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  1. 1. Clinical decision support, medical knowledge representation and workflow technology A seminar in clinical informatics – a subfield of biomedical informatics Vojtech Huser MD PhD RetroGuide
  2. 2. Agenda <ul><li>Clinical Informatics </li></ul><ul><li>Knowledge representation in medicine </li></ul><ul><ul><li>Clinical Decision Support </li></ul></ul><ul><li>Workflow Technology (WT) overview </li></ul><ul><ul><li>Application of WT (my research) </li></ul></ul>http:// www.linkedin.com/in/vojtechhuser
  3. 3. General research interest <ul><li>Research in: Clinical Informatics </li></ul><ul><ul><ul><li>Biomedical informatics (Medical Informatics) </li></ul></ul></ul><ul><ul><li>Lifetime Electronic Health Record (EHR) </li></ul></ul><ul><ul><li>Computable knowledge representation </li></ul></ul><ul><ul><ul><li>Clinical Decision Support systems </li></ul></ul></ul><ul><ul><ul><li>Quality improvement </li></ul></ul></ul><ul><ul><ul><li>Medical research using retrospective data </li></ul></ul></ul>http://clinicalinformatics.stanford.edu/scci_seminars http://en.wikipedia.org/wiki/Clinical_informatics informatician (vs. informaticist)
  4. 4. 1/3: Clinical informatics
  5. 5. Biomedical Informatics <ul><li>Medical Informatics </li></ul><ul><li>+ Genomics (bioinformatics) = BMI </li></ul><ul><li>BMI definition: </li></ul><ul><ul><li>Scientific field that deals with biomedical information, data and knowledge – their storage, retrieval, and optimal use for problem solving and decision making. </li></ul></ul>
  6. 6. Spectrum of fields
  7. 7. Clinical Informatics
  8. 8. Key CI journals <ul><li>Journal of the American Medical Informatics Association </li></ul><ul><li>Methods of Information in Medicine </li></ul><ul><li>Journal of Biomedical Informatics </li></ul><ul><li>Journal of the International Medical Informatics Association </li></ul>
  9. 10. CI diagram
  10. 11. Browse the article
  11. 12. 2/3: Knowledge representation in medicine (sub-topic within clinical informatics)
  12. 13. Medicine <ul><li>Extremely complex field (“black box”) </li></ul><ul><ul><ul><ul><li>(vs. law) </li></ul></ul></ul></ul><ul><ul><li>Sub-specialties </li></ul></ul><ul><li>Represent </li></ul><ul><ul><li>general human biology or anatomy (declarative) </li></ul></ul><ul><ul><ul><li>there are 2 types of lymphocytes: T and B </li></ul></ul></ul><ul><ul><li>clinical knowledge (how to diagnose, treat, deal with the patient) (procedural) </li></ul></ul><ul><ul><ul><li>untreated diabetic keto-acidosis leads to death </li></ul></ul></ul><ul><ul><ul><li>for severe asthma – the treatment plan needs to include maintenance medication and emergency medication </li></ul></ul></ul>
  13. 14. Knowledge representation <ul><li>Knowledge (about a human) </li></ul><ul><ul><ul><li>Ejection fraction (EF) is the fraction of the end-diastolic volume that is ejected with each heart beat. </li></ul></ul></ul><ul><li>Facts (about a particular patient) </li></ul><ul><ul><ul><li>John Smith’s EF changed from 63% (43 old) to 39% (74 old). John Smith’s had pace maker implanted at age 65 and a period of uncontrolled hypertension (48-64). </li></ul></ul></ul>
  14. 15. Three KR domains <ul><li>1. Terminology </li></ul><ul><ul><ul><li>Kidney is an organ in abdomen </li></ul></ul></ul><ul><ul><ul><li>Possible kidney diseases </li></ul></ul></ul><ul><li>2. Facts </li></ul><ul><ul><ul><li>John Smith’s “coded” medical history </li></ul></ul></ul><ul><li>3. Knowledge in Clinical Guidelines </li></ul><ul><ul><ul><li>Diagnosis and management of patients with cough </li></ul></ul></ul><ul><li>Common challenge: computable knowledge representation is difficult </li></ul>
  15. 16. 1. Medical terminologies <ul><li>Clinical: </li></ul><ul><ul><li>SNOMED-CT, LOINC, ICD9-CM </li></ul></ul><ul><li>Administrative: </li></ul><ul><ul><li>CPT, DRG </li></ul></ul><ul><li>Research: </li></ul><ul><ul><li>UMLS, GO, MeSH, NCI Thesaurus </li></ul></ul>
  16. 20. 2. Facts <ul><li>Knowledge about patients </li></ul><ul><li>Data only research on electronic health record </li></ul><ul><ul><li>no intervention, only existing data </li></ul></ul><ul><ul><li>De-identification of data </li></ul></ul><ul><ul><li>IRB approval (waiver of consent) </li></ul></ul>
  17. 21. Clinical data Additional terminologies
  18. 22. 3. Knowledge in Clinical Guidelines <ul><li>Why? </li></ul><ul><ul><li>Physician’s mind capacity is limited </li></ul></ul><ul><li>What is it? </li></ul><ul><ul><li>Executable representation format which is managed/authored by physicians </li></ul></ul><ul><li>Challenge: </li></ul><ul><ul><li>Treatment concepts, therapeutic steps </li></ul></ul><ul><ul><li>linkage to EHR data (different data granularities) </li></ul></ul>
  19. 23. Examples <ul><li>On each encounter, check compliance with medications, take asthma history, record peak flow, look at asthma diary, check inhaler technique, and assess asthma state. For patients taking short-acting β2 agonists and low-dose steroid inhalers, if asthma is not under control, consider stepping up to a medium-dose of steroid or adding a long-acting β2 agonist. </li></ul><ul><li>Patients in the alternative regimen group should receive Adriamycin 60mg/m2 IV every 21 days for 4 cycles, along with Cytoxan 600mg/m2 IV every 21 days for 4 cycles. Patients who are estrogen-receptor positive will receive tamoxifen PO for 5 years. Delay administration of Adriamycin and Cytoxan if there exists > grade 1 granulocytopenia on day 1. </li></ul>
  20. 24. Executable Guidelines <ul><li>Clinician’s decisions (goal, alternatives) </li></ul><ul><li>Temporal dimensions of actions and data </li></ul><ul><li>Abstractions (granulocytopenia) </li></ul><ul><li>Degree of uncertainty </li></ul><ul><li>Targeted recipient (MD, RN, patient) </li></ul><ul><li>Normal case and exceptions </li></ul><ul><li>Medical knowledge vs. events/actions (+participant) </li></ul><ul><li>Two example medical knowledge representation standards </li></ul><ul><ul><li>ARDEN Syntax </li></ul></ul><ul><ul><li>Glif </li></ul></ul>Tu (1999)
  21. 25. Arden Syntax <ul><li>Text based format </li></ul><ul><ul><ul><li>Clinician-friendly syntax </li></ul></ul></ul><ul><li>ANSI approved standard </li></ul><ul><li>Several versions </li></ul><ul><li>Existing vendor implementations </li></ul><ul><li>repository of DSS modules at Columbia U </li></ul><ul><ul><li>283 DSS modules </li></ul></ul>
  22. 26. browse MLM file
  23. 27. GLIF <ul><li>Developed by InferMed collaboration </li></ul><ul><li>Task-network model </li></ul><ul><li>Protégé as the guideline editor </li></ul><ul><li>2 types of graph </li></ul><ul><ul><li>Action map </li></ul></ul><ul><ul><li>Decision map </li></ul></ul><ul><li>Rule-in, rule-out criteria </li></ul><ul><li>Has important similarities with EON, SAGE </li></ul><ul><li>Engine component (experimental vs. actual use) </li></ul>Wang, (1999, 2004) Boxwala, (2004)
  24. 29. GLIF guideline (task-network model) Wang, (1999, 2004) Boxwala, (2004)
  25. 30. End of part 1 and 2
  26. 31. Transition slide <ul><li>Ultra-short questions? </li></ul><ul><li>Summary of part 1 and 2 </li></ul><ul><li>How is this relevant to the CIBM program? </li></ul><ul><ul><li>Bioinformatics (sequence analysis) (health inf. systems) </li></ul></ul><ul><ul><li>Computer Science (intro to data structures) </li></ul></ul><ul><ul><li>Biology (organic chemistry) </li></ul></ul><ul><li>Focus on clinical medicine (last year’s patients) </li></ul><ul><ul><li>Current knowledge: Improve care </li></ul></ul><ul><ul><li>Research settings: Find patients (to collect samples, apply results) (type 2 translation, CTSA, bench to bed) </li></ul></ul><ul><li>The “human clinome project” </li></ul><ul><ul><li>continuously changing library of declarative and procedural knowledge that is computable (can be accessed by computatational means) </li></ul></ul><ul><ul><ul><ul><li>Y.Shahar (Methods of Information Medicine, 2008/4) </li></ul></ul></ul></ul>
  27. 32. 3/3: Workflow Technology (WT) (overview and my research)
  28. 33. Publications http://healthcareworkflow.wordpress.com
  29. 34. Automating Workflow <ul><li>Workflow Management Coalition (WfMC) </li></ul><ul><ul><li>www.wfmc.org </li></ul></ul><ul><ul><ul><li>Terminology and Glossary </li></ul></ul></ul><ul><ul><ul><ul><li>http://www.wfmc.org/standards/docs/TC-1011_term_glossary_v3.pdf </li></ul></ul></ul></ul><ul><li>Workflow </li></ul><ul><ul><li>The automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules. </li></ul></ul><ul><ul><ul><ul><ul><li>WfMS = Workflow Management System </li></ul></ul></ul></ul></ul><ul><li>BPM = Business Process Management </li></ul><ul><ul><ul><ul><ul><li>BPMS = Business Process Management System </li></ul></ul></ul></ul></ul>
  30. 37. Minimum components language editor execution engine
  31. 38. eComm (worklist handler)
  32. 39. Examples of WT use in healthcare Bed management Infections control (MRSA) J. Emanuele and L. Koetter, &quot; Workflow Opportunities and Challenges in Healthcare ,&quot; in 2007 BPM & Workflow Handbook, 2007. L. Koetter, &quot; MRSA infection control with workflow technology ,&quot; Spring AMIA Conference, Orlando, FL, 2007. R. Hess, &quot; The Chester County Hospital: Case Study ,&quot; in 2007 Excellence in Practice: Moving the Goalposts., 2007.
  33. 40. Stroke guideline (WfMS) Quaglini, Panzarasa, et al. (2000,2001,2003,2007)
  34. 41. GLIF guideline (task-network model) Wang, (1999, 2004) Boxwala, (2004)
  35. 42. Examples of workflow editors and engines http://healthcareworkflow.wordpress.com
  36. 44. http://healthcareworkflow.wordpress.com
  37. 46. WT software components <ul><li>Core components </li></ul><ul><ul><li>Editor </li></ul></ul><ul><ul><li>Engine </li></ul></ul><ul><li>Additional components </li></ul><ul><ul><li>Administration application (deplay, terminate, versioning) </li></ul></ul><ul><ul><li>Worklist handler </li></ul></ul><ul><ul><li>User management (LDAP, MS, other) </li></ul></ul><ul><ul><li>Organizational roles </li></ul></ul><ul><ul><li>Monitoring/Analytical application </li></ul></ul><ul><ul><li>Simulation tools </li></ul></ul><ul><ul><li>Workflow mining </li></ul></ul>
  38. 47. Interesting analyses <ul><li>Improving processes </li></ul><ul><ul><li>Allocation of tasks </li></ul></ul><ul><ul><ul><li>Push (human decides) /pull (machine) strategy </li></ul></ul></ul><ul><ul><ul><ul><ul><li>(push to all or to one and then escalate) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Earliest due date, first-in first-out </li></ul></ul></ul><ul><ul><ul><li>Rules: (1)let a resource practice its specialty; (2) do similar task in succession; (3) flexibility of staff (“save the generalist”) </li></ul></ul></ul><ul><ul><li>Bottlenecks </li></ul></ul><ul><ul><ul><li>Number of cases in progress </li></ul></ul></ul><ul><ul><ul><li>Case completion time </li></ul></ul></ul><ul><ul><ul><li>Level of service (customers) </li></ul></ul></ul><ul><li>BPR = business process re-engineering </li></ul>
  39. 48. Workflow mining <ul><li>Traditional approach </li></ul><ul><ul><li>model your process, pilot, deploy </li></ul></ul><ul><li>Alternative </li></ul><ul><ul><li>Take existing event data </li></ul></ul><ul><ul><li>Mine process definition </li></ul></ul><ul><ul><li>Delta analysis </li></ul></ul><ul><ul><ul><li>Discovered process (current) vs. Human modelled process (goal, dream design) </li></ul></ul></ul><ul><ul><ul><li>Migration strategy </li></ul></ul></ul><ul><ul><li> www.processmining.org </li></ul></ul><ul><ul><ul><li> ProM (SourceForge) </li></ul></ul></ul>http://healthcareworkflow.wordpress.com
  40. 49. 3/3: Application of WT (my research)
  41. 50. HL Scenario
  42. 56. Clinical Guidelines Quality Improvement measures
  43. 57. RetroGuide (RG): key concepts <ul><li>Works with retrospective data </li></ul><ul><li>Flowchart layer + code layer (applications) </li></ul><ul><li>Set of RG external applications (RGEAs) </li></ul><ul><li>Single patient execution model (DSS) </li></ul><ul><li>Works with time ordered chart </li></ul><ul><ul><li>Concept of current position in EHR </li></ul></ul><ul><li>Resembles manual chart review </li></ul><ul><li>Use of variables to remember data </li></ul><ul><li>Procedural modeling approach (rather then declarative) </li></ul>
  44. 58. Application areas <ul><li>Research </li></ul><ul><ul><li>Hodgkin's lymphoma and pregnancy </li></ul></ul><ul><ul><li>Hepatitis C </li></ul></ul><ul><li>Decision support </li></ul><ul><ul><li>Computerized glucose management protocol </li></ul></ul><ul><ul><li>Adverse Drug Events (naloxone, respiratory failure) </li></ul></ul><ul><li>Quality improvement </li></ul><ul><ul><li>Osteoporosis </li></ul></ul><ul><ul><li>Cholesterol management </li></ul></ul><ul><ul><li>Blood pressure control in diabetics </li></ul></ul><ul><li>Additional areas of interest (analysis) </li></ul><ul><ul><li>perinatal care, pneumonia, course of care for diabetes, AMI, Barrett’s esophagus, critical labs alerting, chronic kidney disease </li></ul></ul>
  45. 59. Reports generated by RetroGuide
  46. 60. 1. Summary report (population)
  47. 61. 2. Detailed report (execution trace)
  48. 62. 3. Individual patient view
  49. 63. More RetroGuide information
  50. 64. RetroGuide Evaluation <ul><li>Comparison of RG vs. SQL </li></ul><ul><ul><ul><li>Non-expert analyst as subjects (18 participants) </li></ul></ul></ul><ul><li>A) Quantitative results </li></ul><ul><ul><li>Statistically significant difference in scores </li></ul></ul><ul><ul><ul><li>paired t-test, 2-sided </li></ul></ul></ul><ul><ul><ul><ul><li>RG: 11.1 ± 1.8 vs. SQL: 6.3 ± 2.1 (p<<0.0001) </li></ul></ul></ul></ul><ul><li>B) Qualitative results </li></ul><ul><ul><li>Which technology do you prefer (SQL or RG)? </li></ul></ul><ul><ul><ul><ul><li>94% of participants preferred RG </li></ul></ul></ul></ul><ul><ul><li>and why do you prefer it? </li></ul></ul><ul><ul><ul><ul><li>1. easy to learn/use/understand </li></ul></ul></ul></ul><ul><ul><ul><ul><li>2. temporal modeling capabilities </li></ul></ul></ul></ul><ul><ul><ul><ul><li>3. more intuitive/natural/logical </li></ul></ul></ul></ul>
  51. 65. WT, guidelines, RetroGuide <ul><li>Goal: Use of WT within an EHR system </li></ul><ul><li>Why? </li></ul><ul><ul><li>Clinicians are willing to review a flowchart </li></ul></ul><ul><ul><li>Knowledge management should by done by clinicians </li></ul></ul><ul><li>Retrospective mode </li></ul><ul><ul><li>Search EHR data without any intervention </li></ul></ul><ul><ul><li>Identifying opportunities </li></ul></ul><ul><ul><li>Refining the logic </li></ul></ul><ul><ul><li>Demonstrating the potential value to consumers, administrators, physicians </li></ul></ul><ul><li>Prospective mode </li></ul><ul><ul><li>Intervention at the point of care </li></ul></ul>
  52. 66. Clinical informatics context <ul><li>Query EHR data (.sql, .sas, .java --> flowchart) </li></ul><ul><ul><li>Working with lifetime EHR data </li></ul></ul><ul><ul><li>Discovering limitations of this data </li></ul></ul><ul><li>Model decision support </li></ul><ul><ul><li>Sharing problem (flowchart, cross-industry technology) </li></ul></ul><ul><li>Other knowledge domains: </li></ul><ul><ul><ul><li>Quality improvement </li></ul></ul></ul><ul><ul><ul><li>Clinical research </li></ul></ul></ul><ul><li>Knowledge management (and acquisition) </li></ul><ul><ul><ul><li>Clinician-friendly technology </li></ul></ul></ul>
  53. 67. Examples
  54. 68. RetroGuide
  55. 71. http://healthcareworkflow.wordpress.com
  56. 72. Examples using Marshfield Clinic Data
  57. 75. Conclusion <ul><li>Summary </li></ul><ul><ul><li>Clinical Informatics </li></ul></ul><ul><ul><li>Knowledge representation </li></ul></ul><ul><ul><ul><ul><li>Executable Clinical Guidelines </li></ul></ul></ul></ul><ul><ul><li>Workflow technology and my research </li></ul></ul><ul><ul><ul><ul><ul><li>Retrospective analysis of data </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Plans for prospective implementation </li></ul></ul></ul></ul></ul><ul><li>Questions ? </li></ul>