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Research Studies is a monthly compilation of research articles funded by AHRQ or authored by AHRQ researchers and recently published in journals or newsletters.
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1 to 5 of 5 Research Studies Displayed
Graves JA, Nshuti L, Everson J
Breadth and exclusivity of hospital and physician networks in US insurance markets.
The goal of this study was to quantify network breadth and overlap among primary care physician (PCP), cardiology, and general acute care hospital networks for employer-based (large group and small group), individually purchased (marketplace), Medicare Advantage (MA), and Medicaid managed care (MMC) plans. The main outcomes measured were percentage of in-network physicians and/or hospitals within a 60-minute drive from a hypothetical patient in a given zip code (breadth), and the number of physicians and/or hospitals within each network that overlapped with other insurers' networks, expressed as a percentage of the total possible number of shared connections (exclusivity). Networks were categorized by network breadth size and analyzed by insurance type, state, and insurance, physician, and/or hospital market concentration level, as measured by the Hirschman-Herfindahl index. Markets with concentrated primary care and insurance markets had the broadest and least exclusive primary care networks among large-group commercial plans. Markets with the least concentration had the narrowest and most exclusive networks. Rising levels of insurer and market concentration were associated with broader and less exclusive healthcare networks. The authors suggest that this means that patients could switch to a lower-cost, narrow network plan without losing-in-network coverage to their PCP.
AHRQ-funded; HS025976; HS026395.
Citation:
Graves JA, Nshuti L, Everson J .
Breadth and exclusivity of hospital and physician networks in US insurance markets.
JAMA Netw Open 2020 Dec;3(12):e2029419. doi: 10.1001/jamanetworkopen.2020.29419..
Keywords:
Health Insurance, Learning Health Systems, Healthcare Systems, Primary Care, Hospitals, Healthcare Delivery
Hernandez AV, Roman YM, White CM
Developing criteria and associated instructions for consistent and useful quality improvement study data extraction for health systems.
This paper describes AHRQ’s efforts to collate and assess quality improvement studies to support learning health systems (LHS). The authors identified quality improvement studies and evaluated the consistency of data extraction from two experienced independent reviewers at three time points: baseline, first revision, and final revision. Six investigators looked at the data extracted by the independent reviewers and determined the extent of similarity on a scale of 0 to 10. Two LHS participants were then asked to assess the relative value of their criteria. The consistency of extraction improved from a mean 1.17 score at baseline to 6.07 at first revision, and 6.81 at the final revision. There was not a significant improvement from the first to final revision. However, the LHS participants rated the value of these ratings a 9 and a 6, demonstrating that there is value in developing criteria.
AHRQ-funded; 290201500012I.
Citation:
Hernandez AV, Roman YM, White CM .
Developing criteria and associated instructions for consistent and useful quality improvement study data extraction for health systems.
J Gen Intern Med 2020 Nov;35(Suppl 2):802-07. doi: 10.1007/s11606-020-06098-1..
Keywords:
Quality Improvement, Quality of Care, Learning Health Systems, Healthcare Systems, Health Services Research (HSR), Research Methodologies
Lin JS, Murad MH, Leas B
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
This paper addresses when and how the use of health system data might make systematic reviews more useful to decisionmakers. The authors have developed a framework to guide the use of health system data alongside systematic reviews based on a narrative review of the literature and empirical experience. They recommend future methodological work on how best to handle internal and external validity concerns of health system data in the context of systematically reviewed data and work on developing infrastructure to do this type of work.
AHRQ-funded; 290201500007I; 29032001T05; 290201500005I; 290201500009I.
Citation:
Lin JS, Murad MH, Leas B .
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
J Gen Intern Med 2020 Jun;35(6):1830-35. doi: 10.1007/s11606-020-05783-5..
Keywords:
Learning Health Systems, Healthcare Systems, Evidence-Based Practice, Data, Decision Making
Richesson RL, Bray BE, Dymek C
Summary of second annual MCBK public meeting: mobilizing computable biomedical knowledge-a movement to accelerate translation of knowledge into action.
The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18-19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.
AHRQ-authored.
Citation:
Richesson RL, Bray BE, Dymek C .
Summary of second annual MCBK public meeting: mobilizing computable biomedical knowledge-a movement to accelerate translation of knowledge into action.
Learn Health Syst 2020 Apr;4(2):e10222. doi: 10.1002/lrh2.10222..
Keywords:
Implementation, Evidence-Based Practice, Learning Health Systems
Guise JM, Reid E, Fiordalisi CV
AHRQ series on improving translation of evidence: progress and promise in supporting learning health systems.
The authors discuss the articles in the AHRQ EPC series published in this journal over the past six months. They state that satisfaction, care, and costs would all improve if health care delivery were as efficient and effective as possible given current knowledge. They conclude that millions of health decisions must be made by clinicians, patients, and health care systems, and they believe better decisions will be made with evidence.
AHRQ-authored; AHRQ-funded; 290201700003C.
Citation:
Guise JM, Reid E, Fiordalisi CV .
AHRQ series on improving translation of evidence: progress and promise in supporting learning health systems.
Jt Comm J Qual Patient Saf 2020 Jan;46(1):51-52. doi: 10.1016/j.jcjq.2019.10.008..
Keywords:
Implementation, Evidence-Based Practice, Learning Health Systems, Healthcare Systems, Healthcare Delivery, Decision Making
