Decision for Surgery and Referral

The purpose of this pilot study was to identify factors considered by GPs when making mental health referral decisions and to explore referral strategies and related individual differences between GPs. In structured interviews with nine GPs from two surgeries, brief case scenarios were presented. The GPs were asked what treatment or referral option they would recommend in each case, what the main reasons for their choices were and what outcomes they would expect. A short questionnaire was also used. The interviews were transcribed and a content analysis was carried out. Evidence for a variety of patient-related, service-related and doctor-related factors influencing referral decisions was found and compared with previous research findings. In line with results from earlier studies, individual differences between GPs in terms of referral rates, strategies used, confidence and interest in dealing with mental health problems were found. Implications of the findings and of previous research for referrers, service providers and for therapy were discussed. As the present study has obvious limitations, suggestions for future research were made.

Despite a large volume of research, understanding the influences on general practitioner referral behaviour remains limited. This paper argues for the value of an approach to this area based on the analysis of individual referral decisions, rather than aggregated referral statistics. A framework for analysing referral decisions is described, developed from a study that collected accounts of 167 referral decisions from 28 general practitioners. The framework enables referral decisions to be examined at three levels of analysis of increasing generality. It also encompasses factors that influence general practitioners against making a referral as well as those that influence them towards referral, as it was found that both types of influence can be discerned in most cases. The types of study in which the framework would be a useful analytical tool are discussed.

Community-based optometrists, a major provider of primary eye care in the United Kingdom, are the main source of referrals to hospital eye services. The widespread introduction of optical coherence tomography devices in community practices provides community-based optometrists with an opportunity to identify a broader range of treatable diseases. Standard referral pathways do not effectively filter unnecessary referrals, with misclassification of urgency, and erroneous diagnoses. To assess the effectiveness of a teleophthalmology referral pathway between community-based optometrists and hospital eye services for retinal diseases. To measure the accuracy of an artificial intelligence decision support system for diagnosis and referral management of retinal disease. A multicentre, superiority cluster randomised controlled trial to assess the effectiveness of a teleophthalmology referral pathway. A prospective, observational diagnostic accuracy study to measure the performance of artificial intelligence decision support system. A comprehensive economic evaluation was conducted. United Kingdom-based community optometry practices with an optical coherence tomography device and hospital eye services. Adults requiring referral for retinal disease at the opinion of the community-based optometrists. Community optometry practices were randomised 1 : 1 to standard care or teleophthalmology. Referrals sent via the teleophthalmology platform were remotely reviewed by human experts based at the corresponding hospital eye services. A referral decision was provided within 48 hours. Suitable optical coherence tomography scans were solely processed by artificial intelligence decision support system (the 'Octane' model). Cluster randomised controlled trial's primary outcome was the proportion of false-positive referrals (not required or not urgent) per arm in overall participants and in referred-only participants against an independent reference standard. Secondary outcomes included the proportion of wrong diagnosis, wrong referral urgency, false-negative referrals, safely triaged referrals for rare diseases, time from referral to consultation and treatment and cost-effectiveness of teleophthalmology. Primary outcome for the artificial intelligence study was the sensitivity and specificity of artificial intelligence referral decisions against the reference standard. Teleophthalmology significantly reduces the proportion of false-positive urgent referrals by 59% compared to standard care in referred participants. Due to the observed low event rate for false positive referrals, teleophthalmology's role for reducing false positives overall was inconclusive. No significant difference between arms for safety of referral decisions (false negatives) was found. After accounting for external factors, the time to consultation demonstrated both clinically and statistically significant benefits for the teleophthalmology arm. The time to treatment showed a clinically significant benefit. Of 396 recruited participants, the Octane artificial intelligence model processed images contributed by 204 participants (51.5%). For referral decisions, the model showed comparable sensitivity and specificity against its own preset referral rules (rule-based reference standard) (post hoc analysis), but it showed inferior sensitivity and specificity when compared to human expert assessors making these referral decisions (clinical reference standard) (primary AI analysis). The artificial intelligence model presented challenges relating to its generalisability in a real-world evaluation context. Technical limitations in optometry practices, lack of ethnicity data. Asynchronous teleophthalmology reduces the number of unnecessary urgent referrals, the main drivers of increasing hospital capacity pressures, provides more appropriate referral-to-treatment times and is more cost-effective compared to standard care. The Octane artificial intelligence model could not process images from 48.5% of study participants. Compared to hospital-based experts for referral decisions, Octane was less accurate at making routine and urgent referral decisions and of similar accuracy to community optometrists. Applied health research, human-artificial intelligence interaction and artificial intelligence clinical trial design. This trial is registered as ISRCTN18106677. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127773) and is published in full in Health Technology Assessment; Vol. 29, No. 69. See the NIHR Funding and Awards website for further award information.

BackgroundGenerally, there is a higher prevalence of mental health problems among people with a migration background, hereinafter “migrant patients”, than among their native counterparts. They are also more likely to...

Physician experts hired and prepared by the litigants provide most information on standard of care for medical malpractice cases. Since this information may not be objective or accurate, we examined the feasibility and potential value of surveying community physicians to assess standard of care. Seven physician surveys of mutually exclusive groups of randomly selected physicians. Iowa. Community and academic primary care physicians and relevant specialists. Included in each survey was a case vignette of a primary care malpractice case and key quotes from medical experts on each side of the case. Surveyed physicians were asked whether the patient should have been referred to a specialist for additional evaluation. The 7 case vignettes included 3 closed medical malpractice cases, 3 modifications of these cases, and 1 active case. Sixty-three percent of 350 community primary care physicians and 51% of 216 community specialists completed the questionnaire. For 3 closed cases, 47%, 78%, and 88% of primary care physician respondents reported that they would have made a different referral decision than the defendant. Referral percentages were minimally affected by modifying patient outcome but substantially changed by modifying patient presentation. Most physicians, even those whose referral decisions were unusual, assumed that other physicians would make similar referral decisions. For each case, at least 65% of the primary care physicians disagreed with the testimony of one of the expert witnesses. In the active case, the response rate was high (71%), and the respondents did not withhold criticism of the defendant doctor. Randomly selected peer physicians are willing to participate in surveys of medical malpractice cases. The surveys can be used to construct the distribution of physician self-reported practice relevant to a particular malpractice case. This distribution may provide more information about customary practice or standard of care than the opinion of a single physician expert.

BackgroundPoint-of-care ultrasound (POCUS) is recognized as a key imaging modality to bridge the diagnostic imaging gap in Low- and Middle-Income Countries (LMICs). POCUS use has been shown to impact patient management decisions including referral for specialist care. This study explored the impact of POCUS use on referral decisions among trained healthcare providers working in primary rural and peri-urban health facilities in Kenya.MethodsA concurrent mixed methods approach was used, including a locally developed survey (N = 38) and semi-structured interviews of POCUS trained healthcare providers (N = 12). Data from the survey was descriptively analyzed and interviews were evaluated through the framework matrix method.ResultsSurvey results of in-facility access to Xray, Ultrasonography, CT scan and MRI were 49%, 33%, 3% and 0% respectively. Only 54% of the facilities where trainees worked had the capacity to perform cesarean sections, and 38% could perform general surgery. Through a combined inductive and deductive evaluation of interview data, we found that the emerging themes could be organized through the framework of the six domains of healthcare quality as described by the Institute of Medicine: Providers reported that POCUS use allowed them to make referral decisions which were timely, safe, effective, efficient, equitable and patient-centered. Challenges included machine breakdown, poor image quality, practice isolation, lack of institutional support and insufficient feedback on the condition of patients after referral.ConclusionThis study highlighted that in the setting of limited imaging and surgical capacity, POCUS use by trained providers in Kenyan primary health facilities has the potential to improve the patient referral process and to promote key dimensions of healthcare quality. Therefore, there is a need to expand POCUS training programs and to develop context specific POCUS referral algorithms.

Patients look to their family physicians (FPs) for credible information and guidance in making informed choices about genetic testing. FPs are challenged by lack of knowledge and the rapid pace of genetic discovery. There is an urgent need for effective interventions to facilitate integration of genetics into family medicine. To determine if a multi-faceted knowledge translation intervention would improve skills, including referral decisions, confidence in core genetics competencies and knowledge. Randomized controlled trial involving FPs in four communities in Ontario, Canada (two urban and two rural). The intervention consisted of an interactive educational workshop, portfolio of practical clinical genetics tools and knowledge service called Gene Messenger. Outcome measures included appropriate genetics referral decisions in response to 10 breast cancer scenarios, decisional difficulty, self-reported confidence in 11 genetics core competencies, 3 knowledge questions and evaluation of intervention components 6 months afterwards. Among the one hundred and twenty-five FPs randomized, 80 (64%) completed the study (33 control, 47 intervention). Intervention FPs had significantly higher appropriate referral decision scores [6.4/10 [95% confidence interval (CI) 5.8-6.9] control, 7.8/10 (95% CI 7.4-8.2) intervention] and overall self-reported confidence on core genetics competencies [37.9/55 (95% CI 35.1-40.7) control, 47.0/55 (95% CI 44.9-49.2) intervention]. Over 90% of FPs wanted to continue receiving Gene Messengers and would recommend them to colleagues. No significant differences were found in decisional difficulty or knowledge. This study demonstrated that a complex educational intervention was able to significantly improve practice intent for clinical genetics scenarios found in primary care, as well as confidence in genetics skills.

Pay-for-Performance and Selective Referral in Long-Term Care

BackgroundOccupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database.ObjectiveOur key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems.MethodsWe extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process.ResultsWhen analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category “Diseases of the Musculoskeletal System” are least often denied whereas those with diagnoses within the “Mental Illness” category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas “medicaid” holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts.ConclusionsWhile many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients’ needs.

Chronic wounds affect 8.5 million Americans, especially the elderly and patients with diabetes. As regular care is critical for proper healing, many patients receive care in their homes from visiting nurses and caregivers with variable wound expertise. Problematic, non-healing wounds should be referred to experts in wound clinics to avoid adverse outcomes such as limb amputations. Unfortunately, due to the lack of wound expertise, referral decisions made in non-clinical settings can be erroneous, delayed or unnecessary. This paper proposes the Deep Multimodal Wound Assessment Tool (DM-WAT), a novel machine learning framework to support visiting nurses by recommending wound referral decisions from smartphone-captured wound images and associated clinical notes. DM-WAT extracts visual features from wound images using DeiT-Base-Distilled, a Vision Transformer (ViT) architecture. Distillation-based training facilitates representation learning and knowledge transfer from a larger teacher model to DeiT-Base, enabling robust performance on our small wound image dataset of 205 wound images. DM-WAT extracts text features from clinical notes using DeBERTa-base, which comprehends context by disentangling content and position information from clinical notes. Visual and text features are combined using an intermediate fusion approach. To overcome the challenges posed by a small and imbalanced dataset, DM-WAT integrates image and text augmentation along with transfer learning via pre-trained feature extractors to achieve high performance. In rigorous evaluation, DM-WAT achieved an accuracy of 77% pm ~3% and an F1 score of 70% pm ~2%, outperforming the prior state of the art and all baseline single-modality and multimodal approaches. Additionally, to interpret DM-WAT’s recommendations, the Score-CAM and Captum interpretation algorithms provided insights into the specific parts of the image and text inputs that the model focused on during decision-making.

Anticipating post-acute needs and making referrals for follow-up care are crucial for quality discharge planning. Several studies reveal serious gaps in quality for these common and important processes. Compare experts' and hospital clinicians' discharge referral decisions for the same elderly patients, and examine 12-week outcomes for patients whom experts identified for referral but were not referred by hospital clinicians to those who experts and clinicians agreed to refer or not. A comparative, descriptive analysis of referral decisions for 355 elderly patients and their 12-week outcomes. Older adults hospitalized with common medical surgical conditions from 6 hospitals in Northeast United States. Experts identified 183 additional patients for post-acute referral. Experts were 18 times more likely to refer patients than hospital clinicians. Clinicians referred patients with obvious needs for post acute care, and experts with better information and time to consider the perspectives of other disciplines identified additional patients. These patients demonstrated a rehospitalization rate of 23%, not significantly different than those referred (20%), but nearly 5 times higher than patients not identified for referral. Similar to those who received referrals, they were significantly more likely to rate their health fair or poor and their functional status remained significantly worse at 12 weeks than the no referral group. Experts identified additional patients in need of post-acute care. Negative outcomes reflect the costly results of this gap in quality and support the need to improve data presentation and referral decision making.

Eliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges. Describe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts' referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline. De-identified case studies, developed from electronic health records (EHR), contained a comprehensive description of 1,496 acute care inpatients. In teams of three, physicians, nurses, social workers, and physical therapists reviewed case studies and assessed the need for post-acute care referrals; Delphi rounds followed when team members did not agree. Generalized estimating equations (GEEs) compared experts' decisions by discipline, region of the country and to the decisions made by study hospital clinicians, adjusting for the repeated observations from each expert and case. Frequencies determined the most common case characteristics chosen as important by the experts. The experts recommended referral for 80% of the cases; the actual discharge disposition of the patients showed referrals for 67%. Experts from the Northeast and Midwest referred 5% more cases than experts from the West. Physicians and nurses referred patients at similar rates while both referred more often than social workers. Differences by discipline were seen in the factors identified as important to the decision. The method for eliciting expert knowledge enabled national dispersed expert clinicians to anonymously review case summaries and make decisions about post-acute care referrals. Having time and a comprehensive case summary may have assisted experts to identify more patients in need of post-acute care than the hospital clinicians. The methodology produced the data needed to develop an expert decision support system for discharge planning.

To investigate variation in optometric referral decision-making and the influence of experience and continuing education and training (CET). To gain insight into unwarranted variation in referral activity in the United Kingdom (UK): (1) triage data were audited to investigate source of referral, provisional diagnosis, and outcome; (2) an online system was developed to present two sets of 10 vignettes, designed to avoid prompting answers. Participating optometrists completed 10 pre-CET vignettes, recording their tests and management decisions. The main group of participants chose whatever CET they wished over a 6-month period and then completed another 10 post-CET vignettes. A second group of newly-qualified optometrists completed the vignettes before and after a CET course intervention, followed by a third group of pre-registered optometrists with an intervention of 6-months experience of their pre-registration year. The audit identified 1951 optometric referrals and 158 optometrists (211 referrals were from general medical practitioners), with 122 of the 158 optometrists making fewer than ten referrals. Two newly-qualified optometrists generated 12.5% of the total referrals in the audit (N = 2162). Many suspect glaucoma referrals were based on a single suspect measurement resulting in a high discharge rate after community review, as did referrals for certain fundus-related appearances for which no treatment was indicated. The intervention of gaining CET points appeared to have no significant impact (p = 0.37) on referral decision-making, although this part of the study was underpowered. Self-selection bias was confirmed in the main group. When the main group and newly-qualified practitioners were compared, the number of referrals was negatively associated with time since qualification (p = 0.005). When all 20 referral decisions were compared, all optometrists referring more than 10 vignette patients came from a group of newly-qualified practitioners up to 2 years post-qualification. Pre-registered optometrists generally referred more appropriately than newly-qualified. Upon qualification, there was a significant increase in the number of sight tests undertaken per day (p = <0.0005). Gaining CET points alone is unlikely to significantly improve referral decision-making. Mentoring and targeted CET for the newly-qualified up to 2 years post-qualification should be considered. Ophthalmology replies to the referring newly-qualified optometrist are vital for moderating future referrals and developing clinical confidence.

EVALUATION OF THE PEDIATRIC PATIENT WITH A CARDIAC MURMUR

Implementation of indication restrictions made by European regulatory action for antibiotics - amoxicillin.