Developing a Quality Audit Report for General Practice Prescribing for Hypertension: Methodology


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Prof Jim Warren
National Institute for Health Innovation, The University of Auckland

With Rekha Gaikwad, Thusitha Mabotuwana, John Kennelly, Timothy Kenealy

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  • Developing a Quality Audit Report for General Practice Prescribing for Hypertension: Methodology

    1. 1. Developing a Quality Audit Report for General Practice Prescribing for Hypertension: Methodology Prof Jim Warren National Institute for Health Innovation, The University of Auckland With Rekha Gaikwad, Thusitha Mabotuwana, John Kennelly, Timothy Kenealy
    2. 2. ‘ Business Intelligence’ <ul><li>Or we can use some other terms </li></ul><ul><ul><li>Quality assurance </li></ul></ul><ul><ul><li>Continuous process improvement </li></ul></ul><ul><ul><li>Data analysis </li></ul></ul><ul><ul><li>Data mining </li></ul></ul><ul><li>Basically, finding out what’s going on by looking at your computer data (and using that as a basis for change to improve things) </li></ul><ul><li>Great opportunity in NZ (and Aussie) General Practice because we’re world leaders in uptake of General Practice computing </li></ul><ul><ul><li>Reliable prescriptions and lab results, improving problem classifications and observations </li></ul></ul>
    3. 3. Minlaton <ul><li>I’ve been looking at clinical data entry and patterns in GP data since ’89, but recent context starts with… </li></ul><ul><li>Minlaton Medical Centre </li></ul><ul><ul><li>Caters to a combination of farmers and retirees </li></ul></ul><ul><ul><li>Two full-time GPs in 2003-2004 (now three) </li></ul></ul><ul><ul><li>Worked with the GPs to introspect with them on their antihypertensive prescribing </li></ul></ul><ul><ul><li>Using the model of their practice as reflected in their Practice Management System (PMS) data </li></ul></ul><ul><ul><li>With the goal of identifying areas to improve </li></ul></ul>
    4. 4. Tracing individual ‘therapeutic paths’ Nb. Chose these because they’re confounding… most individuals have simple and sensible patterns
    5. 5. ‘ High specificity’ alerts <ul><li>It’s a problem if the software is ‘trigger happy’ </li></ul><ul><ul><li>GPs learn to ignore a plethora of naïve alerts </li></ul></ul><ul><li>Used iterative review of actual therapeutic paths and guidelines to devise a system of alerts </li></ul><ul><ul><li>Aim: to have alerts that are often a cause for action, and virtually always viewed as at least ‘having merit’ </li></ul></ul><ul><li>ALERT </li></ul><ul><li>Alert date: 19 Jan 2005 </li></ul><ul><li>Beta-blocker prescribed after Asthma Dx and regular Asthma </li></ul><ul><li>Medications </li></ul><ul><ul><li>Beta-blocker on 19-Jan-05 </li></ul></ul><ul><ul><li>Asthma Dx on 25-Nov-99 </li></ul></ul><ul><ul><li>Tested by having the GPs blindly critique a sample of cases (and then revealed the alerts – which were on half the cases – and got their feedback) </li></ul></ul><ul><ul><li>87%-96% specificity </li></ul></ul>
    6. 6. So why doesn’t everybody make sense? <ul><li>A range of common stories (from Minlaton) </li></ul><ul><ul><li>Known non-compliance </li></ul></ul><ul><ul><li>Historical arc of patient has taken them to an unusual (but sometimes acceptable) therapeutic mix (it’s tolerated, so don’t change it) </li></ul></ul><ul><ul><li>Extra medication supply through a specialist (in Adelaide, in the Minlaton case) or sample packs </li></ul></ul><ul><ul><li>Historically coded problems may not turn out to be all that severe </li></ul></ul><ul><li>But also, it’s really hard for GP to see all medication persistence and all indicating and contraindicating problems for a substantially chronic/complex patient </li></ul>
    7. 7. Healthwest Fono <ul><li>Largely Pacific practice in West Auckland </li></ul><ul><li>Looking at development of criteria for a quarterly quality audit report around antihypertensive prescribing </li></ul><ul><ul><li>Provide baseline for quality improvement </li></ul></ul><ul><ul><li>Identify specific cases for recall or adjustment at next visit </li></ul></ul><ul><li>Goal is to reduce CVD events (e.g., stroke, MI), and also renal protection </li></ul>
    8. 8. Criteria <ul><li>For a 3-month Evaluation Period some quality improvement opportunities on patients classified with hypertension are indicated by: </li></ul><ul><ul><li>A lapse in anti-hypertensive therapy of >30 days and the lapse extends into the Evaluation Period </li></ul></ul><ul><ul><li>Classified with diabetes mellitus and not on ACEi/ARB at any time during Evaluation Period </li></ul></ul><ul><ul><li>On thiazide(s) and with serum uric acid > 0.42mmo/l at any time during Evaluation Period and not on Allopurinol or Colchicine </li></ul></ul>
    9. 9. Beyond the normal Query Builder <ul><li>These queries are hard to get right with conventional technology </li></ul><ul><li>Mostly we have SQL (structured query language) </li></ul><ul><ul><li>An ISO standard supported by most database management systems with only minor variations) </li></ul></ul><ul><ul><li>SQL has us SELECT <what fields we want> FROM <what tables hold them> WHERE <a certain logical condition applies> </li></ul></ul><ul><li>Great for most things </li></ul><ul><ul><li>We can handle a fixed time range with the WHERE clause (date>=begin and date<=end) </li></ul></ul><ul><ul><li>But harder for discussing time intervals based on data – have to compare rows in a table with other rows in the same table </li></ul></ul><ul><ul><ul><li>This is necessary to query medication persistence (comparing date of one prescription to date of another prescription) </li></ul></ul></ul>
    10. 10. Temporal Queries <ul><li>Explosion of Options </li></ul><ul><ul><li>It is impractical to aspire to a “complete” set of statistics that describe every temporal nuance of the relationships amongst all relevant clinical concepts </li></ul></ul>
    11. 11. ‘ Ontology’ management <ul><li>Significant challenges in capturing all necessary codes </li></ul><ul><ul><li>Problem codes (e.g., all relevant codings of diabetes; maybe excluding gestational) </li></ul></ul><ul><ul><li>Medications (all agents in each therapeutic category, at the desired granularity, possibly not the eye drops) </li></ul></ul><ul><li>These are problems of ontology – the relationships among concepts (esp. subsumption of one group of concepts under another) </li></ul><ul><li>Will be aided significantly if we have widespread uptake of SNOMED </li></ul><ul><ul><li>SNOMED can relate every medical term into a network of semantic relationships </li></ul></ul><ul><ul><li>But still will require technology integration (to use this huge network) and careful query quality assurance (e.g., “hypertension” is part of the term “pulmonary hypertension”) </li></ul></ul>
    12. 12. HealthWest Fono Findings: an Example Case <ul><li>Agree </li></ul><ul><li>CVR 20% </li></ul><ul><li>BP too high </li></ul><ul><li>Needs ACE </li></ul>Yes No Yes 1. A lapse in AHT of >30 days during the EP or the lapse extends into the EP 2. A period of >180 days with no BP measurements extending into the EP Practice Assessment The data from the PMS satisfactorily explains the therapy. The therapy is optimized, or the process of seeking optimized treatment is satisfactory The therapy is free of clinically significant contraindications and interactions. Criteria Assessment
    13. 13. Performance of Criteria Ability to detect positive cases Meaning of a negative result Criteria Assessment Suboptimal therapy (+) Optimal therapy (-) Sensitivity = 76% Specificity = 70% Positive Predictive Value = 65% Negative Predictive Value = 80% 13 4 Suboptimal therapy (+) 7 16 Optimal therapy (-) Practice Assessment
    14. 14. Results Summary <ul><li>Adherence issues dominate </li></ul><ul><ul><li>Forming basis of an HRC application </li></ul></ul><ul><ul><li>Want to proactively remind patients with poor past persistence, particularly to get them back to tune their therapy </li></ul></ul><ul><li>A lot of thought-provoking cases </li></ul><ul><ul><li>Questions around gout, ACEi, thiazides, eGFR, allopurinol that I won’t attempt to get right here </li></ul></ul><ul><ul><li>Seems to be valuable for finding cases on the boundaries of the available evidence </li></ul></ul><ul><ul><li>Possibly good for raising further clinical research questions (highlighting, and quantifying, substantial groups where the evidence is ambiguous) </li></ul></ul>
    15. 15. Building a temporal query support system <ul><li>The types of temporal queries needed fit some very specific categories </li></ul><ul><ul><li>Missing a compellingly indicated treatment (or presence of contra- indication) </li></ul></ul><ul><ul><li>Lack of medication persistence </li></ul></ul><ul><ul><li>Observations persistently outside of goal </li></ul></ul><ul><ul><li>Lack of monitoring observations </li></ul></ul><ul><li>Aim is to develop an easy to use interface tailored to the required concepts </li></ul><ul><ul><li>And that maps the relevant high-level concepts to the specific (and validated) query schemas and detailed sets of problem and drug codes </li></ul></ul>
    16. 16. Adherence <ul><li>Obviously, this is just one step on an adherence pathway </li></ul>Your Prescribing Other docs’ Prescribing Dispensing Reliable Consumption It’s but one step, but it is along the road to more reliable chronic disease management outcomes
    17. 17. Thanks, questions? <ul><li>Prof Jim Warren – </li></ul>??? The Minlaton work: Gadzhanova, S., Iankov, I., Warren, J., Stanek, J., Misan, G., Baig, Z. & Ponte, L., “Developing High-Specificity Anti-hypertensive Alerts by Therapeutic State Analysis of Electronic Prescribing Records,” Journal of the American Medical Informatics Association , Vol 14, No 1, Jan/Feb 2007, pp. 100-109