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A Framework for Assessing Adherence and Persistence to Long-Term Medication
Thusitha Mabotuwana
With: Prof. Jim Warren
31 August 2009
A Framework for Assessing Adherence and Persistence to Long-Term - - PowerPoint PPT Presentation
A Framework for Assessing Adherence and Persistence to Long-Term - - PowerPoint PPT Presentation
A Framework for Assessing Adherence and Persistence to Long-Term Medication Thusitha Mabotuwana With: Prof. Jim Warren 31 August 2009 1 A bit of background Chronic illness is the leading cause of death worldwide - affected 35m (60%) of
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The opportunity
New Zealand in top tier on use of computing in
General Practice medicine (near 100%)
Prescribing and results of tests ordered systemically
present in Practice Management System (PMS)
– Anecdotally, the quality of the record continues to
improve on more ‘voluntary’ fields (e.g., Blood Pressures [BPs], diagnoses)
So the PMS data should tell us a lot about how well a
patient is being managed
Schoen C et al. On the front lines of care: primary care doctors' office systems, experiences, and views in seven countries.. Health Aff (Millwood). 2006 Nov–Dec;25(6):w555-71.
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Adherence and persistence
Adherence refers to coverage of medication
during a particular time period
Persistence is an indication of the time of
continuous therapy.
We all know that taking our medication on
time is important to achieve the full benefit of them – patients need to follow prescribed treatment regimens reasonably closely.
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How should we measure adherence?
No existing gold standard for measuring
adherence
Medication Possession Ratio (MPR) is widely
used as a measure of adherence to long- term medication, such as AHT medication
Andrade SE, Kahler KH, Frech F, Chan KA: Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiology and drug safety 2006, 15(8): 565-574; discussion 575-567
Number of days supply held during evaluation period Number of days in evaluation period X 100
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MPR calculation considerations
We consider only that portion of coverage
which overlaps with evaluation period
Evaluation Period (EP) (12 months) Run-in Period (6 months) AHT Pr1 AHT Pr2 AHT Pr3 AHT Pr4
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MPR calculation considerations
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Traditional MPR will be 90 x 5 / 365 = 123% ‘Our’ MPR is [365 – (30+35)]/365 = 82%
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MPR considerations
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Only patients on monotherapy are considered “to reduce the complexity in measuring medication adherence” Patients on concurrent therapy are excluded due to the difficulty “to define adherence for more than 1 medication concurrently”
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Identifying lapses
A lapse should be running-into, during or at
the end (on-going) of the evaluation period
Evaluation Period (EP) (12 months) Run-in Period (6 months) AHT Pr1 AHT Pr2 AHT Pr3 AHT Pr4 Lapse1 Lapse2 Lapse3
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Framework architecture
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Drug and classification knowledge bases
Mabotuwana, T. and Warren, J., An Ontology Based Approach to Enhance Querying Capabilities of General Practice Medicine for Better Management of
- Hypertension. To appear in: Artificial Intelligence in
Medicine, 2009 (epub available online)
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Specifying criteria details in XML
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Lapse constraints MPR constraints Drugs and diagnoses
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Framework testing and verification
Boundary value analysis (BVA) – eg: 15, 29, 30, 31,
45 day lapses
Equivalence class testing – eg: 45-day nominal
lapses prior to, during and after EP
All-pairs testing – eg: a lapse prior to EP together
with a lapse during EP
Random testing – for 100,000 patients, 41 different
criteria representing the four criteria classes
169 cases covering the first three testing techniques
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Practice adherence and persistence rates
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Maximum lapse duration (days) MPR <80% ≥80% ≥30 235 (27%) 70 (8%) <30 3 (<1%) 573 (65%)
- Antihypertensive adherence
- EP = 1-May-08 to 30-April-09
- 6-month run-in
Maximum lapse duration (days) MPR <80% ≥80% ≥30 398 (42%) 109 (12%) <30 5 (1%) 428 (45%) Npractice-1 = 881 Npractice-2 = 940
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Individual patient adherence and persistence rates
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- ACEi/ARB adherence
- EP = 1-April-06 to 31-March-07
- HT and DM diagnoses
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16 An interactive visualisation tool – non-adherent patient
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17 An interactive visualisation tool – adherent patient
Combination drugs
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Prescribing-dispensing matching
Prescription drugs will work only if you take
them
Some patients collect their prescriptions, but
fail to fill the scripts at the pharmacy
Prescription based adherence calculations
are useful – PPV 81%, NPV is 76%
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Mabotuwana, T., Warren, J., Harrison, J. and Kenealy, T., What Can Primary Care Prescribing Data Tell Us about Individual Adherence to Long-Term Medication? – Comparison to Pharmacy Dispensing Data. Pharmacoepidemiology and Drug Safety, 2009 (Pubmed ref #19609958)
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Key messages
- There’s lots of good information in routinely
collected EMR data that can be used to identify chronic patients whose clinical outcomes can be improved
- The framework can be used to identify cohorts
- f patients with poor adherence and persistence
rates
- Currently looking at a feasibility study to identify
issues behind poor adherence and persistence
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Contact, Further Reading
Thusitha Mabotuwana
thusitha@cs.auckland.ac.nz
Methods/results of two recent studies:
– Mabotuwana, T. and Warren, J., ChronoMedIt – A Computational Quality
Audit Framework for Better Management of Patients with Chronic
- Conditions. Journal of Biomedical Informatics, 2009 (epub available
- nline)
– Mabotuwana, T., Warren, J. and Kennelly, J., A Computational
Framework to Identify Patients with Poor Adherence to Blood Pressure Lowering Medication. International Journal of Medical Informatics, 2009 (epub available online)
Opinion/review piece:
- Warren J, ‘General Practice EMRs: What they can tell us, and how,’ Health
Care and Informatics Review Online, December 2007
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