Optometrist to Operation: Patients' Perspective on the Innovative Use of Quick Response (QR) Code-Linked Patient Information Video on Cataract Surgery.

Background This study aimed to explore patients' subjective utility for a patient information video (PIV) on cataract surgery and analyse the use of a quick response (QR) code as a mode of delivery of the PIV. Methods A total of 500 patients were included in the study. All patients were given a paper form of the patient information leaflet (PIL) as the standard of care (SoC) in addition to a digital QR code to access a supplementary PIV. The questionnaire explored the patients' understanding of cataracts, the risks and benefits of cataract surgery, and their experience accessing and using the QR code. Results A total of 321 responses were collected (64% response rate). The majority were female (55%), with a mean age of 75 years. Among these, 69% (n = 222/321) managed to watch the video. A statistically significant association was reported between prior experience with QR codes and the ability to watch the video (p<0.001). The most common reason for not watching the video was no device (n=54/99, 54%). Ninety-one percent of the patients who watched the video expressed a desire for additional healthcare videos in the future. Overall, most patients (n=170/222, 76%) acknowledged that the PIV was easier to understand when compared to paper-format information, with a minority of patients reporting the PIV missing information that was covered on paper (n=2/222). Conclusions The provision of PIV supplementation as a part of the cataract surgery referral pathway is an innovative method of providing patient information in a more interactive way, with positive feedback from patients.

Neurotrauma Surveillance in National Registries of Low- and Middle-Income Countries: A Scoping Review and Comparative Analysis of Data Dictionaries.

BACKGROUND: Injury is a major global health problem, causing >5 800 000 deaths annually and widespread disability largely attributable to neurotrauma. 89% of trauma deaths occur in low- and middle-income countries (LMICs), however data on neurotrauma epidemiology in LMICs is lacking. In order to support neurotrauma surveillance efforts, we present a review and analysis of data dictionaries from national registries in LMICs. METHODS: We performed a scoping review to identify existing national trauma registries for all LMICs. Inclusion/ exclusion criteria included articles published since 1991 describing national registry neurotrauma data capture methods in LMICs. Data sources included PubMed and Google Scholar using the terms "trauma/neurotrauma registry" and country name. Resulting registries were analyzed for neurotrauma-specific data dictionaries. These findings were augmented by data from direct contact of neurotrauma organizations, health ministries, and key informants from a convenience sample. These data were then compared to the World Health Organization (WHO) minimum dataset for injury (MDI) from the international registry for trauma and emergency care (IRTEC). RESULTS: We identified 15 LMICs with 16 total national trauma registries tracking neurotrauma-specific data elements. Among these, Cameroon had the highest concordance with the MDI, followed by Colombia, Iran, Myanmar and Thailand. The MDI elements least often found in the data dictionaries included helmet use, and alcohol level. Data dictionaries differed significantly among LMICs. Common elements included Glasgow Coma Score, mechanism of injury, anatomical site of injury and injury severity scores. Limitations included low response rate in direct contact methods. CONCLUSION: Significant heterogeneity was observed between the neurotrauma data dictionaries, as well as a spectrum of concordance or discordance with the MDI. Findings offer a contextually relevant menu of possible neurotrauma data elements that LMICs can consider tracking nationally to enhance neurotrauma surveillance and care systems. Standardization of nationwide neurotrauma data collection can facilitate international comparisons and bidirectional learning among healthcare governments.

International practice variation in perioperative laboratory testing in glioblastoma patients-a retrospective cohort study.

PURPOSE: Although standard-of-care has been defined for the treatment of glioblastoma patients, substantial practice variation exists in the day-to-day clinical management. This study aims to compare the use of laboratory tests in the perioperative care of glioblastoma patients between two tertiary academic centers-Brigham and Women's Hospital (BWH), Boston, USA, and University Medical Center Utrecht (UMCU), Utrecht, the Netherlands. METHODS: All glioblastoma patients treated according to standard-of-care between 2005 and 2013 were included. We compared the number of blood drawings and laboratory tests performed during the 70-day perioperative period using a Poisson regression model, as well as the estimated laboratory costs per patient. Additionally, we compared the likelihood of an abnormal test result using a generalized linear mixed effects model. RESULTS: After correction for age, sex, IDH1 status, postoperative KPS score, length of stay, and survival status, the number of blood drawings and laboratory tests during the perioperative period were 3.7-fold (p < 0.001) and 4.7-fold (p < 0.001) higher, respectively, in BWH compared to UMCU patients. The estimated median laboratory costs per patient were 82 euros in UMCU and 256 euros in BWH. Furthermore, the likelihood of an abnormal test result was lower in BWH (odds ratio [OR] 0.75, p < 0.001), except when the prior test result was abnormal as well (OR 2.09, p < 0.001). CONCLUSIONS: Our results suggest a substantially lower clinical threshold for ordering laboratory tests in BWH compared to UMCU. Further investigating the clinical consequences of laboratory testing could identify over and underuse, decrease healthcare costs, and reduce unnecessary discomfort that patients are exposed to.

The low utility of routine cranial imaging after pediatric shunt revision.

OBJECTIVE: Postoperative routine imaging is common after pediatric ventricular shunt revision, but the benefit of scanning in the absence of symptoms is questionable. In this study, the authors aimed to assess how often routine scanning results in a change in clinical management after shunt revision. METHODS: The records of a large, tertiary pediatric hospital were retrospectively reviewed for all consecutive cases of pediatric shunt revision between July 2013 and July 2018. Postoperative imaging was classified as routine (i.e., in the absence of symptoms, complications, or other direct indications) or nonroutine. Reinterventions within 30 days were assessed in these groups. RESULTS: Of 387 included shunt revisions performed in 232 patients, postoperative imaging was performed in 297 (77%), which was routine in 244 (63%) and nonroutine in 53 (14%). Ninety revisions (23%) underwent any shunt-related procedure after postoperative imaging, including shunt reprogramming (n = 35, 9%), shunt tap (n = 10, 3%), and a return to the operating room (OR; n = 58, 15%). Of the 244 cases receiving routine imaging, 241 did not undergo a change in clinical management solely based on routine imaging findings. The remaining 3 cases returned to the OR, accounting for 0.8% (95% CI 0.0%-1.7%) of all cases or 1.2% (95% CI 0.0%-2.6%) of cases that received routine imaging. Furthermore, 27 of 244 patients in this group returned to the OR for other reasons, namely complications (n = 12) or recurrent symptoms (n = 15); all arose after initial routine imaging. CONCLUSIONS: The authors found a low yield to routine imaging after pediatric shunt revision, with only 0.8% of cases undergoing a change in management based on routine imaging findings without corresponding clinical findings. Moreover, routine imaging without abnormal findings was no guarantee of an uneventful postoperative course. Clinical monitoring can be considered as an alternative in asymptomatic, uncomplicated patients.

How much is enough? Exploring the dose-response relationship between cash transfers and surgical utilization in a resource-poor setting.

OBJECTIVE: Cash transfers are a common intervention to incentivize salutary behavior in resource-constrained settings. Many cash transfer studies do not, however, account for the effect of the size of the cash transfer in design or analysis. A randomized, controlled trial of a cash-transfer intervention is planned to incentivize appropriate surgical utilization in Guinea. The aim of the current study is to determine the size of that cash transfer so as to maximize compliance while minimizing cost. METHODS: Data were collected from nine coastal Guinean hospitals on their surgical capabilities and the cost of receiving surgery. These data were combined with publicly available data about the general Guinean population to create an agent-based model predicting surgical utilization. The model was validated to the available literature on surgical utilization. Cash transfer sizes from 0 to 1,000,000 Guinean francs were evaluated, with surgical compliance as the primary outcome. RESULTS: Compliance with scheduled surgery increases as the size of a cash transfer increases. This increase is asymptotic, with a leveling in utilization occurring when the cash transfer pays for all the costs associated with surgical care. Below that cash transfer size, no other optima are found. Once a cash transfer completely covers the costs of surgery, other barriers to care such as distance and hospital quality dominate. CONCLUSION: Cash transfers to incentivize health-promoting behavior appear to be dose-dependent. Maximal impact is likely only to occur when full patient costs are eliminated. These findings should be incorporated in the design of future cash transfer studies.

Surgathon: a new model for creating a surgical innovation ecosystem in low-resource settings.

Innovation ecosystems and emerging technologies can potentially accelerate the access to safe, affordable surgical care in low-resource settings. There is a need to develop localised innovation ecosystems that can establish an initial culture and catalyse the creation, adoption and diffusion of innovation. The surgathon model outlines one approach to seeding surgical innovation ecosystems. International academic institutions collaborated on six global surgery, innovation and ethics-themed hackathons ('surgathons') across India and Rwanda between 2016 and 2019. Over 1598 local multidisciplinary students participated, learning about challenges in the delivery of surgical care and ideating solutions that could leverage appropriate technology and resources for impact. Pursuing student ideas and evaluating their implementation past the surgathons continues to be an active effort. Surgathons have unfolded in different permutations based on local faculty, institution and health system context. The surgathon model is a novel method of priority setting challenges in global surgery and utilises locally driven expertise and innovation capacity to derive ethical solutions. The model offers a path for low-resource setting students and faculty to learn, advocate and innovate for improved surgical care.

Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma.

PURPOSE: The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others. MATERIALS AND METHODS: Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports. RESULTS: In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (ρ = 0.904; P < .001) but not with the frequency distribution of the variables of interest (ρ = 0.179; P = .52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents. CONCLUSION: This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining.

Globalization of national surgical, obstetric and anesthesia plans: the critical link between health policy and action in global surgery.

Efforts from the developed world to improve surgical, anesthesia and obstetric care in low- and middle-income countries have evolved from a primarily volunteer mission trip model to a sustainable health system strengthening approach as private and public stakeholders recognize the enormous health toll and financial burden of surgical disease. The National Surgical, Obstetric and Anesthesia Plan (NSOAP) has been developed as a policy strategy for countries to address, in part, the health burden of diseases amenable to surgical care, but these plans have not developed in isolation. The NSOAP has become a phenomenon of globalization as a broad range of partners - individuals and institutions - help in both NSOAP formulation, implementation and financing. As the nexus between policy and action in the field of global surgery, the NSOAP reflects a special commitment by state actors to make progress on global goals such as Universal Health Coverage and the United Nations Sustainable Development Goals. This requires a continued global commitment involving genuine partnerships that embrace the collective strengths of both national and global actors to deliver sustained, safe and affordable high-quality surgical care for all poor, rural and marginalized people.

Application of Machine Learning to Prediction of Surgical Site Infection.

Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducted in rural Rwanda for the purpose of predicting infection in Cesarean section wounds, which is a leading cause of maternal mortality. Questionnaire and image data were collected from 572 mothers approximately 10 days after surgery at a district hospital. Of the 572 women, 61 surgical wounds were determined to be infected as determined by a physical exam conducted by trained doctors. Machine learning models, logistic regression and Support Vector Machines (SVM), were developed independently for the questionnaire data and the image data. For the questionnaire data, the best results were achieved by the Logistic regression model, with an AUC Accuracy = 96.50% (93.0%-99.3%), Sensitivity = 0.71 (0.33 - 0.92), and Specificity = 0.99 (0.98 - 1.00). The features with the greatest predictive value were the presence of malcolored drainage from the wound and the presence of an odorous discharge from the wound. Using the image data alone, the SVM model performed best, with an AUC Accuracy = 99.5% (99.2%-100%), Sensitivity = 0.99 (0.99 - 1.00), and Specificity = 0.99 (0.99 - 1.00). Combining both questionnaire data and image data, the SVM model achieved an AUC Accuracy = 99.9% (99.7%-100%), Sensitivity = 0.99 (0.99 -1.00), and Specificity = 0.99 (0.99 - 1.00). Results from this initial study are very encouraging and demonstrate that good objective prediction of surgical infection for women in rural Rwanda is feasible using machine learning, even when using image data alone.

Essential Neurosurgical Workforce Needed to Address Neurotrauma in Low- and Middle-Income Countries.

BACKGROUND: Among all trauma-related injuries globally, traumatic brain injury (TBI) and traumatic spine injury (TSI) account for the largest proportion of cases. Where previously data was lacking, recent efforts have been initiated to better quantify the extent of neurotrauma in low- and middle-income countries (LMICs). This information is vital to understand the current neurosurgical deficit so that resources and efforts can be focused on where they are needed most. The purpose of this study is to determine the minimum number of neurosurgeons to address the neurotrauma demand in LMICs and evaluate current evidence to support facility needs so that policy-based recommendations can be made to prioritize development initiatives to scale up neurosurgical services. METHODS: Using existing data regarding the incidence of TBI and TSI in LMICs and current neurosurgical workforce and estimates of case load capacity, the minimum number of neurosurgeons needed to address neurotrauma per population was calculated. Evidence was gathered regarding necessary hospital facilities and disbursement patterns based on time needed to intervene effectively for neurotrauma. RESULTS: There are 4,897,139 total operative cases of TBI and TSI combined in LMICs annually. At minimum, there needs to be 1 neurosurgeon only performing neurotrauma cases per approximately 212,000 people. Evidence suggests that patients should be within 4 hours of a neurosurgical facility at the very least. CONCLUSIONS: The development of neurotrauma systems is essential to address the large burden of neurotrauma in LMICs. The minimum requirements for neurosurgical workforce is 1 neurotrauma surgeon per 212,000 people.