Data mining and analytics in healthcare by Leslie McIntosh from Washington University School of Medicine / BJC Healthcare at the 2014-03-06 TDWI St. Louis chapter meeting. Contact Leslie through LinkedIn (http://www.linkedin.com/pub/leslie-mcintosh/3/176/659)

1. Healthcare Analytics:
Mining Electronic Medical Records
Leslie McIntosh, PhD, MPH

3. Health Informatics
Computer Science/IS
Health/MedicineAnalytics/Statistics

4. https://edgewatertech.wordpress.com

7. Located, Consolidated, and
Standardized the Data

8. Facilities

9. Syntactic interoperability
Use of standardized programming interfaces
 APIs
 Service-oriented architecture
 Messaging standards
Semantic interoperability
Use of controlled vocabularies and ontologies
 ICD9-10 – Diagnosis Codes
 LOINC – Laboratory tests
 SNOMED – Clinical terms (e.g. diagnoses)
 CPT – Medical procedures
 RxNORM – Medications
Facilitating Interoperability

10. Clinical Data
Demographics : name, address, race, gender, phone number
Visits: age, patient type, facility, diagnosis code, procedure code
Labs: age, collection time, facility, lab procedure, lab test name,
specimen type (e.g. serum vs CSF glucose), result
Medications: age, duration, frequency, medication, route & form
Allergies: allergen type, allergy reaction, sensitivity, severity, type, onset
date
Vitals: age, body site, facility, measurement, observation, value and units
Documents: age, document content, document name, facility and
physician
Illicit Drug Use: type, history, positive/negative
Biospecimen Data: Specimen Type, Accession Number and Clinical
diagnosis

11. Natural Language Processing (NLP)
Custom Analytics

12. Acknowledgements
• Leslie D. McIntosh, PhD, MPH – Washington University
• Walton Sumner, MD – Washington University
• Lynn Latham - BJC
• Bijoy George – Washington University
• Pavan Kalantri – Washington University
• Suhas Khot – Washington University
• Anthony Juehne – Washington University
• Rakesh Nagarajan MD, PhD – Washington University

13. Alcohol, Tobacco, and Illicit Drug Use in
Patients are Not Discrete Data
Problem

14. Electronic Medical Documents

15. Rules Creation
Alcohol
Tobacco
Illicit Drugs

16. CIDER

17. Mining Discrete Data
Chronic Diseases associated with
Multiple CTs

18. Unknown what Diagnoses are
Associated with Multiple CT Scans
Problem #1

19. Common Chronic Diseases
Problem #2

20. Acknowledgements
• Richard Griffey, MD, Division of Emergency Medicine, Washington
University School of Medicine
• Leslie McIntosh, PhD, MPH, Center for Biomedical Informatics,
Department of Pathology, Washington University School of Medicine
• Tom Bailey, MD, Division of Infectious Diseases, Department of Medicine,
Washington University School of Medicine

21. Disclosure
• Emergency Medicine Foundation & Emergency Medicine Patient Safety
Foundation Patient Safety Fellowship
• Institutional KM1 Comparative Effectiveness Award Number
KM1CA156708 through the National Cancer Institute (NCI) at the National
Institutes of Health (NIH) and Grant Numbers UL1 RR024992, KL2
RR024994, TL1 RR024995 through The Clinical and Translational Science
Award (CTSA) program of the National Center for Research Resources and
the National Center for Advancing Translational Sciences at the National
Institutes of Health.

22. Background

23. Methods
Design: Exploratory, Retrospective
Step 1
Identify conditions among patients associated with
being in the top 10% of CT study count
Step 2
Test whether among all patients having one of
these conditions increased the odds of being highly
imaged (in the top 10%).

24. Patients with 5+ CT
(2004-2011)
Prior CT at BJH*
(2004-2011)
Unique patients
BJH - ED (2011)
58,079
35,3982 (0)
21,404 (1+ with Dx)
693 (1+ w/o Dx)
18,816 (no) 2,588 (yes)
1. Identify patients in top 10% of CT
ED Visit + CT (ever)
*CTs were limited to those commonly ordered from the ED
(e.g. head, cervical spine, chest, abdomen-pelvis)

25. 2. Identify diagnoses (ICD-9s) associated with these visits*
• Rank by frequency & dual review those appearing >100 times
• Statistical scoring (based on NLP algorithm tf-idf)
• Exclude: cancer diagnoses, non-chronic conditions (e.g.
trauma), those not mapping to an indication for imaging (e.g.
HTN, DM)
1830 malignant neoplasm of ovary
7533 other specified congenital anomalies of kidney
5308 other specified disorders of esophagus
5678 other specified peritonitis
75313 polycystic kidney autosomal dominant
2384 polycythemia vera
7530 renal agenesis and dysgenesis
5582 toxic gastroenteritis and colitis
1551 malignant neoplasm of intrahepatic bile ducts
20210 mycosis fungoides unspecified site
4413 abdomial aortic aneurysm ruptured
1520 malignant neoplasm of duodenum
1541 malignant neoplasm of rectum
53087 mechanical complication of esophagostomy
56489 other functional disorders of intestine
19882 secondary malignant neoplasm of genital organs
4412 thoracic aortic aneurysm without mention of rupture
5187 transfusion related acute lung injury
6190 urinary-genital tract fistula female
99681 complications of transplanted kidney
* Inpatient & ED only

26. Candidate diagnoses
112.5 partial complex seizure
282.6 Sickle cell disease
115.1 schizophrenia
774.0 psychosis
979.7 petite mal seizure
532.4 pulmonary embolus
487.3 quadriplegia
475.8 hemiparesis
784.9 HIV
282.3 Hb-ss disease without crisis
232.0 ulcerative colitis
215.1 lupus
212.3 sickle cell NOS
343.5 migraine
489.6 constipation
234.1 crohn’s disease
299.5 hydrocephalus
214.1 sickle cell pain crisis
HCUP Clinical Classification System
Sickle cell disease
282.3 348.6 594.7 843.6 119.0
298.4 120.9 879.4 282.3 948.1
387.3 282.6 859.1 214.1 902.0
893.0 213.3 912.1 981.1 873.0
Paralysis
349.4 348.6 594.7 843.6 432.4
475.8 120.9 879.4 239.5 948.1
387.3 212.3 859.1 203.1 902.0
893.0 487.3 912.1 981.1 873.0
Regional enteritis
349.4 348.6 594.7 843.6 234.1
298.4 120.9 879.4 239.5 948.1
387.3 212.3 859.1 203.1 902.0
232.0 213.3 912.1 981.1 873.0
Psychosis
349.4 348.6 594.7 843.6 432.4
298.4 120.9 879.4 239.5 948.1
387.3 290.3 859.1 203.1 902.0
893.0 249.3 912.1 115.1 873.0
3. Cluster similar diagnoses in to “conditions”
214.1
282.3
213.3

27. 94
532
1137
6,331
Diagnoses
94 21

28. Methods – Step 2
CT Status
Diagnosis
of Interest
Patients at
BJH (years
in system)
Patients at
BJH(2010)
230,531
102,141
(2+)
23,382 (y)
5,972
(5+)
19,817
(0-4)
78,759 (n)
3,565
(5+)
72,787
(0-4)
128,390
(<2)

29. Big Data Consumable

30. Built a Tool
Trained Clinicians

31. {This page intentionally left blank}

32. People

33. Computer Scientists/Developers

34. Analysts

35. Clinical Researchers

36. In the end, we have…
Developers
Analysts
Clinicians

37. DATA

38. Data Drivers
Liaisons facilitating the
transformation of data to
information to knowledge

39. What’s next