Medical Informatics Congress 2004 Brussels, November 25, 2004 From Patient Data to Information Needs Loes Braun a, Floris Wiesman b, Jaap van den Herik a, Arie Hasman b, Erik Korsten c a Institute for Knowledge and Agent Technology, Maastricht b Academic Medical Center, Amsterdam c Catharina-ziekenhuis, Eindhoven

Presentation outline Introduction Modelling information needs Converting information-need templates Experiments and results Conclusions

Introduction Woman (84), shortness of breath, loss of consciousness Suspected respiratory tract infection: Clarithromycin No admission to ICU, condition worsened Patient died one day after admission to the hospital Suspected cause of death: pneumonia, cardiac failure Autopsy: severe acute pancreatitis Introduction Modelling Converting Experiments and results Conclusions

Information need: formulation of information which is needed to perform a particular task, but is missing Explicit: Information need one is aware of Implicit: Information need one is not aware of Introduction What are the alternatives for Amoxicillin? What are the side effects of Clarithromycin? Introduction Modelling Converting Experiments and results Conclusions

Aim: supporting physicians in retrieving patient-specific literature Task: making a physicians’ implicit information needs explicit automatically Based on the information needs, literature can be retrieved Problem statement: How to make physicians’ implicit information needs explicit in an intelligent automatic way? Introduction Introduction Modelling Converting Experiments and results Conclusions

A physician’s information needs can be made explicit by anticipating them Set of a physician’s potential information needs is required Problems: - set is hard to capture - new information needs are added to the set continuously Possible solution: modelling information needs Modelling Information needs Introduction Modelling Converting Experiments and results Conclusions

Step 1: Identifying information needs Literature survey 8 articles No particular group or geographical region Interviews with physicians 5 physicians 5 different specialisms Modelling Information needs 8Primary careSmith (1996) 2SurgeryReddy (2002) 10VariousJerome (2001) 32VariousGrundmeijer (1999) 16Primary careGorman (1995) 10Family careEly (1999) 77General practice, cardiology, pulmonology, allergologyDe Vries Robbé et al. (1988) 16Outpatient care, inpatient care, internal medicineCucina (2001) # INsIdentification domainSource Literature surveyInterviews 3SurgerySurgeon 3PulmonologyPulmonologist 0NeurologyNeurologist 1CardiologyCardiologist 2AnesthesiologyAnesthesiologist # INsIdentification domainSource Introduction Modelling Converting Experiments and results Conclusions

Step 2: Analyzing information needs Information needs have a certain structure Medical terms can be abstracted Modelling information needs Does Clarithromycin cause high blood pressure? text medical concept text medical concept Introduction Modelling Converting Experiments and results Conclusions

Step 3: Abstracting information needs Based on Ely et al. Replace medical concepts by UMLS Semantic Types Reduce multiple occurrences to one Modelling information needs text medical concept text medical concept Does Clarithromycin cause high blood pressure? Does [CHEMICAL] cause [SIGN OR SYMPTOM]? Norpace fatigue? Introduction Modelling Converting Experiments and results Conclusions Amoxicillinrash? Morphine nausea?

Results: 167 abstracted information needs: information-need templates Modelling information needs How high is the frequency of [FINDING] in case of [DISEASE OR SYNDROME]? How long should [THERAPEUTIC OR PREVENTIVE PROCEDURE] be given in case of [DISEASE OR SYNDROME]? What is the protocol for [DIAGNOSTIC PROCEDURE]? What is the incidence of [DISEASE OR SYNDROME]? Introduction Modelling Converting Experiments and results Conclusions

Converting information-need templates Information-need templates are not patient specific We aim to provide patient-specific literature Information-need templates have to be converted into patient-specific information needs Use of patient data is essential Introduction Modelling Converting Experiments and results Conclusions

Converting information-need templates Electronic Patient Record (EPR) Intensive Care Information System (1999) INAD Computers & Software B.V. Eindhoven, Werkgroep ICIS afd. Intensive Care, Dienst Informatievoorziening, Catharina Ziekenhuis Eindhoven Database with patient data & graphical user interface Hardly any standardization used Introduction Modelling Converting Experiments and results Conclusions

Converting information-need templates Is [CHEMICAL] indicated in a patient with [DISEASE OR SYNDROME]? Step 1: Selecting EPR-queries A list of EPR-queries was defined EPR-query indicates how data of a specific Semantic Type can be found A query is selected for each Semantic Type in the template PatientNummer= WHEREPatientNummer= WHERE OpnameIndicatieFROMMedicatieFROM IndicatieSELECTMedicijnSELECT Introduction Modelling Converting Experiments and results Conclusions

Converting information-need templates Is [CHEMICAL] indicated in a patient with [DISEASE OR SYNDROME]? [CHEMICAL]: Clarithromycin Amoxicillin-Clavulanic Acid Furosemide [DISEASE OR SYNDROME]: Respiratory tract infection Step 2: Executing EPR-queries to retrieve patient data Execution of EPR-queries is handled by database Results in active concepts for each Semantic Type Introduction Modelling Converting Experiments and results Conclusions

Converting information-need templates Is [CHEMICAL] indicated in a patient with [DISEASE OR SYNDROME]? Step 3: Instantiating templates with query results Applicable template: each Semantic Type has at least one active concept Systematically replace each Semantic Type by one of its active concepts for all possible combinations 1. Is Clarithromycin indicated in a patient with respiratory tract infection? 2. Is Amoxicillin-Clavulanic Acid indicated in a patient with respiratory tract infection? 3. Is Furosemide indicated in a patient with respiratory tract infection? Introduction Modelling Converting Experiments and results Conclusions

Experiments Formulation of information needs was tested on EPRs of 85 patients 167 information-need templates were used Resulting number of information needs divided into four categories: (1) no information needs (2) a manageable number of information needs (1-100) (3) a hardly manageable number of information needs ( ) (4) an unmanageable number of information needs (> 1000) Experiments and results Number of information needs formulated Number of patients Introduction Modelling Converting Experiments and results Conclusions

Future improvements Taking the stage of the medical process into account Taking the preferences of the physician into account Experiments and results - applicability of the information-need templates - relevance of patient data - gathering / monitoring information about the physician - monitoring which information the physicians examines Introduction Modelling Converting Experiments and results Conclusions

It is possible to model a physician’s information needs by using information-need templates Our approach for formulating patient-specific information needs automatically is feasible and generalizable The number of formulated information needs is rather high By formulating information needs continuously the number of information needs formulated at once will be reduced By taking two additional parameters into account the total number of formulated information needs may be reduced Conclusions Introduction Modelling Converting Experiments and results Conclusions