1. February 12, 2013: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 12, 2013 Division of General Internal Medicine, and Center for Clinical and Translational Informatics UCSF Electronic Health Records for Clinical Research Copyright Ida Sim, 2013. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.

2. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Summary of Last Class Informatics crucial for making sense of complex data, and crucial for promise of translational research Key informatics challenges –naming data –exchanging data –reasoning to knowledge, capturing knowledge Challenges occur in parallel for clinical care and clinical research

3. February 12, 2012: I. Sim EHRs and Research Medical Informatics Big Picture Take-Home Points Puts care and research together Separates data from the transactional systems used to collect that data Shows need to capture computable knowledge, not just data Clear place for decision support Emphasizes user- centered design as glue

4. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

5. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Promotion of EHR Adoption “Stimulus” Act (2009) directed $19 billion to health IT $17.2 billion Medicare/Medicaid payments for “meaningful use” of EHRs –$44K over 5 years for MDs/clinics/hospitals that achieve meaningful use by 2012-2014 –$6 billion already paid out Medicare fees to be reduced for “non-EHR physician users” starting 2015 UCSF spent $50 mil+ on UCare; over $100m expected total on Epic

6. February 12, 2012: I. Sim EHRs and Research Medical Informatics 8 Types of EHR Functionality

7. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Meaningful Use Stage 1 (2011), basic functions, e.g., –capture vital signs, demographics, active meds, allergies, up-to-date problem lists, smoking status –one clinical decision support rule and track compliance –computer provider order entry (CPOE) (>30% of pts) –electronic prescribing (of >40% of prescriptions) –capability of exchanging key clinical information –report clinical quality measure to CMS or states Stage 2 (2013), reaction to Stage 1 over-reach? –very minor tweaks to above, plus more data to patients Stage 3 (2015)

8. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Certified EHRs EHRs need certification for meeting “meaningful use” http://onc-chpl.force.com/ehrcert http://onc-chpl.force.com/ehrcert –ambulatory practice 2961 products (was 269 in 2011) (Epic products from 2008,2009,2010 listed separately) –inpatient 962 products (was 101 in 2011) GE Centricity (aka UCare) certified, but we dropped them in 2011 due to problems with CPOE Epic (maker of APEX) is market dominant –33-44% of U.S. population has at least one account in Epic

9. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Rising Office-Based EHR Adoption CDC 2012 http://www.cdc.gov/nchs/data/databriefs/db111.htm

10. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

11. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Retrospective cohort study of outpatients Compare 5 year rate for congestive heart failure for diabetics treated with a glitazone vs. not –find diabetics –find whether treated with a glitazone –for these patients, find all subsequent cases of congestive heart failure –analyze at 5 years adjust for age, sex, severity of diabetes, previous CHF, other meds, etc., etc. Outcomes Research Project

12. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Diabetes diagnosis –chart, HgbA1C, meds taken, problem list... Glitazone usage –orders, pharmacy Potential confounders –age, sex, severity, other meds, etc. Types of Data Needed

13. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Community-Based Research For generalizability, and where chronic conditions are, you want to analyze EHR data from community practices Which EHRs products should you work with? Which practices should you approach for participation?

14. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Which EHRs? Should be an ONC-certified EHR that meets (some) Meaningful Use criteria Should provide needed functionality for study protocol –patient demographics –problem list –medication list –clinical documents and notes The more structured and coded the data, the better

15. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Which Practices? Adoption curve –what % of docs using the system? where are they on adoption curve? (takes 6+ months for initial roll- out, 1-2 years for comfortable use) Which functionality being used? –most EHR purchasers do not use all available functionality (e.g., guidelines support) Is there a physician champion? –your best liaison to the practice’s EHR Consider a practice-based research network for outpatient/community clinics

16. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

17. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics How Structured is the Data? Structured data does not equal coded data

18. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics How Coded is the Coded Data? Availability of coding does not mean coding is used! e.g., Problem List –“more than 80% of patients have at least one entry in structured data” (MU Stage 1) –to what vocabulary? who does the coding? gamed”? Malignant neoplasm of colon, unspecified site

19. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics A term is a designation of a concept or an object in a specific vocabulary e.g., English blood = German blut –standardization enables predictable, accurate search and retrieval “Controlled vocabularies” range from simple lists of terms to rich descriptions of knowledge –terminologies: list of terms corresponding to concrete (e.g., heart) and abstract concepts (e.g., hypertension) –ontologies: includes concepts, their definitions, various types of relationships among the concepts, and axioms data (e.g., lisinopril), information (e.g., lisinopril IS-A ACEI) knowledge (e.g., ACEIs lower blood pressure) Standardization of Clinical Terms

20. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Notable Clinical Vocabularies

21. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Terminology Features (e.g, ICD-9) Coverage –is the idea (e.g., SNP) included? Granularity / specificity –do you need left heart failure? subendocardial myocardial infarction? Synonomy –cervical: does this mean related to the neck or or the cervix? Relationships between terms –lisinopril IS-A ACE-inhibitor; see Atomic concepts vs. “post-coordinated” concepts –left heart failure vs. left + heart failure; Usability –can you find the “right” code (SNOMED CT has > 357,000 concepts)

22. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Terminology Features (cont.) Unambiguousness –each concept clearly defined (e.g., immunocompromised) Non-redundancy –each concept has only one corresponding code Consistency –each code has only one meaning in all situations Concept permanence –meaning never changes, even with new versions Versioning –new terms (e.g., SNP), defunct terms (e.g., dropsy), corrected concepts (e.g., rabies not a psychiatric disorder)

23. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics ICD-9 Concept Coverage How well would ICD-9 do in capturing a medical chart? Inpatient and outpatient charts from 4 medical centers abstracted into 3061 concepts [Chute, 96] –diagnoses, modifiers, findings, treatments and procedures, other Matching: 0=no match, 1=partial, 2=complete –1.60 for diagnoses –0.77 overall –ICD-9 augmented with CPT: overall 0.82

24. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics ICD-9 Coding Accuracy VBAC uterine rupture rate –665.0 and 665.1 ICD-9 discharge codes used in study (NEJM 2001;345:3-8) –letter to editor: in 9 years of Massachusetts data 716 patients with 665.0 and 665.1 discharged reviewed 709 charts 363 (51.2%) had actual uterine rupture –others had incidental extensions of C-section incision, or were incorrectly coded or typed 674.1 (dehiscence of the uterine wound) used to code another 197 ruptures (or 35% of confirmed cases of uterine rupture) i.e., sensitivity 65%, specificity 51.2%

25. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics SNOMED-CT “Ontology” To “help structure and computerize the medical record, reducing the variability in the way data is captured, encoded and used for clinical care of patients and medical research” –311,000 unique health care concepts –800,000 descriptions –over 1.36 million relationships between concepts, e.g., Diabetes Mellitus IS_A disorder of glucose regulation Finger PART_OF hand

26. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics SNOMED-CT Structure Formally constructed vocabulary/knowledge map –18 high-level hierarchies e.g. finding, organism, substance, body structure, event, social context –each concept can be described by many attributes e.g., finding site = lung, associated-morphology = inflammation –encodes “knowledge” pneumonia is an infection of the lung by an organism –can “post-coordinate” terms to increase expressive power pneumonia: finding-site=lung ; finding-site=lower lobe; laterality=right; causative agent=pneumococcus; http://nciterms.nci.nih.gov/ncitbrowser/pages/vocabulary.jsf or http://vtsl.vetmed.vt.edu/http://nciterms.nci.nih.gov/ncitbrowser/pages/vocabulary.jsf

27. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics SNOMED-CT Status Best semantic coverage of all existing vocabs de facto standard for EHR clinical vocabulary –owned by newly created International Healthcare Terminology Standards Development Organization (Danish, with 9 founding countries) –site-licensed (i.e., free) in U.S., as a founding country

28. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Coding Barriers Poor inter-coder reliability –3 docs, 5 opthalmology cases, 242 concepts, 2 SNOMED- CT browsers [Chiang M, 2006] reliability between coders (exact term match): 44% and 53% reliability within same coder: 45% over 2 browsers Automatic coding into ICD-9, etc. –precision (true pos) 0.88, recall (sens) 0.9 [Goldstein, 2007] –experts precision 0.6 to 0.9, recall 0.7 - 0.9 –still a major Natural Language Processing (NLP) research challenge in general, let alone with typical clinical notes

29. ICD-9 Going Away… UCSF moving to ICD-10 by 2014. First webinar March 4 Example –W5803XA Crushed by alligator, initial encounter –W5803XD Crushed by alligator, subsequent encounter February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics AMA, 2010 http://www.ama-assn.org/ama1/pub/upload/mm/399/icd10-icd9-differences-fact-sheet.pdf

30. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics EHR for Research Summary Variable adoption of EHRs limits benefit to clinical research Not automatically going to help clinical research –if all unstructured free text, won’t help much at all the more structured it is (i.e., more defined fields), the better –if just coded sporadically in ICD-9 problem with gamed codes, poor semantic coverage ICD-10 transition will be very challenging –very, very few EHRs coded in SNOMED some clinical concepts still not well covered SNOMED is essentially unusable by front-line clinicians general automated coding still some time away, but may be an option for constrained domains (e.g., path, radiology reports)

31. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

32. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Retrospective cohort study of outpatients Compare 5 year rate for congestive heart failure for diabetics treated with a glitazone vs. not –find diabetics –find whether treated with a glitazone –for these patients, find all subsequent cases of congestive heart failure –analyze at 5 years adjust for age, sex, severity of diabetes, previous CHF, other meds, etc., etc. Outcomes Research Project

33. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Diabetes diagnosis –chart, HgbA1C, meds taken, problem list... Glitazone usage –orders, pharmacy Potential confounders –age, sex, severity, other meds, etc. Types of Data Needed

34. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Getting Data Out Cohort identification –how many potentially eligible patients at UCSF? Data extraction –extract particular data items for particular patients? –cannot “go to APEX” to pull out data for outcomes research APEX built for treating one patient at a time backend database (Clarity) is a relational database, but data schema is proprietary

35. March 6, 2012: I. Sim Research Informatics Epi 206 — Medical Informatics MICU Finance Research QA Integrated Data Repository Internet ADT ChemAPEXXRayPBMClaims autofeed nightly, data stored securely with backup Data from APEX to IDR

36. ReplicaSource Systems At UCSF: IDR & My Research – Big Picture Audit DB / IDR Data Warehouse End User Tools Cognos BI Data WarehousingBusiness Intelligence Cohort Selection Tool (i2b2), SAS, STATA, SPSS, Alias.ti, Enterprise Architect MyResearch Portal UCare PICIS Cancer Registry MisysIDXrad Apollo Worx CTMS STOR MAR Flowcast TSI CoPath Kaiser VA ED Epic Extract, Transfer Proxy process and Load Axium SiemensRadiology Transplant Terminal Servers SGD Web top Alfresco REDCap Epic LPPI EMR UCare Will be replaced by Epic Will have interfaces to bring data into Epic SFGH PICIS Cancer Registry CTMS? TSI Kaiser VA Axium Transplant? LPPI EMR SFGH Security Red = Currently Integrated

37. February 12, 2012: I. Sim EHRs and Research Medical Informatics EHR vs. IDR Queries EHR Queries What was Mr. Smith’s last potassium? Does he have an old CXR for comparison? What antihypertensives has he been on before? What did the neurology consult say about his epilepsy? IDR Queries What proportion of diabetics with AMI admissions were discharged on  -blockers? What was the average Medicine length of stay in 2010 compared to 2005? What is the trend in use of head CTs in patients with migraine?

38. February 12, 2012: I. Sim EHRs and Research Medical Informatics EHR/Data Repository Comparison Enterprise viewpoint more appropriate for QI and research Data repository cleans and aggregates data from multiple sources

39. March 6, 2012: I. Sim Research Informatics Epi 206 — Medical Informatics UCSF IDR First version: all UCare data from July 1, 2005 (Ucare roll-out) to mid-2011 –2.875 million records (not all unique) –5 Million encounter records -- manual refresh –Included inpatient data, Dentistry, some billing data, never got STOR/VA/Kaiser/THREDS data yet; APEX data to IDR as of Spring 2012

40. APeX-IDR Information Flow Shadow Server Clarity (Microsoft SQL Server) IDR (HIPAA Limited Data Set*) Epic Production Server (Chronicles Caché) MyResearch (Data Marts / Ontologies) Operational and financial reports Government-mandated reporting Research Patient care Staging Server (Microsoft SQL Server) Medical Center Network (Requires CHR Approval for Access) MyResearch Network (No CHR Approval Required) * “HIPAA Limited Data Set” = No PHI Except Dates of Service

41. IDR Demo Go to MyAccess (myaccess.ucsf.edu) Go to MyResearch Launch Cohort Selection Tool –might need to sign up for account first March 6, 2012: I. Sim Research Informatics Epi 206 — Medical Informatics

42. IDR User Interface UCare Cohort Selection Tool

43. Requesting Data Extraction https://redcap.ucsfopenresearch.org/surveys/?s= SMB9LXhttps://redcap.ucsfopenresearch.org/surveys/?s= SMB9LX –demographic data –diagnostic codes (ICD-9) admit, discharge, outpatient distinction? –procedural codes (CPT) –lab tests No medications yet CHR approval needed for getting identifiable information February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics

44. March 6, 2012: I. Sim Research Informatics Epi 206 — Medical Informatics Limitations Content limitations –“meds, orders, results” on track for March 2013 Search option limitations –ICD-9 terms are cumbersome; ICD-10 is coming –no labs on search interface –very little user support for the interface –no free text or NLP (natural language processing) search Other limitations –Diagnoses include primary, secondary, admit, discharge –Queries are for entire time period since start of IDR –Data is whatever comes out of APEX, errors exist (e.g. 97 married children under 10) Beware!

45. Current Plans on “IDR” A clinical data warehouse needed to serve both research and operational needs –build a new virtual warehouse Rebrand the IDR effort…Enterprise Data Warehouse –“IDR is associated with a project that has not met the requirements of the community; Trust in the product must be built.” (External Advisory Group, 2012) Re-do system architecture –costs: ~$7-12 million/yr for next 3 years Drive work using case studies –arthroplasty and ACOs, OB/neonatal database, etc February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics

46. UC Rex “IDR” of all 5 UC campuses –go live was Dec 31, 2012 –med and lab data loaded –some pilot projects underway February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics

47. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Summary EHRs for Research EHR does not always = easier clinical research –“Frankly, one of the biggest attractions to LastWord (aka UCare) is going to be a boon to clinical research. Information will be accessible in a much more uniform and complete way.” ex-SOM Dean Haile Debas, UCSF Daybreak, 2001 Coding is critical –standardized, coded data trumps free text especially important for research but most controlled vocabularies have insufficient clinical coverage and are difficult to use –automated methods possible in restricted or custom situations Data warehouses are only as good as (and sometimes worse than) the original data sources

48. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

49. Beyond the EHR -- discussion Patient care activities (e.g., orders, referrals, results review) Charting Billing Improving team communication Meeting regulatory requirements Clinical decision making Increasing revenue Clinical research Reducing practice variation Controlling clinician behavior Collecting “big data” to improve care practices Involving the patient in collaborative care Other February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics

50. For Patients http://healthdesignchallenge.com/ February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics

51. ccti.ucsf.edu 51 Context is Automatically Known

52. ccti.ucsf.edu 52 Data Query -- Labs, Notes, Xrays

53. ccti.ucsf.edu 53 Data Capture – Medications

54. ccti.ucsf.edu 54 Data Capture – Physical Exam

55. ccti.ucsf.edu 55 Assessment and Plan

56. ccti.ucsf.edu 56 Finishing Up

57. ccti.ucsf.edu 57 Advanced Medical “Home” 24/7/

58. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics “Meaningful Use” of EHRs EHR Features Affecting Research –functionality and adoption –naming data –getting data out (of APEX) Beyond EHRs Summary Outline

59. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Current State of EHRs HITECH driving adoption of yesterday’s fundamentally mis-conceived technology –lots of activity, churn, money, effort spent to meet Meaningful Use –level of data exchange being mandated is unlikely to improve care quality, decrease cost ACO era starting to align incentives –to drive and reward use of data for care, not billing –to magnify role of patient and teams –to diminish role of hospitals –to upend business roles, business models

60. February 12, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Major barriers still exist to EHR adoption EHR does not always = easier clinical research Coding is critical –standardized, coded data trumps free text especially important for research but most controlled vocabularies have insufficient clinical coverage and are difficult to use –automated methods possible in restricted or custom situations All signs point to coming disruptive change… Take-Home Points

61. February 12, 2012: I. Sim EHRs and Research Medical Informatics Next Class Clinical decision support systems Informatics for clinical research Disruptive change…