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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

Berry, Carolyn A; Dávila Saad, Andrea; Blecker, Saul; Billings, John; Bouchonville, Matthew F; Arora, Sanjeev; Paul, Margaret M
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.
PMID: 37129282
ISSN: 2635-0114
CID: 5502952

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

McNeely, Jennifer; Gallagher, Shane D; Mazumdar, Medha; Appleton, Noa; Fernando, Jasmine; Owens, Elizabeth; Bone, Emmeline; Krawczyk, Noa; Dolle, Johanna; Marcello, Roopa Kalyanaraman; Billings, John; Wang, Scarlett
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.
PMCID:10110762
PMID: 37267184
ISSN: 1935-3227
CID: 5540912

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

McNeely, Jennifer; Gallagher, Shane D; Mazumdar, Medha; Appleton, Noa; Fernando, Jasmine; Owens, Elizabeth; Bone, Emmeline; Krawczyk, Noa; Dolle, Johanna; Marcello, Roopa Kalyanaraman; Billings, John; Wang, Scarlett
OBJECTIVES/OBJECTIVE: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/CONCLUSIONS: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.
PMID: 36255115
ISSN: 1935-3227
CID: 5360342

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

Dragan, Kacie L; Desai, Sunita M; Billings, John; Glied, Sherry A
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.
PMCID:9440394
PMID: 36218926
ISSN: 2689-0186
CID: 5359942

A Project ECHO and community health worker intervention for patients with diabetes

Blecker, Saul; Paul, Margaret M; Jones, Simon; Billings, John; Bouchonville, Matthew F; Hager, Brant; Arora, Sanjeev; Berry, Carolyn A
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 two 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 EHR 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 post-intervention periods (p<0.001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% 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 percent 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%-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. While combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.
PMID: 34973203
ISSN: 1555-7162
CID: 5108412

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

Allen, Philip; Zafar, Farhan; Mi, Junhui; Crook, Sarah; Woo, Joyce; Jayaram, Natalie; Bryant, Roosevelt; Karamlou, Tara; Tweddell, James; Dragan, Kacie; Cook, Stephen; Hannan, Edward L; Newburger, Jane W; Bacha, Emile A; Vincent, Robert; Nguyen, Khanh; Walsh-Spoonhower, Kathleen; Mosca, Ralph; Devejian, Neil; Kamenir, Steven A; Alfieris, George M; Swartz, Michael F; Meyer, David; Paul, Erin A; Billings, John; Anderson, Brett R
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/OBJECTIVE: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.
PMID: 35115103
ISSN: 1558-3597
CID: 5153032

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

Blecker, Saul; Lemieux, Emily; Paul, Margaret M; Berry, Carolyn A; Bouchonville, Matthew F; Arora, Sanjeev; Billings, John
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 &lsqb;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/CONCLUSIONS: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.
PMID: 33471708
ISSN: 1530-891x
CID: 4882082

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

Paul, Margaret M; Saad, Andrea Davila; Billings, John; Blecker, Saul; Bouchonville, Matthew F; Chavez, Cindy; Hager, Brant W; Arora, Sanjeev; Berry, Carolyn A
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.
PMID: 33416685
ISSN: 1548-6869
CID: 4771212

"Sensitivity of paid insurance claims data for identifying hospital patients with opioid use disorder" (MM15) [Meeting Abstract]

McNeely, Jennifer; Owens, Elizabeth; Bone, Emmeline; Appleton, Noa; Fernando, Jasmine; Wang, Scarlett; Dolle, Johanna; Marcello, Roopa Kalyanaraman; Billings, John; Gallagher, Shane
ISI:000603567100083
ISSN: 1940-0640
CID: 4764162

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

Bardsley, Martin; Georghiou, Theo; Spence, Ruth; Billings, John
OBJECTIVE:To identify the relative importance of factors influencing hospital use at the end of life. DESIGN/METHODS: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/METHODS: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/METHODS: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/METHODS: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.
PMID: 27013618
ISSN: 2045-4368
CID: 3052252