Cancer Screening Adherence through Technology-Enhanced ...
A. SPECIFIC AIMS
Colorectal cancer screening (CRCS) is usually discussed between the patient and the primary care (PC) physician in the context of a clinic visit. However, PC physicians face a challenge in promoting CRCS in the face of multiple competing demands [1]. Also, PC physicians’ tendency to recommend colonoscopy over other equally viable CRCS test options could negatively impact shared decision making (SDM) and lead to lower CRCS adherence when patients’ preferences do not match colonoscopy [2, 3]. An intervention that provides decision support and incorporates patient preferences in CRCS test options can aid SDM and may be effective at increasing CRCS rates across diverse populations, as recommended by a recent National Institutes of Health (NIH) State-of-the-Science Conference statement [4].
We have developed Colorectal Web (CW), an interactive decision aid (DA) for CRCS designed to be used prior to a clinic visit to clarify patients’ preferences and promote SDM [5-7]. CW provides a unique interactive Preference Clarification Tool, which helps patients determine the CRCS test option that best matches their preferences. CW was tested in a pilot randomized controlled trial (RCT), which showed that patients using CW were more likely to undergo CRCS than those who used a standard, non-interactive website. This increase in CRCS was similar in Caucasians and African Americans; thus, CW could have higher impact in the latter group, which suffers from higher colorectal cancer (CRC) burden [7]. To our knowledge, our pilot RCT was the first published study to show that a preference-tailored DA improved CRCS adherence. However, patients who used CW did not always complete CRCS through their preferred screening test option. We surmised that the patient-physician communication during the subsequent clinic visit affected the final choice, but the pilot RCT did not directly address this issue. Our proposed project, Decision Aid to Technologically Enhance Shared Decision Making (DATES), will test CW's effectiveness for increasing CRCS and its facilitation of SDM in PC practices through rigorous analysis of the conceptual model illustrated in Figure 2 (Section B-3.1.2). We will test an updated version of CW that is highly innovative. The new CW incorporates interactive patient preference clarification and risk assessment that provide real-time information to the patient and the physician during the clinic visit, making CW more applicable to real-world PC practices. We will perform a 2-armed RCT (300 patients per arm) comparing the Intervention Arm using CW to the Control Arm using a non-interactive control website, Standard Web (SW), in 10 PC practices in Metro Detroit with a large African American population.
Primary Aim 1: To measure the impact of CW on patient uptake of CRCS.
H-1: Patients in the Intervention Arm will have higher rates of CRCS adherence at the 6 month follow-up than those in the Control Arm.
Primary Aim 2: To evaluate the impact of CW on patient determinants, patient preference, and patient intention before the patient-physician encounter.
H2-1: Patients in the Intervention Arm will show greater improvement from baseline in patient determinants (knowledge, attitude, subjective norm, perceived self-efficacy) compared to the Control Arm after the web intervention and before the patient-physician encounter.
H2-2: Patients in the Intervention Arm will be more likely to have a preference for a particular CRCS test option than those in the Control Arm after the web intervention and before the patient-physician encounter.
H2-3: Patients in the Intervention Arm will have higher intention to undergo CRCS than those in the Control Arm after the web intervention and before the patient-physician encounter.
Primary Aim 3: To evaluate the impact of CW on SDM, concordance, and patient intention during and after the patient-physician encounter.
H3-1: Patients in the Intervention Arm will experience a higher level of SDM than those in the Control Arm.
H3-2: Higher rates of concordance will be reached between the patient's preferred CRCS test and the physician's recommended CRCS test in the Intervention Arm than those in the Control Arm.
H3-3: Patient’s intention to undergo CRCS after the patient-physician encounter will be predicted by the study arm, degree of SDM, concordance, and interaction between SDM and concordance.
Secondary analysis will employ a Structural Equation Modeling approach to understand the mechanism of the causal pathway and test the validity of our proposed conceptual model [8-11]. Our study will provide detailed understanding of how an interactive DA that incorporates patient preference clarification and risk assessment, and that provides real-time information to the patient and the physician during the clinic visit, will impact SDM and ultimately, CRCS adherence.
B. RESEARCH STRATEGY
B-1. Significance
CRCS is recommended for all average-risk United States (US) adults aged >50 years, because it reduces CRC death and morbidity [12-20]. Several CRCS options are available, including fecal occult blood test, flexible sigmoidoscopy, double-contrast barium enema, and colonoscopy, unless the adult is at increased risk for CRC and the test option is limited to colonoscopy [12-14, 21]. CRCS rates have shown an upward trend, with overall screening rates increasing from 20% in 1997 to nearly 55% in 2008 [22]. However, millions of eligible people remain unscreened by any method [4, 23]. No strong evidence exists that favors one CRCS test over another for reducing CRC mortality [12, 13]. The US Preventive Services Task Force, the American Cancer Society, and the NIH State-of-the-Science Conference recommend that CRCS should be based on patient preferences in order to optimize the CRCS rate [4, 12, 13, 24, 25]. Patient preferences for CRCS are highly variable and relate to particular test characteristics of efficacy, sensitivity, cost, complexity, and possible harm [26-51]. Patient preference clarification does not mean merely offering choices without guidance: when the information and options are not provided within the context of their preferences and values, patients’ ability to make a decision may actually decrease [52-56]. Physicians are encouraged to incorporate patient values when discussing CRCS and eliciting their screening choice, counseling patients to choose the CRCS test most congruous with their preferences and values [24, 51, 57]. SDM recognizes the central role of patient-physician relationship in helping patients make such decision [57, 58]. However, SDM requires more time and resources than most physicians have for a single issue, especially with multiple, competing agendas [1, 59-61]. Also, physicians do not always correctly perceive and address those factors important to patients and may not have the training and skills to provide for an effective SDM [2, 3, 51, 58, 60, 62, 63].
DAs can potentially facilitate SDM by reducing patient decisional conflict, improving patient knowledge, and stimulating patients to be more active in decision making without increasing anxiety [64]. Studies utilizing DAs by video, informed consent, and analytical hierarchy process have shown small increases in CRCS [65-68]. It is not known, however, whether patients activated by DAs that address patient-specific preferences can positively affect physicians to engage in SDM at a level consistent with the patients’ desires. This is important, because the physician’s recommendation has a significant impact on patients’ behavior change, including CRCS adherence [69-71]. Our study is significant, because it will provide detailed understanding of how an interactive DA that incorporates patient preference clarification and risk assessment, and that provides real-time information to the patient and the physician during the clinic visit, will impact SDM and ultimately, CRCS adherence. Our conceptual model (see Figure 2, Section B-3.1.2.) is based on the Theory of Planned Behavior and outlines how the preference elicitation process will likely influence SDM and, ultimately, patient adherence with screening recommendations [72-77]. The results of our study will not only have important implications for improving SDM for CRCS but can also be applied to other preference-sensitive care situations where underlying risk factors contribute to screening and/or treatment decisions.
B-2. Innovation
The results of our proposed RCT will be among the first to examine the effect of a real-time preference assessment exercise on CRCS and mediators, and, in doing so, will shed light on the patient-physician communication and SDM “black box” that currently exists between the delivery of DAs to patients and the subsequent patient behavior (i.e., CRCS adherence). Our conceptual model will elucidate not only the mechanism of how a DA influences patient behavior, but also how the DA affects the SDM between the patient and the physician [8-11]. In addition, CW has several highly innovative features. First, CW is an interactive web-based DA rigorously developed via focus groups and a modified Delphi technique and extensively tested through user feedback, pilot RCT, and feasibility studies [5-7]. Second, CW assists patients in clarifying their own CRCS preferences and risk. To our knowledge, no previous tools have integrated interactive preference clarification and personal risk assessment to tailor CRCS recommendation, not just assessing them separately [3]. Third, CW offloads the time devoted to providing knowledge, preference clarification, and risk assessment from the clinic visit, permitting the patient and physician to engage in SDM at a more advanced level. Fourth, it can be easily incorporated into routine clinical care.
Our preliminary studies (see Section B-3.1.) strongly suggest that CW will effectively increase completion of CRCS among patients served by PC physicians. In May 2009, CW was evaluated by the International Patient Decision Aid Standards (IPDAS) Collaboration, an international authority on standardized evaluation of DAs [78, 79]: "Overall, the raters felt that the tool was very easy to use and they liked the overall format. The way in which the information regarding the tests was presented was of high quality and the tool facilitates users to make a decision, whilst taking their personal preferences in to consideration. The tool scored highest on areas relevant to information provision and decision support." To our knowledge, this is the first DA focused on CRCS that has been rigorously evaluated by IPDAS.
B-3. Approach
B-3.1. Overall Strategy and Justification
The proposed study will be conducted in 3 phases. In Phase I (Preparation), we will finalize CW, SW, the data collection instruments, and the operational procedures for the 10 PC practices. In Phase II (Implementation), we will recruit 600 patients and perform the RCT (300 per arm). The design is 2-armed to maximize feasibility. The recruited patients will arrive an hour early at the clinic to meet with the Research Assistant (RA). After completing the informed consent, the patients will access the study website and complete the Patient Baseline Survey. Then, they will be automatically randomized to either the Intervention Arm, where the patients will receive CW, or the Control Arm, where the patients will receive SW. Data on the web pages reviewed and time spent will be collected. After completing CW or SW, the Patient Post-Web Survey will be completed. Then, the patient-physician encounter will be audio recorded. After the patient-physician encounter, the Patient Post-Encounter Survey will be completed. Chart audit will be performed 6 months later to determine whether the patient underwent CRCS (Endpoint Chart Audit). In Phase III (Evaluation), we will complete data management tasks and perform final data analyses.
Key members and their efforts over 4 years are listed in Table 1. Note: Sarah Hawley, PhD, MPH, a behavioral/social scientist with expertise in preference elicitation in CRCS, has joined the research team.
|Table 1. Key Members of the Research Team: All departments/institutions are at the University of Michigan. |
|Name |Department/Institution |Role |% Effort |
|Masahito Jimbo, MD, PhD, MPH |Associate Professor of Family Medicine (FM) |Principal Investigator (PI) |35/35/35/35 |
|Mack Ruffin, MD, MPH |Professor and Associate Chair of Research of FM |Co-Investigator (Co-I) |15/15/15/15 |
|Victor Strecher, PhD, MPH |Professor of Health Behavior and Health Education and Director|Co-I |5/5/5/5 |
| |of the Center for Health Communications Research (CHCR) | | |
|Donald Nease, MD |Associate Professor of FM |Co-I |10/10/10/10 |
|Sarah Hawley, PhD, MPH |Associate Professor of Internal Medicine |Co-I |10/15/15/15 |
|Ananda Sen, PhD |Associate Research Scientist, Center for Statistical |Co-I |5/5/5/10 |
| |Consulting and Research | | |
B-3.1.1. Colorectal Web (CW): Ruffin, Strecher, Jimbo
CW, the interactive DA, was originally developed (funding: Michigan Department of Community Health) by Drs. Ruffin and Strecher to help adults aged >50 years make a choice among the CRCS options. First, focus groups of unscreened adults aged 50-64 years in 3 communities (urban, suburban, and rural) in Michigan revealed: clear enthusiasm for something to help individuals decide among the CRCS options, and the Internet as the ideal information source [5]. Next, an Internet search of 65+ English language websites on CRCS targeting US adults revealed: little factual variation, user-directed navigation without guidance, high reading level text format, lack of readily available risk assessment tool, and lack of interactive assistance to establish CRCS preference [6]. These findings led to the development of CW, which was further refined with 30 intensive individual interviews with adults using the program. Users highly rated CW's Grid of the CRCS test options that summarizes and contrasts 10 key issues of each test: Frequency, Preparation, Sedation Required, Discomfort, Embarrassment, Inconvenience, Accuracy, Additional Tests, Risk of Complications, and Cost.
The key feature in CW is the interactivity: it lets the users seek further information by guiding them to make a choice by incorporating a Preference Clarification Tool. In CW, the users select the 3 most important issues to them in choosing a CRCS test, out of the 10 issues mentioned above. The computer uses this information to determine which CRCS option is most likely to be their preferred method. The algorithm used to assign each test, based upon the combination of the 3 issues, was developed through a modified Delphi technique (members: PC physicians and gastroenterologists in academic and community settings, patients having completed >1 CRCS procedures, and public health experts) to arrive at rankings among fecal occult blood test (FOBT), flexible sigmoidoscopy (FS), double-contrast barium enema (DCBE), and colonoscopy (COL) for each item listed. The investigators wrote computer codes to assign a preferred CRCS method based upon which procedure addresses >2 of the 3 issues chosen. In Figure 1-1 example, the user selected the 3 top issues to be: Frequency, Accuracy, and Need for Additional Tests. COL is recommended as the CRCS test option that best matches these 3 issues: it is required just every 10 years, has the best accuracy, and requires no additional tests. In Figure 1-2 example, the user selected the 3 top issues to be: Cost, Discomfort, and Embarrassment. FOBT is recommended as the CRCS test option: it is cheapest, has the least discomfort, and least embarrassing.
The most current version may be accessed at: . (e-mail: test, password: test) The test version already has set answers in the Personal Risk Assessment Tool. However, the interactive feature of the Preference Clarification Tool can still be experienced. Access is not tracked in this test version.
Finally, CW (Intervention Arm) was compared in a pilot RCT to a standard non-interactive electronic web information (Control Arm) about CRCS. This study used the original CW version and recruited 171 community residents aged between 50 and 64 years in need of CRCS, regardless of whether they had a regular physician. At 6 months, the participants in Intervention Arm were significantly more likely to undergo CRCS, 42% vs. 20% (p=0.035) [7]. However, study participants completing CRCS did not always get the CRCS test option they preferred. We hypothesize that CW mechanism of action is via improved knowledge, establishing a preference among the options, and leading to improved patient self-efficacy to complete the preferred CRCS test. However, the patient’s choice is also affected by the subsequent physician recommendation. Lack of data on the actual patient-physician communication that led to the final CRCS test option was a limitation of the pilot RCT. We will obtain this information in our proposed DATES project.
B-3.1.2. Conceptual Model (Figure 2): Jimbo, Ruffin, Hawley
Conceptual model for CW developed by the research team was adapted from the Theory of Planned Behavior [72-75]. It provides a framework that clarifies the relationship between DAs and SDM [76, 77]. The theory incorporates 3 patient determinants (attitude, subjective norm and perceived self-efficacy) that influence patient intention, which in turn influences patient behavior. In our conceptual model, we have added knowledge to patient determinants, since DAs increase knowledge [80, 81]. Additionally, based on our pilot RCT, we believe that the patient developing a preference among the CRCS test options is a key mediator between CW and patient intention to get screened for CRC. Thus, interaction with CW positively affects patient determinants, leading to the patient establishing a preference for a particular CRCS test option. This leads to greater patient intention to get screened and ultimately, higher CRCS rate. This process is also mediated by what occurs between the patient and the physician during the clinic visit to discuss CRCS: the degree of SDM reached and whether the patient and the physician agreed on which CRCS test option to get. The latter will be termed concordance.
B-3.1.3. Improvements to CW: Jimbo, Ruffin, Strecher, Nease
"Cancer Screening Adherence through Technologically-enhanced SDM (CSATS)" was led by Dr. Jimbo in collaboration with Drs. Ruffin, Strecher, and Nease [82]. In this 1-year developmental project funded through CHCR [National Cancer Institute (NCI) 1 P50 CA101451-01], an interactive Personal Risk Assessment Tool was added to CW to enable recommendation stratification by CRC risk. The original CW program did not have an individualized risk assessment, which would affect physician recommendation. In the improved CW, when the patients enter the first screen page, an overview of CW with links to general information, risk assessment, and CRCS test options appears. Next, the patients are guided to answer the Personal Risk Assessment Tool. Then, they are guided to review the Grid, followed by the Preference Clarification Tool. Once the patients select a CRCS test, a Feedback Page that integrates their risk and preference appears: “Average Risk”, “Increased Risk”, “Unknown Risk”, or “Unreported Risk.” For those with average risk, a feedback statement appears that affirms their choice. After this feedback, the patient has a final opportunity to select the preferred CRCS test. For those with increased risk who choose a CRCS test other than COL, a feedback statement appears that alerts them of their possibility of increased risk for CRC, and COL as the recommended CRCS test. Based on this feedback, the patient has a final opportunity to select the preferred CRCS test. For those with unknown risk due to missing information (e.g., history of adoption), a different feedback statement is provided. For those who skipped the Personal Risk Assessment Tool and did the Preference Clarification Tool first, a feedback statement appears encouraging them to take the Personal Risk Assessment. Once they submit their final choice, a Summary Page provides them: their risk, their 3 most important issues, the CRCS test most compatible with the 3 issues, and the test they ultimately selected. CW also underwent improvements in graphics and interactivity, and its overall reading level was lowered from 11th grade to 8th grade.
B-3.1.4. Feasibility of Patients Using CW in PC Practices: Jimbo, Ruffin, Strecher, Nease
Dr. Jimbo also tested the feasibility of the currently proposed approach to implementing and evaluating CW in 2 university-based PC practices (family medicine) in CSATS and a community-based PC practice (internal medicine) in "Streamlining Cancer Screening through Information Technology (SCanIT)," a 1-year developmental study funded by the Michigan Institute for Clinical and Health Research (MICHR), the NIH Clinical and Translational Science Award infrastructure at the University of Michigan. Patient recruitment rates were 20% and 25% respectively, retention rates were 90% in both, and patient and physician satisfaction were 100% in both. All 20 patients recruited in CSATS and 14 patients recruited in SCanIT found the improved CW to be very easy to understand and use. Refinements made during the studies to improve recruitment and retention led to progressively better results. These improvements have been incorporated into the approach of this proposal (see Section B-3.3.).
B-3.2. Study Phases: Phase I (Preparation)
In Phase I (Months 1 to 12), we will finalize CW and SW. We will finalize data collection instruments. We will meet with the participating practices and finalize operational procedures.
B-3.2.1. Finalize CW and Standard Web (SW)
In Months 1-6, the only refinement in CW, i.e., adding computer tomography colonography (CTC) and fecal immunochemical testing (FIT) as CRCS test options to reflect the most recent update in CRCS recommendations, will be completed [12]. Layouts and strategies for including these options have already been explored by the research team. Final readability, which is expected to remain 2 reviewers, RAs and/or the data manager. The PI or a Co-I will review the charts where there is any discrepancy. If there are >2 charts with discrepancies, then all charts for that practice will have 2 auditors review each study participant’s chart.
C-5.5. Quality Assurance
Data quality is fundamental to avoid information bias in analysis. Data will be coded and entered by dedicated RA(s) supervised by the data manager. In case of multiple coders, all the coders will enter data for the same 10 to 20 cases as part of the training and results will be compared before the real data entry starts. Supervision will include data entry training with the help of a manual containing definitions and explanations for every item of the instrument and every field in the database. Data quality will be assured by a combination of random spot checks of records as well as double entry of records. During the data entry we will get periodical descriptive statistics for numeric and character variables to ensure that the values are sensible. Graphical checks will also be performed to identify inconsistencies. In case of missing data every effort will be made to gather the information that is missing.
C-5.6. Regulatory Issues
This study does not involve an intervention, medical device, or medication that is regulated.
C-5.7. Trial Safety
As noted in Section C-1.3., the potential risks to study participants (patients) are violation of their confidential information and exchange with their physician along with psychological distress. Every feasible method to protect against these risks and minimize the risk will be used. In addition, the patients will be meeting with their physician, who can help the patient manage any distress. The participant may withdraw from the study at any time. Same effort to minimize risk to the participating physicians' confidentiality will be used as well.
C-5.8. DSM Administration
The DSM will be administered by the Cancer Prevention DSMC within the University of Michigan Comprehensive Cancer Center. The DSMC reviews projects in cancer prevention/biomarker development upon the request of the PI. The DSMC reviews, makes recommendations, and acts on the following:
Progress towards completion of the trial—recruitment and retention of study subjects.
Insufficient accrual to warrant continuation of the trial.
Evaluation of interim data analyses.
Evaluation of interim new information.
Evaluation of toxicity events including reporting of adverse events.
Timeliness of data.
Quality of data.
1) Ethical conduct of research.
The DSMC is empowered with the authority to recommend a trial be suspended or terminated based upon concerns in any of the above areas of review. The DSMC reviews all serious adverse events and ensures that these events have been correctly reported to all institutional review boards, and that adverse events have been correctly classified as serious or not serious. The institutional review board (IRB) assesses the impact of these events upon the conduct of the clinical trial. The IRB is empowered with the authority to suspend or terminate any trials for which there are concerns of toxicity that endanger human subjects. Monitoring also considers factors external to the study, such as scientific or therapeutic developments that may have an impact on the safety of the subjects or the ethics of the study. Recommendations that emanate from monitoring activities are reviewed by the principal investigator and addressed.
D. Inclusion of Women and Minorities
We plan to recruit 300 subjects in each of 2 study arms (total 600 subjects), with a rough balance across gender. African Americans will be over-sampled to comprise 25% of the total subject number (75 subjects per arm). No gender, racial, or ethnic group will be excluded.
E. Participation of Children
No one aged 50 years. Children will not be included in this study.
F. References
1. Jaen CR, Stange KC, Nutting PA. Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract 1994;38:166-171.
2. McQueen, Bartholomew LK, Greisinger AJ, et al. Behind closed doors: physician-patient discussions about colorectal cancer screening. J Gen Intern Med 2009; 24:1228-1235.
3. Schroy PC, Emmons K, Peters W, et al. The impact of a novel computer-based decision aid on shared decision making for colorectal cancer screening: a randomized trial. Med Decis Making OnlineFirst, doi:10.1177/0272989X10369007.
4. National Institutes of Health State-of-the-Science conference Statement. Available at: Accessed on 5/14/2010.
5. Ruffin M, Creswell JW, Jimbo M, Fetters MD. Factors influencing choices for colorectal cancer screening among previously unscreened African and Caucasian Americans. J Community Health, 2009; 34(2):79-89.
6. Fetters MD, Ivankova NV, Ruffin MT, et al. Developing a web site in primary care. Fam Med 2004; 36:651-659.
7. Ruffin MT, Fetters MD, Jimbo M. Preference-Based Electronic Decision Aid to Promote Colorectal Cancer Screening: Results of a randomized control trial. Prev Med 2007; 45: 267-73.
8. Baron RM and Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers and Soc Psychol 1986; 51:1173-1182.
9. Wu AD and Zumbo BD. Understanding and using mediators and moderators. Soc Indic Res 2008; 87:367-392.
10. Buhi ER, Goodson P, Neilands TB. Structural equation modeling: a primer for health behavior researchers. Am J Health Behav 2007; 31:74-85.
11. DiLalla LF. A structural equation modeling overview for medical researchers. J Dev Behav Pediatr 2008; 29:51-54.
12. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA Cancer J Clin 2008; 58:130-160.
13. U.S. Preventive Services Task Force. Screening for colorectal cancer. Available at: . Accessed on 5/14/2010.
14. Michigan Cancer Consortium. MCC guidelines for the early detection of colorectal cancer, February 2009. Available at: . Accessed on 5/14/2010.
15. Winawer SJ, Flehinger BJ, Schottenfeld D, Miller DG. Screening for colorectal cancer with fecal occult blood testing and sigmoidoscopy. J Natl Cancer Inst 1993; 85:1311-1318.
16. Kewenter J, Bjork S, Haglind E, et al. Screening and rescreening for colorectal cancer. A controlled trial of fecal occult blood testing in 27,700 subjects. Cancer 1988; 62:645-651.
17. Mandel JS, Band JH, Church TR, et al. Reducing mortality from colorectal cancer by screening for fecal occult blood. N Engl J Med 1993; 328:1365-1371.
18. Kronberg O, Fenger C, Olsen J, et al. Randomized study of screening for colorectal cancer with fecal occult blood test. Lancet 1996; 348:1467-1471.
19. Hardcastle JD, Chamberlain JO, Robinson MHE, et al. Randomized controlled trial of fecal occult blood screening for colorectal cancer. Lancet 1996; 348:1472-1477.
20. Robinson MH, Marks CG, Farrands PA, et al. Screening for colorectal cancer with an immunological faecal occult blood test: 2-year follow-up. Br J Surg 1996; 83:500-501.
21. Winawer S, Fletcher R, Rex D, et al. Colorectal cancer screening and surveillance: clinical guidelines and rationale - Update based on new evidence. Gastroenterology 2003; 124:544-560.
22. Behavioral Risk Factor Surveillance System. Available at: . Accessed on 5/14/2010.
23. American Cancer Society. Cancer prevention & early detection facts & figures 2008. Atlanta: American Cancer Society, 2008.
24. Briss P, Rimer B, Reilley B, et al. Promoting informed decisions about cancer screening in communities and healthcare systems. Am J Prev Med 2004; 26:67-80.
25. Michigan Public Health Institute and Michigan Department of Community Health. The Cancer Burden in Michigan: Selected Statistics — 1991-2009. Okemos, MI: September 2009. Available at: . Accessed on 5/14/2010.
26. Selby JV, Friedman GD, Quesenberry CPJ, Weiss NS. A case-control study of screening sigmoidoscopy and mortality from colorectal cancer. N Engl J Med 1992; 326:653-657.
27. Newcomb PA, Norfleet RG, Storer BE, et al. Screening sigmoidoscopy and colorectal cancer mortality. J Natl Cancer Inst 1992; 84:1572-1575.
28. Muller A, Sonnenberg A. Protection by endoscopy against death from colorectal cancer. A case-control study among veterans. Arch Intern Med 1995; 155:1741-1748.
29. Hoff G, Grotmol T, Skovlund E, et al. Risk of colorectal cancer seven years after flexible sigmoidoscopy screening: randomised controlled trial. BMJ 2009; 338:b1846.
30. Rex D. Barium enema and colon cancer screening: finally a study. Am J Gastroenterol 1997; 92:1570-1572.
31. Faivre J, Bader JP, Bertario L, et al. Mass screening for colorectal cancer: Statement of the European groups for colorectal cancer screening. Eur J Cancer Prev 1995; 4:437-439.
32. Cotton PB, et al. Computed tomographic colonography (virtual colonoscopy): a multicenter comparison with standard colonoscopy for detection of colorectal neoplasia. JAMA 2004. 291:1713-1719.
33. Rockey DC, et al. Analysis of air contrast barium enema, computed tomographic colonography, and colonoscopy: prospective comparison. Lancet 2005; 365: 305-311.
34. Pickhardt P, et al. Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N Engl J Med 2003; 349: 2191-2200.
35. Johnson CD, Chen MH, Toledano AY, et al. Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med 2008; 359: 1207-1217.
36. Nelson DB, McQuaid KR, Bond JH, et al. Procedural success and complications of large-scale screening colonoscopy. Gastrointest Endosc 2002; 55:307-314.
37. Lieberman DA, Weiss DG, Bond JH, et al. Use of colonoscopy to screen asymptomatic adults for colorectal cancer. New Engl J Med 2000; 343:162-168.
38. Imperiale TF, Wagner DR, Lin CY, et al. Results of screening colonoscopy among persons 40 to 49 years of age. New Engl J Med 2002; 346:1781-1785.
39. Anderson ML, Pasha TM, Leighton JA. Endoscopic perforation of the colon: lessons for a 10-year study. Am J Gastroenterol 2000; 95:3418-3422.
40. Rex DK, Lieberman DA. Feasibility of colonoscopy screening: discussion of issues and recommendations regarding implementation. Gastrointest Endosc 2001; 54:662-667.
41. Dominitz JA, Provenzale D. Patient preferences and quality of life associated with colorectal cancer screening. Am J Gastroenterol 1997; 92:2171-2178.
42. Leard LE, Savides TJ, Ganiats TG. Patient preferences for colorectal cancer screening. J Fam Pract 1997; 45:211-218.
43. Pignone M, Bucholtz D, Harris R. Patient preferences for colon cancer screening. J Gen Intern Med 1999; 14:432-437.
44. Wolf AM, Schorling JB. Does informed consent alter elderly patients' preferences for colorectal cancer screening? Results of a randomized trial. J Gen Intern Med 2000; 15:24-30.
45. Sheikh RA, Kapre S, Calof OM, et al. Screening preferences for colorectal cancer: a patient demographic study. South Med J 2004; 97:224-230.
46. Nelson RL, Schwartz A. A survey of individual preference for colorectal cancer screening technique. BMC Cancer 2004; 4:76.
47. Frew E, Wolstenholme J, Whynes D. Mass population screening for colorectal cancer: factors influencing subjects' choice of screening test. J Health Serv Res Policy 2001; 6:85-91.
48. Frew E, Wolstenholme JL, Whynes DK. Willingness-to-pay for colorectal cancer screening. Eur J Cancer 2001; 37:1746-1751.
49. Frew EJ, Wolstenholme JL, Whynes DK. Comparing willingness-to-pay: bidding game format versus open ended and payment scale formats. Health Policy 2004; 68:289-98.
50. Frew EJ, Wolstenholme JL, Whynes DK. Eliciting relative preferences for two methods of colorectal cancer screening. Eur J Cancer Care (Engl) 2005; 14:124-131.
51. Ling BS, Moskowitz MA, Wachs D, et al. Attitudes toward colorectal cancer screening tests. J Gen Intern Med 2001; 16:822-830.
52. Elston Lafata J, Divine G, Moon C, Williams L. Patient-physician colorectal cancer screening discussions and screening use. Am J Prev Med 2006; 31:202-209.
53. The Multicentre Australian Colorectal-neoplasia Screening (MACS) Group. A comparison of colorectal neoplasia screening tests: a multicentre community-based study of the impact of consumer choice. Med J Aust 2006; 184:546-550.
54. Salkeld GP, Young JM, Solomon MJ. Consumer choice and the National Bowel Cancer Screening Program. The opportunity for informed choice in screening is limited. Med J Aust 2006; 184:541-542.
55. Segnan N, Senore C, Andreoni B, et al. Randomized trial of different screening strategies for colorectal cancer: patient response and detection rates. J Natl Cancer Inst 2005; 97:347-357.
56. Iyengar SS, Lepper MR. When choice is demotivating: can one desire too much of a good thing? J Pers Soc Psychol 2000; 79:995-1006.
57. Sheridan SL, Harris RP, Woolf SH. Shared decision making about screening and chemoprevention. Am J Prev Med 2004; 26:56-66.
58. Elwyn G, Edwards A, Kinnersley P, Grol R. Shared decision making and the concept of equipoise: the competences of involving patients in healthcare choices. Br J Gen Pract 2000; 50:892-897.
59. Flocke SA, Frank SH, Wenger DA. Addressing multiple problems in the family practice office visit. J Fam Pract 2001; 50:211-216.
60. Elwyn G, Edwards A, Kinnersley P. Shared decision-making in primary care: the neglected second half of the consultation. Br J Gen Pract 1999; 49:477-482.
61. Edwards A, Elwyn G. Inside the black box of shared decision making: distinguishing between the process of involvement and who makes the decision. Health Expectations 2006; 9:307-320.
62. Woolf SH, Chan ECY, Harris R, et al. Promoting informed choice: transforming health care to dispense knowledge for decision making. Ann Intern Med 2005; 143:293-300.
63. Ling BS, Trauth JM, Fine MJ, et al. Informed decision-making and colorectal cancer screening: is it occurring in primary care? Med Care 2008; 46:S23-S29.
64. O’Connor AM, Stacey D, Entwistle V, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev 2003:CD001431. Available at: . Accessed on 5/14/2010.
65. Rimer BK, Briss PA, Zeller PK, et al. Informed decision making: what is its role in cancer screening? Cancer 2004; 101:1214-1228.
66. Pignone M, Harris R, Kinsinger L. Videotape-based decision aid for colon cancer screening. A randomized, controlled trial. Ann Intern Med 2000; 133:761-769.
67. Wolf AM, Schorling JB. Does informed consent alter elderly patients' preferences for colorectal cancer screening? Results of a randomized trial. J Gen Intern Med 2000; 15:24-30.
68. Dolan JG, Frisina S. Randomized controlled trial of a patient decision aid for colorectal cancer screening. Med Decis Making 2002; 22:125-39.
69. Taylor V, Lessler D, Mertens K, et al. Colorectal cancer screening among African Americans: the importance of physician recommendation. J Nat Med Assoc 2003; 95:806-812.
70. Brenes GA, Pakett ED. Predictors of stage of adoption for colorectal cancer screening. Prev Med 2000; 31:410-416.
71. Lipkus IM, Rimer BK, Lyna PR, et al. Colorectal cancer screening patterns and perceptions of risk among African-American users of a community health center. J Community Health 1996; 21:409-427.
72. Ajzen, I. The theory of planned behavior. Organ Behav Hum Dec 1991; 50:179-211.
73. Ajzen, I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol 2002; 32:665-683.
74. Hardeman W, Johnston M, Johnston DW, et al. Application of the theory of planned behaviour change interventions: a systematic review. Psychol Health 2002; 17:123-158.
75. Fishbein M. The role of theory in HIV prevention. AIDS Care 2000; 12:273-278.
76. Makoul G, Clayman ML. An integrative model of shared decision making in medical encounters. Patient Educ Couns 2006; 60:301-312.
77. Frosch DL, Legare F, Mangione CM. Using decision aids in community-based primary care: a theory-driven evaluation with ethnically diverse patients. Patient Educ Couns 2008; 73:490-496.
78. Elwyn G, O'Connor A, Stacey D, et al, International Patient Decision Aids Standards (IPDAS) Collaboration. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ 2006; 333:417-422.
79. Legare F, Elwyn G, Fishbein M, et al. Translating shared decision-making into health care clinical practices: proof of concepts. Implement Sci 2008; 3:2 doi:10.1186/1748-5908-3-2.
80. Barratt A, Trevena L, Davey HM, McCaffery K. Use of decision aids to support informed choices about screening. BMJ 2004; 329:507-510.
81. Barry MJ. Health decision aids to facilitate shared decision making in office practice. Ann Intern Med 2002; 136:127-135.
82. Jimbo M, Ruffin M, Nease D, Fetters, MD, Strecher V, Saunders E. Cancer screening adherence through technology-enhanced shared decision making. Family Medicine Digital Resources Library. Available at: . Accessed on 5/14/2010.
83. Demers RY, Neale AV, Adams R, et al. The impact of physicians' brief smoking cessation counseling: a MIRNET study. J Fam Pract 1990; 31: 625-9
84. Tuakli N, Smith MA, Heaton C. Smoking in adolescence: methods for health education and smoking cessation. A MIRNET study. J Fam Pract 1990; 31:369-374.
85. Klinkman MS, Stevens D, Gorenflo DW. Episodes of Care for Chest Pain: A Preliminary Report from MIRNET. J Fam Pract 1994; 38:345-352.
86. Reddy E, Kreher N, Hickner JM. How concerned are elderly patients without coronary heart disease about hypercholesterolemia and heart disease? An UPRNet study. Upper Peninsula Research Network. J Fam Pract 1998; 46:227-32.
87. Trembath CR, Hickner JM, Bishop SW. Incidental blood pressure elevations: a MIRNET project. J Fam Pract 1991; 32:378-381.
88. Gauthier BM, Hickner JM, Noel MM. High prevalence of overweight children in Michigan primary care practices. An UPRNet study. Upper Peninsula Research Network. J Fam Pract 2000; 49:73-76.
89. Noel M, Hickner J, Ettenhofer T, Gauthier B. The high prevalence of obesity in Michigan primary care practices. An UPRNet study. Upper Peninsula Research Network. J Fam Pract 1998; 47:39-43.
90. Noel MM, Ettenhofer TL, Kreher NE. Heights and weights in an overweight population: an UPRNet study. Upper Peninsula Research Network. J Fam Pract 2000; 49:377-378.
91. Triezenberg DJ, Smith MA, Holmes TM. Cancer screening and detection in family practice: a MIRNET study. J Fam Pract 1995; 40:27-33.
92. Nease DE Jr, Ruffin MT , Klinkman MS, et al. Impact of a generalizable reminder system on colorectal cancer screening in diverse primary care practices: A report from the prompting and reminding at encounters for prevention project. Med Care, 2008; 46(9 Suppl 1):S68-73.
93. Murray E, Pollack L, White M, Lo B. Clinical decision-making: physicians' preferences and experiences. BMC Fam Pract 2007; 8:10 doi:10.1186/1471-2296-8-10.
94. Solberg LI, Kottke TE, Brekke ML. Will primary care clinics organize themselves to improve the delivery of preventive services? A randomized controlled trial. Prev Med 1998; 27:623-631.
95. Crabtree BF, Miller WL, Aita VA, Flocke SA, Stange KC. Primary care practice organization and preventive services delivery: a qualitative analysis. J Fam Pract 1998; 46:403-409.
96. Goodwin MA, Zyzanski SJ, Zronek S, et al. A clinical trial of tailored office systems for preventive service delivery. The study to enhance prevention by understanding practice (STEP-UP). Am J Prev Med 2001; 21:20-28.
97. Leininger LS, Finn L, Dickey L, et al. An office system for organizing preventive services: a report by the American Cancer Society Advisory Group on Preventive Health Care Reminder Systems. Arch Fam Med 1996; 5:108-115.
98. Michigan Cancer Consortium. Early detection and screening for prostate and colorectal cancer: results from the Knowledge, Attitudes and Practice (KAP) survey (2006). Available at: . Accessed on 5/14/2010.
99. Elwyn G, Edwards A, Wensing M, et al. Shared decision making: developing the OPTION scale for measuring patient involvement. Qual Saf Health Care 2003; 12:93-99.
100. Elwyn G, Hutchings H, Edwards A, et al. The OPTION scale: measuring the extent that clinicians involve patients in decision-making process. Health Expectations 2005; 8:34-42.
101. Elwyn G, Edwards A, Wensing M, Grol R. Shared decision making: measurement using the OPTION instrument. Wales: Cardiff University, 2005.
102. Butlow P, Juraskova, Chang S, et al. Shared decision making coding systems: how do they compare in the oncology context? Patient Educ Couns 2010; 78:261-268.
103. Zapka JG. Validation of colorectal cancer screening behaviors. Cancer Epidemiol Biomarkers Prev 2008; 17:745-747.
104. Rauscher GH, Johnson TP, Young IC, Walk JA. Accuracy of self-reported cancer-screening histories: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2008; 17:748-757.
105. Vernon SW, Tiro JA, Vojvodic RW, et al. Reliability and validity of a questionnaire to measure colorectal cancer screening behaviors: does mode of survey administration matter? Cancer Epidemiol Biomarkers Prev 2008;17:758-767.
106. Partin MR, Grill J, Noorbaloochi, et al. Validation of self-reported colorectal cancer screening behavior from a mixed-mode survey of veterans. Cancer Epidemiol Biomarkers Prev 2008; 17:768-776.
107. Jones RM, Mongin SJ, Lazovich D, Church TR, Yeazel MW. Validity of four self-reported colorectal cancer screening modalities in a general population: differences over time and by intervention assignment. Cancer Epidemiol Biomarkers Prev 2008; 17:777-784.
108. Beebe TJ, Jenkins SM, Anderson KJ, Davern ME, Rockwood TH. The effects of survey mode and asking about future intentions on self-reports of colorectal cancer screening. Cancer Epidemiol Biomarkers Prev 2008; 17:785-790.
109. Bastani R, Glenn BA, Maxwell AE, et al. Validation of self-reported colorectal cancer (CRC) screening in a study of ethnically diverse first-degree relatives of CRC cases. Cancer Epidemiol Biomarkers Prev 2008; 17:791-798.
110. Schenck AP, Klabunde CN, Warren JL, et al. Evaluation of claims, medical records, and self-report for measuring fecal occult blood testing among Medicare enrollees in fee for service. Cancer Epidemiol Biomarkers Prev 2008; 17(4):799-804.
111. van Burren S, Boshuizen H, Knook D. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med 1999; 18:681-694.
112. Little RJA, Rubin DB. Statistical analysis with missing data, Hoboken, NJ: John Wiley & Sons, 2002.
113. Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociol Methodol. Washington DC: American Sociological Association, 1982:290-312.
114. MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1996; 1:130-140.
-----------------------
270 Complete Post-Web Survey
270 Complete Audio Recording
270 Complete Post-Encounter Survey
270 Complete Endpoint Chart Audit
1200 Opt Out or
Do Not Respond
300 Complete
Baseline Survey
1500 Invitation Letter Packets
Clinician Review;
375 Excluded
Clinician Review;
1500 Included
1875 Eligible
1875 Excluded
Not Eligible
Review 3750 Charts
per Study Arm
Figure 4: Sample Selection and Attrition
Knowledge, Attitude,
Perceived Self-Efficacy
Subjective Norm
Subjective Norm
CW
Concordance
Physician ID
Physician Style
Subjective Norm
Physician Preference
(strength of)
Intention
Patient Preference
(strength of)
Patient
Behavior
Intention
Intention
SHARED DECISION MAKING
Patient-Physician
Concordance
Patient Preference
CW (Decision Aid)
Patient Behavior
Patient
Intention
Patient Determinants:
Knowledge
Attitude
Subjective Norm
Perceived Self-Efficacy
Baseline Post-Web Post-encounter
Patient Preference
(strength of)
Patient Preference
(strength of)
Concordance
SDM
Patient Background, Experiences
Knowledge, Attitude,
Perceived Self-Efficacy
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related searches
- new prostate cancer screening test
- technology enhanced instruction
- breast cancer screening code icd 10
- cancer screening dx code
- technology enhanced learning for teaching
- how has technology enhanced education
- technology enhanced learning pdf
- technology enhanced learning definition
- technology enhanced education
- breast cancer screening ages
- breast cancer screening guidelines age
- cancer screening tests