Use of a Financial Incentive Code for In-Home COVID-19 Vaccination of Homebound Older Adults.

This study evaluated the uptake of Healthcare Common Procedure Coding System code M0201 after initial implementation to inform future policy related to in-home preventive care.

Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes: A Randomized Clinical Trial.

Importance: Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control. Objective: To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control. Design, Setting, and Participants: In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023. Interventions: Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care. Main Outcomes and Measures: Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis. Results: The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03). Conclusions and Relevance: In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication titration. Trial Registration: ClinicalTrials.gov Identifier: NCT05081011.

Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies.

Importance: A wide variety of novel medical diagnostics and devices are determined safe and effective by the US Food and Drug Administration (FDA) each year, but to our knowledge the literature lacks evidence documenting how long it takes to establish new Medicare coverage for these technologies. Objective: To measure time from FDA authorization to at least nominal Medicare coverage for technologies requiring a new reimbursement pathway. Design, Setting, and Participants: In this cross-sectional study, public databases were used to associate each technology to billing codes, determine the effective date of each code and Medicare coverage decisions, and stratify by the maturity of the Medicare coverage. At least nominal coverage was defined as achievement of explicit coverage milestones through a national coverage determination, local coverage determinations by Medicare administrative contractors, or by implicit coverage aligned to a new billing code. Characterization by product type (acute treatment, chronic or ongoing treatment, diagnostic assay, and diagnostic device), manufacturer size, and evidence level were assessed for association with coverage achievement. The study included new product applications authorized by the FDA through the premarket approval pathway, the de novo pathway, or with breakthrough designation in the 510(k) pathway from January 1, 2016, to December 31, 2019. Data analysis took place between May 1, 2022, and December 31, 2022. Main Outcome Measurement: Time from FDA authorization to the first coverage milestone. Results: Among 281 identified technologies in the total sample, 64 (23%) were deemed novel technologies based on the absence of coverage determinations and/or the use of temporary or miscellaneous billing codes. Twenty-eight of 64 technologies (44%) successfully achieved explicit or implicit coverage following FDA authorization. The median time to at least nominal coverage for the analysis cohort was 5.7 years (90% CI, 4.4-NA years). Analysis of time-to-coverage data highlighted company size (log-rank P<.001) and product type (log-rank P = .01) as significant covariates associated with coverage achievement. No association was observed for technologies with level 1 evidence at FDA authorization and subsequent coverage milestone achievement (log-rank P = .40). Conclusions and Relevance: In this cross-sectional study of 64 novel technologies, only 28 (44%) achieved coverage milestones over the study timeline. The several-year period observed to establish at least nominal coverage suggests existing coverage processes may affect timely reimbursement of new technologies.

Comparison of History of Present Illness Summaries Generated by a Chatbot and Senior Internal Medicine Residents.

This prognostic study assesses the ability of a chatbot to write a history of present illness compared with senior internal medicine residents.

Extent of lymphadenectomy and outcome for patients with stage I nonsmall cell lung cancer.

BACKGROUND: It is uncertain whether lymphadenectomy (LA) affects overall survival (OS) or disease-free survival (DFS) rates for patients with stage I nonsmall cell lung cancer (NSCLC), as is the optimal number of lymph nodes that should be recovered. METHODS: There were 24,273 patients with stage I NSCLC diagnosed from 1992 to 2002 who were included in the Surveillance, Epidemiology, and End Results database and who underwent a definitive surgical procedure. Median follow-up was 35 months. RESULTS: For the entire population, having LA was associated with an increase in the 5-year OS rate from 41.6% to 58.4% (P<.0001) and in DFS from 58.0% to 73.09%, compared with not having LA. Outcome improved with increasing number of recovered lymph nodes, with a plateau at 11 or more lymph nodes. For patients diagnosed from 1998 to 2002 undergoing only N1 or only N2 dissections, LA was also associated with statistically significant improvements in OS in both groups and a significant difference and trend for improved DFS in the 2 groups, respectively. The maximum differences in both OS and DFS between those with no LA and those with LA occurred when 11 to 16 lymph nodes were removed for the former group or 7 to 10 lymph nodes for the latter group, respectively. CONCLUSIONS: LA was associated with increased rates of OS and DFS, compared with no LA. Our results also suggest the minimum number of recovered lymph nodes needed to see the maximum staging accuracy conferred by LA.

A comparison of survival and disease-specific survival in surgically resected, lymph node-positive bronchioloalveolar carcinoma versus nonsmall cell lung cancer: implications for adjuvant therapy.

BACKGROUND: The objective of this study was to assess whether disease-specific survival (DSS) and overall survival (OS) differed among patients who had N1 and N2 bronchioloalveolar carcinoma (BAC) compared with patients who had non-BAC nonsmall cell lung cancer (NSCLC). METHODS: The Surveillance, Epidemiology, and End Results (SEER) Program database from 1992 to 2002 contained 684 patients with BAC and 9809 patients with non-BAC NSCLC who had N1/N2 tumors and who underwent a definitive surgical procedure. OS and DSS rates were compared according to potential prognostic factors, including the use of a matched-pair analysis. RESULTS: The BAC patients with either pathologic N1 or N2 lymph node status were significantly more likely to be women, and nonblack/nonwhite race, but significantly less likely to have poorly differentiated or undifferentiated tumors than patients with non-BAC cancers with comparable lymph node status. The median follow-up of all patients was 29 months. There was a significant difference in DSS between patients with N2 BAC and non-BAC cancers, but not for patients with N1 disease. There was a nonsignificant trend toward longer OS for patients with N2 BAC compared with non-BAC cancers. CONCLUSIONS: Patients with lymph node-positive BAC had distinctly different patient and tumor characteristics than patients with lymph node-positive non-BAC NSCLC. Because DSS appears to be better for patients with N2 BAC, they may not benefit as much from adjuvant therapy as patients with non-BAC NSCLC.

An informative Bayesian structural equation model to assess source-specific health effects of air pollution.

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.

Policies and practices related to breastfeeding in massachusetts: hospital implementation of the ten steps to successful breastfeeding.

BACKGROUND: Hospital policies and practices related to breastfeeding may have long-term health effects. The Ten Steps to Successful Breastfeeding (WHO/UNICEF) provide an evidence-based standard, which may be used to assess individual hospitals. We assessed implementation, and factors related to implementation, of the Ten Steps in Massachusetts hospitals. METHODS: We surveyed postpartum nurse managers at 43 (88%) of the 49 Massachusetts maternity hospitals by telephone. Survey items characterized hospital policies, breastfeeding education, and support practices. We classified hospital implementation of the Ten Steps as high, moderately high, partial, or low and used Massachusetts Department of Public Health hospital data to analyze factors related to implementation. RESULTS: Levels of implementation of the Ten Steps ranged from high to partial. Overall, we classified implementation of 2% of hospitals as high, 58% moderately high, 40% partial, and 0% as low. Hospitals with high/moderately high levels of implementation significantly differed from hospitals with partial implementation with respect to pacifier usage (p=0.0017) and postpartum breastfeeding instruction (p=0.0001). Requirement of a physician order for formula was a statistically significant (p=0.02) predictor of Step 1 implementation but did not reach significance (p=0.14) overall. Acceptance of free formula was significantly associated (p=0.03) with overall Ten Steps implementation. CONCLUSION: Rates of self-reported implementation of the Ten Steps are relatively high in Massachusetts. Step 1 implementation is significantly associated with formula availability, and overall implementation with acceptance of free formula. Continued assessment is needed to confirm these results in larger samples and to examine the relationship of implementation of individual steps, breastfeeding rates, and health outcomes.

Variation in total and specific IgE: effects of ethnicity and socioeconomic status.

BACKGROUND: Asthma is common in minority and disadvantaged populations, whereas atopic disorders other than asthma appear to be less prevalent. It is unclear whether the same holds true for objective markers of sensitization. OBJECTIVE: To determine the association of asthma, atopic disorders, and specific sensitization with race and socioeconomic factors. METHODS: We analyzed total and specific IgE among 882 women (577 white, 169 black, and 136 Hispanic) who delivered a child at a large tertiary hospital in Boston, Mass, and who were screened for participation in a family and birth cohort study. Race/ethnicity and other characteristics were obtained from screening questionnaires. Addresses were geocoded, and 3 census-based geographic area socioeconomic variables were derived from block group information from the 1990 US Census. RESULTS: Black and Hispanic women were more likely to come from areas with low socioeconomic indicators and were more likely to have asthma than white women. However, these women were less likely to have hay fever and eczema than their white counterparts. Compared with white women, black women had higher mean total IgE levels; had greater proportions of sensitization to indoor, outdoor, and fungal allergens; and were more than twice as likely to be sensitized to > or =3 aeroallergens. CONCLUSION: The racial/ethnic disparities in atopic disorders may represent either underdiagnosis or underreporting and suggest that allergy testing may be underused in some populations. Differences in total IgE levels and specific allergen sensitization are likely a result of the complex interplay between exposures associated with socioeconomic disadvantage.