James Harrison, M.D., Ph.D. Associate Professor of Pathology

1. Temporal Sequence Analysis of Clinical Laboratory Results for Patient Follow-up and Effective Data Display James Harrison, M.D., Ph.D. Associate Professor of Pathology Faculty-in-residence, Center for Biomedical Informatics University of Pittsburgh jhrsn@pitt.edu Dept. of Electrical Engineering, Univ. of Pittsburgh February 25, 2004

2. Standard Clinical Data Display

3. Standard Clinical Data Display

4. Phenytoin levels Discharge

5. How does this happen? • Data reduction • Classification • Cross-sectional view • Loss of information

6. Outline for Today • Development and application of a simple temporal event monitor (LabScanner) • Extension of the event monitor: lightweight general-purpose temporal abstraction system (PROTEMPA) • Applications of temporal abstraction • Patient identification and monitoring in the clinical laboratory • Clinical data presentation and decision support

7. Simple Temporal Pattern Recognition • Goal: Process monitor with simple rule maintenance • Case identification for training, Q/A, clinical reporting, consultation • Clinical lab (TDM) perspective • Data access limited to text file export from LIS • No cross-correlation of primary data with other results • Constraints • Need to recognize short patterns, but data sparse and irregular • Underlying physiological model may be changing • Pre-analytic errors, time and procedural errors

8. A Pragmatic Pattern Detection Strategy • Define features of short “problem sequences” from inspection and annotation of real data (heuristics) • Model-independent, based on observable sequence features only • Derive a minimal set of sequence “templates” (Discovery Rules) designed to detect problem sequences • Implement Discovery Rules in data-scanning software

9. Simple Patterns in Patient Data:Sustained high and low values Phenytoin

10. Simple Patterns in Patient Data:Increasing and decreasing trends Phenytoin

11. Simple Patterns in Patient Data:Variability and Frequency Phenytoin

12. General Features of “Discovery Rules” • Pattern features • Value (state), trend, variability, frequency -- plus -- • Patient features • Age, gender, hospital location • Interval features • Number of events, max/min overall time span, max/min time between neighboring events

13. Example Discovery RulesIdentification of Basic Temporal Sequences ||Target (“therapeutic”) range. †Total pattern length < 28 days. JH Harrison and P Rainey. Am J Clin Pathol 1995;103:710-717

14. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? Example: Increasing values?

15. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? Example: Increasing values?

16. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? Example: Increasing values?

17. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves Example: Increasing values?

18. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule Example: Increasing values?

19. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule Example: Increasing values?

20. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule • Persistence phase: If immediately subsequent windows also completely satisfy the same rule, they are concatenated to form a single pattern (rule is “greedy”). Example: Increasing values?

21. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule • Persistence phase: If immediately subsequent windows also completely satisfy the same rule, they are concatenated to form a single pattern (rule is “greedy”). Example: Increasing values?

22. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule • Persistence phase: If immediately subsequent windows also completely satisfy the same rule, they are concatenated to form a single pattern (rule is “greedy”). • End: last point that satisfies the rule. Example: Increasing values?

23. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule • Persistence phase: If immediately subsequent windows also completely satisfy the same rule, they are concatenated to form a single pattern (rule is “greedy”). • End: last point that satisfies the rule. Example: Increasing values?

24. The “Sliding Window” Detection Algorithm • Window size defined in rule • Does the sequence of values within this window completely satisfy a rule? • Four parts of a positive match • Initiation phase: values that contribute to the pattern but do not completely satisfy it by themselves • Trigger and initiation phase together completely satisfy the rule • Persistence phase: If immediately subsequent windows also completely satisfy the same rule, they are concatenated to form a single pattern (rule is “greedy”). • End: last point that satisfies the rule. Example: Increasing values?

25. Initial Implementation: Data Input File downloaded from LIS Flat file import:

26. Rule Management • Pattern Types: • Value comparison • Trend • Variability • Frequency

27. Patient List and Display

28. Incidence of Patterns in Clinical Laboratory Data

29. Printed Reports

30. LabScanner Downloadhttp://jhh.opi.upmc.edu/labscanner/

31. Pattern Incidence in Ten Monitored DrugsJune - September, 2000 • 8311 patients, 35813 TDM values imported • Limit to patients with three or more values • 2927 patients, 27512 values scanned • 9% of values out-of-range high • 16% out-of-range low • 1071 patients with patterns (37%) • 673 patients with persistent patterns (23%) • Average persistence 5.9 days, 5.5 specimens • Average 37 new patterns per day

32. Resource Utilization Associated with Patterns • 52 Pediatric PTN patients with patterns (excluding freq.), Jan - Mar 2001 • 43 Ped. patients with at least 3 PTN values but no patterns (same period) Hospital Days Hospital Costs Resource utilization from the day of the first level to 4 weeks after the last level

33. Summary of Simple Temporal Abstraction • Patterns exist in drug level data that are not clearly recognized by clinicians or laboratories but may identify patients at risk for increased cost of care, suboptimal care, or medical error • Discovery rules can be developed and applied in software that identify patients showing such patterns • We have developed a software tool that can analyze data retrospectively or prospectively to detect such patterns • Large datasets can be processed (ca. 10,000 patients) • Identifying these patients may be useful for education, QA/QC, clinical process improvement, and consultative follow-up • Characteristics of the data patterns, such as persistence, may provide new QA indicators • Tool is limited to single instances of simple patterns; cannot correlate patterns within or across types of events

34. Digoxin Levels 6 5 4 Digoxin (ng/ml) 3 On Quinidine 2 1 Digoxin started 0 0 10 20 30 40 50 Hosp. Day Complex Temporal AbstractionsAggregating Simple Patterns Through Relationships The temporal abstraction task has the goal of abstracting high level concepts from time-stamped data. Digoxin-Quinidine interaction Rising digoxin Declining digoxin Rising digoxin High digoxin On digoxin On quinidine time

35. PROTEMPAA Temporal Abstraction Engine • PRoblem-Oriented TEMPoral Analysis • A symbolic rule-based software framework for specifying, detecting, and visualizing temporal patterns in time series data • Supports simple and complex temporal abstraction • Complex abstractions may include results from multiple clinical laboratory tests or other events • Modular, Java-based; may be implemented stand-alone or in a server environment

36. Temporal Pattern RelationshipsSimple Abstractions Combine to Form Complex Abstractions Disjoint Equals Meets Overlaps Coincident Starts Patterns have: Their own basic characteristics Relationships to other patterns Ends During Contains

37. Features of PROTEMPA Rules • Patient features • Age, gender, location • Interval features • Max/min overall span, max/min point-point distance • Optional fixed min/max start and min/max finish times • Simple pattern features • Value (state), trend, variability, frequency (others possible) • Rule Chain (complex abstraction) features • Listing of contributing simple patterns and their relationships • Specification of at least one of the following defines a relationship: • Max/min time between starts and/or ends • Max/min times between start1-end2 and/or end1-start2 • Represented as 8-tuple • Negated patterns

38. Temporal Constraint NetworksA framework for storage of rule chain data • Nodes are start and end points of intervals • Constraints define spans and relationships of intervals • Constraint values are maintained in matrices • Efficient algorithms are available to test network consistency

39. PROTEMPA Processing Sequence • Locate simple pattern intervals (sliding window) • Aggregate intervals into complex abstractions via constraint network consistency checking • Continue aggregation until no new rule chains are identified Simple Abstraction Modules (Math or stat functions across sequences) Value Complex Abstraction Module (Temporal Constraint Networks) Trend Identified Intervals Report Patterns Raw Data Rule Chain Matching Output Variability Frequency Others Re-analysis

40. PROTEMPA Framework Dataset Input Scanner Engine Temporal Pattern Detector Output Pattern Matches Rules Database Input Encode as Visualize matches as Patterns of Interest Selection Criteria

41. PROTEMPA Stand-alone Interface

42. Application: PatientPatterns • Designed to allow the clinical laboratory to specify temporal patterns for monitoring and follow-up • The PROTEMPA framework is implemented as a Java servlet running in a JBoss/MySQL environment • LIS transactions transferred and scanned every six hours • Rule chains identify situations appropriate for follow-up • Laboratory personnel log on via the web to view found patterns

43. PatientPatterns Interface Prototype implementation: Pattern detection engine runs as a servlet; accesses laboratory data in MySQL DB via JDBC; user interaction through the Web.

44. Rule Entry and Editing

45. Interval Relationship Specification

46. Prototype Web Display

47. Application: Adaptive Clinical Displays • Lab data displays are usually tabular and static • Sequence, form and proximity of displayed data affects decision-making • Graphical data display is best for decisions requiring quantitative comparisons or sequence assessment under time pressure • Clinically significant situations may be recognizable by processing data at display time • Automated aggregation and optimal display of data based on the content of the data may improve clinical decision-making (a form of decision support) • Project funded by NLM; October, 2003

48. Adaptive Display Evaluation Protocol • Set of validated clinical cases for diagnostic training • Extend with one week hospital course including “diagnostic cues” • Physician subjects review and write orders under time pressure using alternative clinical displays • Cognitive analysis: think-aloud protocol, videotape • Transcriptions/tapes coded for searching vs. integration/evaluation activities • Evaluate orders for completeness and accuracy

49. Alternative Displays for Investigation Control: Cerner’s new web display, flowsheet component • Tabular text, static • Organized by laboratory Test: Semigraphical display • PROTEMPA processing on query • Display organization prioritized by rule chains based on data content (“decision cluster”) • Default display format for each test (graphics or text) controlled by rule chains based on data content Both offer similar data access after default display

50. Adaptive Display Hypotheses • The adaptive display will provide aggregated, rapidly-interpretable data for decision-making (a “decision cluster”) • Data viewing and decision-making will occur primarily in the adaptive portion of the display • Cognitive load will be decreased (fewer steps) and the ratio of searching/integrative steps will decrease • Orders will be more complete and accurate • Diagnostic cues will be recognized more frequently