The American Journal of Accountable Care®
December 2014
Volume 2
Issue 4

Pilot-Testing a New Program for Providing Personalized and Patient-Centered Preventive Care

Program that enhances personalized and patient-centered preventive care at a busy inner-city primary care clinic may be associated with improved health outcomes.


Objectives: Evidence-based preventive care is vastly underutilized in the United States, but is necessary to decrease morbidity and mortality. Personalization is essential for successful preventive care implementation. We developed and tested the feasibility of a new program for providing personalized and patient-centered preventive care in a busy, urban ambulatory clinic. We designed our program to be compatible with emerging care models, including the patient-centered medical home model, and to be applicable to diverse pointof- care settings.

Study Design: Pilot-study program with matched controls.

Methods: Participants were 73 nonpregnant adult clinic patients with 73 matched controls from Bellevue Medical Center Ambulatory Care Clinic with the greatest estimated benefit from improvements in adherence to evidence-based preventive care. We used a validated mathematical model based on US Preventive Services Task Force recommendations to quantify and rank the estimated amount of health benefit that would arise from improved adherence to each preventive care guideline. We communicated these personalized estimates graphically to patients. Finally, we engaged the patient in a shared decision making process in which the patient identified which preventive health goals he aimed to achieve, and we set particular action steps to be taken before the next visit that were congruent with these goals.

Results: The program appeared feasible in a busy urban clinic, with no major workflow disruptions and qualitative acceptability to patients and practitioners. There was a trend towards increased life expectancy for program versus control patients by 12.0 months compared with 6.7 months (P = .07), and program participants experienced greater improvements in smoking cessation (P = .05).

Conclusions: It appears feasible to implement a program that enhances personalized and patient-centered preventive care at a busy urban primary care clinic, and that may be associated with improved health outcomes.Although preventable morbidity and mortality in the United States is substantive, evidence-based preventive care is underutilized, as seen in the underutilization of the 60 distinct services recommended by the US Preventive Services Task Force (USPSTF).1-3 For example, only 48% of patients are screened for colorectal cancer, 40% of qualified patients take aspirin, and 28% of patients receive help to quit smoking.4 Further, health disparities are worsened due to unequal distribution of preventive care.4,5

These preventive care deficits may arise partially from limited time for clinicians and patients to address relevant issues.3 Additionally, recommendations for the best patient-centered care6 will sometimes differ from patient to patient, adding further complexity. For example, a general clinical quality metric may encourage tight blood sugar control for a frail 80-year-old female with diabetes, when in practice, for her particular situation, this recommendation might cause more harm than benefit or conflict with her preferences.7,8

It is increasingly appreciated that while primary care should become more personalized and patient-centered, time constraints may oppose these goals. Therefore, our objective was to develop and test the feasibility of a new program for providing personalized and patient-centered preventive care in a busy urban ambulatory clinic. We designed our program to be compatible with emerging care models, including the patient-centered medical home model, and to be applicable to diverse point-of-care settings.


Our program was constructed around the following framework: 1) identify patients who could most benefit from improvements in adherence to preventive care; 2) use a validated mathematical model9 to quantify and rank the estimated health benefit from improved adherence to each USPSTF guideline, personalizing estimates based on risk factors and medical history; 3) communicate this information in a manner reflecting insights from risk communication studies10,11 on patients with low literacy and numeracy; 4) perform shared decision making so the patient chooses preventive health goals aligned with his preferences (for example, lose 30 pounds); and 5) designate action steps to reach those goals (for example, use 9-inch dinner plates rather than larger ones to make food portions fill the plate and visually seem larger).

We employed this framework at Bellevue Medical Center, a safety net center in New York City, New York, with an extremely diverse population: the breakdown is 45% Hispanic; 40% African American, African, or Caribbean; and 10% Asian American; and patients can receive on-demand medical interpretation in 17 languages including Fukinese, Bengali, and Haitian Creole. The Adult Primary Care Center (APCC) at Bellevue has busy clinician loads, complex patients in great need of preventive care, and a recent designation as a patient-centered medical home. Our program supplemented rather than substituted for normally scheduled primary care visits, and was coordinated with these visits whenever possible. It began in May 2012, with intent to pilot for 15 months (3 months enrollment, 12 months follow-up) until August 2013. However, because of the 3-month disruption of operations from Superstorm Sandy, recruitment was extended until November 2013.

Identifying patients who could benefit most from improvements in preventive care

We employed a Markov model that estimates the differences in life expectancy with and without preventive guideline implementation.9 The model is able to consider the impact of all preventive guidelines rated as “A” or “B” by the USPSTF that are applicable to a particular patient, and estimates downstream effects on mortality that are personalized based on an individual’s demographics and risk-factor profile. Its design is described in more detail elsewhere.9

Patients with 1 or more years of potential life expectancy gain (approximately 20% of APCC outpatients) were invited to participate in a quality improvement program intended to personalize and patient-center their care. Patients met this inclusion criterion if they had 1 or more of the following attributes: body mass index >30, hypertension, tobacco use, alcohol dependency, cardiovascular disease but not taking aspirin, or a strong family history of breast cancer. Most patients were prescreened for eligibility using electronic medical records (EMRs). We excluded patients with dominant comorbidities like active cancer or advanced renal disease. The program was endorsed by most primary care providers (PCPs) practicing at the site.

Communicating information in a manner informed by risk communication literature


To ensure that patients had adequate time to understand the personalized information and to make shared decisions, each visit lasted 40 to 60 minutes, split between a nurse practitioner (NP) session and a health coach (HC) session, with oversight from a physician (MD). Scheduled patient visits were monthly for the first 3 months, and then every 3 months for a total duration of 12 months. Additionally, contact was attempted at biweekly intervals between visits by phone call or postcard (for patients without reliable phone numbers). At the initial visit, the NP obtained a full health history. Data were inputted into the mathematical model, and the NP discussed that patient’s personalized results with the aid of a patient-friendly graph that prioritized recommendations and the correlated potential gains in life-years (), and was developed with a risk-communication expert (author AF).

Engaging the patient in a shared decision-making process The patient and NP engaged in shared decision making12 to decide which health recommendations the patient would work on for the next visit. The NP facilitated implementation of these recommendations, including medication adjustments, ordering screening exams, and specialty referrals if needed.

Setting health goals for the next visit

The HC session immediately followed the NP session, aiming to set specific goals and action steps corresponding to the health recommendations endorsed during the NP session. Motivational interviewing techniques were again used, and the patient ended the visit with clear, specific goals. Each patient could designate 1 or more goals. These goals were written, laminated, and presented in a leather binder to add gravitas and symbols of appreciation. Other important educational materials and resources were provided to the patient, such as recommendations of healthy foods, logs to record their food choices, and lists of gyms near their residence. A copy of the patient’s graph was left in their PCP’s mailbox, and EMR notes were documented by the NP and HC.

At follow-up visits, the patient’s goal achievement (or lack thereof) was updated into the model along with their new vital signs, lab work, screenings, and medication changes that occurred since their last visit. If the patient made improvements to their health since the last visit, a smiley-face image was visible on the graph (Figure), symbolizing their accomplishments. Updated reports were provided to the patient’s PCP.

Assessment of feasibility

Because our program of personalized and patient-centered care was a quality improvement project rather than extramurally funded research, we did not employ validated measures or systematic transcripts to assess feasibility. Instead, we sought to note whether there were (1) disruptions to clinician or to clinic workflow, (2) subjective acceptance of the program by patients, and (3) subjective acceptance of the program by PCPs.

Preliminary assessment of outcomes

Although we did not have sufficient statistical power to detect clinically significant differences in process or outcome measures, we did seek to identify trends in these measures. Accordingly, we prespecified a control group of patients who also met program inclusion criteria and expressed interest in attending, but who were unable to attend because of scheduling constraints. Each control patient was matched with an intervention patient based on their date of clinic initiation so they would have a similar duration of follow-up, and similarly timed exposure to the 3-month service disruption caused by Superstorm Sandy.


For the 73 patients enrolled in the program, the median follow-up was 9 months. Their mean age was 54.6 years, 60.3% were female, and they were ethnically diverse: 23.2% black (African American), 10.6% white (non-Hispanic), 47.1% Hispanic (non-black), and 14.7% Asian. Of all patients, 38.4% attended only 1 visit; 16.4%, 2 visits; 16.4%, 3 visits; 8.2%, 4 visits; and 20.5% had 5 or more visits. The most prevalent preventive care goals were reducing obesity (76.7%, N = 56), reducing hypertension (74%, N = 57), diabetes control (47.0%, N = 35), lipid control (38.4%, N = 28), and tobacco cessation (9.6%, N = 7).


There was generally a positive response from patients and PCPs. Only 2 of 12 PCPs opted out of involvement, anecdotally concerned about loss of professional autonomy regarding management of their patients. Work flow disruption issues were minimal because program personnel scheduled visits around the PCP schedule. Patients were proud of their charts and folders and returned with them at follow-up visits.

Motivational interviewing and shared decision making were well received, with those patients motivated to take the most responsibility for their own care making the greatest improvements. For example, after the first program visit of a 40-year-old female of Mexican descent with borderline diabetes and non-alcoholic fatty liver disease, shared decisions resulted in goals directed at reducing obesity, managing hyperlipidemia, and consuming a healthier diet, with an estimated 4 years, 3 months potential gain in life expectancy. She has since lost 32 pounds and has controlled her hyperlipidemia through dietary changes. Her glycated hemoglobin and liver function tests are now normal. She feels well and energetic, and has added an estimated 4 years, 1 month to her life expectancy. She states, “My grandfather was sick with diabetes and I don’t want this disease in my life, so I worked so hard to control and change my diet through this clinic so that I won’t get diabetes or have problems again with fatty liver. I’m not watching my food because I want to lose pounds or be a Barbie [doll]—I’m doing it for my health in the future. When I first came here I ate heavy food every day, and now when I eat heavy food I don’t like it and I notice the difference.”


Table 2

Tables 3


Although this pilot study has insufficient power to detect clinically significant improvements in outcomes, we observed positive trends. Compared with control patients, program patients had a similar distribution of preventive care objectives and burdens (). Participants extended their life expectancy by an additional 5.3 months compared with the control group (P = .07), and they had increased rates of smoking cessation (P = .05) ( and ).


We have implemented a program personalizing and patient-centering care at a busy urban clinic, and our experience suggests the approach is feasible. Our program not only provides personalized information, but patient-centers the decision making to promote active participation in setting and prioritizing health goals.

Prior to program initiation, many clinicians and health policy experts warned us that it would be infeasible to construct a program requiring 40- to 60-minute office visits. We have demonstrated that these predictions were incorrect. Furthermore, we note that the same activities could be accomplished more cost-effectively through additional task shifting: clinic staff could obtain vital signs and utilize routine questionnaires to get results to input into the mathematical model, and a health navigator could coordinate specialty appointments and resolve other logistical burdens (potentially saving 10 minutes each of NP time and HC time). In other words, what was a 60-minute visit divided between NP and HC with MD supervision could be transformed into a visit with those 60 minutes allocated more efficiently and less expensively by task shifting to lesser-skilled personnel: 5 to 10 minutes technician time, 20 minutes NP time, 20 minutes HC time, and 10 to 15 minutes navigator time. Also of note, any program that achieves and sustains a life expectancy increase of 5.3 months would offer favorable value if its additional expenditures (including downstream costs) are lower than $44,000 per patient per year,13 which appears to greatly exceed the costs of our program.


Because program visits were longer than PCP visits, we were unable to discern how much of the improvement in performance was simply due to additional time spent with patients. Future work should allow us to do so. Additionally, while we lacked prespecified quantitative feasibility measures, such as time/motion studies and satisfaction surveys (a situation common to other quality improvement projects), we were able to identify a control group that was closely matched to the participants, and thereby may have avoided overestimation of health benefit because of selection bias or regression to the mean.


We have developed and implemented a program to enhance personalized and patient-centered preventive care at a busy urban primary care clinic. It appears to be feasible and well accepted by patients and practitioners.Source of Funding: Institutional funds from the New York University Department of Population Health.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Address Correspondence to: Melanie Applegate, MSN, FNP, NYU School of Medicine, Department of Population Health, 550 First Ave, VZ30, 6th Fl, 628V, New York, NY 10016. Phone: (646) 501-2554. Fax: (646) 501-2706. E-mail:

1. Published recommendations. 2004. US Preventive Services Task Force website. Accessed December 2, 2014.

2. Tai-Seale M, McGuire TG, Zhang W. Time allocation in primary care office visits.Health Serv Res. 2007;42(5):1871-1894.

3. Yarnall KS, Pollak KI, Østbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention? Am J Public Health. 2003;93(4):635-641.

4. National Commission on Prevention Priorities/Partnership for Prevention. Preventive Care: A National Profile on Use, Disparities, and Health Benefits. Washington, DC: Partnership for Prevention; 2007:1-43.

5. Maciosek MV, Coffield AB, Edwards NM, Flottemesch TJ, Goodman MJ, Solberg LI. Priorities among effective clinical preventive services: results of a systematic review and analysis. Am J Prev Med. 2006;31(1):52-61.

6. Robinson JH, Callister LC, Berry JA, Dearing KA. Patient-centered care and adherence: definitions and applications to improve outcomes. J Am Acad Nurse Pract. 2008;20(12):600-607.

7. Braithwaite RS, Fiellin D, Justice AC. The payoff time: a flexible framework to help clinicians decide when patients with comorbid disease are not likely to benefit from practice guidelines. Med Care. 2009;47(6):610-617.

8. Braithwaite RS. Can life expectancy and QALYs be improved by a framework for deciding whether to apply clinical guidelines to patients with severe comorbid disease? Med Decis Making. 2011;31(4):582-595.

9. Taksler GB, Keshner M, Fagerlin A, Hajizadeh N, Braithwaite RS. Personalized estimates of benefit from preventive care guidelines: a proof of concept. Ann Intern Med. 2013;159(3):161-168.

10. Andrulis DP, Brach C. Integrating literacy, culture and language to improve health care quality for diverse populations. Am J Health Behav. 2007;31(suppl 1):S122-S133.

11. Epstein RM, Alper BS, Quill TE. Communicating evidence for participatory decision making. JAMA. 2004;291(19):2359-2366.

12. Emmons KM, Rollnick S. Motivational interviewing in health care settings. opportunities and limitations. Am J Prev Med. 2001;20(1):68-74.

13. Braithwaite RS, Meltzer DO, King JT Jr, Leslie D, Roberts MS. What does the value of modern medicine say about the $50,000 per quality-adjusted life-year decision rule? Med Care. 2008:46(4):349-356.

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