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.

The principal-agent problem and owner-managers: An instrumental variables application to nursing home quality.

The literature on provider ownership has primarily focused broadly on for-profits compared with nonprofits and chains versus nonchains. However, the understanding of more nuanced ownership arrangements within individual facilities is limited. Utilizing the principal-agent and managerial control frameworks, we study the role of managerial ownership and its relationship to quality among for-profit nursing homes (NHs). We identify NH administrators with more than 5% ownership (owner-manager) from Ohio Medicaid Cost Reports (2005-2010) and link these data to long-stay resident records in the Minimum Data Set. Using differential distance to the nearest NHs with a salaried manager relative to an owner-manager, we address the differential selection into these two types of NHs. After instrumenting for admissions to owner-managed NHs, quality among long-stay residents at owner-managed NHs is generally better than NHs with salaried managers. We find suggestive evidence that the magnitudes of quality difference are larger when the principal-agent problem is likely more pronounced, such as when NHs that are part of a multifacility chain and located in more concentrated markets.

An initial cost-effectiveness analysis of intraoperative magnetic resonance imaging (iMRI) in pediatric epilepsy surgery.

PURPOSE: Previous studies have illustrated the clinical utility of the addition of intraoperative magnetic resonance imaging (iMRI) to conventional microsurgical resection. While iMRI requires initial capital cost investment, long-term reduction in costly follow-up management and reoperation costs may prove economically efficacious. The objective of this study is to investigate the cost-effectiveness of the addition of iMRI utilization versus conventional microsurgical techniques in focal cortical dysplasia (FCD) resection in pediatric patients with medically refractory epilepsy. METHODS: We retrospectively reviewed the medical records of pediatric subjects who underwent surgical resection of FCD at the Children's National Health System between March 2005 and April 2015. Patients were assigned to one of three cohorts: iMRI-assisted resection, conventional resection with iMRI-assisted reoperation, or conventional resection. Direct costs included preoperative, operative, postoperative, long-term follow-up, and antiepileptic drug (AED) costs. The cost-effectiveness was calculated as the sum total of all direct medical costs over the quality-adjusted life years (QALYs). We also performed sensitivity analysis on numerous variables to assess the validity of our results. RESULTS: Fifty-six consecutive pediatric patients underwent resective surgery for medically intractable FCD. Ten patients underwent iMRI-assisted resection; 7 underwent conventional resection followed by iMRI-assisted reoperation; 39 patients underwent conventional microsurgical resection. Taken over the lifetime of the patient, the cumulative discounted QALY of patients in the iMRI-assisted resection cohort was about 2.91 years, versus 2.61 years in the conventional resection with iMRI-assisted reoperation cohort, and 1.76 years for the conventional resection cohort. Adjusting for inflation, iMRI-assisted surgeries have a cost-effectiveness ratio of $16,179 per QALY, versus $28,514 per QALY for the conventional resection with iMRI-assisted reoperation cohort, and $49,960 per QALY for the conventional resection cohort. Sensitivity analysis demonstrated that no one single variable significantly altered cost-effectiveness across all three cohorts compared to the baseline results. CONCLUSION: The addition of iMRI to conventional microsurgical techniques for resection of FCD in pediatric patients with intractable epilepsy resulted in increased seizure freedom and reduction in long-term direct medical costs compared to conventional surgeries. Our data suggests that iMRI may be a cost-effective addition to the surgical armamentarium for epilepsy surgery.

Zero hospital admissions for infection after 577 transperineal prostate biopsies using single-dose cephazolin prophylaxis.

PURPOSE: To determine the rate of hospital admissions for infection after transperineal biopsy of prostate (TPB) with single-dose cephazolin prophylaxis using a prospective database. METHOD: Between April 2013 and February 2016, 577 patients undergoing TPB had 2 g of cephazolin given intravenously at induction of anaesthesia. Data collected from these patients included age, PSA, prostate volume, number of cores taken and post-operative complications. RESULTS: No patients were readmitted to hospital with infection post-TPB. Seven patients developed acute urinary retention, and one patient developed clinical prostatitis that was treated with oral antibiotics in the community. CONCLUSION: It is safe to use single-dose cephazolin only as antibiotic prophylaxis prior to TPB, negating the need for quinolones. This study supports Australia's current Therapeutic Guidelines recommendation for TPB prophylaxis and the existing evidence that sepsis post-TPB is a rare complication. Whether any antibiotic prophylaxis is needed at all for TPB is the subject of a future study.

Transperineal biopsy prostate cancer detection in first biopsy and repeat biopsy after negative transrectal ultrasound-guided biopsy: the Victorian Transperineal Biopsy Collaboration experience.

OBJECTIVES: To present the Victorian Transperineal Biopsy Collaboration (VTBC) experience in patients with no prior prostate cancer diagnosis, assessing the cancer detection rate, pathological outcomes and anatomical distribution of cancer within the prostate. PATIENTS AND METHODS: VTBC was established through partnership between urologists performing transperineal biopsies of the prostate (TPB) at three institutions in Melbourne. Consecutive patients who had TPB, as first biopsy or repeat biopsy after previous negative transrectal ultrasound-guided (TRUS) biopsy, between September 2009 and September 2013 in the VTBC database were included. Data for each patient were collected prospectively (except for TPB before 2011 in one institution), based on the minimum dataset published by the Ginsburg Study Group. Univariate and multivariate analyses were used to identify factors predictive of cancer detection on TPB. RESULTS: In all, 160 patients were included in the study, of whom 57 had TPB as first biopsy and 103 had TPB as repeat biopsy after previous negative TRUS biopsies. The median patient age at TPB was 63 years, with the repeat-biopsy patients having a higher median serum PSA level (5.8 ng/mL for first biopsy and 9.6 ng/mL for repeat biopsy) and larger prostate volumes (40 mL for first biopsy, and 51 mL for repeat biopsy). Prostate cancer was detected in 53% of first-biopsy patients and 36% of repeat-biopsy patients, of which 87% and 81%, respectively, were clinically significant cancers, defined as a Gleason score of ≥7, or more than three positive cores of Gleason 6. Of the cancers detected in repeat biopsies, 75% involved the anterior region (based on the Ginsburg Study Group's recommended biopsy map), while 25% were confined exclusively within the anterior region; a lower proportion of only 5% of cancers detected in first biopsies were confined exclusively within the anterior region. Age, serum PSA level and prostate volume were predictive of cancer detection in repeat biopsies, while only age was predictive in first biopsies. CONCLUSIONS: TPB is an alternative approach to TRUS biopsy of the prostate, offering a high rate of detection of clinically significant prostate cancer. It provides excellent sampling of the anterior region of the prostate, which is often under-sampled using the TRUS approach, and should be considered as an option for all men in whom a prostate biopsy is indicated.

Interlaboratory study to evaluate the robustness of capillary electrophoresis-mass spectrometry for peptide mapping.

A collaborative study on the robustness and portability of a capillary electrophoresis-mass spectrometry method for peptide mapping was performed by an international team, consisting of 13 independent laboratories from academia and industry. All participants used the same batch of samples, reagents and coated capillaries to run their assays, whereas they utilized the capillary electrophoresis-mass spectrometry equipment available in their laboratories. The equipment used varied in model, type and instrument manufacturer. Furthermore, different types of sheath-flow capillary electrophoresis-mass spectrometry interfaces were used. Migration time, peak height and peak area of ten representative target peptides of trypsin-digested bovine serum albumin were determined by every laboratory on two consecutive days. The data were critically evaluated to identify outliers and final values for means, repeatability (precision within a laboratory) and reproducibility (precision between laboratories) were established. For relative migration time the repeatability was between 0.05 and 0.18% RSD and the reproducibility between 0.14 and 1.3% RSD. For relative peak area repeatability and reproducibility values obtained were 3-12 and 9-29% RSD, respectively. These results demonstrate that capillary electrophoresis-mass spectrometry is robust enough to allow a method transfer across multiple laboratories and should promote a more widespread use of peptide mapping and other capillary electrophoresis-mass spectrometry applications in biopharmaceutical analysis and related fields.

Sepsis and 'superbugs': should we favour the transperineal over the transrectal approach for prostate biopsy?

OBJECTIVE: To determine the rate of hospital re-admission for sepsis after transperineal (TP) biopsy using both local data and worldwide literature, as there is growing interest in TP biopsy as an alternative to transrectal ultrasonography (TRUS)-guided biopsy for patients undergoing repeat prostate biopsy. PATIENTS AND METHODS: Pooled prospective databases on TP biopsy from multiple centres in Melbourne were queried for rates of re-admission for infection. A literature review of PubMed and Embase was also conducted using the search terms: 'prostate biopsy, fever, infection, sepsis, septicaemia and complications'. RESULTS: In all, 245 TP biopsies were performed (111 at Alfred Health, 92 at Epworth Healthcare, 38 at Peter MacCallum Cancer Centre, and four at other institutions). The rate of hospital re-admission for infection was zero. The literature review showed that the rate of sepsis after TRUS biopsy appears to be rising with increasing rates of multi-resistant bacteria found in rectal flora, and is as high as 5%. However, the rate of sepsis from published series of TP biopsy approached zero. CONCLUSIONS: Both local and international data suggest a negligible rate of sepsis with TP biopsy. This compares to a concerning rise in the rate of sepsis after TRUS biopsy due to the increasing prevalence of multi-resistant bacteria in rectal flora. Although TRUS biopsy is convenient, cheap and quick to perform, we think that TP biopsy should now be offered as an option, not only to patients undergoing repeat prostate biopsy, but to all patients in whom a prostate biopsy is indicated.

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.

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.

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.

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.

Using large language model to guide patients to create efficient and comprehensive clinical care message.

OBJECTIVE: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations. METHODS: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios. We used 3 LLMs to generate follow-up questions: (1) Comprehensive LLM Artificial Intelligence Responder (CLAIR): a locally fine-tuned LLM, (2) GPT4 with a simple prompt, and (3) GPT4 with a complex prompt. Five physicians rated them with the actual follow-ups written by healthcare providers on clarity, completeness, conciseness, and utility. RESULTS: For five scenarios, our CLAIR model had the best performance. The GPT4 model received higher scores for utility and completeness but lower scores for clarity and conciseness. CLAIR generated follow-up questions with similar clarity and conciseness as the actual follow-ups written by healthcare providers, with higher utility than healthcare providers and GPT4, and lower completeness than GPT4, but better than healthcare providers. CONCLUSION: LLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by healthcare providers.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Myths.

INTRODUCTION: 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 valuable tool to help assess these myths for veracity and inaccuracy; however, they can induce misinformation as well. OBJECTIVE: This study assesses ChatGPT's ability to identify and address AD myths with reliable information. METHODS: We conducted a cross-sectional study of attending geriatric medicine clinicians' evaluation of ChatGPT (GPT 4.0) responses to 16 selected AD myths. We prompted ChatGPT to express its opinion on each myth and implemented a survey using REDCap to determine the degree to which clinicians agreed with the accuracy of each of ChatGPT's explanations. We also collected their explanations of any disagreements with ChatGPT's responses. We used a 5-category 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 = 10) were generally satisfied with ChatGPT's explanations. Among the 16 myths, the clinicians were generally satisfied with these explanations, with [mean (SD) score of 1.1(±0.3)]. Most clinicians selected "Agree" or "Strongly Agree" for each statement. Some statements obtained a small number of "Disagree" responses. There were no "Strongly Disagree" responses. CONCLUSION: Most surveyed health care 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.

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.

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.

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.

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.

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.

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.