Latent Class Analysis of Social Needs in Medicaid Population and Its Impact on Risk Adjustment Models.

BACKGROUND: A growing number of US states are implementing programs to address the social needs (SNs) of their Medicaid populations through managed care contracts. Incorporating SN might also improve risk adjustment methods used to reimburse Medicaid providers. OBJECTIVES: Identify classes of SN present within the Medicaid population and evaluate the performance improvement in risk adjustment models of health care utilization and cost after incorporating SN classes. RESEARCH DESIGN: A secondary analysis of Medicaid patients during the years 2018 and 2019. Latent class analysis (LCA) was used to identify SN classes. To evaluate the impact of SN classes on measures of hospitalization, emergency (ED) visits, and costs, logistic and linear regression modeling for concurrent and prospective years was used. Model performance was assessed before and after incorporating these SN classes to base models controlling for demographics and comorbidities. SUBJECTS: 262,325 Medicaid managed care program patients associated with a large urban academic medical center. RESULTS: 7.8% of the study population had at least one SN, with the most prevalent being related to safety (3.9%). Four classes of SN were determined to be optimal based on LCA, including stress-related needs, safety-related needs, access to health care-related needs, and socioeconomic status-related needs. The addition of SN classes improved the performance of concurrent base models' AUC (0.61 vs. 0.58 for predicting ED visits and 0.61 vs. 0.58 for projecting hospitalizations). CONCLUSIONS: Incorporating SN clusters significantly improved risk adjustment models of health care utilization and costs in the study population. Further investigation into the predictive value of SN for costs and utilization in different Medicaid populations is merited.

Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for All Ages.

BACKGROUND: Classification systems to segment such patients into subgroups for purposes of care management and population analytics should balance administrative simplicity with clinical meaning and measurement precision. OBJECTIVE: To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan. RESEARCH DESIGN AND SUBJECTS: Cross-sectional analyses using insurance claims database for 3.31 Million commercially insured and 1.05 Million Medicaid enrollees under 65 years old; and 5.27 Million Medicare fee-for-service beneficiaries aged 65 and older. MEASURES: The "Patient Need Groups" (PNGs) framework, we developed, classifies each person within the entire 0-100+ aged population into one of 11 mutually exclusive need-based categories. For each PNG segment, we documented a range of clinical and resource endpoints, including health care resource use, avoidable emergency department visits, hospitalizations, behavioral health conditions, and social need factors. RESULTS: The PNG categories included: (1) nonuser, (2) low-need child, (3) low-need adult, (4) low-complexity multimorbidity, (5) medium-complexity multimorbidity, (6) low-complexity pregnancy, (7) high-complexity pregnancy, (8) dominant psychiatric/behavioral condition, (9) dominant major chronic condition, (10) high-complexity multimorbidity, and (11) frailty. Each PNG evidenced a characteristic age-related trajectory across the full lifespan. In addition to offering clinically cogent groupings, large percentages (29%-62%) of patients in two pregnancy and high-complexity multimorbidity and frailty PNGs were in a high-risk subgroup (upper 10%) of potential future health care utilization. CONCLUSIONS: The PNG population segmentation approach represents a comprehensive measurement framework that captures and categorizes available electronic health care data to characterize individuals of all ages based on their needs.

Validation of an algorithm to prioritize patients for comprehensive medication management in primary care settings.

BACKGROUND: Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. AIM: Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. METHOD: We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. RESULTS: Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). CONCLUSION: Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings.

Outcomes of Immune Checkpoint Inhibitor Administration in Hospitalized Patients With Solid Tumor Malignancies.

PURPOSE: More oncologists desire to treat their patients with immune checkpoint inhibitors (ICIs) in the inpatient setting as their use has become more widespread for numerous oncologic indications. This is cost-prohibitive to patients and institutions because of high drug cost and lack of reimbursement in the inpatient setting. We sought to examine current practice of inpatient ICI administration to determine if and in which clinical scenarios it may provide significant clinical benefit and therefore be warranted regardless of cost. METHODS: We conducted a retrospective chart review of adult patients who received at least one dose of an ICI for treatment of an active solid tumor malignancy during hospitalization at a single academic medical center between January 2017 and June 2018. Patient, disease, and admission characteristics including mortality data were examined, and cost analysis was performed. RESULTS: Sixty-five doses of ICIs were administered to 58 patients during the study period. Nearly 40% and 80% of patients died within 30 days and 180 days of ICI administration, respectively. There was a trend toward longer overall survival in patients with good prognostic factors including positive programmed death-ligand 1 (PD-L1) expression or microsatellite instability-high (MSI-H) status. Slightly over 70% of patients were discharged within 7 days of ICI administration. The total cost of inpatient ICI administration over the 18-month study period was $615,016 US dollars. CONCLUSION: Inpatient ICI administration is associated with high costs and poor outcomes in acutely ill hospitalized patients with advanced solid tumor malignancies and therefore should largely be avoided. Careful discharge planning to expedite outpatient treatment after discharge will be paramount in ensuring patients with good prognostic features who will benefit most from ICI therapy can be promptly treated in the outpatient setting as treating very close to discharge in the inpatient setting appears to be unnecessary, regardless of tumor features.

Impact of Social Needs in Electronic Health Records and Claims on Health Care Utilization and Costs Risk-Adjustment Models Within Medicaid Population.

Patients enrolled in Medicaid have significantly higher social needs (SNs) than others. Using claims and electronic health records (EHRs) data, managed care organizations (MCOs) could systemically identify high-risk patients with SNs and develop population health management interventions. Impact of SNs on models predicting health care utilization and costs was assessed. This retrospective study included claims and EHRs data on 39,267 patients younger than age 65 years who were continuously enrolled during 2018-2019 in a Medicaid-managed care plan. SN marker was developed suggesting presence of International Classification of Diseases, 10th revision codes in any of the 5 SN domains. Impact of SN marker was compared across demographic and 2 diagnosis-based (ie, Charlson and Adjusted Clinical Groups risk score) prediction models of emergency department (ED) visit and hospitalizations, and total, medical, and pharmacy costs. After combining data sources, prevalence of documented SN marker increased from 11% and 13% to 18% of the study population across claims, EHRs, and both combined, respectively. SN marker improved predictions of demographic models for all utilization and total costs outcomes (area under the curve [AUC] of ED model increased from 0.57 to 0.61 and R2 of total cost model increased from 10.9 to 12.2). In both diagnosis-based models, adding SN marker marginally improved outcomes prediction (AUC of ED model increased from 0.65 to 0.66). This study demonstrated feasibility of using claims and EHRs data to systematically capture SNs and incorporate in prediction models that could enable MCOs and policy makers to adjust and develop effective population health interventions.