artificial intelligence (AI) technology and advancement in natural language processing NLP, most of the available machine learning-based NLP models targeted research cohort building and healthcare quality. Other healthcare management tasks such as auto-vetting radiology referrals have not been adequately encoded. Furthermore, challenges, including class imbalance and lack of direct comparison with humans, are yet to be investigated sufficiently. In this study, a set of machine learning and deep learning models were developed for auto-vetting of lumbar spine magnetic resonance imaging LSMRI referrals as indicated or not indicated for scanning using referrals from two hospitals. The impact of applying one of the text augmentation techniques on the models' performance has been investigated. In addition, the performance of four different feature extraction techniques has been critically analyzed. Moreover, a comparison has been conducted between the developed models with two expert radiologists who were not involved in establishing the gold standard labels using an unseen dataset. The results show that the models’ performances significantly improved with the augmented data, with an increase in F1 scores ranging from 1% to 8%. Support vector machine with bag of words achieved the highest AUC reaching 0.99. Convolutional neural network model achieved the second-highest model with AUC = 0.97. All models outperformed the two expert radiologists when comparisons were conducted on the unseen dataset.

EP.FRI.178 Audit of rejected radiological referrals at Ninewells Hospital, Dundee: Reasons and outcomes

Abstract Background Rejected radiology requests in emergency-settings can be time-consuming for clinical and radiological staff; and can delay or even affect patient management plans and outcomes. Inappropriate or duplicate radiological requests additionally can be resource-wasting (according to data from Fairfield Independent Hospital, BUPA, St. Helen’s) - the approximate costs / scan are £160 for Ultrasound, £450-£675 for CT, £350- £525 for MRI. Methods Results Commonest reasons for rejection: Conclusions

40 Cattle-Related Trauma: A 5 Year Retrospective Review in An Adult Major Trauma Centre

Abstract Aim Bovine injuries are a common and significant cause of trauma, often requiring admission and operative treatment. We review all bovine related injuries over five years, both emergency and GP referrals at an adult major trauma centre in England. Method Retrospective evaluation was undertaken using keywords through radiology referrals and hospital admissions speciality databases. Demographics were collected as well as the mechanism and the situation of injury; trauma scores were calculated using: Injury Severity Score (ISS) and Probability of Survival (Ps19). Results Sixty-seven patients were identified retrospectively over 5 years, 44 emergency patients (including 23 major traumas) and 23 GP referrals. Combined (Emergency & GP) mean age 52 years old. 67% male. Mean ISS 11. Most common combined mechanism of injury, kicked (n = 23). In emergency patients, trampling injuries were most common. 86% of the trampled patients were major traumas. Indirect injuries mainly involved farm gates (92%). 73% of bull-related injuries were major traumas. In emergency patients’ fractures were the most common primary injury (n = 20), upper limb followed by spine. In GP, soft tissue injuries were the most common. 70% of the emergency referrals required admission and 50% operations. Only one GP referral required an operation. Two patients had a Ps19 score <90. There were two mortalities. Conclusions Cattle related injuries are a significant cause of severe morbidity and mortality. They are under-reported. Patterns of injury are similar to high velocity road traffic collisions and bull-related injuries or trampling in particular, should alert the clinician to more significant trauma.

Assessing the practice of written referrals to neuroradiology and how this process can be improved and standardised

AimsThis quality improvement project aims to improve the quality of information provided in the referrals from the older adult psychiatry department to radiology when requesting neuroradiological imaging.The secondary outcome aims to standardise information on the referral proforma. We hypothesise that this improved referral proforma will lead to improved quality of reporting from the radiology department, which will form the second stage of this quality improvement project.A further area of interest of this exercise is to establish whether standardised radiological scoring systems are requested in the referral, as these can be utilised as a means to standardise reported information.MethodRetrospective electronic case analysis was performed on 50 consecutive radiology referrals for a period of 3 months from November 2019 to January 2020. Data were obtained from generic MRI and CT referral proforma and entered into a specifically designed data collection tool. Recorded were patient demographics, provisional diagnosis, modality of imaging, use of ACE-III cognitive score, radiological scoring systems, and inclusion and exclusion criteria.ResultResults from 50 referrals have shown: 60% were male, 40% female. Average patient age of 74, ranging from 49 to 95. 58% were referred for CT head with 42% for MRI head. More than half of referrals quoted the ACE-III score. 26% of referrals stated exclusion criteria such as space occupying lesions, haemorrhages or infarcts. 10% of referrals requested specific neuro-radiological scoring scales. Specific scales which were requested included GCA (global cortical atrophy), MTA scale (medial temporal atrophy), Koedam scale (evidence of parietal atrophy) and Fazekas (evidence of vascular changes). Only 80% of referrals included the patients GP details on the referral form.Conclusion1. This quality improvement initiative has highlighted that the current level of information in referring patient to radiology is variable and dependent on the referrer.2. All referrals should state exclusion criteria as per the NICE guidelines on neuroimaging in diagnosis of dementia.3. Preliminary evidence suggests that requesting specific radiological rating scales could improve the quality of information received in the imaging report. The second part of this quality improvement initiative will aim to explore the impact of requesting these scales routinely.

Cattle-Related Trauma: A 5-Year Retrospective Review in a Adult Major Trauma Center.

Introduction:Bovine injuries are a common and significant cause of trauma, often requiring admission and operative treatment. We review all bovine-related injuries over 5 years, both emergency and general practitioner (GP) referrals at an adult major trauma center in England.Methods:Retrospective evaluation was undertaken using the keywords through radiology referrals and hospital admissions speciality databases. By searching patient notes, demographics were collected as well as the mechanism and the situation of injury; trauma scores were calculated using: injury severity score (ISS) and probability of survival (Ps19). The results were divided into emergency patients and GP referrals.Results:Sixty-seven patients were identified retrospectively over 5 years, 44 emergency patients (including 23 major traumas), and 23 GP referrals. Combined (emergency and GP) mean age 52 years old; 67% male; and mean ISS 11. Most common combined mechanism of injury, kicked (n = 23). In emergency patients, trampling injuries were the most common. Eighty-six percent of the trampled patients were major traumas and associated with increased ISS (mean 13). Indirect injuries mainly involved farm gates (92%). Seventy-three percent of bull-related injuries were major traumas and had increased ISS scores (mean 17). Orthopaedics was the most common admitting speciality followed by cardiothoracic and neurosurgery. In emergency patients, fractures were the most common primary injury (n = 20), upper limb followed by spine. In GP, soft-tissue injuries were the most common primary injury. Seventy percent of the emergency referrals required admission and 50% operations. Fracture fixation was the most common operative procedure. Only, one GP referral required an operation. There were significant delays in GP patients presenting. Two patients had a Ps19 score <90. There were two mortalities.Conclusion:Cattle-related injuries are a significant cause of severe morbidity and mortality. They are under-reported. Patterns of injury are similar to high-velocity road traffic collisions and bull-related injuries or trampling in particular, should alert the clinician to more significant trauma. Farm gates are a frequent cause of trauma associated with cattle. GP referrals with ongoing symptoms for more than 2 weeks seeking medical advice should alert the clinician to a more serious diagnosis.

Application of qisas and diyat act's terms for reporting in current forensic radiology practice.

To determine the practical use of the application of Qisas and Diyat Act's terms for medicolegal x-ray reporting in the context of present local forensic radiology practice. A cross-sectional study. Place and Duration of the Study: Radiology Department, Dow University of Health Sciences (DUHS) and Civil Hospital, Karachi (CHK), from January to December 2004. The study included all cases referred to radiology department, DUHS and CHK for medicolegal X-ray reporting during the study period where a skeletal injury was suspected. Those X-rays conducted outside Karachi, burns-injury cases, pregnant ladies' cases and technically faulty X-rays were excluded. The studied variables were the demographic characteristics of each case, the mode of injury as alleged, the region and the extent of injury as visible on these x-rays; and the ancillary imaging techniques done to further evaluate the injury. Each injury was reported as per Qisas and Diyat Act 1990's terms and confirmed with medicolegal officer's final report. The frequency distribution, percentages and means were determined for each variable. There were 281/1972 (0.42%) cases of radiographically visible skeletal injury. Those with major degree of trauma (fracture-displaced or otherwise), the injury could be explicitly termed as required by the said ordinance, Jurh Jaifah (fractured ribs), "Jurh Ghair Jaifa Munaqillah' and Hashimah' (peripheral fracture), and 'Shajjah Hashimah' (head and face fractures). 'Shajah Damighah' and 'Ammah' (fractures meninges with intracranial hematoma) could only be diagnosed with CT. 'Jurh-Ghair Jaifah Mudiah' could only be decided by combining physical examination with MRI in two cases-plain x-rays being unhelpful. Radionuclide scan was not called for in a single case. The presently recommended legal terminology for fracture reporting and sub classification is comprehensive, however, reporting the trauma short of fracturing the bone, requires the use of advanced imaging techniques such as the CT scan and the MRI. In the absence of these techniques, the application of the present terms depends mainly on the examination findings of the first examining medical officer rather than radiological evidence such as X-rays. So forensic radiology referrals should utilize CT scan, MRI in skeletal trauma.

Radiographers' assessment of referrals for CT and MR imaging using a web-based data collection tool

IntroductionThere is limited research related to the radiographers' role in assessing of radiology referrals to justify imaging. This study investigated radiographers' compliance with guidelines in the assessment of CT and MRI referrals and factors that influenced their performance. MethodsThis research was facilitated by the EFRS Research Hub at ECR 2019. Five radiology referral scenarios for CT and/or MRI were distributed to radiographers, as determined by their scope of practice, who volunteered at the Research Hub. A web-based data collection tool was used. The radiographers were required to determine the appropriateness of each referral, highlight any concerns and recommend suitable investigations if applicable. Linear regression analysis was used to determine whether postgraduate qualification, grade/role of the radiographer and use of guidelines influenced the radiographers' performance in assessing the referrals. ResultsParticipants originated from 24 countries (n = 51 CT, n = 40 MRI), the majority originating from the UK, Ireland, Italy, Spain, Norway and Austria. Responses consistent with guidelines were 58% and 57% for CT and MRI, respectively. Possession of an MSc qualification in CT was a significant factor of influence for a higher consistency with guidelines (p = 0.02) in CT. Employment as a radiographer in a lead professional role and/or educator was a significant factor of influence for a higher consistency with guidelines in MRI (p = 0.01). ConclusionA total of 58% for CT and 57% for MRI of the radiographers' responses complied with guidelines. Factors such as postgraduate education and leading professional roles are associated with better performance. Implications for practiceConsidering qualifications, experience and managerial role is vital before radiographers are delegated task of justifying CT and MR Imaging.

Exploring the evidence-practice gap in the use of plain radiography for acute abdominal pain and intestinal obstruction: a systematic review and meta-analysis.

Previous studies, some dating back several decades, have recommended that the use of plain abdominal radiography should be curbed, particularly with the growth of more accurate imaging modalities. However, evidence from referral data suggests that plain abdominal radiography continues to be a commonly requested examination. The aim of this review was to explore the gap between evidence and practice by re-examining the evidence using a robust methodology, investigating the diagnostic accuracy of plain abdominal radiography. Studies were identified from electronic databases and reference lists. Eligible studies provided data as to the sensitivity and specificity of plain abdominal radiography for either acute abdominal pain (Group A) or suspected intestinal obstruction (Group B). Version 2 of the Quality Assessment of Diagnostic Accuracy Studies was used to assess the quality of studies and hierarchical summary receiver operator characteristic curves and coupled forest plots were generated. Four studies evaluated plain abdominal radiography for acute abdominal pain (Group A) and 10 for suspected intestinal obstruction (Group B). Two studies investigated both presentations and were included in both groups. Methodological quality of studies was moderately high, though incorporation bias was a common limitation. Sensitivity for Group A studies ranged from 30 to 46%, with specificity from 75 to 88%. For Group B, the range of sensitivity was 48 to 96% and specificity from 50 to 100%. The results suggest that use of plain abdominal radiography could be substantially reduced, particularly for patients with undifferentiated acute abdominal pain. While some guidelines exist, there is sound argument for clinical decision rules for abdominal imaging to inform evidence-based clinical decision-making and radiology referrals.

Assessing practice pattern differences in the treatment of acute low back pain in the United States Military Health System

BackgroundAcute low back pain is one of the most common reasons for individuals to seek medical care in the United States. The US Military Health System provides medical care to approximately 9.4 million beneficiaries annually. These patients also routinely suffer from acute low back pain. Within this health system, patients can receive care and treatment from physicians, or physician extenders including physician assistants and nurse practitioners. Given the diversity of provider types and their respective training programs, it would be informative to evaluate variation in care delivery, adherence to clinical guidelines, and differences within the MHS among a complex mix of provider types.MethodsThis study was a retrospective, cross-sectional quantitative analysis that examined variations in treatment between provider types within the Military Health System in 2015 for treatment of acute low back pain using administrative data. In addition to descriptive and summary statistics, binomial logistic regression models were used to assess variation in practice patterns among physicians and mid-level practitioners for prescribing of non-steroidal anti-inflammatory, opioids, plain radiography, computed tomography, and magnetic resonance imaging.ResultsWith regard to prescribing practices, results indicated that the odds of receiving non-steroidal anti-inflammatory prescriptions increased significantly for both physician assistants and nurse practitioners when compared to physicians. For basic radiological referrals, odds increased significantly for ordering plain radiography for physician assistants and nurse practitioners when compared to physicians. For more advanced imaging, odds significantly decreased for ordering computed tomography (CT) and slightly decreased for magnetic resonance for physician assistants, nurse practitioners and physician residents compared to the physician group. Additionally this study discovered differences in the prescribing patterns between provider categories. Both contractors and civilians had higher odds of prescribing opioids compared to active duty providers.ConclusionsAs physician assistants and nurse practitioners continue to gain popularity as physician extenders in the US and in addressing provider shortages for the Military Health System, further research should be conducted to determine what impact, if any, the differences found in this study have on patient outcomes. In addition, provider type warrants further investigation to determine if labor mix and outsourcing decisions within a single payer system impacts health delivery and value based care.

Incidence of calcaneal apophysitis in Northwest Istanbul

BackgroundCalcaneal apophysitis is a common clinical entity affecting children and adolescents. It is also known as Sever’s disease. Heel pain without a recent trauma is the primary manifestation. There are limited studies on the incidence of this disease. In this study, we aimed to report the regional incidence in Istanbul.MethodsThis retrospective audit of health records of all paediatric patients aged 6–17 years between January 1, 2014, and December 15, 2017 was undertaken. During this period, data were extracted from health records that recorded calcaneal apophysitis as the primary diagnosis.ResultsThe 4-year incidence of calcaneal apophysitis was found to be 0.35% (74 of 20,967 paediatric patients). It commonly affected males, and bilateral cases were more common than unilateral cases. There were more admissions during the spring season, which may indicate a possible association with physical activity.ConclusionAlthough calcaneal apophysitis is a relatively common paediatric foot problem, due to its benign course and spontaneous healing capacity, most physicians are not interested in this topic. However, increased awareness of this diagnosis is important for reducing the rates of unnecessary radiological examinations and orthopaedic referrals. With increased knowledge, most cases may be diagnosed at the family physician level, which may decrease the economic burden on the health system. Incidence reports from various countries and regions may be published in the future.

Geographic Patterns of Radiology Referrals in the United States: A Descriptive Network Analysis

PurposeTo provide a nationwide description of radiology referral patterns for Medicare beneficiaries and network-based measures that can describe and monitor shifts in referral patterns. MethodsWe linked publicly available Medicare data with the National Plan and Provider Enumeration System data set to analyze 3,925,366 encounters representing referrals from nonradiology referrers to diagnostic radiology providers in 2015. We assessed per-state referral volume as well as in-state and in-region referrals and the correlation between state referral volume and in-state referrals. Additionally, we applied the conceptual framework of networks to create two measures to evaluate referrer–radiologist connections within each state: number of referrer relationships and referral density index (RDI). ResultsIn 2015, there was considerable variation in in-state referrals across states, and the correlation between state referral volume and in-state referrals was low (0.18). Across census regions, in-region referrals were high (84.3% in the South to 89.2% in the Midwest). Across states, the mean number of referrer relationships ranged from 7.0 in Utah to 25.0 in California, and the number of referrer relationships varied significantly within states. Radiology provider RDI also varied both within and across states, with mean radiology provider RDI spanning from 0.05 in Kansas to 0.25 in Hawaii. ConclusionsIn a nationwide description of geographic patterns in radiology referrals among Medicare beneficiaries, we demonstrate wide variation in radiology referral patterns and utilized network methods to develop two measures that can be used in the future to monitor shifts in referral patterns.