ABSTRACT
Purpose
Despite the growing interest in implementing genomic medicine into primary care, numerous barriers persist. Herein we present formative work conducted at three primary care clinics to identify barriers to implementation as part of a state‐funded genomic medicine initiative.
Methods
A convergent mixed‐methods case study was conducted across three family practice clinics participating in the Alabama Genomic Health Initiative. Quantitative, cross‐sectional surveys evaluated clinic personnel attitudes, implementation climate, and feasibility. Additionally, qualitative, semi‐structured interviews examined clinic personnel's perceptions of genomic medicine implementation into routine care, identified barriers to this integration, and investigated strategies for overcoming these barriers.
Results
Respondents exhibited positive attitudes towards genomic medicine while voicing concerns regarding time constraints, technological integration, lack of actionable results for certain patients, and inadequate implementation knowledge. Although the primary care climate was generally supportive of change, it was less conducive to the specific implementation of genomic medicine. External facilitators and educational resources were identified as critical for successful implementation.
Conclusion
Despite primary care personnel acknowledging the preventive and diagnostic value of genomic medicine, the effective integration of this practice requires practical solutions to barriers such as resource limitations and specialized training needs, indicating that enhanced infrastructure and focused educational initiatives could facilitate its implementation.
Keywords: electronic health records, genomic medicine, implementation science, mixed methods, pharmacogenetics, preventive health services, primary health care
1. Introduction
Genomic medicine offers numerous benefits for patients, providers, and policy makers. Genomics is an integral component of personalized healthcare given its potential to support more effective and efficient individualized treatments for patients that can improve patient outcomes [1, 2]. This is particularly important in pharmacogenomics, where patients' genetic data provides insight into how they metabolize drugs [1]. Using genomics, clinical practitioners can identify genetic predispositions to specific health conditions, enhancing disease prediction and prevention, particularly for complex and rare conditions [3]. Moreover, investments in genomic medicine can stimulate innovation, leading to new medical breakthroughs and fostering economic growth in the biotechnology and pharmaceutical sectors [4].
In the context of primary care, genomics serves as a foundational resource that supports clinicians in assessing genetic risk, enabling earlier detection of disease, and tailoring both prevention and treatment strategies [1, 2]. Due to primary care being the principal point of continuity for most patient care across the lifespan, implementing genomics here emboldens clinicians to make more informed decisions about screening, risk management, and medication selection [1, 4]. Generating evidence for genomic medicine in the primary care setting is not only essential for enhancing care quality but also ensures that advances in precision medicine reach the broadest patient population and promote more equitable implementation [2, 4].
Given the direct and indirect benefits of genomic medicine, there are growing calls to implement genomic medicine in primary care [5]. However, such implementation faces several barriers. First, primary care providers often lack sufficient knowledge and training in genetics and genomics to integrate genomic tools into their practice effectively [6]. Also, the high costs of genomic tests and the uncertainty around insurance reimbursement inhibit their adoption. Research shows that integrating and interpreting complex genomic data requires sophisticated infrastructure and expertise, which may not be readily available in primary care settings [7]. Another concern is a lack of patient awareness and acceptance, leading to hesitancy and resistance to genetic testing [8]. Addressing these barriers will require concerted efforts from healthcare providers, policymakers, educators, and the broader medical community to realize the full benefits of genomic medicine in primary care.
The purpose of this paper is to present an analysis of recent efforts of the Alabama Genomic Health Initiative (AGHI) to implement and evaluate genomic testing, interpretation, genetic counseling, and pharmacogenetic counseling (“genomic medicine”) in primary care clinics. The following questions guided the evaluation: (1) What are the barriers to implementing genomic medicine in primary care clinics? (2) What strategies exist for overcoming the barriers to implementing genomic medicine in primary care clinics? Although our evaluation focused on primary care clinics in Alabama, findings may provide insights relevant to similar academic primary care settings implementing genomic medicine, while recognizing the context‐specific nature of implementation challenges.
2. The Alabama Genomic Health Initiative
2.1. Population Screening
Supported by the State of Alabama, the AGHI is a collaboration between the University of Alabama at Birmingham (UAB) Medicine and the HudsonAlpha Institute for Biotechnology, providing free genomic testing, interpretation, and counseling for disease risk and pharmacogenetics for all Alabamians [9]. From 2017 to 2020, the AGHI enrolled Alabamians from all 67 counties and conducted population screening for common actionable genomic conditions (ACMG SF v2.0), with follow‐up care recommendations for patients harboring genomic risk [10]. In addition, the AGHI supports genome sequencing to aid in ending the diagnostic odyssey for patients with undiagnosed diseases [9].
2.2. The AGHI Primary Care Program
In June 2020, the AGHI expanded its efforts by implementing genomic medicine in three UAB Family Medicine clinics (two urban, one rural). As part of this expansion, testing was broadened to include genomic screening (ACMG SF v3.1), which returns pathogenic or likely pathogenic variants in medically actionable genes associated with cancer, cardiovascular, metabolic, and connective tissue diseases [11]. In addition, pharmacogenetic (PGx) testing was incorporated, covering 26 genes in 97 markers that meet FDA and CPIC evidence thresholds [12, 13].
Study staff were responsible for recruiting and consenting patients on site, ensuring participants understood the genomic testing process and completed all necessary consent forms and health history questionnaires before enrollment. Once enrolled, participants provided a blood sample for these analyses in clinic. After laboratory analysis, results were returned through a structured process: physicians were notified of their patients' results via the electronic medical record (EMR) system, which included both normal and actionable findings. For participants with actionable findings, study staff—including genetic counselors—directly contacted the patient to discuss the results, provided free genetic counseling, and arranged referrals to specialty providers as needed. Pharmacogenomics‐trained pharmacists provided pharmacogenomic assessments to both providers and participants regarding current and potential medication implications.
In 2022, the AGHI initiated an evaluation of the effort to implement genomic medicine in these three primary care clinics. Using an implementation science‐informed approach, the evaluation sought barriers and facilitators to integrating genomic medicine into primary care clinics in different geographic settings and potential strategies for overcoming identified barriers that could support the expansion of genomic medicine to other primary care clinics.
3. Materials and Methods
3.1. Study Setting
Expansion of the AGHI to UAB Family Medicine clinics (2 urban, 1 rural) provided an opportunity for formative assessments of implementation barriers and facilitators in primary care.
3.2. Design
The evaluation used a mixed method, multiple case study design where each family practice clinic served as a case. One advantage of this design was that each clinic provided a ‘mini evaluation’ to examine clinic members' attitudes toward genomic medicine, the contextual conditions surrounding the implementation of genomic medicine, and common and unique barriers to implementing genomic medicine in primary care settings. With the AGHI goal of scaling up and disseminating genomic medicine to other primary care clinics throughout Alabama, another consideration of the evaluation was the identification of strategies to support implementation in other clinics.
3.3. Data Collection Strategies
The evaluation entailed collecting both quantitative and qualitative data. Quantitative data were used to assess perceived attitudes toward genomic medicine and outcomes related to its implementation. We administered a 15 min survey via Qualtrics to family medicine clinic personnel who were directly involved with implementing and using genomic medicine. Clinic personnel were recruited for participation through direct invitation by the research team during scheduled site visits to each of the three participating family medicine clinics. All clinic staff directly involved with implementing or using genomic medicine in patient care were eligible and invited to participate in both the quantitative survey and qualitative interviews. Participation in both components was voluntary and independent; participants could choose to complete the survey only, participate in interviews only, or engage in both data collection methods. This approach allowed for triangulation of findings while respecting participants' time constraints and preferences for engagement. The survey was based on validated instruments related to clinic context, perceptions of genomic medicine, and implementation outcomes [14, 15, 16, 17]. Specific constructs assessed were the organizational climate (leadership engagement, learning climate, implementation climate, stress), attitudes towards genomic medicine (e.g., compatibility, adaptability, observability), and key implementation outcomes (acceptability, appropriateness, feasibility, satisfaction). Each multi‐item scale used a 5‐point Likert response format. This format ranged from 1 (“strongly disagree”) to 5 (“strongly agree”), with higher scores reflecting a more favorable perception or a stronger agreement with the construct. Mean (M) scale scores and standard deviations (SD) are reported to summarize respondents' perceptions; categorical variables are reported as frequencies. Appendix 1 provides a detailed enumeration of these survey instruments and constructs. The survey also included three questions related to the respondent's role/position in the clinic, the number of years of experience, and the number of years in their current position.
Qualitative data were used to describe the process clinics used to incorporate genomic medicine results in routine care, identify barriers to integrating genomic medicine into work processes, and strategies that clinics have (or could use) to overcome barriers to using genomic medicine in routine care. We conducted semi‐structured interviews, approximately 30 min in duration, with 7 clinic personnel involved with the implementation and/or use of genomic medicine in each clinic. Interview questions were based on the Consolidated Framework for Implementation Research (CFIR; see Appendix 2) and focused on clinic personnel attitudes toward and experiences using genomic medicine and how the internal and external context influenced and will continue to influence the use of genomic medicine in their clinic.
3.4. Data Analysis
Quantitative data analysis relied on univariate statistics, primarily means and standard deviations for summated scales and frequencies for categorical attributes related to survey respondents. Given the case study design of our evaluation, we also report univariate statistics for our summated scales by clinic and respondent role (i.e., physician vs. other); however, given the exploratory nature of our evaluation, we do not formally test differences between these groupings.
3.5. For Qualitative Data Analysis of the Interviews
We used a framework approach. The process began with the research team familiarizing themselves with the interview contents by reading the transcripts in detail before making any efforts to code the data. Next, two researchers independently coded the transcripts using the CFIR. This established implementation framework is commonly used to systematically examine the context surrounding the implementation of evidence‐based interventions in health care settings. The CFIR distinguishes between five major domains that affect implementation: 1. Outer setting (e.g., policies, reimbursement); 2. Inner setting (e.g. implementation climate, organizational resources); 3. Process (e.g., plans for implementation); 4. Intervention characteristics (e.g., complexity, ease of use); and 5. Individual characteristics (e.g., openness to change, perceived self‐efficacy). In the third phase, the two researchers charted the interview data into a framework matrix (i.e., individual cases as rows and CFIR‐related codes as columns). In this case, charting entailed writing a brief, 1‐sentence statement summarizing a participant's response to the interview question and, where possible, an illustrative quote corresponding with that summary statement. In the final phase, the two researchers compared the charted data across study participants to identify commonalities and differences.
4. Results
4.1. Quantitative Results
4.1.1. Survey Respondents
We received 17 of 46 responses to the survey (response rate = 37.0%; Table 1). Approximately equal numbers of respondents indicated they were physicians (N = 6, 35.3%) or some other professional role (N = 7, 41.2%). On average, respondents had 15.0 years of work experience (range = 7–25 years) and had been in their current positions for 6.2 years (range = 1–25 years).
Table 1.
Survey respondent characteristics.
| Overall | Site 1 | Site 2 | Site 3 | |
|---|---|---|---|---|
| Professional role, N (%) | ||||
| Physician | 6 (37.5) | 2 (50.0) | 1 (33.3) | 3 (33.3) |
| Nurse | 1 (6.3) | 0 (0) | 1 (33.3) | 0 (0) |
| Licensed practical nurse | 1 (6.3) | 0 (0) | 0 (0) | 1 (11.1) |
| Navigator | 1 (6.3) | 0 (0) | 0 (0) | 1 (11.1) |
| Other | 7 (43.8) | 2 (50.0) | 1 (33.3) | 4 (44.4) |
| Number of years of health care experience, Mean (SD) | 15.0 (6.5) | 15.1 (7.4) | 12 (8.7) | 15.9 (6.0) |
| Number of years in current position, Mean (SD) | 6.2 (6.2) | 11.0 (9.7) | 2.0 (1.4) | 5.0 (4.0) |
| N | 16 | 4 | 3 | 9 |
4.1.2. Clinic Climate
On average, respondents felt most positive about leadership engagement (M = 3.85, SD = 0.58) and learning climate (M = 3.61, SD = 0.49) domains of the clinic (Table 2). In contrast, respondents felt the least positive about the implementation climate (M = 3.02, SD = 0.80) and the level of stress within the clinic (M = 3.12, SD = 0.51).
Table 2.
Organizational climate descriptive statistics, overall and by clinic and role.
| Clinic | Role | |||||
|---|---|---|---|---|---|---|
| Overall (N = 17) | Clinic 1 (N = 4) | Clinic 2 (N = 3) | Clinic 3 (N = 9) | Physician (N = 6) | Other (N = 11) | |
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | |
| Culture | 3.38 (0.44) | 3.69 (0.43) | 3.59 (0.23) | 3.14 (0.37) | 3.46 (0.58) | 3.33 (0.37) |
| Culture stress | 3.12 (0.51) | 3.30 (0.60) | 3.08 (0.63) | 3.03 (0.46) | 3.21 (0.53) | 3.07 (0.51) |
| Imp. Climate | 3.02 (0.80) | 3.05 (1.23) | 3.33 (n/a) | 2.97 (0.59) | 3.01 (1.04) | 3.03 (0.65) |
| Learning climate | 3.61 (0.49) | 4.00 (0.00) | 4.00 (0.00) | 3.27 (0.48) | 3.63 (0.45) | 3.60 (0.54) |
| Leader engagement | 3.85 (0.58) | 4.15 (0.49) | 4.14 (0.55) | 3.58 (0.56) | 3.69 (0.43) | 3.93 (0.65) |
| Resources | 3.32 (0.70) | 3.63 (1.00) | 3.33 (0.17) | 3.15 (0.62) | 2.92 (0.58) | 3.55 (0.68) |
| Change readiness | ||||||
| Motivation | 3.29 (1.00) | 3.43 (1.12) | 3.57 (n/a) | 3.16 (1.05) | 2.93 (1.18) | 3.55 (0.79) |
| Capacity | 3.29 (0.95) | 3.40 (0.94) | 3.40 (n/a) | 3.20 (1.07) | 3.02 (1.00) | 3.49 (0.74) |
4.1.3. Attitudes Toward Genomic Medicine
Overall, respondents were slightly positive in their attitudes toward genomic medicine (M = 3.28, SD = 0.72; Table 3). With respect to specific aspects of genomic medicine, respondents were most consistently positive in their attitudes about the compatibility of genomic medicine with their clinic activities. For example, respondents reported a mean agreement of 3.33 (SD = 0.65) to the statement “Using genomic medicine fits well with the way I like to work” and 3.42 (SD = 0.90) to the statement “Genomic medicine is aligned with my clinical judgment.” Similarly, respondents tended to think genomic medicine could be adapted to their clinic setting. Respondents reported a mean agreement of 3.45 (SD = 0.69) to the statement “Genomic medicine can be adapted to fit my treatment setting” and a mean agreement of 3.45 (SD = 0.93) to the statement “Genomic medicine can be adapted to meet the needs of my patients.” In contrast, respondents were least positive about the observability of genomic medicine, reporting a mean agreement of 2.82 (SD = 1.08) to the statement “It is easy to tell whether patients are benefitting from genomic medicine” and a mean agreement of 2.91 (SD = 0.83) to the statement “Genomic medicine produces improvements in my patients that I can actually see.”
Table 3.
Attitudes toward genomic medicine descriptive statistics, overall and by clinic and role.
| Clinic | Role | |||||
|---|---|---|---|---|---|---|
| Overall (N = 12) | Clinic 1 (N = 4) | Clinic 2 (N = 1) | Clinic 3 (N = 7) | Physician (N = 5) | Other (N = 7) | |
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | |
| Aggregate | 3.28 (0.72) | 3.48 (0.41) | 3.58 (n/a) | 3.13 (0.90) | 3.02 (1.00) | 3.48 (0.43) |
| Compatibility | ||||||
| Using genomic medicine fits well with the way I like to work. | 3.33 (0.65) | 3.48 (0.41) | 3.58 (n/a) | 3.29 (0.90) | 3.20 (0.84) | 3.43 (0.53) |
| Genomic medicine is aligned with my clinical judgment. | 3.42 (0.90) | 3.50 (0.58) | 3.00 (n/a) | 3.14 (1.07) | 3.20 (1.30) | 3.57 (0.53) |
| Complexity | ||||||
| Genomic medicine is clear and understandable. | 3.17 (0.94) | 3.75 (0.50) | 4.00 (n/a) | 3.14 (1.07) | 2.80 (1.30) | 3.43 (0.53) |
| Genomic medicine is easy to use. | 3.25 (1.06) | 3.00 (0.82) | 3.00 (n/a) | 3.29 (1.11) | 2.80 (1.30) | 3.57 (0.79) |
| Trialability | ||||||
| Genomic medicine can be tested out with patients without disrupting their overall therapy. | 3.73 (0.47) | 4.00 (0.00) | 4.00 (n/a) | 3.14 (1.21) | 3.80 (0.45) | 3.67 (0.52) |
| It is easy to try out genomic medicine and see how it performs. | 3.27 (1.01) | 3.67 (0.58) | 4.00 (n/a) | 3.57 (0.53) | 2.80 (1.30) | 3.67 (0.52) |
| Observability | ||||||
| It is easy to tell whether patients are benefitting from genomic medicine. | 2.82 (1.08) | 3.00 (1.00) | 3.00 (n/a) | 3.00 (1.15) | 2.20 (1.30) | 3.33 (0.52) |
| Genomic medicine produces improvements in my patients that I can actually see. | 2.91 (0.83) | 3.00 (1.00) | 3.00 (n/a) | 2.71 (1.25) | 2.40 (0.89) | 3.33 (0.52) |
| Potential for reinvention | ||||||
| Genomic medicine can be adapted to fit my treatment setting. | 3.45 (0.69) | 4.00 (0.00) | 3.00 (n/a) | 2.86 (0.90) | 3.40 (0.89) | 3.50 (0.55) |
| 10. Genomic medicine can be adapted to meet the needs of my patients. | 3.45 (0.93) | 4.00 (0.00) | 4.00 (n/a) | 3.29 (0.76) | 3.40 (1.34) | 3.50 (0.55) |
| Task issues | ||||||
| Using genomic medicine improves the quality of work that I do. | 3.36 (1.03) | 3.67 (0.57) | 4.00 (n/a) | 3.14 (1.07) | 3.40 (1.52) | 3.33 (0.52) |
| Using genomic medicine makes it easier to do my job. | 3.09 (0.94) | 3.00 (1.00) | 4.00 (n/a) | 3.14 (1.21) | 2.80 (1.30) | 3.33 (0.52) |
4.1.4. Implementation Outcomes
On average, respondents felt moderately positive about genomic medicine's acceptability (M = 3.77, SD = 1.06), appropriateness (M = 3.73, SD = 1.11), and feasibility (M = 3.66, SD = 1.06) in their clinic (Table 4), while their overall satisfaction with genomic medicine was slightly lower (M = 3.36, SD = 1.02). There was notable variability in perceptions of genomic medicine across the three sites, with respondents from the clinic in the rural setting consistently reporting more negative scores of acceptability, appropriateness, and feasibility. Similarly, physicians consistently reported more negative perceptions of genomic medicine acceptability, appropriateness, and feasibility than other clinic members, yet reported higher levels of overall satisfaction with genomic medicine.
Table 4.
Implementation outcome descriptives, overall and by clinic and role.
| Clinic | Role | |||||
|---|---|---|---|---|---|---|
| Overall (N = 11) | Highlands (N = 3) | Hoover (N = 1) | Selma (N = 7) | Physicians (N = 5) | Other (N = 6) | |
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | |
| Acceptability | 3.77 (1.06) | 3.92 (0.14) | 4.00 (0.00) | 3.68 (1.36) | 3.75 (1.46) | 3.79 (0.75) |
| Appropriateness | 3.73 (1.11) | 4.00 (0.00) | 3.75 (0.00) | 3.61 (1.41) | 3.60 (1.53) | 3.83 (0.75) |
| Feasibility | 3.66 (1.06) | 3.92 (0.14) | 3.75 (0.00) | 3.54 (1.34) | 3.60 (1.53) | 3.71 (0.60) |
| Overall satisfaction | 3.36 (1.02) | 3.67 (0.57) | 3.00 (n/a) | 3.29 (1.25) | 3.40 (1.52) | 3.33 (0.52) |
4.2. Qualitative Results
Our qualitative analysis identified several notable challenges to implementing genomic medicine in primary care.
4.2.1. Tension Between Providing More Information and Time Constraints
Time constraints are a well‐known challenge in primary care and providing additional information in the form of genetic testing results and additional treatment options during a patient visit can exacerbate this challenge [18].
I think it's just the amount of information. You have a very limited amount of time with an individual and they come in for an acute issue and you have 15, 20 min to make that decision. You're going through family history, you're going through allergies, you're going through their symptoms and adding in…genomic medicine. It's just, it's not something that, at least for me, that I naturally think to pull in yet. It takes an extra step to think, okay, maybe I do need to take a look, and have they had this done and would this be applicable to their current situation?
(Physician assistant)
4.2.2. Clinic Technology Not Set Up to Integrate Genomic Information Into Clinical Routines
Another challenge highlighted by interviewees was the ability of existing clinic technology to provide easy access to genetic information for patients during a visit. Providers need to seek out this information actively, and it was not always apparent to some providers that this information could be found in a patient's medical record.
Honestly, that has not been something that I have done regularly. The information is in the chart obviously on the patients that have had it but it's something that you have to go and look for. It's not something that's listed on their problem list that pulls into your note or something like that. It's something that you have to seek out.
(Physician assistant)
4.2.3. Clinical Uncertainty for Results That Do Not Change Current Therapy
Without clear next steps regarding clinical treatment for some genetic results, the value proposition for genomic testing was unclear for some clinicians.
I think one of the other concerns that I've seen and had a conversation with some of the residents is to make sure that they don't see this as another thing that they're having to do…I've had conversations with them questioning, “Why are we finding out if there's nothing that we can do about it?
(Clinical Research Coordinator)
4.2.4. Facilitators
Our interviews also highlighted several things that participants felt made it easier to integrate genomic medicine into their care routines.
4.2.5. Emphasize the Value of Genomic Medicine for Prevention
Over half of the study interviewees described genomic medicine's capabilities for supporting front line preventive care as an important message to support its adoption by primary care providers.
In primary care, I feel like we're in the beginning phases versus other realms in the oncology world where they're using it a little bit more regularly. I think in the primary care world, you're on the front end of saying, “Hey, look, there is some information available that's gonna really impact our ability to choose the right medications and look at the genetic risk profile of these patients in an actual way on the front end, rather than letting this being able to rely on specialists to manage this. I think that empowering primary care providers to have this information is, in my mind, the way of the future.
(Physician assistant)
Again, it's a tool. It's almost like a no‐brainer for me to have that tool. It's kinda like a mechanic havin' a tool where you can find out what's wrong with the car, so you know what to fix. I know it's not expansive—it's only the genes that we screen for, but the value in it, and bein' able to educate their patients and to focus more on preventive care versus findin' it out down the road where they're in—I think it's minimizing chronic illnesses.
(Clinical Research Coordinator)
I think it goes back to our focus on prevention, so I think highlighting, that is what we are trying to do as primary care providers, so that, the focus on prevention and also being able to just fine tune what we're doing in terms of prescribing medications.
(Physician 2)
4.2.6. Education
Participants noted the importance of education that uses tangible learning tools such as examples of genomic medicine reports. Such learning aides helped end users make the somewhat tacit and abstract aspects of genomic medicine more explicit, and in doing so, helped clinicians more easily see the value of genomic medicine by connecting it more directly to patient care.
Then, what made it even better, you receive your first report and you're going through it and you're like ‘Oh, man, this is cool! Because you don't talk about those types of things. You talk about them but there's not like this tangible thing…it [the report] solidifies it. Then you're able to say ‘Hey, you will benefit from this. Then you're actively seeking out patients to benefit from getting tested.
(Physician 1)
Again, I think having the information on the front end of what these reports are gonna look like, when we get this report, here's how it's gonna look, here's what's actual, here's what's not. I think having that education on the front end as far as them getting these reports back and saying, “Okay. Well, this looks familiar. I know what to do with it.” Having that part on the front end is very helpful…
(Physician assistant)
4.2.7. Robust External Facilitation to Support Patient Enrollment
Several interviewees also discussed the importance of external facilitation to support their efforts to integrate genomic medicine into their clinical practice. First, the use of external facilitators as part of the site‐level implementation team provided genomic medicine expertise that was not readily available in the clinics. Second, using external facilitators as part of the on‐site implementation helped mitigate some of the new workload required to successfully implement genomic medicine, including initial patient education about genetic testing and some of the procedures needed to generate and integrate genetic testing results.
The enthusiasm, not necessarily from me but from the staff actually in the clinic, I think, is the bonus of this whole situation. Because, if it [enrollment] was outside of the clinic and it was something that we were saying ‘Hey, just stop by this building’ or ‘Hey, just do this’ or ‘Come back.’, but because they were right there, able to do this, the teaching and getting the blood and everything right on site…
(Physician 1)
As far as the recruitment, I think you guys do a very good job of giving pre‐information to the patients before. It's not relying on us. Saying, “Hey, we also have this thing. It puts the onus on the patient a little bit to have that information first and then we can talk about it if they need it….I mean, I think the biggest part is having someone who's part of the implementation team, who can do heavy lifting with the patient education. I think that as far as us having the time to do that in the clinic visit before we send them out the door to go talk to you guys I think having, again, y'all [an external facilitator], having someone who pre‐calls the patient and does that introduction and gets the ball rolling a little bit is very helpful. Rather than relying on the providers to say, “Oh, by the way, we have this group that's here doing a research study and let me start from scratch and talk about it. I mean, I think that has helped. Because honestly, in a short visit, I think that it would be hard to start that conversation.
(Physician assistant)
5. Discussion
The AGHI's efforts to integrate genomic medicine into primary care clinics has provided several critical insights. Our quantitative and qualitative results provide three primary takeaways (Table 5). First, while in our study participating clinicians generally recognized the value of genomic medicine for their patients, they lack the resources and knowledge about how to best use it in clinical practice. This was highlighted by relatively positive perceptions of acceptability, appropriateness, and compatibility with their clinical work but clear identification of EMR integration and time constraints as formidable barriers to routinely using genomic medicine in their care routines. Such challenges are not unique to efforts to implement genomic medicine, as other studies have identified insufficient time and technology as barriers to implementing evidence‐based interventions [5, 19]. On one hand, these findings are encouraging, as these other studies may provide some insight into approaches to overcome these same barriers. On the other hand, the fact that these same barriers are persistent over time and intervention type suggests that they are deep‐rooted challenges that defy simple solutions. Regardless, the fact that the majority of respondents in our study reported seeing the value and relevance of genomic medicine for their work suggests that, at least among this self‐selected group, the primary issue may be less about convincing clinicians to adopt genomic medicine and more about showing them how it can work in their clinic. However, given the low response rate (37%), these views may not be representative of all clinic personnel, and it is possible that non‐respondents hold less favorable attitudes.
Table 5.
Joint display of quantitative and qualitative results.
| Quantitative results | Qualitative results | ||
|---|---|---|---|
| Theme | Source: Web‐based survey of clinic personne | Source: Qualitative interviews of clinic personnel | Integration |
| Genomic medicine is valued but resources and knowledge are lacking. |
Mean acceptability = 3.77 (SD = 1.06) Mean appropriateness =3.73 (SD = 1.11) Mean feasibility = 3.66 (SD = 1.06) “Genomic medicine is aligned with my clinical judgment” = 3.42 (SD = 0.90) |
“Honestly, that has not been something that I have done regularly. The information is in the chart obviously on the patients that have had it but it's something that you have to go and look for. It's not something that's listed on their problem list that pulls into your note or something like that. It's something that you have to seek out. In the note that you pull up, your family history, past medical history, surgical history, all those things pull in genetic information, pharmacogenetics, that's stuff that's not—you have to seek that out. It's a matter of getting used to that being another piece of information that is available.” | Primary care clinicians generally recognize the value of genomic medicine for their patients; however, they lack the resources and knowledge about how to best use it in clinical practice. Thus, moving forward the primary may not be convincing primary care providers to adopt genomic medicine but rather showing them how it can be implemented in their clinics and supporting their efforts to do so. |
| The general environment in primary care settings is supportive of change; however, the climate for implementing genomic medicine specifically is not as conducive. |
Mean learning climate = 3.61 (SD = 0.49) Mean leadership engagement = 3.85 (SD = 0.58) Mean implementation climate = 3.02 (SD = 0.80) Mean change readiness for genomic medicine motivation = 3.29 (SD = 1.00) Mean change readiness for genomic medicine capacity = 3.29 (0.95) |
“I think it's just the amount of information. You have a very limited amount of time with an individual and they come in for an acute issue and you have 15, 20 min to make that decision. You're going through family history, you're going through allergies, you're going through their symptoms and adding in that other piece, the genomic medicine. It's just, it's not something that, at least for me, it's not something that I naturally think to pull in yet. It takes an extra step to think, okay, maybe I do need to take a look, and have they had this done and would this be applicable to their current situation?” “As far as the recruitment, I think you guys do a very good job of giving pre‐information to the patients before. It's not relying on us. Saying, “Hey, we also have this thing. It puts the onus on the patient a little bit to have that information first and then we can talk about it if they need it….I mean, I think the biggest part is having someone who's part of the implementation team, who can do the heavy lifting with the patient education. I think that as far as us having the time to do that in the clinic visit before we send them out the door to go talk to you guys I think having, again, y'all, having someone who pre‐calls the patient and does that introduction and gets the ball rolling a little bit is very helpful.” |
The general environment in primary care settings is supportive of change; however, the climate for implementing genomic medicine specifically is not as conducive. One reason for this disconnect is the specialized, information‐intensive nature of genomic medicine vis‐à‐vis the time constraints faced by many primary care physicians. External facilitation can help mitigate this constraint. |
| The value of genomic medicine is evident, however, its application to all patients is less clear for some clinicians due to its complexity and its inability to provide actionable results for all patients. |
Mean acceptability = 3.77 (SD = 1.06) Mean appropriateness = 3.73 (SD = 1.11) Mean “Genomic medicine is aligned with my clinical judgment” = 3.42 (SD = 0.90) Mean “Genomic medicine is clear and understandable” = 3.17 (SD = 0.94) Mean “It is easy to tell whether patients are benefitting from genomic medicine.” = 2.82 (SD = 1.03) |
“I think one of the other concerns that I've seen and had a conversation with some of the residents is to make sure that they don't see this as another thing that they're having to do…I've had conversations with them questioning, “Why are we finding out if there's nothing that we can do about it?” I've had conversations as far as BRCA gene. They understand that. If I get that information, there's things that I can do as far as, maybe twice mammogram, versus once. There's other things that I can do with that, but there's some genes I think they don't understand, and they may even see screening as being an obstacle for them. Because if I get those results back, then what? What do I do with that?” “I think it goes back to our focus on prevention, so I think highlighting, that is what we are trying to do as primary care providers, so that, the focus on prevention and also being able to just fine tune what we're doing in terms of prescribing medications.” |
Despite primary care physicians recognizing the value of genomic medicine, its application to all patients is less clear for some clinicians due to its complexity and its inability to provide actionable results for all patients. Early efforts to educate providers that provide tangible materials and examples, as well as emphasizing the role of genomic medicine for prevention, may help mitigate these issues. |
Second, our findings suggest that among the clinic personnel who participated in our evaluation across these three academic family medicine clinics, perceptions of the broader clinic environment were relatively supportive of change (e.g., positive perceptions of the learning climate and leadership support), whereas the climate for implementing genomic medicine specifically appeared less conducive (e.g., change motivation and change capacity for genomic medicine were modest in comparison). These perceptions reflect a self‐selected subset of providers and may not generalize to the wider clinic workforce or to primary care settings without similar institutional support. One reason why the implementation climate for genomic medicine specifically may be less supportive is the absence of time that many primary care physicians, including those in our study, report having to provide current levels of care, in combination with the characteristics of genomic medicine—i.e., an information‐intense intervention that requires specialized knowledge. Indeed, interviewees noted the tension between providing more information, like genetic testing results, and patient visits that are already pressed for time given other clinical and administrative requirements. This may be one reason why stress was rated as one of the most salient aspects of the clinic climate in our study (M = 3.02). It may also be why study participants highlighted the value of external facilitators with specialized expertise as an important facilitator for promoting the use of genomic medicine in primary care.
Finally, a close review of our findings indicates that while the potential value of genomic medicine was recognized by most respondents, its relevance and application to all patients/situations was murkier for some, due in part to the complexity of genomic medicine and its inability to provide clear and actionable results for all patients (e.g., modest observability). Educational programs that provide tangible materials and examples can help offset this issue, especially if provided early on. Likewise, emphasizing the value of genomic medicine for supporting preventive medicine may help overcome reservations on the part of primary clinicians who may question its broad value for all patients.
The findings from our evaluation are generally consistent with other studies that have considered how to implement genomic medicine in primary care. For example, Khoury and Holt and Strianese et al. [1, 2] highlighted the potential of genomics to revolutionize personalized healthcare and provide personalized treatments as well as provide better diagnostic accuracy but also noted the challenges that come with integrating genomics into clinical practice, especially in primary care settings, where providers may lack the necessary training and resources. Likewise, Thomas et al. identified a lack of sufficient knowledge and training in genetics among primary care providers as a significant barrier [6]. Stranneheim et al. also highlighted the infrastructure demands of integrating genomic sequencing into clinical settings, which—alongside the high costs of genomic tests and uncertainty around insurance reimbursement—can inhibit adoption [7].
6. Clinical Implications
The findings of our studies have numerous implications for clinical practice. First, they highlight the need for education and training for primary care providers to effectively use genomic information. This is important to improve the diagnostic accuracy and treatment efficacy of genomic medicine. Second, the study highlights the importance of genomic data integration into electronic health records to facilitate its use during patient visits, at point of care. This requires investment in health information technology and the development of user‐friendly systems that allow providers to easily access and interpret genomic data to make treatment decisions. Moreover, positive attitudes towards genomic medicine are promising. With proper support and resources, primary care clinics can successfully implement genomic medicine practices. This has the potential to transform primary care and provide more personalized and precise treatments. The proactive approach to health and prevention (highlighted by our qualitative findings) suggests that genomic medicine can support a more holistic and patient‐centered approach to health care delivery and help patients take ownership of their health and engage in more effective preventive care.
7. Study Limitations and Recommendations for Future Research
The findings of our study should be interpreted considering several limitations. The study was conducted among three primary care clinics affiliated with an academic medical center in a limited geographic region, all of which may limit the generalizability of the study findings. Indeed, clinicians in our study clinics were provided with substantial education and leadership support. Thus, these clinics may represent the best‐case scenario' for implementing genomic medicine in primary care. Moreover, the response rate to the survey was relatively low. Because participation in both the survey and interviews was voluntary, respondents may have been more favorably disposed toward genomic medicine and the implementation effort than non‐respondents, so the perspectives described in the Discussion should be interpreted as reflecting this self‐selected subset of clinic personnel rather than the full clinic workforce. Our qualitative analysis was based on a limited number of interviews and may not capture all possible perspectives. Additionally, the study did not include patient perspectives, which are important when considering the full range of factors that may affect the implementation of genomic medicine in primary care. Furthermore, this study was conducted in clinics receiving substantial support—including external facilitation, dedicated education, and active leadership encouragement, which may not reflect conditions in randomly selected clinics without such resources. This limits the transferability of findings to unsupported settings. Future studies should include a larger and more diverse sample to enhance the generalizability of the findings.
Finally, our study was cross‐sectional in design and provided only a snapshot of the current state of genomic medicine implementation but does not capture changes over time. Longitudinal studies are needed to understand whether and how efforts to implement genomic medicine may change over time. Additionally, the study relied on self‐reported data, which may be subject to response biases. We recommend that more studies incorporate objective measures of genomic medicine implementation and outcomes to provide a more accurate assessment.
8. Conclusion
The findings of this study provide valuable insights into the factors that facilitate and complicate the implementation of genomic medicine in primary care. Despite enthusiasm and recognition of the value of genomic medicine, practical challenges such as time constraints, insufficient technological integration, and a lack of specialized knowledge still present formidable barriers to the broad clinical adoption of genomic medicine in these settings. Overcoming these barriers will require the efforts of many different stakeholders, including healthcare providers, policymakers, and educators. Only then can we realize the full potential of genomic medicine and transform primary care into a more personalized, precise, and proactive healthcare system.
Author Contributions
Conceptualization: Larry R. Hearld, Kristine R. Hearld, Nita A. Limdi, Irene P. Moss, Bruce R. Korf. Formal analysis: Larry R. Hearld, Kristine R. Hearld, Nita A. Limdi. Funding acquisition: Larry R. Hearld, Kristine R. Hearld, Nita A. Limdi, Bruce R. Korf. Investigation: Larry R. Hearld, Kristine R. Hearld. Methodology: Larry R. Hearld, Kristine R. Hearld. Project administration: Larry R. Hearld, Nita A. Limdi. Resources: Bruce R. Korf, Nita A. Limdi. Supervision: Larry R. Hearld. Visualization: Nita A. Limdi, Larry R. Hearld. Writing – original draft: Larry R. Hearld, Nita A. Limdi, Emory W. Heffernan. Writing – review and editing: All authors.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supplementary.
Acknowledgements
The authors would like to thank the staff and participants of the UAB Family Medicine clinics for their valuable contributions to this study. We also acknowledge the support of the Alabama Genomic Health Initiative (AGHI) team for their assistance in study implementation. This work was partially supported by grants from the Alabama Genomic Health Initiative, funded by the state of Alabama (N.A.L, L.R.H.); NIH T32TR004770 (E.W.H); NIH UM1TR004771 (N.A.L.); NIH K24HL133373 (N.A.L.).
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. Strianese O., Rizzo F., Ciccarelli M., et al., “Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease,” Genes 11, no. 7 (2020): 747, 10.3390/genes11070747. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Khoury M. J. and Holt K. E., “The Impact of Genomics on Precision Public Health: Beyond the Pandemic,” Genome Medicine 13, no. 1 (2021): 67, 10.1186/s13073-021-00886-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Weisschuh N., Mazzola P., Zuleger T., et al., “Diagnostic Genome Sequencing Improves Diagnostic Yield: A Prospective Single‐Centre Study in 1000 Patients With Inherited Eye Diseases,” Journal of Medical Genetics 61, no. 2 (2024): 186–195, 10.1136/jmg-2023-109470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Stark Z., Dolman L., Manolio T. A., et al., “Integrating Genomics Into Healthcare: A Global Responsibility,” American Journal of Human Genetics 104, no. 1 (2019): 13–20, 10.1016/j.ajhg.2018.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wildin R. S., Giummo C. A., Reiter A. W., Peterson T. C., and Leonard D. G. B., “Primary Care Implementation of Genomic Population Health Screening Using a Large Gene Sequencing Panel,” Frontiers in Genetics 13 (2022): 867334, 10.3389/fgene.2022.867334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Thomas J., Keels J., Calzone K. A., et al., “Current State of Genomics in Nursing: A Scoping Review of Healthcare Provider Oriented (Clinical and Educational) Outcomes (2012–2022),” Genes 14, no. 11 (2023): 2013, 10.3390/genes14112013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Stranneheim H., Lagerstedt‐Robinson K., Magnusson M., et al., “Integration of Whole Genome Sequencing Into a Healthcare Setting: High Diagnostic Rates Across Multiple Clinical Entities in 3219 Rare Disease Patients,” Genome Medicine 13, no. 1 (2021): 40, 10.1186/s13073-021-00855-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Pearce A., Mitchell L. A., Best S., Young M. A., and Terrill B., “Publics' Knowledge of, Attitude to and Motivation Towards Health‐Related Genomics: A Scoping Review,” European Journal of Human Genetics 32, no. 7 (2024): 747–758, 10.1038/s41431-024-01547-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Alabama Genomic Health Initiative. UAB Medicine. Published May 15, 2024, accessed June 18, 2025, https://www.uabmedicine.org/specialties/aghi/.
- 10. Kalia S. S., Adelman K., Bale S. J., et al., “Recommendations for Reporting of Secondary Findings in Clinical Exome and Genome Sequencing, 2016 Update (ACMG SF v2.0): A Policy Statement of the American College of Medical Genetics and Genomics,” Genetics in Medicine 19, no. 2 (2017): 249–255, 10.1038/gim.2016.190. [DOI] [PubMed] [Google Scholar]
- 11. Miller D. T., Lee K., Abul‐Husn N. S., et al., “ACMG SF v3.1 List for Reporting of Secondary Findings in Clinical Exome and Genome Sequencing: A Policy Statement of the American College of Medical Genetics and Genomics (ACMG),” Genetics in Medicine 24, no. 7 (2022): 1407–1414, 10.1016/j.gim.2022.04.006. [DOI] [PubMed] [Google Scholar]
- 12.Center for Drug Evaluation and Research. Table of pharmacogenomic biomarkers. U.S. Food and Drug Administration, accessed June 11, 2025, https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling.
- 13.Clinical Pharmacogenetics Implementation Consortium (CPIC). Genes–Drugs, accessed June 18, 2025, https://cpicpgx.org/genes-drugs/.
- 14. Fernandez M. E., Walker T. J., Weiner B. J., et al., “Developing Measures to Assess Constructs From the Inner Setting Domain of the Consolidated Framework for Implementation Research,” Implementation Science 13, no. 1 (2018): 52, 10.1186/s13012-018-0736-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Shea C. M., Jacobs S. R., Esserman D. A., Bruce K., and Weiner B. J., “Organizational Readiness for Implementing Change: A Psychometric Assessment of a New Measure,” Implementation Science 9 (2014): 7, 10.1186/1748-5908-9-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Cook J. M., Thompson R., and Schnurr P. P., “Perceived Characteristics of Intervention Scale: Development and Psychometric Properties,” Assessment 22, no. 6 (2015): 704–714, 10.1177/1073191114561254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Weiner B. J., Lewis C. C., Stanick C., et al., “Psychometric Assessment of Three Newly Developed Implementation Outcome Measures,” Implementation Science 12, no. 1 (2017): 108, 10.1186/s13012-017-0635-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Nguyen M. L. T., Honcharov V., Ballard D., Satterwhite S., McDermott A. M., and Sarkar U., “Primary Care Physicians' Experiences With and Adaptations to Time Constraints,” JAMA Network Open 7, no. 4 (2024): e248827, 10.1001/jamanetworkopen.2024.8827. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Horwood C., Luthuli S., Mapumulo S., et al., “Challenges of Using E‐Health Technologies to Support Clinical Care in Rural Africa: A Longitudinal Mixed Methods Study Exploring Primary Health Care Nurses' Experiences of Using an Electronic Clinical Decision Support System (CDSS) in South Africa,” BMC Health Services Research 23, no. 1 (2023): 30, 10.1186/s12913-022-09001-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.