Upcoming SlideShare
×

201011 vhuser research-uk_005 ukukuk

223 views
180 views

Published on

0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
223
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
1
0
Likes
0
Embeds 0
No embeds

No notes for slide
• open Tset
• 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.
• 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.
• 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.
• (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.
• 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.
• 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.
• 201011 vhuser research-uk_005 ukukuk

1. 1. Research Interests Vojtech Huser MD PhD
2. 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. 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
4. 4. Vojtech Huser, MD, PhD 4
5. 5. Vojtech Huser, MD, PhD 5
6. 6. Vojtech Huser, MD, PhD 6
7. 7. Vojtech Huser, MD, PhD 7
8. 8. Vojtech Huser, MD, PhD 8
9. 9. Vojtech Huser, MD, PhD 9 HMO Research Network (VDW) http://www.hmoresearchnetwork.org
10. 10. Vojtech Huser, MD, PhD 10I2b2 (tool for basic EHR data querying), With experimental local codes for laboratory results
11. 11. Vojtech Huser, MD, PhD 11 Work with data within a database, 4-10GB datasets shown)
12. 12. Vojtech Huser, MD, PhD 12 Work with complex database data structures (EHR observations database
13. 13. Vojtech Huser, MD, PhD 13 Research data collection within EHR within my past research project
14. 14. Vojtech Huser, MD, PhD 14 Statistical analysis and data manipulation (R; also knowledge of SAS, SPSS, Stata)
15. 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. 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. 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. 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