From the point of view of clinical data representation, this study attempted to identify obstacles in translating clinical narrative guidelines into computer interpretable format and integrating the guidelines with data in Electronic Health Records in China.
Based on SAGE and K4CARE formulism, a Chinese clinical practice guideline for hypertension was modeled in Protégé by building an ontology that had three components:
A set of flowcharts was built. A
The obstacles in modeling a clinical guideline and integrating with data in Electronic Health Records include narrative ambiguity of the guideline, gaps and inconsistencies in representing some data items between the guideline and the patient' records, and unavailability of a unified medical coding system. Therefore, collaborations among various participants in developing guidelines and Electronic Health Record specifications is needed in China.
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 [
Hypertension is a significant risk factor for people's health. It is estimated that there are currently 200 million hypertension patients in China [
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 [
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.
We modeled the hypertension guideline based on SAGE and K4CARE formalism [
First, we extracted and formalized the narrative guideline as an ontology. The ontology has three successive components:
Second, we reviewed the data items and metadata defined in the national specification of patients' EHRs. By comparing them with those in the guideline ontology, conflicts and gaps were identified.
Finally, based on the previous steps, obstacles in computerizing guidelines were inferred and corresponding recommendations for the development of clinical guidelines and patient health record systems were made.
Based on clinical regulation, the hypertension guideline was disassembled into 6 parts: identification, risk stratification, classification of hypertension management, lifestyle intervention, medication, and care goals. These 6 parts and corresponding flowcharts are shown in
All the rules in the
In structuring and conceptualizing the guideline, one problem we encountered is inexplicit and imprecise narratives. Some statements, such as
There are 140 data items extracted from node of the guideline ontology, which are classified into 4 classes in the
In defining all the data items, we also tried to localize the values of some attributes according to codes, identifiers, vocabularies, etc., provided in the target guideline.
There were two major challenges in standardizing the data elements in localizing vMR DAM. The first challenge is how to tailor or specify the general definitions of vMR to satisfy the need to exactly represent the specific rules and concepts in the guideline. For example,
We reviewed the data items defined in the national specification of patients' EHRs of hypertension and analyzed the definitions provided in support documents. By comparing them with the standardized data definitions mentioned above, we found that among the data items extracted from the guideline, 8 items were left out in patients' records: age of family members when hypertension occurred, the cause of death of family members, measurement of urinary trace albumin and urinary albumin/creatinine, daily intake of sodium, and target for body weight control. Additionally, we found 10 data items that have definitions that are different from those of the guideline. Taking
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 [
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.
This research was supported by National High-Tech R&D Program (863 Program) of China (No. 2012AA02A603).
No potential conflict of interest relevant to this article was reported.
Flowchart for routine monitoring of BP in adults. BP: blood pressure, SBP: systolic BP, DBP: diastolic BP.
Browsing and editing class
Content of hypertension guideline
BP: blood pressure, ACEI: angiotensin-converting enzyme inhibitor, ARB: angiotensin receptor blocker.
Data items and their definitions in