A Framework For Health Care       Measurement                     2012.03.09
Physician’s overestimate performance by at least 10% (30% for LDL in diabetes)         McCrate et al. CFP 2010
Hypertension and BP < 140/90  Steinman et al. Am J Med 2004
Montano et al, 1994 – American Journal of Public Health
Practice Routinely Receives and Reviews Data on                 Patient Clinical OutcomesPercent100          89 75        ...
Practice Routinely Receives Data Comparing           Clinical Performance to Other PracticesPercent100 75          65 50  ...
Don’t just do something, stand there!     Think about the system…
Information Flow - CDM
Measures• Process oriented “Are we doing what we  set out to do?”  – Processes are designed to achieve an    outcome• Outc...
Measures must be connected to         the doers  Meaningful documentation   Value add from system
Point of Care                Practice   Population
Point of Care                Practice   Population
Population Level Measurement• Privacy  – Patient (Can you send de-identified    information without consent?)  – Provider•...
Aggregates are often good enough for the population level• Can’t be reworked or linked (“mining”)• Provides pointer to pop...
Model• Collect aggregate data –  numerator/denominator e.g. 71 of 81  diabetics with A1c measured in last 6  months• Magic...
Data Stewardship Considerations• Physician governance• Choice of metrics• Permissions for viewing• Ownership of data (phys...
FI rewall        Generate                                     AggregateEMR    Aggregates    Interface                     ...
PG Attachment
Visits Per Patient Per Year
FI rewall        Generate       Aggregates                    AggregateEMR                  Interface      from Queries   ...
Aggregates      Replicated                AggregateEMR      EMR Data                   Collector                     Queries
Aggregates       Replicated                   AggregateEMR       EMR Data                      Collector                  ...
Variation• Natural “common cause”  – Biologic (e.g. tight control of BP and blood sugar    different in the elderly/frail ...
Ann Intern Med: 2011;154:627-634
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
E4 Gathering Data Differently:  New Approaches to Data Collection Through Technology - B. Clifford
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E4 Gathering Data Differently: New Approaches to Data Collection Through Technology - B. Clifford

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  • Diabetes composite 2.5%
  • E4 Gathering Data Differently: New Approaches to Data Collection Through Technology - B. Clifford

    1. 1. A Framework For Health Care Measurement 2012.03.09
    2. 2. Physician’s overestimate performance by at least 10% (30% for LDL in diabetes) McCrate et al. CFP 2010
    3. 3. Hypertension and BP < 140/90 Steinman et al. Am J Med 2004
    4. 4. Montano et al, 1994 – American Journal of Public Health
    5. 5. Practice Routinely Receives and Reviews Data on Patient Clinical OutcomesPercent100 89 75 71 68 65 50 43 41 40 25 24 25 17 12 0 UK SWE NZ NET US GER ITA NOR AUS CAN FR Source: 2009 Commonwealth Fund International Health Policy Survey of Primary Care Physicians.
    6. 6. Practice Routinely Receives Data Comparing Clinical Performance to Other PracticesPercent100 75 65 50 39 38 28 26 25 23 25 14 11 3 0 UK SWE FR US NZ NET GER AUS CAN NOR ITA** Question asked differently in Italy. Source: 2009 Commonwealth Fund International Health Policy Survey of Primary Care Physicians.
    7. 7. Don’t just do something, stand there! Think about the system…
    8. 8. Information Flow - CDM
    9. 9. Measures• Process oriented “Are we doing what we set out to do?” – Processes are designed to achieve an outcome• Outcome oriented (sometimes surrogate outcomes) “Are we achieving what we set out to accomplish?”
    10. 10. Measures must be connected to the doers Meaningful documentation Value add from system
    11. 11. Point of Care Practice Population
    12. 12. Point of Care Practice Population
    13. 13. Population Level Measurement• Privacy – Patient (Can you send de-identified information without consent?) – Provider• Right measures• Timely• Available to those who need information
    14. 14. Aggregates are often good enough for the population level• Can’t be reworked or linked (“mining”)• Provides pointer to population which can then apply usual research methods (ethics/consent)
    15. 15. Model• Collect aggregate data – numerator/denominator e.g. 71 of 81 diabetics with A1c measured in last 6 months• Magic of 5 – Don’t contribute data with a denominator of 5 or less – Restrict view of other practices or practitioners unless by invitation or by groups of 5 or more
    16. 16. Data Stewardship Considerations• Physician governance• Choice of metrics• Permissions for viewing• Ownership of data (physicians overwhelmingly want to own it)• Use of data• No warrantee of accuracy
    17. 17. FI rewall Generate AggregateEMR Aggregates Interface Collector from Queries
    18. 18. PG Attachment
    19. 19. Visits Per Patient Per Year
    20. 20. FI rewall Generate Aggregates AggregateEMR Interface from Queries Collector
    21. 21. Aggregates Replicated AggregateEMR EMR Data Collector Queries
    22. 22. Aggregates Replicated AggregateEMR EMR Data Collector Scripts Decision Messaging Support / Broker Reporting Tool
    23. 23. Variation• Natural “common cause” – Biologic (e.g. tight control of BP and blood sugar different in the elderly/frail often not wise) – Preference – Resource availability• System caused – Lack of awareness – Failure to execute
    24. 24. Ann Intern Med: 2011;154:627-634

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