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Research ArticleOriginal Research

Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case

Alex H. Krist, Steven H. Woolf, Camille Hochheimer, Roy T. Sabo, Paulette Kashiri, Resa M. Jones, Jennifer Elston Lafata, Rebecca S. Etz and Shin-Ping Tu
The Annals of Family Medicine May 2017, 15 (3) 217-224; DOI: https://doi.org/10.1370/afm.2063
Alex H. Krist
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
2Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia
MD, MPH
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  • For correspondence: ahkrist@vcu.edu
Steven H. Woolf
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
2Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia
3Center on Society and Health, Virginia Commonwealth University, Richmond, Virginia
MD, MPH
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Camille Hochheimer
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
4Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia
BA
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Roy T. Sabo
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
4Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia
PhD
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Paulette Kashiri
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
MPH
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Resa M. Jones
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
2Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia
MPH, PhD
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Jennifer Elston Lafata
5Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina Chapel Hill, Chapel Hill, North Carolina
6Lineberger Comprehensive Cancer Center, University of North Carolina Chapel Hill, Chapel Hill, North Carolina
7Institute for Healthcare Quality Improvement, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, North Carolina
PhD
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Rebecca S. Etz
1Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, Virginia
PhD
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Shin-Ping Tu
8School of Public Health, University of Washington, Seattle, Washington
MD, MPH
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  • Figure 1
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    Figure 1

    Conceptual model for engaging patients through an informed decision-making module embedded in a patient portal and electronic health record.

    Note: To engage patients in their decision, the informed decision-making (IDM) module guides patients and clinicians through a series of 7 steps that can be applied to a wide range of decisions beyond the test case (cancer screening) investigated in this study. The IDM module (1) reaches patients outside the confines of an office visit to explore a potential decision by completing the module; (2) walks patients through an intake that assesses personal preferences, knowledge, and needs, and patients’ readiness to make a decision; (3) provides personalized educational material tailored to patients’ stated preferences and decision stage; (4) allows patients to share their preferences and decision needs with their clinician; (5) prompts patients and clinicians to use the reported information to make a decision; (6) guides the patient to make a choice, which can include deferring the decision; and (7) invites patients and clinicians to provide input after the encounter.

  • Figure 2
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    Figure 2

    Relationship of the informed decision-making module with follow-up visits and with breast, colorectal, and prostate cancer screening.

    PSA = prostate-specific antigen.

    Note: Percentages of patients who received screening tests were derived from electronic health record data for a period of 3 months after completion of the decision module. Although the colorectal cancer screening rate appears low, this study included only the subset of practice patients overdue for that screening. On the basis of prior studies and practice quality program participation, about 70% of patients in the study practices have been screened for colorectal cancer.46

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    Table 1

    Patients Starting and Completing the Informed Decision-Making Module by Study Phase and Cancer Screening Decision

    Module UseCancer Screening Decision
    ColonBreastProstateTotal
    Overall use: phases 1, 2, and 3 combineda
    Eligible, No.6,3293,7331,396 11,458
    Starters, No. (%)1,249 (19.7)638 (17.1)468 (33.5)2,355 (20.6)
    Completers, No.489190224903
     Of starters, %39.29.847.938.3
     Of eligible, %7.75.16.07.9
    Phase 1: prompt on MyPreventiveCare log-ina
    Eligible, No.5422971711,010
    Starters, No. (%)154 (28.4)70 (23.6)86 (50.3)310 (30.7)
    Completers, No.39122172
     Of starters, %25.317.124.423.2
     Of eligible, %7.24.012.37.1
    Phase 2: prompt via e-mail before appointmenta
    Eligible, No.35417185610
    Starters, No. (%)140 (39.5)53 (31)53 (62.4)246 (40.3)
    Completers, No.751935129
     Of starters, %53.635.966.052.4
     Of eligible, %21.211.141.221.1
    Phase 3: prompt via e-mail without an appointmenta
    Eligible, No.5,1363,2201,0809,436
    Starters, No. (%)469 (9.1)264 (8.2)189 (17.5)922 (9.8)
    Completers, No.1747787338
     Of starters, %37.129.246.036.7
     Of eligible, %3.42.48.13.6
    • ↵a Phases 1, 2, and 3 combined: January 2, 2014, through August 15, 2014. Phase 1: January 2, 2014, through February 16, 2014. Phase 2: February 17, 2014, through May 25, 2014. Phase 3: May 26, 2014, through August 15, 2014.

    • Note: The difference in starting and completing the decision module was statistically different for breast vs colorectal (P =.001), breast vs prostate (P <.001), and colorectal vs prostate cancer (P <.001) screening decisions. The difference in starting and completing the decision module was statistically different for phase 1 vs phase 2 (P <.001), phase 1 vs phase 3 (P <.001), and phase 2 vs phase 3 (P <.001).

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    Table 2

    Use of the Informed Decision-Making Module by Demographic Characteristics

    CharacteristicModule Starters Module Nonstarters
    NoncompletersCompleters
    Age, mean (SD), y53.5 (8.3)54.9 (8.2)52.4 (8.2)
    Total, No. (%)1,452 (12.7)903 (7.9)9,103 (79.4)
    Sex
     Male595 (13.9)472 (11.1)3,201 (75.0)
     Female857 (11.9)431 (6.0)5,902 (82.1)
    Race, No. (%)
     Asian132 (11.4)60 (5.2)966 (83.4)
     African American62 (12.6)63 (12.8)368 (74.6)
     White1,019 (13.7)618 (8.3)5,788 (78.0)
     Other67 (11.7)43 (7.5)464 (80.8)
     Unreported172 (9.5)119 (6.6)1,517 (83.9)
    Ethnicity, No. (%)
     Hispanic43 (9.7)38 (8.5)364 (81.8)
     Non-Hispanic1,065 (13.5)658 (8.4)6,153 (78.1)
     Unknown344 (11.0)207 (6.6)2,586 (82.4)
    Language, No. (%)
     English1,249 (13.3)770 (8.2)7,375 (78.5)
     Other203 (9.8)133 (6.5)1,728 (83.7)
    Insurance type, No. (%)
     Commercial1,304 (13.8)782 (8.3)7,349 (77.9)
     Medicare92 (17.2)69 (12.9)374 (69.9)
     Medicaid1 (20.0)0 (0)4 (80.0)
     None55 (3.7)52 (3.5)1,376 (92.8)
    Prior screening, No. (%)
     Yes538 (14.3)347 (9.3)2,861 (76.4)
     No914 (11.9)556 (7.2)6,242 (80.9)
    • Note: Given the large sample size, all differences across groups (noncompleters, completers, and nonstarters) were statistically significant (P <.001) with the exception of Medicaid insurance type.

    • View popup
    Table 3

    Satisfaction With the Informed Decision-Making Module and Reported Impact on Care

    Statement or MeasurePatient Agreement, % (n = 277)aClinician Agreement, % (n = 281)b
    Doctor believed to have seen response summary at time of appointment
     Yes57.850.0
     No21.150.0
     Cannot remember21.10
    Doctor discussed screening test at visit
     Yes70.765.5
     No20.724.6
     Cannot remember8.69.9
    How use of module changed the conversation
     Motivated patient to talk with doctor39.041.3
     Prompted doctor to talk with patient28.139.7
     Did not change anything47.333.3
     Other2.19.5
    How conversation helped patient with fears or worries ranked as most important on module
     Reduced fears or worries80.9NA
     Did not help with fears or worries19.1NA
    Doctor recalled addressing patients’ fears or worries about cancer screening
     YesNA39.0
     NoNA29.3
     Cannot rememberNA31.7
    Strongly/somewhat agree vs strongly/somewhat disagree regarding completion of module and forwarding of summaryc
     Look and layout were easy to understand56.0 vs 9.7NA
     Took too long to complete34.3 vs 29.2NA
     Was easy to complete72.2 vs 11.1NA
     Helped patient with cancer screening decision42.6 vs 20.444.9 vs 8.2
     Made visit more productive40.7 vs 17.638.1 vs 16.3
     Got patient more involved with the decision47.7 vs 17.651.7 vs 6.1
     Helped to change patient’s screening plans22.7 vs 30.113.6 vs 17.0
     Improved patient-doctor communication37.5 vs 16.742.2 vs 12.2
     Improved patient’s knowledge before visit48.1 vs 15.745.6 vs 6.1
     Made the doctor more sensitive to patient’s needs27.3 vs 11.648.3 vs 10.2
    • NA = not applicable.

    • ↵a Response rate = 44.7%.

    • ↵b Response rate = 45.3%.

    • ↵c Response options were strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, and strongly disagree. Values for neither agree nor disagree are not reported in table.

Additional Files

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  • The Article in Brief

    Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case

    Alex H. Krist , and colleagues

    Background In this study, researchers examine the potential of health information technology to systematically guide patients through decision making processes for three cancer screening choices. Specifically, the study evaluated how clinicians and patients used an automated decision module that promoted the 2012 prostate, 2009 breast and 2008 colon cancer screening recommendations made by the U.S. Preventive Services Task Force and how that module effected care.

    What This Study Found Although automated decision aids have the potential to make office visits more efficient and effective, cultural, workflow and technical changes are needed before widespread implementation. Practices had a large decision burden -- with one in five patients facing a cancer screening decision over the one-year study period. Yet, of the 11,458 patients who faced a screening decision for colorectal cancer, breast cancer or prostate cancer, only 21 percent started and 8 percent completed the decision module. User data showed patients reviewed a range of topics while in the module and 47 percent of the module completers elected to forward a summary to their clinician. After their next office visit, both patients and clinicians reported that module completion helped with decisions: 41 percent said it made their appointment more productive, 48 percent said it helped engage them in the decision, 48 percent said it broadened their knowledge and 38 percent said it improved communication.

    Implications

    • The authors conclude that while the model is appealing, a clear challenge is getting patients to use such a system. If future research confirms the benefits of this approach -- yielding more informed patients, better decisions and wiser use of encounter time --the return on investment could offset the implementation costs and improve care.
  • Supplemental Appendixes

    Supplemental Appendixes

    Files in this Data Supplement:

    • Supplemental data: Appendixes - PDF file
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Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case
Alex H. Krist, Steven H. Woolf, Camille Hochheimer, Roy T. Sabo, Paulette Kashiri, Resa M. Jones, Jennifer Elston Lafata, Rebecca S. Etz, Shin-Ping Tu
The Annals of Family Medicine May 2017, 15 (3) 217-224; DOI: 10.1370/afm.2063

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Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case
Alex H. Krist, Steven H. Woolf, Camille Hochheimer, Roy T. Sabo, Paulette Kashiri, Resa M. Jones, Jennifer Elston Lafata, Rebecca S. Etz, Shin-Ping Tu
The Annals of Family Medicine May 2017, 15 (3) 217-224; DOI: 10.1370/afm.2063
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