Download Why we need Datamart? - Becker`s Hospital Review

7/8/2015
Building Data Warehouse for Research,
Reporting and Quality. 6 Years of ICU
DataMart Experience
Vitaly Herasevich, MD, PhD, MSc
Associate Professor of Anesthesiology and Medicine,
Department of Anesthesiology,
Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.)
[email protected]
Jul 2015
Why we need Datamart?
EHR
Herasevich et al. Medical informatics in ICU., in Principles of Critical Care, 4th, 2015
1
7/8/2015
Data volume before and in ICU
Labs
Drug Orders
Microbiology
X ray
Vitals
Average data points per day
Per Patient
Per 24 bedded ICU
60
1440
10
240
2
48
2
48
1950
46800
Microbiology, labs, medications, chest X-ray, Nurses flowsheet,
Clinical notes (history and impression/plan) – Vitals excluded
Herasevich V, Litell J, Pickering B. Electronic medical records and mHealth anytime, anywhere. Biomed
Instrum Technol. 2012 Fall;Suppl:45-8. PMID: 23039776.
Why we need Datamart?
• 1) So much clinical data
• 2) Physically data stored in different databases
• 3) Make some sense out of data…
©2011 MFMER | slide-5
BIG data, data minng…
2
7/8/2015
The market for analytics solutions is not small — more
than 100 vendors currently offer big data tools and products.
©2011 MFMER | slide-7
Association is not causation
http://www.tylervigen.com/view_correlation?id=1597
©2011 MFMER | slide-8
Big Data = Predictive Analysis = Data Mining
• Data Mining is an analytic process designed to
explore data (usually large amounts of data typically business or market related - also
known as "big data") in search of consistent
patterns and/or systematic relationships
between variables.
• The ultimate goal of data mining is prediction
- and predictive data mining is the most
common type of data mining and one that has
the most direct business applications.
©2011 MFMER | slide-9
3
7/8/2015
Myths of data mining
• Myth #1: Data mining provides instant crystal ball
predictions
• Myth #2: Data mining is not yet viable for medicine
• Myth #3: Data mining requires separate, dedicated
database
• Myth #4: Only PhDs can do data mining
• Myth #5: Data mining is for large companies with lots
of customer data
Data mining is not:
• Data mining is a tool, not a magic.
• Data mining will not automatically discover
solutions without guidance.
• Data mining will not sit inside of your database and
send you an email when some interesting pattern is
discovered.
• Data mining may find interesting patterns, but it
does not tell you the value of such patterns.
• Data mining does not infer causality.
©2011 MFMER | slide-11
What can data mining do?
• Helps to determine relationships among "internal"
factors such as price, product positioning, or staff skills,
and "external" factors such as economic indicators,
competition, and customer demographics.
• Helps to determine the impact on sales, customer
satisfaction, and corporate profits.
• Helps them to "drill down" into summary information
Primarily used today by companies with a strong
consumer focus - retail, financial, communication,
and marketing organizations.
©2011 MFMER | slide-12
4
7/8/2015
Common Data Mining Applications
• Market analysis
• Risk analysis and management
• Fraud detection and detection of unusual patterns
(outliers)
• Text mining (news group, email, documents) and
Web mining
• Real time data mining
• DNA and bio-data analysis
“Big Data” Applications in Health Science
• Drug discovery and functional genomics
• Analysis of DNA micro-array data
• Gene, disease and drug interaction
• Genomics, proteomics and metabolomics
• Biomedical text mining (finding relations between
experimental data and published literature)
• Estimating outcomes of patients
• Epidemiology
• Data mining electronic patient records
ICU Datamart (METRIC Datamart)
5
7/8/2015
Critical Care at Mayo
208 ICU beds
CPOE
ICU demographics
Enterprise orders
HRBS
Nursing Flow Sheet
Monitored data
MICS Lastword
Chart+
Clinical notes
Emergency acute area
MCLS Lastword
YES
Key Facts
HL7
Radiology Reports
Fluids: in/out
RIMS
Chart+
~ 15,000 admissions per year
~ 1,000,000 vital records per week
Transfusion Orders
DSS • Data available from 2003
MYSIS
Updated every hour in average (15 min Microbiology
for vitals)Reports
Past• history
•
ICD-9
•
PPI •
Near real-time
HRBS
APACHE
Surgical schedule
APACHE
Surgical
Historical
Drug orders
REP
Herasevich V, et al. ICU data mart: a non-iT
approach. Healthc Inform. 2011;28(11):42,
44–5. PMID: 22121570
HRBS
OR Data mart
ICU Data mart
Labs
HRBS
METRIC datamart workflow
Li M, Pickering BW, Smith VD, Hadzikadic M, Gajic O, Herasevich V. Medical informatics: an essential tool for health sciences research in acute
care. Bosn J Basic Med Sci. 2009;9 Suppl 1:34–9. PMID: 19912124
6
7/8/2015
Some available data
2001
2002
2003
2004
2005
2006
2011
2015
Monthly
average
90,000
Drugs orders
3,000
Radiology reports
330,000
Laboratory tests
6,000
Transfusions
3,000
Microbiology tests
6,000,000
Vital signs (150 var.)
370,000
CPOE orders
1,300
Demographics
220,000
Fluids (intake/output)
Approach
7
7/8/2015
Rule zero
Rule one: lego bricks
8
7/8/2015
Rule two: UNIXsh - no user interface
• No formal web/query Interface
• ODBC connection allows query from any app (JMP, Excel, SAS…)
Approach: technically
• SQL server with institutional support
• Tables divided by years
• In “Current tables” only patients who in currently in ICU
• EAV (entity – attribute – value) structure
• Continuously “Testing – production”
• Test –> production DBs
9
7/8/2015
Data integrity
Statistical control
Real time monitoring
Validation is key.
Physiological parameters
• Near 100% accurate
Herasevich V, Pickering BW, Dong Y, et al. Informatics
infrastructure for syndrome surveillance, decision support,
reporting, and modeling of critical illness. Mayo Clin Proc
2010;85(3):247-254. (PMID: 20194152)
Herasevich V, Kor D, Li M, et al. ICU Data Mart: A Non-IT
Approach. Healthcare Informatics 2011;28(11):42-45. (PMID:
22121570)
10
7/8/2015
Areas of implementation
APACHE replacement project
APACHE replacement
1994 - 2009
11
7/8/2015
Free text search for medical
admission diagnoses
Chandra S, et al. Mapping physicians’ admission diagnoses to structured concepts towards fully automatic calculation of acute physiology
and chronic health evaluation score. BMJ Open. 2011;1(2):e000216. PMID: 22102639
Clinical reports
Effective management
Joint Commission on Healthcare
Organizations (JCAHO) measurement
of ICU performance.
•
•
•
•
•
Mortality report
Length of Stay Review
ICU Death Review
ICU admission Low Risk Monitor Review
ICU Readmission Review
12
7/8/2015
METRIC Reports
1. Hospital Length of Stay for ICU Graduates –
Unadjusted
2. ICU Length of Stay – Unadjusted
3. ICU Length of Stay – Adjusted
4. ICU Readmission Rate
5. ICU Admissions
6. ICU Admission Source and Service
7. Duration of Mechanical Ventilation
8. ICU Mortality Rate – Unadjusted
9. Hospital Mortality Rate – Adjusted
10. ICU Admissions for Low-Risk Monitoring
11. ICU Census - Hourly Utilization
• Monthly reports
• Ad-hock reports
• Customized reports
AWARE real time administrative
dashboard
© 2014 Mayo Foundation for Medical Education and Research
Sniffers
13
7/8/2015
Sniffers – rule based DSS
Herasevich V, Pickering BW, Dong Y, Peters SG, Gajic O. Informatics infrastructure for syndrome surveillance, decision support, reporting,
and modeling of critical illness. Mayo Clin Proc. 2010;85(3):247–54. PMID: 20194152
Sniffers
OR Datamart
1.
No Temp: If the temperature recordings were not started after 45 minutes into OR location.
2.
Hypothermia: If there are three consecutive low temps < 35 after 60 minutes into OR location.
3.
VILI – High Tidal volume: If there are three consecutive high tidal volumes after 60 minutes into
OR.
4.
VILI – Peak airway pressure: if there are three consecutive peak airway pressures > 35 cm H20
after 60 minutes into OR.
5.
Hypoglycemia: If the patient glucose level dropped < 70
6.
Glucose Check alert: If the patient had insulin administered and had no glucose test done within
2 hours from the last glucose test.
7.
Pressor alert: to alert the covering anesthesiologist when thresholds are exceeded for
intravenous pressor support.
8.
IV Administration of greater than 10 ml pressor (100 mcg/ml phenylephrine and/or 50 mg/ml
ephedrine) in a 30 minutes period.
ICU datamart
1.
Hip Arthroplasty Study Alert : patients list who scheduled the hip arthroplasty surgery. Knee
Arthroplasty Study Alert: patients list who scheduled the Knee arthroplasty surgery.
2.
Surgery Glucose Study Alert: Patient list who scheduled Thoracic surgery with glucose
problems.
3.
Pepsin Study Alert: Patients list who scheduled Thoracic and other surgeries. For Blood Draw
study.
4.
Septic-sniffer alert: the patient list who are the ICU patients and suspicious developed septic.
5.
Neuromyopathy-alert : Basically the septic patients for the pediatric ICU(s) patients.
6.
Pectus excavatum repair alert: Patients list who scheduled pectus excavatum repair surgery.
7.
QTC sniffer: new born lists for the QTC >475 ms. PI:
Notable sniffers
ALI
VILI
Herasevich V, Yilmaz M,
Khan H, et al. Validation of an
electronic surveillance system
for acute lung injury. Intensive
Care Med 2009;35(6):10181023. (PMID: 19280175)
Herasevich V, Tsapenko M,
Kojicic M, et al. Limiting
ventilator-induced lung injury
through individual electronic
medical record surveillance.
Crit Care Med 2011;39(1):3439. (PMID: 20959788)
Septic Shock
Herasevich V, Pieper MS,
Pulido J, et al. Enrollment
into a time sensitive clinical
study in the critical care
setting: results from
computerized septic shock
sniffer implementation. J Am
Med Inform Assoc 2011.
(PMID: 21508415)
14
7/8/2015
Data retrieval for research
Olmsted county – unique for
population based research
Olmsted county
admission
METRIC datamart
Clinical studies
• Enrollment to time
sensitive trials
• Retrospective
studies for Quality
Improvement an
research
15
7/8/2015
Data mining
Visual mining
In conclusion
16
7/8/2015
Datamart usage
1. Administrative reporting
2. Clinical research, including population
based
3. Quality improvement: Point of care novel
user interfaces, alerts and decision
supports tools
4. Predictive analytics
Chest. 2014;145(6):1190. doi:10.1378/chest.145.6.1190
[email protected]
17