Roger Brice

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Patient Compliance

• Roger Brice

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Patient Compliance - Summary

• Compliance depends on
• Patient - beliefs, attitudes expectations
• Physician - interaction with patient
• Therapy - form, taste, price, dose schedule
• Physician cannot predict non-compliant patient
• No such person as the non-compliant patient
• Patients fail to comply
• In different ways
• For different reasons
• With different consequences
• Any support to promote compliance must be
  tailored to individual patients

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Understanding Compliance

• Health belief models have proved to be poor
  predictors of compliance
• Patient beliefs, perceptions expectations
  change over time
• Often as a post-event rationalisation to justify
  compliance behaviour
• Conclusion
• Any health belief model should include a
  measurement of beliefs, etc at time of first
  diagnosis as well as at time of compliance
  classification

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Developing a Compliance Programme

• MEASUREMENT
• Defining extent of problem
• eg lost sales, cost of outcome
• UNDERSTANDING
• Determining drivers of compliance
• PREDICTING
• Likely compliance on an individual patient basis
• JUSTIFYING
• Economic arguments to justify cost of programme
  based on the outcome of improved compliance
• PROGRAMME
• Supporting both patient and physician
• Tailored to the needs of individual patients

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Measuring Compliance
Diagnosis 1st Prescription
Initial Drop Out (up to 20)
Rx filled
Rx not filled
Taken as directed
Not taken as directed
Feedback to physician apparent lack of efficacy
(up to 50)
Taken in time frame
Not taken in time frame
most are partial compliers minority
are poor compliers
Drop Out(40 on HRT after 1 year) (50 of
hypertensives after 1 year) (up to 70 of
hypertensives within 1-5 years)
Refill/Repeat Prompt
Refill/Repeat Delayed
No Refill/Repeat
Refill/Repeat Rx
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Understanding Compliance
CHANGE ?
Attitudes beliefs
Attitudes beliefs
usual measurement
causal?
predictive
Diagnosis 1st Script
Measurement of Compliance Status
TIME

• Attitudes beliefs at time of non-compliance may
  reflect a post-event rationalisation
• Therefore causal predictive nature of
  relationship between compliance status and
  attitudes/beliefs at the time of non-compliance
  must be questioned
• Need to also measure attitudes and beliefs at
  time of first prescription

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Developing Strategies to Improve Compliance

• Emotional vs. Problem Focused Coping
• Most strategies are designed for problem focused
  reactions
• Yet many (most?) patients react to problems
  emotionally
• Patients need to be
• Educated about their disease and their therapy
• Reminded to take their therapy
• Physicians need to be
• Able to identify non-compliers
• OR
• Able to predict non-compliers
• AND
• Recognise, and be able to implement, the most
  appropriate strategy,
• on an individual patient basis

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Examples of Strategies to Improve Compliance

• Novo Nordisk/Kliofem
• patient video sent to patients Rxd Kliofem (UK)
• Pfizer/Norvasc
• MEMS - electronic cap for drug container
• Belgium to be extended to Canada
• Bayer/diabetes therapy
• Electronic clinical trial registration device
• MEMS Track Cap
• Novo Nordisk/diabetes
• Disease State Management tools linked to
  electronic medical record (in development)
• Roche
• COMPAGE
• pager-based patient reminder device (in field
  test)
• Lilly/diabetes
• internet
• patient accessed information
• Schering AG/cancer
• internet
• patient group

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Developing an Instrument to Predict Future
Compliance
Outline of a suggested methodology - chronic
therapy area
1. Qualitative research/brainstorming to generate
list of possible predictor variables
(questions) 2. Convert to a structured
questionnaire (eg 40 possible predictor
questions) 3. Interview patients at various
stages of complying with their therapy 4. Multivar
iate method (eg discriminant analysis) to
determine key predictor variables (typically 7 to
12) 5. Convert to PC-based instrument (see
following 2 pages)
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Predicting Future Patient Compliance Hypertension