Why we need Datamart?

Transcription

1 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.) Jul 2015 herasevich.vitaly@mayo.edu Why we need Datamart? EHR Herasevich et al. Medical informatics in ICU., in Principles of Critical Care, 4th,

2 Data volume before and in ICU Average data points per day Per Patient Per 24 bedded ICU Labs Drug Orders Microbiology 2 48 X ray 2 48 Vitals 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 Fall;Suppl:45-8. PMID: 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

3 The market for analytics solutions is not small more than 100 vendors currently offer big data toolsand products MFMER slide-7 Association is not causation MFMER slide-8 Big Data = Predictive Analysis = Data Mining Data Miningis 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 MFMER slide-9 3

4 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 automaticallydiscover solutions without guidance. Data mining will notsit inside of your database and send you an when some interesting pattern is discovered. Data mining may find interesting patterns, but it does not tell you the valueof such patterns. Data mining does not infer causality 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 MFMER slide-12 4

5 Common Data Mining Applications Market analysis Risk analysis and management Fraud detection and detection of unusual patterns (outliers) Text mining (news group, , documents) and Web mining Realtimedatamining 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

6 Critical Care at Mayo 208 ICU beds CPOE Enterprise orders Nursing Flow Sheet MICS Lastword ICU demographics HRBS Monitored data Chart+ Clinical notes MCLS Lastword Radiology Reports RIMS ICD-9 DSS Past history PPI APACHE APACHE Key Facts HL7 Emergency acute area YES Fluids: in/out Chart+ Transfusion Orders MYSIS ~ 15,000 admissions per year ~ 1,000,000 vital records per week Data available from 2003 Updated every hour in average (15 min for vitals) Near real-time Microbiology Reports HRBS Surgical schedule Surgical Historical REP Drug orders HRBS Herasevich V, et al. ICU data mart: a non-it approach. Healthc Inform. 2011;28(11):42, PMID: 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:

7 Some available data Monthly average Drugs orders 90,000 Radiology reports 3,000 Laboratory tests 330,000 Transfusions 6,000 Microbiology tests 3,000 Vital signs (150 var.) 6,000,000 CPOE orders 370,000 Demographics 1,300 Fluids (intake/output) 220,000 Approach 7

8 Rule zero Rule one: lego bricks 8

9 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

10 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): (PMID: ) Herasevich V, Kor D, Li M, et al. ICU Data Mart: A Non-IT Approach. Healthcare Informatics 2011;28(11): (PMID: ) 10

11 Areas of implementation APACHE replacement project APACHE replacement

12 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):e PMID: 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

13 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

14 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 ClinProc. 2010;85(3): PMID: 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 < Glucose Check alert: If the patient had insulin administered and had no glucose test done within 2 hours from the last glucose test. 7. Pressoralert: 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. Pectusexcavatumrepair alert: Patients list who scheduled pectusexcavatumrepair surgery. 7. QTC sniffer: new born lists for the QTC >475 ms.pi: Notable sniffers ALI VILI Septic Shock Herasevich V, Yilmaz M, Khan H, et al. Validation of an electronic surveillance system for acute lung injury. Intensive Care Med 2009;35(6): (PMID: ) 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): (PMID: ) 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 (PMID: ) 14

15 Data retrieval for research Olmsted county unique for population based research Olmsted county admission METRIC datamart Enrollment to time sensitive trials Retrospective studies for Quality Improvement an research Clinical studies 15

16 Data mining Visual mining In conclusion 16

17 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: /chest