Hierarchical Condition Category Score Research Articles

Trends in Patient Complexity, Practice Setting, and Surgeon Reimbursement for Urolithiasis: Do Rural Urologists Pay the Price?

ObjectivesTo evaluate the relationship between patient complexity, practice setting, and surgeon reimbursement for ureteroscopy and percutaneous nephrolithotomy (PCNL). MethodsThe “2021 Medicare Physician and Other Provider” file was used to collect Rural-Urban Commuting Area (RUCA) codes and hierarchical condition category (HCC) scores of urologists. Higher HCC score corresponds to higher medical complexity and higher RUCA code corresponds to a more rural area. Medicare reimbursement for ureteroscopy and PCNL were collected. Linear regressions were performed to predict change in reimbursement based on RUCA and HCC scores. ResultsIn 2021, 52,816 procedures under CPT code 52356 (ureteroscopy) and 1649 procedures under 50080 or 50081 (PCNL) were billed to Medicare. Mean reimbursement was $338.24 for ureteroscopy and $957.89 for PCNL. For ureteroscopy, higher HCC score predicted lower reimbursement (p<0.001). Higher HCC score predicted higher reimbursement for PCNL (p<0.01). Average RUCA for ureteroscopy was higher than for PCNL (p=0.02). Rural location predicted lower reimbursement for ureteroscopy (p<0.001), however there was no association for PCNL. ConclusionsFor ureteroscopy, higher-risk patients are associated with lower reimbursement while the opposite holds true for PCNL. Rural practices were associated with lower reimbursement for ureteroscopy, but there was no association between location and PCNL reimbursement. Together, these findings suggest practice pattern variation between ureteroscopy and PCNL and highlight gaps in reimbursement policy. Risk-adjusted reimbursement should be considered to incentivize urologists to treat complex patients within their practice scope.

Medicare Advantage Health Risk Assessments Contribute Up To $12 Billion Per Year To Risk-Adjusted Payments.

With Medicare Advantage (MA) enrollment surpassing 50percent of Medicare beneficiaries, accurate risk-adjusted plan payment rates are essential. However, artificially exaggerated coding intensity, where plans seek to enhance measured health risk through the addition or inflation of diagnoses, may threaten payment rate integrity. One factor that may play a role in escalating coding intensity is health risk assessments (HRAs)-typically in-home reviews of enrollees' health status-that enable plans to capture information about their enrollees. In this study, we evaluated the impact of HRAs on Hierarchical Condition Categories (HCC) risk scores, variation in this impact across contracts, and the aggregate payment impact of HRAs, using 2019 MA encounter data. We found that 44.4percent of MA beneficiaries had at least one HRA. Among those with at least one HRA, HCC scores increased by 12.8percent, on average, as a result of HRAs. More than one in five enrollees had at least one additional HRA-captured diagnosis, which raised their HCC score. Potential scenarios restricting the risk-score impact of HRAs correspond with $4.5-$12.3billion in reduced Medicare spending in 2020. Addressing increased coding intensity due to HRAs will improve the value of Medicare spending and ensure appropriate payment in the MA program.

Restrictiveness of Medicare Advantage provider networks across physician specialties.

The objective was to measure specialty provider networks in Medicare Advantage (MA) and examine associations with market factors. We relied on traditional Medicare (TM) and MA prescription drug event data from 2011 to 2017 for all Medicare beneficiaries in the United States as well as data from the Area Health Resources File. Relying on a recently developed and validated prediction model, we calculated the provider network restrictiveness of MA contracts for nine high-prescribing specialties. We characterized network restrictiveness through an observed-to-expected ratio, calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based on the prediction model. We assessed the relationship between network restrictiveness and market factors across specialties with multivariable linear regression. Prescription drug event data for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017. Provider networks in MA varied in restrictiveness. OB-Gynecology was the most restrictive with enrollees seeing 34.5% (95% CI: 34.3%-34.7%) as many providers as they would absent network restrictions; cardiology was the least restrictive with enrollees seeing 58.6% (95% CI: 58.4%-58.8%) as many providers as they otherwise would. Factors associated with less restrictive networks included the county-level TM average hierarchical condition category score (0.06; 95% CI: 0.04-0.07), the county-level number of doctors per 1000 population (0.04; 95% CI: 0.02-0.05), the natural log of local median household income (0.03; 95% CI: 0.007-0.05), and the parent company's market share in the county (0.16; 95% CI: 0.13-0.18). Rurality was a major predictor of more restrictive networks (-0.28; 95% CI: -0.32 to -0.24). Our findings suggest that rural beneficiaries may face disproportionately reduced access in these networks and that efforts to improve access should vary by specialty.

Trends in the Adoption of Outpatient Joint Arthroplasties and Patient Risk: A Retrospective Analysis of 2019 to 2021 Medicare Claims Data.

Total joint arthroplasties (TJAs) have recently been shifting toward outpatient arthroplasty. This study aims to explore recent trends in outpatient total joint arthroplasty (TJA) procedures and examine whether patients with a higher comorbidity burden are undergoing outpatient arthroplasty. Medicare fee-for-service claims were screened for patients who underwent total hip, knee, or shoulder arthroplasty procedures between January 2019 and December 2022. The procedure was considered to be outpatient if the patient was discharged on the same date of the procedure. The Hierarchical Condition Category Score (HCC) and the Charlson Comorbidity Index (CCI) scores were used to assess patient comorbidity burden. Patient adverse outcomes included all-cause hospital readmission, mortality, and postoperative complications. Logistic regression analyses were used to evaluate if higher HCC/CCI scores were associated with adverse patient outcomes. A total of 69,520, 116,411, and 41,922 respective total knee, hip, and shoulder arthroplasties were identified, respectively. Despite earlier removal from the inpatient-only list, outpatient knee and hip surgical volume did not markedly increase until the pandemic started. By 2022Q4, 16%, 23%, and 36% of hip, knee, and shoulder arthroplasties were discharged on the same day of surgery, respectively. Both HCC and CCI risk scores in outpatients increased over time ( P < 0.001). TJA procedures are shifting toward outpatient surgery over time, largely driven by the COVID-19 pandemic. TJA outpatients' HCC and CCI risk scores increased over this same period, and additional research to determine the effects of this should be pursued. Level III, therapeutic retrospective cohort study.

The mechanics of risk adjustment and incentives for coding intensity in Medicare.

To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024. Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees. We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records. We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018. Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change. Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.

Changes in Diagnoses and Site of Care for Patients Receiving Hospice Care From Agencies Acquired by Private Equity Firms and Publicly Traded Companies

Private equity firms and publicly traded companies have been acquiring US hospice agencies; an estimated 16% of US hospice agencies are owned by private equity (PE) firms or publicly traded companies (PTC). To examine the association of PE and PTC acquisitions of hospices with Medicare patients' site of care and clinical diagnoses. This cohort study of US hospice agencies used a novel national database of acquisitions merged with the Medicare Post-Acute Care and Hospice Public Use File for 2013 to 2020. Changes in sites of care and patient characteristics for hospice agencies acquired by PE or PTCs were compared with changes for patients in nonacquired for-profit hospice agencies. Private equity and publicly traded company acquisitions. This study used a difference-in-differences approach within an event-study framework to examine the association of PE and PTC acquisitions of hospice agencies with changes in patient diagnoses and sites of care. Dependent variables were annual hospice-level measures of the Hierarchical Condition Category (HCC) score and proportion of patients diagnosed with cancer or dementia. Sites of care included the proportion of patients receiving hospice care in their personal home, nursing home, or assisted living facility. A total of 158 hospice agencies acquired by PEs, 250 acquired by PTCs, and 1559 other for-profit hospice agencies were included. Preacquisition, hospice agencies that would later be acquired by PE or PTC served a mean (IQR) 30.1% (12.0%-44.0%) and 29.4% (13.0%-43.0%) of their patients in nursing homes respectively, a greater proportion compared with the 27.1% (8.0%-43.8%) served by for-profit hospices that were never acquired. Agencies acquired by PE between 2014 and 2019 saw a significant relative increase of 5.98% in dementia patients (1.38 percentage points; 95% CI, 0.35-2.40 percentage points; P = .008). In PTC-owned hospices, the proportion of patients receiving care at home increased by 5.26% (2.98 percentage points; 95% CI, 1.46-4.51 percentage points; P < .001), the proportion of dementia patients rose by 13.49% (3.11 percentage points; 95% CI, 2.14-4.09 percentage points; P < .001), and the HCC score decreased by 1.37% (-3.19 percentage points; 95% CI, -5.92 to -0.47 percentage points; P = .02). These findings suggest that PE and PTCs select patients and sites of care to maximize profits.

Abstract 14878: Natural Language Processing of Unstructured Medical Record Can Predict Cardiac Arrhythmia by Quantifying Chronic Disease Burden

Introduction: Prediction of atrial fibrillation (AF) and sick sinus syndrome (SSS) is difficult. CMS Hierarchical Condition Categories scores (CMS-HCC) is a risk adjustment score used by CMS to account to risk adjust quality and cost metrics by quantifying the burden of chronic disease and patient complexity. Its calculation is based on patient’s demographics and ICD10 diagnostic codes. We have created an algorithm (MYTH) that can determine ICD10 diagnostic codes from unstructured medical chart using Natural Language Processing through syntax analysis, knowledge graph and regular expressions. The ICD-10 codes can then be used to calculate CMS-HCC. Because chronic disease burden may increase the probability of cardiac arrhythmia, we hypothesize that CMS-HCC can predict the onset of atrial fibrillation and sick sinus syndrome. Methods: We used MYTH to automate ICD10 coding and HCC score calculation based on textual outpatient cardiology records of 926 patients. The algorithm was additionally applied to calculate CHA2DS2-VASc scores. These were applied to predict the diagnosis of AF and SSS on clinical follow-up retrospectively. Results: CMS-HCC scores outperforms CHA2DS2-VASc scores in the prediction of onset of AF: (AUC: CHA2DS2-VASc 0.703, CMS-HCC 0.778, difference P=0.0006, individually P&lt;0.0001). While HCC score is predictive of the onset of SSS (P&lt;0.001), its improvement over CHA2DS2-VASc score does not reach statistical significance (AUC: CHA2DS2-VASc 0.702; CMS-HCC 0.744, difference: P=0.297, individually P&lt;0.001). Conclusions: Natural Language Processing algorithms can understand unstructured medical text, automate ICD coding and automate CMS-HCC score calculation. The burden of chronic diseases, as reflected by the CMS-HCC score, is highly predictive of future atrial fibrillation and modestly predictive of sick sinus syndrome.

Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

The Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery. To augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery. The National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization. We analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score. The addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

Patients From Medically Underserved Areas Are at Increased Risk for Nonhome Discharge and Emergency Department Return After Total Joint Arthroplasty.

Maryland Health Enterprise Zones (MHEZs) were introduced in 2012 and encompass underserved areas and those with reduced access to healthcare providers. Across the United States many underserved and minority populations experience poorer total joint arthroplasty (TJA) outcomes seemingly because they reside in underserved areas. The purpose of this study is to identify and quantify the relationship between living in an MHEZ and TJA outcomes. Retrospective review of 11,451 patients undergoing primary TJA at a single institution from July 1, 2014 to June 30, 2020 was conducted. Patients were classified based on whether they resided in an MHEZ. Statistical analyses were used to compare outcomes for TJA patients who live in MHEZ and those who do not. Of the 11,451 patients, 1057 patients lived in MHEZ and 10,394 patients did not. After risk adjustment, patients who live in an MHEZ were more likely to return to the emergency department within 90 days postoperatively and were less likely to be discharged home than those patients who do not live in an MHEZ. Total joint arthroplasty patients residing in MHEZ appear to present with poorer overall health as measured by increased American Society of Anesthesiologists and Hierarchical Condition Categories scores, and they are less likely to be discharged home and more likely to return to the emergency department within 90 days. Several factors associated with these findings such as socioeconomic factors, household composition, housing type, disability, and transportation may be modifiable and should be targets of future population health initiatives.

The Impact of Telemedicine on Quality of Care for Patients with Diabetes After March 2020

BackgroundThe impact of telemedicine on ambulatory care quality is a key question for policymakers as they navigate payment reform for remote care.ObjectiveTo evaluate whether utilizing telemedicine in the first 9 months of the COVID-19 pandemic impacted performance on a diabetes quality of care measure for patients at a large academic medical center. We hypothesized care quality would reduce less among telemedicine users.DesignQuasi-experimental design using binomial logistic regression. Covariates included age, gender, race, ethnicity, type of insurance, hierarchical condition category score, primary language at the individual level, and zip code–level income.ParticipantsAll adult patients younger than 75 years of age diagnosed with type 2 diabetes mellitus (N = 16,588) as of 3/19/2020 at a single academic health center.InterventionsCompletion of one or more telemedicine encounters with an institutional primary care physician or endocrinologist between 3/19/2020 and 12/19/2020.Main MeasuresThe components met in a five-item composite measure of diabetes quality of care, as of patients’ last clinical encounter. Items were (1) systolic blood pressure less than 140 mmHg, (2) hemoglobin A1c less than 8.0%, (3) using a statin and (4) aspirin, and (5) tobacco non-use.Key ResultsFrom the pre- to post-period, the probability of meeting any given component of the composite measure for patients only utilizing in-person care was 21% lower (OR, 95% CI 0.79; 0.76, 0.81) and for the telemedicine users 2% lower (OR 0.98; 0.85, 1.13). There was an increased likelihood of meeting any given component among telemedicine users compared to in-person care alone (OR 1.25; 1.08, 1.44).ConclusionsPatients with diabetes utilizing telemedicine performed similarly on a composite measure of diabetes care quality compared to before the pandemic. Those not utilizing telemedicine had reductions. Telemedicine use maintained quality of care for patients with diabetes during the first 9 months of the COVID-19 pandemic.

Do rural oral and maxillofacial surgeons prescribe more postoperative opioids?

This study assesses rural-urban differences in opioid prescription practices among oral and maxillofacial surgeons (OMSs) who treated Medicare beneficiaries in 2017. This cross-sectional study examines the 2017 Medicare Provider Utilization and Payment Dataset. The primary predictor variable was provider Rural-Urban Commuting Area code (rural versus urban). The primary outcome variable was mean opioid claims per Medicare beneficiary. Additional outcomes include total opioid claims volume, average Medicare beneficiaries and opioid cost per provider, mean days' supply of opioids per opioid claim, and average percentage of Medicare Part D claims represented by opioid claims. Mann-Whitney U tests compared continuous variables. A least-squares regression identified correlates of opioid claims volume. Rural OMSs demonstrated a higher mean opioid claims per OMS and opioid cost per provider compared to urban surgeons. Urban OMSs prescribed a greater mean days' supply of opioids per opioid claim. A larger percentage of Medicare Part D claims were represented by opioid claims for rural OMSs compared to urban OMSs. There were no differences in mean opioid claims per Medicare beneficiary. Male provider gender, female Medicare beneficiary gender, total number of beneficiaries, and a higher hierarchical condition category score were correlated with increased opioid claims per Medicare beneficiary for urban providers only. Urban and rural OMSs prescribe a similar volume of opioids per Medicare beneficiary, with rural providers prescribing higher total volumes of opioids due to larger patient panels. This work indicates that rural and urban OMSs have similar opioid prescribing practices.

Abstract 10404: The Role of Patient, Community and Healthcare System Factors in the Variability of Heart Failure Hospital Readmissions

Introduction: The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals by reducing Medicare (CMS) reimbursement for those institutions with higher rates of readmissions following hospitalizations for heart failure (HF). Understanding the contributors to readmission following HF hospitalization can identify meaningful areas for intervention. We examine whether community and patient or healthcare system factors contribute more to variability in HF readmissions. Methods: We compiled CMS hospital data on HF readmissions, CMS physician reimbursement data, and 2020-21 County Health Rankings (Credit: University of Wisconsin). The outcome was the ratio of a hospital’s HF readmissions to HF discharges. Physician/healthcare system factors included total number of cardiac subspecialists, presence of all subspecialties, and the mean proportion of reimbursed charges. Patient/community factors included mean Hierarchical Condition Category (HCC) risk score (quantifies expected healthcare costs for CMS beneficiaries based on ICD-10 coding), median household income, the proportion of residents reporting no physical activity, and access to food stores. Models were created using linear regression. Model of best fit was determined using R 2 , AIC, BIC in model training data and mean absolute prediction error (MAPE) and root mean square prediction error (RMSE) in test data. Results: The linear regression model incorporating community and patient factors explained greater variability in HF readmission rates and performed with greater accuracy in the test data (R 2 0.177; AIC -6879.1; BIC -6840.542; MAPE 0.030; RMSE 0.039) than the model incorporating physician/healthcare system factors (R 2 0.021; AIC -6566.7; BIC -6539.191; MAPE 0.032; RMSE 0.041). HCC score and level of physical activity were associated with the greatest effect sizes on the outcome. Conclusions: At a county level, community/patient factors appear to contribute more to variability in hospitals’ HF readmission rates than physician/healthcare system factors. Further analyses should explore other community, patient, and provider factors that contribute. The HRRP does not account for community or patient factors, indicating its limitations.

Abstract 10400: The Role of Patient, Community, and Healthcare System Factors in Variability of Hospital Readmissions After Myocardial Infarction

Introduction: The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals by reducing Medicare (CMS) reimbursement for those institutions with higher rates of readmissions following hospitalizations for myocardial infarction (MI). Understanding the contributors to readmission following MI hospitalization can identify meaningful areas for intervention. We examine whether community and patient or healthcare system factors contribute more to variability in MI readmissions. Methods: We compiled CMS hospital data on MI readmissions, CMS physician reimbursement data, and 2020-21 County Health Rankings (Credit: University of Wisconsin). The outcome was the ratio of a hospital’s MI readmissions to MI discharges. Physician/healthcare system factors included total number of cardiac subspecialists, presence of all subspecialties, and the mean proportion of reimbursed charges. Patient and community factors included mean Hierarchical Condition Category (HCC) risk score (quantifies expected healthcare costs for CMS beneficiaries based on ICD-10 coding), median household income, the proportion of residents reporting no physical activity, and access to food stores. Models were created using linear regression. Model of best fit was determined using R 2 , AIC, BIC in model training data and mean absolute prediction error (MAPE) and root mean square prediction error (RMSE) in test data. Results: The linear regression model incorporating community and patient factors explained greater variability in MI readmission rates and performed with greater accuracy in the test data (R 2 0.21; AIC -40.2; BIC -5.1; MAPE 0.030; RMSE 0.042) than the model incorporating physician/healthcare system factors (R 2 0.04; AIC 170.0; BIC 195.0; MAPE 0.033; RMSE 0.045). HCC score, level of physical activity, median household income, and number of cardiac specialists were associated with significant effect sizes on the outcome. Conclusions: At a county level, community and patient factors appear to contribute more to variability in hospitals’ MI readmission rates. Further analyses should explore other community, patient, and provider factors that contribute. The HRRP does not account for community or patient factors, indicating its limitations.

Abstract 10388: The Role of Patient, Community and Healthcare System Factors in the Variability of Cardiovascular Hospital Readmissions

Introduction: The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals by reducing Medicare (CMS) reimbursement for institutions with higher rates of readmissions following hospitalizations for myocardial infarction (MI), coronary artery bypass grafting (CABG) and heart failure (HF). Understanding the contributors to these readmissions can identify meaningful areas for intervention. We examine whether community and patient or healthcare system factors contribute more to variability in cardiac readmissions. Methods: We compiled CMS hospital data on MI, CABG and HF readmissions, CMS physician reimbursement data, and 2020-21 County Health Rankings (Credit: University of Wisconsin). The outcome was the ratio of a hospital’s composite readmissions for MI, CABG and HF to discharges for the same conditions. Physician/healthcare system factors included total cardiac subspecialists, presence of all subspecialties, and the mean proportion of reimbursed charges. Patient and community factors included mean Hierarchical Condition Category (HCC) risk score, median household income, proportion of residents reporting no physical activity, and access to food stores. Linear regression models were compared using R 2 , AIC, BIC in model training data and mean absolute prediction error (MAPE) and root mean square prediction error (RMSE) in test data. Results: The linear regression model incorporating community and patient factors explained greater variability in readmission rates in training data and performed with greater accuracy in the test data (R 2 0.35; AIC -1839; BIC -1811; MAPE 0.017; RMSE 0.022) than the model incorporating physician/healthcare system factors (R 2 0.04; AIC - 1685; BIC - 1664; MAPE 0.022; RMSE 0.027). HCC score, level of physical activity, and number of cardiac specialists were associated with the largest effect sizes on the outcome. Conclusions: At a county level, community and patient factors appear to contribute more to variability in hospitals’ MI, CABG and and HF readmission rates. Further analyses should explore other community, patient, and provider factors that may contribute. The HRRP does not account for community/patient factors, indicating its limitations.

Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models

We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus <1%). Similarly, the PCF’s downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.

Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis

BackgroundThe COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown.ObjectiveWe examined the temporal association of...

Clinician Care Team Composition and Health Care Utilization.

ObjectiveTo test the hypothesis that a greater proportion of physician time on primary care teams are associated with decreased emergency department (ED) visits, hospital admissions, and readmissions, and to determine clinician and care team characteristics associated with greater utilization.Patients and MethodsWe retrospectively analyzed administrative data collected from January 1 to December 31, 2017, of 420 family medicine clinicians (253 physicians, 167 nurse practitioners/physician assistants [NP/PAs]) with patient panels in an integrated health system in 59 Midwestern communities serving rural and urban areas in Minnesota, Wisconsin, and Iowa. These clinicians cared for 419,581 patients through 110 care teams, with varying numbers of physicians and NP/PAs. Primary outcome measures were rates of ED visits, hospitalizations, and readmissions.ResultsThe proportion of physician full-time equivalents on the team was unrelated to rates of ED visits (rate ratio [RR] = 0.826; 95% confidence interval [CI], 0.624 to 1.063), hospitalizations (RR = 0.894; 95% CI, 0.746 to 1.072), or readmissions (RR = –0.026; 95% CI, 0.364 to 0.312). In separate multivariable models adjusted for clinician and practice-level characteristics, the rate of ED visits was positively associated with mean panel hierarchical condition category (HCC) score, urban vs rural setting, NP/PA vs physician, and lower years in practice. The rate of inpatient admissions was associated with HCC score, and 30-day hospital readmissions were positively associated with HCC score, lower years in practice, and male clinicians.ConclusionCare team physician and NP/PA composition was not independently related to utilization. More complex panels had higher rates of ED visits, hospitalization, and readmissions. Statistically significant differences between physician and NP/PA panels were only evident for ED visits.

Disparities in the Use of In-Person and Telehealth Primary Care Among High- and Low-Risk Medicare Beneficiaries During COVID-19.

We conducted a retrospective cohort study using a difference-in-differences design to estimate differences in primary care outpatient clinic visit utilization among high- and low-risk Medicare aging beneficiaries from an Accountable Care Organization during the COVID-19 pandemic compared to a control cohort from the previous year. High-risk was defined as having a Hierarchical Condition Category score of 2 or higher. A total of 582 101 patient-month records were analyzed. After adjusting for patient characteristics, those in the high-risk group had 339 (95% CI [333, 345]) monthly outpatient encounters (in-person and telehealth) per 1000 patients compared to 186 (95% CI [182, 190]) in the low-risk group. This represented a 22.8% and 26.5% decline from the previous year in each group, respectively. Within each group, there was lower utilization among those who were older, male, or dually eligible for Medicaid in the high-risk group and among those who were younger, male, or non-white in the low-risk group. Telehealth use was less common among patients who were older, dually eligible for Medicaid or living in rural/suburban areas compared to urban areas. All results were significant at the 95% level. We found significant disparities based on age, gender, insurance status, and non-white race in primary care utilization during the pandemic among Medicare beneficiaries. With the exception of gender, these disparities differed between high- and low-risk groups. Interventions targeting these vulnerable groups may improve health equity in the setting of public health emergencies.

Preoperative Frailty Assessment, Operative Severity Score, and Early Postoperative Loss of Independence in Surgical Patients Age 65 Years or Older

BackgroundPreoperative discussions around postoperative discharge planning have been amplified by the COVID pandemic. We wished to determine whether our preoperative frailty screen would predict postoperative loss of independence (LOI).Study DesignThis single-institutional study included demographic, procedural, and outcomes data from patients 65 years or older who underwent frailty screening before a surgical procedure. Frailty was assessed using the Edmonton Frail Scale. The Operative Severity Score was used to categorize procedures. The Hierarchical Condition Category risk-adjustment score, as calculated by the Centers for Medicare and Medicaid Services, was included. LOI was defined as an increase in support outside of the home after discharge. Univariable, multivariable logistic regressions, and adjusted postestimation analyses for predictive probabilities of best fit were performed.ResultsFive hundred and thirty-five patients met inclusion criteria and LOI was seen in 38 patients (7%). Patients with LOI were older, had a lower BMI, a higher Edmonton Frail Scale score (7 vs 3.0; p < 0.001), and a higher Hierarchical Condition Category score than patients without LOI. Being frail and undergoing a procedure with an Operative Severity Score of 3 or higher was independently associated with an increased risk of LOI. In addition, social dependency, depression, and limited mobility were associated with an increased risk for LOI. On multivariable modeling, frailty status, undergoing an operation with an Operative Severity Score of 3 or higher, and having a Hierarchical Condition Category score ≥1 were the most predictive of LOI (odds ratio 12.72; 95% CI, 12.04 to 13.44; p < 0.001). In addition, self-reported depression, weight loss, and limited mobility were associated with a nearly 11-fold increased risk of postoperative LOI.ConclusionsThis study was novel, as it identified clear, generalizable risk factors for LOI. In addition, our findings support the implementation of preoperative assessments to aid in care coordination and provide specific targets for intervention.

Improvement in Mortality With Early Fluid Bolus in Sepsis Patients With a History of Congestive Heart Failure.

ObjectiveTo determine whether rapid administration of a crystalloid bolus of 30 mL/kg within 3 hours of presentation harms or benefits hypotensive patients with sepsis with a history of congestive heart failure (CHF).Patients and MethodsA retrospective cohort study using Medicare claims data enhanced by medical record data from members of the High Value Healthcare Collaborative from July 1, 2013, to June 30, 2015, examining patients with a history of CHF who did (fluid bundle compliant [FBC]) or did not (NFBC) receive a volume bolus of 30 mL/kg within 3 hours of presentation to the emergency department. A proportional Cox hazard model was used to evaluate the association of FBC with 1-year survival.ResultsOf the 211 patients examined, 190 were FBC and 21 were NFBC. The FBC patients had higher average hierarchical condition category scores but were otherwise similar to NFBC patients. The NFBC patients had higher adjusted in-hospital and postdischarge mortality rates. The risk-adjusted 1-year mortality rate was higher for NFBC patients (hazard ratio, 2.18; 95% CI, 1.2 to 4.0; P=.01) than for FBC patients.ConclusionIn a retrospective claim data-based study of elderly patients with a history of CHF presenting with severe sepsis or septic shock, there is an association of improved mortality with adherence to the initial fluid resuscitation guidelines as part of the 3-hour sepsis bundle.