Effect of Individualized Preventive Care Recommendations vs Usual Care on Patient Interest and Use of Recommendations

Key Points Question Do patients benefit from an evidence-based tool individualized for patient risk factors that helps prioritize preventive services based on their potential to improve life expectancy? Findings In this pilot randomized clinical trial including 104 patients and 20 physicians, intervention patients found an individualized decision tool helpful and wanted to use it again. Compared with the control group, intervention patients more often correctly identified the service least likely (46.2% vs zero) to improve their life expectancy. Meaning An individualized preventive care decision tool was feasible, acceptable, and improved patient understanding of primary prevention.


Introduction
Preventable risk factors contributed to an estimated 61% of US deaths in 2019, 1,2 and health risks increase with age. 3 Prior work suggests that optimal preventive care use could add over 2 million healthy life-years nationwide. 4,5 The US Preventive Services Task Force (USPSTF) recommends 25 preventive services for middle-aged adults aged 50 to 64 years, but in 2015 only 8% of adults 35 years or older received all high-priority services. 6 A recent study found that adherence to hypertension control, a highly effective intervention, declined over the past decade. 7 National prevention guidelines present several challenges. First, they scarcely account for individuals' unique characteristics, such as comorbid conditions, which affect 70% of middle-aged adults. 8 This standardized approach hinders physicians' ability to identify which preventive services provide maximum benefit for a specific patient. Second, few guidelines consider patient preferences, including views about side effects, convenience, lifestyle, and cost. [9][10][11] This generic approach may contribute to low attainment of prevention targets. 8,12 Third, clinical time is limited, forcing physicians to prioritize among recommended guidelines without having tools to do so. For example, a physician may know that both mammograms and colonoscopies are "life-saving," but not understand their relative benefits. 13 In this pilot study, we evaluated the potential of an individualized decision tool to help patients and physicians better understand the net benefits of all major, USPSTF-recommended preventive services and improve preventive care delivery.

Methods
We pilot-tested feasibility and interest in a decision tool that was based on a previously published mathematical model that individualizes preventive care recommendations. 14,15 The model measures change in life expectancy associated with guideline adherence to each of 25 preventive services rated A or B by the USPSTF 9,14,15 and management of 6 closely related asymptomatic conditions (ie, control of hypertension, hyperlipidemia, diabetes, and overweight or obesity; cessation of tobacco use or alcohol misuse). Results are individualized for patient age, sex, race, medical history, family history, and lifestyle. For example, while studies suggest that in the general population, colorectal cancer screening adds 270 life-years per 1000 individuals (0.27 years per person), 16 the individualized model assigns greater benefits for patients with family history of colorectal cancer but lower benefits for those with uncontrolled diabetes (because of shorter life expectancy). 17 Based on patient focus groups and a national survey, we developed a preliminary visual aid to communicate model recommendations. 18 This study was approved by Cleveland Clinic's institutional review board (protocol and statistical analysis plan in Supplement 1) and prospectively registered (NCT03023813). 19 Physician informed consent was obtained. For patients, a waiver of informed consent was issued for use of the decision tool (because physicians retained discretion in ordering) and written or verbal informed consent was obtained before a survey in accordance with institutional review board requirements. We followed Consolidated Standards of Reporting Trials (CONSORT) reporting guideline for trial studies.

Setting
The study began February 22, 2017, and because of the COVID-19 pandemic, terminated February 17, 2021, at the Cleveland Clinic Health System. (The last participant enrolled March 12, 2020.) The system comprises a large academic medical center, 13 regional hospitals, 21 family health centers, and over 75 outpatient locations. The pilot was conducted at 3 ambulatory clinics: at the main campuswhich draws a variety of patient demographics, including many employees-and facilities in underserved East Cleveland (90.3% African American, 41.8% impoverished) 20 and suburban Beachwood, Ohio.

Design
The pilot included 2 phases: development and a nonmasked randomized clinical trial (RCT). In the development phase (ended October 31, 2017), we convened a patient-physician advisory panel for 6 meetings (typically 6 to 9 patients and 6 to 7 physicians per session). Topics included general impressions, the tool's visual design, and shared decision-making. [21][22][23] We also brainstormed visual design with 8 graphic designers. To account for clinical workflow, we consulted nurses, medical assistants, operational staff, and departmental leadership. Additionally, we enrolled primary care patients to receive individualized preventive care recommendations during regularly scheduled visits. After each encounter, we requested patient and physician feedback to iteratively improve the tool. Finally, after reaching saturation of comments, we transitioned to a pilot RCT comparing individualized preventive care recommendations (intervention) with usual care (control), with an optional postvisit patient survey in both arms.

Physician Recruitment
During both phases, we recruited physicians through departmental meetings and in-person, telephone, or email requests. Physicians were asked to try the decision tool at least once and spend their usual amount of time discussing preventive care. However, rather than discussing each service sequentially, we asked physicians to utilize the tool to engage in holistic, shared decision-making about the different preventive services available while incorporating patient values and preferences.
Study staff reviewed tenets of shared decision-making with each physician 24 and emailed them a 4-minute video. 25

Patients
Eligible patients were between ages 45 and 70 years with 2 or more of the following characteristics or test results: currently smoking, body mass index 25 or above (BMI, calculated as weight in kilograms divided by height in meters squared), blood pressure (BP) 140/90 or above, hemoglobin A 1c (HbA 1c ) levels 9% or higher, 10-year atherosclerotic cardiovascular disease risk (ASCVD) risk 7.5% or above, alcohol misuse, depression, history of sexually transmitted infection, and being overdue for colorectal, cervical, breast, or lung cancer screenings. Exclusion criteria were active cancer (other than nonmelanoma skin), end-stage kidney disease, moderate to severe congestive heart failure or chronic obstructive pulmonary disease, and other comorbidities with limited life expectancy.
Approximately once a week, a nurse reviewed appointment schedules for primary care annual wellness visits to identify eligible patients. Wellness visits focused on prevention and lasted 30 to 40 minutes vs 15 to 20 minutes for routine appointments. For each eligible patient, we computed his or her individualized preventive care recommendations and documented them in a research database.
Additionally, for each intervention patient, we created a 1-page graphic handout showing the individualized recommendations and provided a copy to the physician.
On the encounter day, a team member approached the patient in a waiting room to introduce the study, inform them that their physician was participating, and invite them to complete an optional postvisit survey in exchange for a $25 gift card. Finally, for intervention patients, the team member placed the individualized recommendations in a bin outside the examination room with hard copies for the physician, patient, and a companion.

Randomization
A biostatistician (B.H.) generated a block randomization sequence by patient (sizes 2, 4, 6) with a 1:1 parallel allocation ratio. Since we needed to create individualized recommendations in advance, randomization occurred 1 week before scheduled encounters. If a patient did not enroll, that sequence was skipped.

Outcomes
The primary outcome was patient self-reported interest in individualized preventive care recommendations. Secondary outcomes included use of shared decision-making (using SDM-Q-9 26-28 ), decisional comfort (decisional conflict scale 29 ), readiness to change (transtheoretical model 30 ), and preventive services received within 1 year.
Most outcomes were based on survey responses. Patients could complete the survey immediately by computer in a dedicated clinical room, alone or with help from a team member, or by phone or online within 3 days. The survey asked about current health, preventive services discussed during the encounter, preventive services patients thought were the most and least likely to improve their overall health, primary and secondary study outcomes, demographics (ie, age, sex, race To assess preventive care utilization we reviewed patient medical records approximately 1 year postencounter. Data were made available for all encounters within the health system, including visits with specialists. Then, for each preventive service recommended to at least 10 control and 10 intervention patients (except healthy diet and exercise, which had no EHR data), we employed a generalized linear mixed regression model with 1 row per follow-up encounter. The dependent variable was the relevant preventive service outcome (ie, tobacco use, BMI, systolic BP, HbA 1c , 10-year ASCVD risk, receipt of cancer screenings). Independent variables were receipt of the intervention (yes or no) and baseline value (baseline HbA 1c ), with a random effect for each patient.
For binary outcomes, we included a logit link.

Statistical Analysis
Power was based on use of shared decision-making (SDM-Q-9). 26 Assuming a baseline mean (standard error) score of 31 (9) on a 45-point scale, 130 surveys provided 80% power to detect a 15% improvement. 26,37 We employed 2-sided t tests (α = .05) but as a pilot study, our main goal was learning whether individualized recommendations showed promise for further development and testing. Secondary outcomes were not adjusted for multiple comparisons and should be considered exploratory. Analyses were conducted in Stata/MP version 15.1 (StataCorp).

Development Phase
In early weeks, patients and physicians found the visual aid too long; we reduced length from 8 pages (eFigure 1 in Supplement 2) to 1 page. After 12 major iterations (eTable 1 in Supplement 2), feedback was consistently positive without further suggestions for change. Figure 2 shows the final design. At top was an individualized statement; eg, "You are 60 years old but have the health of a 69 year old." To do so, our model estimated a patient's life expectancy and converted it into "true age," the age most commonly associated with that life expectancy. Below, a bar graph showed the improvement in true age if a patient obtained all recommended preventive services (eg, "10 years younger") and the change in true age associated with each service. To avoid overprecision, changes were rounded to the nearest year (or month if less than 1 year), with more than 10 years expressed as, "More than 10 years younger."  Text below each bar conveyed the effort required to follow the recommendation (easy, medium, or hard) and a short description (eg, "Usually, it takes people at least 7 tries to quit") based on patient-advisory panel feedback. The final design showed 2 weight loss categories: "Lose Weight" with a 25 BMI goal-a difficult, if not impossible, task for many patients 30 -and "Start by Losing 10 lbs," intended to proxy a more achievable 5% weight loss goal. 10,11 The survey underwent 3 revisions based on patient and physician feedback (eMethods in Supplement 2). Table 2 shows RCT results. Patients were eligible for a median (IQR) 6 (5-6) preventive services. Their true age was a mean (SD) 7.7 (4.0) years older than their biological age. Weight loss, healthy diet and exercise, cholesterol reduction, and colorectal cancer screening were recommended to most patients.

Comprehension
Intervention patients demonstrated comprehension of the decision tool. Following the visit, compared with controls, intervention patients were more likely to identify the preventive service In a question added late in the study, we asked 26 patients to compare their true age with their biological age. Twelve intervention patients (85%) correctly did so compared with no patients in the control group (6-category drop-down ranging from "[age − 1] years old or younger" to "[age + 10] years old or older").

Primary Outcome
Intervention patients had strongly favorable impressions of the decision tool. When asked, "Overall, how helpful did you find the written material (handouts)?" and "In the future, would you like to see     Abbreviations: DCS, Decisional Conflict Scale; NA, not applicable; SDM-Q-9, 9-item Shared Decision-Making Questionnaire.

JAMA Network Open | Health Policy
Details on the rank-order and magnitude of life expectancy gain associated with each individualized preventive service recommendation are shown in eFigures 2 and 3 in Supplement 2, respectively. a After the randomized trial began, some physicians expressed concern about correlation between diet, exercise, and weight loss, so for some patients we removed this service from the decision tool.

Physician Feedback
Physicians found the intervention impactful, compelling, desirable, and helpful (Box). Nineteen of 20 physicians wanted to continue using the decision tool in the future. Physicians remarked that the intervention added about 10 minutes to the encounter for the first few uses, and minimal time thereafter.

Box. Qualitative Feedback From Patients and Physicians
Patients What Did You Like BEST About the Written Material (handouts) and Conversation About It With Your Doctor?
• Personalization: "What I like best about the written material [is] it explains that I am 61 and my health is of a 64 [year old], this is a great concern for me and I plan to work on improving this."… "Said I am 62 but the health of a 67 year old, that is 5 years I want back"… "Personalized for me as an individual-I really liked that." • Design: "Very simple to read, don't have to try to figure out"… "It was to the point"… "Easy to understand"… "I think the visual presentation was more impactful than reading a few paragraphs."… "I like the years for quitting smoking, losing weight etc."… "Straight to the point." • Importance: "Well it was a good explanation on how to help me live longer, what I need to do to do that"… "It showed me the importance of taking medications daily to improve my overall health and to get my numbers in safe zone."… "Teaches you how to become more healthy. "This has to get in Epic." "Overall, I would love if this was expanded to all patients so that we can prioritize our interventions given limited time and resources." "I think it's a good visual aid. If integrated in Epic, it would be nice if we printed as part of their after visit summary (nice take home after counseling during the visit). It can also be a 'report card' type of thing." • Patient-physician discussion: "Handout [was] useful and we had a nice discussion using it. Patient appreciated the line about effort even though unfortunately wasn't ready to move further towards stopping smoking." "I think it went well. The patient appreciated it. I liked the individualized breakdown and the graphics to help explain to patient."

Support Staff
• Impactful: "The fact that participant could take packet home to read, re-read, and possibly act on was great. Having the practitioner go over packet also allowed them a second opportunity to reinforce teaching and/or touch on an area they may have missed." • Workflow: [Question: Does the research in any way affect your workflow?] "No, it's perfect. We have it all worked out. We go in, get 'em set up, the doctor goes in, we go back in to finish up, and then they go into the research room [for the survey]." Follow-up: Is there anything we could do better? "Nope, it's all smooth." After each encounter in both the development phase and the randomized clinical trial phase, we asked the patient and physician for feedback. We also periodically asked support staff (eg, nurses, medical assistants, patient registration/check-in) for feedback.
a The final version of the decision tool provided 2 weight loss recommendations, "Lose weight" and "Start by losing 10 lbs." b This feedback was provided in an early version of the decision tool. Physicians stated that too much information was provided, so their focus was on reviewing the items rather than shared decision-making with the patient. This eventually led to a 1-page design that was well received.
c After new evidence was released on aspirin and a physician expressed concern, we added a footnote: "[W]e are less sure about aspirin than other ways to improve your health. New research suggests that the benefits of aspirin may be much lower." After the physician next tested the tool, they responded, "This works well. I don't think we need to get rid of [the aspirin recommendation] entirely. I have just been a bit less impressed by aspirin lately and my desire to start it in general has gone down."

Discussion
We Patients had a mean true age 7.7 years older than their biological age, providing important health perspective, and were eligible for 5 to 6 preventive services. Our greatest enrollment barriers were patient cancellations and no shows, comprising 1 in 3 screened patients (consistent with general practice at our 3 sites), but among remaining encounters, only 17 patients and 1 physician declined participation. Postenrollment, intervention patients expressed strong satisfaction with the decision tool and demonstrated greater comprehension of prevention priorities than controls. Similarly, 19 of 20 physicians wanted to keep using the tool and supported its integration into the EHR. Their feedback that the tool added minimal time to the encounter after the first few uses was consistent with earlier studies finding that shared decision-making adds only 2 to 3 minutes to visits. 38 Finally, early evidence suggests that the decision tool may improve outcomes. Use of shared decision-making exceeded the range found in a meta-analysis of shared decision-making interventions (range, 42-75 on a 100-point scale), 28 and the tool improved readiness to change for all preventive services. All coefficients were in the expected direction and magnitudes were clinically meaningful but would require further testing to establish efficacy. One possible reason is that our holistic approach affected patients' understanding of their own health, a hypothesis consistent with intervention patients rating their overall health lower than controls. By opening discussion to all preventive services simultaneously rather than sequentially, we allowed patients to express their goals and constraints (eg, transportation, costs) for health improvement. Another possibility is that the tool helped physicians better understand the relative benefits of preventive services. Prior work suggests that physicians care about the potential of preventive services to improve patient length and quality of life, but they need help individualizing these metrics for specific patients. 13 Taken together, our findings suggest strong potential for individualized, prioritized recommendations to improve preventive care delivery among middle-aged adults. Future work should seek to confirm our results in a larger RCT and establish whether heightened, individualized understanding of preventive care is sufficient to change behavior over time. This work may be particularly important amid the COVID-19 pandemic, as early evidence suggests increased alcohol misuse and less healthy diet and physical activity. 45-47