Thusitha Mabotuwana

An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension

BACKGROUND Hypertension is one of the most prevalent chronic conditions and is directly correlated to deadly risks; yet, despite the availability of effective treatment, there is still clear room for improving patient outcomes. Use of relational databases is widespread for storing patient data, but formulating queries to identify patients whose clinical management can be improved is challenging due to the temporal nature of chronic illness and the mismatch in levels of abstraction between key management concepts and coded clinical data. OBJECTIVE The objective of this work is to develop a sharable and extensible analysis tool that can be used to identify hypertensive patients who satisfy any of a set of evidence-based criteria for quality improvement potential. METHODS We developed an ontology driven framework to enhance and facilitate some important temporal querying requirements in general practice medicine, focusing on prescribing for hypertension. The Web Ontology Language has been used to develop the ontology and the specific queries have been written in Semantic Query-enhanced Web Rule Language. We have used production electronic medical record (EMR) data from a General Medical Practice in New Zealand to populate the ontology. RESULTS A unified patient management ontology consisting of a disease management ontology, a patient data ontology, and a plan violation taxonomy has been created and populated with EMR data. We have queried this ontology to determine patient cohorts that satisfy any of eight quality audit criteria, thereby identifying patients whose clinical management can be improved. A prescription timeline visualisation tool has also been developed to aid a clinician in understanding a patients antihypertensive prescribing patterns, as well as visually validating the query results. CONCLUSIONS The presented framework shows potential to provide answers to clinically relevant queries with complex temporal relationships. The framework can be used to successfully identify hypertensive patients who need to be followed-up/recalled.

What can primary care prescribing data tell us about individual adherence to long-term medication?—comparison to pharmacy dispensing data†

To assess the predictive value of general practice electronic prescribing records with respect to adherence to long‐term medications as compared to claims‐based pharmacy dispensing data.

A computational framework to identify patients with poor adherence to blood pressure lowering medication

BACKGROUND Blood pressure (BP) lowering medications have impressive efficacy in reducing cardiovascular and renal events; but low adherence threatens their effectiveness. Analysis of patterns in electronic prescribing from electronic medical records (EMRs) may have the potential to reveal cohorts of patients with significant adherence problems. METHODS We developed a computational framework to identify patient cohorts with poor adherence to long-term medication through analysis of electronic prescribing patterns. A range of quality reporting criteria can be specified (as an XML document). We illustrate the framework by application to the EMRs of a New Zealand general practice with a focus on adherence to angiotensin-converting enzyme inhibitors (ACE-inhibitors) and/or angiotensin II receptor blockers (ARBs) in patients classified with hypertension and diabetes. We analyse medication supply based on Medication Possession Ratio (MPR) and duration of lapse in ACE-inhibitors/ARBs over a 12-month evaluation period. We describe graphical tools to assist visualisation of prescribing patterns and relationship of the analysis outputs to controlled blood pressure. RESULTS Out of a cohort of 16,504 patient EMRs, 192 patients were found classified with both hypertension and diabetes and under active ACE-inhibitor and/or ARB management. Of these, 107 (56%) patients had an ACE-inhibitor/ARB MPR less than 80% together with a lapse in ACE-inhibitors/ARBs for greater than 30 days. We find non-adherent patients (i.e. MPR <80% or lapse >30 days) are three times more likely to have poor BP than adherent patients (odds ratio=3.055; p=0.012). CONCLUSIONS We have developed a generic computational framework that can be used to formulate and query criteria around issues of adherence to long-term medication based on practice EMRs. Within the context of the example we have used, the observed adherence levels indicate that a substantial proportion of patients classified with hypertension and diabetes have poor adherence, associated with poorer rates of blood pressure control, that can be detected through analysis of electronic prescribing. Further work is required to identify effective interventions using the reporting information to reduce non-adherence and improve patient outcomes.

ChronoMedIt - A computational quality audit framework for better management of patients with chronic conditions

BACKGROUND Quality audit and feedback to general practice is an important aspect of successful chronic disease management. However, due to the complex temporal relationships associated with the nature of chronic illness, formulating clinically relevant queries within the context of a specific evaluation period is difficult. METHODS We abstracted requirements from a set of previously developed criteria to develop a generic criteria model that can be used to formulate queries related to chronic condition management. We implemented and verified the framework, ChronoMedIt, to execute clinical queries within the scope of the criteria model. RESULTS Our criteria model consists of four broad classes of audit criteria - lapse in indicated therapy, no measurement recording, time to achieve target and measurement contraindicating therapy. Using these criteria classes as a guide, ChronoMedIt has been implemented as an extensible framework. ChronoMedIt can produce criteria reports and has an integrated prescription and measurement timeline visualisation tool. We illustrate the use of the framework by identifying patients on suboptimal therapy based on a range of pre-determined audit criteria using production electronic medical record data from two general medical practices for 607 and 679 patients with hypertension. As the most prominent result, we find that 59% (out of 607) and 34% (out of 679) of patients with hypertension had at least one episode of >30day lapse in their antihypertensive therapy over a 12-month evaluation period. CONCLUSIONS ChronoMedIt can reliably execute a wide range of clinically useful queries to identify patients whose chronic condition management can be improved.

A Semantic Web Technology Based Approach to Identify Hypertensive Patients for Follow-Up/Recall

We present an ontology based approach to identify hypertensive patients who show non-adherence to prescribed medication. Using the Web ontology language (OWL), we have developed an ontology that includes patient prescription details, medication possession ratios (MPRs) and blood pressure measurements (together with other patient related information) that has been populated with production electronic medical record (EMR) data from a general medical practice in New Zealand. We have written queries using the semantic query-enhanced Web rule language (SQWRL) to query this ontology to determine patients who have lapsed medication while having a low MPR. We also discuss some practical issues related to patient recall based on EMR data, as well as the suitability of the proposed scheme.

Using the general practice EMR for improving blood pressure medication adherence.

PURPOSE Analysis of practice electronic medical records (EMRs) demonstrated widespread antihypertensive medication adherence problems in a Pacific-led general practice serving a predominantly Pacific (majority Samoan) caseload in suburban New Zealand. Adherence was quantified in terms of medication possession ratio (MPR, percent of days covered by medication supply) from the practices prescribing data. We studied the effectiveness of general practice staff follow-up guided by EMR data to improve medication adherence. METHODS A framework for identification of suboptimal long-term condition management from routinely-collected EMR data, the ChronoMedIt (Chronological Medical Audit) tool, was applied to data of two Pacific-led general practices to identify patients with low MPR. One practice undertook intervention, the other provided usual care. A cohort was based on MPR<80% for antihypertensive medication in a baseline 6-month period. At the intervention practice a team was established to provide reminders and motivation for these patients and discuss their specific needs for assistance to improve adherence for 12 months. MPR and systolic blood pressure (SBP) was collected at baseline and for last six months of intervention based on practice EMRs; national claims data provided assessment of MPR based on dispensing. Nursing notes were analysed, and patient and provider focus groups were conducted. RESULTS Of the 252 intervention patients with MPR<80% initially, MPR improved 12.0% (p=0.0002) and systolic blood pressure was 3.5mmHg lower (p=0.07) as compared to the control cohort. MPR from national claims data improved by 11.5% (p=0.0001) as compared to the control. Patients welcomed the approach as caring and useful. Providers felt the approach worthy of wider deployment but that it required dedicated staffing. DISCUSSION AND CONCLUSIONS Systematic follow-up of patients with demonstrated poor medication possession appears effective in the context of a Pacific-led general practice serving a largely Pacific caseload. It was possible to exploit the EMR database to identify patients with low antihypertensive medication possession and to raise their level of medication possession significantly. The measured effect on systolic BP was only marginally significant, leaving open the question of the precise value of the intervention in terms of morbidity and mortality. The intervention was found to be feasible and was met with good acceptance from the intervention patients, who appreciated the concern reflected in the follow-up effort. The intervention practice is continuing use of ChronoMedIt to guide long-term condition management with extension to cholesterol and blood sugar.