IPDRA: Intelligent Patient Data Review Assistant Presented to Walter Reed Army Institute of Research By Jim Ong / Stottler Henke 22 March 2002 Stottler Henke Smarter Software Solutions

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Stottler Henke  40 person applied research / advanced development company  San Mateo, CA  Seattle, WA  Boston, MA  Core competency: artificial intelligence (AI)  Intelligent tutoring and knowledge management  Decision-support  Intelligent information access and data mining  Planning and scheduling

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Project Goals Help clinicians review patient data more quickly and effectively before and during patient encounters Patient databases: current approaches –Uniform web browser user interface to relational database (web application server generates html) –Provides source-oriented data views –Minimal use of graphical data presentation Limitations of current approaches –Time-consuming to pick out relevant patient data –Hard to integrate and interpret data spanning multiple pages.

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Project Team Stottler Henke Jim Ong (PI) Software developers Consultants Jane Dowling David Fram Stephen Porter Seth Powsner Ida Sim AI, clinical & drug safety DBs, data visualization Java, web computing, AI programming nursing, info architecture, med device UIs medical databases, data mining, data visualization emergency medicine, pediatrics, clinical systems UI psychiatry, medical data visualization and software internal medical, informatics for evidence-based medicine

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Phase I Project Activities Review: –Literature: Concept queries (Zeng), graphical patient summaries (Powsner/Tufte, Plaisant/Schneiderman) –Other patient DB systems: CHCS, ICDB, Logician, –Relevant technologies: Apelon, UMLS, 3M Define requirements –Questions: clinical goals, info needs, desired capabilities –Methods: project team discussions, clinician questionnaires, observations, abstract user needs Define and analyze technology approaches Preliminary technical and operational design Implement and demo software prototype

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Design Goals and Assumptions Clinicians are busy before, during, after visits Organizations and clinicians vary –preferences for data content and presentation method –willingness to delegate / trust –proficiency (using software and visualizing data) IPDRA should: –adapt to the user, patient, type of visit, problem, etc. –be predictable and understandable –be extensible and maintainable –provides default behavior via automation using KB –give precedence to user-supplied rules and decisions –encourage best clinical practices

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Example Clinician Data Review Goals current health, recent treatments, and response patient data related to current problem notice other patient problems requiring attention health maintenance identify compliance issues identify patient conditions affecting treatment selection graph of most recent vitals, labs, medications, If current problem = headache, review cardiovascular disease risk factors if BP>135/85, see BP meds and/or other BP readings to determine if patient not Dx with HTN or inadequately treated immunizations up to date? last pap, mammogram/breast exam, psa, etc. compare data about when patient came for refill (stored in pharmacy DB) with treatment plan allergies, other meds, contraindications

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Type of visit Type of user Current problems Age / Gender Individual user Relevance of Patient Data Depends Upon Routine, Well, Acute, Emergency primary care, cardiology

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Example: routine visit Current concerns Health state Last regular check up: Chronic conditions: Health maintenance: Current medications: Current allergies: Current vitals: Current weight: Is anything bothering this individual? Patient’s perception of own health status? When was it? Any issues that require monitoring? Known Dx? Related symptoms and health state. Recommended testing based on age and gender. Results of previous health maintenance studies. Prescriptions? Over the counters? Alternatives? To what meds, environmental/food elements. Reactions? Heart rate and BP compared to normal limits. Compared with 1 year ago and with norms

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Example: problem visit / known issue Current concerns Health state Last problem visit: Health maintenance: Current medications: Current vitals: Current weight: Is anything bothering this individual? Patient’s perception of own health status? Of illness- specific health status? Change since last assessment? What were the active issues? What was the intervening plan of action? (testing, treatment, etc.) Test results? What testing is required for health maintenance on this problem? Have meds been altered from last visit? Patient response to any change in regimen? Refills required? Any blood tests required based on current meds?. Heart rate and BP compared to normal limits. Compared with 1 year ago and with norms

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Approach Modular, re-usable data display objects (text and graphics) –Hierarchy of objects: pages, panels, components –Components retrieve and display data Manual authoring of display objects via configuration (w/o programming) Automated creation of display objects via rules Incremental learning of data display rules Concept-oriented queries Support best clinical practices by displaying clinical guidelines data

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Plaisant/Schneiderman LifeLine Time-aligned display of clinical events (timepoints) and states (time intervals)

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Powsner/Tufte Patient Summary High density display of many quantitative variables

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Patient Overview Using Data Summarization User can click on each data summary (e.g., active problems) to request detailed view

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Authoring of Display Objects User configures display component to specify data content and appearance 1 IBUPROFEN 600 MG TAB Qty: 90 for 30 days MAGNESIUM OXIDE 400 MG Tqy: 90 for 90 days ACETAMINOPHEN 325 MG TAB: Qty: 100 for 30 days User includes components in a display panel to create reusable set of related data components 2

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Authoring of Display Objects (2) IBUPROFEN 600 MG TAB Qty: 90 for 30 days MAGNESIUM OXIDE 400 MG Tqy: 90 for 90 days ACETAMINOPHEN 325 MG TAB: Qty: 100 for 30 days T P R BP 96.7 F /80 display components display panels User include panels in a page 3

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Rule-driven Creation of Display Objects Configuring components Adding components to a panel Adding panels to a page Including pages in a patient report If Then set property P to value V If Then add component C to panel P If Then add panel P to page Page-1 If Then add page P1 to report R Conditions can be: –simple comparisons (if visit type = “well”) or complex data patterns that apply medical relationships –highly-specific or broad

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Mix Authored and Auto-Gen Display Objects Page A Page BPage C Always included Conditionally included Panel 1Panel 4Panel 3Panel 2Panel 5 C1C2C3C4 C5C6C7C8C9C10 Always display this standard page Sometimes display this standard page Always display these alerts

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Incremental Learning of Data Display Rules User can identify data object requested ad hoc and –define a rule that specifies when to include the data object within its parent object –add the data object to its parent unconditionally

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Medical Concept Queries Query = medical concept (candidate Dx, body system, drug) Approach explored by Zeng & Cimino, mixed results Issues: scalability, data granularity, source of medical relationships

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March UMLS: Unified Medical Language System Medical knowledge base –lexicon –concepts –relationships (is-a, part-of, etc.) Developed by Lexical Technologies (now, Apelon) with funding from the Nat’l Library of Medicine Can be used by IPDRA to support concept queries: User enters candidate Dx 1 IPDRA identifies related body system 2 IPDRA identifies and presents display objects related to body system 3

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March Capability: Support Best Clinical Practices Target value for clinical variable –BP, LDL, hemoglobin A1C Recommended diagnostics and therapies –Mammograms, flu vaccinations Target drug levels

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Software Implementation Library of display components, as Java classes –data retrieval –data presentation –data navigation (to next page) Run-time system, as Java applet class Authoring tool for creating display objects Rule base and editing tools Knowledge base and editing tools (may include 3rd party KBs, like UMLS)

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Architecture Display objects Knowledge Base Rule Base Patient Database IPDRA run- time system IPDRA authoring tool KB Editor Rule Editor

IPDRA: Intelligent Patient Data Review Assistant Stottler Henke / 22 March IPDRA Phase II: Access ICDB Data HL7 CHCS database ICDB database ICDB web app server IPDRA web app server