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.

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study.

Importance: Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning. Objective: To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources. Design: Multi-center, randomized clinical vignette study. Setting: The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions. Participants: Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine. Interventions: Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams. Main Outcomes and Measures: The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis. Results: 50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group. Conclusions and Relevance: In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

Evidence for Changes in Screen Use in the United States During Early Childhood Related to COVID-19 Pandemic Parent Stressors: Repeated Cross-Sectional Study.

BACKGROUND: The COVID-19 pandemic transformed the home lives of many families in the United States, especially those with young children. Understanding the relationship between child and parent screen time and family stressors exacerbated by the pandemic may help inform interventions that aim to support early child development. OBJECTIVE: We aim to assess the changing relationship between family screen time and factors related to pandemic-induced remote work and childcare or school closures. METHODS: In the spring of 2021 we administered a survey, similar to one administered in the spring of 2019, to a national sample of parents of young children (aged 6 to 60 months). Using iterative sampling with propensity scores, we recruited participants whose sociodemographic characteristics matched the 2019 survey. Participants were aged >18 years, proficient in English or Spanish, and residing in the United States. The main outcomes were changes in child screen time (eg, mobile phone, tablet, computer, and television) and parenting technoference, defined as perceived screen-related interference with parent-child interactions. Additional survey items reported pandemic-related job loss, and changes to work hours, work location, caregiving responsibilities, day care or school access, and family health and socioeconomic status. RESULTS: We enrolled 280 parents, from diverse backgrounds. Parents reported pandemic-related changes in child screen time (mean increase of 1.1, SD 0.9 hours), and greater parenting technoference (3.0 to 3.4 devices interfering per day; P=.01). Increased child screen time and parenting technoference were highest for parents experiencing job loss (mean change in child screen time 1.46, SD 1.03; mean parenting technoference score 3.89, SD 2.05), second highest for working parents who did not lose their job (mean change in child screen time 1.02, SD 0.83; mean parenting technoference score 3.37, SD 1.94), and lowest for nonworking parents (mean change in child screen time 0.68, SD 0.66; mean parenting technoference score 2.66, SD 1.70), with differences significant at P<.01. School closure and job loss were most associated with increased child screen time during the pandemic after controlling for other stressors and sociodemographic characteristics (d=0.52, P<.001; d=0.31, P=.01). Increased child screen time and school closure were most associated with increased parenting technoference (d=0.78, P<.001; d=0.30, P=.01). CONCLUSIONS: Work and school changes due to the COVID-19 pandemic were associated with increased technology interference in the lives of young children. This study adds to our understanding of the interaction between technology use at home and social factors that are necessary to support early childhood health and development. It also supports possible enhanced recommendations for primary care providers and childcare educators to guide parents in establishing home-based "screen time rules" not only for their children but also for themselves.

Association between maternal employment status and presence of children with major congenital anomalies in Denmark.

IMPORTANCE: The burden of caring for children with complex medical problems such as major congenital anomalies falls principally on mothers, who in turn suffer a variety of potentially severe economic consequences. As well, health consequences of caregiving often further impact the social and economic prospects of mothers of children with major congenital anomalies (MCMCAs). Evaluating the long-term economic consequences of extensive in-home caregiving among MCMCAs can inform strategies to mitigate these effects. OBJECTIVE: To assess whether MCMCAs face reduced employment and increased need for disability benefits over a 20-year period. DESIGN: A population-based matched cohort study. SETTING: Denmark. PARTICIPANTS: All women who gave birth to a singleton child with a major congenital anomaly in Denmark between January 1, 1997 and December 31, 2017 (n = 23,637) and a comparison cohort of mothers matched by maternal age, parity, and infant's year of birth (n = 234,586). EXPOSURES: Liveborn infant with a major congenital anomaly. MAIN OUTCOMES AND MEASURES: The primary outcome was mothers' employment status, stratified by their child's age. Employment status was categorized as employed, outside the workforce (on temporary leave, holding a flexible job, or pursuing education), or unemployed; the number of weeks in each category was measured over time. The secondary outcome was time to receipt of a disability pension, which in Denmark implies permanent exit from the labor market. We used a negative binomial regression model to estimate the number of weeks in each employment category, stratified by the child's age (i.e., 0-1 year, > 1-6 years, 7-13 years, 14-18 years). A Cox proportional hazards regression model was used to compute hazard ratios as a measure of the relative risk of receiving a disability pension. Rate ratios and hazard ratios were adjusted for maternal demographics, pregnancy history, health, and infant's year of birth. RESULTS: During 1-6 years after delivery, MCMCAs were outside the workforce for a median of 50 weeks (IQR, 6-107 weeks), while members of the comparison cohort were outside the workforce for a median of 48 weeks (IQR, 4-98 weeks), corresponding to an adjusted rate ratio [ARR] of 1.05 (95% confidence interval [CI], 1.04-1.07). During the first year after delivery, MCMCAs were more likely to be employed than mothers in the comparison cohort (ARR, 1.08; 95% CI, 1.06-1.10). At all timepoints thereafter, MCMCAs had a lower rate of workforce participation. The rate of being outside the workforce was 5% higher than mothers in the comparison cohort during 1-6 years after delivery (ARR, 1.05; 95% CI, 1.04-1.07), 9% higher during 7-13 years after delivery (ARR, 1.09; 95% CI, 1.06-1.12), and 12% higher during 14-18 years after delivery (ARR, 1.12; 95% CI, 1.07-1.18). Overall, MCMCAs had a 20% increased risk of receiving a disability pension during follow-up than mothers in the matched comparison cohort [incidence rates 3.10 per 1000 person-years (95% CI, 2.89-3.32) vs. 2.34 per 1000 person-years (95% CI, 2.29-2.40), adjusted hazard ratio, 1.20; 95% CI, 1.11-1.29]. CONCLUSION AND RELEVANCE: MCMCAs were less likely to participate in the Danish workforce, less likely to be employed, and more likely to receive disability pensions than mothers of unaffected children. The rate of leaving the workforce intensified as their affected children grew older. The high demands of caregiving among MCMCAs may have long-term employment consequences even in nations with comprehensive and heavily tax-supported childcare systems, such as Denmark.

Innovation Diffusion Across 13 Specialties and Associated Clinician Characteristics.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation - artificial intelligence (AI) - for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes.

In a randomized, pre-post intervention study, we evaluated the influence of a large language model (LLM) generative AI system on accuracy of physician decision-making and bias in healthcare. 50 US-licensed physicians reviewed a video clinical vignette, featuring actors representing different demographics (a White male or a Black female) with chest pain. Participants were asked to answer clinical questions around triage, risk, and treatment based on these vignettes, then asked to reconsider after receiving advice generated by ChatGPT+ (GPT4). The primary outcome was the accuracy of clinical decisions based on pre-established evidence-based guidelines. Results showed that physicians are willing to change their initial clinical impressions given AI assistance, and that this led to a significant improvement in clinical decision-making accuracy in a chest pain evaluation scenario without introducing or exacerbating existing race or gender biases. A survey of physician participants indicates that the majority expect LLM tools to play a significant role in clinical decision making.

Caring for People With Depression: Costs Among 43 Million Commercially Insured Patients With or Without Comorbid Illnesses.

BACKGROUND: Depression is a common comorbidity for patients with chronic medical conditions. Although the costs of treating chronic medical illness in combination with depression are believed to be significantly higher than the costs of treating each condition independently, few studies have formally modeled the cost consequences of mental health comorbidity. PURPOSE: To estimate the relative magnitude of the independent and synergistic contributions to health care costs from depression diagnosis and other chronic physical health conditions. METHODS: Cross-sectional, observational study using all individuals >18 years of age in the national Blue Cross Blue Shield (BCBS) Axis claims database (N = 43,872,144) from calendar year 2018. General linear models with and without interaction terms were used to assess the relative magnitude of independent and synergistic contributions to total annual health care costs of depression alone and in combination with coronary heart disease, chronic kidney disease, chronic obstructive pulmonary disease, diabetes (both types 1 and 2), hypertension, and arthritis. RESULTS: The incremental annual cost associated with having a diagnosis of depression was $2,951 compared to $1,986-$6,251 for the other chronic physical conditions. The interaction between depression and chronic conditions accounted for less than one-hundredth of the amount of variation in costs explained by the main effects of depression and each chronic physical condition. CONCLUSIONS: The independent increase in total annual health care costs associated with a depression diagnosis was comparable to that of many common physical chronic conditions. This finding underscores the importance of health care service and payment models that acknowledge depression as an equal contributor to overall health care costs. The combination of depression and another chronic condition did not synergistically increase total annual health care costs beyond the increases in costs associated with each condition independently. This finding has implications for simplifying risk adjustment models.

It is widely believed that depression, when combined with other chronic physical conditions, systematically inflates health care costs. For example, it is assumed that the costs of caring for a patient with both depression and heart disease are higher than the costs of caring for each condition independently. Using a database that included 43 million commercially insured people in the United States, we found that the costs of care for patients with depression were comparable to the costs for patients with other chronic medical conditions. This result supports the need for mental health parity and for trained mental health care providers in medical settings. We then considered the costs of caring for people with depression with or with or without one of seven common chronic physical condition. Contrary to expectation, the combination of depression and any of the diagnoses appeared to have largely independent relationships with health care costs. The results contradict the suggestion that depression and chronic condition diagnoses act synergistically to inflate health care expenditures.

Using Case Mix Index within Diagnosis-Related Groups to Evaluate Variation in Hospitalization Costs at a Large Academic Medical Center.

In analyzing direct hospitalization cost and clinical data from an academic medical center, commonly used metrics such as diagnosis-related group (DRG) weight explain approximately 37% of cost variability, but a substantial amount of variation remains unaccounted for by case mix index (CMI) alone. Using CMI as a benchmark, we isolate and target individual DRGs with higher than expected average costs for specific quality improvement efforts. While DRGs summarize hospitalization care after discharge, a predictive model using only information known before admission explained up to 60% of cost variability for two DRGs with a high excess cost burden. This level of variability likely reflects underlying patient factors that are not modifiable (e.g., age and prior comorbidities) and therefore less useful for health systems to target for intervention. However, the remaining unexplained variation can be inspected in further studies to discover operational factors that health systems can target to improve quality and value for their patients. Since DRG weights represent the expected resource consumption for a specific hospitalization type relative to the average hospitalization, the data-driven approach we demonstrate can be utilized by any health institution to quantify excess costs and potential savings among DRGs.

Effect of a Biopsychosocial Intervention or Postural Therapy on Disability and Health Care Spending Among Patients With Acute and Subacute Spine Pain: The SPINE CARE Randomized Clinical Trial.

Importance: Low back and neck pain are often self-limited, but health care spending remains high. Objective: To evaluate the effects of 2 interventions that emphasize noninvasive care for spine pain. Design, Setting, and Participants: Pragmatic, cluster, randomized clinical trial conducted at 33 centers in the US that enrolled 2971 participants with neck or back pain of 3 months' duration or less (enrollment, June 2017 to March 2020; final follow-up, March 2021). Interventions: Participants were randomized at the clinic-level to (1) usual care (n = 992); (2) a risk-stratified, multidisciplinary intervention (the identify, coordinate, and enhance [ICE] care model that combines physical therapy, health coach counseling, and consultation from a specialist in pain medicine or rehabilitation) (n = 829); or (3) individualized postural therapy (IPT), a postural therapy approach that combines physical therapy with building self-efficacy and self-management (n = 1150). Main Outcomes and Measures: The primary outcomes were change in Oswestry Disability Index (ODI) score at 3 months (range, 0 [best] to 100 [worst]; minimal clinically important difference, 6) and spine-related health care spending at 1 year. A 2-sided significance threshold of .025 was used to define statistical significance. Results: Among 2971 participants randomized (mean age, 51.7 years; 1792 women [60.3%]), 2733 (92%) finished the trial. Between baseline and 3-month follow-up, mean ODI scores changed from 31.2 to 15.4 for ICE, from 29.3 to 15.4 for IPT, and from 28.9 to 19.5 for usual care. At 3-month follow-up, absolute differences compared with usual care were -5.8 (95% CI, -7.7 to -3.9; P < .001) for ICE and -4.3 (95% CI, -5.9 to -2.6; P < .001) for IPT. Mean 12-month spending was $1448, $2528, and $1587 in the ICE, IPT, and usual care groups, respectively. Differences in spending compared with usual care were -$139 (risk ratio, 0.93 [95% CI, 0.87 to 0.997]; P = .04) for ICE and $941 (risk ratio, 1.40 [95% CI, 1.35 to 1.45]; P < .001) for IPT. Conclusions and Relevance: Among patients with acute or subacute spine pain, a multidisciplinary biopsychosocial intervention or an individualized postural therapy intervention, each compared with usual care, resulted in small but statistically significant reductions in pain-related disability at 3 months. However, compared with usual care, the biopsychosocial intervention resulted in no significant difference in spine-related health care spending and the postural therapy intervention resulted in significantly greater spine-related health care spending at 1 year. Trial Registration: ClinicalTrials.gov Identifier: NCT03083886.

Care teams misunderstand what most upsets patients about their care.

BACKGROUND: Negative healthcare delivery experiences can cause lasting patient distress and medical service misuse and disuse. Yet no multi-site study has examined whether care-team members understand what most upsets patients about their care. METHODS: We interviewed 373 patients and 360 care-team members in the medical oncology and ambulatory surgery clinics of 11 major healthcare organizations across six U.S. census regions. Patients deeply upset by a service-related experience (n = 99, 27%) answered questions about that experience, while care-team members (n = 360) answered questions about their beliefs regarding what most upsets patients. We performed content analysis to identify memorably upsetting care (MUC) themes; a generalized estimating equation to explore whether MUC theme mention frequencies varied by participant role (care-team member vs. patient), specialty (oncology vs. surgery), facility (academic vs. community), and gender; and logistic regressions to investigate the effects of participant characteristics on individual themes. RESULTS: MUC themes included three systems issues (inefficiencies, access barriers, and facilities problems) and four care-team issues (miscommunication, neglect, coldness, and incompetence). MUC theme frequencies differed by role (all Ps < 0.001), with more patients mentioning care-team coldness (OR = 0.37; 95% CI, 0.23-0.60) and incompetence (OR = 0.17; 95% CI, 0.09-0.31); but more care-team members mentioning system inefficiencies (OR = 7.01; 95% CI, 4.31-11.40) and access barriers (OR, 5.48; 95% CI, 2.81-10.69). CONCLUSIONS: When considering which service experiences most upset patients, care-team members underestimate the impact of their own behaviors and overestimate the impact of systems issues. IMPLICATIONS: Healthcare systems should reconsider how they collect, interpret, disseminate, and respond to patient service reports. LEVEL OF EVIDENCE: Level III.

Improving adherence to guidelines for spine pain care: what tools could support primary care clinicians in conforming to guidelines?

BACKGROUND: Spine pain is one of the most common conditions seen in primary care and is often treated with ineffective, aggressive interventions, such as prescription pain medications, imagery and referrals to surgery. Aggressive treatments are associated with negative side effects and high costs while conservative care has lower risks and costs and equivalent or better outcomes. Despite multiple well-publicised treatment guidelines and educational efforts recommending conservative care, primary care clinicians (PCCs) widely continue to prescribe aggressive, low-value care for spine pain. METHODS: In this qualitative study semistructured interviews were conducted with PCCs treating spine pain patients to learn what prevents clinicians from following guidelines and what tools or support could promote conservative care. Interviews were conducted by telephone, transcribed and coded for thematic analysis. RESULTS: Forty PCCs in academic and private practice were interviewed. Key reflections included that while familiar with guidelines recommending conservative treatment, they did not find guidelines useful or relevant to care decisions for individual patients. They believed that there is an insufficient body of real-world evidence supporting positive outcomes for conservative care and guidance recommendations. They indicated that spine pain patients frequently request aggressive care. These requests, combined with the PCCs' commitment to reaching shared treatment decisions with patients, formed a key reason for pursuing aggressive care. PCCs reported not being familiar with risk-screening tools for spine patients but indicated that such screens might increase their confidence to recommend conservative care to low-risk patients. CONCLUSIONS: PCCs may be more willing to give conservative, guideline-consistent care for spine pain if they had tools to assist in making patient-specific evaluations and in countering requests for unneeded aggressive care. Such tools would include both patient risk screens and shared decision-making aids that include elements for resolving patient demands for inappropriate care.

Identifying solutions to meet unmet needs of family caregivers using human-centered design.

BACKGROUND: Given the rapidly aging society, shrinking workforce, and reducing dependency ratio, there is an increasing challenge for family members to provide care for older adults. While a broad understanding of caregiver burden and its consequences have been studied across various contexts, there is a need to better understand this challenge among family caregivers in Asian societies. METHODS: This study is a cross-sectional observational study. A total of 20 dyads of community-based older adults, who required assistance with at least one activities of daily living, and family caregivers in Thailand participated in the study. We used the first three stages out of five stages of human-centered design: empathize, define, and ideate. RESULTS: On average caregivers were 59.2 years old, with 43% still employed. Of the older adult participants, 10 were interviewed, the others had moderate-to-severe cognitive impairment. Based on the analysis, six caregiver personas (i.e. semi-fictional characters) are identified. Caregiver personas of "The 2-Jober" and "My Life Purpose" has the highest caregiver burden score whereas "The Spouse" has the lowest. Based on the specific needs of the caregiver persona "My Life Purpose", the team brainstormed more than 80 potential solutions which were classified into three categories of solutions that satisfied the metrics of desirability, feasibility and viability: distributed medical care system, technology-charged care network, and community gathering for rest and recuperation. CONCLUSIONS: These solutions are culturally sensitive given that they are built around established behavioral patterns. This is an illustration of a method of innovation that can be applied to bring a culturally specific understanding, and to develop products and services to enable further independent aging.

Distilling innovative US autism care programs that address widely perceived unmet patient and family needs.

LAY ABSTRACT: Currently, the quality of care for autistic individuals is not good. Many care services for autistic individuals are not well coordinated, nor are they tailored. We wanted to find out a better model for autism care and believed that the autism community knows where these programs are. So, we had conversations with and surveyed 55 autistic adults, family members, clinicians, and researchers. They shared 90 innovative autism care programs that had been collaboratively designed with patients and families and that are likely to improve the quality of life of autistic individuals and their families. We then narrowed down the 90 nominated programs to 15 programs across the United States by applying researcher-selected criteria, such as providing services actively and having data on program effectiveness. We compiled a list of these innovative, quality autism care programs.

Estimating health care delivery system value for each US state and testing key associations.

OBJECTIVE: To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value. DATA SOURCES: Annual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts. STUDY DESIGN: Using non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991-2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: US state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01). CONCLUSIONS: Substantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.

A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency.

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.

Mothers of children with major congenital anomalies have increased health care utilization over a 20-year post-birth time horizon.

OBJECTIVE: This population-based, matched cohort study aimed to evaluate utilization of health care services by mothers of children with major congenital anomalies (MCAs), compared to mothers of children without MCAs over a 20-year post-birth time horizon in Denmark. METHODS: Our analytic sample included mothers who gave birth to an infant with a MCA (n = 23,927) and a cohort of mothers matched to them by maternal age, parity and infant's year of birth (n = 239,076). Primary outcomes were period prevalence and mothers' quantity of health care utilization (primary, inpatient, outpatient, surgical, and psychiatric services) stratified by their child's age (i.e., ages 0-6 = before school, ages 7-13 = pre-school + primary education, and ages 14-18 = secondary education or higher). The secondary outcome measure was length of hospital stays. Outcome measures were adjusted for maternal age at delivery, parity, marital status, income quartile, level of education in the year prior to the index birth, previous spontaneous abortions, maternal pregnancy complications, maternal diabetes, hypertension, alcohol-related diseases, and maternal smoking. RESULTS: In both cohorts the majority of mothers were between 26 and 35 years of age, married, and employed, and 47% were primiparous. Mothers of infants with anomalies had greater utilization of outpatient, inpatient, surgical, and psychiatric services, compared with mothers in the matched cohort. Inpatient service utilization was greater in the exposed cohort up to 13 years after a child's birth, with the highest risk in the first six years after birth [adjusted risk ratio, 1.13; 95% confidence interval (CI), 1.12-1.14], with a decrease over time. Regarding the quantity of health care utilization, the greatest difference between the two groups was in inpatient service utilization, with a 39% increased rate in the exposed cohort during the first six years after birth (adjusted rate ratio, 1.39; 95% CI, 1.37-1.42). During the first 6 years after birth, mothers of children with anomalies stayed a median of 6 days (interquartile range [IQR], 3-13) in hospital overall, while the comparison cohort stayed a median of 4 days (IQR, 2-7) in hospital overall. Rates of utilization of outpatient clinics (adjusted rate ratio, 1.36; 95% CI, 1.29-1.42), as well as inpatient (adjusted rate ratio, 1.77; 95% CI, 1.68-1.87), and surgical services (adjusted rate ratio, 1.33; 95% CI, 1.26-1.41) was higher in mothers of children with multiple-organ MCAs during 0 to 6 years after birth. Among mothers at the lowest income levels, utilization of psychiatric clinic services increased to 59% and when their child was 7 to 13 years of age (adjusted rate ratio, 1.59; 95% CI, 1.24-2.03). CONCLUSION: Mothers of infants with a major congenital anomaly had greater health care utilization across services. Health care utilization decreased over time or remained stable for outpatient, inpatient, and surgical care services, whereas psychiatric utilization increased for up to 13 years after an affected child's birth. Healthcare utilization was significantly elevated among mothers of children with multiple MCAs and among those at the lowest income levels.

Design of the Spine Pain Intervention to Enhance Care Quality And Reduce Expenditure Trial (SPINE CARE) study: Methods and lessons from a multi-site pragmatic cluster randomized controlled trial.

BACKGROUND: Low back and neck pain (together, spine pain) are among the leading causes of medical visits, lost productivity, and disability. For most people, episodes of spine pain are self-limited; nevertheless, healthcare spending for this condition is extremely high. Focusing care on individuals at high-risk of progressing from acute to chronic pain may improve efficiency. Alternatively, postural therapies, which are frequently used by patients, may prevent the overuse of high-cost interventions while delivering equivalent outcomes. METHODS: The SPINE CARE (Spine Pain Intervention to Enhance Care Quality And Reduce Expenditure) trial is a cluster-randomized multi-center pragmatic clinical trial designed to evaluate the clinical effectiveness and healthcare utilization of two interventions for primary care patients with acute and subacute spine pain. The study was conducted at 33 primary care clinics in geographically distinct regions of the United States. Individuals ≥18 years presenting to primary care with neck and/or back pain of ≤3 months' duration were randomized at the clinic-level to 1) usual care, 2) a risk-stratified, multidisciplinary approach called the Identify, Coordinate, and Enhance (ICE) care model, or 3) Individualized Postural Therapy (IPT), a standardized postural therapy method of care. The trial's two primary outcomes are change in function at 3 months and spine-related spending at one year. 2971 individuals were enrolled between June 2017 and March 2020. Follow-up was completed on March 31, 2021. DISCUSSION: The SPINE CARE trial will determine the impact on clinical outcomes and healthcare costs of two interventions for patients with spine pain presenting to primary care. TRIAL REGISTRATION NUMBER: NCT03083886.

Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos.

Parkinson's disease (PD) is a brain disorder that primarily affects motor function, leading to slow movement, tremor, and stiffness, as well as postural instability and difficulty with walking/balance. The severity of PD motor impairments is clinically assessed by part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a universally-accepted rating scale. However, experts often disagree on the exact scoring of individuals. In the presence of label noise, training a machine learning model using only scores from a single rater may introduce bias, while training models with multiple noisy ratings is a challenging task due to the inter-rater variabilities. In this paper, we introduce an ordinal focal neural network to estimate the MDS-UPDRS scores from input videos, to leverage the ordinal nature of MDS-UPDRS scores and combat class imbalance. To handle multiple noisy labels per exam, the training of the network is regularized via rater confusion estimation (RCE), which encodes the rating habits and skills of raters via a confusion matrix. We apply our pipeline to estimate MDS-UPDRS test scores from their video recordings including gait (with multiple Raters, R=3) and finger tapping scores (single rater). On a sizable clinical dataset for the gait test (N=55), we obtained a classification accuracy of 72% with majority vote as ground-truth, and an accuracy of ∼84% of our model predicting at least one of the raters' scores. Our work demonstrates how computer-assisted technologies can be used to track patients and their motor impairments, even when there is uncertainty in the clinical ratings. The latest version of the code will be available at https://github.com/mlu355/PD-Motor-Severity-Estimation.