ChronoMedIt : a computational quality audit framework for better management of patients with chronic disease

Abstract

Chronic disease is a leading cause of death worldwide accounting for around 60% of all deaths. An important aspect of successful chronic disease management is quality audit and feedback to clinicians. However, due to the complex temporal relationships inherent in chronic disease, formulating clinically relevant queries is difficult using the querying tools often built into commercial practice management systems. The onset of this PhD research involved working with staff of a general practice clinic to develop a set of explicit quality audit indicators for blood pressure control. Eight indicators were identified as most relevant to the practice. The ability to compute these indicators reliably from routinely collected electronic medical records (EMRs) was validated by clinical panel assessment. These eight indicators informed formulation of a model of chronic disease audit with four broad classes of indicators: (1) persistence to indicated medication; (2) timely measurement recording; (3) time to achieve target; and (4) measurement contraindicating therapy. The four broad indicator classes have been implemented within the ChronoMedIt (Chronological Medical audIt) framework as an extensible and configurable architecture. The main components of the ChronoMedIt architecture are: an XML based specification for indicator formulation (with an associated XML-Schema), a drug and classification knowledge base maintained using Semantic Web technologies, a C# based criteria processing engine, a SQL-Server based patient database with related stored procedures and a graphical user interface to formulate queries and generate reports. ChronoMedIt can produce patient-specific audit reports as well as reports to benchmark an entire practice for a given evaluation period. A visualisation tool has been developed to provide an alternate representation of patient prescribing and measurement histories. By modifying the indicator specification and knowledge base an analyst can address a wide array of chronic disease management queries as specific instances of the four broad indicator classes. The framework's core computation has been verified using redundant query implementations on a battery of simulated case data and is illustrated against the EMRs of several practices. ChronoMedIt has been applied in several real-world settings; notably, identifying patients with poor antihypertensive medication adherence profiles for a feasibility study of nurse-led adherence promotion.