M. Christopher Roebuck

A comparison of 2013 and 2022 data shows that cancer costs are not uniformly rising. Costs decreased for metastatic colorectal cancer but increased for both metastatic non–small cell lung cancer and multiple myeloma.

Economic incentives and patent protections drive development of innovative medications. Drug prices are determined by consumer demand, not production costs. Therefore, governmental regulation risks future investment in research and development.

Background: Nissen Fundoplication (NF) and Transoral Incisionless Fundoplication (TIF) are established procedures for the treatment of gastroesophageal reflux disease (GERD). However, the surgically induced plication can loosen over time. This multicenter study aims to evaluate the safety and efficacy of Antireflux Mucosectomy (ARMS) and Resection and Plication (RAP) in symptomatic patients with prior NF or TIF that has become loose. Patients and methods: Eighteen patients were enrolled in the study. Ten had prior TIF, while eight had prior NF. Half of these patients had a Hill Grade 3 Valve while the other half had a Hill Grade 2 valve. Endoscopic submucosal dissection (ESD) was performed in six patients, while endoscopic mucosal resection (EMR) was performed in twelve patients. A follow-up endoscopy was performed at 4–12 weeks. Results: At follow-up, 11 patients had a Hill Grade 1 valve, and seven patients had a Hill Grade 2 valve. All patients had improvement in symptoms for up to 32 months. Conclusions: In this pilot study, ARMS/RAP appears to be an effective option in patients who had prior NF or TIF with recurrent GERD symptoms.

Background: Anal fissures cause severe pain and can be difficult to treat. Medical therapy is initially used, followed by sigmoidoscopy-guided botox injections if the medical therapy is not successful. With this technique, however, it is not clear whether botox is injected into the muscle layer or submucosa. Aim: To evaluate the efficacy of EUS-guided botox injection directly into the internal sphincter. Methods: Consecutive patients with chronic anal fissure refractory to conventional endoscopic botulinum toxin type A injection were enrolled in the study. EUS was performed using a linear array echoendoscope, and a 25 G needle was used to inject botox. All patients were followed up at one- and two-month intervals. Results: Eight patients with chronic anal fissures were included in the study. Six patients had an excellent response to botox at the two-month interval using a visual analog pain scale, while one patient had a moderate response with a pain score reduction of 40%. One patient had no response. No complications were noted. An improvement in visual analog scale (pre-score > post-score) was statistically significant at the p < 0.01 level. Conclusion: EUS-guided botox injection into the internal sphincter appears to be a promising technique for patients with refractory anal fissure with pain.

Background: Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process. Objective: This study aims to evaluate the ability of an AI clinical decision support system (CDSS) to identify eligible patients for a set of clinical trials. Methods: This study included the deidentified data from a cohort of patients with breast cancer seen at the medical oncology clinic of an academic medical center between May and July 2017 and assessed patient eligibility for 4 breast cancer clinical trials. CDSS eligibility screening performance was validated against manual screening. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for eligibility determinations were calculated. Disagreements between manual screeners and the CDSS were examined to identify sources of discrepancies. Interrater reliability between manual reviewers was analyzed using Cohen (pairwise) and Fleiss (three-way) κ, and the significance of differences was determined by Wilcoxon signed-rank test. Results: In total, 318 patients with breast cancer were included. Interrater reliability for manual screening ranged from 0.60-0.77, indicating substantial agreement. The overall accuracy of breast cancer trial eligibility determinations by the CDSS was 87.6%. CDSS sensitivity was 81.1% and specificity was 89%. Conclusions: The AI CDSS in this study demonstrated accuracy, sensitivity, and specificity of greater than 80% in determining the eligibility of patients for breast cancer clinical trials. CDSSs can accurately exclude ineligible patients for clinical trials and offer the potential to increase screening efficiency and accuracy. Additional research is needed to explore whether increased efficiency in screening and trial matching translates to improvements in trial enrollment, accruals, feasibility assessments, and cost.

BACKGROUND Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process. OBJECTIVE This study aims to evaluate the ability of an AI clinical decision support system (CDSS) to identify eligible patients for a set of clinical trials. METHODS This study included the deidentified data from a cohort of patients with breast cancer seen at the medical oncology clinic of an academic medical center between May and July 2017 and assessed patient eligibility for 4 breast cancer clinical trials. CDSS eligibility screening performance was validated against manual screening. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for eligibility determinations were calculated. Disagreements between manual screeners and the CDSS were examined to identify sources of discrepancies. Interrater reliability between manual reviewers was analyzed using Cohen (pairwise) and Fleiss (three-way) κ, and the significance of differences was determined by Wilcoxon signed-rank test. RESULTS In total, 318 patients with breast cancer were included. Interrater reliability for manual screening ranged from 0.60-0.77, indicating substantial agreement. The overall accuracy of breast cancer trial eligibility determinations by the CDSS was 87.6%. CDSS sensitivity was 81.1% and specificity was 89%. CONCLUSIONS The AI CDSS in this study demonstrated accuracy, sensitivity, and specificity of greater than 80% in determining the eligibility of patients for breast cancer clinical trials. CDSSs can accurately exclude ineligible patients for clinical trials and offer the potential to increase screening efficiency and accuracy. Additional research is needed to explore whether increased efficiency in screening and trial matching translates to improvements in trial enrollment, accruals, feasibility assessments, and cost.

PURPOSE: To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. PATIENTS AND METHODS: A cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage. RESULTS: Treatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)–positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio [OR], 1.58; P < .05) and less likely in those with stage IIA (OR, 0.29; P < .05) or IIIA cancer (OR, 0.08; P < .01). Reasons cited for changes included consideration of the CDSS therapeutic options (63% of patients), patient factors highlighted by the tool (23%), and the decision logic of the tool (13%). Patient age and oncologist experience were not associated with decision changes. Adherence to NCCN treatment guidelines increased slightly after using the CDSS (0.5%; P = .003). CONCLUSION: Use of an artificial intelligence–based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant ( P = .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology.

To quantify the burden of illness of chronic hepatitis C virus (HCV) infection and estimate the impact of interferon-free direct-acting antiviral treatment on healthcare costs in Medicaid. Observational, retrospective analysis. Medicaid claims data from 2012 for nonelderly adult enrollees with chronic HCV in 16 states were used to estimate the burden of HCV in Medicaid. Annual measures of health services utilization and cost for patients with HCV were compared with a control group of patients without HCV exactly matched on a robust set of individual characteristics and stratified according to liver disease severity, Medicaid eligibility group, and plan type. Subsequently, HCV burden-of-illness estimates were used in a separate analysis of Medicaid State Drug Utilization Data on interferon-free drug utilization and expenditures to estimate the annual and cumulative impact of these curative medications on national Medicaid costs from 2013 through 2022. Annual per-person Medicaid healthcare costs attributed to HCV infection were estimated to range from $10,561 for noncirrhotic disabled adults to $46,263 for nondisabled adults with end-stage liver disease. The costs were due mainly to inpatient hospitalizations and outpatient hospital visits, prescription drug utilization, outpatient physician's office/clinic visits, and laboratory tests. By 2014, the first full year following the approval of interferon-free treatment, an estimated 12,175 adults with HCV were cured in Medicaid nationwide, each avoiding an estimated $15,907 per year in healthcare costs associated with the disease. As more patients in Medicaid are treated and net savings continue to grow year after year-due to recurring avoidance of health services use and declining drug prices-total cumulative treatment costs since 2013 are expected to be fully offset by total cumulative healthcare expenditure reductions by the end of 2019. By 2022, the recurrent annual avoidance of healthcare costs will have delivered an estimated $12 billion in total cumulative savings to Medicaid, net of DAA drug expenditures. The introduction of interferon-free HCV treatments enables the avoidance of significant healthcare costs previously associated with treating the disease year after year, producing annual cumulative Medicaid savings beginning in 2019. A main finding from this study is that the cost of a complete DAA treatment course, at 2018 estimated net prices, can be expected to be fully offset by healthcare cost savings after only 16 months, on average, on a per-person basis. Given the tremendous value provided by these curative drugs, Medicaid policies aimed toward restricting access to these treatments based on disease severity or other requirements would be shortsighted.

Objective: To examine the impact of adherence to chronic disease medications on health services utilization among Medicaid enrollees. Subjects: Eligibility, claims, and encounter data from the Medicaid Analytic Extract files from 10 states (Alabama, California, Florida, Illinois, Indiana, Louisiana, New Hampshire, New Mexico, New York, and Virginia) were used to construct a 3-year (2008–2010), longitudinal dataset of Medicaid recipients 18–64 years of age, including 656,646 blind/disabled individuals and 704,368 other adults. Patients were classified as having ≥1 of 7 chronic conditions: (1) congestive heart failure; (2) hypertension; (3) dyslipidemia; (4) diabetes; (5) asthma/chronic obstructive pulmonary disease; (6) depression; and (7) schizophrenia/bipolar. Methods: Poisson regression was used to estimate associations between medication adherence [continuous and categorical proportion of days covered (PDC)] and 3 dependent variables: number of inpatient hospitalizations, emergency department visits, and outpatient physician/clinic visits. Results: Full adherence was associated with 8%–26% fewer hospitalizations and 3%–12% fewer emergency department visits among those with congestive heart failure, hypertension, diabetes, and schizophrenia/bipolar. In all analyses, full adherence was associated with up to 15% fewer outpatient physician/clinic visits. Moreover, low and moderate levels of adherence were also related to less health care use. Conclusions: Substantial reductions in health services utilization and costs may be realized with improved medication adherence in Medicaid. These benefits begin to accrue at adherence levels below the common 0.80 PDC threshold. Therefore, interventions should focus not just on perfecting moderate adherers, but also on encouraging Medicaid patients with chronic conditions to initiate pharmacotherapy.

We used data on more than 1.5 million Medicaid enrollees to examine the impact of changes in prescription drug use on medical costs. For three distinct groups of enrollees, we estimated the effects of aggregate prescription drug use-and, more specifically, the use of medications to treat eight chronic noncommunicable diseases-on total nondrug, inpatient, outpatient, and other Medicaid spending. We found that a 1 percent increase in overall prescription drug use was associated with decreases in total nondrug Medicaid costs by 0.108 percent for blind or disabled adults, 0.167 percent for other adults, and 0.041 percent for children. Reductions in combined inpatient and outpatient spending from increased drug utilization in Medicaid were similar to an estimate for Medicare by the Congressional Budget Office. Moving forward, policy makers evaluating proposed changes that alter medication use among the nearly seventy million Medicaid recipients should consider the net effects on program spending to ensure that scarce federal and state health care dollars are allocated efficiently.