To encourage evidence-based practice, clinical decision support systems (CDSSs) that provide digitized clinical practice guidelines (CPGs) and critical pathways are being actively introduced into the medical field for the health professional's use in clinical settings [1,2]. CDSSs can also help people with chronic diseases manage their own health by providing instant knowledge and recommendations [3,4]. One of the key steps in developing a CPG-based CDSS is building computer interpretable guidelines (CIGs), also called guideline ontology through guideline representation languages, which define the declarative knowledge of complex medical pathways. Internationally, there are several languages and models to represent guidelines, such as GLIF3, SAGE, PROforma, etc. [5,6]. No matter what kind of techniques a CDSS uses, it delivers alerts, reminders, and suggestions based on not only decision rules specified in CPGs but also relevant patient data describing patient's health status collected by Electronic Health Record (EHR) systems [7]. Therefore, a large amount of routine clinical data needed by a CDSS has to be represented in an interoperable way with unambiguous meanings, and it must be consistent with those in CIGs so as to be retrieved and used by CDSS. To represent clinical data in a standard way for CDSSs, HL7 developed a data model called virtual medical record (vMR) based on the HL7 Reference Information Model (RIM) [8]. vMR represents clinical information inputs and outputs that can be exchanged between CDS engines and clinical information systems through mechanisms such as CDS services. The Domain Analysis Model (DAM) of vMR includes structural specifications for inputs and outputs of CDSS engines, which consists of several information classes, such as observation, encounter, problem, adverse reaction, goal, procedure, medication order, etc., each representing a specialization of the RIM Act class. vMR is employed by SAGE to support information communication between CDSSs and local clinical information systems [9].

Hypertension is a significant risk factor for people's health. It is estimated that there are currently 200 million hypertension patients in China [10,11]. To cope with the prevalence and the characteristic dangers of hypertension, the Chinese government has established a strategy of "focusing on prevention and transferring the healthcare downwards to primary health sectors." Consequently, hypertension has been one of major chronic diseases managed by primary healthcare organizations at the community level, and a clinical practice guideline specifically for primary healthcare of hypertension was developed in 2009, in which general knowledge about the disease was documented comprehensively to guide general practitioners to monitor, evaluate, medicate, and instruct hypertension patients [10]. Meanwhile, the national government imposed a set of requirements for the delivery of public health services and required that the data needed for chronic disease management, including hypertension, must be recorded by EHR systems in primary healthcare organizations for the purposes of evidence-based, continuing patient healthcare and public health policy making [12]. The data items and their formats in EHR systems that already exist or are implemented in primary healthcare facilities are also stated in the national specification.

Like many other clinical guidelines, the hypertension guideline has not been fully followed in daily healthcare practice. Several factors restricting physicians' adherence to clinical guidelines have been identified [13]. Investigations in other countries have revealed that data standardization is a critical factor for CDSS development [14,15,16,17]. In China, there are very few computerized guidelines to date in routine clinical use. Furthermore, there have not been investigations yet on whether the existing narrative guidelines in Chinese are able to be computerized or whether the data that information systems collected following the announcement of national specifications can meet the needs of CDSS implementation.

Therefore, this research tried to identify underlying obstacles in implementing clinical guidelines from the aspect of clinical data representation in the context of hypertension by investigating the medical statements in both the guideline and electronic patient record. Finally, we suggest some considerations that need to be taken into account in the development of clinical guidelines and EHR systems in China.

The clinical guidelines provided by real-time CDSSs require that they are seamlessly integrated with existing patient information systems to enable the automatic provision of advice at the time and place at which decisions are made [25]. Focusing on hypertension, by modeling the knowledge and rules, our research first developed a guideline ontology to formulate the guideline and define the data items it contains. Secondly, we reviewed the health record specification for hypertension patients and compared the data definitions with those in the ontology to investigate whether they are mutually compatible in scope and semantics. Our investigation demonstrated that it is basically feasible to represent the guideline in a computer interpretable format. However, problems arose in defining semantics for some data items because of narrative ambiguity of the guideline, gaps and inconsistencies in representing some data items between the guideline and patient' records, and unavailability of unified medical coding systems. These problems might be common and need to be dealt with when any clinical guidelines are to be computerized. Accordingly, the following major suggestions for the development of guidelines and data specifications can be made.

Participation of people in different disciplines and at various levels is vital. Firstly, the development of a guideline requires input from, not only sufficiently qualified medical professionals who are able to make the right decisions in complex clinical situations, but also primary healthcare practitioners who need definitive directives when facing multiple choices. The participation of primary healthcare practitioners is even more important in the development of guidelines that target the management of long-term chronic diseases, such as hypertension, where general practitioners play leading roles. Therefore, the statements in a guideline should be more precise or knowledge intensive, making computerized CPGs and CDSSs more usable for people who really need them. Secondly, disease management specifications, which in China are designed mainly by public health agencies and address data recording issues, also need to be harmonious and consistent with the guidelines developed by clinical experts so as to facilitate guideline implementation. Obviously, this harmonization relies on effective communication and collaboration among all participants. Thirdly, difficulties in translating narrative guidelines to a computer interpretable format and in normalizing data definitions result partly from a lack of informaticians in the development of guideline and patients' record specifications. By identifying what kind of data standards or coding systems will be required in computerizing a specific guideline and whether they are available and applicable, informatics experts can certainly be helpful in solving some problems that have been identified in this research.

More attention should be paid to data standard development and adoption issues. Computable representation of clinical information requires lots of standards, especially medical terminologies so as to name, identify, and code medical concepts consistently. Unfortunately, few such standards have been developed domestically in China, and international standards have not been available so far. Therefore, we suggest that the government, healthcare, and health information communities make great effort to identify strategies, mechanisms, and technical solutions for clinical data standardization. Otherwise, it will be impossible to implement the guideline by CDSSs and to integrate data across various health information systems to build patient-centered, longitudinal EHR and clinical data repositories.

This study had several limitations. The guideline ontology built in this study is based on a guideline document modeled by our research team. It remains to be validated by clinical professionals in terms of its meaningfulness and accuracy. Additionally, this research investigated clinical data standardization issues only by focusing on the example of hypertension. It cannot be considered comprehensive, and there may be some other significant challenges remain unidentified. After all, integrating all the related data and knowledge in a CDSS has proven to be very complicated, while the variety of health information systems and clinical guidelines makes it even more difficult. Thus, the data standardization issue discussed herein is far from enough to execute a CDSS.