Effect of the Affordable Care Act on diabetes care at major health centers: newly detected diabetes and diabetes medication management

Furmanchuk, Al’ona; Liu, Mei; Song, Xing; Waitman, Lemuel R.; Meurer, John R.; Osinski, Kristen; Stoddard, Alexander J.; Chrischilles, Elizabeth A.; McClay, James C.; Cowell, Lindsay G.; Tachinardi, Umberto; Embí, Peter J.; Mosa, Abu Saleh Mohammad; Mandhadi, Vasanthi; Shah, Raj C.; Garcia, Diana; Angulo, Francisco; Patino, Alejandro; Trick, William; Markossian, Talar; Rasmussen‐Torvik, Laura J.; Kho, Abel N.; Black, Bernard S.

doi:10.1136/bmjdrc-2021-002205

Cited by 4 publications

(5 citation statements)

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“…Cohort identification. We utilized the framework from Furmanchuk (2021) 37 to identify the diabetes mellitus (DM) cohort from our MU CDM based on the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) algorithm. the SUPREME-DM DataLink is one example of a distributed registry developed for studying Any-DM (mixed Type 1 DM and Type 2 DM codes) using a standardized data extraction approach based on diagnosis, labs, and medications [38][39][40] .…”

Section: Data Preparationmentioning

confidence: 99%

Task-Oriented Predictive (Top)-BERT: Novel Approach for Predicting Diabetic Complications Using a Single-Center EHR Data

Islam,

Bartlett,

Pierce

et al. 2024

Preprint

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In this study, we assess the capacity of the BERT (Bidirectional Encoder Representations from Transformers) framework to predict a 12-month risk for major diabetic complications-retinopathy, nephropathy, neuropathy, and major adverse cardiovascular events (MACE) using a single-center EHR dataset. We introduce a task-oriented predictive (Top)-BERT architecture, which is a unique end-to-end training and evaluation framework utilizing sequential input structure, embedding layer, and encoder stacks inherent to BERT. This enhanced architecture trains and evaluates the model across multiple learning tasks simultaneously, enhancing the model's ability to learn from a limited amount of data. Our findings demonstrate that this approach can outperform both traditional pretraining-finetuning BERT models and conventional machine learning methods, offering a promising tool for early identification of patients at risk of diabetes-related complications. We also investigate how different temporal embedding strategies affect the model's predictive capabilities, with simpler designs yielding better performance. The use of Integrated Gradients (IG) augments the explainability of our predictive models, yielding feature attributions that substantiate the clinical significance of this study. Finally, this study also highlights the essential role of proactive symptom assessment and the management of comorbid conditions in preventing the advancement of complications in patients with diabetes.

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Section: Data Preparationmentioning

confidence: 99%

Task-Oriented Predictive (Top)-BERT: Novel Approach for Predicting Diabetic Complications Using a Single-Center EHR Data

Islam,

Bartlett,

Pierce

et al. 2024

Preprint

Self Cite

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“…Health outcomes are partially associated with the environment in which patients live, and the PCORnet CDM stores patients’ addresses at various levels of resolution (county, 5 and 9 digit postal codes, street address, and detail) and address changes over time to support geographic linkage (ie, geocoding 26 ). Publicly available community-level data provides a way to characterize social and economic factors affecting patients 27 (eg, social deprivation indices incorporating Census tract median incomes) as used in a study of the effect of the Affordable Care Act on diabetes care 28 . PCORnet participating sites linked with the US Census Bureau’s American Community Survey (ACS) data 29,30 and may also leverage more recent investments 31 such as the Agency for Healthcare Research and Quality’s (AHRQ) database on Social Determinants of Health (SDOH) 32 and environmental exposure databases on water, 33 soil, 34 air, 35 and climate 36 released by Environmental Protection Agency (EPA).…”

Section: Area-level Measuresmentioning

confidence: 99%

“…Another area of ongoing concern is the impact of record linkages on the privacy of individuals, groups of individuals, and participating health systems. Ongoing efforts by research teams associated with PCORnet participants are addressing these issues in a systematic way 9,28 . It is incumbent on the PCORnet research community to clarify these limitations and invest in engagement efforts to advance trustworthy research that might now holistically evaluate health choices in relation to the financial stability and livelihoods of our patients, their families, and communities.…”

Section: Limitations and Privacy Considerationsmentioning

confidence: 99%

“…Publicly available community-level data provides a way to characterize social and economic factors affecting patients 27 (eg, social deprivation indices incorporating Census tract median incomes) as used in a study of the effect of the Affordable Care Act on diabetes care. 28 PCORnet participating sites linked with the US Census Bureau’s American Community Survey (ACS) data 29 , 30 and may also leverage more recent investments 31 such as the Agency for Healthcare Research and Quality’s (AHRQ) database on Social Determinants of Health (SDOH) 32 and environmental exposure databases on water, 33 soil, 34 air, 35 and climate 36 released by Environmental Protection Agency (EPA). Economic variables at the zip code or census tract level provide contextual information for studying associations between health outcomes and socioeconomic factors of day-to-day life in communities, like computer and internet access/use, labor force participation, and home ownership.…”

Section: Area-level Measuresmentioning

confidence: 99%

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Avenues for Strengthening PCORnet’s Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

Waitman,

Bailey,

Becich

et al. 2023

Medical Care

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PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system “sites.” As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient’s health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI’s authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI’s expanded scope and PCORnet’s phase 3 beginning in January 2022, there are opportunities to strengthen the network’s ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

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“…The network aims to provide a platform and framework for natural experiment research focused on diabetes and builds upon activities of the NEXT-D (2010-2015) [19 •] and NEXT-D2 (2015-2020) [20•] networks that were funded by the Centers for Disease Control and Prevention (CDC)'s Division of Diabetes Translation, the National Institute of Digestive and Diabetes and Kidney Diseases (NIDDK), and the Patient-Centered Outcomes Research Institute (PCORI). Reports from NEXT-D and NEXT-D2 have described the impact of population-level policies (e.g., integrating diabetes screening prompts into city-wide electronic medical records, employer-sponsored lifestyle modification programs, targeted cost-sharing reductions for diabetes care, HDHPs, insurance expansion, universal preventive coverage for obesity screening, Medicaid innovations in care for high-cost, and high-need patients) on diabetes care, outcomes, and costs [16,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: History Description and Goals Of The Next-d3 Networkmentioning

confidence: 99%

Evaluating Natural Experiments that Impact the Diabetes Epidemic: an Introduction to the NEXT-D3 Network

et al. 2022

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Effect of the Affordable Care Act on diabetes care at major health centers: newly detected diabetes and diabetes medication management

Abstract: Song X, et al. Effect of the Affordable Care Act on diabetes care at major health centers: newly detected diabetes and diabetes medication management.

Cited by 4 publications

References 5 publications

Task-Oriented Predictive (Top)-BERT: Novel Approach for Predicting Diabetic Complications Using a Single-Center EHR Data

Task-Oriented Predictive (Top)-BERT: Novel Approach for Predicting Diabetic Complications Using a Single-Center EHR Data

Avenues for Strengthening PCORnet’s Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

Evaluating Natural Experiments that Impact the Diabetes Epidemic: an Introduction to the NEXT-D3 Network

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