A quality improvement collaborative increased preventive education and screening rates for women at high-risk for type 2 diabetes mellitus in primary care settings

Lorenz, Allison; Oza-Frank, Reena; May, Sara M.; Conrey, Elizabeth J.; Panchal, Bethany; Brill, Seuli; RajanBabu, Arun; Howard, Kristin

doi:10.1016/j.pcd.2019.09.010

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…To decrease the rates of T2DM among women after a GDM-affected pregnancy, it is necessary to understand women’s health beliefs and perceptions related to lifestyle modification. 34,35 Support during behavior change was a major aspect of Dyad appointments; respondents shared PCPs and resources helped in their postpartum period transition and acknowledging their future health risks. Within a survey of 217 women with a history of GDM, 90% recognized GDM was a risk factor for diabetes, whereas only 16% believed that they had a high likelihood of developing T2DM.…”

Section: Discussionmentioning

confidence: 99%

Mothers’ Perspectives on a Mother/Infant Dyad Postpartum Primary Care Program Following Gestational Diabetes Mellitus: A Qualitative Pilot Study

Brown

Leonard

Clinton

et al. 2022

The Science of Diabetes Self-Management and Care

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Purpose: The purpose of this study is to characterize mothers’ experiences within a mother/infant dyad postpartum primary care program (Dyad) following gestational diabetes mellitus (GDM) to inform improvements in the delivery of care. Methods: A qualitative pilot study of women (n = 10) enrolled in a mother/infant Dyad program was conducted in a primary care practice at a large, urban academic medical center. Respondents were asked a series of open-ended questions about their experience with GDM, the Dyad program, and health behaviors. Interviews were audio-recorded, transcribed verbatim, and analyzed using ground theory with NVivo 12 Plus software. Results: Three key themes emerged: (1) Dyad program experience, (2) implementation of health behavior changes, and (3) acknowledgment of future GDM and type 2 diabetes mellitus (T2DM) health risks. Respondents found the Dyad program respondents felt that the program conveniently served mother and infant health care needs in a single appointment. Respondents also valued support from primary care providers when implementing health behavior changes. The Dyad program provided an opportunity for respondents to understand their current and future risk for developing GDM and T2DM. Conclusions: Postpartum women enrolled in the Dyad program received highly personalized primary care services. The results of our study will help integrate patient-centered strategies into models for GDM care to maintain patient engagement in postpartum clinical services.

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Section: Discussionmentioning

confidence: 99%

Mothers’ Perspectives on a Mother/Infant Dyad Postpartum Primary Care Program Following Gestational Diabetes Mellitus: A Qualitative Pilot Study

Brown

Leonard

Clinton

et al. 2022

The Science of Diabetes Self-Management and Care

Self Cite

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“…The general challenges of frontline care clinical workflow have been well documented in the literature (4,18). Standardization of patient intake, streamlining of care management processes, patient flow coordination, office visit documentation, and use of electronic record systems have been extensively measured and optimized in the primary setting (19)(20)(21)(22)(23).…”

Section: Autonomous Ai For the Detection Of Diabetic Retinopathymentioning

confidence: 99%

Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data

Goldstein,

Weitzman,

Lemerond

et al. 2023

Front. Digit. Health

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Autonomous Artificial Intelligence (AI) has the potential to reduce disparities, improve quality of care, and reduce cost by improving access to specialty diagnoses at the point-of-care. Diabetes and related complications represent a significant source of health disparities. Vision loss is a complication of diabetes, and there is extensive evidence supporting annual eye exams for prevention. Prior to the use of autonomous AI, store-and-forward imaging approaches using remote reading centers (asynchronous telemedicine) attempted to increase diabetes related eye exams with limited success. In 2018, after rigorous clinical validation, the first fully autonomous AI system [LumineticsCore™ (formerly IDx-DR), Digital Diagnostics Inc., Coralville, IA, United States] received U.S. Food and Drug Administration (FDA) De Novo authorization. The system diagnoses diabetic retinopathy (including macular edema) without specialist physician overread at the point-of-care. In addition to regulatory clearance, reimbursement, and quality measure updates, successful adoption requires local optimization of the clinical workflow. The general challenges of frontline care clinical workflow have been well documented in the literature. Because healthcare AI is so new, there remains a gap in the literature about challenges and opportunities to embed diagnostic AI into the clinical workflow. The goal of this review is to identify common workflow themes leading to successful adoption, measured as attainment number of exams per month using the autonomous AI system against targets set for each health center. We characterized the workflow in four different US health centers over a 12-month period. Health centers were geographically dispersed across the Midwest, Southwest, Northeast, and West Coast and varied distinctly in terms of size, staffing, resources, financing and demographics of patient populations. After 1 year, the aggregated number of diabetes-related exams per month increased from 89 after the first month of initial deployment to 174 across all sites. Across the diverse practice types, three primary determinants underscored sustainable adoption: (1) Inclusion of Executive and Clinical Champions; (2) Underlining Health Center Resources; and (3) Clinical workflows that contemplate patient identification (pre-visit), LumineticsCore Exam Capture and Provider Consult (patient visit), and Timely Referral Triage (post-visit). In addition to regulatory clearance, reimbursement and quality measures, our review shows that addressing the core determinants for workflow optimization is an essential part of large-scale adoption of innovation. These best practices can be generalizable to other autonomous AI systems in front-line care settings, thereby increasing patient access, improving quality of care, and addressing health disparities.

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“…[30,38] Improvement of patient safety culture was the subject of three studies. [23,25,36] Improving care in different clinical conditions was the subject of 14 reports such as: neonatal death, [42] care with tracheostomies, [46] diagnosis, [41] child health, [18,27,45] HIV transmission or treatment, [16,22,43] mental health, [24] women's health, [31,34,44] and stroke. [48] Prevention of adverse events was the subject of four studies.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…[19,26,38] A lack of recording in the results about unintended consequences, such as unexpected bene ts, problems, failures, or costs associated with the intervention or details about lost data, was observed in 38% of the studies. Thus, 44.1% of the articles were classi ed as high quality with 5 to 6 Squire items; [15,[21][22][23]25,26,28,29,34,38,39,41,42,45,48] 44.1% were classi ed as average quality with 3 to 4 Squire items [16][17][18][19][20]24,27,[30][31][32][33]37,40,43,44] and 11.8% were classi ed as low quality with 1 to 2 Squire items. [35,36,46,47]…”

Section: Study Characteristicsmentioning

confidence: 99%

Relation Between Quality Improvement Collaboratives and Non-technical Skills Development: a Systematic Review of Complex Interventions

Freitas

Paiva

Junqueira

et al. 2021

Preprint

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Background: Improving the quality of care contributes to the Sustainable Development Goals. Non-technical skills (NTS) such as leadership, teamwork, communication, and use of data for decision-making are the strong points of the learning process of collaboratives for quality improvement in health services.Objective: To evaluate the relationship between the collaboratives and the development of NTS by participating health professionals.Methods: We searched PUBMED, WEB OF SCIENCE, SCOPUS and GOOGLE SCHOLAR (the first 30 pages) and performed a manual search on the IHI (Institute for Healthcare Improvement) website, from Jan 2010 to Dec 2019, for articles published in English, Spanish, and Portuguese. IHI Breakthrough Series Collaborative model project studies with pre- and post-data, and SQUIRE compliant titles were included. The qualitative analysis evaluated the study design, year, country, context, target for improvement, health services, duration of intervention, number of participating professionals, evidence of improvement, effectiveness of the intervention, and NTSResults: From 701 identified abstracts, 34 studies were included for data extraction and qualitative analysis. Improvement of the intended results was reported by 76.5% (26/34), of which only 38.5% (10/26) showed sustainability for six or more months. Improvement in NTS was assessed in five studies, but none assessed their contribution to the dissemination and sustainability of improvement.Conclusions: Collaborative initiatives train professionals in improvement science to incorporate evidence-based practices. It is necessary to include, in the measurement plan of these improvement projects, the assessment of the NTS acquired by the professionals and its relationship with the results achieved, the dissemination level, and the success in terms of sustainability of those gains and best practices.Systematic review registration number: Prospero - CRD42020164190

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A quality improvement collaborative increased preventive education and screening rates for women at high-risk for type 2 diabetes mellitus in primary care settings

Cited by 5 publications

References 25 publications

Mothers’ Perspectives on a Mother/Infant Dyad Postpartum Primary Care Program Following Gestational Diabetes Mellitus: A Qualitative Pilot Study

Mothers’ Perspectives on a Mother/Infant Dyad Postpartum Primary Care Program Following Gestational Diabetes Mellitus: A Qualitative Pilot Study

Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data

Relation Between Quality Improvement Collaboratives and Non-technical Skills Development: a Systematic Review of Complex Interventions

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