Artificial intelligence in Medicare: utilization, spending, and access to AI-enabled clinical software.

OBJECTIVES: In 2018, CMS established reimbursement for the first Medicare-covered artificial intelligence (AI)-enabled clinical software: CT fractional flow reserve (FFRCT) to assist in the diagnosis of coronary artery disease. This study quantified Medicare utilization of and spending on FFRCT from 2018 through 2022 and characterized adopting hospitals, clinicians, and patients. STUDY DESIGN: Analysis, using 100% Medicare fee-for-service claims data, of the hospitals, clinicians, and patients who performed or received coronary CT angiography with or without FFRCT. METHODS: We measured annual trends in utilization of and spending on FFRCT among hospitals and clinicians from 2018 through 2022. Characteristics of FFRCT-adopting and nonadopting hospitals and clinicians were compared, as well as the characteristics of patients who received FFRCT vs those who did not. RESULTS: From 2018 to 2022, FFRCT billing volume in Medicare increased more than 11-fold (from 1083 to 12,363 claims). Compared with nonbilling hospitals, FFRCT-billing hospitals were more likely to be larger, part of a health system, nonprofit, and financially profitable. FFRCT-billing clinicians worked in larger group practices and were more likely to be cardiac specialists. FFRCT-receiving patients were more likely to be male and White and less likely to be dually enrolled in Medicaid or receiving disability benefits. CONCLUSIONS: In the initial 5 years of Medicare reimbursement for FFRCT, growth was concentrated among well-resourced hospitals and clinicians. As Medicare begins to reimburse clinicians for the use of AI-enabled clinical software such as FFRCT, it is crucial to monitor the diffusion of these services to ensure equal access.

Can a private sector engagement intervention that prioritizes pro-poor strategies improve healthcare access and quality? A randomized field experiment in Kenya.

Private sector engagement in health reform has been suggested to help reduce healthcare inequities in sub-Saharan Africa, where populations with the most need seek the least care. We study the effects of African Health Markets for Equity (AHME), a cluster randomized controlled trial carried out in Kenya from 2012 to 2020 at 199 private health clinics. AHME included four clinic-level interventions: social health insurance, social franchising, SafeCare quality-of-care certification programme and business support. This paper evaluates whether AHME increased the capacity of private health clinics to serve poor clients while maintaining or enhancing the quality of care provided. At endline, clinics that received AHME were 14.5 percentage points (pp) more likely to be empanelled with the National Health Insurance Fund (NHIF), served 51% more NHIF clients and served more clients from the middle three quintiles of the wealth distribution compared to control clinics. Comparing individuals living in households near AHME treatment and control clinics (N = 8241), AHME led to a 6.7-pp increase in the probability of holding any health insurance on average. We did not find any additional effect of AHME on insurance holding among poor households. We measured quality of care using a standardized patient (SP) experiment (N = 596 SP-provider interactions) where recruited and trained SPs were randomized to present as either 'not poor', and able to afford all services provided, or 'poor' by telling the provider they could only afford ∼300 Kenyan Shillings (US$3) in fees. We found that poor SPs received lower levels of both correct and unnecessary services, and AHME did not affect this. More work must be done to ensure that clients of all wealth levels receive high-quality care.

Do private providers give patients what they demand, even if it is inappropriate? A randomised study using unannounced standardised patients in Kenya.

INTRODUCTION: Low and varied quality of care has been demonstrated for childhood illnesses in low-income and middle-income countries. Some quality improvement strategies focus on increasing patient engagement; however, evidence suggests that patients demanding medicines can favour the selection of resistant microbial strains in the individual and the community if drugs are inappropriately used. This study examines the effects on quality of care when patients demand different types of inappropriate medicines. METHODS: We conducted an experiment where unannounced standardised patients (SPs), locally recruited individuals trained to simulate a standardised case, present at private clinics. Between 8 March and 28 May 2019, 10 SPs portraying caretakers of a watery diarrhoea childhood case scenario (in absentia) conducted N=200 visits at 200 private, primary care clinics in Kenya. Half of the clinics were randomly assigned to receive an SP demanding amoxicillin (an antibiotic); the other half, an SP demanding albendazole (an antiparasitic drug often used for deworming), with other presenting characteristics the same. We used logistic and linear regression models to assess the effects of demanding these inappropriate medicines on correct and unnecessary case management outcomes. RESULTS: Compared with 3% among those who did not demand albendazole, the dispensing rate increased significantly to 34% for those who did (adjusted OR 0.06, 95% CI 0.02 to 0.22, p<0.0001). Providers did not give different levels of amoxicillin between those demanding it and those not demanding it (adjusted OR 1.73, 95% CI 0.51 to 5.82). Neither significantly changed any correct management outcomes, such as treatment or referral elsewhere. CONCLUSION: Private providers appear to account for both business-driven benefits and individual health impacts when making prescribing decisions. Additional research is needed on provider knowledge and perceptions of profit and individual and community health trade-offs when making prescription decisions after patients demand different types of inappropriate medicines. TRIAL REGISTRATION NUMBERS: American Economic Association Registry (#AEARCTR-0000217) and Pan African Clinical Trial Registry (#PACTR201502000770329).

How scheduling systems with automated appointment reminders improve health clinic efficiency.

Missed clinic appointments or no-shows burden health care systems through inefficient use of staff time and resources. Scheduling software with automatic appointment reminders shows promise to improve clinics' management through timely cancellations and re-scheduling, but at-scale evidence is missing. We study a nationwide text message appointment reminder program in Chile implemented at primary care clinics for patients with chronic disease. Using longitudinal clinic-level data, we find that the program did not change the number of visits by chronic patients eligible to receive the reminder but visits from other patients ineligible to receive reminders increased by 5.0% in the first year and 7.4% in the second. Clinics treating more chronic patients and those with a relatively younger patient population benefited more from the program. Scheduling systems with automatic appointment reminders were effective in increasing clinics' ability to care for more patients, likely due to timely cancellations and re-scheduling.

A Text Messaging Intervention (StayWell at Home) to Counteract Depression and Anxiety During COVID-19 Social Distancing: Pre-Post Study.

BACKGROUND: Social distancing and stay-at-home orders are critical interventions to slow down person-to-person transmission of COVID-19. While these societal changes help contain the pandemic, they also have unintended negative consequences, including anxiety and depression. We developed StayWell, a daily skills-based SMS text messaging program, to mitigate COVID-19-related depression and anxiety symptoms among people who speak English and Spanish in the United States. OBJECTIVE: This paper describes the changes in StayWell participants' anxiety and depression levels after 60 days of exposure to skills-based SMS text messages. METHODS: We used self-administered, empirically supported web-based questionnaires to assess the demographic and clinical characteristics of StayWell participants. Anxiety and depression were measured using the 2-item Generalized Anxiety Disorder (GAD-2) scale and the 8-item Patient Health Questionnaire-8 (PHQ-8) scale at baseline and 60-day timepoints. We used 2-tailed paired t tests to detect changes in PHQ-8 and GAD-2 scores from baseline to follow-up measured 60 days later. RESULTS: The analytic sample includes 193 participants who completed both the baseline and 60-day exit questionnaires. At the 60-day time point, there were significant reductions in both PHQ-8 and GAD-2 scores from baseline. We found an average reduction of -1.72 (95% CI -2.35 to -1.09) in PHQ-8 scores and -0.48 (95% CI -0.71 to -0.25) in GAD-2 scores. These improvements translated to an 18.5% and 17.2% reduction in mean PHQ-8 and GAD-2 scores, respectively. CONCLUSIONS: StayWell is an accessible, low-intensity population-level mental health intervention. Participation in StayWell focused on COVID-19 mental health coping skills and was related to improved depression and anxiety symptoms. In addition to improvements in outcomes, we found high levels of engagement during the 60-day intervention period. Text messaging interventions could serve as an important public health tool for disseminating strategies to manage mental health. TRIAL REGISTRATION: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23592.

A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial.

BACKGROUND: Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. OBJECTIVE: In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. METHODS: The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. RESULTS: The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. CONCLUSIONS: Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23592.

Proximate determinants of tuberculosis in Indigenous peoples worldwide: a systematic review.

BACKGROUND: Indigenous peoples worldwide carry a disproportionate tuberculosis burden. There is an increasing awareness of the effect of social determinants and proximate determinants such as alcohol use, overcrowding, type 1 and type 2 diabetes, substance misuse, HIV, food insecurity and malnutrition, and smoking on the burden of tuberculosis. We aimed to understand the potential contribution of such determinants to tuberculosis in Indigenous peoples and to document steps taken to address them. METHODS: We did a systematic review using seven databases (MEDLINE, Embase, CINAHL, Global Health, BIOSIS Previews, Web of Science, and the Cochrane Library). We identified English language articles published from Jan 1, 1980, to Dec 20, 2017, reporting the prevalence of proximate determinants of tuberculosis and preventive programmes targeting these determinants in Indigenous communities worldwide. We included any randomised controlled trials, controlled studies, cohort studies, cross-sectional studies, case reports, and qualitative research. Exclusion criteria were articles in languages other than English, full text not available, population was not Indigenous, focused exclusively on children or older people, and studies that focused on pharmacological interventions. FINDINGS: Of 34 255 articles identified, 475 were eligible for inclusion. Most studies confirmed a higher prevalence of proximate determinants in Indigenous communities than in the general population. Diabetes was more frequent in Indigenous communities within high-income countries versus in low-income countries. The prevalence of alcohol use was generally similar to that among non-Indigenous groups, although patterns of drinking often differed. Smoking prevalence and smokeless tobacco consumption were commonly higher in Indigenous groups than in non-Indigenous groups. Food insecurity was highly prevalent in most Indigenous communities evaluated. Substance use was more frequent in Indigenous inhabitants of high-income countries than of low-income countries, with wide variation across Indigenous communities. The literature pertaining to HIV, crowding, and housing conditions among Indigenous peoples was too scant to draw firm conclusions. Preventive programmes that are culturally appropriate targeting these determinants appear feasible, although their effectiveness is largely unproven. INTERPRETATION: Indigenous peoples were generally reported to have a higher prevalence of several proximate determinants of tuberculosis than non-Indigenous peoples, with wide variation across Indigenous communities. These findings emphasise the need for community-led, culturally appropriate strategies to address smoking, food insecurity, and diabetes in Indigenous populations as important public health goals in their own right, and also to reduce the burden of tuberculosis. FUNDING: Canadian Institutes of Health Research.

Exploring genetic suppression interactions on a global scale.

Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression.

Spectrum of MKS1 and MKS3 mutations in Meckel syndrome: a genotype-phenotype correlation. Mutation in brief #960. Online.

Meckel syndrome (MKS) is a rare autosomal recessive lethal condition characterized by central nervous system malformations (typically occipital meningoencephalocele), postaxial polydactyly, multicystic kidney dysplasia, and ductal proliferation in the portal area of the liver. MKS is genetically heterogeneous and three loci have been mapped respectively on 17q23 (MKS1), 11q13 (MKS2), and 8q24 (MKS3). Very recently, two genes have been identified: MKS1/FLJ20345 on 17q in Finnish kindreds, carrying the same intronic deletion, c.1408-35_c.1408-7del29, and MKS3/TMEM67 on 8q in families from Pakistan and Oman. Here we report the genotyping of the MKS1 and MKS3 genes in a large, multiethnic cohort of 120 independent cases of MKS. Our first results indicate that the MKS1 and MKS3 genes are each responsible for about 7% of MKS cases with various mutations in different populations. A strong phenotype-genotype correlation, depending on the mutated gene, was observed regarding the type of central nervous system malformation, the frequency of polydactyly, bone dysplasia, and situs inversus. The MKS1 c.1408-35_1408-7del29 intronic mutation was identified in three cases from French or English origin and dated back to 162 generations (approx. 4050 years) ago. We also identified a common MKS3 splice-site mutation, c.1575+1G>A, in five Pakistani sibships of three unrelated families of Mirpuri origin, with an estimated age-of-mutation of 5 generations (125 years).