Implementation and Impact of a Risk-Stratified Prostate Cancer Screening Algorithm as a Clinical Decision Support Tool in a Primary Care Network



Implementation methods of risk-stratified cancer screening guidance throughout a health care system remains understudied.


Conduct a preliminary analysis of the implementation of a risk-stratified prostate cancer screening algorithm in a single health care system.


Comparison of men seen pre-implementation (2/1/2016–2/1/2017) vs. post-implementation (2/2/2017–2/21/2018).


Men, aged 40–75 years, without a history of prostate cancer, who were seen by a primary care provider.


The algorithm was integrated into two components in the electronic health record (EHR): in Health Maintenance as a personalized screening reminder and in tailored messages to providers that accompanied prostate-specific antigen (PSA) results.

Main Measures

Primary outcomes: percent of men who met screening algorithm criteria; percent of men with a PSA result. Logistic repeated measures mixed models were used to test for differences in the proportion of individuals that met screening criteria in the pre- and post-implementation periods with age, race, family history, and PSA level included as covariates.

Key Results

During the pre- and post-implementation periods, 49,053 and 49,980 men, respectively, were seen across 26 clinics (20.6% African American). The proportion of men who met screening algorithm criteria increased from 49.3% (pre-implementation) to 68.0% (post-implementation) (p < 0.001); this increase was observed across all races, age groups, and primary care clinics. Importantly, the percent of men who had a PSA did not change: 55.3% pre-implementation, 55.0% post-implementation. The adjusted odds of meeting algorithm-based screening was 6.5-times higher in the post-implementation period than in the pre-implementation period (95% confidence interval, 5.97 to 7.05).


In this preliminary analysis, following implementation of an EHR-based algorithm, we observed a rapid change in practice with an increase in screening in higher-risk groups balanced with a decrease in screening in low-risk groups. Future efforts will evaluate costs and downstream outcomes of this strategy.

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This work was supported by grants from the National Cancer Institute (P30CA014236) and the Duke Institute for Health Innovation (DIHI).

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Corresponding author

Correspondence to Kevin Shah MD, MBA.

Ethics declarations

This study was reviewed and deemed exempt by the Duke Institutional Review Board.

Conflict of Interest

Thomas J. Polascik reports consultancies in the last 3 years and honoraria in the last 3 years from Healthtronics [training agreement]. Terry Hyslop reports consultancies in the last 3 years from AbbVie. Glenn M. Preminger reports consultancies in the last 3 years from Boston Scientific, Auris Robotics, and Kalera Medical and reports other relationships as an Associate Editor for Up to Date. Kevin Shah reports stock ownership/options other than mutual funds from Infinity Pharmaceuticals. Anand Shah reports stock ownership/options other than mutual funds from Pfizer Inc. All other authors report no conflict of interest.

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Shah, A., Polascik, T.J., George, D.J. et al. Implementation and Impact of a Risk-Stratified Prostate Cancer Screening Algorithm as a Clinical Decision Support Tool in a Primary Care Network. J GEN INTERN MED 36, 92–99 (2021).

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