Clinical Medicine &amp Research Journal Impact Factor & Information

Publisher: Marshfield Clinic

Journal description

Clinical Medicine & Research is a new peer reviewed publication of original scientific medical research, that is relevant to a broad audience of medical researchers and health care professionals. Published quarterly manuscripts on the following topics may be submitted for publication: medicine, medical research, evidence based medicine, preventive medicine rural health, epidemiology, basic science, history of medicine, the art of medicine, non-clinical aspects of medicine and science.

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Clinical Medicine and Research website
Other titles Clinical medicine & research, Clinical medicine and research, CM & R
ISSN 1539-4182
OCLC 49497616
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Detect the main predictive non-motor factors related to independent community ambulation after stroke. Furthermore, we propose a scale to estimate the probability of a stroke patient getting independent community ambulation after six months of rehabilitation.
    Clinical Medicine &amp Research 11/2014; n/a:n/a. DOI:10.3121/cmr.2014.1232
  • Clinical Medicine &amp Research 09/2014; 12(1-2):4. DOI:10.3121/cmr.2014.1231
  • Clinical Medicine &amp Research 09/2014; 12(1-2):5. DOI:10.3121/cmr.2014.1240
  • Clinical Medicine &amp Research 09/2014; 12(1-2):73-6. DOI:10.3121/cmr.2014.1251
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    ABSTRACT: Background/Aims: Recent estimates indicate 10-15% of all babies born in the U.S. are admitted to the neonatal intensive care unit (NICU) which along with evidence that coordinated prenatal care is positively correlated with better birth outcomes (fewer low birth weight and premature babies, and fewer infants transferred to NICU’s) suggest the potential for improvement in perinatal care quality and cost. The aim of this analysis is to examine the economic impact of an evidence- and guideline-based standardized coordinated perinatal care and electronic health record measurement process (Geisinger Health System (GHS) Perinatal ProvenCare® (PPC)) for both the mother and infant implemented in a large integrated U.S. healthcare delivery system to test whether it reduces the total cost of care. Methods: GHS PPC applies to care over the entire gestational period (antepartum, intrapartum and postpartum) using a single standardized pathway with 103 best practice measures grouped in five clinically relevant bundles and automated reporting for all patients across 22 practice sites and four hospitals. Geisinger Health Plan claims data from 2007 to 2010 for 3,369 mother-infant combinations were used to calculate total costs of care per live birth for mothers and infants for PPC and control groups. A difference-in-difference method was used to estimate the cost impact accounting for baseline differences between groups and the secular trend in the control group. A set of multivariate regression models was developed to calculate regression-adjusted cost estimates. Results: Average total cost of care per live birth in the PPC group was approximately 26% (p=0.001) lower compared to the control group. Much of this cost savings was attributable to reductions in the cost associated with infant care, including lower utilization of expensive NICU services. Conclusions: This study demonstrates the potential for reduction in medical care costs of a standardized perinatal care delivery process based on Geisinger’s experience. The findings suggest that cost savings are attributable to prevention of adverse patient outcomes. If applied more broadly, including to state Medicaid programs which cover almost half of U.S. births, similarly implemented standardized processes could result in better health outcomes and significant cost savings.
    HMO Research Network Conference, Phoenix, AZ; 04/2014
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    ABSTRACT: Background/Aims: Policymakers are exploring bundled payments to providers, but little is known about their application in ambulatory care. As an example, this study examines the cost of acute bronchitis episodes. Methods: Data were from electronic health records and claims of a large ambulatory group practice with mixed payment sources. Optum’s Symmetry Grouper was used to create episode treatment groups (ETGs) for all services. There were 78,828 episodes of acute bronchitis, cared for by 427 primary care physicians/urgent care centers (PCPs) (1,568 PCP-years) in 2007-2011. Costs included standardized fees for physicians, laboratory/imaging ordered, and specialist services. The grouper extends an episode indefinitely with continuing related services, so we separately considered episodes closed in: (a) 1 day, (b) within 30 days, and (c) those extending beyond 30 days. In a nested model, we focused on lead physician ‘effects,’ controlling for patient characteristics. Results: Of the total episodes, 78% closed in 1 day, 19% closed in 2 to 30 days, and 3% took >30 days. One-day episode costs were most stable (mean cost=$77, coefficient of variation (CV)=0.36), followed by 30-day episodes (mean cost=$181, CV=0.56), and longer than 30-day episodes (mean cost=$268, CV=0.78). Among 1-day episodes, 21% of the PCP-years (with 166 unique PCPs) had costs significantly below the average. Only 22% of the episodes of these PCPs extended beyond 1 day vs. 32% for all other PCPs (p<0.01), and the costs of their longer episodes averaged $176 vs. $205 (p<0.01). Among the 91 PCPs with 2 or more years of significantly lower 1-day costs, 85% (77 PCPs) did not have elevated rates of longer episodes or significantly above average costs for longer episodes. Conclusions: Focusing on 1-day acute bronchitis episodes (78% of the total) markedly reduces the variance across episodes, but substantial variation in episode costs across PCPs remains. With some PCPs repeatedly achieving low 1-day costs without evidence of subsequent problems, episode-based payment may lead to improved resource use. Grant support: This study was funded by the Robert Wood Johnson Foundation (PI: Dr. Luft; grant number: 70045). Category: health service research/health policy Keywords: episode-based payment, cost, physician compensation
    HMO Research Network Confernce, Phoenix AZ; 04/2014
  • Clinical Medicine &amp Research 10/2013; 11(3):126-126. DOI:10.3121/cmr.2013.1176.ps1-8
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    ABSTRACT: Background/Aims Health organizations are beginning to apply predictive analytics as a central and critical tool for more effective healthcare management. However, the art is still far from maturity, and it is necessary to develop and perfect the requisite analytic tools. A need exists for methods to measure illness burden and identify patients for targeted interventions. Most commercial programs are unable to use all of the data we have available for analysis. Their input is limited to age, gender, diagnoses and medications, while our database also contains a wide range of demographic, socioeconomic, clinical and financial data at the patient level. We hypothesized that utilizing the richer data would generate robust analytic and predictive capabilities. We then developed a predictive analytical system that accesses our entire database. The design requirements included flexible and generic database mapping and transparency of any algorithm's internal processes. In addition, the system has embedded quality assurance processes and maintains an historical record of all analytical models and results. Methods Data sources included approximately 15 years of history of physician and other medical professional visits, hospitalizations, emergency room visits, diagnoses, medications, laboratory results, imaging studies, pathology results, and extensive socio-economic, demographic data and associated costs of all medical expenditures. Analytics techniques used included linear regression, classification trees, and additional data mining methods. Models developed included: predicted annual cost and prediction of re-hospitalization within 30 days. Models were validated using R(2), C-statistics and Positive Predictive Value (PPV). Results The first model (R(2) ~ 0.36) was used to create reports for risk adjustment and phyician profiling. The second model (PPV 54%) was incorporated into an existing program for preventing re-hospitalization. Conclusions The Maccabi analytical tool has a robust predictive ability and has been successfully used for physician profiling and predicting re-hospitalization. We suggest evaluating this tool on different databases to yield insight into its transferability and robustness. The minimal required data set for use in other organizations needs to be determined.
    Clinical Medicine &amp Research 09/2013; 11(3):154-155. DOI:10.3121/cmr.2013.1176.ps2-37
  • Clinical Medicine &amp Research 05/2013; DOI:10.11648/j.cmr.20130202.12
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    ABSTRACT: Background/Aims: Blood pressure (BP) is not controlled in the majority of people with hypertension (HTN) in the U.S. We describe a study of long-term hypertension outcomes that compares home BP telemonitoring and pharmacist case management vs. usual care.
    Clinical Medicine &amp Research 12/2010; DOI:10.3121/cmr.2010.943.ps1-04
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    ABSTRACT: Background/Aims: There are few prospective evaluations of the effectiveness of worksite wellness programs in achieving improved lifestyle and health. Our study has two objectives:
    Clinical Medicine &amp Research 12/2010; DOI:10.3121/cmr.2010.943.c-a1-02
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    ABSTRACT: Background and Aims: The Centers for Disease Control estimated the obesity rate in New Mexico for 2008 to be 25.2%. Sources estimate the following associations between obesity and type 2 diabetes (80%); cardiovascular disease (70%); hypertension (26 %). Yet obesity is infrequently coded as a secondary diagnosis among providers submitting claims. This study examines the frequency with which obesity is documented on claims forms, the relationship between age, gender, and obesity coding, and the relationship between obesity coding and healthcare utilization.
    Clinical Medicine &amp Research 12/2010; DOI:10.3121/cmr.2010.943.ps2-15