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Can we measure structured chronic care ?

• Michel Wensing
• Jochen Gensichen
• John Tooker

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Contents the workshop

• Why measurement is important
• Patient and provider reports on chronic care
• Examples from U.S.A, and Europe
• Discussion on desired research and implications
  for practice and policy

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Why measure chronic care ?

• To be able to optimize it (formative evaluation
  and internal improvement)
• To show its value (summative evaluation for
  transparancy, contracts, public reporting, P4P)
• But is it measurable? Some say that structured
  chronic care is too complex to be measured

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Specific challenges for measurement

• Chronic care often includes a range of health
  professionals - certainly from a patient or
  system perspective
• Chronic care implies things that may be absent
  and unknown to patients (and perhaps providers)
• Measure disease specific or generic aspects of
  chronic care?

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Netherlands PACIC questionnaire in general
practice

• Validation study in 165 patients from 4 practices
• Wensing
• Van Lieshout
• Jung
• Hermsen
• Rosemann

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Methods

• Diabetes patients and COPD patients, randomly
  sampled from practice registers
• PACIC (20 items) forward and backward
  translation, interviews with 15 patients, and
  adaptations
• Postal survey with reminders (70 response rate)

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Description of the patients (n165)

Mean age (SD) 68.0 (10.3)
Percentage women 47
Percentage medium/high education 36
Percentage good/excellent health status 55
Percentage who rated GP care as excellent 57
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Floor and ceiling effects (examples)
in lowest category in highest category
PA given choices about treatment to think about 25 20
DS given a written list of thinkgs I should do to improve my health 39 24
GS Encouraged to go to a specific group or class to help me copy with my chronic illness 76 10
FU Encouraged to attend programmes in the community that could help me 78 10
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PACIC domains metrics
Overall PA DS GS PS FU
Nr items 20 3 3 5 4 5
Mean 2.9 3.2 3.5 2.5 3.3 3.1
Missing 31 21 20 25 28 25
Alpha 0.93 0.85 0.75 0.81 0.87 0.71
ICC 0.91 0.85 0.66 0.76 0.86 0.66
Europep effect
PEI effect - NS - - - -
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Diabetes versus COPD patients

• Diabetes patients scored higher than COPD
  patients on 14 of the 20 PACIC items
• This might be explained by better structured
  chronic care for diabetes patients, or by patient
  characteristics

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Conclusions

• A translated and validated Dutch version of PACIC
  is available
• Reasonably good measurement characteristics, but
  some problems
• About 25 non responders
• Floor and ceiling effects
• Unexpected assocation with PEI

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Chronic care and physician workload

• Secondary analysis of EPA data from 140 practices
  in 10 countries
• Wensing
• Van den Hombergh
• Van Doremalen
• Grol
• Szescenyi

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Chronic care and physician workload in European
primary care

• Secondary analysis of data from the EPA project

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Background

• Delivery of chronic care is an important task of
  primary care
• Primary care practices are relatively small
• A higher volume of chronic patients may be
  associated with better performance and higher
  efficiency
• Many factors could influence such associations
  international research needed

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Methods

• Data from 140 practices in 10 countries
  (convenience samples)
• Physician workload working hours per 1000
  yearly attending patients
• Post-hoc measures based on EPA to measure aspects
  of the chronic care model
• Practice size number of yearly attending
  patients
• Non-physician staff total units of full time
  equivalance staff in the practice
• Mixed linear regression analysis models

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Some descriptive figures (n140 practices)
Mean
Yearly attending patients 4337
Physician hours / 1000 patients 15.0
Fte Non physician staff / 1000 patients 0.81
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Structured chronic care (n140 practices)
Theoretical range Mean
Presence of staff in team meetings 0 6 1.5
Procedures for preventive services 0 5 3.0
Use of disease classification 0 6 3.0
Use of email and internet 0 - 3 2.2
Computerized medical records 0 3 2.9
Use of advanced sotfware 0 3 2.4
Access to sources of evidence 0 4 2.3
Use of patiëet education materials 0 - 4 2.8
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Main findings

• Practice size was the single most important
  predictor of physician workload per 1000
  patients each additional 1000 patients was
  associated with 1.29 fewer working hours per week
  per 1000 patients
• More non-physician staff was associated with
  higher physician workload each additional 0.1
  fte led to an additional 1.6 physician hours per
  week per 1000 patients

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Conclusions

• Practice size, not chronic care delivery, was the
  most important determinant of physician worklload
• Warning observational research
• Physician workload per 1000 patients is a proxy
  measure for physician efficiency larger
  practices are more efficient
• Involving more nurses in primary does not imply
  reduced physician workload, and may in fact imply
  higher workload

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• www.topaseurope.eu

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Discussion

• Further research and development
• Implementation in policy and practice