scholarly journals Development and implementation of standardized study performance metrics for a VA healthcare system clinical research consortium

2021 ◽  
pp. 106505
Author(s):  
Marcus R. Johnson ◽  
Merritt Raitt ◽  
Aliya Asghar ◽  
Debra L. Condon ◽  
Danielle Beck ◽  
...  
Author(s):  
Parasuram Krishnamoorthy ◽  
Andriy Vengrenyuk ◽  
Brian Wasielewski ◽  
Nitin Barman ◽  
Jeffrey Bander ◽  
...  

Abstract Technological advancements have transformed healthcare. System delays in transferring patients with ST- segment elevation myocardial infarction (STEMI) to a primary percutaneous coronary intervention (PCI) center are associated with worse clinical outcomes. Our aim was to design and develop a secure mobile application, STEMIcathAID, streamlining communication and coordination between the STEMI care teams to reduce ischemia time and improve patient outcomes. The app was designed for transfer of patients with STEMI to a cardiac catheterization laboratory (CCL) from an emergency department (ED) of either a PCI capable or a non-PCI capable hospital. When a suspected STEMI arrives to a non-PCI hospital ED, the ED physician uploads the EKG and relevant patient information. An instant notification is simultaneously sent to the on-call CCL attending and transfer center. The attending reviews the information, makes a video call and decides to either accept or reject the transfer. If accepted, on-call CCL team members receive an immediate push notification and begin communicating with the ED team via a HIPPA compliant chat. The app provides live GPS tracking of the ambulance and frequent clinical status updates of the patient. In addition, it allows for screening of STEMI patients in cardiogenic shock. Prior to discharge important data elements have to be entered to close the case. In conclusion, we developed a novel mobile app to optimize care for STEMI patients and facilitate electronic extraction of relevant performance metrics to improve allocation of resources and reduction of costs.


2020 ◽  
Vol 4 (6) ◽  
pp. 485-492
Author(s):  
Stephanie Schuckman ◽  
Lynn Babcock ◽  
Cristina Spinner ◽  
Opeolu Adeoye ◽  
Dina Gomaa ◽  
...  

AbstractIntroduction:Acute care research (ACR) is uniquely challenged by the constraints of recruiting participants and conducting research procedures within minutes to hours of an unscheduled critical illness or injury. Existing competencies for clinical research professionals (CRPs) are gaining traction but may have gaps for the acute environment. We sought to expand existing CRP competencies to include the specialized skills needed for ACR settings.Methods:Qualitative data collected from job shadowing, clinical observations, and interviews were analyzed to assess the educational needs of the acute care clinical research workforce. We identified competencies necessary to succeed as an ACR-CRP, and then applied Bloom’s Taxonomy to develop characteristics into learning outcomes that frame both knowledge to be acquired and job performance metrics.Results:There were 28 special interest competencies for ACR-CRPs identified within the eight domains set by the Joint Task Force (JTF) of Clinical Trial Competency. While the eight domains were not prioritized by the JTF, in ACR an emphasis on Communication and Teamwork, Clinical Trials Operations, and Data Management and Informatics was observed. Within each domain, distinct proficiencies and unique personal characteristics essential for success were identified. The competencies suggest that a combination of competency-based training, behavioral-based hiring practices, and continuing professional development will be essential to ACR success.Conclusion:The competencies developed for ACR can serve as a training guide for CRPs to be prepared for the challenges of conducting research within this vulnerable population. Hiring, training, and supporting the development of this workforce are foundational to clinical research in this challenging setting.


2018 ◽  
Vol 4 (4) ◽  
pp. 308-317 ◽  
Author(s):  
S. L. Filipp ◽  
M. Cardel ◽  
J. Hall ◽  
R. Z. Essner ◽  
D. J. Lemas ◽  
...  

2006 ◽  
Vol 14 (7S_Part_12) ◽  
pp. P658-P659
Author(s):  
Adam L. Boxer ◽  
Hilary W. Heuer ◽  
Ping Wang ◽  
Katya Rascovsky ◽  
Howard J. Rosen ◽  
...  

2018 ◽  
Vol 154 (6) ◽  
pp. S-306-S-307 ◽  
Author(s):  
Irene Sarosiek ◽  
Goro Yamada ◽  
Henry P. Parkman ◽  
Braden Kuo ◽  
Kenneth L. Koch ◽  
...  

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1505-1505 ◽  
Author(s):  
Rami S Komrokji ◽  
Xiao-Feng Wang ◽  
Najla H Al Ali ◽  
Guillermo Garcia-Manero ◽  
David P. Steensma ◽  
...  

Abstract Background MDS are a spectrum of diseases commonly divided clinically into lower- and higher-risk subtypes to reflect underlying disease biology and to guide treatment. The International Prognostic Scoring System (IPSS), the most widely used tool for risk stratification is limited in its ability to identify poor prognosis lower-risk patients (pts). A prognostic scoring system specifically for lower-risk MDS pts (LR-PSS) was developed (Garcia-Manero Leukemia 2008) based on unfavorable (non-del(5q), non–diploid) cytogenetic, hemoglobin (hgb) <10g/dl, platelet count (plt) <50 k/uL or 50-200k/uL, bone marrow blast %≥4, and age ≥60 years. The newly revised IPSS (IPSS-R) also addressed some IPSS limitations. We examined the prognostic utility of IPSS, LR-PSS and the IPSS-R in a large cohort of lower-risk MDS pts, including those with secondary (s) MDS and chronic myelomonocytic leukemia (CMML) – both excluded from IPSS and IPSS-R - within the MDS Clinical Research Consortium. Methods MDS pts with IPSS Score <1.5 were identified at Moffitt Cancer Center (MCC) or Cleveland Clinic (CC) from 2002-2012 and included if adequate data for analyses were available. Overall Survival (OS) was calculated from diagnosis. The Kaplan–Meier method was used to estimate median OS. Univariable analyses were performed using the log-rank test and adjusted for multiple comparisons; multivariable analyses used a Cox proportional hazards model. Harrell's c index and the Akaike information criteria (AIC) were used to assess the discriminatory power of the models and relative goodness of fit, respectively. Results The analysis included 1196 MDS patients with IPSS scores <1.5. Comparing MCC (n=668) to CC (n=528), baseline characteristics were similar except plt <50k/uL: 16% vs. 22% (p=0.01); ANC <1.5 k/uL: 40% vs. 28% (p<0.0001); blasts <4%: 78% vs. 72% (p=0.02); sMDS: 11% vs. 6% (p=0.0024); and CMML: 2% vs. 10% (p<0.001 for WHO subgroup). R-IPSS cytogenetic groups were very good/ good/ int/ poor/ and very poor in 2/74/16/6/3 % at MCC and 2/63/19/7/10 % at CCF (p < 0.001). The median OS was 47 months (95% C.I. 44 - 52) and median follow-up of patients still alive was 62 months (range 2-326). LR-PSS and IPSS-R classifications for MCC and CCF Pts and OS are summarized in Table 1 and Figure 1. In univariable analyses, The IPSS, LR-PSS, and IPSS-R were all predictive of OS (p<0.0001 for all). Multivariable analyses confirmed the overall predictive abilities of the 3 prognostic tools adjusted for Hgb, plt, and age (p <0.0001). Compared to the IPSS-R, the LR-PSS had the higher (better) Harrell's c value (.66 vs. .60) and lower (better) AIC (5600 vs. 5605), and both were superior to the IPSS (.46 and 5609, respectively). The LR-PSS upstaged 302 pts (25%) from IPSS low or Int-1 to LR-PSS Category 3, and downstaged 104 pts (8.6%) from Int-1 to Category 1. The IPSS-R upstaged 449 pts (37%) from IPSS low or Int-1 to IPSS-R Categories ≥Intermediate, and downstaged 18 pts (1.5% ) from Int-1 to Very Low. Conclusions The LR-PSS and IPSS-R are superior tools for distinguishing outcome among pts previously thought to have lower-risk disease by the IPSS, including those with sMDS and CMML. Upstaged pts may benefit from earlier interventions with disease-modifying therapies, and should be considered in trials targeting higher-risk MDS pts. The LR-PSS appears to provide slightly better prognostic information. Disclosures: No relevant conflicts of interest to declare.


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