Citations

Problem definition: Coordinated care network (CCN) is a burgeoning paradigm where patients’ diagnosis and treatment plans are developed based on collaboration between multiple, colocated medical specialties to holistically address patients’ health needs. A primary performance metric for CCNs is how quickly patients can complete their itinerary of appointments at multiple medical services in the network. Rapid completion is critical to care delivery but also presents a major operational challenge. Because information about a patient’s condition and treatment options evolves over the course of the itinerary, care paths are not known a priori. Thus, appointments (except for the first one) cannot be reserved in advance, which may result in significant delays if capacity is not allocated properly. Methodology/results: We study capacity allocation for the patient’s first (root) appointment as the primary operational lever to achieve rapid itinerary completion in CCNs. We develop a novel queueing-based analytical framework to optimize this root appointment allocation, maximizing the proportion of patients completing care by prespecified deadlines. Our framework accounts for the complex interactions among all patients in the network through the blocking process, which contrasts with conventional siloed planning. We provide an exact characterization of the itinerary time and develop a mean-field approximation with convergence guarantees that permits tractable solutions for large-scale network problems. In a simulation case study of Mayo Clinic, our solution improves on-time completion from 60% under the current plan to more than 93%. Managerial implications: We demonstrate that root appointment allocation is a multifaceted problem and that ignoring any of those facets can lead to poor performance. Simultaneously accounting for all of these complexities makes manual template design or traditional optimization methods inadequate, highlighting the significance of our integrated approach. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0649 .

Background: Value is defined as the ratio of patient outcomes to the cost of care. One method to assess value is through patient-level value analysis (PLVA). To our knowledge, this tool has not previously been implemented in the setting of total hip arthroplasty (THA). The purposes of this study were to perform PLVA for a 1-year episode of care among patients undergoing primary THA and to identify characteristics that affect value in a metropolitan health-care system. Methods: The patient-reported outcome (PRO) measure database of the institution was queried for all primary THAs from 2018 to 2019. The PRO measure that was utilized was the Hip disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS-JR). Cost was evaluated with use of time-driven activity-based costing (TDABC) for a 1-year episode of care (consisting of the day of surgery, inpatient stay, discharge facility, postoperative appointments, and physical therapy). The primary outcome was the 1-year value quotient, or the ratio of the 1-year change in HOOS-JR to the cost of the episode of care (V HOOS ). The value quotient was compared among all included patients and evaluated for variables that may affect the overall value of the episode of care. Results: In total, 480 patients were included in the analysis. The mean improvement in the HOOS-JR was +34.9 ± 16.1 (95% confidence interval [CI]: 33.5, 36.3). The mean cost was $13,835 ± $3,471 (95% CI: $13,524, $14,146). The largest contributor to cost was implants (39.0%), followed by post-recovery care (i.e., inpatient stay and specialized nursing facilities; 24.1%). Change in the HOOS-JR was poorly correlated with the cost of care (r = −0.06; p = 0.19). THAs performed at an ambulatory surgery center (ASC) with discharge to home demonstrated higher value (V HOOS = 0.42) than THAs performed at a hospital with discharge to a rehabilitation facility (V HOOS = 0.15; analysis of variance F-test, p < 0.01). Conclusions: Our study found that PROs did not correlate with costs in the setting of primary THA. Implants were the largest cost driver. Surgical setting (an ASC versus a hospital) and discharge destination influenced value as well. PLVA is a value measurement tool that can be utilized to optimize components of the care delivery pathway. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Congenital cardiac anesthesiologists practice in a unique environment with high risk for morbidity and mortality. Quality metrics can be used to focus clinical initiatives on evidence-based care and provide a target for local quality improvement measures. However, there has been no comprehensive review on appropriate quality metrics for congenital cardiac anesthesia to date. Members of the Quality and Safety Committee for the Congenital Cardiac Anesthesia Society proposed 31 possible candidate topics for metrics. Using a scoping review strategy, 3649 abstracts were reviewed with 30 articles meeting final criteria. Of these, 5 candidate metrics were unanimously proposed for local collection and national benchmarking efforts: use of a structured handover in the intensive care unit, use of an infection prevention bundle, use of blood conservation strategies, early extubation of cardiopulmonary bypass cases, and cardiac arrest under the care of a cardiac anesthesiologist. Many metrics were excluded due to a lack of primary data and perceived complexity beyond the scope of cardiac anesthesia practice. There is a need to develop more primary data including linking process measures with outcomes, developing risk-stratification for our patients, and collecting national data for benchmarking purposes.

Background/Objectives: Our objective was to review published studies of the intestinal barrier and permeability, the deleterious effects of dietary components (particularly fat), the impact of altered intestinal permeability in disease models and human diseases, the role of the microbiome and epigenomics in control of barrier function, and the opportunities to restore normal barrier function with dietary interventions and products of the microbiota. Methods: We conducted a literature review including the following keywords alone or in combination: intestinal barrier, permeability, microbiome, epigenomics, diet, irritable bowel syndrome, inflammatory bowel disease, probiotics. Results: Intestinal permeability is modified by a diet including fat, which increases permeability, and nutrients such as fiber, glutamine, zinc, vitamin D, polyphenols, emulsifiers, and anthocyanins, which decrease permeability. There is significant interaction of the microbiome and barrier function, including the inflammatory of luminal/bacterial antigens, and anti-inflammatory effects of commensals or probiotics and their products, including short-chain fatty acids. Epigenomic modification of barrier functions are best illustrated by effects on junction proteins or inflammation. Detailed documentation of the protective effects of diet, probiotics, prebiotics, and microbiota is provided. Conclusion: intestinal permeability is a critical factor in protection against gastrointestinal diseases and is impacted by nutrients that preserve or heal and repair the barrier and nurture anti-inflammatory effects.

Background: Immune checkpoint inhibitors (ICIs) have been successfully used in the previous decade for the treatment of a variety of malignancies. Adverse events (AEs) can cause many symptoms, most notably cardiac. We analyzed the frequency of these adverse events, comparing pembrolizumab and other ICIs. Methods: Using the Food and Drug Administration (FDA) adverse event reporting database (FAERS), we searched for all adverse events of interest reported for every ICI included in this study. After obtaining the data, we conducted a disproportionality analysis using the reporting odds ratio (ROR) and the information component (IC). Results: A total of 6719 ICI-related cardiac adverse events of interest were reported in the database. Serious outcomes were reported in 100% of the cases, with 34.3% of the cases ending fatally. Compared with all other medications in the database, pembrolizumab use was more frequently associated with myocarditis, pericardial disease, heart failure, and atrial fibrillation. No difference was found in cardiotoxicity between different ICIs. Conclusions: Although infrequent, cardiac AEs in pembrolizumab use are associated with serious outcomes and high mortality. Prospective studies are needed to further research the connection between ICI use and cardiotoxicity.

Background: Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate. Methods: The challenge is that it is often difficult for radiologists reading prostate-specific imaging such as magnetic resonance images (MRIs) to differentiate malignant lymph nodes from non-malignant ones. An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. Earlier research focused on implementing texture algorithms to extract imaging features used in classification models. More recently, researchers began studying the use of deep learning for both stand-alone feature extraction and end-to-end classification tasks. In order to tackle the challenges inherent in small datasets, this study was designed as a scalable hybrid framework utilizing pre-trained ResNet-18, a deep learning model, to extract features that were subsequently fed into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer. For comparison, two texture algorithms were implemented, namely the gray-level co-occurrence matrix (GLCM) and Gabor. Results: Using an institutional prostate lymph node dataset (42 positives, 84 negatives), the proposed framework achieved an accuracy of 76.19%, a sensitivity of 79.76%, and a specificity of 69.05%. Using GLCM features, the classification achieved an accuracy of 61.90%, a sensitivity of 74.07%, and a specificity of 42.86%. Using Gabor features, the classification achieved an accuracy of 65.08%, a sensitivity of 73.47%, and a specificity of 52.50%. Conclusions: Our results demonstrate that a hybrid approach, i.e., using a pre-trainined deep learning model for feature extraction, followed by a machine learning classifier, is a viable solution. This hybrid approach is especially useful in medical-imaging-based applications with small datasets.

The mature splenic B‐cell lymphomas are an enigmatic group of lymphoid neoplasms that have long caused significant difficulty for the practicing pathologist due to overlapping diagnostic features among entities and the decreasing availability of splenic tissue for assessment. While some entities have highly characteristic and specific clinicopathological features (e.g. hairy cell leukaemia), others are substantially more difficult to recognise (e.g. splenic diffuse red pulp lymphoma). At the same time, classification systems have been evolving, resulting in multiple changes to the boundaries among these entities and even the existence of some entities in their own right. Moreover, unbiased multi‐omic interrogation (whole genome/transcriptome sequencing, methylome) of the splenic B‐cell lymphomas over the past decade has given us significant insights into the underling biology of these neoplasms. We present a clinicopathological perspective on the historical, current and future state of the diagnosis and classification of splenic B‐cell lymphomas integrating multi‐omic data and highlighting areas of focus for the field in order to continue to strive to improve patient outcomes through accurate diagnosis.

Here we review the ‘what and how’ of molecular techniques used in the context of haematopathological diagnostics of both lymphoid and myeloid neoplasms. Keeping in mind that the required resources for molecular testing are not universally available, we will not only discuss novel and emerging techniques that allow more high‐throughput and sophisticated analyses of lymphoid and myeloid neoplasms, but also the more classical, low‐cost alternatives and even some workarounds for molecular testing approaches. In this review we also address other key aspects around molecular techniques for haematopatholgy diagnostics, including preanalytics, data interpretation, and data management, bioinformatics, and interlaboratory precision and performance evaluation.

ABSTRACTBackgroundPatients with ulcerative colitis (UC) who are hospitalised for acute severe flares represent a high‐risk orphan population.AimTo provide guidance for clinical trial design methodology in these patients.MethodsWe created a multi‐centre consortium to design and conduct a clinical trial for a novel therapeutic intervention (hyperbaric oxygen therapy) in patients with UC hospitalised for moderate–severe flares. During planning, we identified and addressed specific gaps for inclusion/exclusion criteria; disease activity measures; pragmatic trial design considerations within care pathways for hospitalised patients; standardisation of care delivery; primary and secondary outcomes; and sample size and statistical analysis approaches.ResultsThe Truelove‐Witt criteria should not be used in isolation. Endoscopy is critical for defining eligible populations. Patient‐reported outcomes should include rectal bleeding and stool frequency, with secondary measurement of urgency and nocturnal bowel movements. Trial design needs to be tailored to care pathways, with early intervention focused on replacing and/or optimising responsiveness to steroids and later interventions focused on testing novel rescue agents or strategies. The PRECIS‐2 framework offers a means of tailoring to local populations. We provide standardisation of baseline testing, venous thromboprophylaxis, steroid dosing, discharge criteria and post‐discharge follow‐up to avoid confounding by usual care variability. Statistical considerations are provided given the small clinical trial nature of this population.ConclusionWe provide an outline for framework decisions made for the hyperbaric oxygen trial in patients hospitalised for UC flares. Future research should focus on the remaining gaps identified.