Differences in healthcare utilization between enrollees of fully integrated dual eligible special needs plans versus non-fully integrated plans.

BACKGROUND: Policymakers advocate care integration models to enhance Medicare and Medicaid service coordination for dually eligible individuals. One rapidly expanding model is the fully integrated dual eligible (FIDE) plan, a sub-type of the dual eligible special needs plan (D-SNP) in which a parent insurer manages Medicare and Medicaid spending for dually eligible individuals. We examined healthcare utilization differences among dually eligible individuals aged 65 years or older enrolled in D-SNPs by plan type (FIDE vs non-FIDE). METHODS: Using 2018 Medicare Advantage encounters and Medicaid claims of FIDE and non-FIDE enrollees in six states (AZ, CA, FL, NY, TN, WI), we compared healthcare utilization between plan types, adjusting for enrollee characteristics and county indicators. We applied propensity score weighting to address differences between FIDE and non-FIDE plan enrollees. RESULTS: In our main analysis, which included all dually eligible individuals in our sample, we observed no significant difference in healthcare utilization between FIDE and non-FIDE plan enrollees. However, we identified some differences in healthcare utilization between FIDE and non-FIDE plan enrollees in subgroup analyses. For example, among home and community-based service (HCBS) users, FIDE plan enrollees had 6.0 fewer hospitalizations per 1000 person-months (95% CI: -7.9, -4.0) and were 7.0 percentage points more likely to be discharged to home (95% CI: 2.6, 11.5) after hospitalization, compared to non-FIDE plan enrollees. CONCLUSION: While we found no differences in healthcare utilization between FIDE and non-FIDE plan enrollees when considering all dually eligible individuals in our sample, some differences emerged when focusing on subgroups. For example, HCBS users with FIDE plans had fewer hospitalizations and were more likely to be discharged to their home following hospitalization, compared to HCBS users with non-FIDE plans. These findings suggest that FIDE plans may improve care coordination for specific subsets of dually eligible individuals.

Leveraging large language models for generating responses to patient messages-a subjective analysis.

OBJECTIVE: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal. MATERIALS AND METHODS: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness. RESULTS: The dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness. CONCLUSION: This subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts. MATERIALS AND METHODS: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness. RESULTS: Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001). CONCLUSION: End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

The 21st Century Cures Act Information Blocking Rule in Post-Acute Long-Term Care.

Included as part of the 21st Century Cures Act, the information blocking rule entered the first compliance phase in April 2021. Under this rule, post-acute long-term care (PALTC) facilities must not engage in any activity that interferes with accessing, using, or exchanging electronic health information. In addition, facilities must respond to information requests in a timely fashion and allow records to be readily available to patients and their delegates. Although hospitals have been slow to adapt to these changes, skilled nursing and other PALTC centers have been even slower. With a Final Rule enacted in recent years, awareness of the information-blocking rules became more crucial. We believe this commentary will help our colleagues interpret the rule for the PALTC setting. In addition, we provide points of emphasis to help guide those providers and administrative staff workers toward compliance and avoid potential penalties.

A Cost Savings Analysis of Topical Estrogen Therapy in Urinary Tract Infection Prevention Among Postmenopausal Women.

INTRODUCTION: UTIs are some of the most common infections in geriatric patients, with many women experiencing recurrent infections after menopause. In the US, annual UTI-related costs are $2 billion, with recurrent infections creating a significant economic burden. Given the data published on topical estrogen in reducing the number of infections for postmenopausal women with recurrent UTI, we sought to evaluate how this would translate to cost savings. METHODS: We performed a systematic literature review of UTI reduction secondary to topical estrogen utilization in postmenopausal female patients. The cost per UTI was determined based on published Medicare spending on UTI per beneficiary, weighted on reported likelihood of complicated and resistant infections. For a patient with recurrent infections, topical estrogen therapy reported on average can reduce infections from 5 to 0.5 to 2 times per person per year. RESULTS: At a calculated cost per UTI of $1222, the reduction in UTI spending can range between $3670 and $5499 per beneficiary per year. Per-beneficiary spending on topical estrogen therapies was $1013 on average ($578-$1445) in 2020. After including the cost of the therapy, overall cost savings for topical estrogen therapies were $1226 to $4888 annually per patient. CONCLUSIONS: Topical estrogens are a cost-conscious way to improve the burden of UTI on postmenopausal women with the potential for billions of dollars in Medicare savings. System-wide efforts should be made to have these therapies available as prophylaxis for postmenopausal patients and to ensure they are affordable for patients.

State-Level Adverse Outcomes Among Long-Term Services and Supports Users With Alzheimer's Disease and Related Dementias.

Home- and community-based services (HCBS) users, on average, experience hospitalizations more frequently than nursing facility residents. However, little is known about state-level variation in such adverse events among these groups. Using 2018 Medicare and Medicaid claims for dual-eligible beneficiaries with Alzheimer's disease and related dementias, we described hospitalization and emergency department (ED) visit rates among HCBS users and nursing facility residents and observed substantial state-level variation. In addition, consistent with prior evidence, we found more frequent hospitalizations and ED visits among HCBS users than nursing facility residents. The magnitude of this difference varied considerably across states, and the degree of variation was greatest among beneficiaries with six or more comorbid conditions. Our findings represent a crucial initial exploration of the state-level variation in adverse events among HCBS users and nursing facility residents, paving the way for further investigations into factors that contribute to this variability.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Statements with Varying Degrees of Misinformation.

Background: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models such as ChatGPT, may be a useful tool to help assess these myths for veracity and inaccuracy. However, they can induce misinformation as well. The objective of this study is to assess ChatGPT's ability to identify and address AD myths with reliable information. Methods: We conducted a cross-sectional study of clinicians' evaluation of ChatGPT (GPT 4.0)'s responses to 20 selected AD myths. We prompted ChatGPT to express its opinion on each myth and then requested it to rephrase its explanation using a simplified language that could be more readily understood by individuals with a middle school education. We implemented a survey using Redcap to determine the degree to which clinicians agreed with the accuracy of each ChatGPT's explanation and the degree to which the simplified rewriting was readable and retained the message of the original. We also collected their explanation on any disagreement with ChatGPT's responses. We used five Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' agreement in each aspect of the evaluation. Results: The clinicians (n=11) were generally satisfied with ChatGPT's explanations, with a mean (SD) score of 1.0(±0.3) across the 20 myths. While ChatGPT correctly identified that all the 20 myths were inaccurate, some clinicians disagreed with its explanations on 7 of the myths.Overall, 9 of the 11 professionals either agreed or strongly agreed that ChatGPT has the potential to provide meaningful explanations of certain myths. Conclusions: The majority of surveyed healthcare professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation. However, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.

Leveraging Large Language Models for Generating Responses to Patient Messages.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal. Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate the fine-tuned models, we used ten representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness. Results: The dataset consisted of a total of 499,794 pairs of patient messages and corresponding responses from the patient portal, with 5,000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness. Conclusion: Leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and primary care providers.

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.

Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

HCV universal EHR prompt successfully increases screening, highlights potential disparities.

BACKGROUND & OBJECTIVES: Screening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region. METHODS: Data was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates. For a limited period centered on the implementation of the HCV alert, mixed effects multivariable regression analyses were performed to compare the timeline and characteristics of those screened and un-screened. The final models included socio-demographic covariates of interest, time period (pre/post) and an interaction term between time period and sex. We also examined a model with time as a monthly variable to look at the potential impact of COVID-19 on screening for HCV. RESULTS: Absolute number of screens and screening rate increased by 103% and 62%, respectively, after adopting the universal EHR alert. Patients with Medicaid were more likely to be screened than private insurance (ORadj 1.10, 95% CI: 1.05, 1.15), while those with Medicare were less likely (ORadj 0.62, 95% CI: 0.62, 0.65); and Black (ORadj 1.59, 95% CI: 1.53, 1.64) race more than White. CONCLUSIONS: Implementation of universal EHR alerts could prove to be a critical next step in HCV elimination. Those with Medicare and Medicaid insurance were not screened proportionately to the national prevalence of HCV in these populations. Our findings support increased screening and re-testing efforts for those at high risk of HCV.

Use of long-term services and supports among dual-eligible beneficiaries with Alzheimer's disease and related dementias.

BACKGROUND: To respect people's preference for aging in place and control costs, many state Medicaid programs have enacted policies to expand home and community-based services as an alternative to nursing facility care. However, little is known about the use of Medicaid long-term services and supports (LTSS) at a national level, particularly among dual-eligible beneficiaries with Alzheimer's disease and related dementias (ADRD). METHODS: Using Medicare and Medicaid claims of 30 states from 2016, we focused on dual-eligible beneficiaries 65 years or older with ADRD and described their use of any form of LTSS and sub-types of LTSS (home-based, community-based, and nursing facility services) across states. RESULTS: We found that 80.5% of dual-eligible beneficiaries with ADRD received some form of Medicaid LTSS in 2016. The most common LTSS setting was nursing facility (46.7%), followed by home (31.5%) and community (12.2%). There was sizeable state variation in the percentage of dual-eligible beneficiaries with ADRD who used any form of LTSS (ranging from 61% in Maine to 96% in Montana). The type of LTSS used also varied widely across states. For example, home-based service use ranged from 9% in Maine, Arizona, and South Dakota to 62% in Oregon. Nursing facility services were the most common type of LTSS in most states. However, home-based service use exceeded nursing facility use in Oregon, Alaska, and California. CONCLUSIONS: Our findings suggest substantially different use of LTSS across states among dual-eligible beneficiaries with ADRD. Given the importance of LTSS for this population and their families, a deeper understanding of state LTSS policies and other factors that contribute to wide state variation in LTSS use will be necessary to improve access to LTSS across states.

Comparing Genetic Risk and Clinical Risk Classification in Luminal-like Breast Cancer Patients Using a 23-Gene Classifier.

Background: A 23-gene classifier has been developed based on gene expression profiles of Taiwanese luminal-like breast cancer. We aim to stratify risk of relapse and identify patients who may benefit from adjuvant chemotherapy based on genetic model among distinct clinical risk groups. Methods: There were 248 luminal (hormone receptor-positive and human epidermal growth factor receptor II-negative) breast cancer patients with 23-gene classifier results. Using the modified Adjuvant! Online definition, clinical high/low-risk groups were tabulated with the genetic model. The primary endpoint was a recurrence-free interval (RFI) at 5 years. Results: There was a significant difference between the high/low-risk groups defined by the 23-gene classifier for the 5-year prognosis of recurrence (16 recurrences in high-risk and 3 recurrences in low-risk; log-rank test: p < 0.0001). Among the clinically high-risk group, the 5-year RFI of high risk defined by the 23-gene classifier was significantly higher than that of the low-risk group (15 recurrences in high-risk and 2 recurrences in low-risk; log-rank test: p < 0.0001). Conclusion: This study showed that 23-gene classifier can be used to stratify clinically high-risk patients into distinct survival patterns based on genomic risks and displays the potentiality to guide adjuvant chemotherapy. The 23-gene classifier can provide a better estimation of breast cancer prognosis which can help physicians make a better treatment decision.

Cost-Effectiveness Analysis from a Societal Perspective of Recurrence Index for Distant Recurrence (RecurIndex) in Women with Hormone Receptor-Positive and HER2-Negative Early-Stage Breast Cancer.

PURPOSE: A clinical-genomic prognostic multigene panel (RI-DR assay, RecurIndex®), predicting the risk level of distant recurrence (DR) in early-stage breast cancer (EBC) patients with an Asian background, has been validated as a valuable tool for identifying high-risk patients to develop distant recurrence (metastasis). Although the clinical benefit of adjuvant chemotherapy from the assay's prediction is already proved, its affordability remains uncertain. This study is the first time in which the long-term cost-effectiveness of the RI-DR assay is evaluated. PATIENTS AND METHODS: A lifetime Markov decision-analytic model was developed from a societal perspective to estimate the life-years gained (LYGs), quality-adjusted life-years (QALYs), medical costs, and incremental cost-effectiveness ratios (ICERs), comparing EBC women with and without RI-DR genomic testing. A decision tree was used to classify patients in one of the fifteen end nodes (by order, each arm was stratified by a patient being tested or not with the RI-DR assay, being treated or not with adjuvant chemotherapy and had no, minor, major, or fatal toxicity after adjuvant chemotherapy). Health utilities, costs, transition probabilities, and survival data were extracted from the scientific literature. Deterministic sensitivity analysis (DSA) and probabilistic sensitivity analysis (PSA) were performed on variables to assess the robustness of the model. A willingness-to-pay (WTP) threshold of 790,000 NT$ per QALY gained was considered as a cost-effectiveness criterion. RESULTS: The incremental cost per QALY gained under base-case assumptions of the model was 173,842 NT$. Findings on the variation in model input parameters were robust and confirmed that every key variable was cost-effective for the benefit of RI-DR testing. CONCLUSION: The clinical-genomic RI-DR assay is cost-effective in guiding adjuvant chemotherapy decisions compared to current clinical practice guidelines.

Cloud Computing Synthetic Syndromic Surveillance Systems: Opioid Epidemic in Illinois.

With an increasing number of overdose cases yearly, the city of Chicago is facing an opioid epidemic. Many of these overdose cases lead to 911 calls that necessitate timely response from our limited emergency medicine services. This paper demonstrates how data from these calls along with synthetic and geospatial data can help create a syndromic surveillance system to combat this opioid crisis. Chicago EMS data is obtained from the Illinois Department of Public Health with a database structure using the NEMSIS standard. This information is combined with information from the RTI U.S. Household Population database, before being transferred to an Azure Data Lake. Afterwards, the data is integrated with Azure Synapse before being refined in another data lake and filtered with ICD-10 codes. Afterwards, we moved the data to ArcGIS Enterprise to apply spatial statistics and geospatial analytics to create our surveillance system.

The Determinants and Variation of Nursing Home Private-Pay Prices: Organizational and Market Structure.

Nursing home (NH) care is arguably the most significant financial risk faced by the elderly without long-term care insurance or Medicaid coverage. Annual out-of-pocket expenditures for NH care can easily exceed $70,000. However, our understanding of private-pay prices is limited by data availability. Utilizing a unique data set on NH prices from 2005 through 2010 across eight states, we find that NH price growth has consistently outpaced growth in consumer and medical care prices. After adjusting for geographical and facility differences, for-profit chains charge the lowest prices, independently operated for-profit and nonprofit NHs have similar prices, and nonprofit chains charge the highest prices. Adjusted prices are also likely to be higher when NHs have higher occupancy rates and markets are more concentrated. The significant differences in price across organizational and market structures suggest private-pay prices can be an important factor when evaluating and comparing the value of NH care.

A Multimodal Sepsis Quality-Improvement Initiative Including 24/7 Screening and a Dedicated Sepsis Response Team-Reduced Readmissions and Mortality.

OBJECTIVES: To evaluate if a hospitalwide sepsis performance improvement initiative improves compliance with the Centers for Medicare and Medicaid Services-mandated sepsis bundle interventions and patient outcomes. STUDY DESIGN: Retrospective analysis comparing 6 months before and 14 months after intervention. SETTING: Tertiary teaching hospital in Washington, DC. SUBJECTS: Patients admitted with a diagnosis of sepsis to a tertiary hospital. INTERVENTIONS: Implementation of a multimodal quality-improvement initiative. MEASUREMENTS AND MAIN RESULTS: A total of 4,102 patients were diagnosed with sepsis, severe sepsis, or septic shock during the study period, 861 patients (21%) were diagnosed during a 6-month preintervention period, and 3,241 (79%) were diagnosed in a 13-month postintervention period. Adjusted for patient case-mix, the prevalence of simple sepsis increased by 12%, but it decreased for severe sepsis and septic shock by 5.3% and 6.9%, respectively. Compliance with all sepsis bundle interventions increased by 31.1 percentage points (p < 0.01). All-cause hospital readmission and readmission due to infection were both reduced by 1.6% and 1.7 percentage points (p < 0.05). Death from any sepsis diagnosis was reduced 4.5% (p < 0.01). Death from severe sepsis and septic shock both was reduced by 5% (p < 0.01) and 6.5% (p < 0.01), respectively. CONCLUSIONS: After the implementation of multimodal sepsis performance initiatives, we observed a higher prevalence of sepsis secondary to screening but a lower prevalence of severe sepsis and septic shock, an improvement in compliance with the sepsis bundle interventions bundle, as well as reduction in hospital readmission and all- cause mortality rate.

Relationship of Hospital Star Ratings to Race, Education, and Community Income.

INTRODUCTION: The Centers for Medicare & Medicaid Services (CMS) publishes hospital quality ratings to provide more transparent and useable quality information to patients and stakeholders. However, there is a gap in the literature regarding the geographic distribution of the hospitals with higher star ratings. In this paper, we focus on the associations between star ratings and community characteristics, including racial/ethnic mix, household income, educational attainment, and regional difference. METHODS: A retrospective study and cross-sectional logistic and multinomial logistic regression analyses. RESULTS: According to the multivariate regression results, hospitals in areas with lower income, lower educational attainment, and higher minority population shares have lower quality ratings (lower income: odds ratio [OR] 0.67; 95% CI, 0.49-0.91; lower education: OR 0.66; 95% CI, 0.51-0.85; higher minority: OR 0.52; 95% CI, 0.40-0.69). Compared with hospitals in the Midwest, hospitals in Northeast, South, and West regions have lower quality ratings (Northeast: OR 0.37; 95% CI, 0.25-0.56; South: OR 0.68; 95% CI, 0.51-0.91; West: OR 0.69; 95% CI, 0.49-0.97). DISCUSSION AND CONCLUSION: Overall, our results show that hospitals with higher star ratings are less likely to be located in communities with higher minority populations, lower income, and lower levels of educational attainment. Findings contribute to the discussion of integrating social factors in hospital quality star rating calculation methodologies.

An Initial Cost-Effectiveness Analysis of Magnetic Resonance-Guided Laser Interstitial Thermal Therapy in Pediatric Epilepsy Surgery.

INTRODUCTION: Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) is a new technology that provides a clinically efficacious and minimally invasive alternative to conventional microsurgical resection. However, little data exist on how costs compare to traditional open surgery. The goal of this paper is to investigate the cost-effectiveness of MRgLITT in the treatment of pediatric epilepsy. METHODS: We retrospectively analyzed the medical records of pediatric patients who underwent MRgLITT via the Visualase® thermal therapy system (Medtronic, Inc., Minneapolis, MN, USA) between December 2013 and September 2017. Direct costs associated with preoperative, operative, and follow-up care were extracted. Benefit was calculated in quality-adjusted life years (QALYs), and the cost-effectiveness was derived from the discounted total direct costs over QALY. Sensitivity analysis on 4 variables was utilized to assess the validity of our results. RESULTS: Twelve consecutive pediatric patients with medically refractory epilepsy underwent MRgLITT procedures. At the last postoperative follow-up, 8 patients were seizure free (Engel I, 66.7%), 2 demonstrated significant improvement (Engel II, 16.7%), and 2 patients showed worthwhile improvement (Engel III, 16.7%). The average cumulative discounted QALY was 2.11 over the lifetime of a patient. Adjusting for inflation, MRgLITT procedures had a cost-effectiveness of USD 22,211 per QALY. Our sensitivity analysis of cost variables is robust and supports the procedure to be cost--effective. CONCLUSION: Our data suggests that MRgLITT may be a cost-effective alternative to traditional surgical resection in pediatric epilepsy surgery.