GMB Group Benefit Services provides tailored benefit strategies and ongoing administration and communication services to employer-sponsored benefit programs. Their mission is to provide clients with unique benefit strategies based on demographics and goals. They focus on seamless administration and one-on-one employee education to improve understanding and appreciation of provided benefits. GMB takes a four stage approach: discovery of needs, planning a strategic benefits plan, implementation including product selection and rollout, and periodic reviews to ensure plans stay aligned over time.
4. Total Benefit Strategy TEXT TEXT TEXT TEXT TEXT Your Business Here! Supplemental Insurance Property / Casualty Workers Comp Group Retirement Planning Individual Planning Services Personal Lines Major Medical Ancillary Benefits Retirement Planning
5. Our Team Employee Benefits Commercial Lines Individual Planning Office Locations Brent Blakely Benefit Advisor Mooresville [email_address] Kate Overton Customer Service Mooresville, [email_address] Lynn Williams Administrative Assistant Mooresville [email_address] Ashton Loyd Commercial Lines Gasoline Alley [email_address] Rob Griffin Workers Comp Gasoline Alley [email_address] Charles Moore, III Executive Benefits Plaza Office moorec12@nationwide.com Lynn Williams Customer Service Plaza Office [email_address] Group Benefit Services 227 West Plaza Drive, Mooresville, NC 704-664-9111 ext .255 Griffin Moore Ins. Agency 227 West Plaza Drive, Mooresville, NC 704-664-9111 ext.239 Griffin Insurance Agency 135 Gasoline Alley, Mooresville, NC 704-664-9111 Tarheel Insurance Agency 135 Gasoline Alley, Mooresville, NC 704-768-2001 Griffin Insurance Agency 125 E. Front St, Statesville, NC 704-871-8002 Griffin Insurance Agency 208 Stanford Road, Lincolnton, NC 704-732-6856 Griffin Insurance (Hispanic) 227 West Plaza Drive, Mooresville, NC 704-664-9111 ext. 147
11. At Group Benefit Services , we bring years of combined experience that includes developing working relationships and strategic alliances with some of the industry's leading insurance carriers and service providers. Through these relationships, Group Benefit Services offers comprehensive solutions to your benefit needs. We seek relationships with providers who share our goals and demonstrate financial stability, superior service commitments and high industry ratings. Our Partners
Desire to identify tumor markers, and markers of disease progression. Hope to distinguish tumor stage and outcomes. Used a sample pooling strategy, prior to gene expression profiling. Differs from clustering individual samples expression profiles, and loses distinction of gene expression profiles between samples. Comparison of gene expression values relative to normal were calculated as ratios (i.e. fold change). Candidate tumor markers were selected based on more than 2-fold expression difference vs. normal mucosa. Candidate tumor progression markers were selected based on overall pattern of progressively increasing expression in line with advancing tumor stage, with overall 2-fold change considered minimum required to be significant. Similar analysis was done to identify those genes progressively decreasing with tumor stage. Osteopontin most consistently progressive, and greatest differential expression.
Treatment combinations are different based on specific diagnosis of sub-type. Remissions can be achieved using corresponding treatments, however cure rates are greatly reduced, and unwarranted toxicities occur. Created an expression vector, highlighting an idealized expression pattern that would uniformly distinguish between the two classes. Training set identifies correlation of each gene’s expression level with the class distinction (AML vs. ALL). Identified genes that were more highly correlated with the class distinction than would be expected by chance. Used set of known samples (training set) to identify genes most strongly correlated with the class distinction. Cross-validation: Withhold 1 training sample; build predictor; test if withheld sample is correctly classified; repeat with a different test sample. Somewhat arbitrarily selected 50 most strongly correlated genes, but discovered that predictors based on 10 – 200 samples were as accurate. Some genes confirmed with independent knowledge; e.g. surface proteins for which monoclonal antibodies have been used to distinguish lymphoid from myeloid lineage cells. Others correlated with pathogenesis (e.g. chromatin remodeling, or known oncogenes) or pharmacology (e.g. topoisomerase II, target of etoposide).
Provides further validation, substantiation that the conclusions drawn from the individual models are accurate; if models work in concert, and agree, adds evidence that conclusions are valid.
Beginning Embedded Analytics: Scenario: A patient is admitted with a presumptive diagnosis of community-acquired pneumonia (CAP). Assuming a standard (uncomplicated) course of treatment, all of the resources required and their required displacement over time and space can be projected. This enables: An ongoing assessment: Is the patient on-track, on-protocol, on-standard of care? If not, why? And what factors are contributing to their deviating from standard? Looking at all similar patients in the aggregate, and considering their individual positions and distribution along their care pathways, are we adequately and appropriately deployed to meet the forecast and emerging resource needs of this patient population? If not, are we able to forecast and anticipate shortfalls, or overages? Can we exploit this information to re-deploy? This capabilities will also provide for LOS monitoring: are we achieving the required progress in the care of these patients, that we can anticipate achieving the required LOS and therefore the appropriate costs of care? Is our discharge planning keeping pace with the anticipated and/or desired course of care?
Process issues such as cross-specialty communications and hand-offs. Identify participant’s role in the process, in the incident, and their relationship to the provider enterprise, to determine risk and exposure.
Requires a core foundation of good, high quality, well-understood data