Andrew Kramer
President & Co-Founder at Prescient Healthcare Consulting
President & Co-Founder of Prescient Healthcare Consulting. Respected leader in creating predictive models for outcomes in intensive care units.
Established leader in the field of healthcare analytics.  Co-creator of > 100 predictive models in use at hospitals worldwide. Inventor on three patents with another under consideration. Developer of novel algorithm for near-real time predictive analytics of deleterious outcomes in intensive care units.  Strong record of collaborating with researchers worldwide. 

Authored or co-authored over 75 published manuscripts.

Presented at conferences in the US, Canada, England, Sweden, Iceland, and Belgium.

Serves as ad-hoc reviewer for Critical Care Medicine, Am. Journal Resp. Crit. Care, and JAMA.

Member and past Chair of Society of Critical Care Medicine's subcommittee on Clinical Research & Epidemiology.

Graduate Adviser, University of Oxford. Two of my students received their D.Phil. 

Adjunct Professor, Kansas University Medical Center, Department of Biostatistics, Feb 2009 to present
Prescient Healthcare Consulting
https://www.linkedin.com/company/prescient-healthcare-consulting
"Prescient" is focused on delivering predictive models and intelligent decision support to acute and intensive healthcare facilities. We have developed revolutionary methods for using streaming physiologic data to prospectively identify patients at a very high risk of deterioration. Patent issued in 2020.

Please visit our website: www.prescient-healthcare.com
High Point, NC
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President & Co-Founder
Cerner Corporation
https://www.linkedin.com/company/cerner-corporation
Oversaw company’s entire research program within critical care, with recent expansion to include acute care. Designed and created an analytical ecosystem that encompasses all phases of predictive model development, testing, and maintenance,
•	Created and instituted suite of 23 predictive models for use in benchmarking U.S. hospitals. 
•	Supervised agile team in employing data mining techniques to generate a novel severity of illness score for acute care patients (patent pending). 
•	Led team in creating clinical data mart using EMR data for population studies. 
•	Fostered collegial relationships with leaders in the field of critical care research and expanded company’s research involvement overseas. 
•	Completely overhauled critical care analytics, growing an analytical data mart from scratch to over 750,000 patients, and then mining that data. Subsequently co-developed an analytical system that predicted > 70 outcomes . Published findings in high-impact scientific journals.

Interacted with national entities such as JCAHO, NQF, and REDCap.
Vienna, VA
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Senior Manager, Critical Care Research
Independent Consultant
Provided data mining solutions and statistical consulting for researchers in the medical community. Work included analyzing data from gene expression arrays, developing an algorithm distinguishing between COPD and severe asthma, using neural networks to predict length of stay, etc....
Middleburg, VA
Principal
PhD
Human Genetics
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Virginia Commonwealth University
https://www.linkedin.com/school/virginia-commonwealth-university/
Postdoctoral Fellow
Epidemiology
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State University of New York at Buffalo
https://www.linkedin.com/school/universityatbuffalo/
B.S.
Minor in Statistics.
Zoology
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University of Maryland College Park
https://www.linkedin.com/school/university-of-maryland/
I've been playing a key role in developing the framework for a national research registry of critical care data.
Society of Critical Care Medicine
Member, Clinical Research & Epidemiology Subcommittee
United States Critical Illness and Injury Trials Group
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Using genetic algorithms and particle swarm optimization we were able to come up with a parsimonious severity of illness metric. This new score, "OASIS", matches up very well against APACHE yet requires fewer data elements. It should supplant APACHE II for those researchers who are still using that version of APACHE.
ANew Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy
Critical Care Medicine
This paper looked at readmission rates among ICUs and compared that metric with risk-adjusted outcomes. It clearly demonstrated that using readmission rate as a benchmark measure was counter-productive. Instead healthcare agencies should utilize outcome measures such as mortality and length of stay, provided they are adjusted for patient case-mix.
The association between intensive care unit readmission rate and patient outcomes
Critical Care Medicine
Objective: To examine the Hosmer-Lemeshow test’s sensitivity in evaluating the calibration of models predicting hospital mortality in large critical care populations. 

Study Design, Patients: Data sets were simulated representing the approximate number of patients used in earlier versions of critical care predictive models (n = 5,000 and 10,000) and more recent predictive models (n=50,000). Each patient had a hospital mortality probability generated as a function of 23 risk variables. 

Methods and Main Results: Data sets of 5,000, 10,000, and 50,000 patients were replicated 1,000 times. Logistic regression models were evaluated for each simulated data set. This process was initially carried out under conditions of perfect fit (observed mortality = predicted mortality; standardized mortality ratio = 1.000) and repeated with an observed mortality that differed slightly (0.4%) from predicted mortality. Under conditions of perfect fit the Hosmer-Lemeshow test was not influenced by the number of patients in the data set. In situations where there was a slight deviation from perfect fit the Hosmer-Lemeshow test was sensitive to sample size. For populations of 5,000 patients 10% of the Hosmer-Lemeshow tests were significant at p<0.05, while for 10,000 patients 34% of the Hosmer-Lemeshow tests were significant at p<0.05. When the number of patients matched contemporary studies, i.e. 50,000 patients the Hosmer-Lemeshow test was statistically significant in 100% of the models. 

Conclusion: A significant Hosmer-Lemeshow test does not necessarily mean that a predictive model is not useful or suspect. While decisions concerning a mortality model’s suitability should include the Hosmer-Lemeshow test additional information needs to be taken into consideration. This includes the overall number of patients, the observed and predicted probabilities within each decile, and adjunct measures of model calibration.
Assessing the calibration of mortality                                        benchmarks in critical care: The  Hosmer-Lemeshow test revisited
Critical Care Medicine
http://journals.lww.com/ccmjournal/toc/2007/09000
This paper was a Feature Article in Critical care Medicine. It described the hospital mortality prediction in the new APACHE IV model. Subsequent manuscripts would describe additional equations in APACHE IV. This new version of APACHE represented the first major upgrade to the APACHE critical care package since APACHE III was published in 1991. 
NOTE: As of 6/19/2013 this article has been cited 240 times.
“Acute Physiology and Chronic Health Evaluation (APACHE)  IV: Hospital Mortality Assessment for Today’s Critically Ill  Patients ”,
Critical Care Medicine
American College of Critical Care Medicine
www.sccm.org
Medicine
FCCM
https://www.sccm.org/Professional-Development/ACCM/Apply-for-FCCM
https://www.linkedin.com/in/annabel-henry-76603711a
New York City Metropolitan Area
Annabel Henry
Senior Director at Mount Sinai Health System
https://www.linkedin.com/in/john-anthony-vullo-017702149
New York, NY
John Anthony Vullo
Cardiothoracic Anesthesiologist & Intensivist
Assistant Program Director, ACTA Fellowship
https://www.linkedin.com/in/donald-cardone-emt-p-cic-chpcp-596b34a
Brooklyn, NY
Donald Cardone, EMT-P, CIC, CHPCP
Senior Director of Emergency Management Mount Sinai Health System, Mount Sinai Hospital and the Icahn School of Medicine
https://www.linkedin.com/in/corey-scurlock-md-mba-7abab4221
New York, NY
Corey Scurlock, MD, MBA
Chief Executive Officer at Equum Medical
https://www.linkedin.com/in/kristin-myers-42673531
New York, NY
Kristin Myers
Chief Information Officer - Digital and Technology Partners at Mount Sinai Health System
https://www.linkedin.com/in/michael-weinman-6b935410
San Francisco, CA
Michael Weinman
Attorney at Law Office of Michael Weinman
https://www.linkedin.com/in/michael-weinman-5397998
Tacoma, WA
Michael Weinman
Owner, Weinman Consulting
https://www.linkedin.com/in/gus-slotman-10a3854a
Moorestown, NJ
GUS SLOTMAN
Head and Neck Surgical Oncology and Director of Resident Research at Inspira Health Network, Vineland, NJ
https://www.linkedin.com/in/kathy-thomas-a8744245
Many, LA
Kathy Thomas
Child Welfare Supervisor at State of Louisiana//Department of Children and Family Services
https://www.linkedin.com/in/tony-varner-8a01295
Fort Polk South, LA
Tony Varner
Chief Human Resources Officer at Bayne Jones Community Hospital
https://www.linkedin.com/in/charles-pearson-82692424
Many, LA
Charles Pearson
ED Medical Director at Charles R. Pearson, MD, LLC
https://sy.linkedin.com/in/osama-thaljeh-63642962
Syria
Osama Thaljeh
Software Engineer at BTS
https://www.linkedin.com/in/teleise-mitchell-31a2751b0
Fort Polk, LA
Teleise Mitchell
Home Health Aide at companion care
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Greater Alexandria, Louisiana
Julie Everette
RN at Christus St Frances Cabrini
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Leesville, LA
Candace Turner
CNA at Byrd Regional
https://www.linkedin.com/in/jannese-ryland-a608926b
Haughton, LA
Jannese Ryland
Administrative Coordinator at LSU Health Sciences Center
https://www.linkedin.com/in/jennings-lisa-663267b0
Leesville, LA
Jennings Lisa
Medical Assistant at QTC
Jack Zimmerman
      
          



            
              
        
              
  

    
        Andrew is an outstanding statistician, health care outcomes researcher, and program manager. He has authored and co-authored multiple publications with many of the world’s most accomplished scientists. In addition to his technical and scientific knowledge, he is incredibly productive, an excellent speaker, and team leader. I recommend him most highly and without reservation. Richardson Jim
      
          



            
              
        
              
  

    
        Andrew has an outstanding knowledge of statistics, has leadership experience, and would be an asset to any company
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Celebrating the little things is the best part of my job! Congrats on your first college hit Sean Lane #DirtyTerps
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We're hiring at the Temerty Centre for AI Research and Education in Medicine (TCAIREM)! Come join us as we build a totally new way to share health…
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Scottsdale, AZ
Andrew Kramer
Deputy County Attorney at Maricopa County Attorney's Office
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Greater Cleveland
Andrew Kramer
President at Andrew Kramer Productions
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St Louis, MO
Andrew Kramer
Associate Financial Advisor at Banderman Financial Group
https://www.linkedin.com/in/andrew-kramer-b871597
New York, NY
Andrew Kramer
Equity Derivatives
https://www.linkedin.com/in/andrew-kramer
Port Washington, NY
Andrew Kramer
President
Data Mining Professionals
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Data! Data! Data!
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University of Maryland, College Park Alumni Association (Official)
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Predictive Analytics Professionals
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Data Miners
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EPIDEMIOLOGY AND BIOSTATISTICS
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CBIC - Charlottesville Business Innovation Council
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Data Scientists
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Physician Leaders Transforming Healthcare
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Cerner Global Alumni
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SAS Users Group
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Accountable Care and Value Based Payment News
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Critical Care data-mining
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Data Mining, Statistics, Big Data, Data Visualization, AI, Machine Learning, and Data Science
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