Safety vs price in the generic drug market: metformin.

OBJECTIVES: Generic medications represent 90% of prescriptions in the US market and provide a tremendous financial benefit for patients. Recently, multiple generic drugs have been recalled due to the presence of carcinogens, predominantly N-nitrosodimethylamine (NDMA), including an extensive recall of extended-release (ER) metformin products in 2020. STUDY DESIGN: Primary pharmaceutical quality testing and database analysis. METHODS: We tested marketed metformin immediate-release (IR) and ER tablets from a wide sample of generic manufacturers for the presence of carcinogenic impurities NDMA and N,N-dimethylformamide (DMF). We examined the association of level of impurity with drug price and the impact of the 2020 FDA recalls on unit price and prescription fill rate. RESULTS: Postrecall NDMA levels were significantly lower in metformin ER samples (standardized mean difference = -2.0; P = .01); however, we found continued presence of carcinogens above the FDA threshold in 2 of 30 IR samples (6.67%). Overall, the presence of contaminant levels was not significantly associated with price for either IR (NDMA: R2 = 0.142; P = .981; DMF: R2 = 0.382; P = .436) or ER (NDMA: R2 = 0.124; P = .142; DMF: R2 = 0.199; P = .073) samples. Despite recalls, metformin ER prescription fills increased by 8.9% while unit price decreased by 19.61% (P < .05). CONCLUSIONS: Recalls of metformin ER medications were effective in lowering NDMA levels below the FDA threshold; however, some samples of generic metformin still contained carcinogens even after FDA-announced recalls. The absence of any correlation with price indicates that potentially safer products are available on the market for the same price as poorer-quality products.

Assessment of Price Variation in Coronary Artery Bypass Surgery at US Hospitals.

BACKGROUND: Little is known about hospital pricing for coronary artery bypass grafting (CABG). Using new price transparency data, we assessed variation in CABG prices across US hospitals and the association between higher prices and hospital characteristics, including quality of care. METHODS AND RESULTS: Prices for diagnosis related group code 236 were obtained from the Turquoise database and linked by Medicare Facility ID to publicly available hospital characteristics. Univariate and multivariable analyses were performed to assess factors predictive of higher prices. Across 544 hospitals, median commercial and self-pay rates were 2.01 and 2.64 times the Medicare rate ($57 240 and $75 047, respectively, versus $28 398). Within hospitals, the 90th percentile insurer-negotiated price was 1.83 times the 10th percentile price. Across hospitals, the 90th percentile commercial rate was 2.91 times the 10th percentile hospital rate. Regional median hospital prices ranged from $35 624 in the East South Central to $84 080 in the Pacific. In univariate analysis, higher inpatient revenue, greater annual discharges, and major teaching status were significantly associated with higher prices. In multivariable analysis, major teaching and investor-owned status were associated with significantly higher prices (+$8653 and +$12 200, respectively). CABG prices were not related to death, readmissions, patient ratings, or overall Centers for Medicare and Medicaid Services hospital rating. CONCLUSIONS: There is significant variation in CABG pricing, with certain characteristics associated with higher rates, including major teaching status and investor ownership. Notably, higher CABG prices were not associated with better-quality care, suggesting a need for further investigation into drivers of pricing variation and the implications for health care spending and access.

Place of Death Before and During the COVID-19 Pandemic.

This quality improvement study examines the national and ongoing impact of the COVID-19 pandemic with the place of death among individuals in the US.

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.

Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic.

BACKGROUND: Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations. METHODS: We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic. RESULTS: We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=-0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation. CONCLUSIONS: Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need.

Spatial access to healthcare facilities (i.e., how long people need to travel to reach care) is important for understanding public health, but hard to measure. Most research so far has focused on theoretical (potential) travel times. Using anonymized smartphone location history data, we measure actual (revealed) travel times to healthcare facilities in over 100 countries. We find that revealed travel times exceed theoretical travel times in many regions of the world, meaning that in reality people travel longer to get healthcare. Our data also show that inequities in travel time became worse during the COVID-19 pandemic. When combined with other data, these results can help policymakers identify areas and populations at need, and direct resources to improve public health.

AI Alone Will Not Reduce the Administrative Burden of Health Care.

This Viewpoint considers AI's limits in solving the medical billing quagmire and argues that standardizing health insurance claim forms and simplifying billing must occur before AI can shoulder the load.

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.

Engaging Medicare Beneficiaries in Coverage Choices.

This Viewpoint discusses the recently announced monthly Medicare Part B premium hike and the limited role beneficiaries play in decisions about their coverage, and proposes ways to engage Medicare beneficiaries in program decisions.

Employer-Based Health Insurance and Employee Compensation.

This Viewpoint expounds on how the cost of health insurance is consuming an ever-greater share of total compensation for employers and employees, stagnating real incomes and calling into question its real value.

Maintaining Health Care Innovations After the Pandemic.

This Viewpoint discusses evaluating and perhaps extending the record of successful innovation arising from the COVID-19 pandemic.

Randomized Clinical Trial to Evaluate an Atrial Fibrillation Stroke Prevention Shared Decision-Making Pathway.

Background Oral anticoagulation reduces stroke and disability in atrial fibrillation (AF) but is underused. We evaluated the effects of a novel patient-clinician shared decision-making (SDM) tool in reducing oral anticoagulation patient's decisional conflict as compared with usual care. Methods and Results We designed and evaluated a new digital decision aid in a multicenter, randomized, comparative effectiveness trial, ENHANCE-AF (Engaging Patients to Help Achieve Increased Patient Choice and Engagement for AF Stroke Prevention). The digital AF shared decision-making toolkit was developed using patient-centered design with clear health communication principles (eg, meaningful images, limited text). Available in English and Spanish, the toolkit included the following: (1) a brief animated video; (2) interactive questions with answers; (3) a quiz to check on understanding; (4) a worksheet to be used by the patient during the encounter; and (5) an online guide for clinicians. The study population included English or Spanish speakers with nonvalvular AF and a CHA2DS2-VASc stroke score ≥1 for men or ≥2 for women. Participants were randomized in a 1:1 ratio to either usual care or the shared decision-making toolkit. The primary end point was the validated 16-item Decision Conflict Scale at 1 month. Secondary outcomes included Decision Conflict Scale at 6 months and the 10-item Decision Regret Scale at 1 and 6 months as well as a weighted average of Mann-Whitney U-statistics for both the Decision Conflict Scale and the Decision Regret Scale. A total of 1001 participants were enrolled and followed at 5 different sites in the United States between December 18, 2019, and August 17, 2022. The mean patient age was 69±10 years (40% women, 16.9% Black, 4.5% Hispanic, 3.6% Asian), and 50% of participants had CHA2DS2-VASc scores ≥3 (men) or ≥4 (women). The primary end point at 1 month showed a clinically meaningful reduction in decisional conflict: a 7-point difference in median scores between the 2 arms (16.4 versus 9.4; Mann-Whitney U-statistics=0.550; P=0.007). For the secondary end point of 1-month Decision Regret Scale, the difference in median scores between arms was 5 points in the direction of less decisional regret (P=0.078). The treatment effects lessened over time: at 6 months the difference in medians was 4.7 points for Decision Conflict Scale (P=0.060) and 0 points for Decision Regret Scale (P=0.35). Conclusions Implementation of a novel shared decision-making toolkit (afibguide.com; afibguide.com/clinician) achieved significantly lower decisional conflict compared with usual care in patients with AF. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04096781.

A data-driven quality-score system for rating drug products and its implications for the health care industry.

The quality of drug products in the United States has been a matter of growing concern. Buyers and payers of pharmaceuticals have limited insight into measures of drug-product quality. Therefore, a quality-score system driven by data collection is proposed to differentiate between the qualities of drug products produced by different manufacturers. The quality scores derived using this proposed system would be based upon public regulatory data and independently-derived chemical data. A workflow for integrating the system into procurement decisions within health care organizations is also suggested. The implementation of such a quality-score system would benefit health care organizations by including the consideration of the quality of products while also considering price as a part of the drug procurement process. Such a system would also benefit the U.S. health care industry by bringing accountability and transparency into the drug supply chain and incentivizing manufacturers to place an increased emphasis on the quality and safety of their drug products.

Pre-pandemic assessment: a decade of progress in electronic health record adoption among U.S. hospitals.

As the COVID-19 pandemic loomed, the adoption of electronic health records (EHRs) in US hospitals became a pivotal concern. This study provides a pre-pandemic assessment, highlighting a decade of progress in EHR adoption from 2009 to 2019, with the last available survey conducted from January to June of 2020. It delves into the current EHR adoption rates, variations across different hospital categories, the influence of major vendors, and the challenges in implementing these systems. The study found that basic EHR adoption surged from 6.6% to 81.2%, while comprehensive systems increased from 3.6% to 63.2%. Despite this growth, the findings point to enduring disparities among hospitals, a concentrated market share by 6 vendors (90%), and significant concerns regarding maintenance costs. These insights provide an invaluable snapshot of the state of EHR adoption at the brink of the pandemic, serving as a benchmark to assess hospitals' readiness to utilize digital infrastructure in health care. The conclusions underscore the necessity for strategic policy interventions to encourage a competitive landscape and guarantee equitable access, ultimately strengthening the health care system's responsiveness to global health crises such as COVID-19.