Linking guidelines to Electronic Health Record design for improved chronic disease management.

Linking Guidelines to Electronic Health Record Design for Improved Chronic Disease Management Sistine A. Barretto, BIT(hons)1, Jim Warren, PhD1, Andrew Goodchild, PhD2, Linda Bird, PhD2, Sam Heard, MBBS3, Markus Stumptner, Dr.techn.1 1 Advanced Computing Research Centre, University of South Australia 2 Distributed Systems Technology Centre 3 Ocean Informatics ABSTRACT The promise of electronic decision support to promote evidence based practice remains elusive in the context of chronic disease management. We examine the problem of achieving a close relationship of Electronic Health Record (EHR) content to other components of a clinical information system (guidelines, decision support and workflow), particularly linking the decisions made by providers back to the guidelines. We use the openEHR architecture, which allows extension of a core Reference Model via Archetypes to refine the detailed information recording options for specific classes of encounter. We illustrate the use of openEHR for tracking the relationship of a series of clinical encounters to a guideline via a case study of guideline-compliant treatment of hypertension in diabetes. This case study shows the contribution guideline content can have on problemspecific EHR structure and demonstrates the potential for a constructive interaction of electronic decision support and the EHR. INTRODUCTION A good case can be made for the use of Electronic Health Records (EHRs) in Chronic Disease Management (CDM). A case study that looked into the effect of using electronic data exchange in a diabetes coordinated care environment found that communication between health care providers increased, they had better access to data, and there was a small improvement in patients’ health over a short period of time [1]. The question remains, is it possible to reap further benefits in CDM via the use of guidelines? PRODIGY Phase Two results estimate that if all General Practitioners (GPs; i.e., “family physicians”) prescribed the same way as PRODIGY-compliant GPs on three ‘tracer conditions’, the savings would be approximately £14 million per quarter [2]. Experience in PRODIGY Phase Two, however, indicates challenges for achieving effective decision support for CDM [2-3]. These challenges include the need to provide guidance using information across successive consultations; provision of structured guidance within a minimal user interaction; and providing guidance-position- ing information [3]. The Phase Three architecture aims to address these problems – a key feature being clinical scenarios (patient states) with sets of available actions associated with each scenario. Actions taken indicate scenario transitions for following consultations. Despite the innovations, however, recent evaluation using the scenariobased decision support in general practice shows no effect on management of chronic conditions [4], most likely due to the significant barriers to its usability [5]. Three of the authors have had a related experience from work in one of the Australian Commonwealth’s HealthConnect projects [6]. We observed that, in concert with domain experts, one can design an event summary data collection form that describes all information that is potentially needed for a given event (e.g., GP contact with a diabetes patient). However, clinicians find these unwieldy because the form documents a maximal data set, too much to record in a given consultation, and it is unclear when to record which information. The authors suggest that a promising way of solving this problem is to introduce more specific linkage of the associated guidelines to the EHR content items. In this way, information that is considered a priority for a given encounter can be clearly identified in the point-of-care application. In this paper, we present a model and architecture aimed at facilitating the development of systems to achieve the yet-unrealised potential of guidelines in CDM. Our approach emphasises representation of the content to be recorded in the EHR specific to the role of any given consultation in the CDM process with clear linkage of each provider decision back to the guideline. This model and architecture exploits the openEHR approach to allow extension of the Reference Architecture for specific EHR refinement as requirements are identified. GUIDELINES Guidelines are natural-language documents resulting from a process of consolidating and localising medical evidence. A widely accepted definition of clinical guidelines is: “systematically developed AMIA 2003 Symposium Proceedings − Page 66 descriptive tools or standardized specifications for care to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances” [7]. The technology of electronic guidelines is advancing beyond merely making the guideline be “on line” as multimedia or hypermedia. Such representations include GLIF (GuideLine Interchange Format) [8] and EON [9]. Objectoriented GLIF3 enables guidelines to be abstracted into three levels: (1) conceptual (flowchart representation for human-readability), (2) computable (algorithm), and (3) implementable (integration into a clinical information system). Moreover, GLIF3 supports linkage to other domain ontologies such as the HL7 v3 Reference Information Model (RIM), medical vocabularies (e.g. UMLS) and knowledge bases. EON is also object-oriented, uses flowchart representation and an ontology approach to mapping patient data encoded in guidelines to an external EHR. EON also supports reusability of medical domain knowledge, temporal queries and abstractions. Current guideline models vary depending on the type of processes they try to express. A typology of four modelling formalisms used by guideline models is identified in [10]: (1) flowcharts for algorithmic problem-solving processes; (2) diseasestate maps to relate decisions made during the course of patient care; (3) sequencing of activities in care plans that aim to meet goals; and (4) workflows to model care processes in an organisation. We take the position that, in general, engineering of a given guideline for use in clinical information systems with electronic decision support produces a number of artefacts (figure 1). Guideline EHR content Workflow schemas Computerinterpretable clinical guidelines (CIGs) Hypermedia (human-readable electronic guidelines) Figure 1. Artefacts from engineering of guidelines Guidelines allow us to specify what needs to be recorded (EHR content), when to record, and how to evaluate/make decisions (computer interpretable clinical guidelines, CIGs), and what needs to be done (workflow schemas that may include a combination of clinician and system dependent actions). Also, we can produce a human-readable electronic version of the guideline as hypermedia. Maintaining a clear relationship among these artefacts during the execution of the system is key to successful computer support in CDM. iour of healthca (...truncated)