Patient Perception of Physician Reimbursement by Medicare for Artificial Urinary Sphincter Placement.

INTRODUCTION: Patient perceptions of physician reimbursement commonly differ from actual reimbursement. This study aims to improve health care cost transparency and trust between patients, physicians, and the health care system by evaluating patient perceptions of Medicare reimbursement for artificial urinary sphincter (AUS) placement. METHODS: We identified patients who underwent AUS placement at a single institution from 2014 to 2023. After obtaining informed consent, we administered a telephone survey to ask patients about their perceptions of Medicare reimbursement for AUS surgery and the amount they felt the physician should be compensated. RESULTS: Sixty-four patients were enrolled and completed the survey. On average, patients estimated Medicare physician reimbursement to be $18,920, 25 times the actual average procedure reimbursement. Once informed that the actual amount was $757.52, 97% of respondents felt that the reimbursement was "somewhat lower" (13%) or "much lower" (84%) than what they considered fair. The average amount that patients felt the physician should be paid was $8,844, 12 times the actual average procedure reimbursement. Fifty-four percent of patients estimated their physician's reimbursement to be higher than what they later reported as being "fair," representing a presurvey belief that their physician was overpaid. CONCLUSIONS: Patient perceptions of physician reimbursement for AUS are vastly different than the actual amount paid. The discordance between patient perception and actual reimbursement could impact how patients view health care costs and the relationship with their provider.

Expanding the role of justice in secondary research using digital psychological data.

Secondary analysis of digital psychological data (DPD) is an increasingly popular method for behavioral health research. Under current practices, secondary research does not require human subjects research review so long as data are de-identified. We argue that this standard approach to the ethics of secondary research (i.e., de-identification) does not address a range of ethical risks and that greater emphasis should be placed on the ethical principle of justice. We outline the inadequacy of an individually focused research ethic for DPD and describe unaddressed "social risks" generated by secondary research of DPD. These risks exist in the "circumstances of justice": that is, a circumstance where individuals must cooperate to create a public good (e.g., research knowledge), and where it is impractical to individually exempt individuals. This requires researchers to emphasize the just allocation of benefits and burdens against a background of social cooperation. We explore six considerations for researchers who wish to conduct research with DPD without explicit consent: (a) create socially valuable knowledge, (b) fairly share the benefits and burdens of research, (c) be transparent about data use, (d) create mechanisms for withdrawal of data, (e) ensure that stakeholders can provide input into the design and implementation of the research, and (f) responsibly report results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome.

Importance: Many clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered studies. Objective: To evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention. Design, Setting, and Participants: This diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study included hospitalized patients aged 55 years or older with serious illness enrolled between April 23, 2020, and March 26, 2021, in a pragmatic randomized clinical trial of a communication intervention in a multihospital US academic health system. Main Outcomes and Measures: Main outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-recall (PR) analyses and examined the effects of misclassification on power using mathematical substitution and Monte Carlo simulation. Results: A total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set identified patients with documented goals-of-care discussions with moderate accuracy (maximal F1 score, 0.82; area under the ROC curve, 0.924; area under the PR curve, 0.879). Manual abstraction of the outcome from the trial data set would require an estimated 2000 abstractor-hours and would power the trial to detect a risk difference of 5.4% (assuming 33.5% control-arm prevalence, 80% power, and 2-sided α = .05). Measuring the outcome by NLP alone would power the trial to detect a risk difference of 7.6%. Measuring the outcome by NLP-screened human abstraction would require 34.3 abstractor-hours to achieve estimated sensitivity of 92.6% and would power the trial to detect a risk difference of 5.7%. Monte Carlo simulations corroborated misclassification-adjusted power calculations. Conclusions and Relevance: In this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies using NLP would be beneficial.

Characteristics of Online Crowdfunding Campaigns for Urological Cancers in the United States.

INTRODUCTION: We examined the characteristics and financial outcomes of online crowdfunding campaigns for patients with major urological cancers in the U.S. METHODS: This cross-sectional study analyzed publicly available data from GoFundMe, the largest online medical crowdfunding service, via automated web scraping. Online campaigns from 2010 to 2018 with the following primary cancer types were included: kidney, prostate, bladder and testicular. Financial outcomes were compared using Kruskal-Wallis and Wilcoxon rank-sum tests. Multivariable analyses were utilized to identify predictors of campaign financial outcomes. RESULTS: Kidney cancers were the most frequent online campaign type (478), followed by prostate (379), bladder (202) and testicular (175) malignancies. Urological cancer campaign recipients frequently requested funding for medical expenses (71%) during active treatment (57%). After adjustment, testicular cancer and children's cancer campaigns generated more donations than other urological and adult cancer campaigns (p <0.05). Family and friend-authored campaigns generated more donations and average donation amounts than self-authored campaigns (p <0.05). Campaign narratives focused on disheartening circumstances received fewer donations than narratives focused on the recipient's high moral character or contributions to society (p <0.05), and unclear narratives received the smallest donation amounts (p <0.05). CONCLUSIONS: Urological cancer crowdfunding in the U.S. is primarily used to finance uncovered costs associated with medical care during active treatment. Crowdfunding financial outcomes are likely related to the campaign recipient's age, malignancy type, social network and primary appeal of the narrative. Urologists should be aware of trends in medical crowdfunding in order to better understand the financial burden this patient population faces.

Elective Percutaneous Coronary Intervention in Ambulatory Surgery Centers.

OBJECTIVES: The aim of this study was to explore characteristics and outcomes of patients undergoing elective percutaneous coronary intervention (PCI) in ambulatory surgery centers (ASCs). BACKGROUND: Little is known about patients who underwent ASC PCI before Medicare reimbursement was instituted in 2020. METHODS: Using commercial insurance claims from MarketScan, adults who underwent hospital outpatient department (HOPD) or ASC PCI for stable ischemic heart disease from 2007 to 2016 were studied. Propensity score analysis was used to measure the association between treatment setting and the primary composite outcome of 30-day myocardial infarction, bleeding complications, and hospital admission. RESULTS: The unmatched sample consisted of 95,492 HOPD and 849 ASC PCIs. Patients who underwent ASC PCI were more likely to be younger than 65 years, to live in the southern United States, and to have managed or consumer-driven health insurance. ASC PCI was also associated with decreased fractional flow reserve utilization (odds ratio [OR]: 0.31; 95% confidence interval [CI]: 0.20 to 0.48; p < 0.001). In unmatched, multivariate analysis, ASC PCI was associated with increased odds of the primary outcome (OR: 1.25; 95% CI: 1.01 to 1.56; p = 0.039) and bleeding complications (OR: 1.80; 95% CI: 1.11 to 2.90; p = 0.016). In propensity-matched analysis, ASC PCI was not associated with the primary outcome (OR: 1.23; 95% CI: 0.94 to 1.60; p = 0.124) but was significantly associated with increased bleeding complications (OR: 2.49; 95% CI: 1.25 to 4.95; p = 0.009). CONCLUSIONS: Commercially insured patients undergoing ASC PCI were less likely to undergo fractional flow reserve testing and had higher odds of bleeding complications than HOPD-treated patients. Further study is warranted as Medicare ASC PCI volume increases.

Physical and Occupational Therapy Use and Cost After Common Hand Procedures.

PURPOSE: The use of routine physical therapy (PT) and occupational therapy (OT) after certain hand procedures, such as carpal tunnel release, remains controversial. The objective of this study was to evaluate baseline use, the change in use, variation in prescribing patterns by region, and costs for PT/OT after common hand procedures. METHODS: Outpatient administrative claims data from patients who underwent procedures for carpal tunnel syndrome, trigger finger, carpometacarpal arthritis, de Quervain tenosynovitis, wrist ganglion cyst, and distal radius fracture were abstracted from the Truven Health MarketScan database from 2007 to 2015. The incidence of therapy and total reimbursement of therapy per patient were collected for each procedure over a 90-day postoperative observational period. Trends in use of therapy over time were described with average compound annual growth rates (CAGRs), a way of quantifying average growth over a specified observation period. Variations in the incidence of PT/OT use across 4 census regions were assessed. RESULTS: The incidence of 90-day utilization of PT and OT after hand procedures was 14.0% and increased for all procedures during the observation period with an average CAGR of 8.3%. Cost per therapy visit was relatively stable when adjusted for inflation, with an average CAGR of 0.63%. Patients in the northeast had a significantly higher incidence of PT/OT use than those in the south and west for all procedures except carpometacarpal arthritis. CONCLUSIONS: Use of PT and OT has increased over time after common hand procedures. Geographical variation in the utilization rate of these services is substantial. Limiting unwarranted variation of care is a health policy strategy for increasing value of care. TYPE OF STUDY/LEVEL OF EVIDENCE: Outcomes Research II.

Assessing Predictors of Early and Late Hospital Readmission After Kidney Transplantation.

BACKGROUND: A better understanding of the risk factors of posttransplant hospital readmission is needed to develop accurate predictive models. METHODS: We included 40 461 kidney transplant recipients from United States renal data system (USRDS) between 2005 and 2014. We used Prentice, Williams and Peterson Total time model to compare the importance of various risk factors in predicting posttransplant readmission based on the number of the readmissions (first vs subsequent) and a random forest model to compare risk factors based on the timing of readmission (early vs late). RESULTS: Twelve thousand nine hundred eighty-five (31.8%) and 25 444 (62.9%) were readmitted within 30 days and 1 year postdischarge, respectively. Fifteen thousand eight hundred (39.0%) had multiple readmissions. Predictive accuracies of our models ranged from 0.61 to 0.63. Transplant factors remained the main predictors for early and late readmission but decreased with time. Although recipients' demographics and socioeconomic factors only accounted for 2.5% and 11% of the prediction at 30 days, respectively, their contribution to the prediction of later readmission increased to 7% and 14%, respectively. Donor characteristics remained poor predictors at all times. The association between recipient characteristics and posttransplant readmission was consistent between the first and subsequent readmissions. Donor and transplant characteristics presented a stronger association with the first readmission compared with subsequent readmissions. CONCLUSIONS: These results may inform the development of future predictive models of hospital readmission that could be used to identify kidney transplant recipients at high risk for posttransplant hospitalization and design interventions to prevent readmission.

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data.

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell's fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan's Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.

Water Footprint Allocation under Equity and Efficiency Considerations: A Case Study of the Yangtze River Economic Belt in China.

From a water footprint perspective, this paper adopts Gross Domestic Product (GDP) as the influencing factor to construct a lexicographical optimization framework for optimizing water resources allocation under equity and efficiency considerations. This approach consists of a lexicographic allocation of water footprints (LAWF) model and an input-output capacity of water footprints (IOWF) model. The proposed methodology is then applied to allocate water resources in the Yangtze River Economic Belt (YREB) by employing the 2013 cross-sectional data in the area. The results show that: (1) The LAWF scheme signifies reductions in water footprints in each of the YREB administrative units, thereby significantly strengthening their IOWFs. (2) IOWFs are affected by industrial attributes and natural endowments, and the impact tends to vary across different industries and regions. (3) Policy suggestions are proposed to effectively enhance the IOWFs of the weakest industries across the three YREB regions to exploit their natural endowments.

Assessment of Persistent and Prolonged Postoperative Opioid Use Among Patients Undergoing Plastic and Reconstructive Surgery.

IMPORTANCE: Although the development of persistent opioid use after surgical procedures has garnered much attention in recent years, large-scale studies characterizing patterns of persistent opioid use among patients undergoing plastic and reconstructive surgery procedures are lacking. OBJECTIVE: To assess the prevalence of immediate and long-term postoperative opioid use after plastic and reconstructive surgery procedures. DESIGN, SETTING, AND PARTICIPANTS: In this population-based cohort study, patients who underwent 5 classes of plastic and reconstructive procedures (nasal, eye, breast, abdomen, and soft tissue reconstruction) between January 1, 2007, and December 31, 2015, were identified using IBM MarketScan Commercial and Medicare Supplemental research databases. Patients were excluded if they were younger than 18 years, lacked continuous insurance coverage for 1 year preoperatively and postoperatively, had a second anesthesia event within 1 year postoperatively, and filled an opioid prescription within the year prior to surgery. MAIN OUTCOMES AND MEASURES: Analgesic prescription patterns in the immediate postoperative period. The primary outcome was rates of persistent opioid use (opioid prescriptions filled 90-180 days postoperatively). The secondary outcome was rates of prolonged opioid use (opioid prescriptions filled 90-180 days postoperatively and again 181-365 days postoperatively). Explanatory variables included patient demographics, procedure type, and relevant comorbidities. RESULTS: Of the 466 677 patients who met inclusion criteria, 96 397 (45.3%) were men, and the mean (SD) age was 46.8 (17.7) years. Furthermore, 212 387 (54.6%) of the patients filled prescriptions for postoperative analgesics, with 212 387 (91.5%) of analgesic prescriptions filled being for opioids. Persistent opioid use occurred in 30 865 (6.6%) patients (5.1%-13.5% across procedure classes), while prolonged opioid use occurred in 10 487 (2.3%) patients (1.7%-5.6% across procedure classes). Patients who filled prescriptions for opioids in the perioperative period were significantly more likely to exhibit persistent (odds ratio [OR], 2.87; 95% CI, 2.80-2.94) and prolonged (OR, 2.90; 95% CI, 2.77-3.02) opioid use than those who did not fill perioperative opioid prescriptions, with the greatest odds for persistent use found in patients who underwent breast (OR, 4.36; 95% CI, 4.10-4.63) and nasal (OR, 3.51; 95% CI, 3.30-3.73) procedures. On multivariable logistic regression analysis, independent risk factors for persistent and prolonged opioid use included perioperative opioid use, procedure type, and prior-year mental health (depression and anxiety) and substance abuse diagnoses. CONCLUSIONS AND RELEVANCE: Given the significant risk of persistent opioid use after plastic and reconstructive procedures, it is imperative to develop best practices guidelines for postoperative opioid prescription practices in this population. LEVEL OF EVIDENCE: NA.

Aspirin Exacerbated Respiratory Disease: Epidemiology, Pathophysiology, and Management.

The correlation between aspirin sensitivity, asthma, and nasal polyposis was recognized in the early 20th century. Today, this classic triad of symptoms, eponymously named Samter's Triad, is known as aspirin exacerbated respiratory disease (AERD). Aspirin exacerbated respiratory disease affects approximately 0.3⁻0.9% of the general population in the USA and approximately 7% of asthmatic patients. The management of AERD is challenging as no single modality has proven to have high rates of symptom control. Consequently, disease management typically involves a multimodality approach across both medical and surgical disciplines. This review describes the epidemiology of AERD and the current state-of-the-art as it relates to the underlying pathophysiologic mechanisms of this disease process. A significant proportion of the review is focused on the appropriate diagnostic workup for AERD patients including the utility of aspirin provocation testing. The spectrum of medical treatments, including aspirin desensitization and recently introduced immunotherapies, are discussed in detail. Furthermore, surgical approaches to disease control, including advanced endoscopic techniques, are reviewed and treatment outcomes presented.

Diagnostic staging laparoscopy in gastric cancer treatment: A cost-effectiveness analysis.

BACKGROUND AND OBJECTIVES: Accurate preoperative staging helps avert morbidity, mortality, and cost associated with non-therapeutic laparotomy in gastric cancer (GC) patients. Diagnostic staging laparoscopy (DSL) can detect metastases with high sensitivity, but its cost-effectiveness has not been previously studied. We developed a decision analysis model to assess the cost-effectiveness of preoperative DSL in GC workup. METHODS: Analysis was based on a hypothetical cohort of GC patients in the U.S. for whom initial imaging shows no metastases. The cost-effectiveness of DSL was measured as cost per quality-adjusted life-year (QALY) gained. Drivers of cost-effectiveness were assessed in sensitivity analysis. RESULTS: Preoperative DSL required an investment of $107 012 per QALY. In sensitivity analysis, DSL became cost-effective at a threshold of $100 000/QALY when the probability of occult metastases exceeded 31.5% or when test sensitivity for metastases exceeded 86.3%. The likelihood of cost-effectiveness increased from 46% to 93% when both parameters were set at maximum reported values. CONCLUSIONS: The cost-effectiveness of DSL for GC patients is highly dependent on patient and test characteristics, and is more likely when DSL is used selectively where procedure yield is high, such as for locally advanced disease or in detecting peritoneal and superficial versus deep liver lesions.

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.