Denial-Artificial Intelligence Tools and Health Insurance Coverage Decisions.

This JAMA Forum discusses the potential and the pitfalls in the use of artificial intelligence in the coverage decisions made by health insurance companies.

Conditional cross-design synthesis estimators for generalizability in Medicaid.

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases-lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.

Many Clinicians Implement Digital Equity Strategies To Treat Opioid Use Disorder.

Drawing upon a longitudinal survey of clinicians who treat patients with opioid use disorder (OUD), we report changes over time in telemedicine use, clinicians' attitudes, and digital equity strategies. Clinicians reported less use of telemedicine (both video and audio-only) in 2022 than in 2020. In March 2022, 77.0 percent of clinician respondents reported implementing digital equity strategies to help patients overcome barriers to video visits.

Uncertainty in lung cancer stage for survival estimation via set-valued classification.

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set-valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step. We demonstrate this process using SEER cancer registry data linked with Medicare claims data.

Association Between Telemedicine Use in Nonmetropolitan Counties and Quality of Care Received by Medicare Beneficiaries With Serious Mental Illness.

Importance: Access to specialty mental health care remains challenging for people with serious mental illnesses, such as schizophrenia and bipolar disorder. Whether expansion of telemedicine is associated with improved access and quality of care for these patients is unclear. Objective: To assess whether greater telemedicine use in a nonmetropolitan county is associated with quality measures, including use of specialty mental health care and medication adherence. Design, Setting, and Participants: In this cohort study, the variable uptake of telemental health visits was examined across a national sample of fee-for-service claims from Medicare beneficiaries in 2916 nonmetropolitan counties between January 1, 2010, and December 31, 2018. Beneficiaries with schizophrenia and related psychotic disorders and/or bipolar I disorder during the study period were included. For each year of the study, each county was categorized based on per capita telemental health service use (none, low, moderate, and high). The association between telemental health service use in the county and quality measures was tested using a multivariate model controlling for both patient characteristics and county fixed effects. Analyses were conducted from January 1 to April 11, 2022. Before the COVID-19 pandemic, telemedicine reimbursement was limited to nonmetropolitan beneficiaries. Main Outcomes and Measures: Receipt of a minimum of 2 specialty mental health service visits (telemedicine or in-person) in the year, number of months per year with medication, hospitalization rate, and outpatient follow-up visits after a mental health hospitalization in a year. Results: In 2018, there were 2916 counties with 118 170 patients (77 068 [65.2%] men; mean [SD] age, 58.3 [15.6] years) in the sample. The fraction of counties that had high telemental health service use increased from 2% in 2010 to 17% in 2018. In 2018 there were 1.08 telemental health service visits per patient in the high telemental health counties. Compared with no telemental health care in the county, patients in high-use counties were 1.2 percentage points (95% CI, 0.81-1.60 percentage points) (8.0% relative increase) more likely to have a minimum number of specialty mental health service visits, 13.7 percentage points (95% CI, 5.1-22.3 percentage points) (6.5% relative increase) more likely to have outpatient follow-up within 7 days of a mental health hospitalization, and 0.47 percentage points (95% CI, 0.25-0.69 percentage points) (7.6% relative increase) more likely to be hospitalized in a year. Telemental health service use was not associated with changes in medication adherence. Conclusions and Relevance: The findings of this study suggest that greater use of telemental health visits in a county was associated with modest increases in contact with outpatient specialty mental health care professionals and greater likelihood of follow-up after hospitalization. No substantive changes in medication adherence were noted and an increase in mental health hospitalizations occurred.

Disruptions in Care for Medicare Beneficiaries With Severe Mental Illness During the COVID-19 Pandemic.

Importance: Little is known about changes in care for individuals with severe mental illness during the COVID-19 pandemic. Objective: To examine changes in mental health care during the pandemic and the use of telemedicine in outpatient care among Medicare beneficiaries with severe mental illness. Design, Setting, and Participants: This population-based cohort study included Medicare beneficiaries (age ≥18 years) diagnosed with schizophrenia and schizophrenia-related disorders or bipolar I disorder. Care patterns during January to September 2020 for a cohort defined in 2019 were compared with those during January to September 2019 for a cohort defined in 2018. Exposures: Start of COVID-19 pandemic in the United States, defined as week 12 of 2020. Main Outcomes and Measures: Use of mental health-related outpatient visits, emergency department visits, inpatient care, and oral prescription fills for antipsychotics and mood stabilizers during 4-week intervals. Multivariable logistic regression analyses examined whether the pandemic was associated with differential changes in outpatient care across patient characteristics. Results: The 2019 cohort of 686 214 individuals included 389 245 (53.8%) women, 114 073 (15.8%) Black and 526 301 (72.8%) White individuals, and 477 353 individuals (66.0%) younger than 65 years; the 2020 cohort of 723 045 individuals included 367 140 (53.5%) women, 106 699 (15.6%) Black and 497 885 (72.6%) White individuals, and 442 645 individuals (64.5%) younger than 65 years. Compared with 2019, there were large decreases during the pandemic's first month (calendar weeks 12-15) in individuals with outpatient visits (265 169 [36.7%] vs 200 590 [29.2%]; 20.3% decrease), with antipsychotic and mood stabilizer medication prescription fills (216 468 [29.9%] vs 163 796 [23.9%]; 20.3% decrease), with emergency department visits (12 383 [1.7%] vs 8503 [1.2%]; 27.7% decrease), and with hospital admissions (11 564 [1.6%] vs 7912 [1.2%]; 27.9% decrease). By weeks 32 to 35 of 2020, utilization rebounded but remained lower than in 2019, ranging from a relative decrease of 2.5% (outpatient visits) to 12.9% (admissions). During the full pandemic period (weeks 12-39) in 2020, 1 556 403 of 2 743 553 outpatient visits (56.7%) were provided via telemedicine. In multivariable analyses, outpatient visit use during weeks 12 to 25 of 2020 was lower among those with disability (odds ratio, 0.95; 95% CI, 0.93-0.96), and during weeks 26 to 39 of 2020, it was lower among Black vs non-Hispanic White individuals (OR, 0.97; 95% CI, 0.95-0.99) and those with dual Medicaid eligibility (OR, 0.96; 95% CI, 0.95-0.98). Conclusions and Relevance: In this cohort study, despite greater use of telemedicine, individuals with severe mental illness experienced large disruptions in care early in the pandemic. These narrowed but persisted through September 2020. Disruptions were greater for several disadvantaged populations.

Identifying undercompensated groups defined by multiple attributes in risk adjustment.

OBJECTIVE: To identify undercompensated groups in plan payment risk adjustment that are defined by multiple attributes with a systematic new approach, improving on the arbitrary and inconsistent nature of existing evaluations. METHODS: Extending the concept of variable importance for single attributes, we construct a measure of 'group importance' in the random forests algorithm to identify groups with multiple attributes that are undercompensated by current risk adjustment formulas. Using 2016-2018 IBM MarketScan and 2015-2018 Medicare claims and enrolment data, we evaluate two risk adjustment scenarios: the risk adjustment formula used in the individual health insurance Marketplaces and the risk adjustment formula used in Medicare. RESULTS: A number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is many times larger than with single attributes. No complex groups were found to be consistently undercompensated or overcompensated in the Medicare risk adjustment formula. CONCLUSIONS: Our method is effective at identifying complex undercompensated groups in health plan payment risk adjustment where undercompensation creates incentives for insurers to discriminate against these groups. This work provides policy-makers with new information on potential targets of discrimination in the healthcare system and a path towards more equitable health coverage.

Ethical Machine Learning in Healthcare.

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

Patterns of Mental Health Care Before Initiation of Telemental Health Services.

BACKGROUND: Use of telemental health has increased among rural Medicare beneficiaries, particularly among individuals with serious mental illness (SMI). Little is known about what leads to the initiation of telemental health. OBJECTIVE: To categorize the different patterns of mental health care use before initiation of telemental health services among individuals with SMI. METHODS: A cohort of rural beneficiaries with SMI (defined as schizophrenia/related psychotic disorders or bipolar disorder) with an index telemental health visit in 2010-2017 was built using claims for a 20% random sample of fee-for-service Medicare beneficiaries. The authors used latent class analysis to identify classes of mental health care use in the 6 months before the index telemental health visits. Across the classes, the authors also described characteristics of index and subsequent mental health visits. RESULTS: The cohort included 4930 rural Medicare beneficiaries with SMI. Three classes of mental health care use before initiation of telemental health were identified. The largest class (n=3066) had minimal use of primary care provider mental health care and the second largest class (n=1537) had minimal specialty mental health care. The smallest class (n=327) was characterized by recent hospitalization or emergency department care. In the overall cohort, index visits were frequently established visits and were often with specialty prescribers. CONCLUSIONS: Our findings highlight 3 distinct patterns of care before telemental health initiation, providing insight into the role that telemedicine may play in mental health care for rural Medicare beneficiaries with SMI. Overall, telemental health was most often used to maintain care with existing providers.

Association of Nondiscrimination Policies With Mental Health Among Gender Minority Individuals.

Importance: In the past decade, many states have implemented policies prohibiting private health insurers from discriminating based on gender identity. Policies banning discrimination have the potential to improve access to care and health outcomes among gender minority (ie, transgender and gender diverse) populations. Objective: To evaluate whether state-level nondiscrimination policies are associated with suicidality and inpatient mental health hospitalizations among privately insured gender minority individuals. Design, Setting, and Participants: In this cohort study, difference-in-differences analysis comparing changes in mental health outcomes among gender minority enrollees before and after states implemented nondiscrimination policies in 2009-2017 was conducted. A sample of gender minority children and adults was identified using gender minority-related diagnosis codes obtained from private health insurance claims. The present study was conducted from August 1, 2018, to September 1, 2019. Exposure: Living in states that implemented policies banning discrimination based on gender identity in 2013, 2014, 2015, and 2016. Main Outcomes and Measures: The primary outcome was suicidality. The secondary outcome was inpatient mental health hospitalization. Results: The study population included 28 980 unique gender minority enrollees (mean [SD] age, 26.5 [15] years) from 2009 to 2017. Relative to comparison states, suicidality decreased in the first year after policy implementation in the 2014 policy cohort (odds ratio [OR], 0.72; 95% CI, 0.58-0.90; P = .005), the 2015 policy cohort (OR, 0.50; 95% CI, 0.39-0.64; P < .001), and the 2016 policy cohort (OR, 0.61; 95% CI, 0.44-0.85; P = .004). This decrease persisted to the second postimplementation year for the 2014 policy cohort (OR, 0.48; 95% CI, 0.41-0.57; P < .001) but not for the 2015 policy cohort (OR, 0.81; 95% CI, 0.47-1.38; P = .43). The 2013 policy cohort experienced no significant change in suicidality after policy implementation in all 4 postimplementation years (2014: OR, 1.19; 95% CI, 0.85-1.67; P = .31; 2015: OR, 0.94; 95% CI, 0.73-1.20; P = .61; 2016: OR, 0.82; 95% CI, 0.65-1.03; P = .10; and 2017: OR, 1.29; 95% CI, 0.90-1.88; P = .18). Mental health hospitalization rates generally decreased or stayed the same for individuals living in policy states vs the comparison group. Conclusions and Relevance: Implementation of a state-level nondiscrimination policy appears to be associated with decreased or no changes in suicidality among gender minority individuals living in states that implemented these policies from 2013 to 2016. Given high rates of suicidality among gender minority individuals in the US, health insurance nondiscrimination policies may offer a mechanism for reducing barriers to care and mitigating discrimination.

Impact of an Episode-Based Payment Initiative by Commercial Payers in Arkansas on Procedure Volume: an Observational Study.

BACKGROUND: Episode-based payment (EBP) is gaining traction among payers as an alternative to fee-for-service reimbursement. However, there is concern that EBP could influence the number of episodes. OBJECTIVE: To examine how procedure volume changed after the introduction of EBP in 2013 and 2014 under the Arkansas Health Care Payment Improvement Initiative. DESIGN: Using 2011-2016 commercial claims data, we estimate a difference-in-differences model to assess the impact of EBP on the probability of a beneficiary having an episode for four procedures that were reimbursed under EBP in Arkansas: total joint replacement, cholecystectomy, colonoscopy, and tonsillectomy. PARTICIPANTS: Commercially insured beneficiaries in Arkansas serve as our treatment group, while commercially insured beneficiaries in neighboring states serve as our comparison group. INTERVENTIONS: Statewide implementation of EBP for various clinical conditions by two of Arkansas' largest commercial insurers. MAIN MEASURES: For a given procedure type, the primary outcomes are the annual rate of procedures (number of procedures per 1000 beneficiaries) and the probability of a beneficiary undergoing that procedure in a given quarter. KEY RESULTS: The relationship between EBP and procedure volume varies across procedures. After EBP was implemented, the probability of undergoing colonoscopy increased by 17.2% (point estimate, 2.63; 95% CI, 1.18 to 4.08; p < 0.001; Arkansas pre-period mean, 15.29). The probability of undergoing total joint replacement increased by 9.9% (point estimate, 0.091; 95% CI, - 0.011 to 0.19; p = 0.08; Arkansas pre-period mean, 0.91), though this effect is not significant. There is no discernable impact on cholecystectomy or tonsillectomy volume. CONCLUSIONS: We do not find clear evidence of deleterious volume expansion. However, because the impact of EBP on procedure volume may vary by procedure, payers planning to implement EBP models should be aware of this possibility.

Fair regression for health care spending.

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrates that these new fair regression methods may lead to massive improvements in group fairness (eg, 98%) with only small reductions in overall fit (eg, 4%).

Estimating the Health Status of Privately Insured Gender Minority Children and Adults.

Purpose: The purpose of this study was to characterize the health status of privately insured gender minority individuals. Methods: We created a diagnosis-based algorithm to identify gender minority children and adults in the 2009-2015 IBM® MarketScan® Commercial Database. We compared the age-adjusted health status among individuals with and without gender minority-related diagnosis codes. Results: The percentage of the privately insured population with gender minority-related diagnosis codes increased from 0.004% in 2009 to 0.026% in 2015. Age-adjusted analyses demonstrated that individuals with gender minority-related diagnosis codes were more likely to have diagnoses for mental health disorders (odds ratio [OR] = 8.5; 95% confidence interval [CI] = 8.1-9.0), substance use disorders (OR = 3.4; 95% CI = 2.9-3.9), and diabetes (OR = 1.4; 95% CI = 1.2-1.6), driven by high prevalence of these conditions among individuals younger than 18 years. Conclusions: Our findings highlight a markedly greater prevalence of mental health and substance use disorder diagnoses among privately insured gender minority individuals. These results establish a reference point for evaluating the impact of federal- and state-level policies that ban health insurance discrimination based on gender identity on the health and health care use of gender minority individuals.

Data transformations to improve the performance of health plan payment methods.

The conventional method for developing health care plan payment systems uses observed data to study alternative algorithms and set incentives for the health care system. In this paper, we take a different approach and transform the input data rather than the algorithm, so that the data used reflect the desired spending levels rather than the observed spending levels. We present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our systematic approach for data transformations in two Medicare applications: underprovision of care for individuals with chronic illnesses and health care disparities by geographic income levels. Empirically comparing our method to two other common approaches shows that the "side effects" of these approaches vary by context, and that data transformation is an effective tool for addressing misallocations in individual health insurance markets.

Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches.

PURPOSE: Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from claims data. METHODS: Our study used the linked SEER-Medicare data. The patient samples included fee-for-service Medicare beneficiaries diagnosed with lung cancer from 2010 to 2011 (development cohort) and 2012 to 2013 (validation cohort) who received chemotherapy. Classification algorithms considered Medicare Part A and B claims for care in the 3 months before and after chemotherapy initiation. We developed a clinical algorithm to predict stage IV (v I to III) cancer on the basis of treatment patterns (surgery, radiotherapy, chemotherapy). We also considered an ensemble of claims-based machine learning algorithms. Classification methods were trained in the development cohort, and performance was measured in both cohorts. The SEER data were the gold standard for cancer stage. RESULTS: Development and validation cohorts included 14,760 and 14,620 patients with lung cancer, respectively. Validation analyses assessed clinical, random forest, and simple logistic regression algorithms. The best performing classifier within the development cohort was the random forests, but this performance was not replicated in validation analysis. Logistic regression had stable performance across cohorts. Compared with the clinical algorithm, the 14-variable logistic regression algorithm demonstrated higher accuracy in both the development (77% v 71%) and validation cohorts (77% v 73%), with improved specificity for stage IV disease. CONCLUSION: Machine learning algorithms have potential to improve lung cancer stage classification but may be prone to overfitting. Use of ensembles, cross-validation, and external validation can aid generalizability. Degradation of accuracy between development and validation cohorts suggests the need for caution in implementing machine learning in research or care delivery.

Potential Impact of Initial Clinical Data on Adjustment of Pediatric Readmission Rates.

OBJECTIVE: Comparison of readmission rates requires adjustment for case-mix (ie, differences in patient populations), but previously only claims data were available for this purpose. We examined whether incorporation of relatively readily available clinical data improves prediction of pediatric readmissions and thus might enhance case-mix adjustment. METHODS: We examined 30-day readmissions using claims and electronic health record data for patients ≤18 years and 29 days of age who were admitted to 3 children's hospitals from February 2011 to February 2014. Using the Pediatric All-Condition Readmission Measure and starting with a model including age, gender, chronic conditions, and primary diagnosis, we examined whether the addition of initial vital sign and laboratory data improved model performance. We employed machine learning to evaluate the same variables, using the L2-regularized logistic regression with cost-sensitive learning and convolutional neural network. RESULTS: Controlling for the core model variables, low red blood cell count and mean corpuscular hemoglobin concentration and high red cell distribution width were associated with greater readmission risk, as were certain interactions between laboratory and chronic condition variables. However, the C-statistic (0.722 vs 0.713) and McFadden's pseudo R2 (0.085 vs 0.076) for this and the core model were similar, suggesting minimal improvement in performance. In machine learning analyses, the F-measure (harmonic mean of sensitivity and positive predictive value) was similar for the best-performing model (containing all variables) and core model (0.250 vs 0.243). CONCLUSIONS: Readily available clinical variables do not meaningfully improve the prediction of pediatric readmissions and would be unlikely to enhance case-mix adjustment unless their distributions varied widely across hospitals.

Gatekeeping and patterns of outpatient care post healthcare reform.

OBJECTIVES: As US healthcare spending increases, insurers are focusing attention on decreasing potentially avoidable specialist care. Little recent research has assessed whether the design of modern health maintenance organization (HMO) insurance is associated with lower utilization of outpatient specialty care versus less restrictive preferred provider organization (PPO) plans. STUDY DESIGN: Observational study of Massachusetts residents aged 21 to 64 years with any HMO or PPO insurance coverage from 2010 to 2013. METHODS: We examined rates and patterns of primary care visits, new specialist visits, and specialist spending among HMO versus PPO enrollees. We estimated multivariable regression models for each outcome, adjusting for patient and insurance characteristics. RESULTS: From 2010 to 2013, 546,397 and 295,427 individuals had continuous HMO or PPO coverage, respectively. HMO patients had fewer annual new specialist visits per member versus PPO patients (unadjusted, 0.37 vs 0.43), a difference after adjustment of 0.05 annual visits, or a 12% relative decrease among HMO members (P <.001). These visits were more likely to be with a specialist in the same health system as the patient's primary care physician (44.9% vs 40.7%; adjusted difference, 2.8 percentage points; P <.001). Mean annual spending on new specialist visits and subsequent follow-up per member was lower in HMO versus PPO patients (unadjusted, $104.10 vs $128.10), translating to 12% lower annual spending (adjusted difference, -$16.26; P <.001). CONCLUSIONS: Having HMO insurance was associated with lower rates of new specialist visits and lower spending on specialist visits, and these visits were less likely to occur across multiple health systems. The impact of this change on overall spending and clinical outcomes remains unknown.

Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data.

OBJECTIVE: To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment. DATA SOURCES: Medicare enrollment and claims data from 2008 to 2011. DATA EXTRACTION: Risk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files. STUDY DESIGN: A propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries. PRINCIPAL FINDINGS: Matching improved balance on observables, but performance metrics were similar when comparing risk adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample. CONCLUSIONS: Fitting MA risk adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for risk adjustment.