A Facility-Level Analysis of Nursing Home Compare's Five-Star Rating and Maryland's Family Satisfaction with Care Survey.

BACKGROUND AND OBJECTIVES: Nursing facilities care for individuals with cognitive and/or physical disabilities. Poor quality is associated with greater disease and mortality. Quality comprises many factors and different stakeholders value different factors. This study aimed to compare two care quality frameworks, one based on observable factors and one on family satisfaction. RESEARCH DESIGN AND METHODS: We merged publicly available 2021 Maryland nursing facility data. The Maryland Health Care Commission surveys long-term care residents' family satisfaction across seven domains. Centers for Medicare and Medicaid Services' (CMS) 5-star ratings aggregate inspections, staffing, and quality measures. We used univariate and bivariate statistics to compare the frameworks. RESULTS: The data set included 220 facilities and 4,610 survey respondents. The average facility rating was 7.70/10 and overall 77% of respondents would recommend the facility. Eighty-six percent of respondents from 5-star facilities, 79% from 4-star facilities, and 76% from 3-star facilities would recommend the facility compared to 65% from 1-star facilities (p < .001, p < .01, and p < .05, respectively). Four or 5-star facilities received significantly higher ratings (8.33, p < .001; 7.75, p < .05, respectively) than 1-star facilities (7.07). DISCUSSION AND IMPLICATIONS: Our results corroborated earlier findings of strong associations between CMS ratings and satisfaction at the extremes of the 5-star system. These associations are inconsistent across family-reported domains. This suggests overlap between the frameworks. CMS ratings address care quality; family satisfaction measures quality of life and care quality. High satisfaction is associated with high care quality and quality of life; lower satisfaction is associated with lower care quality.

Addressing housing-related social needs for Medicaid beneficiaries: a qualitative assessment of Maryland's Medicaid §1115 waiver program.

BACKGROUND: While health care payers are increasingly considering approaches that help support stable and affordable housing for their beneficiaries, experience with these initiatives is limited. Through its §1115 HealthChoice waiver, Maryland Medicaid has begun experimenting with programs designed to pay for housing and tenancy support/case management services. This study investigates barriers and facilitators to the success of Maryland's pilot program initiative - Assistance in Community Integration Services (ACIS). METHODS: The study focused on key stakeholders employed by the four Lead Entities that currently participate in the ACIS program. The stakeholders included members of each Lead Entity's administration, direct service providers, state and local government officials, and case managers from local hospitals. The convenience sample was selected through an initial list of stakeholders and was supplemented using snowball sampling methods. Interviews were audio recorded and turned into transcripts via Otter.ai and then analyzed using NVivo by two independent reviewers. RESULTS: A total of 23 interviews were conducted between February 2022 and May 2022, representing a broad range of stakeholders across different Maryland geographies. A total of 4 themes were identified through the course of the interviews. Stakeholders identified difficulty finding housing for the target population in a tight housing market, challenges with communication within the program and with its clients, and problems with non-healthcare providers documenting services for reimbursement. At the same time, ACIS was seen as creating opportunities for organizations to work together across siloes in meeting client needs. CONCLUSIONS: The findings of this study helps to highlight Medicaid §1115 waivers as a novel approach to using Medicaid funds to support tenancy-based services, such as ACIS and to improve the lives of individuals while reducing healthcare costs. Implementation of the ACIS program in Maryland has been a resounding success in helping individuals obtain and sustain stable housing. However, continued efforts to align capacity with demand, streamline billing and reimbursement and improve communication with clients and across partners will need to be prioritized. The program also highlights the growing need to address root causes of housing insecurity including the limited supply of affordable housing.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events.

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

The Role of Dementia and Residential Service Agency Characteristics in the Care Experiences of Maryland Medicaid Home and Community-Based Service Participants and Family and Unpaid Caregivers.

In Maryland, residential service agencies deliver Medicaid Home and Community-Based Services (HCBS) to older adults with disabilities through direct care workers (e.g., personal care aides). Leveraging survey data from residential service agency administrators, linked to interRAI Home Care assessments for 1144 participants, we describe agency characteristics, and participant and family caregiver experiences by participant dementia status. Most (61.7%) participants experienced low social engagement, and roughly 10.0% experienced a hospitalization or emergency room visit within 90 days. Few (14.4%) participants were served by agencies requiring dementia-specific direct care worker training, and most were served by agencies offering supplemental services, or in which direct care workers helped with health information technology (81.2% and 72.8%, respectively). Few caregivers reported negative care experiences. Participants with dementia and those served by agencies with training and support more often reported negative care experiences. Findings lay the foundation for future longitudinal and embedded interventions within Medicaid HCBS.

Predicting Hospital Readmissions in a Commercially Insured Population over Varying Time Horizons.

BACKGROUND: Reducing hospital readmissions is a federal policy priority, and predictive models of hospital readmissions have proliferated in recent years; however, most such models tend to focus on the 30-day readmission time horizon and do not consider readmission over shorter (or longer) windows. OBJECTIVES: To evaluate the performance of a predictive model of hospital readmissions over three different readmission timeframes in a commercially insured population. DESIGN: Retrospective multivariate logistic regression with an 80/20 train/test split. PARTICIPANTS: A total of 2,213,832 commercially insured inpatient admissions from 2016 to 2017 comprising 782,768 unique patients from the Health Care Cost Institute. MAIN MEASURES: Outcomes are readmission within 14 days, 15-30 days, and 31-60 days from discharge. Predictor variables span six different domains: index admission, condition history, demographic, utilization history, pharmacy, and environmental controls. KEY RESULTS: Our model generates C-statistics for holdout samples ranging from 0.618 to 0.915. The model's discriminative power declines with readmission time horizon: discrimination for readmission predictions within 14 days following discharge is higher than for readmissions 15-30 days following discharge, which in turn is higher than predictions 31-60 days following discharge. Additionally, the model's predictive power increases nonlinearly with the inclusion of successive risk factor domains: patient-level measures of utilization and condition history add substantially to the discriminative power of the model, while demographic information, pharmacy utilization, and environmental risk factors add relatively little. CONCLUSION: It is more difficult to predict distant readmissions than proximal readmissions, and the more information the model uses, the better the predictions. Inclusion of utilization-based risk factors add substantially to the discriminative ability of the model, much more than any other included risk factor domain. Our best-performing models perform well relative to other published readmission prediction models. It is possible that these predictions could have operational utility in targeting readmission prevention interventions among high-risk individuals.

Designing optimal recommended budgeting thresholds for a Medicaid program.

OBJECTIVES: To develop and test a methodology for optimally setting automatic auditing thresholds to minimize administrative costs without encouraging overall budget growth in a state Medicaid program. STUDY DESIGN: Two-stage optimization using administrative Maryland Medicaid plan-of-service data from fiscal year (FY) 2019. METHODS: In the first stage, we use an unsupervised machine learning method to regroup acuity levels so that plans of service with similar spending profiles are grouped together. Then, using these regroupings, we employ numerical optimization to estimate the recommended budget levels that could minimize the number of audits across those groupings. We simulate the effects of this proposed methodology on FY 2019 plans of service and compare the resulting number of simulated audits with actual experience. RESULTS: Using optimal regrouping and numerical optimization, this method could reduce the number of audits by 10.4% to 36.7% relative to the status quo, depending on the search space parameters. This reduction is a result of resetting recommended budget levels across acuity groupings, with no anticipated increase in the total recommended budget amount across plans of service. These reductions are driven, in general, by an increase in recommended budget level for acuity groupings with low variance in plan-of-service spending and a reduction in recommended budget level for acuity groupings with high variance in plan-of-service spending. CONCLUSIONS: Using machine learning and optimization methods, it is possible to design recommended budget thresholds that could lead to significant reductions in administrative burden without encouraging overall cost growth.

The actual, long-term cost of intentional injury care among a cohort of Maryland Medicaid recipients.

BACKGROUND: Intentional injury (both self-harm and interpersonal) is a major cause of morbidity and mortality, yet there are little data on the per-person cost of caring for these patients. Extant data focus on hospital charges related to the initial admission but does not include actual dollars spent or follow-up outpatient care. The Affordable Care Act has made Medicaid the primary payor of intentional injury care (39%) in the United States and the ideal source of cost data for these patients. We sought to determine the total and per-person long-term cost (initial event and following 24 months) of intentional injury among Maryland Medicaid recipients. METHODS: Retrospective cohort study of Maryland Medicaid claims was performed. Recipients who submitted claims after receiving an intentional injury, as defined by the International Classification of Diseases, Tenth Revision, between October 2015 and October 2017, were included in this study. Subjects were followed for 24 months (last participant enrolled October 2017 and followed to October 2019). Our primary outcome was the dollars paid by Medicaid. We examined subgroups of patients who harmed themselves and those who received repeated intentional injury. RESULTS: Maryland Medicaid paid $11,757,083 for the care of 12,172 recipients of intentional injuries between 2015 and 2019. The per-person, 2-year health care cost of an intentional injury was a median of $183 (SD, $5,284). These costs were highly skewed: min, $2.56; Q1 = 117.60, median, $182.80; Q3 = $480.82; and max, $332,394.20. The top 5% (≥95% percentile) required $3,000 (SD, $6,973) during the initial event and $8,403 (SD, $22,024) per served month thereafter, or 55% of the overall costs in this study. CONCLUSION: The long-term, per-person cost of intentional injury can be high. Private insurers were not included and may experience different costs in other states. LEVEL OF EVIDENCE: Economic and Value Based Evaluations; level III.

Predicting avoidable hospital events in Maryland.

OBJECTIVE: To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. DATA SOURCES: Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. STUDY DESIGN: Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. DATA COLLECTION/EXTRACTION METHODS: One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. PRINCIPAL FINDINGS: We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. CONCLUSIONS: A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.

Screening and Treatment of Sexually Transmitted Infections Among Medicaid Populations-A 2-State Analysis.

BACKGROUND: Chlamydia, gonorrhea, and syphilis are common, treatable sexually transmitted infections (STIs) that are highly prevalent in the general US population. Costs associated with diagnosing and treating these conditions for individual states' Medicaid participants are unknown. The purpose of this study was to estimate the cost of screening and treatment for 3 common STIs for state Medicaid program budgets in Maryland and South Carolina. METHODS: A retrospective, cross-sectional study was conducted using Medicaid administrative claims data over a 2-year period. Claims were included based on the presence of one of the 3 study conditions in either diagnosis or procedure codes. Descriptive analyses were used to characterize the participant population and expenditures for services provided. RESULTS: Total Medicaid expenditures for STI care in state fiscal years 2016 and 2017 averaged $43.5 million and $22.3 million for each year in Maryland and South Carolina, respectively. Maryland had a greater proportion of costs associated with outpatient hospital and laboratory settings. Costs for care provided in the emergency department were highest in South Carolina. CONCLUSIONS: Diagnosis and treatment of commonly reported STIs may have a considerable financial impact on individual state Medicaid programs. Public health activities directed at STI prevention are important tools for reducing these costs to states.

Cost-Effective Care Coordination for People With Dementia at Home.

BACKGROUND AND OBJECTIVES: People with dementia (PWD) represent some of the highest-need and highest-cost individuals living in the community. Maximizing Independence (MIND) at Home is a potentially cost-effective and scalable home-based dementia care coordination program that uses trained, nonclinical community workers as the primary contact between the PWD and their care partner, supported by a multidisciplinary clinical team with expertise in dementia care. RESEARCH DESIGN AND METHODS: Cost of care management services based on actual time spent by care management personnel over first 12 months of MIND at Home intervention was calculated for 342 MIND at Home recipients from Baltimore, Maryland and surrounding areas participating in a Centers for Medicare and Medicaid Services (CMS) funded Health Care Innovation Award demonstration project. Difference-in-differences analysis of claims-based Medicaid spending of 120 dually-eligible MIND at Home participants with their propensity score matched comparison group (n = 360). RESULTS: The average cost per enrollee per month was $110, or $1,320 per annum. Medicaid expenditures of dually-eligible participants grew 1.12 percentage points per quarter more slowly than that of the matched comparison group. Most savings came from slower growth in inpatient and long-term nursing home use. Net of the cost of the 5-year MIND at Home intervention, 5-year Medicaid savings are estimated at $7,052 per beneficiary, a 1.12-fold return on investment. DISCUSSION AND IMPLICATIONS: Managed care plans with the flexibility to engage community health workers could benefit from a low-cost, high-touch intervention to meet the needs of enrollees with dementia. Limitations for using and reimbursing community health workers exist in Medicare fee-for-service, which CMS should address to maximize benefit for PWD.

Expanding Medicaid Coverage for Community-Based Long-Term Services and Supports: Lessons From Maryland's Community First Choice Program.

Community First Choice (CFC) is a Medicaid state plan option authorized through the Affordable Care Act (ACA) that supports the delivery of long-term services and supports in home and community settings. We interviewed stakeholders in Maryland, one of the first states to adopt CFC, to assess challenges, benefits, and potential implications of this Medicaid option for state and federal policy makers. Study findings suggest that expanding coverage for home- and community-based services through CFC in Maryland has been financially feasible, expanded the personal care workforce, and supported a more equitable approach to personal care services. We conclude that greater coverage for home- and community-based long-term services is a promising avenue to improve access to care for high-need Medicaid beneficiaries.

Medicaid Cost Savings of a Preventive Home Visit Program for Disabled Older Adults.

BACKGROUND/OBJECTIVES: Little is known about cost savings of programs that reduce disability in older adults. The objective was to determine whether the Community Aging in Place, Advancing Better Living for Elders (CAPABLE) program saves Medicaid more money than it costs to provide. DESIGN: Single-arm clinical trial (N = 204) with a comparison group of individuals (N = 2,013) dually eligible for Medicaid and Medicare matched on baseline geographic and demographic characteristics, chronic conditions, and healthcare use. We used finite mixture model regression estimates in a Markov model. SETTING: Baltimore, MD PARTICIPANTS: Individuals aged 65 and older with reported difficulty with at least one activity of daily living. INTERVENTION: CAPABLE is a 5-month program to reduce the health effects of impaired physical function in low-income older adults by addressing individual capacity and the home environment. CAPABLE uses an interprofessional team (occupational therapist, registered nurse, handyman) to help older adults attain self-identified functional goals. MEASUREMENTS: Monthly average Medicaid expenditure and likelihood of high- or low-cost use of eight healthcare service categories. RESULTS: Average Medicaid spending per CAPABLE participant was $867 less per month than that of their matched comparison counterparts (observation period average 17 months, range 1-31 months). The largest differential reduction in expenditures were for inpatient care and long-term services and supports. CONCLUSION: CAPABLE is associated with lower likelihood of inpatient and long-term service use and lower overall Medicaid spending. The magnitude of reduced Medicaid spending could pay for CAPABLE delivery and provide further Medicaid program savings due to averted services use. CLINICAL TRIAL REGISTRATION: CAPABLE for Frail dually eligible older adults NCT01743495 https://clinicaltrials.gov/ct2/show/NCT01743495.