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Implementing a Telehealth Shared Counseling and Decision-Making Visit for Lung Cancer Screening in a Veterans Affairs Medical Center
Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.
The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9
We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.
Implementation
We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.
Screening Referrals
When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11
Shared Decision-Making Telephone Visit
The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.
LDCT Imaging
The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.
Evaluating Shared Decision Making
We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.
We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.
We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15
The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.
Clinical Outcomes
We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.
Initial Findings
We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2). 
We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).
Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale. 
Implementing SDM
The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.
Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.
After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.
We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.
Discussion
Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.
Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.
Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.
Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20
The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.
Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.
SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.
We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.
Challenges and Limitations
We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.
The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.
Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.
Conclusions
A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26
Acknowledgments
The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.
1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434
2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7
3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873
4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771
5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001
6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274
7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304
8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680
9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004
10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117
11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070
12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453
13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf
14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008
15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009
16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085
17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.
18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659
19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9
20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558
21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063
22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027
23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1
24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807
25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5
26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046
Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.
The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9
We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.
Implementation
We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.
Screening Referrals
When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11
Shared Decision-Making Telephone Visit
The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.
LDCT Imaging
The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.
Evaluating Shared Decision Making
We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.
We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.
We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15
The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.
Clinical Outcomes
We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.
Initial Findings
We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2).
We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).
Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale.
Implementing SDM
The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.
Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.
After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.
We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.
Discussion
Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.
Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.
Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.
Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20
The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.
Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.
SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.
We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.
Challenges and Limitations
We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.
The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.
Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.
Conclusions
A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26
Acknowledgments
The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.
Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.
The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9
We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.
Implementation
We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.
Screening Referrals
When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11
Shared Decision-Making Telephone Visit
The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.
LDCT Imaging
The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.
Evaluating Shared Decision Making
We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.
We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.
We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15
The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.
Clinical Outcomes
We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.
Initial Findings
We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2).
We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).
Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale.
Implementing SDM
The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.
Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.
After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.
We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.
Discussion
Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.
Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.
Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.
Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20
The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.
Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.
SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.
We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.
Challenges and Limitations
We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.
The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.
Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.
Conclusions
A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26
Acknowledgments
The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.
1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434
2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7
3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873
4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771
5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001
6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274
7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304
8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680
9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004
10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117
11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070
12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453
13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf
14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008
15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009
16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085
17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.
18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659
19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9
20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558
21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063
22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027
23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1
24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807
25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5
26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046
1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434
2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7
3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873
4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771
5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001
6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274
7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304
8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680
9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004
10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117
11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070
12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453
13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf
14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008
15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009
16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085
17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.
18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659
19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9
20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558
21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063
22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027
23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1
24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807
25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5
26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046
Decision-Aids for Prostate Cancer Screening
There is growing interest in the medical community about the development and dissemination of health care information to assist patients in making more informed choices. The proponents of this new health care consumerism assume that patients want to be informed about their choices and want to be active partners in making those decisions. Along with global advances in informatics technologies, there has been attention to and development of consumer health care informatics tools. Terms such as informed consent1 and shared decision making2 are becoming increasingly common in the medical literature and are associated with the development of many of these new consumer tools. The number of these decision-aids is increasing, as is the amount of literature evaluating their use in clinical practice.
Screening for prostate cancer has become a serious clinical concern in primary care, where physicians are the central players in an ongoing debate about offering preventive health services of unknown benefit and significant risk to patients who may not be aware of this uncertainty. Schapira and VanRuiswyk3 present a randomized comparative trial of a written decision-aid for prostate cancer screening. In their well-designed clinical trial, patients receiving an illustrated pamphlet showed greater knowledge about the accuracy of prostate cancer screening tests than did control-group patients, while no difference in the rate of screening was observed. These investigators compared 2 versions of a written pamphlet on prostate cancer screening. The comparison intervention pamphlet contained written information on prostate cancer epidemiology, symptoms, screening methods, and the benefits of screening. The decision-aid pamphlet included the same basic information plus a graphical design using human figures to represent the accuracy (sensitivity and specificity) of a combined screening strategy. The authors of this study evaluated the added impact of a graphical presentation of the accuracy of screening on patients’ knowledge, beliefs, and behaviors associated with screening for prostate cancer. It is not surprising that patients who received the decision-aid showed greater knowledge about the accuracy of prostate cancer screening.
The conceptual basis of decision-aids
How might we characterize the kind of decision-aid developed by Schapira and VanRuiswyk? In an excellent overview of the field of health care informatics and decision making, Hersey and colleagues4 draw a distinction between educational tools (which are preparatory and anticipatory of a decision that has already been made) and decision analysis tools (which are used to foster an informed decision by the patient). Similarly, O’Connor and coworkers5 use the term “tailored decision aids” to refer to patient education tools based on expected value decision theories in which models are developed to represent the structure of a decision, the probability of certain outcome events, and the patient’s valuation of those outcomes. Decision-aids can be prescriptive, using clinical decision-analysis to arrive at an optimal strategy on the basis of the expected value of the options considered. Descriptive decision-aids present probabilities and values to clarify the options and provide insight into the decision-making process.5 The tool developed by Schapira and VanRuiswyk would be considered a descriptive decision-aid, because it presents the probabilities of the accuracy of prostate cancer screening and encourages clarification of patients’ values associated with those outcomes.
Proponents of the paternalism model, which has dominated contemporary medicine, presume that the physician is the sole decision-maker and the patient plays a limited or no formal role in choosing a course of action. In contrast, the informed decision-making model and the shared decision-making model by Charles and colleagues6 include the active involvement of the patient. In the informed model, the patient is provided with all information relevant to making a decision and assumes final authority. In the shared model, patients are provided with all the pertinent information, and they work with the health care provider to come to a decision consistent with their personal values. The implementation of the decision-aid developed by Schapira and VanRuiswyk is consistent with an informed decision-making approach. Almost all decision-aids are associated with this approach.
Selecting clinical questions for decision-aids
Although it might be argued that all clinical decisions should involve various degrees of patient input, not all clinical decisions warrant the development of formal decision-aids. We suggest 3 criteria that should be met for a clinical decision to be considered appropriate for an informed decision-making intervention. First, there must be uncertainty; the optimal strategy must be unclear. Second, using a term by Kassirer,7 the decision must be “utility sensitive” that is, a patient’s preferences for the outcomes of treatment should be central to determining the optimal strategy. Finally, a patient’s preferences for the outcomes of treatment must vary sufficiently to warrant an individualized approach to assessment.
The question of screening for prostate cancer appears to meet these 3 criteria. There is uncertainty about the benefit of screening, and treatment holds a potential for significant complications. Previous studies using clinical decision-analysis have shown that patients’ preferences for the outcomes of prostate cancer treatment are central to determining the optimal screening strategy.8,9 Finally, patients’ (and spouses’) preferences for these outcomes vary markedly.10
Decision-aids and prostate cancer
In the study by Schapira and VanRuiswyk, the use of prostate cancer screening after the intervention was not significantly different for the experimental and control groups (more than 80% were screened). What might explain the intervention’s lack of impact on screening behavior? A summary of clinical trials evaluating decision-aids appears to suggest that the effect of a decision-aid on screening behavior varies by subject population Table 1. In studies of unselected patients, decision-aids for prostate cancer screening appear to decrease the rate of screening. (The Mantel-Haenszel pooled relative risk estimate for these studies of unselected patients is 0.35, suggesting that decision-aids decrease screening behaviors.) Similar reductions had been observed in studies where the outcomes were intention or interest in screening.1,11 In contrast, for studies where patients were self-referred, such as men presenting for free prostate-specific antigen testing, decision-aids appear to have little effect on screening behavior. Schapira and VanRuiswyk solicited their subjects by letter. This self-selection, as the authors note, may have led to the formation of a sample of patients who were more favorably inclined to select screening. Previous research on decision-aids suggests that a predisposition for a course of action can have an impact on the choices patients make. For example, when considering the decision to circumcise a male newborn, a decision-making tool has little effect on the rate of circumcision; parents have strong preferences before receiving the intervention and are not swayed by learning more about the risks and benefits of the procedure.4
Future challenges
The literature on decision-aids shows that knowledge tends to improve the situation: patients become more certain (or less conflicted) about the choices they make, and they favorably evaluate the experience.12
So what are the goals of informed patient decision making? O’Connor13 has made the astute observation that cognitively oriented decision-aids should be expected to have their greatest impact on cognitive outcomes (eg, knowledge). It seems reasonable to expect that a principal outcome of any informed decision-making intervention will be to increase patient awareness of the core issues surrounding the options they face. Reductions in decision-associated conflict, more accurate perceptions of personal risk, and satisfaction with the decision-making process are also important outcomes. Whether such interventions change behavior appears to be a secondary concern.
Perhaps the greatest challenge for this new field of patient informatics will occur as our attention turns from the efficacy of decision-aids (the effect of the intervention in highly controlled protocol-driven clinical trials) to evaluating their effectiveness (implementation in the real world of clinical practice). What seems certain is that patients will continue to want this kind of information, with many playing a more active role in decision making and looking to their health care providers for information and guidance.
1. AM, Becker DM. Cancer screening and informed patient discussions: truth and consequences. Arch Intern Med 1996;156:1069-72.
2. J. Shared decision making and the future of managed care. Dis Manage Clin Outcomes 1997;1:15-6.
3. MM, VanRuiswyk J. The effect of an illustrated pamphlet decision-aid on the use of prostate cancer screening tests. J Fam Pract 2000;49:418-424.
4. JC, Matheson J, Lohr KN. Consumer health informatics and patient decision-making: final report. Rockville, Md: US Department of Health and Human Services, Agency for Health Care Policy and Research; 1997.
5. AM, Tugwell P, Wells GA, et al. Randomized trial of a portable, self-administered decision aid for postmenopausal women considering long-term preventive hormone therapy. Med Decis Making 1998;18:295-303.
6. C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med 1997;44:681-92.
7. JP. Incorporating patients’ p into medical decisions. N Engl J Med 1994;330:1895-6.
8. SB, Spann SJ, Volk RJ, Cardenas MP, Warren MM. Prostate cancer screening: a decision analysis. J Fam Pract 1995;41:33-41.
9. RJ, Cantor SB, Spann SJ, Cass AR, Cardenas MP, Warren MM. P of husbands and wives for prostate cancer screening. Arch Fam Med 1997;6:72-6.
10. SB, Volk RJ, Krahn MD, Cass AR, Spann SJ. Couples’ p for prostate cancer health states. Med Decis Making 1999;19:537.-
11. RJ, Cass AR, Spann SJ. A randomized controlled trial of shared decision making for prostate cancer screening. Arch Fam Med 1999;8:333-40.
12. AM, Rostom A, Fiset V, et al. Decision aids for patients facing health treatment or screening decisions: systematic review. BMJ 1999;319:731-4.
13. AM. A call to standardize measures for judging the efficacy of interventions to aid patients’ decision making. Med Decis Making 1999;19:504-5.
14. AB, Wennberg JE, Nease RF, Jr, Fowler FJ, Jr, Ding J, Hynes LM. The importance of patient preference in the decision to screen for prostate cancer: Prostate Patient Outcomes Research Team. J Gen Intern Med 1996;11:342-9.
15. RJ, Cass AR, Spann SJ. A randomized, comparative trial of shared decision making for prostate cancer screening: 1-year follow-up. Med Decis Making 1998;18:477.-
16. EG, Lowery JC, Hamill JB. The impact of shared decision making in prostate specific antigen (PSA) screening. Med Decis Making 1999;19:525.-
There is growing interest in the medical community about the development and dissemination of health care information to assist patients in making more informed choices. The proponents of this new health care consumerism assume that patients want to be informed about their choices and want to be active partners in making those decisions. Along with global advances in informatics technologies, there has been attention to and development of consumer health care informatics tools. Terms such as informed consent1 and shared decision making2 are becoming increasingly common in the medical literature and are associated with the development of many of these new consumer tools. The number of these decision-aids is increasing, as is the amount of literature evaluating their use in clinical practice.
Screening for prostate cancer has become a serious clinical concern in primary care, where physicians are the central players in an ongoing debate about offering preventive health services of unknown benefit and significant risk to patients who may not be aware of this uncertainty. Schapira and VanRuiswyk3 present a randomized comparative trial of a written decision-aid for prostate cancer screening. In their well-designed clinical trial, patients receiving an illustrated pamphlet showed greater knowledge about the accuracy of prostate cancer screening tests than did control-group patients, while no difference in the rate of screening was observed. These investigators compared 2 versions of a written pamphlet on prostate cancer screening. The comparison intervention pamphlet contained written information on prostate cancer epidemiology, symptoms, screening methods, and the benefits of screening. The decision-aid pamphlet included the same basic information plus a graphical design using human figures to represent the accuracy (sensitivity and specificity) of a combined screening strategy. The authors of this study evaluated the added impact of a graphical presentation of the accuracy of screening on patients’ knowledge, beliefs, and behaviors associated with screening for prostate cancer. It is not surprising that patients who received the decision-aid showed greater knowledge about the accuracy of prostate cancer screening.
The conceptual basis of decision-aids
How might we characterize the kind of decision-aid developed by Schapira and VanRuiswyk? In an excellent overview of the field of health care informatics and decision making, Hersey and colleagues4 draw a distinction between educational tools (which are preparatory and anticipatory of a decision that has already been made) and decision analysis tools (which are used to foster an informed decision by the patient). Similarly, O’Connor and coworkers5 use the term “tailored decision aids” to refer to patient education tools based on expected value decision theories in which models are developed to represent the structure of a decision, the probability of certain outcome events, and the patient’s valuation of those outcomes. Decision-aids can be prescriptive, using clinical decision-analysis to arrive at an optimal strategy on the basis of the expected value of the options considered. Descriptive decision-aids present probabilities and values to clarify the options and provide insight into the decision-making process.5 The tool developed by Schapira and VanRuiswyk would be considered a descriptive decision-aid, because it presents the probabilities of the accuracy of prostate cancer screening and encourages clarification of patients’ values associated with those outcomes.
Proponents of the paternalism model, which has dominated contemporary medicine, presume that the physician is the sole decision-maker and the patient plays a limited or no formal role in choosing a course of action. In contrast, the informed decision-making model and the shared decision-making model by Charles and colleagues6 include the active involvement of the patient. In the informed model, the patient is provided with all information relevant to making a decision and assumes final authority. In the shared model, patients are provided with all the pertinent information, and they work with the health care provider to come to a decision consistent with their personal values. The implementation of the decision-aid developed by Schapira and VanRuiswyk is consistent with an informed decision-making approach. Almost all decision-aids are associated with this approach.
Selecting clinical questions for decision-aids
Although it might be argued that all clinical decisions should involve various degrees of patient input, not all clinical decisions warrant the development of formal decision-aids. We suggest 3 criteria that should be met for a clinical decision to be considered appropriate for an informed decision-making intervention. First, there must be uncertainty; the optimal strategy must be unclear. Second, using a term by Kassirer,7 the decision must be “utility sensitive” that is, a patient’s preferences for the outcomes of treatment should be central to determining the optimal strategy. Finally, a patient’s preferences for the outcomes of treatment must vary sufficiently to warrant an individualized approach to assessment.
The question of screening for prostate cancer appears to meet these 3 criteria. There is uncertainty about the benefit of screening, and treatment holds a potential for significant complications. Previous studies using clinical decision-analysis have shown that patients’ preferences for the outcomes of prostate cancer treatment are central to determining the optimal screening strategy.8,9 Finally, patients’ (and spouses’) preferences for these outcomes vary markedly.10
Decision-aids and prostate cancer
In the study by Schapira and VanRuiswyk, the use of prostate cancer screening after the intervention was not significantly different for the experimental and control groups (more than 80% were screened). What might explain the intervention’s lack of impact on screening behavior? A summary of clinical trials evaluating decision-aids appears to suggest that the effect of a decision-aid on screening behavior varies by subject population Table 1. In studies of unselected patients, decision-aids for prostate cancer screening appear to decrease the rate of screening. (The Mantel-Haenszel pooled relative risk estimate for these studies of unselected patients is 0.35, suggesting that decision-aids decrease screening behaviors.) Similar reductions had been observed in studies where the outcomes were intention or interest in screening.1,11 In contrast, for studies where patients were self-referred, such as men presenting for free prostate-specific antigen testing, decision-aids appear to have little effect on screening behavior. Schapira and VanRuiswyk solicited their subjects by letter. This self-selection, as the authors note, may have led to the formation of a sample of patients who were more favorably inclined to select screening. Previous research on decision-aids suggests that a predisposition for a course of action can have an impact on the choices patients make. For example, when considering the decision to circumcise a male newborn, a decision-making tool has little effect on the rate of circumcision; parents have strong preferences before receiving the intervention and are not swayed by learning more about the risks and benefits of the procedure.4
Future challenges
The literature on decision-aids shows that knowledge tends to improve the situation: patients become more certain (or less conflicted) about the choices they make, and they favorably evaluate the experience.12
So what are the goals of informed patient decision making? O’Connor13 has made the astute observation that cognitively oriented decision-aids should be expected to have their greatest impact on cognitive outcomes (eg, knowledge). It seems reasonable to expect that a principal outcome of any informed decision-making intervention will be to increase patient awareness of the core issues surrounding the options they face. Reductions in decision-associated conflict, more accurate perceptions of personal risk, and satisfaction with the decision-making process are also important outcomes. Whether such interventions change behavior appears to be a secondary concern.
Perhaps the greatest challenge for this new field of patient informatics will occur as our attention turns from the efficacy of decision-aids (the effect of the intervention in highly controlled protocol-driven clinical trials) to evaluating their effectiveness (implementation in the real world of clinical practice). What seems certain is that patients will continue to want this kind of information, with many playing a more active role in decision making and looking to their health care providers for information and guidance.
There is growing interest in the medical community about the development and dissemination of health care information to assist patients in making more informed choices. The proponents of this new health care consumerism assume that patients want to be informed about their choices and want to be active partners in making those decisions. Along with global advances in informatics technologies, there has been attention to and development of consumer health care informatics tools. Terms such as informed consent1 and shared decision making2 are becoming increasingly common in the medical literature and are associated with the development of many of these new consumer tools. The number of these decision-aids is increasing, as is the amount of literature evaluating their use in clinical practice.
Screening for prostate cancer has become a serious clinical concern in primary care, where physicians are the central players in an ongoing debate about offering preventive health services of unknown benefit and significant risk to patients who may not be aware of this uncertainty. Schapira and VanRuiswyk3 present a randomized comparative trial of a written decision-aid for prostate cancer screening. In their well-designed clinical trial, patients receiving an illustrated pamphlet showed greater knowledge about the accuracy of prostate cancer screening tests than did control-group patients, while no difference in the rate of screening was observed. These investigators compared 2 versions of a written pamphlet on prostate cancer screening. The comparison intervention pamphlet contained written information on prostate cancer epidemiology, symptoms, screening methods, and the benefits of screening. The decision-aid pamphlet included the same basic information plus a graphical design using human figures to represent the accuracy (sensitivity and specificity) of a combined screening strategy. The authors of this study evaluated the added impact of a graphical presentation of the accuracy of screening on patients’ knowledge, beliefs, and behaviors associated with screening for prostate cancer. It is not surprising that patients who received the decision-aid showed greater knowledge about the accuracy of prostate cancer screening.
The conceptual basis of decision-aids
How might we characterize the kind of decision-aid developed by Schapira and VanRuiswyk? In an excellent overview of the field of health care informatics and decision making, Hersey and colleagues4 draw a distinction between educational tools (which are preparatory and anticipatory of a decision that has already been made) and decision analysis tools (which are used to foster an informed decision by the patient). Similarly, O’Connor and coworkers5 use the term “tailored decision aids” to refer to patient education tools based on expected value decision theories in which models are developed to represent the structure of a decision, the probability of certain outcome events, and the patient’s valuation of those outcomes. Decision-aids can be prescriptive, using clinical decision-analysis to arrive at an optimal strategy on the basis of the expected value of the options considered. Descriptive decision-aids present probabilities and values to clarify the options and provide insight into the decision-making process.5 The tool developed by Schapira and VanRuiswyk would be considered a descriptive decision-aid, because it presents the probabilities of the accuracy of prostate cancer screening and encourages clarification of patients’ values associated with those outcomes.
Proponents of the paternalism model, which has dominated contemporary medicine, presume that the physician is the sole decision-maker and the patient plays a limited or no formal role in choosing a course of action. In contrast, the informed decision-making model and the shared decision-making model by Charles and colleagues6 include the active involvement of the patient. In the informed model, the patient is provided with all information relevant to making a decision and assumes final authority. In the shared model, patients are provided with all the pertinent information, and they work with the health care provider to come to a decision consistent with their personal values. The implementation of the decision-aid developed by Schapira and VanRuiswyk is consistent with an informed decision-making approach. Almost all decision-aids are associated with this approach.
Selecting clinical questions for decision-aids
Although it might be argued that all clinical decisions should involve various degrees of patient input, not all clinical decisions warrant the development of formal decision-aids. We suggest 3 criteria that should be met for a clinical decision to be considered appropriate for an informed decision-making intervention. First, there must be uncertainty; the optimal strategy must be unclear. Second, using a term by Kassirer,7 the decision must be “utility sensitive” that is, a patient’s preferences for the outcomes of treatment should be central to determining the optimal strategy. Finally, a patient’s preferences for the outcomes of treatment must vary sufficiently to warrant an individualized approach to assessment.
The question of screening for prostate cancer appears to meet these 3 criteria. There is uncertainty about the benefit of screening, and treatment holds a potential for significant complications. Previous studies using clinical decision-analysis have shown that patients’ preferences for the outcomes of prostate cancer treatment are central to determining the optimal screening strategy.8,9 Finally, patients’ (and spouses’) preferences for these outcomes vary markedly.10
Decision-aids and prostate cancer
In the study by Schapira and VanRuiswyk, the use of prostate cancer screening after the intervention was not significantly different for the experimental and control groups (more than 80% were screened). What might explain the intervention’s lack of impact on screening behavior? A summary of clinical trials evaluating decision-aids appears to suggest that the effect of a decision-aid on screening behavior varies by subject population Table 1. In studies of unselected patients, decision-aids for prostate cancer screening appear to decrease the rate of screening. (The Mantel-Haenszel pooled relative risk estimate for these studies of unselected patients is 0.35, suggesting that decision-aids decrease screening behaviors.) Similar reductions had been observed in studies where the outcomes were intention or interest in screening.1,11 In contrast, for studies where patients were self-referred, such as men presenting for free prostate-specific antigen testing, decision-aids appear to have little effect on screening behavior. Schapira and VanRuiswyk solicited their subjects by letter. This self-selection, as the authors note, may have led to the formation of a sample of patients who were more favorably inclined to select screening. Previous research on decision-aids suggests that a predisposition for a course of action can have an impact on the choices patients make. For example, when considering the decision to circumcise a male newborn, a decision-making tool has little effect on the rate of circumcision; parents have strong preferences before receiving the intervention and are not swayed by learning more about the risks and benefits of the procedure.4
Future challenges
The literature on decision-aids shows that knowledge tends to improve the situation: patients become more certain (or less conflicted) about the choices they make, and they favorably evaluate the experience.12
So what are the goals of informed patient decision making? O’Connor13 has made the astute observation that cognitively oriented decision-aids should be expected to have their greatest impact on cognitive outcomes (eg, knowledge). It seems reasonable to expect that a principal outcome of any informed decision-making intervention will be to increase patient awareness of the core issues surrounding the options they face. Reductions in decision-associated conflict, more accurate perceptions of personal risk, and satisfaction with the decision-making process are also important outcomes. Whether such interventions change behavior appears to be a secondary concern.
Perhaps the greatest challenge for this new field of patient informatics will occur as our attention turns from the efficacy of decision-aids (the effect of the intervention in highly controlled protocol-driven clinical trials) to evaluating their effectiveness (implementation in the real world of clinical practice). What seems certain is that patients will continue to want this kind of information, with many playing a more active role in decision making and looking to their health care providers for information and guidance.
1. AM, Becker DM. Cancer screening and informed patient discussions: truth and consequences. Arch Intern Med 1996;156:1069-72.
2. J. Shared decision making and the future of managed care. Dis Manage Clin Outcomes 1997;1:15-6.
3. MM, VanRuiswyk J. The effect of an illustrated pamphlet decision-aid on the use of prostate cancer screening tests. J Fam Pract 2000;49:418-424.
4. JC, Matheson J, Lohr KN. Consumer health informatics and patient decision-making: final report. Rockville, Md: US Department of Health and Human Services, Agency for Health Care Policy and Research; 1997.
5. AM, Tugwell P, Wells GA, et al. Randomized trial of a portable, self-administered decision aid for postmenopausal women considering long-term preventive hormone therapy. Med Decis Making 1998;18:295-303.
6. C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med 1997;44:681-92.
7. JP. Incorporating patients’ p into medical decisions. N Engl J Med 1994;330:1895-6.
8. SB, Spann SJ, Volk RJ, Cardenas MP, Warren MM. Prostate cancer screening: a decision analysis. J Fam Pract 1995;41:33-41.
9. RJ, Cantor SB, Spann SJ, Cass AR, Cardenas MP, Warren MM. P of husbands and wives for prostate cancer screening. Arch Fam Med 1997;6:72-6.
10. SB, Volk RJ, Krahn MD, Cass AR, Spann SJ. Couples’ p for prostate cancer health states. Med Decis Making 1999;19:537.-
11. RJ, Cass AR, Spann SJ. A randomized controlled trial of shared decision making for prostate cancer screening. Arch Fam Med 1999;8:333-40.
12. AM, Rostom A, Fiset V, et al. Decision aids for patients facing health treatment or screening decisions: systematic review. BMJ 1999;319:731-4.
13. AM. A call to standardize measures for judging the efficacy of interventions to aid patients’ decision making. Med Decis Making 1999;19:504-5.
14. AB, Wennberg JE, Nease RF, Jr, Fowler FJ, Jr, Ding J, Hynes LM. The importance of patient preference in the decision to screen for prostate cancer: Prostate Patient Outcomes Research Team. J Gen Intern Med 1996;11:342-9.
15. RJ, Cass AR, Spann SJ. A randomized, comparative trial of shared decision making for prostate cancer screening: 1-year follow-up. Med Decis Making 1998;18:477.-
16. EG, Lowery JC, Hamill JB. The impact of shared decision making in prostate specific antigen (PSA) screening. Med Decis Making 1999;19:525.-
1. AM, Becker DM. Cancer screening and informed patient discussions: truth and consequences. Arch Intern Med 1996;156:1069-72.
2. J. Shared decision making and the future of managed care. Dis Manage Clin Outcomes 1997;1:15-6.
3. MM, VanRuiswyk J. The effect of an illustrated pamphlet decision-aid on the use of prostate cancer screening tests. J Fam Pract 2000;49:418-424.
4. JC, Matheson J, Lohr KN. Consumer health informatics and patient decision-making: final report. Rockville, Md: US Department of Health and Human Services, Agency for Health Care Policy and Research; 1997.
5. AM, Tugwell P, Wells GA, et al. Randomized trial of a portable, self-administered decision aid for postmenopausal women considering long-term preventive hormone therapy. Med Decis Making 1998;18:295-303.
6. C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med 1997;44:681-92.
7. JP. Incorporating patients’ p into medical decisions. N Engl J Med 1994;330:1895-6.
8. SB, Spann SJ, Volk RJ, Cardenas MP, Warren MM. Prostate cancer screening: a decision analysis. J Fam Pract 1995;41:33-41.
9. RJ, Cantor SB, Spann SJ, Cass AR, Cardenas MP, Warren MM. P of husbands and wives for prostate cancer screening. Arch Fam Med 1997;6:72-6.
10. SB, Volk RJ, Krahn MD, Cass AR, Spann SJ. Couples’ p for prostate cancer health states. Med Decis Making 1999;19:537.-
11. RJ, Cass AR, Spann SJ. A randomized controlled trial of shared decision making for prostate cancer screening. Arch Fam Med 1999;8:333-40.
12. AM, Rostom A, Fiset V, et al. Decision aids for patients facing health treatment or screening decisions: systematic review. BMJ 1999;319:731-4.
13. AM. A call to standardize measures for judging the efficacy of interventions to aid patients’ decision making. Med Decis Making 1999;19:504-5.
14. AB, Wennberg JE, Nease RF, Jr, Fowler FJ, Jr, Ding J, Hynes LM. The importance of patient preference in the decision to screen for prostate cancer: Prostate Patient Outcomes Research Team. J Gen Intern Med 1996;11:342-9.
15. RJ, Cass AR, Spann SJ. A randomized, comparative trial of shared decision making for prostate cancer screening: 1-year follow-up. Med Decis Making 1998;18:477.-
16. EG, Lowery JC, Hamill JB. The impact of shared decision making in prostate specific antigen (PSA) screening. Med Decis Making 1999;19:525.-