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

  • Anand Shah
  • , Thomas J. Polascik
  • , Daniel J. George
  • , John Anderson
  • , Terry Hyslop
  • , Alicia M. Ellis
  • , Andrew J. Armstrong
  • , Michael Ferrandino
  • , Glenn M. Preminger
  • , Rajan T. Gupta
  • , W. Robert Lee
  • , Nadine J. Barrett
  • , John Ragsdale
  • , Coleman Mills
  • , Devon K. Check
  • , Alireza Aminsharifi
  • , Ariel Schulman
  • , Christina Sze
  • , Efrat Tsivian
  • , Kae Jack Tay
  • Steven Patierno, Kevin C. Oeffinger, Kevin Shah

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Background: Implementation methods of risk-stratified cancer screening guidance throughout a health care system remains understudied. Objective: Conduct a preliminary analysis of the implementation of a risk-stratified prostate cancer screening algorithm in a single health care system. Design: Comparison of men seen pre-implementation (2/1/2016–2/1/2017) vs. post-implementation (2/2/2017–2/21/2018). Participants: Men, aged 40–75 years, without a history of prostate cancer, who were seen by a primary care provider. Interventions: 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). Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)92-99
Number of pages8
JournalJournal of general internal medicine
Volume36
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Internal Medicine

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