The incidence of chronic diseases around the world is increasing because of increased environmental pollution, incorrect eating habits, and a decrease in physical activity [1]. According to a survey conducted by the National Statistical Office, the three major causes of death in Korea in 2011 were malignant neoplasms, cerebral vascular disease, and heart disease, which had a high probability of being caused by chronic diseases, such as high blood pressure, hyperlipidemia, and diabetes. Further, chronic diseases accounted for 47.4% of the total cause of death. Therefore, it is necessary to provide appropriate guidelines to patients with chronic diseases and to warn them of the risks of such diseases [2].

Chronic diseases cannot be cured through treatment or surgery; however, continuous management is required by patients. They need to modify their daily guidelines and change their lifestyle habits. Therefore, communication is important between clinicians and patients. However, continuously providing customized guidelines is unrealistic because of the continuous changes in the state of patients. Hence, the u-healthcare system is drawing attention as a new system that uses wired and wireless networking information technology (IT) for health management and provides health services beyond the limitations of time and place. The u-healthcare system enables real-time delivery and analysis of data [3,4,5,6,7]. In this system, guidelines are developed for patient management by determining the state of patients through personal health records (PHRs). PHR is a tool to safely manage and record all medical and health information [8]. The proposed system uses Electronic Medical Records (EMRs) [9] in the hospital information system instead of paper sheets by introducing the concept of PHRs and IT in health institutions; the system provides clinical guidelines by considering the lifestyle information of chronic patients, which includes smoking status, exercise, and alcohol consumption, as well as clinical risk variables (total cholesterol, blood pressure, body mass index [BMI], etc.). Information exchange is performed using the standard protocol, Health Level 7 (HL7), of the medical information transmission interface, and mutual operability is ensured independent of a specific hospital system by enabling a linkage between independent systems [10,11].

A healthcare decision support system (HDSS) is the tool selected to provide guidelines, which uses the concept of a clinical decision support system (CDSS) appropriately modified with respect to chronic patients [12]. The HDSS is a newly named decision support system designed in this study. The purpose of the HDSS and the range of user groups are newly established; it mainly focuses on the health management of patients with chronic diseases to distinguish it from CDSS that underscores practicality in the clinical field. The core difference between a HDSS and a CDSS is the service user groups. A CDSS is a physician-centered system that shortens treatment time, prevents the risk of misprescription or misdiagnosis, and systematically focuses on healthcare providers including hospital management. On the other hand, a HDSS presents the unique characteristics of chronic disease patients by consistently providing health content tailored for patients with chronic conditions and aiming for supporting education on treatment and schedule management, in addition to self-management by checking patient behaviors and receiving feedback. A CDSS is an expert system in the medical field, and they are now gradually being used as intelligent computing systems to improve treatment efficiency. Such an intelligent computing system provides clinical guidelines and patients information to physicians and patients [5,13,14,15,16,17,18]. The HDSS could play a significant role in the health management of patients by enabling physicians to produce a customized patient clinical guide through PHR, which includes further lifestyle information; the developed guidelines consider patient feedback.

Figure 1 shows an interface roadmap for accessing the PHRs of patients in the HDSS. The interface roadmap provides high reusability and interoperability of message exchange of medical information, which is analyzed using a medical information messaging international standard, HL7. The HDSS knowledge builder and inference engine provide a Web service interface, which is based on the standard message transmission protocol. A knowledge base is developed using synchronous message communication based on the simple object access protocol and asynchronous communication for a stateful session. Then, the developed knowledge base is transmitted to a service terminal. The knowledge base has the characteristics of modifiability and it is developed such that it can incorporate new production, removal, modification, and integration of data; further, business logic is not hard-coded in the service implementation process. The service is based on the interface standard protocol for increasing availability, and the service interface can be produced according to various clients by publishing through Web service description language. Similarly, a linkage between different hospital information systems (HISs) of various hospitals is enabled through compliance with the standard protocol; thus, the usability of an independent system on a client platform is increased.

The proposed system utilizes personal health information at the client level based on PHRs through the service interface to provide a wide range of services for patients. Chronic disease patient's PHRs used in the system are chiefly divided into three types. The first type of health information is the personal health management history recorded during health screening, and it is used to provide tailored clinical guidelines established by the identification of the illness and health status of patients based on surgeries, hospital visits, admissions, and prescriptions in the HDSS. Moreover, PHRs can be applied in consultation service for scheduling of screening and intelligent counseling on a patient's conditions based on health records. Likewise, the content management system (CMS) provides data on related disease management for patients according to their chronic illnesses. The second type of health information comprises medical records on lifestyle habits. The CMS keeps records of nutritional intake status including health functional foods and physical activity levels in a diet/exercise application, manages nutritional supply of chronic disease patients in order for them to sustain safe eating habits appropriate for unique chronic conditions, and it provides advice regarding exercise methods. Finally, the third type of health information is PHRs in the emergency medical system classifying allergy and idiosyncrasies. These data are applied in determining emergency situations according to the conditions of chronic disease patients in the emergency situation management of the HDSS. In addition, the information aids systematic management appropriate for different situations by classifying response measures suitable for each patient and emergency stages.

The construction of the HDSS system establishes manifold linkage and enables physicians to provide guidelines corresponding to the state of patients on a clinical basis by ensuring a standard patient information exchange among health institutions. The service is provided to chronic disease patients though each layer of the service platform and enables the creation of rules by physicians. Table 1 shows the roles of patients and physicians separately.

Further, the HDSS provides a customized service to patients through multiple algorithms and a single variable, by constructing a deductive inference engine and providing service according to the state of patients.

A knowledge base is a set of rules for reproducing the knowledge of a domain. The knowledge base outputs a conclusion message through a pattern matcher & solution searcher by combining with the attribute information of objects possessed by the entity of a patient. The pattern matcher generates a hypothesis by matching the variable value of objects (patients and HDSS message), input to the inference engine with the individual rules of the knowledge base. The working memory performs the networking of the generated hypotheses and increases the networking until further chaining is not possible; finally, the obtained hypotheses are combined as conclusions to produce the output.

The content set corresponding to the health state of a user is generated, and messages are provided. These messages are personalized and finally output to users through the user interface. Figure 5 shows the process of HDSS message output for patients with normal attributes except for lower levels of BMI.

A rule-based inference has certain attributes, which lead to conclusions by deductive derivation of a new fact from the facts (attributes) of any object [22]. A single rule consists of 'antecedent' → 'consequent,' and the storage place of this set is the knowledge base. New hypotheses are generated by the application of rules; an inference is continued until further hypothesis is not possible. When an event generation score of probability is granted to an individual rule, a basis is provided, which enables the selection of an optimum conclusion candidate from various conclusions. The inference progress of the HDSS implements the inference process by the forward chaining method.

Figure 6 shows the increase in inference by the attribute values of BMI level, total cholesterol, and systolic/diastolic blood pressure.

Figure 7 shows an expert system model based on a general rule [23]. Figure 8 shows the HDSS model constructed on the basis of a standard protocol, service construction, and a clinical evidence concept based on machine learning.

The present HDSS system establishes data linkage among the HISs of different hospitals using the PHR interface HL7, which includes a standardized external data module. An external program was developed by using the service-oriented architecture method comprising service aspects by providing a HDSS; a CMS; medical counseling services; diet, nutrition, and exercise services; and individual health record information. Patient accessibility was increased by extension of the service through mobile applications, Web services, and home nurses in smart homes. In addition, the HDSS considered rules corresponding to the core service mentioned earlier. The database was also divided inside and outside based on service aspects, and the basic rules were processed, thereby ensuring the scalability and the flexibility of the HDSS. Finally, clinical decision-making was supported for each service user, patient, and physician through the customized interface of the HDSS engine.

The present system provides life-friendly guidelines to chronic patients by collecting information through the EMRs of HISs of different hospitals, thus overcoming limitations, such as patient information collection, rule construction, and modifications, faced by the CDSS used in traditional medical institutions. Chronic conditions are difficult to manage and prevent. The current system aids decision-making by analyzing the correlation of prescription, diagnosis, and treatment with their results. Meanwhile, the proposed system is meaningful in that it accurately identifies risk factors based on habit changes to suggest measures to tackle the problems by recommending practical guidelines for the daily lives of patients with chronic illnesses. The knowledge base was constructed to enable service users to follow the guidelines of health management of chronic patients during drug prescription by physicians and diagnostic procedures, such as surgery. Maximum effort was made to maximize convenience for physicians by considering study results and visualization methods while ensuring clinical evidence used in the formulation of the guidelines. A model was designed for use in various clinical environments by designing the HDSS structure using a simple rule-based expert system and a differentiated model. The rule-based expert system requires the user to obtain the knowledge of doctors and clinical domain experts and to create guidelines beforehand; further, it is difficult to access clinicians during the modification and addition of rules. The proposed HDSS system is intended to gradually improve its user interface by using an open visualization tool, such as Carrot, and to provide an intuitive interface for both physicians and patients. Future studies will be aimed at developing expert knowledge and formulating customized guidelines by studies supporting intuitive understanding, and obtaining information, such as IT affinity, and behavior patterns of patients along with an analysis of dietary patterns. Moreover, further studies will be performed to expand the usability of the new system by developing and advancing decision-making algorithms to establish intelligent HDSS considering all situational factors, including diet, physical activity level, allergy, emergency care, and other PHRs based on verification of the established system.