SlideShare a Scribd company logo
1 of 18
Research Interests
Vojtech Huser MD PhD
Vojtech Huser, MD, PhD
2
Introduction
 Medical Doctor
 PhD in Medical Informatics
 Research experience at several academic
institutions
 Excellent knowledge of large healthcare
systems EHR infrastructure
 Comparable to NHS collaboration settings
Vojtech Huser, MD, PhD
3
Research interest
 Major
 Health services research and EHR data analysis
 quality improvement in healthcare
 Other
 data warehousing
 medical terminologies
 personal health record (consumer informatics)
 knowledge representation
 clinical research informatics
Vojtech Huser, MD, PhD
4
Vojtech Huser, MD, PhD
5
Vojtech Huser, MD, PhD
6
Vojtech Huser, MD, PhD
7
Vojtech Huser, MD, PhD
8
Vojtech Huser, MD, PhD
9
HMO Research Network (VDW)
http://www.hmoresearchnetwork.org
Vojtech Huser, MD, PhD
10I2b2 (tool for basic EHR data querying),
With experimental local codes for laboratory results
Vojtech Huser, MD, PhD
11
Work with data within a database, 4-10GB datasets shown)
Vojtech Huser, MD, PhD
12
Work with complex database data structures (EHR observations
database
Vojtech Huser, MD, PhD
13
Research data collection within EHR within my past research project
Vojtech Huser, MD, PhD
14
Statistical analysis and data manipulation
(R; also knowledge of SAS, SPSS, Stata)
Vojtech Huser, MD, PhD
15
Example 1
 5. Huser V, Rocha RA, Huser M, Conducting Time Series Analyses on Large Data
Sets: a Case Study With Lymphoma, Medinfo 2007.
 6. Huser V, Rocha, RA, Graphical Modeling of HEDIS Quality Measures and
Prototyping of Related Decision Support Rules to Accelerate Improvement, fall
AMIA symposium, 2007
 Intermountain Healthcare, 3.2 M patients,
comprehensive data warehouse with coded
administrative, clinical and payer data (health plan)
 Methods: data pre-processing, R statistical package,
SQL and other tools
Vojtech Huser, MD, PhD
16
Example 1
 Lymphoma
 780 patients with HL (140 met all inclusion criteria)
 Preservation of reproductive function after toxic cancer therapy
 Experimental analysis of data concerning: stages of the Hodgkin disease, cycles
and doses of chemotherapy, detection of relapses, levels of hormones
indicating premature ovarian failure or prescribed contraception methods
 Similar results to comparable prospective observational study done by Franchi-
Rezghui (2003) (36.9%) (84 subjects)
 Quality measures
 2 measures studied: Osteoporosis, cholesterol management in cardiovascular
patients
 1400+ patients
 Cholesterol management results (inclusion criteria:history of AMI, CABG or
PTCA):

43.24% had proper cholesterol screening, 31.53% in good control

Additional sub-analyses: close to the threshold level (100-130 mg/dL) and on a low
dose of a lipid-lowering agent (2.66%).
In 13.38% of the non-compliant patients we found evidence of 2+ laboratory-test-
episodes or 3+ encounters within a 12 month window
Vojtech Huser, MD, PhD
17
Example 2
 10. Huser V, Starren JB, EHR Data Pre-processing Facilitating
Process Mining: an Application to Chronic Kidney Disease.
AMIA Annu Symp Proc 2009
 Analysis of stages of CKD progression

laboratory onset, formal diagnosis establishment, first analysis, regular
dialysis, transplant, death

Using manual as well as data mining methods
 15. Huser V, A Methodology for Quantitative Measurement
of Quality and Comprehensiveness of a Research Data
Repository, Proc of 16th Annual HMORN Conference 2010

Received Young investigator award for this submission

Evaluation of data warehouses of multiple institutions [consortium]

Set of qualitative measures used for comparisons inter-institutions and
intra-institution (yearly progress)
Vojtech Huser, MD, PhD
18
Summary
 Educationally well-qualified researcher
 History of past publications and successful
grant applications
 Apart from health services research, additional
knowledge of the field of health informatics
and interventional clinical projects (via informatics
methods)
 Publications available at an “internal-use-only” URL:
 http://minfor.wikispaces.com/publications

More Related Content

What's hot

Personalized medicine tools for clinical trials - Kuchinke
Personalized medicine tools for clinical trials - KuchinkePersonalized medicine tools for clinical trials - Kuchinke
Personalized medicine tools for clinical trials - Kuchinke
Wolfgang Kuchinke
 

What's hot (19)

Precision Medicine: Opportunities and Challenges for Clinical Trials
Precision Medicine: Opportunities and Challenges for Clinical TrialsPrecision Medicine: Opportunities and Challenges for Clinical Trials
Precision Medicine: Opportunities and Challenges for Clinical Trials
 
Recruitment Metrics from TogetherRA: A Study in Rheumatoid Arthritis Patients...
Recruitment Metrics from TogetherRA: A Study in Rheumatoid Arthritis Patients...Recruitment Metrics from TogetherRA: A Study in Rheumatoid Arthritis Patients...
Recruitment Metrics from TogetherRA: A Study in Rheumatoid Arthritis Patients...
 
PCOR PPT
PCOR PPTPCOR PPT
PCOR PPT
 
Clinical Trials 101
Clinical Trials 101Clinical Trials 101
Clinical Trials 101
 
5 malaga kalseth
5 malaga kalseth5 malaga kalseth
5 malaga kalseth
 
Msrc types research (1)
Msrc types research (1)Msrc types research (1)
Msrc types research (1)
 
Overuse of Stress Ulcer prophylaxis (SUP)
Overuse of Stress Ulcer prophylaxis (SUP)Overuse of Stress Ulcer prophylaxis (SUP)
Overuse of Stress Ulcer prophylaxis (SUP)
 
effective chronic diseses
effective chronic diseseseffective chronic diseses
effective chronic diseses
 
Prescription event monitoring- rumana hameed
Prescription event monitoring- rumana hameedPrescription event monitoring- rumana hameed
Prescription event monitoring- rumana hameed
 
2 amaddeo donisi tedeschi cephos enmesh 2015
2  amaddeo donisi tedeschi cephos enmesh 20152  amaddeo donisi tedeschi cephos enmesh 2015
2 amaddeo donisi tedeschi cephos enmesh 2015
 
Tackling the U.S. Healthcare System’s Infectious Disease Management Problem
Tackling the U.S. Healthcare System’s Infectious Disease Management ProblemTackling the U.S. Healthcare System’s Infectious Disease Management Problem
Tackling the U.S. Healthcare System’s Infectious Disease Management Problem
 
Session 3: Ahmed Aboulghate
Session 3: Ahmed AboulghateSession 3: Ahmed Aboulghate
Session 3: Ahmed Aboulghate
 
Building a National Data Infrastructure to Advance Patient-Centered Comparati...
Building a National Data Infrastructure to Advance Patient-Centered Comparati...Building a National Data Infrastructure to Advance Patient-Centered Comparati...
Building a National Data Infrastructure to Advance Patient-Centered Comparati...
 
Clinical oncology-can-observational-research-impact-clinical-decision-making
Clinical oncology-can-observational-research-impact-clinical-decision-makingClinical oncology-can-observational-research-impact-clinical-decision-making
Clinical oncology-can-observational-research-impact-clinical-decision-making
 
Costs of ADRs
Costs of ADRsCosts of ADRs
Costs of ADRs
 
RDD Conf Day 2: Josh Lounsberry (Canadian Neuromuscular Disease Network)
RDD Conf Day 2: Josh Lounsberry (Canadian Neuromuscular Disease Network)RDD Conf Day 2: Josh Lounsberry (Canadian Neuromuscular Disease Network)
RDD Conf Day 2: Josh Lounsberry (Canadian Neuromuscular Disease Network)
 
Personalized medicine tools for clinical trials - Kuchinke
Personalized medicine tools for clinical trials - KuchinkePersonalized medicine tools for clinical trials - Kuchinke
Personalized medicine tools for clinical trials - Kuchinke
 
Strom11206
Strom11206Strom11206
Strom11206
 
Personalized Medicine
Personalized MedicinePersonalized Medicine
Personalized Medicine
 

Viewers also liked

Internet scams
Internet scamsInternet scams
Internet scams
brittanyc
 
Smart Objects for Human Computer Interaction, Experimental Study
Smart Objects for Human Computer Interaction, Experimental Study Smart Objects for Human Computer Interaction, Experimental Study
Smart Objects for Human Computer Interaction, Experimental Study
Jeroen Doggen
 
Career choice
Career choiceCareer choice
Career choice
sa200293
 
World wide learning
World wide learningWorld wide learning
World wide learning
sa200293
 

Viewers also liked (6)

Internet scams
Internet scamsInternet scams
Internet scams
 
5 vogais
5 vogais5 vogais
5 vogais
 
Smart Objects for Human Computer Interaction, Experimental Study
Smart Objects for Human Computer Interaction, Experimental Study Smart Objects for Human Computer Interaction, Experimental Study
Smart Objects for Human Computer Interaction, Experimental Study
 
Pai pe de_pai
Pai pe de_paiPai pe de_pai
Pai pe de_pai
 
Career choice
Career choiceCareer choice
Career choice
 
World wide learning
World wide learningWorld wide learning
World wide learning
 

Similar to 201011 vhuser research-uk_005 ukukuk

Predictive analytics for personalized healthcare
Predictive analytics for personalized healthcarePredictive analytics for personalized healthcare
Predictive analytics for personalized healthcare
John Cai
 
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxChapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
christinemaritza
 
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
Mina Max
 
humanastatinarticle
humanastatinarticlehumanastatinarticle
humanastatinarticle
newtonsapple
 
Electronic Medical Records: From Clinical Decision Support to Precision Medicine
Electronic Medical Records: From Clinical Decision Support to Precision MedicineElectronic Medical Records: From Clinical Decision Support to Precision Medicine
Electronic Medical Records: From Clinical Decision Support to Precision Medicine
Kent State University
 

Similar to 201011 vhuser research-uk_005 ukukuk (20)

Predicting Diabetic Readmission Rates: Moving Beyond HbA1c
Predicting Diabetic Readmission Rates: Moving Beyond HbA1cPredicting Diabetic Readmission Rates: Moving Beyond HbA1c
Predicting Diabetic Readmission Rates: Moving Beyond HbA1c
 
CPT.RWE for decision making.2016
CPT.RWE for decision making.2016CPT.RWE for decision making.2016
CPT.RWE for decision making.2016
 
Trial Types_PIIS0092867420302099.pdf
Trial Types_PIIS0092867420302099.pdfTrial Types_PIIS0092867420302099.pdf
Trial Types_PIIS0092867420302099.pdf
 
Predictive analytics for personalized healthcare
Predictive analytics for personalized healthcarePredictive analytics for personalized healthcare
Predictive analytics for personalized healthcare
 
Translational Genomics towards Personalized medicine - Medhavi Vashisth.ppt
Translational Genomics towards Personalized medicine - Medhavi Vashisth.pptTranslational Genomics towards Personalized medicine - Medhavi Vashisth.ppt
Translational Genomics towards Personalized medicine - Medhavi Vashisth.ppt
 
Tools and Technology for Advancing Rare Disease Research and Drug Development
Tools and Technology for Advancing Rare Disease Research and Drug DevelopmentTools and Technology for Advancing Rare Disease Research and Drug Development
Tools and Technology for Advancing Rare Disease Research and Drug Development
 
Embi cri review-2013-final
Embi cri review-2013-finalEmbi cri review-2013-final
Embi cri review-2013-final
 
The role of patients and healthcare providers in translational medicine
The role of patients and healthcare providers in translational medicineThe role of patients and healthcare providers in translational medicine
The role of patients and healthcare providers in translational medicine
 
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxChapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docx
 
Towse NDDP implications for drug development
Towse NDDP implications for drug developmentTowse NDDP implications for drug development
Towse NDDP implications for drug development
 
MedicalResearch.com: Medical Research Interviews
MedicalResearch.com:  Medical Research InterviewsMedicalResearch.com:  Medical Research Interviews
MedicalResearch.com: Medical Research Interviews
 
CIBM
CIBMCIBM
CIBM
 
08 exemplo de revisão sistemática
08   exemplo de revisão sistemática08   exemplo de revisão sistemática
08 exemplo de revisão sistemática
 
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
Chemotherapy+with+or+without+gefitinib+in+patients+with+advanced+non small-ce...
 
humanastatinarticle
humanastatinarticlehumanastatinarticle
humanastatinarticle
 
The Role of Real-World Data in Clinical Development
The Role of Real-World Data in Clinical DevelopmentThe Role of Real-World Data in Clinical Development
The Role of Real-World Data in Clinical Development
 
ERAS and regional anesthesia at PGA 2015
ERAS and regional anesthesia at PGA 2015ERAS and regional anesthesia at PGA 2015
ERAS and regional anesthesia at PGA 2015
 
"How Scientific Wellness will Drive The Future of Health" - Nathan Price (Pro...
"How Scientific Wellness will Drive The Future of Health" - Nathan Price (Pro..."How Scientific Wellness will Drive The Future of Health" - Nathan Price (Pro...
"How Scientific Wellness will Drive The Future of Health" - Nathan Price (Pro...
 
Electronic Medical Records: From Clinical Decision Support to Precision Medicine
Electronic Medical Records: From Clinical Decision Support to Precision MedicineElectronic Medical Records: From Clinical Decision Support to Precision Medicine
Electronic Medical Records: From Clinical Decision Support to Precision Medicine
 
Clinical Research Informatics (CRI) Year-in-Review 2014
Clinical Research Informatics (CRI) Year-in-Review 2014Clinical Research Informatics (CRI) Year-in-Review 2014
Clinical Research Informatics (CRI) Year-in-Review 2014
 

Recently uploaded

會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
中 央社
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
中 央社
 
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
EADTU
 

Recently uploaded (20)

The Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDFThe Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDF
 
How to Manage Website in Odoo 17 Studio App.pptx
How to Manage Website in Odoo 17 Studio App.pptxHow to Manage Website in Odoo 17 Studio App.pptx
How to Manage Website in Odoo 17 Studio App.pptx
 
8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management
 
Scopus Indexed Journals 2024 - ISCOPUS Publications
Scopus Indexed Journals 2024 - ISCOPUS PublicationsScopus Indexed Journals 2024 - ISCOPUS Publications
Scopus Indexed Journals 2024 - ISCOPUS Publications
 
Climbers and Creepers used in landscaping
Climbers and Creepers used in landscapingClimbers and Creepers used in landscaping
Climbers and Creepers used in landscaping
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio App
 
OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...
 
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
 
Trauma-Informed Leadership - Five Practical Principles
Trauma-Informed Leadership - Five Practical PrinciplesTrauma-Informed Leadership - Five Practical Principles
Trauma-Informed Leadership - Five Practical Principles
 
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge App
 
e-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopale-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopal
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptx
 
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
 
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMDEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
 
Including Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdfIncluding Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdf
 
male presentation...pdf.................
male presentation...pdf.................male presentation...pdf.................
male presentation...pdf.................
 
An overview of the various scriptures in Hinduism
An overview of the various scriptures in HinduismAn overview of the various scriptures in Hinduism
An overview of the various scriptures in Hinduism
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 

201011 vhuser research-uk_005 ukukuk

  • 2. Vojtech Huser, MD, PhD 2 Introduction  Medical Doctor  PhD in Medical Informatics  Research experience at several academic institutions  Excellent knowledge of large healthcare systems EHR infrastructure  Comparable to NHS collaboration settings
  • 3. Vojtech Huser, MD, PhD 3 Research interest  Major  Health services research and EHR data analysis  quality improvement in healthcare  Other  data warehousing  medical terminologies  personal health record (consumer informatics)  knowledge representation  clinical research informatics
  • 9. Vojtech Huser, MD, PhD 9 HMO Research Network (VDW) http://www.hmoresearchnetwork.org
  • 10. Vojtech Huser, MD, PhD 10I2b2 (tool for basic EHR data querying), With experimental local codes for laboratory results
  • 11. Vojtech Huser, MD, PhD 11 Work with data within a database, 4-10GB datasets shown)
  • 12. Vojtech Huser, MD, PhD 12 Work with complex database data structures (EHR observations database
  • 13. Vojtech Huser, MD, PhD 13 Research data collection within EHR within my past research project
  • 14. Vojtech Huser, MD, PhD 14 Statistical analysis and data manipulation (R; also knowledge of SAS, SPSS, Stata)
  • 15. Vojtech Huser, MD, PhD 15 Example 1  5. Huser V, Rocha RA, Huser M, Conducting Time Series Analyses on Large Data Sets: a Case Study With Lymphoma, Medinfo 2007.  6. Huser V, Rocha, RA, Graphical Modeling of HEDIS Quality Measures and Prototyping of Related Decision Support Rules to Accelerate Improvement, fall AMIA symposium, 2007  Intermountain Healthcare, 3.2 M patients, comprehensive data warehouse with coded administrative, clinical and payer data (health plan)  Methods: data pre-processing, R statistical package, SQL and other tools
  • 16. Vojtech Huser, MD, PhD 16 Example 1  Lymphoma  780 patients with HL (140 met all inclusion criteria)  Preservation of reproductive function after toxic cancer therapy  Experimental analysis of data concerning: stages of the Hodgkin disease, cycles and doses of chemotherapy, detection of relapses, levels of hormones indicating premature ovarian failure or prescribed contraception methods  Similar results to comparable prospective observational study done by Franchi- Rezghui (2003) (36.9%) (84 subjects)  Quality measures  2 measures studied: Osteoporosis, cholesterol management in cardiovascular patients  1400+ patients  Cholesterol management results (inclusion criteria:history of AMI, CABG or PTCA):  43.24% had proper cholesterol screening, 31.53% in good control  Additional sub-analyses: close to the threshold level (100-130 mg/dL) and on a low dose of a lipid-lowering agent (2.66%). In 13.38% of the non-compliant patients we found evidence of 2+ laboratory-test- episodes or 3+ encounters within a 12 month window
  • 17. Vojtech Huser, MD, PhD 17 Example 2  10. Huser V, Starren JB, EHR Data Pre-processing Facilitating Process Mining: an Application to Chronic Kidney Disease. AMIA Annu Symp Proc 2009  Analysis of stages of CKD progression  laboratory onset, formal diagnosis establishment, first analysis, regular dialysis, transplant, death  Using manual as well as data mining methods  15. Huser V, A Methodology for Quantitative Measurement of Quality and Comprehensiveness of a Research Data Repository, Proc of 16th Annual HMORN Conference 2010  Received Young investigator award for this submission  Evaluation of data warehouses of multiple institutions [consortium]  Set of qualitative measures used for comparisons inter-institutions and intra-institution (yearly progress)
  • 18. Vojtech Huser, MD, PhD 18 Summary  Educationally well-qualified researcher  History of past publications and successful grant applications  Apart from health services research, additional knowledge of the field of health informatics and interventional clinical projects (via informatics methods)  Publications available at an “internal-use-only” URL:  http://minfor.wikispaces.com/publications

Editor's Notes

  1. open Tset
  2. Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
  3. Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
  4. Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
  5. (180 met all criteria) Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
  6. Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
  7. Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.