Empowering patients with essential information and communication support in the context of diabetes

International Journal of Medical Informatics (2006) 75, 577—596

Empowering patients with essential information and communication support in the context of diabetes Chunlan Ma a, Jim Warren a,∗, Patrick Phillips b, Jan Stanek a a

Advanced Computing Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia b Department of Endocrinology, The Queen Elizabeth Hospital, Adelaide, Australia Received 15 December 2004 ; received in revised form 1 September 2005; accepted 2 September 2005 KEYWORDS Consumer health information; Internet; Tailoring; Doctor—patient relationship; Diabetes

∗

Summary Objectives: Patients with diabetes need to be aware of essential information to be involved in decision-making, manage diabetes properly, and communicate with doctors and other healthcare providers effectively. We have developed Violet Technology (VT) to provide features beyond previous health information tailoring systems by dynamically prioritizing diabetes learning topics and providing integrated direct support for patient-provider communication through formulation of individualized agendas to take to healthcare encounters. Methods: A particular feature of the VT approach is a Diabetes Information Profile (DIP) that models psychosocial and educational exposure features, as well as clinical characteristics, and considers expressed patient information preferences and recent information browsing history. The agenda facility recommends questions that the patients may have based on their profile, as well as helping to initialize a patient empowerment protocol. The technology uses a modular and extensible approach for key components, including consumer health information, prioritization rules, and methods of instantiating the DIP. VT has been implemented into a web portal for patient use. Two phases of evaluation studies have been conducted to collect patient and healthcare provider feedback. Results and conclusions: Results indicate that VT prioritizes relevant and important information for individual patients. Moreover, both patients and providers indicate that formulating an agenda of questions is important for patients. More extensive system use is needed to establish if the technology can deliver an improved patientprovider partnership and, ultimately, improved health outcomes. © 2005 Elsevier Ireland Ltd. All rights reserved.

Corresponding author. Tel.: +61 8 8302 3446; fax: +61 8 8302 3988. E-mail address: [email protected] (J. Warren).

1386-5056/$ — see front matter © 2005 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ijmedinf.2005.09.001

578

1. Background Although comprehensive guidelines for diabetes management have been developed, many patients with diabetes do not achieve optimal outcomes and still experience devastating complications due to the current healthcare system, which is designed for acute rather than chronic disease [1]. In diabetes management, more than 95% of diabetes care is done by patients at their home base [2]. Patients need to set their own goals and priorities within the context of their health issues, living environment and family demands. Health professionals have little control on how patients manage their diabetes. The reality is that patients do not normally receive necessary training or support for effective care from the current health care system [2]. A qualitative study showed that people with diabetes generally have a poor knowledge of care and that there is inconsistency in the way information is delivered to patients [3]. As a result, there is a gap between the ideal doctor—patient partnership and what actually occurs, and this gap forms a source of frustration for both patients and healthcare professionals [4]. To close this gap, facilitating the collaborative relationships between doctors and patients — ‘‘doctor—patient partnership’’ — and fostering patient-centered practices are the key [1]. In diabetes management, doctor—patient partnership in both diabetes care and doctor—patient communication improves patients’ compliance and outcome [2,5,6]. This partnership can be perceived as health professionals bringing expertise about diabetes and its treatment, and patients bringing their expertise about their own life. A shared decisionmaking model has been developed for achieving the doctor—patient partnership [7,8]. In this model, the information exchange between these two parties is in two ways. The patient is put at the center of the healthcare process and patients have to take their own responsibilities in this process. Consequently, patients need to be empowered with essential knowledge and skills in order to be involved in decision-making, manage diabetes properly, and communicate with doctors and other healthcare providers (such as nurse educators and pharmacists) effectively. Patient empowerment is defined as helping the patient discover and develop the inherent capacity to be responsible for one’s own life [1]. Tailored and personalized online information is considered one of the approaches for empowering patients and consumers to enable the partnership [9]. Lewis [10] reports that computer-based education is an effective strategy for transferring knowledge and skill development for patients with

C. Ma et al. chronic disease. However, she says that there has been little research aimed at providing customized information that is flexible enough to adapt to the dynamic nature of patients’ ongoing information needs and changes in their personal health and social circumstances. We believe this deficiency relates to a number of factors: (1) neglecting patients’ change in knowledge of the specific disease; (2) largely ignoring patients’ psychosocial and lifestyle status, i.e., considering only clinical factors; (3) the absence of information prioritization in current tailoring systems; and (4) disregarding patients’ information preference in the information tailoring. In addition, provision of personalized information only indirectly supports patients in their formulation of questions. Unvoiced agenda during the health encounter is a common and serious problem leading to poor doctor—patient communication [11—13]. We believe the patients should be directly supported in articulating their questions to formulate as agendas for subsequent encounters with healthcare providers. Our research aims to formulate an information technology (IT) framework to support doctor—patient partnership through: (1) providing essential information to individual patients—– information that is not only relevant, but also prioritized; and (2) providing, in close integration with the information prioritization, direct support for patients to generate personalized agendas prior to scheduled health visits. We have designed, implemented and undertaken preliminary evaluation of a web portal for diabetes patients. The suite of integrated components we have developed to create this portal is called Violet Technology (VT). The VT components are described in the next section. Section 3 describes the web portal itself in terms of overall architecture and user interface. Section 4 describes our two phases of evaluation study undertaken to date—–the first aimed at evaluating the prioritization of information topics, and the second a field trial of the integrated portal. Section 5 discusses our findings, including comparison with other technologies reported in the literature. Section 6 provides summary conclusions and directions for further research.

2. Violet Technology The innovative technologies that have been developed for implementing our approach to IT for patient-provider partnership include: a comprehensive Diabetes Information Profile (DIP); information tailoring and prioritization algorithms (supporting an Information Service); quiz tailoring and prioriti-

Empowering patients with essential information and communication support zation algorithms (supporting a Quizzing Service); and agenda personalization algorithms (supporting formulation of patient question sets as an Agenda Service). Collectively, the implementation of these approaches, including its embodiment in a web portal for diabetes patients, is called Violet Technology. VT has an extensible architecture based on Extensible Mark-up Language (XML) and Java technologies. This section describes the design of the major VT components.

579

The second part is the patient’s information browsing history, i.e., the IDs of information items that the patient has opened at least three times on different sessions, and each viewing time lasting for at least 10 s. The third part is the information about the patient’s information preferences. The fourth part is the information about the patient’s quizzing history—–his/her answers to quiz questions. The final part is the information about the patient’s history of agenda generation. The utilization of each part of the DIP is detailed in the next section.

2.1. Diabetes Information Profile 2.2. Adaptation mechanisms The essential role of user modeling is to support adaptation of the system to the needs of the user according to the model [14]. Sources of a relevant patient profile for a health application include a patient’s Electronic Medical Record (EMR) [15,16] and medical history collected through computerized medical forms filled out by patients (as in [17]), or from both patients and health providers (as in [18,19]). Our approach uses both EMRs and computerized forms filled out by patients, the latter providing additional more patient-centered information, covering a broader range of issues than those usually contained within the EMR. The portal provides a service for the patient to view and edit this information. We have developed a comprehensive user profile called the Diabetes Information Profile. This profile consists of five parts (see Table 1). The first part is information about a patient’s diabetes-related situations, such as current lifestyle, physical and laboratory examination, medications and so forth. Table 1

The system’s adaptive mechanisms include the information service (provision of tailored information), the quizzing service (provision of tailored quiz) and the agenda service (provision of tailored agenda question pool). The adaptation mechanisms of the information service have two stages: filtering and prioritization (see Fig. 1). Filtering retains the information and quiz questions that the patient needs to know and that which is nice to know, and removes information and quiz questions that are irrelevant in light of the patient’s profile. After the filtering process the contents of the user specific information and quiz questions are all relevant to the user’s profile. Prioritization matches the patient’s profile to the prioritization rules so as to assign appropriate weights (numeric scores) to the relevant information and quiz questions. Prioritization rule matching uses Boolean comparison of rule criteria to the DIP. The weights assigned to information items or quiz questions are used to sort

Components of the Diabetes Information Profile (DIP)

Profile component

Contents of each component

Diabetes-related situation

• • • • • • • • •

History of information browsing

• IDs of the information items that have been viewed each time for at least 10 seconds and the corresponding date. • IDs of the information items with patient’s self rating (not interesting, somewhat interesting, interesting, very interesting) and the corresponding date. • Dates, quiz question IDs and results of the patient’s answers to quiz questions.

Patient information preference History of quizzing History of agenda generation

Current lifestyle Diabetes educational exposure Diabetic complication risk factors Psychosocial issues Risk factors of diabetic complications Behaviour change due to diabetes Self blood glucose tests Physical and laboratory examination Medications

• Dates and the descriptions of agenda questions added/created by patients. • Dates and the descriptions of the agenda questions deleted by patients. • Dates and agenda list generated by patients.

580

C. Ma et al.

Fig. 1 Process of information/quiz questions filtering and prioritization using Violet Technology (VT).

content in a meaningful order. There is no filtering process involved in the mechanisms of the agenda service. The following sections introduce the adaptation mechanisms of the three services.

2.3. Mechanisms of the information service Filtering and prioritization are applied, respectively. 2.3.1. Information filtering algorithm We apply straightforward filtering rules that first eliminate major topics of information that are irrelevant (e.g., female issues for men), then perform finer-grained filtering of items and sub-items (e.g., remove discussion of insulin if it is not used by the particular patient). The filtering rules have only two outcome values: relevant and irrelevant. The patient’s profile is matched against these rules. The mapping results determine the actual contents of the user specific diabetes information resource, i.e., only true result makes the corresponding information topics, items or sub-items are copied to the user specific diabetes information—–an XML document. It is important to note that the information filtering approach does not mean that the information must be inaccessible to a patient interacting with a system based on this method—–the information stopped by the filtering will not be presented as priority information, but can still appear, for example, on a table of contents. This is significant with respect to user control (to access what they want), will be important to some users who would otherwise be disoriented by perceived incompleteness of available information, and is essential to support a user who may, for instance, be looking up information for their spouse.

Table 2

2.3.2. Information prioritization After filtering out the irrelevant information, the next step is to prioritize the relevant information for the patient. The question is how to assign a priority (i.e., weight) to an information item. Beeney et al. mentioned in [20] that it might not be sufficient to only rely on the perceptions of a health professional to determine the educational priorities for the patient with diabetes. Beeney’s study shows GPs (General Practitioners, i.e., family physicians) significantly overestimate complications as a concern for patients at diagnosis. A patient’s emotional supports and information preference should be considered when individualizing his/her educational priority. According to the suggestions from the literature [21,22], a patient’s acute physiological needs, self-beliefs and knowledge level on diabetes should be taken into account in setting priorities for information needs. Consequently, we categorize the prioritization rules into three groups: significant data oriented prioritization rules (SigPRule); patient’s knowledge level oriented (KnowPRule); and patient’s information preference oriented (PrefPRule). SigPRule: Significant data mean the factors that have the greatest adverse effects on diabetes management outcome (e.g., excess alcohol consumption, smoking, high fat diet) or that represent an urgent situation needing to be dealt with (e.g., hypoglycemia or the presence of ketoses in urine). In each rule, there are criteria for significant data and corresponding information IDs (UMLs Concept Unique Identifiers [CUIs]) with appropriate predefined weight. Table 2 shows one of the SigPRule contents. The rules are represented in an XML document format. The recommendations in Diabetes & You—–The essential guide [23] provide our starting point

Sample content of one SigPRule

Name of the rule

Criteria

Information IDs (UMLS CUIs)

Weight

Hyperglycemia

Blood glucose test (before meals): >=8 mmol/L & <=10 mmol/L

C0020456 (Hyperglycemia), C0020615 C0020456 (Hypoglycemia Hyperglycemia)

0.8

Empowering patients with essential information and communication support for the rules and information items. This book, published by Diabetes Australia, is a professionally vetted and endorsed reference for people with diabetes. In terms of how to determine the value of the weight assigned to the related information item(s), the severity and likelihood of consequences, as well as the preventable or actionable nature of the issues, are considered. We have implemented about 25 different types of significant data-oriented prioritization rules. The rules cover the patient’s blood glucose level, excess alcohol consumption, smoking, high fat and sugar diet, frequency of dining out, traveling, checking feet, psychosocial issues due to diabetes, body mass index (BMI), HbA1c (glycosylated hemoglobin, which is a measure of blood glucose control), lipid profile, blood pressure, podiatry test, and risk factors of retinopathy, neuropathy, vascular disease and nephropathy. These rules represent our knowledge engineering of the domain, based on: firstly, the content of the source reference [23] itself; secondly, a review of the related medical literature; thirdly, the understanding of the clinically-trained authors (Ma, Phillips and Stanek); and fourthly, an extensive review with other healthcare providers and consumers, notably diabetic nurse educators at The Queen Elizabeth Hospital (TQEH) Diabetes Centre. However, the rule content and in particular the weights the rules assign, must be viewed as a matter of opinion. KnowPRule: Our information prioritization takes into account the patient’s estimated diabetes knowledge level in terms of educational exposure. This stands in contrast to a proficiencyadapted hypermedia educational system (e.g., for learning a programming language [24]), where a student’s knowledge is estimated by evaluating the student’s attainment of key concepts and concept-related practices. However, patient education is not a linear learning process. Rather it is a situation-centered learning process. For example, if a patient knows how to reduce fat in one’s diet, it does not necessarily mean that the patient must know the amount of food he/she needs a day, or the symptoms of hyperglycemia. For this reason, we do not directly use quizzing to estimate knowledge level. We categorize both patients and all information items into three levels. With respect to patients, Level 1 means the patient knows little or nothing about diabetes. Level 2 means the patient knows basic information and survival information about diabetes, but does not know enough about diabetes self-management. Level 3 means the patient knows almost everything about dia-

581

betes self-management and feels quite confident in their knowledge level. With respect to information items, Level 1 contains the most basic and necessary information for a newly diagnosed patient, such as explanation of diabetes, survival information, insulin injection and available essential resources for support (National Diabetes Supply Scheme, Diabetes Centre, etc.). Level 2 contains further necessary information to perfect the patient’s self-management skills, including label reading, diet management and insulin dose adjustment. Level 3 mainly contains information on diabetic complications, such as retinopathy and neuropathy. The correspondence of the patient’s estimated knowledge level and the rated level of the information item provides a basis for prioritizing relevant information according to patient knowledge as well as significant data. We assign patients to knowledge levels, 1, 2 or 3 on the basis of diabetes educational exposure. Table 3 illustrates the exposure details and how weight for information items is assigned on the basis of the match of patient level and information level. In addition to considering the knowledge level, we use currency to indicate the patient’s familiarity with a piece of information. If a patient has read an information item several times, this item will no longer be relevant to this patient. If a patient has opened an information item three times in three different log-ins, and each time the item is opened for more than 10 s, this information is assigned a weight of −1. For knowledge refreshing purposes, this weight is valid for 120 days. PrefPRules: apart from the above factors, patients’ information preferences are also considered during the prioritization. Patients can rate each piece of information as Not interesting, Somewhat interesting, Interesting, and Very interesting, and the corresponding weight is −1, 0, 0.5 and 1, respectively. We simply use the sum of all the four priorities (significant data, knowledge level, currency and preference), as the final weight to sort the information items at this time.

2.4. Mechanisms of quizzing service The source of quiz questions, answers and explanations is ‘‘Novo Nordisk: Diabetes Web World1 ’’. Like the information service, this quizzing service uses filtering and prioritization. The quiz filtering rules are matched against the patient’s DIP. Only if 1

http://www.yourdiabetesworld.com/health/dwk/ info/ydww/index.asp.

582

C. Ma et al.

Table 3 Educational exposure collection questionnaire—–assessing patient knowledge level and weight assignment to information items by level Educational exposure data collection questionnaire items

Median score of the educational exposure questionnaire

Knowledge level

Information level 1

2

3

• Do you discuss your diabetes-related problems with your GP? (Scores: 0—3)

Median sore <2

Level 1

0.8

0.5

0.2

• 2 <= median score <3 OR • Patient has ever been a carer for a person with diabetes OR • Patient is/was a diabetes related health professional

Level 2

0.2

0.8

0.5

Median score >= 3

Level 3

0.5

0.2

0.8

• How many education sessions on diabetes have you attended? (Scores: 0—5) • How many times have you seen a diabetes educator? (Scores: 0—5) • How many times have you seen a dietician to learn about a diabetic meal plan or diet? (Scores: 0—5) • Are you a diabetes-related health professional (e.g., GP, nurse, pharmacist) or have you ever been a carer for a person with diabetes? • How frequently have you sought diabetes information from other resources, such as diabetes information booklets, TV, magazine, or library, etc.? (Scores: 0—3)

the matching result is true, the corresponding quiz questions are copied to an XML file—–‘‘Tailored quiz questions.’’ However, a number of quiz questions are not modeled in the quiz filtering rules and are directly copied to the ‘‘Tailored quiz questions’’ file. The prioritization mechanisms of the Quizzing service uses three mapping functions: significant data mapping; patient’s educational exposure mapping; and mapping the patient’s response to quiz questions. If a patient answers a question correctly, it may be assumed that the patient knows the question-specific knowledge. Therefore, this question will be given a weight of −1 in order that it will not appear next time when the same patient takes the quiz; this weight has a valid period of 120 days (after which the question may regain priority as a ‘refresher’ for the patient if still relevant according to the profile).

2.5. Mechanisms of agenda service Four sources form the patient-specific agenda question pool: (1) the patient’s greatest difficulties in diabetes management; (2) the patient’s diabetes-

related issues; (3) the information items that the patient added during information browsing; and (4) the questions that the patient created for herself/himself. The first source is the initialization of a patient empowerment protocol. This protocol was designed to help patients participate in decisionmaking by developing their knowledge, skills, attitudes and degree of self-awareness [25]. It has been shown that both patients and health professionals responded positively to an empowerment protocolbased program [26]. Furthermore, the empowerment protocol philosophy was able to establish doctor—patient partnership and patient-centred practices, and improve doctor—patient communication and diabetes management outcomes [1]. Thus, we prompt for information to initiate the patient empowerment protocol as the first component of all agendas. For the second source, if a patient is found to match any of the significant data rules, these issues are put into the agenda question pool. The third source relates to the patient explicitly selecting, via the portal interface, that an information item should be added to his/her agenda. The fourth source consists of questions authored by the patient in the agenda section of the portal, pre-

Empowering patients with essential information and communication support sumably outside of the question pool offered by our knowledge base.

3. The VT Web portal A web-based portal using the VT has been implemented. The following sections introduce its architecture and how the adaptation services are presented to users.

3.1. Architecture The web portal is an adaptive system. The information presented is changed dynamically with the change of each user’s DIP. Extensible Markup Language is used to model: the consumer diabetes information base; the tailored, patient-specific diabetes information; the quiz questions pool; the tailored, patient-specific quiz questions; and the patient-specific agenda pool. In addition, the information filtering and prioritization rules, the quiz filtering and prioritization rules are also XMLstructured. Extensible Stylesheet Language Transformations (XSLT) and XML Path Language (XPath) are used to copy, refer to, and present part of the XML documents. JAXP and SAX in JAVA technology (j2sdk-1 4 2 version) and Xalan (version 2.02 , for processing of XML documents) have been employed in implementing the information-tailoring prototype. Fig. 2 shows the web portal’s high level architecture. The system inputs are the consumer diabetes information (CDI), quiz question pool and patient as a user, and the outputs are the presentation of the tailored diabetes information, tailored quiz questions and tailored agenda question pool. The DIP collector collects data about the patient’s DIP (see Table 1). Generally speaking, there are two ways of user profile collection: explicit and implicit. Explicit collection involves asking the user to provide the information directly, while implicit collection occurs when the system observes and uses user—system interactions to infer the patient’s situation. In this web portal, except for collecting the history of a patient’s information browsing, profile collections require the patient to fill in corresponding questionnaires. All collected data are put into the DIP. The DIP is stored in a relational database implemented by mySQL. The Information inference engine generates the Tailored & prioritized information — an XML document — on the basis of the patient’s DIP. Likewise, 2

Xalan-J 2.0 Design, http://xml.apache.org/xalan-j/design/ design2 0 0.html.

583

the Quiz inference engine generates the Tailored & prioritized quiz questions. Both inference engines have two stages: filtering and prioritization. The DIP evaluator determines whether the patient’s DIP satisfies the specific condition required by the Information prioritization rules. The Agenda generator creates XML-structured agenda questions, including questions generated by the patients themselves, system recommended questions and the patients’ previous agenda lists. The VT web portal uses the XSLT processor to transform the XML-structured Tailored & prioritized information, Agenda question pool and Tailored & prioritized quiz questions based on the corresponding transformation scripts.

3.2. User interface The portal has three main adaptation services that can be accessed by diabetes patients using the system over the Web: Information Service, Quizzing Service and Agenda Service. 3.2.1. Information Service The Information service provides the patient with relevant and prioritized information. Fig. 3 shows a screen snapshot of this service. The diabetes information source is modeled hierarchically, from category, topic, to item. The menu bar at the left hand side provides the top five important information topics. The menu bar displays five prioritized information topics at a time. The ‘‘More’’ link under the top five information topics brings the patient to the next five important topics. The main window displays the details of one topic. Information items of each topic are also prioritized and ranked in the descending order of importance (see ‘‘Most important to you’’ in Fig. 4). Similarly, five information items are presented at one time. Patients can go to the next five information items by clicking the ‘‘More’’ link. The ‘‘Stars’’ indicate the importance assessment of the corresponding information item. The ‘‘Thumbs Up’’ conveys the patient’s rating of the information item. If the patient has recently viewed the item a number of times, the ‘‘Stars’’ fade. Patients can also go through the information items of each topic one by one by using the ‘‘Next’’ and ‘‘Previous’’ links. There are two actions users can undertake with each information item. The ‘‘Rate this information’’ button at the bottom of the main window allows users to rate the presented item. The system will use the patient’s rating to re-rank the order of the tailored information items. The other action is that the patient can add the information items into the agenda by clicking the ‘‘Add to my agenda’’ button.

584

C. Ma et al.

Fig. 2 High level architecture of the VT Web portal.

3.2.2. Quizzing Service The quiz questions are also ranked in descending order of importance based on the patient’s DIP. The question description, the right answer, the patient’s answer and the further explanation of the answer are displayed. The purpose of the Quizzing Service is not to test patients’ diabetes knowledge, but rather to provide an alternative way of learning. If the patient’s answer is correct, this particular question will not be shown on the next patient log-in.

3.2.3. Agenda Service The four components of the Agenda Service have been introduced in Section 2.4. Fig. 4 shows the first and second components of the Agenda Service. The first component asks a patient to indicate: ‘‘What part of living with diabetes is the most difficult or unsatisfying for you?’’ This question initializes a patient empowerment protocol [25]. The second component is the patient’s potential questions detected by the VT system.

Empowering patients with essential information and communication support

585

Fig. 3 Snapshot of the Information Service.

This component shows both the questions and the patient’s status that are related to the questions. For example, Fig. 4 shows the first question of the second component is the ‘‘Vascular disease risk factors,’’ and the patient’s status of the vascular disease risk factors are high blood

pressure, smoking, and abnormal total cholesterol. Fig. 5 shows the third and fourth components of the Agenda Service, and the patient’s previous agenda lists. The third component is the information selected by the patient during information

Fig. 4 First two components of the Agenda Service: (1) incorporation of the patient empowerment protocol; and (2) patient’s potential issues.

586

C. Ma et al.

Fig. 5 Final two components of the Agenda Service: (1) the information items selected by the patient; and (2) patient’s own questions.

browsing. The fourth component is the agenda questions created by the patient herself/himself. For example, one of the questions (or ‘issues’) of this component is ‘‘How to adjust insulin’’. The patient’s previous agenda list allows the patient to review previous asked questions or re-select the previously asked questions for the next agenda list. For each component, the patient is able to delete the questions they think are no longer useful. However, if the patient deletes the diabetes-related issues (i.e., the questions of the second component), the deleted issues will appear again after 3 months since the deletion time. The changes in a patient’s DIP are the only way to change the diabetes-related contents issues permanently. Apart from the above adaptation services, the portal also provides the patient with the entire diabetes information knowledge base without any filtering and prioritization. This facility gives the patient user-controlled access to the total information along the lines of a traditional table of contents.

4. Evaluation of the Violet Technology Two phases of evaluation have been conducted. The first phase assessed the validity of the information tailoring and prioritization algorithms in isolation, seeking patient and healthcare provider feedback for algorithm improvement. The second phase of evaluation involved a field trial using the VT web portal with patients as end users. All participants in these two evaluation studies were recruited from

the Endocrinology Department and the Diabetes Centre of a metropolitan hospital (The Queen Elizabeth Hospital, Adelaide, South Australia). Both phases received approval by the human research ethics committees of the University of South Australia and the Adelaide North West Area Health Service.

4.1. Phase 1 evaluation: validity of information tailoring and prioritization algorithms 4.1.1. Study design and methods The evaluation participants were patients with diabetes (a random sample of those identified as diagnosed in the past year through the hospital database, recruited by mail-out with telephone follow-up) and selected healthcare providers who were familiar and involved in diabetes management (two doctors—–an endocrinologist and a GP; two diabetes nurse educators; and one dietician). The research aimed to establish the degree to which patients and providers found topics prioritized by VT to be relatively important, whether VT’s assessment of knowledge level from educational exposure agreed with patient self-assessment, and to seek specific feedback for refining VT’s filtering and prioritization rules. Patients were required to complete two questionnaires each. The first questionnaire collected the data required by the DIP, with clinical components obtained from the patients’ hospital records. These data were then entered (remote from the patients) into the VT prototype by the first author. The VT algorithms were used to produce three lists

Empowering patients with essential information and communication support of 20 information items (consumer education topics) each: • InfoGroupR—–a random selection from the CDI source file; • InfoGroupS—–the top priority scores, after filtering, as weighted by the significant data-oriented prioritization rules (SigPRules); and • InfoGroupB—–top priority scores as per InfoGroupS with additional weighting based on estimated patient Knowledge Level (a subset of the KnowPRules). The three item lists were presented in random order, with the patient blind to the nature of each list, as a component of the second questionnaire. Patients were asked to score each information group in terms of its overall relevance and importance to their current situation (from 1—10, 10 meaning most relevant) and to provide, from the lists or otherwise, the 10 items they felt most relevant, in descending order of importance. The scores of each information group on each of these two measures (i.e., the relevance score and the number of items in the top-10 list) were contrasted using a paired t-test on the difference per patient to examine contrasts among the information groups. Fig. 6 shows an instance of a completed second questionnaire. After submitting (by post) the completed second questionnaire, a half hour semi-structured face-toface interview was conducted by the first author. Each interview was audio-taped and investigated:

587

whether the tailored topic lists (both InfoGroupB and InfoGroupS) missed any topics that the patients wanted; why patients preferred a particular information group; whether the lists contained any information that the patient did not think relevant; and what the patients’ own estimates of their diabetes knowledge level was (based on definitions, as per Section 2.3). After completing the patient questionnaires and interviews, healthcare providers participated in an audio-taped, one-hour focus group, facilitated by the first author. One focus group meeting was with the two doctors and a separate focus group was held with the two nurses and dietician (to reduce the doctors’ influence over the other healthcare providers). In these sessions, the providers gave feedback on the DIPs and resultant InfoGroupB lists for three patients (one patient randomly selected from each Knowledge Level as estimated by the VT algorithm). Providers were required to comment on whether they thought the tailored topic list was relevant and important to the patient, in light of the patient’s DIP data and whether there were any important topics missing. 4.1.2. Results Twelve patients (five females and seven males) agreed to participate in the study and returned the completed consent forms. One female dropped out from the study due to acute illness. The age of the participants ranged from 38 to 62 years (mean 53). Ten patients had type 2 diabetes and one had

Fig. 6 A sample of Questionnaire 2 of phase 1 evaluation (actual size is A3).

588

C. Ma et al.

Table 4 Patient relevance ratings and number of patients’ 10 most relevant items in list for random (InfoGroupR), significant data only (InfoGroupS) and both significant data and knowledge level (InfoGroupB) prioritized information items Relevance rating (1—10) (n = 11)

InfoGroupR InfoGroupS InfoGroupB InfoGroupS—InforGroupR InfoGroupB—InfoGroupR InfoGroupB—InfoGroupS

Number in 10 most relevant (n = 11)

Mean (standard deviation)

P (paired-t)

Mean (standard deviation)

P (paired-t)

7.27 (2.05) 7.91 (1.51) 8.00 (1.48) 0.636 (2.84) 0.727 (2.61) 0.901 (2.43)

N/A N/A N/A 0.47 0.38 0.90

2.63 4.27 4.27 1.64 1.64 0.00

N/A N/A N/A 0.074 0.068 1.00

type 1 diabetes. The period of diagnosis with diabetes was 2—13 months at the time of interview (mean 7.5 months). All 11 patients completed and returned the first and second questionnaires. Fig. 6 shows one example of the completed second questionnaire. Table 4 summarizes findings on patient-assigned relevance scores for each information group and for number of patients’ 10 most-relevant information items appearing in each information group. InfoGroupR, InfoGroupS and InforGroupB have the expected pattern in terms of means (S and B higher than R, B highest) for relevance scores, but provide no statistically significant differences. The number of information items from each group that appear in the patients’ 10 most-relevant items list shows InfoGroupS and InfoGroupB with equal means and a p-value between 0.05 and 0.10 for their superiority over InfoGroupR. Thirty-four percent of patients’ 10 most relevant items were covered in neither InfoGroupS nor InfoGroupB. Reasons for omission were largely due to information preferences outside the scope of the diabetes per se (e.g., depression, back pain and headache) or outside the scope of the system implementation at the time of the study (e.g., ‘sexual health’). Also, patient priority sometimes differed from the priority as assigned by our rules—–e.g., ‘overview of diabetes and eyes’ was ranked in the top 10 by a newly diagnosed patient who had no eye problems. During the interview sessions, all patients were able to point out at least some irrelevant information items from InfoGroupR; and 8 out of 11 patients (81.81%) agreed that the tailored information (InfoGroupS and InfoGroupB) was relevant. When asked why they preferred either InfoGroupB or InfoGroupS to InfoGroupR, reasons included: (1) the tailored information answered their information preferences or interests better; (2) the tailored information was directly related to their current issues; (3) the tailored information met their com-

(1.91) (1.90) (2.10) (2.73) (2.65) (1.10)

prehension level; (4) the tailored information was relevant to their family history; and (5) the tailored information was ranked in an appropriate order. On the other hand, some patients did not think some of the tailored information items listed were relevant because: (1) they had not experienced the related problems yet; (2) they felt they knew the information; and/or (3) they did not understand the information. Two participants preferred randomly selected information despite the fact that some it was not relevant to their situation. This is because they liked the diversity of topics and wider scope. Patient-estimated Knowledge Level was similar to the system estimate based on educational exposure (as per Table 3) with patients’ mean estimated Knowledge Level at 2.23 and the system estimate at 2.00. System and patient estimates never differed by more than 1 (Pearson correlation coefficient is 0.55). In the health provider focus groups, participants agreed that the tailored information was relevant to the corresponding patient profiles. However, they provided several suggestions for more information and prioritization rules, including: information on the National Diabetes Services Scheme of Diabetes Australia; information on sexual issues; and use of early symptoms of diabetic complications as triggers for information on diabetic complications. A number of rule categories entered discussion and received approval including those concerning patient behaviour changes, exercise, diet control, frequency of dining out, medications and psychosocial problems. Moreover, the providers gave input on tuning the priority of different information topics. Providers largely agreed with the basis of Knowledge Level estimation but were sceptical that a person who had ever been a carer for a person with diabetes would know the correct skills of diabetes management. They also thought that counting a patient’s visits to diabetes educators was redundant with counting the patient’s diabetes education sessions attended.

Empowering patients with essential information and communication support Table 5

589

Web portal usage statistics

Patient registration

Registration period: 23a to 126 days (median 73 days) Log-in times: 82 in total, individual patients had 2—15 log-ins (median 6)

Usage of the Information Service

Access to the service: all 12 patients Access to the information items: 82 out of 257 information items have been visited for more than 10 s; individual patients accessed 1—55 items (mean 20.2, very bimodal)

Usage of the Quizzing Service

Access to the service: 5 out of 12 patients Number of quiz questions done: individual patients did 5—110 quiz questions (mean 61.8)

Usage of the Agenda Service

Generation of an agenda: 1 out of 12 patients

a

Some patients experienced significant delays in accessing the system, greatly reducing the nominal 90-day trial period before statistics were collected.

Observations from phase 1 were used to refine the VT system, notably the SigPRule base, which was then used for a field trial as a consumer education portal.

4.2. Phase 2 evaluation: field trial 4.2.1. Study design and methods Patients were recruited by response to flyers and face-to-face invitation (by the first author) at the waiting rooms of the Diabetes Centre and Endocrinology Department. Patients had to agree to using the web portal for 3 months (via home or public library Internet access), with the offer of training as required. Healthcare providers for focus group feedback were the same as in phase 1. The aim was to assess the usability of the VT web portal, to see how it was used, what patients thought of it, and what providers thought about the appropriateness of the web environment for diabetes patients. Each patient was given a web portal account (username and password). At the time of first log-in they were directed through the registration process, leading to specification of DIP data (clinical values were verified remotely by the investigators using data from the hospital database). Each patient received a phone call reminding him/her to use the VT system, and to answer profile questions (if outstanding) every 2 weeks. An automated logging system was used throughout the field trial. At the end of the study, patients were asked to complete a questionnaire collecting their feedback on the VT system. Half of the patients were requested to attend an audio-taped, half-hour, face-to-face interview that firstly, investigated why they liked or did not like the system, and secondly, sought further suggestions for improving it. After the patients had completed 3 months of VT portal use, two provider focus groups were conducted (one for doctors and one for the nurses and

the dietician, as in phase (1). For the two most active portal users, the providers were shown the patients’ DIPs, tailored and prioritized information topics, tailored and prioritized quiz questions and personalized agenda questions. After a review of each case, each provider completed a questionnaire collecting his/her feedback on the system behaviour with respect to that patient. In addition, the providers gave general comments on the VT system. 4.2.2. Results Twelve patients were recruited3 , seven males and five females ranging in age from 35 to 65 years (mean 51); all 12 had type 2 diabetes. There were three patients who could not access the portal due to not having a computer at home or other reasons, so the first author helped them to register with the VT system in the Diabetes Centre and they were encouraged to use the VT system in a nearby public library. None of the patients asked for training even though they knew that training was available on request. Table 5 provides statistics on the patients’ usage of the portal. Nine patient questionnaires were returned. Table 6 summarizes the questionnaire responses. Six patients were randomly selected to attend a half-hour, semi-structured face-to-face interview. In line with the questionnaire responses, most patients agreed that the VT system is easy to learn and easy to use. Newly diagnosed patients found the tailored information relevant. Patients using quizzes felt they were a good way to learn. Moreover, use of the portal inspired some patients 3

Actually, 24 patients were recruited but half were randomly assigned to a control group intended to commence portal use three months after the initial group. For logistic reasons (including a high drop-out rate, probably exacerbated by the Christmas/New Year holiday season), however, the use of the control group was abandoned.

590 Table 6

C. Ma et al. Patient questionnaire responses on use of the VT-based web portal

Question

Patient responses (n = 9) Response

Count

The prototype is easy to use

Agree No opinion Disagree

7 1 1

The tailored information is relevant to my diabetes

Agree No opinion Disagree

7 1 1

The tailored information is useful

Strongly Agree Agree No opinion Disagree

1 6 1 1

The tailored agenda is useful

Agree No opinion Disagree

7 1 1

I would recommend this program to other people with diabetes

Strongly agree Agree No opinion Disagree

4 2 2 1

The questionnaires take too long to complete

Agree No opinion Disagree

4 2 3

The tailored information is easy to follow

Agree No opinion

6 3

The tailored information meets my information needs

Agree No opinion Disagree

6 1 2

The tailored agenda questions are appropriate

Agree No opinion Disagree

6 2 1

The tailored agenda questions are easy to follow

Agree No opinion

6 3

I suspect that the tailored agenda reminds me with important questions that I might not have asked my health providers otherwise

Strongly agree Agree No opinion

5 2 2

The tailored information is much more specific to my personal situation

Strongly agree Agree No opinion Disagree

1 5 2 1

Would you like to continue using the web system?

Yes No response No

5 3 1

Empowering patients with essential information and communication support to see the relevance of computers. On the other hand, experienced patients often found that the tailored information was not useful because they already knew it. A common suggestion was to provide greater depth by linking on from the tailored information to external web sites. The portal was not considered very useful for patients in several specific situations: • the patient was very experienced and aware of all the tailored information; • the patient’s diabetes was already wellcontrolled, and the patient was very comfortable with his/her current knowledge and did not think it necessary to seek more information; • the patient could not access the VT system easily for some reasons (e.g., did not have computer at home); and • the patient preferred general information to tailored information. Table 7 shows questionnaire results for the healthcare providers in response to a review of the portal screens for the two most active users, Case A and Case B. In focus group discussions, the providers suggested that there was some information missing in both Case A and Case B, including cardiovascular risk factors and psychosocial issues. Moreover, it

Table 7

591

was suggested that the priority of the tailored information should be adjusted so psychosocial issues had the highest priority (in the Information Service and as suggested issues in the Agenda Service). Comments on the VT system from providers reinforced the appropriateness of the items suggested for the patient agenda, including: I think the agenda is good because the patients have four questions, and they may forget two. (- GP) The agenda did come through strong — cardiovascular at the top, the cardiovascular, blood pressure and foot care, whatever, so it seems to be prioritized. . . (- Nurse) Health professional suggestions for general system improvement included: • A default page should be available for patients who have well-controlled diabetes, such as page of key consumer resources; • The system should detect trend in weight or HbA1c so that the appropriate information or agenda questions could be given to the patients at an early stage; • Patients should receive summary feedback of their quiz results;

Healthcare providers’ questionnaire responses on VT portal with respect to two cases

Question

Healthcare provider responses (n = 5) Response

Case A

Case B

The prototype is easy to use

Agree

5

5

The tailored information is relevant to the patient

Strongly agree Agree Disagree

1 3 1

0 3 2

There is much important information missing in the tailored information

Agree Disagree

2 3

3 2

The tailored information is important to the patient

Agree

5

5

The tailored agenda is useful

Strongly agree Agree Disagree

1 4 0

0 4 1

The tailored agenda questions are important for the patient to know

Strongly agree Agree

2 3

1 4

I suspect that the tailored agenda provided important questions that I might not have discussed with the patient otherwise

Strongly agree Agree No opinion Disagree

0 3 1 1

1 3 0 1

I would recommend this program to my patients with diabetes

Strongly agree Agree

2 3

1 4

592 • More information is required in the CDI base, such as how to deal with psychosocial issues; and • The VT system should allow patients to track individual targets, plans or goals.

5. Discussion We have developed a novel approach and architecture — VT — to help patients become empowered and participate more in their own care through greater understanding and encouragement to ask questions of their healthcare providers. Patient and healthcare provider responses to the technology, as revealed through two phases of evaluation study, are encouraging and have provided direction for improving the technology. Generally, the results show support that VT and the VT-based portal have substantial ‘face validity’ (i.e., it looks right) as a mechanism to prioritize learning and promote a focus on essential information. Face validity is demonstrated in three major ways: Patient use and usability—–for patients willing to participate in the field trial, there was minimal expression of difficulty in using or understanding the system. Basically, if they could (and would) make it onto the Internet, they could use the system; there was minimal seeking of offered help, and nil expression of confusion. Patient feedback—–patient feedback was almost exclusively supportive with respect to the system features and their presentations. This is not to say that some patients would not like more features or to act outside of those features. Healthcare provider feedback—–healthcare providers gave strong support for the value of the system as a whole in the context of the specific patients reviewed. Critical feedback of healthcare providers became constrained to limited and specific areas with the refinements between phase 1 and phase 2. The providers are empowered, knowledgeable and passionate about quality diabetes care—–they are not inclined to ‘hold back’ on criticism if any deficit can be discerned.

C. Ma et al. Agreement of all five healthcare providers (Agree or Strongly Agree in the contexts of both cases) that: they would recommend the system to their patients with diabetes; the tailored information and agenda suggestions were important; and they believed the portal was easy to use; and Mixed levels of provider support (although always with majority support) for the utility of the agenda function and the quality of prioritization (relevance of tailored information and suggested agenda questions) with respect to specific cases. This demonstrates the willingness of the providers to disagree when a flaw could be identified. The individuality of patients stands out, especially in the phase 1 results. We were surprised to find no significant superiority in patient rating of the relevance of VT-selected topic lists as compared to random topics. Moreover, there is only weakly-positive association (p < 0.10) to the greater number of items in the VT-prioritized lists of topics appear in the patients’ own Top 10 topic lists. This owed to a range of individual priorities, such as patients preferring random topic lists due to their greater breadth and patients’ Top 10 topics being outside of diabetes. These sorts of individual priorities re-emerged in the phase 2 interviews. Moreover, from phase 2 we can identify two (not mutually exclusive) patient groups for which prioritization may have minimal benefit: (a) those with well-controlled diabetes; and (b) those who are very confident in their diabetes knowledge.

5.1. Limitations

Quantitative findings have to be viewed as preliminary and suggestive due to the scale of the evaluations; however, the questionnaire responses are particularly worthy of highlighting, including the following:

The findings are obviously limited by the sample sizes in terms of patients and providers, but are also limited by the duration of the reported field trial. Diabetes patients are typically expected to visit their doctor once every 6 months. A trial, therefore, should probably span several 6-month periods in order to observe progressive improvement in patient-provider partnership due to online learning and agenda-formulation support. Low usage of the Agenda Service in the field trial is unsurprising since many patients did not attend a routine doctor visit during the field trial period. Recruitment of participants for phase 2 was surprisingly challenging. Levels of Internet use in Australia are high4 , yet the majority of patients approached to use the web portal declined, usually on the grounds that they were not interested,

Strong support (seven-to-one) for ease-of-use, relevance of tailored information, and utility of tailored information and agenda suggestions from the field trial participants;

4 66.4% penetration, per Nielsen, as at February 2005, as compared to 67.8% for the United States (www.internetworldstats. com/stats.htm).

Empowering patients with essential information and communication support comfortable or able to access the Internet. There would be some age and socio-economic bias against Internet use in the population approached, but the level of resistance was still significant. From an engineering perspective, more content is needed. Our key information resource [23], is, by title and design, an ‘essential guide,’ yet we see a number of issues that are not covered. While a book for patients can suffer from intimidating thickness, this is less of a problem for a web resource. More coverage on approaches to psychosocial issues would be useful, as well as more detail on areas particularly actionable by the patient, such as more detailed food tips and even healthy recipes. At a different level, a segment of patients expressed frustration at the limitations of a fixed information source, and a desire for integration with Internet search facilities.

5.2. Related work Many systems for provision of personalized healthcare information have been developed. Herein we discuss a few that are most relevant to the approach we have taken with VT. A significant consumer health information system is CHESS [27]. In CHESS, its ‘‘Information Services’’ provide frequently asked questions and answers to some specific contexts, but the questions are not tailored to the patient’s personal circumstance. The ‘‘Analysis Services’’ collect patient data (both the data on health status and other relevant data) and provides feedback and/or relevant materials according to these data, which can be considered as tailored information. However, CHESS neither systematically prioritizes information based on the patient’s status nor does it take into account the patient’s knowledge level in providing tailored information. The management of patient questions has been addressed in a variety of ways. PEAS [17] provides patients with relevant information concerning their personal status with related questions and answers, so as to encourage patients to formulate their own questions prior to a health visit. CHESS [27] provides disease-related frequently asked questions. However, both of these systems are aimed more at providing questions with answers for knowledge transfer, rather than stimulating patient-provider communication. CareLink was developed to support families with premature infants [28]. CareLink facilitates patients’ communication with health professionals through online messaging. The communication facility provided by CareLink exemplifies use of the Internet as an alternative communication channel between doctors and patients,

593

but provides no deep support for question formulation. The Accu-Chek Interview (ACI) is a CDROM-based psychosocial assessment tool [29]. The assessment is implemented via a questionnaire with 20 questions, and the assessment covers the areas of diabetes-related emotional distress, major depression, hypoglycemia, and smoking. The system presents five self-care topics for patients to select one for discussion with their health providers; it also allows patients to type in their own topics or select ‘‘No topic today.’’ The systems described above largely circumscribe the approach to question management we have taken with VT. We believe, however, we have gone a step further in the representation of the patient questions as persistent objects (essentially part of the health record), where these questions can be managed into compositions such as an agenda, and where the basis for suggested questions is deeply integrated with the information service, including patient knowledge level and preferences. A system for migraine sufferers by Buchanan et al. [18,19] uses natural language (NL) generation technologies to create personalized explanations with a focus on the system’s estimated communicative goals of the user. This system is exceptional for incorporating results of a study of patient information desires, including common fears and misconceptions (e.g., concern that the migraine may indicate a brain tumor). It collects a user’s medical history and — based on these data — dynamically generates explanations of key concepts in a fashion tailored to that particular user. It conducts implicit reasoning on the user’s knowledge level by observing the user’s reading history. If a user asks the same question twice, the system assumes that there is a state of user confusion or dissatisfaction with some aspect of the explanation and, as a result of this, the system selects a different strategy for generating an answer. The different explanation aims to facilitate the user’s understanding by introducing the information from different angles. The disadvantage is that changeable information could confuse the user, and may be confusing if the user simply wants to review an explanation for reinforcement. While we have been inspired by their use of patient profiles, we have chosen not to emulate Buchanan et al. on the use of NL generation. The engineering costs of developing NL generation systems are high and, recently, some doubt has been cast on the effectiveness of such methods. STOP generates tailored smoking cessation letters for smokers [30]. A clinical trial with 2553 smokers showed no significant difference in cessation rates

594 between smokers who received a tailored letter and those who received a non-tailored letter. More importantly, however, we believe NL generation comes too close to aiming to supplant rather than support patient-provider communication. Generating dynamic, personalized natural language serves to simulate a human healthcare provider, which we believe is an inappropriate metaphor for online learning for patient self-management.

5.3. Implications The motivation to continue investigating consumer information prioritization in chronic disease management is high. Both patients and providers strongly supported the importance of the tailored information and generally supported the priorities assigned. The average amount of online reading conducted by patients in the field trial was fairly limited at a mean of around two items per week (see Table 5), indicating a great deal of value in ‘pushing’ a couple of truly essential topics. Patient feedback indicates that the greatest benefit of this technology may lie with relatively newly diagnosed patients. Both patient and provider feedback suggested provision of a display of patient selfmonitoring results. A monitoring-centered display may be the more appropriate home base in a portal for stable and well-informed patients. Patient desire for links to Internet searching came through strongly in interviews, aligning with high use of the Internet for health queries in those with serious conditions generally (e.g., see [31]). Satisfying this desire for broad searching would clearly help to address user interest in the portal. It is a somewhat different issue, however, as to whether this would make the promotion of better patient outcomes more effective. The VT system is designed to promote, not supplant, patientprovider interaction. The source material indexed by the portal stays close to this philosophy, but the same could not be said of all health materials on the Internet. We believe it is the identification of essential topics to promote patientprovider interaction, not their ‘answer’ through online reading, that is a key contribution of our architecture. Better integration of the portal with the mainstream healthcare system seems likely to improve its uptake, as well as having potential to improve promotion of the patient-provider partnership. Our recruitment protocol for the field trial did not permit direct ‘referral’ of the patient by their healthcare provider. While recommendation was possible, there was no specific trigger for this in the processes at the participating clinical settings. VT-type priori-

C. Ma et al. tization technology could add a significant element of dynamism to existing technologies for health consumer online learning, especially if integrated with electronic health records and personal health records.

6. Conclusion and future research Even though doctor—patient partnership has been advocated for nearly two decades, using IT approaches to support the partnership have not been adequately explored. Consumer education is clearly a requirement for effective communication, partnership and empowerment, but to be most effective this education should be focused on opportunities for action. We have taken an approach based on customization of online learning for consumers incorporating emphasis of individual needs, the fact that individual needs are dynamic, and that providing the right information requires addressing psychosocial issues and preference, as well clinical issues per se. We have developed an innovative architecture that emphasizes assignment of priority to consumer information needs as the underlying mechanism for individualized partnership support. The items of highest priority are emphasized as essential information for online learning, based on readings and quizzes, in a dynamic web portal. In addition, the portal provides an agenda service to facilitate consumer compilation of questions into hardcopy agendas that they can take to appointments with their healthcare providers. The agenda service, working integrally with the prioritization algorithms for online learning, facilitates placement of user and machine-selected items of high priority and/or interest into the agenda. Prioritization algorithms give weight to topics on significant clinical and psychosocial issues, as well as educational exposure and user-assigned priority. The portal is underpinned by a modular architecture that makes extensive use of XML technologies (notably, to present the core consumer information, quizzes, and prioritization rules) and features a Diabetes Information Profile that supports the diverse patient data requirements of the prioritization algorithms. We refer to the components collectively as Violet Technology. Evaluation of the VT and the VT-based web portal has proceeded through two phases. The first phase focused on information topic prioritization in a laboratory setting, and the second phase involved a general field trial by diabetic consumers. While the scope and format of these studies permit only preliminary findings, we can say that the information

Empowering patients with essential information and communication support and agenda services have ‘face validity’ in light of consumer and healthcare provider feedback. Moreover, usability issues appear minimal among those consumers willing to enrol (at least those who think they want to use it can). Each evaluation phase has provided us with directions for enhancing the system. From phase 1 we identified several areas for improving the prioritization rules (including use of early symptoms of complications and location (Australia) specific information on support services), which were incorporated for phase 2. Phase 2 revealed some areas for further rule refinements (e.g., greater responsiveness to psychological issues) and (largely related) areas where it would be useful to incorporate more consumer information for prioritization. The greatest consumer interest revealed in phase 2 was for the ability to move on from the information provided within the portal to search external sources for more extensive and advanced reading. This demand is shaping our ongoing research toward using the VT components for consumer search query expansion and reformulation. Provider feedback indicated considerable interest in adding features to the portal of a more general Personal Health Record nature, such as trend graphs of consumer monitored data. A more extensive field trial, both in terms of duration and patient numbers, is needed to provide the data to assess quantitative changes in indicators of patient-provider partnership (e.g., number of questions asked in healthcare visits) and ultimately in indicators of clinical outcomes (notably improved glycaemic control as measured by HbA1c). We are currently working to incorporate an automated user account and profile creation procedure at our university Podiatry Clinic (which has a high diabetic case load) as a mechanism for larger-scale enrolment in the VT-based web portal.

What is already known on this topic In diabetes management, doctor—patient partnership in both diabetes care and doctor—patient communication improves patients’ compliance and outcomes. Computer-based education is an effective strategy for transferring knowledge and skill development for patients with chronic disease.

What this study adds A modular architecture using XML technologies can be used to map diabetes consumer profiles to priori-

595

tized consumer education topics for online learning and consumer agenda creation. The resultant web portal and prioritized topic lists have face validity and are supported by positive responses from consumers and healthcare provider focus groups regarding ease-of-use, relevance and utility. Consumers show remarkable individuality in their ranking of topic relevance and in the topics they identify as most relevant or important to their current situation.

Acknowledgements We extend our thanks to all the patients of The Queen Elizabeth Hospital Diabetes Centre who participated in our evaluation studies, as well as those who provided feedback in the formative stages of the project, and also to Dr. Stephen Leow, Dr. Catherine Chittleborough, Dr. George Phillipov, Luisa Pinto, Wendy Martin, Jane Giles, Melissa Carapetis, Teresa Sgardelis, Lesley Roberts and all the Diabetes Center staff for their support. We appreciate the valuable feedback from Jean-Pierre Calabretto, Eric Brown, Sistine A. Barretto and Phillip Lock. We thank Amy Gordon for her contributions in graphic design. We also thank Diabetes Australia, Novo Nordisk and Kidney Health Australia for approving the use of their materials.

References [1] M.M. Funnell, R.M. Anderson, Empowerment and selfmanagement of diabetes, Clin. Diabetes 22 (3) (2004) 123—127. [2] M.M. Funnell, R.M. Anderson, The problem with compliance in diabetes, JAMA 284 (13) (2000) 1709. [3] J. Hiscock, R. Legard, Snape D., Listening to Diabetes Service Users: Qualitative findings for the Diabetes National Service Framework, National Centre for Social Research, 2001. [4] M.M. Funnell, R.M. Anderson, Working toward the next generation of diabetes self-management education, Am. J. Prev. Med. 22 (4, Suppl. 1) (2002) 3—5. [5] C.G. Pooley, C. Gerrard, S. Hollis, S. Morton, J. Astbury, ’Oh it’s a wonderful practice . . . you can talk to them’: a qualitative study of patients’ and health professionals’ views on the management of type 2 diabetes, Health Soc. Care Community 9 (5) (2001) 318—327. [6] R. Street, V. Piziak, W. Carpentier, J. Herzog, J. Hejl, G. Skinner, L. McLellan, Provider-patient communication and metabolic control, Diabetes Care 16 (5) (1993) 714— 721. [7] C. Charles, A. Gafni, T. Whelan, Shared decision-making in the medical encounter — what does it mean — (or it takes at least two to tango), Soc. Sci. Med. 44 (5) (1997) 681—692. [8] C. Charles, A. Gafni, T. Whelan, Decision-making in the physician-patient encounter: revisiting the shared treat-

596

[9] [10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

C. Ma et al. ment decision-making model, Soc. Sci. Med. 49 (5) (1999) 651—661. G. Eysenbach, Possibilities for fundamental change in the health system in the electronic world (2002). D. Lewis, Computer-based approaches to patient education: a review of the literature, J. Am. Med. Inform. Assoc. 6 (4) (1999) 272—282. C.A. Barry, C.P. Bradley, N. Britten, F.A. Stevenson, N. Barber, Patients’ unvoiced agendas in general practice consultations: qualitative study, BMJ 320 (7244) (2000) 1246—1250. C. Charles, A. Gafni, T. Whelan, How to improve communication between doctors and patients, BMJ 320 (7244) (2000) 1220—1221. P. Little, M. Dorward, G. Warner, K. Stephens, J. Senior, M. Moore, Importance of patient pressure and perceived pressure and perceived medical need for investigations, referral, and prescribing in primary care: nested observational study, BMJ 328 (7437) (2004) 444. B. Shapira, P. Shoval, U. Hanani, Stereotypes in information filtering systems, Inform. Process. Manage. 33 (3) (1997) 273—287. Generating personalised information using the medical record, in: K. Binsted, A. Cawsey, R. Jones (Eds.), Proceedings of AIME 95, Artif. Intell. Med., Springer-Verlag, Berlin, New York, 1995, pp. 29—41. S.S.R. Abidi, C.Y. Han, S.R. Abidi. Patient Empowerment via ‘Pushed’ Delivery of Personalised Healthcare Educational Content Over the Internet. MEDINFO 2001, in: Proceedings of the 10th World Congress on Medical Informatics, Amsterdam, IOS Press, 2001, pp. 1425— 1429. S.W. McRoy, A. Liu-Perez, S.S. Ali, Interactive computerized health care education, J. Am. Med. Inform. Assoc. 5 (4) (1998) 347—356. B.G. Buchanan, G. Carenini, V.O. Mittal, J.D. Moore, Designing computer-based frameworks that facilitate doctor—patient collaboration, Artif. Intell. Med. 12 (2) (1998) 169—191. B.G. Buchanan, J.D. Moore, D.E. Forsythe, G. Carenini, S. Ohlsson, G. Banks, An intelligent interactive system

[20]

[21] [22] [23] [24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

for delivering individualized information to patients, Artif. Intell. Med. 7 (2) (1995) 117—154. L.J. Beeney, A.A. Bakry, S.M. Dunn, Patient psychological and information needs when the diagnosis is diabetes, Patient Educ. Counsel. 29 (1) (1996) 109—116. K. Lorig and associates, Patient education: a practical approach, Sage Publications, Inc., 2001. S.H. Rankin, K.D. Stallings, Patient Education: Issues, Principles, Practices, Lippincott, Philadelphia, 1990. P. Phillips, L. Brown, T. Dunning, Diabetes & You—–The Essential Guide, Diabetes Australia, 2002. L. Calvi, P. De Bra, Proficiency-adapted information browsing and filtering in hypermedia educational systems, User Modeling and User-Adapted Interaction 7 (4) (1997) 257—277. R.M. Anderson, M. Pibernik-Okanovic, The patient empowerment approach to diabetes care, Diabetologia Croatica 28 (3) (1999). M.M. Funnell, R. Nwankwo, M.L. Gillard, R.M. Anderson, T.S. Tang, Implementing an empowerment-based diabetes self-management education program, Diabetes Educator 31 (1) (2005) 53—61. D.H. Gustafson, R.P. Hawkins, E.W. Boberg, F. McTavish, B. Owens, M. Wise, H. Berhe, S. Pingree, CHESS: 10 years of research and development in consumer health informatics for broad populations, including the underserved, Int. J. Med. Inform. 65 (3) (2002) 169—177. H.S. Goldberg, A. Morales, L. Gottlieb, L. Meador, C. Safran. Reinventing Patient-centered Computing for the Twentyfirst Century, MEDINFO, Amsterdam, IOS Press, 2001, pp. 1455—1458. G. Welch, D.W. Guthrie, Supporting lifestyle change with a computerized psychosocial assessment tool, Diabetes Spectr. 15 (3) (2002) 203—207. E. Reiter, R. Robertson, L.M. Osman, Lessons from a failure: generating tailored smoking cessation letters, Artif. Intell. 144 (1—2) (2003) 41—58. S. Dickerson, A.M. Reinhart, T.H. Feeley, R. Bidani, E. Rich, V.K. Garg, C.O. Hershey, Patient internet use for health information at three urban primary care clinics, J. Am. Med. Inform. Assoc. 11 (6) (2004) 499—504.