Ontology Based Semantic Recommendations for Discharge Summary Medication Information for Patients Mehnaz Adnan, Jim Warren, Martin Orr Department of Computer Science / National Institute for Health Innovation, University of Auckland, Auckland, New Zealand
[email protected] Abstract
discharge care. These summaries typically includes a synopsis of care provided, a list of discharge medications, relevant laboratory results, required follow-up along with the advice of ongoing management for patients. In this project we have been working with Waitemata District Health Board (WDHB), Auckland to produce more readable Electronic Discharge Summary (EDS) contents for consumers through interactive computer-based support both at the authoring and in the reading of EDSs. We randomly collected 200 de-identified EDSs, 50 each from medicine, surgery, emergency and older adult health services in two secondary care hospitals (North Shore and Waitakere hospitals) managed by WDHB. As a first phase in this research we have analysed the text characteristics of EDS contents and found that the patient advice section is a very brief component of the total document and does not provide complete information about the condition, treatment plan and medication side effects to patients. While the other sections of EDS which are primarily written for a clinical audience provide comprehensive information about in-hospital and post-discharge care but contain clinical jargon and abbreviations, hence imposing a comprehension barrier for a lay person [5]. In a follow-up hospital panel assessment [6], we found deficiencies in the quality of information, notably in the sections relating to ongoing management advice for patients and primary care physicians. In the second phase of this project, we used semantic annotations to provide consumer friendly synonyms and dynamic hyperlinks for the difficult text in EDSs to provide readability support for consumers [7] . In the current phase, we developed a Clinical Decision Support System (CDSS) by incorporating medication advice recommendations as interactive feedback that will allow EDS authors to utilize the discharge medication information at the point of authoring patient advice.
Patients’ limited knowledge about medications is a key factor in post-discharge adverse drug events. This can be improved by providing optimal advice to patients in discharge summaries for managing their post-discharge care. This paper presents a prototype system, Medication Information Decision Support System (MI-DSS), which brings together natural language processing and ontology based semantic annotations to develop a clinical decision support system to assist Electronic Discharge Summary (EDS) authors in providing medication advice to patients in EDSs. Our approach involved the selection and modeling of high risk discharge medications and their consumer related advice. We present our medication information ontology which models the medication knowledge necessary for consumers to manage their post-discharge self care. This ontology serves as the knowledge source to semantically annotate the EDS text and determine the medication-specific recommendations in the MI-DSS.
1. Introduction A recent study showed that 11% of patients had an adverse drug event following discharge from hospital, the majority of which were related to medication management [1]. Patients’ limited knowledge about their medications is identified as one of the problems responsible for post-discharge adverse drug events. This can be improved by educating patients about drug therapies, side effects and what to do if specific problem develops [1, 2]. A Discharge Summary (DS) communicates a post-discharge framework of care between hospital physicians and primary care as well as patients and their families [3]. Patient use their DS information for later consultation and self care management [4], therefore optimal advice for patients in these summaries is critical for successful post-
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In this paper, we present an ontology-based, Medication Information Decision Support System (MIDSS) to provide patient advice recommendations for a set of identified high risk discharge medications. The main component of the MI-DSS is a medication information ontology that models the medication knowledge specific to post discharge patient information which serves as knowledge source in providing medication-specific patient advice. We present the ontology engineering process that highlights the specification of our high risk discharge medication ontology in terms of clinical concepts and the relationships between the ontology concepts/classes and instances. We also describe the natural language processing approach employed to identify and semantically annotate the ontology concepts and instances in free text of EDSs. In the next section, we present the ontology model to express knowledge about the side effects, required patient actions and follow up for the identified high risk discharge medications. Section 3 describes the ontology-based natural language processing (NLP) and semantic recommendations in detail. The prototype implementation is described in Section 4. Section 5 discusses previous work relevant to this paper. Finally, Section 6 concludes the paper with some directions for future work.
2. Medication information ontology modeling Medication information ontology modeling involved selection of high risk discharge medications and identification of the medication knowledge needed to support consumers in their post-discharge self care. We reviewed available literature to identify medication classes and specific drugs to include in our ontology model which are attributable to causing most postdischarge adverse drug events. For modeling drug specific patient information we augmented the evidence from available literature with patient advice available on MIMS (www.mimsonline.co.nz) to create a standard advice text for each of the included medication. Next, difficult terms and phrases were replaced in these advice texts with their consumer friendly synonyms provided by the open-access collaborative Consumer Health Vocabulary (CHV) [8] to make the contents more readable for consumers. Finally, by using OWL and the Protégé ontology editing environment we create a high risk discharge medication information (MI) ontology model as shown in Figure 1.
Figure 1. Medication Information Ontology
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2.1. Specifying MI ontology classes
relationships among the classes using the class properties, and the approach can be best understood by following the modeling of this patient advice statement for a patient who has “Warfarin” in his discharge medications list: “Notify doctor in case of coughing up blood… your follow up is to test your blood for INR.” Side_Effect has an object type property called has_Side_Effect whose value is “Coughing_up_Blood” which is an instance to the class Warfarin_Side_Effect (Figure 1). As such, it can be inferred that if a patient is taking Warfarin then the advice to patient should include advice related to “coughing up blood,” and if not then the critiquing rules flag the absence of this concept for the author.
Three main classes, Patient, High_Risk_Discharge_Medication, and Patient_Information are created to model patient details, discharge medications and patient advice, respectively. Five child classes, Anticoagulant, Cardiovascular, Anti-infective, Analgesic and Corticosteroid are created for the High_Risk_Discharge_Medication class. One specific drug class is included as their child to model drug specific patient information. This hierarchy is shown in Medication Category Layer of Figure 1. For modeling medication patient information, we identified three main sub classes, Side_Effect, Patient_Action and Follow_up, each containing subclasses for medications included in Medication Category Layer and shown in the Medication Information Layer of Figure1.
3. Recommendation execution architecture The purpose of MI-DSS is to provide writing support for the EDS author at the point of authoring patient advice so that appropriate corrective actions can be taken when the text in the advice section is missing necessary advice. Based on the MI ontology concepts and the concept found in the EDS of the current patient, the MI-DSS is able to recommend the auto text as well as a critique on the missing concepts in the patient’s advice section, which may reduce the time and effort for the clinician in providing necessary information to consumers. Computing the patient advice content recommendation consists of two steps as shown in Figure 2. First, the Semantic Annotation engine annotates the MI ontology concepts and instances in the patient details, discharge medications and patient advice sections using ontology-based natural language processing in GATE (the General Architecture of Text Engineering [9]). The GATE pipeline used in this process is shown in Figure 2(a). The text is split into words by the GATE Tokenizer. For addressing term variance problems in free text a custom GATE Gazetteer was built which includes all the synonyms for the terms present in medication information instances using SNOMED CT and WorldNet [10] as vocabulary sources. Another challenge is to associate the related co-occurring terms to annotate patient information instances in a meaningful way and to filter the phrases indicating negated instances. For associating co-occurring terms we used GATE’s Part of Speech Tagger within the context of a sentence to identify and annotate Patient_Information instances. Finally, a JAPE grammar was written to annotate text chunks in the text as the instances of MI ontology classes and instances. The negated instances were filtered in an earlier phase of a JAPE grammar by a custom implementation of the NegEx algorithm [11].
2.1. Specifying class instances In the next step of MI ontology engineering, we specified the individuals for the conditional recommendations of the discharge medications advice. A list of side effects, patient actions and follow-up was created from the standard advice content to identify the individuals of classes in information layer of Figure 1 and added as instances of appropriate classes. For example, individuals for class Warfarin_Side_Effect include Abnormal_Urine, Severe_Headache, Diarrhea, Coughing_up_Blood, Easy_Bruisng and Abnormal_Bleeding. Similarly, individuals of class Warfarin include all of the brand names specified in MIMS, i.e. Marevan and Warfarin Sodium. In a few cases, the classes shared some instances, for example Diarrhea is an instance of two classes, Warfarin_Side_Effect and Amoxicillin_Side_Effect. 2.1. Specifying class properties Patient is the most important class in determining patient specific medication information in MI-DSS because the conditional recommendations are tailored for the current patient. To specify patient characteristics, we defined two properties, age and sex, to represent the patient profile – i.e. an instance of the Patient class. These properties are used to associate the recommendations with a patient profile. For example, contraception advice is included only if the patient is female and less than fifty years old. 2.2. Specifying relationships between the classes The MI ontology allows establishing inter-class relationships to represent the knowledge inherent within patient medication information. We modeled
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Figure 2. Patient Advice Recommendation Process Following is an example of JAPE grammar to annotate the instance of Coughing_up_Blood in the sentence “contact your doctor if you have blood in your sputum” in the patient advice section.
for the patient having an instance of Warfarin in their discharge medications is shown below: IF
Patient hasHighRiskDischargeMedication = Marevan (Individual of Warfarin) Patient_Information hasSideEffect = Diarrhea (Individual of WarfarinSideEffect) Patient_Information hasFollowup = INR (Individual of WarfarinFollowup) THEN Patient Advice Recommendation = Coughing up Blood, Severe Headache, Easy Bruising, Red or Brown Urine, Gum Nose Bleeding
(({Lookup.majorType=="blood"}) {Token.category=="IN"} {Token.category=="PRP$"} ({Lookup.majorType=="sputum"}) ):temp --> :temp.PatientAdvice {type = warfarinAdvice kind = coughing_up_blood"} The above rule specifies that one or more words having a Lookup annotation of type ‘blood,’ followed by a Part of Speech category of preposition (“IN”), possessive pronoun (“PRP$”) and a Lookup annotation of type ‘sputum’ should be annotated as PatientAdvice with type warfarinAdvice and kind ‘coughing_up_blood’. The Lookup annotations and Part of Speech categories are created by the gazetteer lookup module and GATE Part of Speech tagger, discussed above. The second step, ontology-based semantic recommendation, is shown in Figure 2(b). The semantic recommendation engine retrieves the EDS annotations from the semantic annotation engine and compares the retrieved annotations in the patient details, discharge medications and patient advice sections to execute the decision rules for advice recommendations. The decision rules to execute the recommendations, based on EDS annotations, were designed as forward IF-THEN rules consisting of a list instances of medication information which constitutes the conclusion of the rule. The example decision rule
In this way, the Recommendation engine creates a list of missing concepts in patient advice to relate with the patients’ discharge medications. Each step of the recommendation is performed by exploiting knowledge about the patient, discharge medications, and the patient advice. The ontology provides persistent storage and efficient retrieval of such knowledge.
4. Prototype implementation With the proposed recommendation approach, we built a semantic medication information recommender system using the GATE API and Java (JDK1.5). The workings of the MI-DSS involves the following: when the EDS author starts typing patient advice a reminder message generated by the system would say that the patient is having high risk discharge medication and offers to include the related medication advice in the patient advice section as an auto text. The EDS author
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ontology and semantic annotations for providing clinical recommendations to support clinical authors is a relatively new topic. Ontology based CDSS has only been investigated in a few systems in the past few years. SemMed [12] is designed to assist healthcare professionals in medication prescription by incorporating drug-drug interactions in an ontology. The proposed system takes diagnosis, medicine groups being taken by the patient and allergies as input and uses JENA rule engine to recommend the possible drugs which can be used to treat the given diagnosis for the current patient. Similarly, BCF-CDSS [13] is a decision support system which supports family physicians by providing breast cancer follow up and determining patient specific recommendations. In BCS-DSS, ontology is developed to model the breast cancer clinical practice guideline knowledge and the decision rules are implemented using JENA which infers recommendations based on ontology and patient data. Complementary to our approach, these systems use ontologies to model domain knowledge but take structured data to provide recommendations for physicians. In contrast, our system uses natural language processing as the basis of the semantic annotations, processing free clinical text to drive the execution of decision rules for providing EDS content recommendations. Our approach has a number of limitations. First, it uses only a limited set of specific drugs classed as high risk as reference drugs for recommendations. It has been noted that CDSS which generate a large number
can also enter any patient advice using the MI-DSS user interface (Figure 3) and in return is provided with a set of relevant critiques. For a 75-year-old female patient having discharge medications including Marevan and Prednisone, the system will look up their decision rules to compile related auto text based on the MI ontology classes, instances and properties. If the EDS author wants, the auto text is inserted in the bottom section of the Patient Advice window, which consists of the required patient action, medications side effects and follow up. EDS authors can also enter any advice from scratch or make any changes in the inserted auto text. When the user clicks the enter button to indicate that editing Patient Advice is finished, the system computes missing advice and prompts the user with a list of missing Patient_Information instances as per the ontology in the bottom window of the Patient Advice section.
5. Discussion In this paper we presented an ontology based medication information model and decision support system to promote the completeness of the patientspecific medication information in the patient advice section of EDSs to assist health care professionals in providing optimal medication advice for patients. There has been much work done in the area of CDSS over the past decade. The objects manipulated by CDSS include clinical guideline, medical problems and clinical workflow. Compared with these fields, using
Figure 3: MI-DSS User Interface
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hospital discharge," Journal of General Internal Medicine, vol. 20, Apr 2005, pp. 317-23. [2] M. J. Maniaci, M. G. Heckman, and N. L. Dawson, "Functional health literacy and understanding of medications at discharge," Mayo Clinic Proceedings, vol. 83, May 2008, pp. 554-8. [3] C. Walraven, "What is Necessary for High-Quality Discharge Summaries?," American Journal of Medical Quality, vol. 14, 1999, p. 10. [4] L. R. Maloney and M. E. Weiss, "Patients' perceptions of hospital discharge informational content," Clinical Nursing Research, vol. 17, Aug 2008, pp. 200-19. [5] M. Adnan, J. Warren, and M. Orr, "Assessing Text Characteristics of Electronic Discharge Summaries and their Implications for Patient Readability," in Second Australasian Workshop on Health Data and Knowledge Management 2010. [6] M. Adnan, J. Warren, and M. Orr, "Quality of Electronic Discharge Summaries for Post-Discharge Care: A Hospital Panel Assessment," in Health Informatics New Zealand (HINZ) Wellington, 2010. [submitted for review] [7] M. Adnan, J. Warren, and M. Orr, "Enhancing Patient Readability of Discharge Summaries with Automatically Generated Hyperlinks," Health Care and Informatics Review Online, December 2009. [8] A. Keselman, T. Tse, J. Crowell, A. Browne, L. Ngo, and Q. Zeng, "Assessing consumer health vocabulary familiarity: an exploratory study," Journal of Medical Internet Research, vol. 9, 2007, p. e5. [9] H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan, "GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications," in 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02), Philadelphia, July 2002. [10] A. M. George, "WordNet: a lexical database for English," Commun. ACM, vol. 38, 1995, pp. 39-41. [11] W. W. Chapman, W. Bridewell, P. Hanbury, G. F. Cooper, and B. G. Buchanan, "A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries," Journal of Biomedical Informatics, vol. 34, 2001, pp. 301-310. [12] A. Rodriguez, E. Jimenez, J. Fernandez, M. Eccius, J. M. Gomez, G. Alor-Hernandez, R. Posada-Gomez, and C. Laufer, "SemMed: Applying Semantic Web to Medical Recommendation System," in Intensive Applications and Services, 2009. INTENSIVE '09. , Valencia 2009. [13] S. Hussain, S. R. Abidi, and S. S. R. Abidi, "Semantic Web Framework for Knowledge-Centric Clinical Decision Support Systems," LECTURE NOTES IN COMPUTER SCIENCE, vol. 4594, 2007, p. 5. [14] H. van der Sijs, J. Aarts, A. Vulto, and M. Berg, "Overriding of Drug Safety Alerts in Computerized Physician Order Entry," Journal of the American Medical Informatics Association, vol. 13, 2006, pp. 138-147. [15] N. R. Shah, A. C. Seger, D. L. Seger, J. M. Fiskio, G. J. Kuperman, B. Blumenfeld, E. G. Recklet, D. W. Bates, and T. K. Gandhi, "Improving acceptance of computerized prescribing alerts in ambulatory care," Journal of the American Medical Informatics Association, vol. 13, Jan-Feb 2006, pp. 5-11.
of inappropriate alerts cause “alert fatigue” and hence are frequently overridden by clinicians [14]. However, it has been observed that CDSS generating high severity alerts have higher rates of clinicians’ acceptance [15]. For this reason, we chose key high risk drugs for patient advice recommendations to create alerts that are less likely to be ignored by the EDS author. Another limitation is that we used JAPE grammar to associate co-occurring terms or entities to annotate advice concepts which could lead to false positive annotations. Using machine learning with a large sample of data (much larger than our sample of 200 EDS) could ameliorate this problem.
6. Conclusion and future work In this paper we demonstrated a prototype ontologybased decision support system, MI-DSS, which generates semantic recommendations for authoring medication information in EDSs. The MI-DSS contains two sub engines, a semantic annotation engine and a recommendation engine. A semantic annotation and ontology based recommendation approach is also presented in this paper. As future work, we are including additional drugs in the primary high risk medication classes which are most frequently used in our sample data and have potential to impose postdischarge risks. A further future direction is to propose the incorporation and integration of this system with commercial EDS systems such as Orion’s Soprano application currently being used in WDHB. Another direction is not only to recommend consumer medication information but also give more information that would complete the patient follow up information, such as advice to General Practitioner and follow up plans. Finally, a more reliable natural language processing approach could be derived using machine learning techniques to address the performance and optimization issues of recommendations.
10. Acknowledgement This work was supported by a Higher Education Commission, Pakistan scholarship. The research protocol was approved by the University of Auckland Human Participants Ethics Committee under protocol number 2008/221 and by the Waitemata District Health Board Knowledge Centre.
11. References [1] A. J. Forster, H. J. Murff, J. F. Peterson, T. K. Gandhi, and D. W. Bates, "Adverse drug events occurring following
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