Disability, Health, and Multiple Chronic Conditions Among People Eligible for Both Medicare and Medicaid, 2005–2010

Fox, Michael H.; Reichard, Amanda

doi:10.5888/pcd10.130064

Cited by 52 publications

(35 citation statements)

References 15 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Those with public insurance often have multiple chronic conditions that require regular care. 45 In particular, compared with private insurance patients, public insurance patients have higher topic enrichment values for atrial fibrillation (0.24 v 0.013), pacemakers (0.023 v 0.014), and dialysis (0.023 v 0.013). However, compared with public insurance patients, private insurance patients have higher topic enrichment values for fractures (0.035 v 0.012), lymphoma (0.030 v 0.015), and aneurysms (0.028 v 0.016).…”

Section: Results: Enrichment Of Topic Modeled Notesmentioning

confidence: 99%

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Chen¹,

Szolovits²,

Ghassemi³

2019

AMA Journal of Ethics

215

View full text Add to dashboard Cite

Background: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve for all. Methods:Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status.Results: Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings. Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission.Conclusions: This analysis can provide a framework for assessing and identifying disparate impacts of artificial intelligence in health care. Bias in Machine Learning ModelsWhile health care is an inherently data-driven field, most clinicians operate with limited evidence guiding their decisions. Randomized trials estimate average treatment effects for a trial population, but participants in clinical trials often aren't representative of the patient population that ultimately receives the treatment with respect to race and gender. 1,2 As a result, drugs and interventions are not tailored to historically mistreated groups; for example, women, minority groups, and obese patients tend to have generally poorer treatment options and longitudinal health outcomes. [3][4][5][6][7][8][9] Advances in artificial intelligence (AI) and machine learning offer the potential to provide personalized care by taking into account granular patient differences. Machine learning using images, clinical notes, and other electronic health record (EHR) data has been Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. She is also pursuing a graduate education in medical sciences certificate in the Harvard-MIT Program in Health Sciences and Technology. She received a bachelor of arts degree in applied math-economics and computer science and a master of science degree in computational science and engineering from Harvard University.

show abstract

Section: Results: Enrichment Of Topic Modeled Notesmentioning

confidence: 99%

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Chen¹,

Szolovits²,

Ghassemi³

2019

AMA Journal of Ethics

215

View full text Add to dashboard Cite

show abstract

“…As technology rapidly improves, many individuals with health problems turn to the Internet to seek out relevant health information as an active coping strategy. Recently, the Pew Internet & American Life Project conducted a national survey and found that 86% of Internet users living with disability or chronic illness have looked online for information (Fox, ). Related data indicate that approximately 8 million American adults with health concerns searched for information on at least one health topic on a typical day.…”

mentioning

confidence: 99%

Predictors of Online Health Information Seeking Among Women with Breast Cancer: The Role of Social Support Perception and Emotional Well-Being

Kim

Shah

Namkoong

et al. 2013

J Comput-Mediat Comm

View full text Add to dashboard Cite

This study attempts to examine the role of social support perception and emotional well-being on online information seeking among cancer patients within the context of CHESS, a well-established Interactive Cancer Communication System (ICCS). Factor and regression analyses conducted among 231 breast cancer patients revealed that social support perception and emotional well-being interacted with each other to influence online health information seeking. Patients with low social support perception and high emotional well-being were most likely to seek health information, whereas patients with high social support perception and high emotional well-being sought out the same information least. Practical implications of the study findings were further discussed.

show abstract

“…In 2019, CMS began to compare readmission rates and determine penalties in the Hospital Readmission Reduction Program based on each hospital's proportion of Medicare beneficiaries who were enrolled in Medicaid . These methods are intended to account for the higher average risk of dual enrollees, which are a reflection of the pathways to eligibility for both programs: Patients establish eligibility for Medicare if they are elderly or have a qualifying disability, and can receive Medicaid if they have low income and assets, or incur high medical expenses relative to income in states with Medically Needy programs …”

Section: Introductionmentioning

confidence: 99%

State variation in the characteristics of Medicare‐Medicaid dual enrollees: Implications for risk adjustment

Roberts

Mellor

McInerney

et al. 2019

Health Services Research

View full text Add to dashboard Cite

Objective To examine between‐state differences in the socioeconomic and health characteristics of Medicare beneficiaries dually enrolled in Medicaid, focusing on characteristics not observable to or used by policy makers for risk adjustment. Data Source 2010‐2013 Medicare Current Beneficiary Survey. Study Design Retrospective analyses of survey‐reported health and socioeconomic status (SES) measures among low‐income Medicare beneficiaries and low‐income dual enrollees. We used hierarchical linear regression models with state random effects to estimate the between‐state variation in respondent characteristics and linear models to compare the characteristics of dual enrollees by state Medicaid policies. Principal Findings Between‐state differences in health and socioeconomic risk among low‐income Medicare beneficiaries, as measured by the coefficient of variation, ranged from 17.5 percent for an index of socioeconomic risk to 20.3 percent for an index of health risk. Between‐state differences were comparable among the subset of low‐income beneficiaries dually enrolled in Medicare and Medicaid. Dual enrollees with incomes below the Federal Poverty Level were in better health and had higher SES in states that offered Medicaid to individuals with relatively higher incomes. Duals' average incomes were higher in states with Medically Needy programs. Conclusions Characteristics of dual enrollees differ substantially across states, reflecting differences in states' low‐income Medicare populations and Medicaid policies. Risk‐adjustment methods using dual enrollment to proxy for poor health and low SES should account for this state‐level heterogeneity.

show abstract

Disability, Health, and Multiple Chronic Conditions Among People Eligible for Both Medicare and Medicaid, 2005–2010

Cited by 52 publications

References 15 publications

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Predictors of Online Health Information Seeking Among Women with Breast Cancer: The Role of Social Support Perception and Emotional Well-Being

State variation in the characteristics of Medicare‐Medicaid dual enrollees: Implications for risk adjustment

Contact Info

Product

Resources

About