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Colorectal cancer screening: overview & background

Colorectal cancer screening: overview & background

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David's Slides from AHRQ Kick-Off Event David's Slides from AHRQ Kick-Off Event Presentation Transcript

  • Colorectal cancer screening: overview & background January 8,2007 David A. Haggstrom, MD, MAS LEADERS SYMPOSIUM “ Strategic Planning to Inform a Funded Project on how to Achieve Workflow Integration in Developing and Implementing CDS for CRC Screening”
  • Outline
    • CRC screening practice guidelines
    • Competing clinical demands for CRC screening
    • Applied research for screening
      • Clinical decision support
        • Facilitators & barriers
      • Practice-based interventions
  • Colorectal cancer screening Clinical practice guidelines
    • Target population : men & women 50 years of age & older at average risk for colorectal cancer
    • Caveat (VA/DoD) : providers should discuss screening with patients ages 80 & older, taking into account estimated life expectancy & presence of co-morbid disease
  • Colorectal cancer screening Clinical practice guidelines (USPSTF) Test Interval
    • Fecal occult blood testing (FOBT)
      • 3 cards done at home
    Annually Flexible sigmoidoscopy Every 5 years Colonoscopy Every 10 years Double-contrast barium enema Every 5 years
  • Competing clinical demands
      • Most clinical practice guidelines (CPGs) did not address their applicability for older patients with multiple comorbidities
      • Most didn’t discuss
        • burden, short- & long-term goals
        • give guidance for incorporating patient preferences into treatment plans (Boyd, Wu, JAMA , 2005)
      • To fully satisfy all USPSTF recommendations
        • 7.4 hrs/working day is needed for the provision of preventive services by physicians
    (Yarnall et al., AJPH , 2003)
  • Computer reminders – Regenstrief Institute
    • Clinical focus : FOBT, mammography, & Pap testing
    • Study design : 6-mo. RCT
    • Population : 31 GIM faculty & 145 residents at Indiana University
    • Intervention : “directed reminders” vs. routine reminders
      • 1) done/order today 3) patient refused
      • 2) NA to patient 4) next visit
    • Primary outcome : compliance with reminder
      • “ directed reminders” overall (46% vs. 38%, p = 0.002)
      • FOBT (61% vs. 49%, p = 0.0007)
    • Secondary outcomes :
      • 21% of time : NA to patient - due to inadequate data in pt’s EMR
      • 10% of time : patient refused
    • Conclusions :
      • Requiring MDs to respond to computer-generated reminders improved their compliance
      • However, 100% compliance with cancer screening reminders will be unattainable due to clinical appropriateness & patient refusal
    (Litzelman, Tierney, JGIM , 1993)
  • Electronic health record – Partners HealthCare Barriers to use
    • 24% of physicians “never/sometimes” used any EHR functionality during patient visit
    • Barriers to EHR use:
      • Loss of eye contact with patients (62%)
      • Falling behind schedule (52%)
      • Computers being too slow (49%)
      • Inability to type quickly enough (32%)
      • Using computer in front of patient is rude (31%)
      • Preferring to write long prose notes (28%)
    (Linder, AMIA Annu Symp Proc , 2006)
  • Computer reminders - VA Facilitators to adherence
    • In VA, overall adherence rate to 15 CRs:
      • 86% (67% - 97%)
      • Variation by clinic, individual clinician, & individual CR
    • Positive influence upon reminder completion rate:
      • full utilization of support staff in completion process
      • receiving frequent individual feedback on completion
    • No influence:
      • provider demographics
      • provider attitudes towards reminders
    (Mayo-Smith, Abha Agrawal, 2004 & 2006)
  • Computer reminders - VA Barriers to reminders
    • HIV clinical reminders
    • Design: ethnographic observations & semi-structured interviews
    • Barriers to effective use:
      • Workload
      • Time to remove inapplicable reminders
      • False alarms
      • Reduced eye contact
      • Use of paper forms rather than software
    (Patterson, Doebbeling, Asch et al., J Biomed Inform , 2005)
  • Colorectal cancer screening Primary care-based interventions
    • Practice-individualized facilitation of implementation of tools:
      • Group randomized clinical trial
      • 77 community family practices
    • Intervention:
      • 1-day practice assessment - nurse facilitator observed practice MDs & staff
      • 1.5 hour meeting with practice day after
      • frequent visits thereafter (unknown dose effect)
    • Outcomes at 12 months
      • Summary scores of preventive service delivery rates:
        • Intervention: 42% vs. 31%
        • Control: 37% vs. 35% (p=0.015)
      • Screening services, (p=0.048), not immunization services
    • Sustained after 24 months
    (STEP-UP - Study to Enhance Prevention by Understanding Practice) (Stange, Goodwin, Am J Prev Med , 2001 & 2003)
  • CRC screening in primary care practices
    • Most CRC screening interventions focus on either
      • patients or individual clinicians
      • without examining the office context
    • Methods:
      • chart review (795 pts eligible for CRC screening)
      • practice surveys (22 family medicine practices)
    • Factors associated with higher CRC screening:
      • Using nursing or health educator staff to provide behavioral counseling
      • Reminder system use
    (Hudson & Crabtree, Can Det Prev , 2007)
  • Conclusions
    • Generally positive, but sometimes mixed, results for clinical, computer reminders
      • Direct observation & qualitative methods provide opportunity to understand potential pathways for effectiveness of clinical reminders
    • Computer reminders
      • Need not only to incorporate evidence base, but address patient preferences & comorbidities
    • Prior positive experience with practice change
      • Computer reminder often key component
      • Team-based approach also important, particularly to help address competing time demands
    • Questions or comments?
  • Systems engineering framework
    • Identify system of interest
    • Choose appropriate performance measure
    • Select best modeling tool
    • Study model properties & behavior under variety of scenarios
    • Make design & operation decisions for implementation
    • previous applications in hemodialysis, radiation therapy, & patient flow modeling
    (Kopach-Konrad, Doebbeling et al., JGIM , 2007)