Scanning the aged to minimize missed injury: An EAST multicenter study.

BACKGROUND: Despite the high incidence of blunt trauma in older adults, there is a lack of evidence-based guidance for computed tomography (CT) imaging in this population. We aimed to identify an algorithm to guide use of a Pan-Scan (Head/C-spine/Torso) or a Selective Scan (Head/C-spine ± Torso). We hypothesized that a patient's initial history and exam could be used to guide imaging. METHODS: We prospectively studied blunt trauma patients aged 65+ at 18 Level I/II trauma centers. Patients presenting >24 h after injury or who died upon arrival were excluded. We collected history and physical elements and final injury diagnoses. Injury diagnoses were categorized into CT body regions of Head/C-spine or Torso (chest, abdomen/pelvis, and T/L spine). Using machine learning and regression modeling as well as a priori clinical algorithms based, we tested various decision rules against our dataset. Our priority was to identify a simple rule which could be applied at the bedside, maximizing sensitivity (Sens) and negative predictive value (NPV) to minimize missed injuries. RESULTS: We enrolled 5,498 patients with 3,082 injuries. Nearly half (47.1%, n = 2,587) had an injury within the defined CT body regions. No rule to guide a Pan-Scan could be identified with suitable Sens/NPV for clinical use. A clinical algorithm to identify patients for Pan-Scan, using a combination of physical exam findings and specific high-risk criteria, was identified and had a Sens of 0.94 and NPV of 0.86 This rule would have identified injuries in all but 90 patients (1.6%) and would theoretically spare 11.9% (655) of blunt trauma patients a torso CT. CONCLUSIONS: Our findings advocate for Head/Cspine CT in all geriatric patients with the addition of torso CT in the setting of positive clinical findings and high-risk criteria. Prospective validation of this rule could lead to streamlined diagnostic care of this growing trauma population. LEVEL OF EVIDENCE: Level 2, Diagnostic Tests or Criteria.

Racial Differences in Psychiatric Symptoms, Maladaptive Eating, and Lifestyle Behaviors After Bariatric Surgery.

There are several psychological and behavioral factors associated with poorer outcomes following bariatric surgery, yet it is unknown whether and how these factors may differ by race. In this cross-sectional study, individuals who underwent bariatric surgery from 2018 to 2021 and up to 4 years post-surgery were invited to complete an online survey. Psychiatric symptoms, maladaptive eating patterns, self-monitoring behaviors, and exercise frequency were examined. Participants (N = 733) were 87% women, 63% White, with a mean age of 44 years. Analyses of covariance demonstrated that White individuals endorsed greater anxiety symptoms (p =.01) and emotional eating due to depression (p = .01), whereas Black individuals endorsed greater depression severity (p = .02). Logistic regression analyses demonstrated that White individuals were more likely to experience loss of control eating (OR= 1.7, p = .002), grazing (OR= 2.53, p <.001), and regular self-weighing (OR= 1.41, p <.001) than Black individuals, and were less likely to skip meals (OR= .61, p = .04), or partake in nighttime eating (OR= .40, p <.001). There were no racial differences in binge eating, emotional eating due to anxiety or frustration, use of a food diary, or exercise. Thus, depressive symptoms, skipping meals, and nighttime eating may be important, modifiable intervention targets to optimize the benefits of bariatric surgery and promote equitable outcomes.

Suicide attempts after bariatric surgery: comparison to a nonsurgical cohort of individuals with severe obesity.

BACKGROUND: The rate of suicide is higher among individuals following bariatric surgery compared with the general population; however, it is not clear whether risk is associated with bariatric surgery beyond having severe obesity. OBJECTIVE: To compare the risk of a suicide attempt among those who had bariatric surgery versus a nonsurgical cohort with severe obesity. SETTING: Aggregate count data were collected from 5 healthcare systems. METHODS: Individuals were identified in the surgical cohort if they underwent bariatric surgery between 2009 and 2017 (n = 35,522) and then were compared with a cohort of individuals with severe obesity who never had bariatric surgery (n = 691,752). Suicide attempts were identified after study enrollment date using International Classification of Diseases, Ninth and Tenth Editions (ICD-9 and ICD-10) diagnosis codes from 2009 to 2021. RESULTS: The relative risk of a suicide attempt was 64% higher in the cohort with bariatric surgery than that of the nonsurgical cohort (2.2% versus 1.3%; relative risk = 1.64; 95% CI, 1.53-1.76). Within the cohort with bariatric surgery, suicide attempts were more common among the 18- to 39-year age group (P < .001), women (P = .002), Hawaiian-Pacific Islanders (P < .001), those with Medicaid insurance (P < .001), and those with a documented mental health condition at baseline (in the previous 2 years; P < .001). CONCLUSIONS: The relative risk of suicide attempts was higher among those who underwent bariatric surgery compared with a nonsurgical cohort, though absolute risk remained low. Providers should be aware of this increased risk. Screening for suicide risk after bariatric surgery may be useful to identify high-risk individuals.

Human Papillomavirus vaccination clinical decision support for young adults in an upper midwestern healthcare system: a clinic cluster-randomized control trial.

INTRODUCTION: Human papillomavirus (HPV) vaccination rates are low in young adults. Clinical decision support (CDS) in primary care may increase HPV vaccination. We tested the treatment effect of algorithm-driven, web-based, and electronic health record-linked CDS with or without shared decision-making tools (SDMT) on HPV vaccination rates compared to usual care (UC). METHODS: In a clinic cluster-randomized control trial conducted in a healthcare system serving a largely rural population, we randomized 34 primary care clinic clusters (with three clinics sharing clinicians randomized together) to: CDS; CDS+SDMT; UC. The sample included young adults aged 18-26 due for HPV vaccination with a study index visit from 08/01/2018-03/15/2019 in a study clinic. Generalized linear mixed models tested differences in HPV vaccination status 12 months after index visits by study arm. RESULTS: Among 10,253 patients, 6,876 (65.2%) were due for HPV vaccination, and 5,054 met study eligibility criteria. In adjusted analyses, the HPV vaccination series was completed by 12 months in 2.3% (95% CI: 1.6%-3.2%) of CDS, 1.6% (95% CI: 1.1%-2.3%) of CDS+SDMT, and 2.2% (95% CI: 1.6%-3.0%) of UC patients, and at least one HPV vaccine was received by 12 months in 13.1% (95% CI: 10.6%-16.1%) of CDS, 9.2% (95% CI: 7.3%-11.6%) of CDS+SDMT, and 11.2% (95% CI: 9.1%-13.7%) of UC patients. Differences were not significant between arms. Females, those with prior HPV vaccinations, and those seen at urban clinics had significantly higher odds of HPV vaccination in adjusted models. DISCUSSION: CDS may require optimization for young adults to significantly impact HPV vaccination. TRIAL REGISTRATION: clinicaltrials.gov NCT02986230, 12/6/2016.

Clinical Decision Support with or without Shared Decision Making to Improve Preventive Cancer Care: A Cluster-Randomized Trial.

BACKGROUND: Innovative interventions are needed to address gaps in preventive cancer care, especially in rural areas. This study evaluated the impact of clinical decision support (CDS) with and without shared decision making (SDM) on cancer-screening completion. METHODS: In this 3-arm, parallel-group, cluster-randomized trial conducted at a predominantly rural medical group, 34 primary care clinics were randomized to clinical decision support (CDS), CDS plus shared decision making (CDS+SDM), or usual care (UC). The CDS applied web-based clinical algorithms identifying patients overdue for United States Preventive Services Task Force-recommended preventive cancer care and presented evidence-based recommendations to patients and providers on printouts and on the electronic health record interface. Patients in the CDS+SDM clinic also received shared decision-making tools (SDMTs). The primary outcome was a composite indicator of the proportion of patients overdue for breast, cervical, or colorectal cancer screening at index who were up to date on these 1 y later. RESULTS: From August 1, 2018, to March 15, 2019, 69,405 patients aged 21 to 74 y had visits at study clinics and 25,198 were overdue for 1 or more cancer screening tests at an index visit. At 12-mo follow-up, 9,543 of these (37.9%) were up to date on the composite endpoint. The adjusted, model-derived percentage of patients up to date was 36.5% (95% confidence interval [CI]: 34.0-39.1) in the UC group, 38.1% (95% CI: 35.5-40.9) in the CDS group, and 34.4% (95% CI: 31.8-37.2) in the CDS+SDM group. For all comparisons, the screening rates were higher than UC in the CDS group and lower than UC in the CDS+SDM group, although these differences did not reach statistical significance. CONCLUSION: The CDS did not significantly increase cancer-screening rates. Exploratory analyses suggest a deeper understanding of how SDM and CDS interact to affect cancer prevention decisions is needed. Trial registration: ClinicalTrials.gov ID: NCT02986230, December 6, 2016.

Primary care clinicians' opinions before and after implementation of cancer screening and prevention clinical decision support in a clinic cluster-randomized control trial: a survey research study.

BACKGROUND: Electronic health record (EHR)-linked clinical decision support (CDS) may impact primary care clinicians' (PCCs') clinical care opinions. As part of a clinic cluster-randomized control trial (RCT) testing a cancer prevention and screening CDS system with patient and PCC printouts (with or without shared decision-making tools [SDMT]) for patients due for breast, cervical, colorectal, and lung cancer screening and/or human papillomavirus (HPV) vaccination compared to usual care (UC), we surveyed PCCs at study clinics pre- and post-CDS implementation. Our primary aim was to learn if PCCs' opinions changed over time within study arms. Secondary aims including examining whether PCCs' opinions in study arms differed both pre- and post-implementation, and gauging PCCs' opinions on the CDS in the two intervention arms. METHODS: This study was conducted within a healthcare system serving an upper Midwestern population. We administered pre-implementation (11/2/2017-1/24/2018) and post-implementation (2/2/2020-4/9/2020) cross-sectional electronic surveys to PCCs practicing within a RCT arm: UC; CDS; or CDS + SDMT. Bivariate analyses compared responses between study arms at both time periods and longitudinally within study arms. RESULTS: Pre-implementation (53%, n = 166) and post-implementation (57%, n = 172) response rates were similar. No significant differences in PCC responses were seen between study arms on cancer prevention and screening questions pre-implementation, with few significant differences found between study arms post-implementation. However, significantly fewer intervention arm clinic PCCs reported being very comfortable with discussing breast cancer screening options with patients compared to UC post-implementation, as well as compared to the same intervention arms pre-implementation. Other significant differences were noted within arms longitudinally. For intervention arms, these differences related to CDS areas like EHR alerts, risk calculators, and ordering screening. Most intervention arm PCCs noted the CDS provided overdue screening alerts to which they were unaware. Few PCCs reported using the CDS, but most would recommend it to colleagues, expressed high CDS satisfaction rates, and thought patients liked the CDS's information and utility. CONCLUSIONS: While appreciated by PCCs with high satisfaction rates, the CDS may lower PCCs' confidence regarding discussing patients' breast cancer screening options and may be used irregularly. Future research will evaluate the impact of the CDS on cancer prevention and screening rates. TRIAL REGISTRATION: clinicaltrials.gov , NCT02986230, December 6, 2016.

Implementing a Screening and Brief Intervention Protocol for Excessive Alcohol Use in a Trauma Center: A Healthcare Improvement Project.

OBJECTIVE: The aim of this healthcare improvement project was to evaluate healthcare provider use of screening and brief interventions (SBIs) for patients screening positive for alcohol at an upper Midwestern adult trauma center transitioning from Level II to Level I. METHOD: Trauma registry data for 2,112 adult patients with trauma who screened positive for alcohol were compared between three periods: pre-formal-SBI protocol (January 1, 2010, to November 29, 2011); first post-SBI protocol (February 6, 2012, to April 17, 2016) after protocol implementation, healthcare provider training, and documentation changes; and second post-SBI protocol (June 1, 2016, to June, 30, 2019) after additional training and process improvements. Data analysis included descriptive statistics and logistic regression for comparisons over time and between admitting services. RESULTS: For the trauma admitting service, SBI rates increased from 32% to 90% over time, compared with 18%-51% for other admitting services combined. Trauma-service-admitted patients screening positive for alcohol had higher odds of receiving a brief intervention than other admitting services in each period in adjusted models: pre-SBI (OR = 1.99, 95% CI [1.15, 3.43], p = .014), first post-SBI (OR = 2.89, 95% CI [2.04, 4.11], p < .001), and second post-SBI (OR = 11.40, 95% CI [6.27, 20.75], p < .001) protocol periods. Within trauma service admissions, first post-SBI protocol (OR = 2.15, 95% CI [1.64, 2.82], p < .001) and second post-SBI protocol (OR = 21.56, 95% CI [14.61, 31.81], p < .001) periods had higher rates and odds of receiving an SBI than the pre-SBI protocol period. CONCLUSION: The number of SBIs completed with alcohol-positive adult patients with trauma significantly increased over time through SBI protocol implementation, healthcare provider training, and process improvements, suggesting other admitting services with lower SBI rates could adopt similar approaches.

Patient Perceptions of Using Clinical Decision Support for Cancer Screening and Prevention: "I wouldn't have thought about getting screened without it."

PURPOSE: We sought to gain an understanding of cancer prevention and screening perspectives among patients exposed to a clinical decision support (CDS) tool because they were due or overdue for certain cancer screenings or prevention. METHODS: Semi-structured qualitative interviews were conducted with 37 adult patients due or overdue for cancer prevention services in 10 primary care clinics within the same health system. Data were thematically segmented and coded using qualitative content analysis. RESULTS: We identified three themes: 1) The CDS tool had more strengths than weaknesses, with areas for improvement; 2) Many facilitators and barriers to cancer prevention and screening exist; and 3) Discussions and decision-making varied by type of cancer prevention and screening. Almost all participants made positive comments regarding the CDS. Some participants learned new information, reporting the CDS helped them make a decision they otherwise would not have made. Participants who used the tool with their provider had higher self-reported rates of deciding to be screened than those who did not. CONCLUSIONS: Learning about patients' perceptions of a CDS tool may increase understanding of how patient-tailored CDS impacts cancer screening and prevention rates. Participants found a personalized CDS tool for cancer screening and prevention in primary care useful and a welcome addition to their visit. However, many providers were not using the tool with eligible patients.

Is it a matter of time? The effect of transfer time on femur fracture outcomes.

BACKGROUND: Trauma systems in rural areas often require longdistance transfers for definitive care. Delays in care, such as delayed femurfracture repair have been reported to be associated with poorer outcomes, butlittle is known about how transfer time affects time to repair or outcomesafter femur fractures. METHODS: We conducted a retrospective review of all trauma patients transferred to our level 1 rural trauma center between May 1, 2016-April 30, 2019. Patient demographics and outcomes were abstracted from chart and trauma registry review. All patients with femur fractures were identified. Transfer time was defined as the time from admission at the initial hospital to admission at the trauma center, and time to repair was defined as time from admission to the trauma center until operative start time. Our outcome variables were mortality, in-hospital complications, and hospital length of stay (LOS). RESULTS: Over the study period1,887 patients were transferred to our level 1 trauma center and 398 had afemur fracture. Compared to the entire transfer cohort, femur fracture patientswere older (71 versus 57 years), and more likely to be female (62% versus 43%). The majority (74%) of patients underwent fracture repair within 24hours. Delay in fracture fixation >24 hours wasassociated with increased length of stay (5 days versus 4 days; p<0.001), higher complication rates (23% versus 12%; p=0.01), and decreased dischargehome (19% vs. 32%, pp=0.02), but was not associated with mortality (6% versus5%; p=0.75). Transfer time and time at the initial hospital were not associatedwith mortality, complication rate, or time to femur fixation. DISCUSSION: Fixation delay greater than 24 hours associated with increased likelihood of in-hospital complications, longer length of stay, and decreased likelihood of dischargehome. Transfer time not related to patient outcomes or time to femur fixation. LEVEL OF EVIDENCE: Level III; therapeutic/care management.

The impact of personalized clinical decision support on primary care patients' views of cancer prevention and screening: a cross-sectional survey.

BACKGROUND: Few studies have assessed the impact of clinical decision support (CDS), with or without shared decision-making tools (SDMTs), on patients' perceptions of cancer screening or prevention in primary care settings. This cross-sectional survey was conducted to understand primary care patient's perceptions on cancer screening or prevention. METHODS: We mailed surveys (10/2018-1/2019) to 749 patients aged 18 to 75 years within 15 days after an index clinical encounter at 36 primary care clinics participating in a clinic-randomized control trial of a CDS system for cancer prevention. All patients were overdue for cancer screening or human papillomavirus vaccination. The survey compared respondents' answers by study arm: usual care; CDS; or CDS + SDMT. RESULTS: Of 387 respondents (52% response rate), 73% reported having enough time to discuss cancer prevention options with their primary care provider (PCP), 64% reported their PCP explained the benefits of the cancer screening choice very well, and 32% of obese patients reported discussing weight management, with two-thirds reporting selecting a weight management intervention. Usual care respondents were significantly more likely to decide on colorectal cancer screening than CDS respondents (p < 0.01), and on tobacco cessation than CDS + SDMT respondents (p = 0.02) and both CDS and CDS + SDMT respondents (p < 0.001). CONCLUSIONS: Most patients reported discussing cancer prevention needs with PCPs, with few significant differences between the three study arms in patient-reported cancer prevention care. Upcoming research will assess differences in screening and vaccination rates between study arms during the post-intervention follow-up period. TRIAL REGISTRATION: clinicaltrials.gov , NCT02986230 , December 6, 2016.

Design and rationale of an intervention to improve cancer prevention using clinical decision support and shared decision making: A clinic-randomized trial.

BACKGROUND: Despite decades of research the gap in primary and secondary cancer prevention services in the U. S. remains unacceptably wide. Innovative interventions are needed to address this persistent challenge. Electronic health records linked with Web-based clinical decision support may close this gap, especially if delivered to both patients and their providers. OBJECTIVES: The Cancer Prevention Wizard (CPW) study is an implementation, clinic-randomized trial designed to achieve these aims: 1) assess impact of the Cancer Prevention Wizard-Clinical Decision Support (CPW-CDS) alone and CPW-CDS plus Shared Decision Making Tools (CPW + SDMTs) compared to usual care (UC) on tobacco cessation counseling and drugs, HPV vaccinations, and screening tests for breast, cervical, colorectal, or lung cancer; 2) assess cost of the CPW-CDS intervention; and 3) describe critical facilitators and barriers for CPW-CDS implementation, use, and clinical impact using a mixed-methods approach supported by the CFIR and RE-AIM frameworks. METHODS: 34 predominantly rural, primary care clinics were randomized to CPW-CDS, CPW + SMDTs, or UC. Between August 2018 and October 2020, primary care providers and their patients who met inclusion criteria in intervention clinics were exposed to the CPW-CDS with or without SDMTs. Study outcomes at 12 months post index visit include patients up to date on screening tests and HPV vaccinations, overall healthcare costs, and diagnostic codes and billing levels for cancer prevention services. CONCLUSIONS: We will test in rural primary care settings whether CPW-CDS with or without SDMTs can improve delivery of primary and secondary cancer prevention services. The trial and analyses are ongoing with results expected in 2021.

Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system.

BACKGROUND: Cancer is a leading cause of death in the United States. Primary care providers (PCPs) juggle patient cancer prevention and screening along with managing acute and chronic health problems. However, clinical decision support (CDS) may assist PCPs in addressing patients' cancer prevention and screening needs during short clinic visits. In this paper, we describe pre-implementation study design and cancer screening and prevention CDS changes made to maximize utilization and better fit a healthcare system's goals and culture. We employed the Consolidated Framework for Implementation Research (CFIR), useful for evaluating the implementation of CDS interventions in primary care settings, in understanding barriers and facilitators that led to those changes. METHODS: In a three-arm, pragmatic, 36 clinic cluster-randomized control trial, we integrated cancer screening and prevention CDS and shared decision-making tools (SDMT) into an existing electronic medical record-linked cardiovascular risk management CDS system. The integrated CDS is currently being tested within a predominately rural upper Midwestern healthcare system. Prior to CDS implementation, we catalogued pre-implementation changes made from 2016 to 2018 based on: pre-implementation site engagement; key informant interviews with healthcare system rooming staff, providers, and leadership; and pilot testing. We identified influential barriers, facilitators, and changes made in response through qualitative content analysis of meeting minutes and supportive documents. We then coded pre-implementation changes made and associated barriers and facilitators using the CFIR. RESULTS: Based on our findings from system-wide pre-implementation engagement, pilot testing, and key informant interviews, we made changes to accommodate the needs of the healthcare system based on barriers and facilitators that fell within the Intervention Characteristics, Inner Setting, and Outer Setting CFIR domains. Changes included replacing the expansion of medical assistant roles in one intervention arm with targeted SDMT, as well as altering cancer prevention CDS and study design elements. CONCLUSIONS: Pre-implementation changes to CDS may help meet healthcare systems' evolving needs and optimize the intervention by being responsive to real-world implementation barriers and facilitators. Frameworks like the CFIR are useful tools for identifying areas where pre-implementation barriers and facilitators may result in design changes, both to research studies and CDS systems. TRIAL REGISTRATION: NCT02986230.

Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study.

BACKGROUND: In the United States, primary care providers (PCPs) routinely balance acute, chronic, and preventive patient care delivery, including cancer prevention and screening, in time-limited visits. Clinical decision support (CDS) may help PCPs prioritize cancer prevention and screening with other patient needs. In a three-arm, pragmatic, clinic-randomized control trial, we are studying cancer prevention CDS in a large, upper Midwestern healthcare system. The web-based, electronic health record (EHR)-linked CDS integrates evidence-based primary and secondary cancer prevention and screening recommendations into an existing cardiovascular risk management CDS system. Our objective with this study was to identify adoption barriers and facilitators before implementation in primary care. METHODS: We conducted semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) with 28 key informants employed by the healthcare organization in either leadership roles or the direct provision of clinical care. Transcribed interviews were analyzed using qualitative content analysis. RESULTS: EHR, CDS workflow, CDS users (providers and patients), training, and organizational barriers and facilitators were identified related to Intervention Characteristics, Outer Setting, Inner Setting, and Characteristics of Individuals CFIR domains. CONCLUSION: Identifying and addressing key informant-identified barriers and facilitators before implementing cancer prevention CDS in primary care may support a successful implementation and sustained use. The CFIR is a useful framework for understanding pre-implementation barriers and facilitators. Based on our findings, the research team developed and instituted specialized training, pilot testing, implementation plans, and post-implementation efforts to maximize identified facilitators and address barriers. TRIAL REGISTRATION: clinicaltrials.gov , NCT02986230 , December 6, 2016.

The measurement invariance of the Patient Health Questionnaire-9 for American Indian adults.

BACKGROUND: American Indian people have high suicide rates. However, little epidemiological data is available on depression prevalence, a suicide risk factor, in this population. Some research suggests that depression scales may perform differently for American Indian people. However, the Patient Health Questionnnaire-9 (PHQ-9), a depression scale widely-used in clinical practice, had not been assessed for cross-cultural measurement invariance with American Indian people. METHODS: In this retrospective study of existing electronic health record (EHR) data in an upper Midwestern healthcare system, we assessed the measurement invariance of the standard one-factor PHQ-9 and five previously identified two-factor models for 4443 American Indian and 4443 Caucasian American adults (age >= 18) with a PHQ-9 in the EHR from 12/1/2005 to 12/31/2017. We also conducted subgroup analyses with adults ages >= 65. RESULTS: Models showed good fits (e.g., CFI > 0.99, RMSEA < 0.05) and internal consistency reliability (ordinal alpha > 0.80). All models displayed measurement invariance between racial groups. Factor correlation was high for two-factor models, providing support for the one-factor model. American Indian adults had significantly higher odds of PHQ-9 total scores >= 10 and >= 15 than Caucasian American adults. LIMITATIONS: Data came from a single healthcare system. CONCLUSIONS: The PHQ-9 exhibited cross-cultural measurement invariance between American Indian and Caucasian American adults, supporting the PHQ-9 as a depression screening tool in this clinical care population. American Indian adults also had higher levels of depression than Caucasian Americans. Future research could confirm the generalizability of our findings to other American Indian populations.

Understanding primary care providers' perceptions of cancer prevention and screening in a predominantly rural healthcare system in the upper Midwest.

BACKGROUND: Cancer is the leading cause of death in the United States, with the burden expected to rise in the coming decades, increasing the need for effective cancer prevention and screening options. The United States Preventive Services Task Force has suggested that a shared decision-making process be used when clinicians and patients discuss cancer screening. The electronic medical record (EMR) often provides only reminders or alerts to primary care providers (PCPs) when screenings are due, a strategy with limited efficacy. METHODS: We administered a cross-sectional electronic survey to PCPs (n = 165, 53% response rate) at 36 Essentia Health primary care clinics participating in a large, National Cancer Institute-funded study on a cancer prevention clinical decision support (CDS) tool. The survey assessed PCP demographics, perceptions of the EMR's ability to help assess and manage patients' cancer risk, and experience and comfort level discussing cancer screening and prevention with patients. RESULTS: In these predominantly rural clinics, only 49% of PCPs thought the EMR was well integrated to help assess and manage cancer risk. Both advanced care practitioners and physicians agreed that cancer screening and informed discussion of cancer risks are important; however, only 53% reported their patients gave cancer screening a high priority relative to other health issues. CONCLUSIONS: The impact of EMR-linked CDS delivered to both patients and PCPs may improve cancer screening, but only if it is easy to use and saves PCPs time.