Piwek, Paul

Power, Richard

Williams, Sandra

English

Medical information is notoriously difficult to convey to patients because the content is complex, emotionally sensitive, and hard to explain without recourse to technical terms. We describe a pilot system for communicating the contents of electronic health records (EHRs) to patients. It generates two alternative presentations, which we have compared in a preliminary evaluation study: the first takes the form of a monologue, which elaborates the information taken from the patient’s EHR by adding explanations of some concepts and procedures; the second takes the form of a scripted dialogue, in which the content is recast as a series of questions, answers and statements assigned to two characters in the dialogue, a senior and a junior nurse. Our discourse planning method designs these presentations in tandem, first producing a monologue plan which is then elaborated into a dialogue plan

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Open Research OnlineThe Open University’s repository of research publicationsand other research outputsGenerating monologue and dialogue to presentpersonalised medical information to patientsConference or Workshop ItemHow to cite:Williams, Sandra; Piwek, Paul and Power, Richard (2007). Generating monologue and dialogue to presentpersonalised medical information to patients. In: Proceedings of the 11th European Workshop on Natural LanguageGeneration, 17-20 Jun 2007.For guidance on citations see FAQs.c© [not recorded]Version: [not recorded]Link(s) to article on publisher’s website:http://enlg07.dfki.de/Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukGenerating monologue and dialogue to present personalisedmedical information to patientsSandra Williams, Paul Piwek and Richard PowerDepartment of Computing ScienceThe Open UniversityWalton Hall, Milton Keynes, MK7 6AA, U.K.{s.h.williams, p.piwek, r.power}@open.ac.ukAbstractMedical information is notoriously difficult to con-vey to patients because the content is complex, emo-tionally sensitive, and hard to explain without re-course to technical terms. We describe a pilot sys-tem for communicating the contents of electronichealth records (EHRs) to patients. It generates twoalternative presentations, which we have comparedin a preliminary evaluation study: the first takesthe form of a monologue, which elaborates the in-formation taken from the patient’s EHR by addingexplanations of some concepts and procedures; thesecond takes the form of a scripted dialogue, in whichthe content is recast as a series of questions, answersand statements assigned to two characters in the di-alogue, a senior and a junior nurse. Our discourseplanning method designs these presentations in tan-dem, first producing a monologue plan which is thenelaborated into a dialogue plan.1 IntroductionIncreasingly, health service providers are storing pa-tient medical histories in machine-usable form – i.e.,in databases of Electronic Health Records (EHRs)– and are obliged by legislation to make this infor-mation accessible to patients. We explore here anapplication in which material from EHRs is selectedand organised for presentation to patients either asa monologue or as a script for a dialogue. Emphat-ically these are not intended to replace face-to-faceconsultation with a specialist. Rather, the aim is tohelp patients use consultations to better effect.We have constructed a natural language genera-tion (NLG) system that generates monologue or di-alogue scripts which are spoken by synthetic voices.If a patient were to listen to a review of her recentrecord (but not, of course, containing any new diag-noses of which she was previously unaware) it couldpotentially help her in her next consultation in anumber of ways:• by reminding her of her own case history;• by giving her practical examples of the meaningand usage of medical terms relating to her case;• by demonstrating how to ask practical medicalquestions relating to her case (dialogues only).We generate dialogue because it opens up newways of making rhetorical and argumentative infor-mation explicit (Piwek et al., 2005). Also, as Craiget al. (2000) and Cox et al. (1999) found, it wouldhelp the patient recall the current state of her treat-ment, it would encourage her to ask questions duringthe subsequent consultation by reminding her of theexact meaning of technical terms, and would give hermore confidence in expressing herself both in tech-nical language and informal language. It could alsosave doctors’ time by providing an additional sourceof explanation for patients.The CLEF system (Hallett and Scott, 2005) sum-marises EHRs of breast cancer patients for clini-cal staff. We have implemented a pilot system forpatients that selects and describes events from thesame EHR repository. At present the wording of ut-terances is somewhat stilted, since our initial focus ison designing a discourse planner for both monologueand dialogue. Below is an extract from a monologuegenerated by the current system.On November 27th you had an examination.The doctor found that you had lymphadenopa-thy in your right axillary lymphnodes. Lym-phadenopathy is swelling of the lymph nodeswhich the doctor can feel when you are exam-ined. The commonest cause of lymphadenopa-thy is infection. Lympadenopathy can also becaused by a build up of cancer cells withinthe lymph nodes. Axillary lymphnodes arerounded masses of tissue under the arm con-taining white blood cells.A dialogue script generated from the same datafollows. The characters are two nurses, one juniorand one senior, where the junior is an eager butnot very knowledgeable character while the senior isan experienced, trustworthy instructor whose wordscarry weight. The junior reads from a report whichcontains some terms that she does not understand;the senior nurse explains them.Junior Nurse: On November 27th she hadan examination.Senior Nurse: What did the examinationfind?Junior Nurse: The doctor found thatshe had lymphadenopathy. What is lym-phadenopathy?Senior Nurse: Lymphadenopathy is swellingof the lymph nodes which the doctor can feelwhen you are examined. The commonestcause of lymphadenopathy is infection. Lym-padenopathy can also be caused by a build upof cancer cells within the lymph nodes.Junior Nurse: The lymphadenopathy was inher right axillary lymphnodes. What are axil-lary lymphnodes?Senior Nurse: Axillary lymphnodes arerounded masses of tissue under the arm con-taining white blood cells.We have piloted the system with colleagues to findout whether monologue and dialogue would be suc-cessful at conveying information in the EHR. Manyuseful ideas for improving the system emerged, ascan be seen in Section 4.2 Generating monologues anddialogues2.1 InputThe system’s input comes from a relational databaserepresenting medical histories of simulated breastcancer patients. The patient simulator was devel-oped by Rogers et al. (2006) for the CLEF project.Database entries describe medical interventions, in-vestigations, problems, drugs and patients. Primarykeys provide relational links between database ta-bles in the usual manner; additionally, a Relationstable specifies explicit rhetorical relations – e.g., theevidential relation HAS FINDING which indicatesthat an investigation provided evidence for diagno-sis of a medical problem. Twenty types of relationare present in the Relations table, although our cur-rent prototype only describes a subset of them.2.2 Discourse planningDocument planning has two or four stages depend-ing on whether monologue or dialogue output is re-quired. First, we select content and arrange it intoa discourse structure. Second, we add explanationrelations. At this stage, the discourse structure iscomplete for monologue generation and the final taskis to recast it into a series of sentence templates.Discourse planning for dialogue output has two fur-ther stages: the third adds questions and answers,and the last allocates portions of the discourse struc-ture to each conversational partner and recasts theminto a series of utterance templates from which di-alogue turns are to be generated. These stages aredescribed in more detail below.Content selection (stage 1a) queries thedatabase for records on a particular simulated pa-tient. These are scanned for recent medical investi-gations and interventions that are linked to medicalproblems via explicit evidential and motivational re-lations present in the Relations table. Selection ofEHR data is the same for dialogue and monologue.Initial discourse structures for medicalepisodes are built from the selected content (stage1b). Fig. 1 (A) shows a medical episode where In-vestigation and Problem concepts are linked to eachother by a HAS FINDING relation and to a Lo-cus concept by HAS TARGET and HAS LOCUSrelations. The nodes in the diagram represent in-stances of concepts, while the arcs represent rela-tions. Instances generally represent records foundin database tables; here they are records from theInvestigation, Problem and Locus tables; the nodescontain all the fields that were present in the record,e.g. Fig. 1 (A) shows the “Name” fields “exami-nation”, “lymphadenopathy” and “axillary lymphn-odes”. The arcs in the diagram represents relationspresent in the Relations table mentioned above.Medical episodes like the one in Fig. 1 (A) are or-dered sequentially according to the date fields of theretrieved data.Explanations are added (stage 2) as shown inFig. 1 (B). The program consults a table of term def-initions and introduces EXPLANATION relationslinking instances of concepts to glosses that explainthese concepts. At present, we do not have an au-tomatic procedure for deciding which concepts re-quire explanations; concepts like “examination” areleft unexplained because they seem intuitively well-known, while less familiar concepts like “axilla” areexplained. A search in the BNC confirmed that rawBNC frequencies correspond well with our intuitions:e.g., “examination” has high frequency, whereas “ax-illa” has low frequency. Our technical term def-inition glossary currently has 73 entries compris-ing canned phrases and sentences from trusted sitessuch as Cancer Research UK patient informationpages (www.cancerresearchuk.org). Where appro-priate these have been simplified slightly and theirtenses have been changed.Questions and Answers are added (stage 3)through the relations RAISES Q (i.e., a conceptraises a question) and HAS ANS (i.e., a given ques-tion has an answer), as shown in Fig. 1 (C).Two question types are illustrated: firstly “What isit?” questions, which are subgraphs spanning EX-PLANATION relations; and secondly a “What wasfound?” question asking about a specific argumentin a specific relation (in this case about the find-ing of the HAS FINDING relation, i.e., the ProblemFigure 1: Stages in discourse planning.that the evidence suggested). Note that questionsask about both concepts and relations.Assignment to dialogue turns (stage 4) isachieved by mapping parts of the discourse structureto utterance specifications (i.e. questions, answersand statements) and by using dialogue templates toassign these to the dialogue partners. Below is alist of utterance specifications generated from Fig. 1(C). The resulting dialogue script is given in section1 where, for example, “On November 27th she hadan examination” realises the utterance specificationinform(Investigation).Speaker SpecificationJunior inform(Investigation)Senior question(HAS FINDING)Junior answer(Problem)Junior question(Problem.Name)Senior answer(Gloss)Junior inform(HAS LOCUS)Junior question(Locus.Name)Senior answer(Gloss)Note that a single turn can contain several movesand that both characters ask questions, thus avoid-ing ‘conversational ping-pong’ (Davis, 1998) — i.e.,exchanges where turns degenerate into nothing morethan a sequence of alternating questions and an-swers.2.3 Microplanning, realisation and outputThese are achieved through simple string processingwith reference to a discourse history which maintainsa list of entities mentioned so far, prohibits questionsand explanations being generated more than once,and allows determiners such as “another” to be gen-erated when an action is performed repeatedly. Theoutput is formatted as web pages with embeddedActive X commands to control text-to-speech. Ex-amples of the system’s output were given in section1.3 Pilot evaluationAs a preliminary test of our system, we piloted theeffectiveness of communicating medical informationthrough scripted dialogue and monologue by con-ducting a small evaluation with eleven colleaguesfrom The Open University, divided randomly intotwo groups, Group A (five people) and Group B (sixpeople).Materials: We generated a monologue and dia-logue from the same simulated patient’s EHR. Lo-quendo text-to-speech voice Kate read the mono-logue, while in the dialogue Simon read the juniornurse and Kate the senior. Section 1 illustrates frag-ments of the materials that were actually used in thepilot. A practice monologue was also produced byhand, with both voices reading alternate paragraphsfrom a cancer patient web site.Method: Both groups listened to the practicemonologue and answered questions about the voicesand a practice multiple-choice comprehension ques-tion. Group A then listened to the monologue andanswered multiple-choice questions. Afterwards,this group listened to the dialogue, stated their pref-erences, and were asked for comments. Group Blistened to the dialogue first, answered questions,listened to the monologue, stated their preferences,and added comments.Results: Participants’ comments were very infor-mative. People particularly did not like pronuncia-tions of technical terms and thought that the syn-thetic voices spoke too fast; these are problems thatcan be addressed by configuring the text-to-speechsystem. Furthermore, the general impression wasthat too much information was given too quickly, al-though one person commented that the dialogue wasbetter because information was presented in shorter“chunks”.Both groups performed well on comprehension(their mean scores out of 10 were 8.6 for Group Aand 7.5 for Group B), and an independent samplest-test showed no significant difference between them.A few people told us that they already knew someof the medical concepts queried by the comprehen-sion questions; however, performance on questionsfor which the answers could not have been knownin advance was also slightly (but not significantly)higher in Group A (monologue).A Pearson chi-square test showed no difference be-tween groups’ monologue/dialogue preferences. De-spite that, none of the participants were neutralabout the choice — i.e., none selected the option“I have no preference”.Discussion: The most important outcome is thatthe system ought to communicate information at aslower rate and present it in smaller chunks. Thiscould be achieved by adding conversational paddingthat would enable the density of new informationto be finely tuned. Piwek and van Deemter (forth-coming) have proposed a possible solution to thisproblem.4 ConclusionPatients accessing their EHRs have very differentneeds from doctors: configuring a suitable presen-tation is not merely a question of replacing techni-cal language. Doctors have to treat many patientsevery day, under severe time pressure; what theywant is a condensed version of the facts relating toeach individual case. Patients have plenty of time toview a single presentation of intense personal inter-est, and so are likely to prefer a gentler informationflow with informal touches (i.e., dialogue rather thanmonologue) and some easily assimilated instruction.We have not tried to eliminate technical terms alto-gether, but to give patients some mastery of techni-cal concepts that are highly relevant to their case.This is an original communication genre with subtleaims: the patient learns by viewing a conversationin which she is not addressed directly.Technically, the planning of the patient commu-nications is achieved through a process in which aninitial plan, similar to that for generating condensedreports for doctors (Hallett and Scott, 2005), is pro-gressively overlaid with further rhetorical and se-mantic content — first, by adding explanations, sec-ondly by recasting assertions as conversational re-sponse pairs, and thirdly by grouping moves intoturns. Our pilot evaluation suggests that the re-sulting dialogues are still too dense: they lack thereassurance that comes from a lighter, more discur-sive style, and some repetition of information thatis already familiar — the equivalent of small talk.There are obvious more superficial ways in which ourdemonstrator could be improved (e.g., using morepronouns instead of ponderous repetition of nounslike ‘lymphadenopathy’), but the crucial challengelies in pushing our approach to discourse planningmuch further, so that the content from the EHR isfolded into a plan that meets the special needs ofthis new genre.ReferencesCox, R., McKendree, J., Tobin, R., Lee, J. andMayes, T. (1999) Vicarious learning from dialogueand discourse: a controlled comparison. Instruc-tional Science 27: 431-458.Craig, S D., Gholson, B., Ventura, M., Graesser, A(2000) Overhearing Dialogues and Monologues inVirtual Tutoring Sessions: Effects on Questioningand Vicarious Learning. International Journal ofArtificial Intelligence in Education, 11, 242-25.Davis, R. (1998) Writing Dialogue for Scripts, A &C Black Ltd., London, ISBN 0-7136-4802-3.Hallett, C., and Scott, D. (2005). Structural vari-ation in generated health reports. Proceedings ofthe 3rd International Workshop on Paraphrasing.Piwek, P., Power, R., Scott, D., and van Deemter,K. (2005) Generating Multimedia Presentations:From Plain Text to Screen Play. In O. Stock andM. Zancanaro, eds., Multimodal Intelligent Infor-mation Presentation. Springer.Piwek, P., van Deemter, K. (forthcoming). Gen-erating under Global Constraints: the Case ofScripted Dialogue. Research on Language andComputation. Kluwer.Rogers, J., Puleston, C., Rector, A. (2006) TheCLEF Chronicle: Patient Histories derived fromElectronic Health Records. Proc. of the 22ndInternational Conference on Data EngineeringWorkshops , IEEE.

Generating monologue and dialogue to present personalised medical information to patients

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Generating monologue and dialogue to present personalised medical information to patients

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