Chronic Medication Burden After Cardiac Surgery for Pediatric Medicaid Beneficiaries.

BACKGROUND: Congenital heart defects are the most common and resource-intensive birth defects. As children with congenital heart defects increasingly survive beyond early childhood, it is imperative to understand longitudinal disease burden. OBJECTIVES: The purpose of this study was to examine chronic outpatient prescription medication use and expenditures for New York State pediatric Medicaid enrollees, comparing children who undergo cardiac surgery (cardiac enrollees) and the general pediatric population. METHODS: This was a retrospective cohort study of all Medicaid enrollees age <18 years using the New York State Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources database (2006-2019). Primary outcomes were total chronic medications per person-year, enrollees per 100 person-years using ≥1 and ≥3 medications, and medication expenditures per person-year. We described and compared outcomes between cardiac enrollees and the general pediatric population. Among cardiac enrollees, multivariable regression examined associations between outcomes and clinical characteristics. RESULTS: We included 5,459 unique children (32,131 person-years) who underwent cardiac surgery and 4.5 million children (22 million person-years) who did not. More than 4 in 10 children who underwent cardiac surgery used ≥1 chronic medication compared with approximately 1 in 10 children who did not have cardiac surgery. Medication expenditures were 10 times higher per person-year for cardiac compared with noncardiac enrollees. Among cardiac enrollees, disease severity was associated with chronic medication use; use was highest among infants; however, nearly one-half of adolescents used ≥1 chronic medication. CONCLUSIONS: Children who undergo cardiac surgery experience high medication burden that persists throughout childhood. Understanding chronic medication use can inform clinicians (both pediatricians and subspecialists) and policymakers, and ultimately the value of care for this medically complex population.

Sensitivity of Medicaid Claims Data for Identifying Opioid Use Disorder in Patients Admitted to 6 New York City Public Hospitals.

OBJECTIVES: Behavioral health diagnoses are frequently underreported in administrative health data. For a pragmatic trial of a hospital addiction consult program, we sought to determine the sensitivity of Medicaid claims data for identifying patients with opioid use disorder (OUD). METHODS: A structured review of electronic health record (EHR) data was conducted to identify patients with OUD in 6 New York City public hospitals. Cases selected for review were adults admitted to medical/surgical inpatient units who received methadone or sublingual buprenorphine in the hospital. For cases with OUD based on EHR review, we searched for the hospitalization in Medicaid claims data and examined International Classification of Diseases, Tenth Revision discharge diagnosis codes to identify opioid diagnoses (OUD, opioid poisoning, or opioid-related adverse events). Sensitivity of Medicaid claims data for capturing OUD hospitalizations was calculated using EHR review findings as the reference standard measure. RESULTS: Among 552 cases with OUD based on EHR review, 465 (84.2%) were found in the Medicaid claims data, of which 418 (89.9%) had an opioid discharge diagnosis. Opioid diagnoses were the primary diagnosis in 49 cases (11.7%), whereas in the remainder, they were secondary diagnoses. CONCLUSION: In this sample of hospitalized patients receiving OUD medications, Medicaid claims seem to have good sensitivity for capturing opioid diagnoses. Although the sensitivity of claims data may vary, it can potentially be a valuable source of information about OUD patients.

Comparison of Care Provided to Underserved Patients With Diabetes by a Telementoring Model of Care to Care Provided by a Specialty Clinic: Endo ECHO Versus an Academic Specialty Clinic.

PURPOSE: The purpose of the study was to examine differences among adult patients with diabetes who receive care through a telementoring model versus care at an academic specialty clinic on guideline-recommended diabetes care and self-management behaviors. METHODS: Endocrinology-focused Extension for Community Healthcare Outcomes (ECHO Endo) patients completed surveys assessing demographics, access to care, health care quality, and self-management behaviors at enrollment and 1 year after program enrollment. Diabetes Comprehensive Care Center (DCCC) patients completed surveys at comparable time points. RESULTS: At baseline, ECHO patients were less likely than DCCC patients to identify English as their primary language, have postsecondary education, and private insurance. One year postenrollment, ECHO patients visited their usual source of diabetic care more frequently. There were no differences in A1C testing or feet checking by health care professionals, but ECHO patients were less likely to report eye exams and smoking status assessment. ECHO and DCCC patients did not differ in consumption of high-fat foods and soda, physical activity, or home feet checks. ECHO patients were less likely to space carbohydrates evenly and test glucose levels and more likely to have smoked cigarettes. CONCLUSIONS: Endo ECHO is a suitable alternative to specialty care for patients in underserved communities with restricted access to specialty care. Results support the value of the Project ECHO telementoring model in addressing barriers to high-quality care for underserved communities.

Long-Term Health Care Utilization After Cardiac Surgery in Children Covered Under Medicaid.

BACKGROUND: Understanding the longitudinal burden of health care expenditures and utilization after pediatric cardiac surgery is needed to counsel families, improve care, and reduce outcome inequities. OBJECTIVES: The purpose of this study was to describe and identify predictors of health care expenditures and utilization for Medicaid-insured pediatric cardiac surgical patients. METHODS: All Medicaid enrolled children age <18 years undergoing cardiac surgery in the New York State CHS-COLOUR database, from 2006 to 2019, were followed in Medicaid claims data through 2019. A matched cohort of children without cardiac surgical disease was identified as comparators. Expenditures and inpatient, primary care, subspecialist, and emergency department utilization were modeled using log-linear and Poisson regression models to assess associations between patient characteristics and outcomes. RESULTS: In 5,241 New York Medicaid-enrolled children, longitudinal health care expenditures and utilization for cardiac surgical patients exceeded noncardiac surgical comparators (cardiac surgical children: $15,500 ± $62,000 per month in year 1 and $1,600 ± $9,100 per month in year 5 vs noncardiac surgical children: $700 ± $6,600 per month in year 1 and $300 ± $2,200 per month in year 5). Children after cardiac surgery spent 52.9 days in hospitals and doctors' offices in the first postoperative year and 90.5 days over 5 years. Being Hispanic, compared with non-Hispanic White, was associated with having more emergency department visits, inpatient admissions, and subspecialist visits in years 2 to 5, but fewer primary care visits and greater 5-year mortality. CONCLUSIONS: Children after cardiac surgery have significant longitudinal health care needs, even among those with less severe cardiac disease. Health care utilization differed by race/ethnicity, although mechanisms driving disparities should be investigated further.

Association of Insurance Mix and Diagnostic Coding Practices in New York State Hospitals.

Importance: Given higher reimbursement rates, hospitals primarily serving privately insured patients may invest more in intensive coding than hospitals serving publicly insured patients. This may lead these hospitals to code more diagnoses for all patients. Objective: To estimate whether, for the same Medicaid enrollee with multiple hospitalizations, a hospital's share of privately insured patients is associated with the number of diagnoses on claims. Design, Setting, and Participants: This cross-sectional study used patient-level fixed effects regression models on inpatient Medicaid claims from Medicaid enrollees with at least 2 admissions in at least 2 different hospitals in New York State between 2010 and 2017. Analyses were conducted from 2019 to 2021. Exposures: The annual share of privately insured patients at the admitting hospital. Main Outcomes and Measures: Number of diagnostic codes per admission. Probability of diagnoses being from a list of conditions shown to be intensely coded in response to payment incentives. Results: This analysis included 1 614 630 hospitalizations for Medicaid-insured patients (mean [SD] age, 48.2 [20.1] years; 829 684 [51.4%] women and 784 946 [48.6%] men). Overall, 74 998 were Asian (4.6%), 462 259 Black (28.6%), 375 591 Hispanic (23.3%), 486 313 White (30.1%), 128 896 unknown (8.0%), and 86 573 other (5.4%). When the same patient was seen in a hospital with a higher share of privately insured patients, more diagnoses were recorded (0.03 diagnoses per percentage point [pp] increase in share of privately insured; 95% CI, 0.02-0.05; P < .001). Patients discharged from hospitals in the bottom quartile of privately insured patient share received 1.37 more diagnoses when they were subsequently discharged from hospitals in the top quartile, relative to patients whose admissions were both in the bottom quartile (95% CI, 1.21-1.53; P < .001). Those going from hospitals in the top quartile to the bottom had 1.67 fewer diagnoses (95% CI, -1.84 to -1.50; P < .001). Diagnoses in hospitals with a higher private payer share were more likely to be for conditions sensitive to payment incentives (0.08 pp increase for each pp increase in private share; 95% CI, 0.06-0.10; P < .001). These findings were replicated in 2016 to 2017 data. Conclusions and Relevance: In this cross-sectional study of Medicaid enrollees, admission to a hospital with a higher private payer share was associated with more diagnoses on Medicaid claims. This suggests payment policy may drive differential investments in infrastructure to document diagnoses. This may create a feedback loop that exacerbates resource inequity.

Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2).

BACKGROUND: As the cardiac community strives to improve outcomes, accurate methods of risk stratification are imperative. Since adoption of International Classification of Disease-10th Revision (ICD-10) in 2015, there is no published method for congenital heart surgery risk stratification for administrative data. OBJECTIVES: This study sought to develop an empirically derived, publicly available Risk Stratification for Congenital Heart Surgery (RACHS-2) tool for ICD-10 administrative data. METHODS: The RACHS-2 stratification system was iteratively and empirically refined in a training dataset of Pediatric Health Information Systems claims to optimize sensitivity and specificity compared with corresponding locally held Society of Thoracic Surgeons-Congenital Heart Surgery (STS-CHS) clinical registry data. The tool was validated in a second administrative data source: New York State Medicaid claims. Logistic regression was used to compare the ability of RACHS-2 in administrative data to predict operative mortality vs STAT Mortality Categories in registry data. RESULTS: The RACHS-2 system captured 99.6% of total congenital heart surgery registry cases, with 1.0% false positives. RACHS-2 predicted operative mortality in both training and validation administrative datasets similarly to STAT Mortality Categories in registry data. C-statistics for models for operative mortality in training and validation administrative datasets-adjusted for RACHS-2-were 0.76 and 0.84 (95% CI: 0.72-0.80 and 0.80-0.89); C-statistics for models for operative mortality-adjusted for STAT Mortality Categories-in corresponding clinical registry data were 0.75 and 0.84 (95% CI: 0.71-0.79 and 0.79-0.89). CONCLUSIONS: RACHS-2 is a risk stratification system for pediatric cardiac surgery for ICD-10 administrative data, validated in 2 administrative-registry-linked datasets. Statistical code is publicly available upon request.

A Project ECHO and Community Health Worker Intervention for Patients with Diabetes.

BACKGROUND: Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes. We studied the effect of an intervention that combined these 2 approaches on glycemic control. METHODS: Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same electronic health records databases to estimate the average treatment effect. RESULTS: Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus postintervention periods (P < .001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% confidence interval [CI] -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percentage of patients with HbA1c <8% in the late follow-up cohort (8.1%, 95% CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5% to 8.7%) DISCUSSION: The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. Although combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.

Improving Longitudinal Outcomes, Efficiency, and Equity in the Care of Patients With Congenital Heart Disease.

BACKGROUND: Longitudinal follow-up, resource utilization, and health disparities are top congenital heart research and care priorities. Medicaid claims include longitudinal data on inpatient, outpatient, emergency, pharmacy, rehabilitation, home health utilization, and social determinants of health-including mother-infant pairs. OBJECTIVES: The New York Congenital Heart Surgeons Collaborative for Longitudinal Outcomes and Utilization of Resources linked robust clinical details from locally held state and national registries from 10 of 11 New York congenital heart centers to Medicaid claims, building a novel, statewide mechanism for longitudinal assessment of outcomes, expenditures, and health inequities. METHODS: The authors included all children <18 years of age undergoing cardiac surgery in The Society of Thoracic Surgeons Congenital Heart Surgery Database or the New York State Pediatric Congenital Cardiac Surgery Registry from 10 of 11 New York centers, 2006 to 2019. Data were linked via iterative, ranked deterministic matching on direct identifiers. Match rates were calculated and compared. Proportions of the linked cohort trackable over 3, 5, and 10 years were described. RESULTS: Of 14,097 registry cases, 59% (n = 8,322) reported Medicaid use. Of these, 7,414 were linked to New York claims, at an 89% match rate. Of matched cases, the authors tracked 79%, 74%, and 65% of children over 3, 5, and 10 years when requiring near-continuous Medicaid enrollment. Allowing more lenient enrollment criteria, the authors tracked 86%, 82%, and 76%, respectively. Mortality over this time was 7.7%, 8.4%, and 10.0%, respectively. Manual validation revealed ∼100% true matches. CONCLUSIONS: This establishes a novel statewide data resource for assessment of longitudinal outcome, health expenditure, and disparities for children with congenital heart disease.

Impact of a Primary Care Provider Tele-Mentoring and Community Health Worker Intervention on Utilization in Medicaid Patients with Diabetes.

OBJECTIVE: The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico. METHODS: Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients. RESULTS: Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio, 1.57; 95% confidence interval [CI], 1.43 to 1.72). The intervention was associated with an increase in emergency department (ED) visits (rate ratio, 1.30; 95% CI, 1.04 to 1.63) but no change in hospitalizations (rate ratio, 1.47; 95% CI, 0.95 to 2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI, 4.0% to 13.6%). We found similar increases in use of statins (DID, 8.5%; 95% CI, 3.2% to 13.8%), angiotensin-converting enzyme inhibitors (DID, 9.5%; 95% CI, 3.5% to 15.4%), or antidepressant therapies (DID, 9.4%; 95% CI, 1.1% to 18.1%). CONCLUSION: Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization.

A Telementoring Intervention Leads to Improvements in Self-Reported Measures of Health Care Access and Quality among Patients with Complex Diabetes.

Individuals living with complex diabetes experience limited access to endocrine care due to a nationwide shortage of endocrinologists. Project ECHO (Extension for Community Healthcare Outcomes) is an innovative, scalable model of health care that extends specialty care to medically underserved areas through ongoing telementorship of community primary care providers. We evaluated the effects of an endocrine-focused ECHO program (Endo ECHO) on patients with type 1 and complex type 2 diabetes, and report here on changes in patient-reported measures of health care access and quality from baseline to one year aft er program enrollment. Patients were eligible for Endo ECHO if they were 18 years or older with complex diabetes. Aft er participating in Endo ECHO, access to health care and diabetes-related quality of care improved dramatically. Our results suggest that Endo ECHO may be a suitable intervention for extending best practices in diabetes care to medically underserved patients.

Study protocol for a pragmatic trial of the Consult for Addiction Treatment and Care in Hospitals (CATCH) model for engaging patients in opioid use disorder treatment.

BACKGROUND: Treatment for opioid use disorder (OUD) is highly effective, yet it remains dramatically underutilized. Individuals with OUD have disproportionately high rates of hospitalization and low rates of addiction treatment. Hospital-based addiction consult services offer a potential solution by using multidisciplinary teams to evaluate patients, initiate medication for addiction treatment (MAT) in the hospital, and connect patients to post-discharge care. We are studying the effectiveness of an addiction consult model [Consult for Addiction Treatment and Care in Hospitals (CATCH)] as a strategy for engaging patients with OUD in treatment as the program rolls out in the largest municipal hospital system in the US. The primary aim is to evaluate the effectiveness of CATCH in increasing post-discharge initiation and engagement in MAT. Secondary aims are to assess treatment retention, frequency of acute care utilization and overdose deaths and their associated costs, and implementation outcomes. METHODS: A pragmatic trial at six hospitals, conducted in collaboration with the municipal hospital system and department of health, will be implemented to study the CATCH intervention. Guided by the RE-AIM evaluation framework, this hybrid effectiveness-implementation study (Type 1) focuses primarily on effectiveness and also measures implementation outcomes to inform the intervention's adoption and sustainability. A stepped-wedge cluster randomized trial design will determine the impact of CATCH on treatment outcomes in comparison to usual care for a control period, followed by a 12-month intervention period and a 6- to 18-month maintenance period at each hospital. A mixed methods approach will primarily utilize administrative data to measure outcomes, while interviews and focus groups with staff and patients will provide additional information on implementation fidelity and barriers to delivering MAT to patients with OUD. DISCUSSION: Because of their great potential to reduce the negative health and economic consequences of untreated OUD, addiction consult models are proliferating in response to the opioid epidemic, despite the absence of a strong evidence base. This study will provide the first known rigorous evaluation of an addiction consult model in a large multi-site trial and promises to generate knowledge that can rapidly transform practice and inform the potential for widespread dissemination of these services. TRIAL REGISTRATION: NCT03611335.

Factors associated with variation in hospital use at the end of life in England.

OBJECTIVE: To identify the relative importance of factors influencing hospital use at the end of life. DESIGN: Retrospective cohort study of person and health system effects on hospital use in the past 12 months modelling differences in admissions, bed days and whether a person died in hospital. SETTING: Residents in England for the period 2009/2010 to 2011/2012 using Hospital Episodes Statistics (HES) data from all acute care hospitals in England funded by the National Health Service (NHS). PARTICIPANTS: 1 223 859 people registered with a GP in England who died (decedents) in England (April 2009-March 2012) with a record of NHS hospital care. MAIN OUTCOME MEASURES: Hospital admissions, and hospital bed days and place of death (in or out of hospital) in the past 12 months of life. RESULTS: The mean number of admissions in the past 12 months of life averaged 2.28 occupying 30.05 bed days-excluding 9.8% of patients with no hospital history. A total of 50.8% of people died in hospital. Difference in hospital use was associated with a range of patient descriptors (age, gender and ethnicity). The variables with the greatest 'explanatory power' were those that described the diagnoses and causes of death. So, for example, 65% of the variability in the model of hospital admissions was explained by diagnoses. Only moderate levels of variation were explained by the hospital provider variables for admissions and deaths in hospital, though the impacts on total bed days was large. CONCLUSIONS: Comparative analyses of hospital utilisation should standardise for a range of patient specific variables. Though the models indicated some degree of variability associated with individual providers, the scale of this was not great for admissions and death in hospital but the variability associated with length of stay differences suggests that attempts to optimise hospital use should look at differences in lengths of stay and bed use. This study adds important new information about variability in admissions by diagnostic group, and variability in bed days by diagnostic group and eventual cause of death.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.

Taking Telemedicine to the Next Level in Diabetes Population Management: a Review of the Endo ECHO Model.

Worldwide increases in diabetes prevalence in the face of limited medical resources have prompted international interest in innovative healthcare delivery models. Project ECHO (Extension for Community Healthcare Outcomes) is a "telementoring" program which has been shown to increase capacity for complex disease management in medically underserved regions. In contrast to a traditional telemedicine model which might connect a specialist with one patient, the ECHO model allows for multiple patients to benefit simultaneously by building new expertise. We recently applied the ECHO model to improve health outcomes of patients with complex diabetes (Endo ECHO) living in rural New Mexico. We describe the design of the Endo ECHO intervention and a 4-year, prospective program evaluation assessing health outcomes, utilization patterns, and cost-effectiveness. The Endo ECHO evaluation will demonstrate whether and to what extent this intervention improves outcomes for patients with complex diabetes living in rural New Mexico, and will serve as proof-of-concept for academic medical centers wishing to replicate the model in underserved regions around the world.

A pilot study of implantable cardiac device interrogation by emergency department personnel.

BACKGROUND: Implanted devices (eg, pacemakers and defibrillators) provide valuable information and may be interrogated to obtain diagnostic information and to direct management. During admission to an emergency department (ED), significant time and cost are spent waiting for device manufacturer representatives or cardiologists to access the data. If ED personnel could safely interrogate implanted devices, more rapid disposition could occur, thus leading to potentially better outcomes at a reduced cost. This was a pilot study examining the feasibility of ED device interrogation. METHODS: This was a prospective convenience sample study of patients presenting to the ED with any chief complaint and who had an implantable device capable of being interrogated by a Medtronic reader. After obtaining informed consent, study patients underwent device interrogation by ED research personnel. After reviewing the device data, the physician documented their opinions of the value of data in aiding care. Patients were followed up at intervals ranging from 30 days out to 1 year to determine adverse events relating to interrogation. RESULTS: Forty-four patients underwent device interrogation. Their mean age was 56 ± 14.7 years (range, 28-83), 75% (33/44) were male and 75% (33/44) were hospitalized from the ED. The interrogations took less than 10 minutes 89% of the time. In 60% of the cases, ED physicians reported the data-assisted patient care. No adverse events were reported relating to the ED interrogations. CONCLUSIONS: In this pilot study, we found that ED personnel can safely and quickly interrogate implantable devices to obtain potentially useful clinical data.