Medicaid coverage accuracy in electronic health records

Applying Machine Learning Techniques to Implementation Science

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information - A merging of two administrative data sources

OBJECTIVE

Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders.

METHODS

Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims.

RESULTS

A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition.

CONCLUSION

The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.

An observational study of health insured visits for children following Medicaid eligibility expansion for adults among a linked cohort of parents and children

Despite its focus on adults, the Affordable Care Act (ACA) Medicaid expansion led to increased health insurance enrollment for children in the United States. Previous studies looked at parent and child insurance changes separately, or used a single survey response item to understand changes in health insurance for parents and children. It is, however, important to understand the connection between parent and child insurance changes together (not individually) using data sources that account for insurance over time. Therefore, to understand the association of parental health insurance on their children's coverage, leveraging a cohort of linked families seen in community health centers (CHCs), we used electronic health records to link a cohort of parents and children with ≥1 visit to a CHC in a Medicaid expansion state pre- (1/1/2012-12/31/2013) and ≥1 visit post-ACA (1/1/2014-12/31/2018) and determined primary payer type for all visits. This observational, cohort study assessed the rate of insured visits for children pre- to post-ACA across four parental insurance groups (always insured, gained Medicaid, discontinuously insured, never insured) using Poisson mixed effects models. We included 335 CHCs across 7 United States. Insurance rates were highest (~95 insured visits/100 visits) for children of parents who were always insured; rates were lowest for children of parents who were never insured (~83 insured visits/100 visits). Children with a parent who gained Medicaid had 4.4% more insured visits post- compared to pre-ACA (adjusted relative rates  = 1.044, 95% confidence interval: 1.014, 1.074). When comparing changes from pre- to post-ACA between parent insurance groups, children's insured visit rates were significantly higher for children of parents who gained Medicaid (reference) compared to children of parents who were always insured (adjusted ratio of rate ratio: 0.963, confidence interval: 0.935-0.992). Despite differences in Medicaid eligibility for children and adults, health insurance patterns were similar for linked families seen in CHCs. Findings suggest consideration should be paid to parent health insurance options when trying to increase children's coverage.

Racial and Ethnic Disparities in Acute Care Use for Pediatric Asthma

PURPOSE

Previous work has shown that asthma-related emergency department (ED) use is greatest among Black and Latine populations, but it is unknown whether health care use for exacerbations differs across settings (outpatient, ED, inpatient) and correlates with use of routine outpatient services. We aimed to measure disparities by race, ethnicity, and language in pediatric acute asthma care using data from US primary care community health centers.

METHODS

In an observational study using electronic health records from community health centers in 18 states, we compared non-Hispanic Black, English-preferring Latine, Spanish-preferring Latine, and non-Hispanic White children aged 3 to 17 years on visits for clinic-coded asthma exacerbations (2012-2018). We further evaluated asthma-related ED use and inpatient admissions in a subsample of Oregon-Medicaid recipients. Covariate-adjusted odds ratios (ORs) and rate ratios (RRs) were derived using logistic or negative binomial regression analysis with generalized estimating equations.

RESULTS

Among 41,276 children with asthma, Spanish-preferring Latine children had higher odds of clinic visits for asthma exacerbation than non-Hispanic White peers (OR = 1.10; 95% CI, 1.02-1.18). Among the subsample of 6,555 children insured under Oregon-Medicaid, non-Hispanic Black children had higher odds and rates of asthma-related ED use than non-Hispanic White peers (OR = 1.40; 95% CI, 1.04-1.89 and RR = 1.49; 95% CI, 1.09-2.04, respectively). We observed no differences between groups in asthma-related inpatient admissions.

CONCLUSIONS

This study is the first to show that patterns of clinic and ED acute-care use differ for non-Hispanic Black and Spanish-preferring Latine children when compared with non-Hispanic White peers. Non-Hispanic Black children had lower use of clinics, whereas Spanish-preferring Latine children had higher use, including for acute exacerbations. These patterns of clinic use were accompanied by higher ED use among Black children. Ensuring adequate care in clinics may be important in mitigating disparities in asthma outcomes.VISUAL ABSTRACT.

Effectiveness of an insurance enrollment support tool on insurance rates and cancer prevention in community health centers: a quasi-experimental study

Background

Following the ACA, millions of people gained Medicaid insurance. Most electronic health record (EHR) tools to date provide clinical-decision support and tracking of clinical biomarkers, we developed an EHR tool to support community health center (CHC) staff in assisting patients with health insurance enrollment documents and tracking insurance application steps. The objective of this study was to test the effectiveness of the health insurance support tool in (1) assisting uninsured patients gaining insurance coverage, (2) ensuring insurance continuity for patients with Medicaid insurance (preventing coverage gaps between visits); and (3) improving receipt of cancer preventive care.

Methods

In this quasi-experimental study, twenty-three clinics received the intervention (EHR-based insurance support tool) and were matched to 23 comparison clinics. CHCs were recruited from the OCHIN network. EHR data were linked to Medicaid enrollment data. The primary outcomes were rates of uninsured and Medicaid visits. The secondary outcomes were receipt of recommended breast, cervical, and colorectal cancer screenings. A comparative interrupted time-series using Poisson generalized estimated equation (GEE) modeling was performed to evaluate the effectiveness of the EHR-based tool on the primary and secondary outcomes.

Results

Immediately following implementation of the enrollment tool, the uninsured visit rate decreased by 21.0% (Adjusted Rate Ratio [RR] = 0.790, 95% CI = 0.621–1.005, p = .055) while Medicaid-insured visits increased by 4.5% (ARR = 1.045, 95% CI = 1.013–1.079) in the intervention group relative to comparison group. Cervical cancer preventive ratio increased 5.0% (ARR = 1.050, 95% CI = 1.009–1.093) immediately following implementation of the enrollment tool in the intervention group relative to comparison group. Among patients with a tool use, 81% were enrolled in Medicaid 12 months after tool use. For the 19% who were never enrolled in Medicaid following tool use, most were uninsured (44%) at the time of tool use.

Conclusions

A health insurance support tool embedded within the EHR can effectively support clinic staff in assisting patients in maintaining their Medicaid coverage. Such tools may also have an indirect impact on evidence-based practice interventions, such as cancer screening.

Trial registration

This study was retrospectively registered on February 4th, 2015 with Clinicaltrials.gov (#NCT02355262). The registry record can be found at https://www.clinicaltrials.gov/ct2/show/NCT02355262.

The Impact of COVID-19 on Cancer Screening: Challenges and Opportunities

Cancer is a leading cause of death in the United States and across the globe. Cancer screening is an effective preventive measure that can reduce cancer incidence and mortality. While cancer screening is integral to cancer control and prevention, due to the COVID-19 outbreak many screenings have either been canceled or postponed, leaving a vast number of patients without access to recommended health care services. This disruption to cancer screening services may have a significant impact on patients, health care practitioners, and health systems. In this paper, we aim to offer a comprehensive view of the impact of COVID-19 on cancer screening. We present the challenges COVID-19 has exerted on patients, health care practitioners, and health systems as well as potential opportunities that could help address these challenges.

Using Electronic Health Records in Longitudinal Studies: Estimating Patient Attrition

BACKGROUND

Electronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.

OBJECTIVE

The objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.

RESEARCH DESIGN

A retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.

SUBJECTS

A total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.

MEASURES

Percentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.

RESULTS

Attrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.

CONCLUSION

This study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Network Engagement in Action: Stakeholder Engagement Activities to Enhance Patient-centeredness of Research

BACKGROUND

Stakeholders (ie, patients, policymakers, clinicians, advocacy groups, health system leaders, payers, and others) offer critical input at various stages in the research continuum, and their contributions are increasingly recognized as an important component of effective translational research. Natural experiments, in particular, may benefit from stakeholder feedback in addressing real-world issues and providing insight into future policy decisions, though best practices for the engagement of stakeholders in observational studies are limited in the literature.

METHODS

The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) network utilizes rigorous methods to evaluate natural experiments in health policy and program delivery with a focus on diabetes-related outcomes. Each of the 8 partnering institutions incorporates stakeholder engagement throughout multiple study phases to enhance the patient-centeredness of results. NEXT-D2 dedicates a committee to Engagement for resource sharing, enhancing engagement approaches, and advancing network-wide engagement activities. Key stakeholder engagement activities include Study Meetings, Proposal Development, Trainings & Educational Opportunities, Data Analysis, and Results Dissemination. Network-wide patient-centered resources and multimedia have also been developed through the broad expertise of each site's stakeholder group.

CONCLUSIONS

This collaboration has created a continuous feedback loop wherein site-level engagement approaches are informed via the network and network-level engagement efforts are shaped by individual sites. Emerging best practices include: incorporating stakeholders in multiple ways throughout the research, building on previous relationships with stakeholders, enhancing capacity through stakeholder and investigator training, involving stakeholders in refining outcome choices and understanding the meaning of variables, and recognizing the power of stakeholders in maximizing dissemination.

Disparities in Biomarkers for Patients With Diabetes After the Affordable Care Act

BACKGROUND

Racial and ethnic minorities are disproportionately affected by diabetes and at greater risk of experiencing poor diabetes-related outcomes compared with non-Hispanic whites. The Affordable Care Act (ACA) was implemented to increase health insurance coverage and reduce health disparities.

OBJECTIVE

Assess changes in diabetes-associated biomarkers [hemoglobin A1c (HbA1c) and low-density lipoprotein] 24 months pre-ACA to 24 months post-ACA Medicaid expansion by race/ethnicity and insurance group.

RESEARCH DESIGN

Retrospective cohort study of community health center (CHC) patients.

SUBJECTS

Patients aged 19-64 with diabetes living in 1 of 10 Medicaid expansion states with ≥1 CHC visit and ≥1 HbA1c measurement in both the pre-ACA and the post-ACA time periods (N=13,342).

METHODS

Linear mixed effects and Cox regression modeled outcome measures.

RESULTS

Overall, 33.5% of patients were non-Hispanic white, 51.2% Hispanic, and 15.3% non-Hispanic black. Newly insured Hispanics and non-Hispanic whites post-ACA exhibited modest reductions in HbA1c levels, similar benefit was not observed among non-Hispanic black patients. The largest reduction was among newly insured Hispanics versus newly insured non-Hispanic whites (P<0.05). For the subset of patients who had uncontrolled HbA1c (HbA1c≥9%) within 3 months of the ACA Medicaid expansion, non-Hispanic black patients who were newly insured gained the highest rate of controlled HbA1c (hazard ratio=2.27; 95% confidence interval, 1.10-4.66) relative to the continuously insured group.

CONCLUSIONS

The impact of the ACA Medicaid expansion on health disparities is multifaceted and may differ across racial/ethnic groups. This study highlights the importance of CHCs for the health of minority populations.

Following Uninsured Patients Through Medicaid Expansion: Ambulatory Care Use and Diagnosed Conditions

PURPOSE

The Patient Protection and Affordable Care Act (ACA) has improved access to health insurance, yet millions remain uninsured. Many patients who remain uninsured access care at community health centers (CHCs); however, little is known about their health conditions and health care use. We assessed ambulatory care use and diagnosed health conditions among a cohort of CHC patients uninsured before enactment of the ACA (pre-ACA: January 1, 2012 to December 31, 2013) and followed them after enactment (post-ACA: January 1, 2014 to December 31, 2015).

METHODS

This retrospective cohort analysis used electronic health record data from CHCs in 11 US states that expanded Medicaid eligibility. We assessed ambulatory care visits and documented health conditions among a cohort of 138,246 patients (aged 19 to 64 years) who were uninsured pre-ACA and either remained uninsured, gained Medicaid, gained other health insurance, or did not have a visit post-ACA. We estimated adjusted predicted probabilities of ambulatory care use using an ordinal logistic mixed-effects regression model.

RESULTS

Post-ACA, 20.9% of patients remained uninsured, 15.0% gained Medicaid, 12.4% gained other insurance, and 51.7% did not have a visit. The majority of patients had ≥1 diagnosed health condition. The adjusted proportion of patients with high use (≥6 visits over 2 years) increased from pre-ACA to post-ACA among those who gained Medicaid (pre-ACA: 23%, post-ACA: 34%, P <.001) or gained other insurance (pre-ACA: 29%, post-ACA: 48%, P <.001), whereas the percentage fell slightly for those continuously uninsured.

CONCLUSIONS

A significant percentage of CHC patients remained uninsured; many who remained uninsured had diagnosed health conditions, and one-half continued to have ≥3 visits to CHCs. CHCs continue to be essential providers for uninsured patients.

Study protocol: a mixed-methods study of women's healthcare in the safety net after Affordable Care Act implementation - EVERYWOMAN

Background

Evidence-based reproductive care reduces morbidity and mortality for women and their children, decreases health disparities and saves money. Community health centres (CHCs) are a key point of access to reproductive and primary care services for women who are publicly insured, uninsured or unable to pay for care. Women of reproductive age (15–44 years) comprise just of a quarter (26%) of the total CHC patient population, with higher than average proportions of women of colour, women with lower income and educational status and social challenges (e.g. housing). Such factors are associated with poorer reproductive health outcomes across contraceptive, preventive and pregnancy-related services. The Affordable Care Act (ACA) prioritised reproductive health as an essential component of women’s preventive services to counter these barriers and increase women’s access to care. In 2012, the United States Supreme Court ruled ACA implementation through Medicaid expansion as optional, creating a natural experiment to measure the ACA’s impact on women’s reproductive care delivery and health outcomes.

Methods

This paper describes a 5-year, mixed-methods study comparing women’s contraceptive, preventive, prenatal and postpartum care before and after ACA implementation and between Medicaid expansion and non-expansion states. Quantitative assessment will leverage electronic health record data from the ADVANCE Clinical Research Network, a network of over 130 CHCs in 24 states, to describe care and identify patient, practice and state-level factors associated with provision of recommended evidence-based care. Qualitative assessment will include patient, provider and practice level interviews to understand perceptions and utilisation of reproductive healthcare in CHC settings.

Discussion

To our knowledge, this will be the first study using patient level electronic health record data from multiple states to assess the impact of ACA implementation in conjunction with other practice and policy level factors such as Title X funding or 1115 Medicaid waivers. Findings will be relevant to policy and practice, informing efforts to enhance the provision of timely, evidence-based reproductive care, improve health outcomes and reduce disparities among women. Patient, provider and practice-level interviews will serve to contextualise our findings and develop subsequent studies and interventions to support women’s healthcare provision in CHC settings.