Introduction
Although delivery of medical care according to evidence-based practices has become an accepted standard in medicine, the ability to consistently implement these practices is often difficult [1]. While clinical guidelines are essential for synthesizing evidence and standardizing quality care, traditional dissemination to individual clinicians is often insufficient for translating new evidence into practice across diverse settings [2, 3]. Indeed, it has been shown that, on average, evidence-based practices take 17 years to be integrated into routine care, with many stalling in the pipeline from research to practice [4].
Cardiovascular disease prevention is no exception, with care gaps identified in risk factor screening, treatment initiation, and long-term adherence to recommended management [1, 3].
For example, providing patients with a 10-year cardiovascular disease risk calculation, as part of medical decision making, is limited in practice, despite guidelines incorporating this recommendation for over a decade [5]. In practice, research suggests that using a risk score often does not change patient or clinician behaviors [5]. Familial hypercholesterolemia (FH) provides another example of opportunities to improve guidelines-based care for cardiovascular risk management.
FH is an inherited disorder of cholesterol metabolism that increases LDL cholesterol at a young age, thus leading to significant risk for premature heart attacks and strokes [3]. Identification of FH allows for appropriate management of cholesterol, including the potential for targeted therapies, in patients and their close family members. However, numerous care gaps in both the diagnosis and management of FH have been identified in the current International Atherosclerosis Society evidence-based guideline [3].
Implementation science has emerged as a scientific discipline to address the gap between randomized controlled clinical trials, which demonstrate the efficacy of an intervention in rigorously controlled scenarios, and practice, where there may be barriers to successful adoption of interventions. Implementation science methods provide a structured approach for (1) identifying contextual factors affecting the adoption of interventions at the patient, clinician, and systems levels, (2) assessing barriers and facilitators (determinants) of adoption, (3) adapting interventions and designing implementation strategies to address determinants identified, and (4) studying outcomes of implementation to ensure continued effectiveness over time. Implementation science methods can be used across implementation phases, from planning a project, to adopting and implementing a new practice, to sustaining the practice as part of routine care [2, 6–8].
Evidence-based implementation strategies may be included in clinical guidelines to facilitate the process of implementation [2]. Ultimately, the goal of implementation science is to ensure patients will benefit from evidence-based care as implementation and sustainment is systematically adapted across diverse medical settings [1, 6, 7].
The purpose of this overview is to explore how implementation science research can be conducted in an integrated learning health system, using examples from our ongoing research. At Geisinger, several projects are using implementation science methodology to identify patients with significantly increased risk for atherosclerotic cardiovascular disease (ASCVD) from FH with the goal of improving their care. By discussing these three implementation research studies, set in a single learning health system, we aim to demonstrate how these methods can lead to improved implementation of guideline-based care for patients with ASCVD risk; many of these strategies may be transferable across health systems and to risk scenarios other than FH.
Implementation Science Overview
Implementation science methods are a systematic approach to design, test, adapt, and evaluate interventions and implementation strategies using a range of tools. Health care policy, health care systems, clinical practice models, characteristics and perspectives of patients and clinicians, and other factors may all be explored as these factors influence the adoption and sustainment of interventions [2]. Implementation science methods typically use a pragmatic approach to data collection (whether quantitative, qualitative, or mixed methods), and the generalizability of findings is enhanced through disseminating detailed information about practice adaptations, implementation strategies, and context [8–10].
Theories, models, and frameworks (hereafter: frameworks) provide a conceptual roadmap for structuring the implementation of evidenced-based practices. Frameworks allow researchers to systematically design, select, and adapt implementation strategies, to successfully adopt an implementation strategy as a practice, and to provide guidance on selecting relevant outcomes for implementation evaluation [8, 11–13]. Ideally, an implementation science researcher uses a framework (or multiple frameworks, if needed) to guide a project from developing the research question to designing the study protocol, and through to dissemination of outcomes and sustainability plans [13].
It is well recognized that adaptations to interventions and implementation strategies are context dependent and will need further adaptation to fit in contexts different from where initial research was performed. Tracking adaptations using structured frameworks enhances transferability to new settings [14]. Finally, it is critical to evaluate sustainability in order to overcome barriers to sustained integration over time so that practices continue even after project funding ends [15].
Common Implementation Strategies in Healthcare
Implementation strategies are specific techniques used to facilitate implementation of an evidence-based practice and include methods such as audit and feedback, education outreach, and quality monitoring [16]. Educational strategies targeted towards clinicians and/or patients and use of electronic health record (EHR) tools are commonly leveraged for implementation of practices in healthcare. Education is necessary when introducing any initiative, as factors such as improved knowledge about a condition or access to resources have been shown to change clinician behavior [17, 18].
The Cochrane Effective Practice and Organisation of Care Group (EPOC) has evaluated six types of educational strategies for healthcare clinicians: (1) disseminating educational materials; (2) large-scale educational meetings; (3) small-scale educational meetings; (4) outreach visits; (5) electronic learning; and (6) education provided by opinion leaders [19]. To have a higher probability of achieving effectiveness, educational programs should include a needs assessment, identify key opinion leaders (such as influential peers), avoid one-day interventions, and include champions of the evidence based practice who are known for active participation or high interest in the topic [17, 20]. Importantly, educational interventions must be delivered as part of a multi-pronged approach rather than rely on a single best strategy [20, 21].
For example, a growing volume of evidence documents patient health education approaches aimed at improving adherence to desired health behaviors [22]. Thus, Including patient-level education in implementation of new practices may help to improve the number of patients reached by those practices.
In addition to clinician and patient knowledge enhancement through education, it is critical to consider the broader environment in which clinicians operate. Clinical workflows, tools, resources, and how clinicians are evaluated are examples of important contexts which influence the implementation of evidence-based practices. EHRs, as the primary tool clinicians use to interact with patients and their clinical information, provide the opportunity to deliver clinical decision support regarding evidence-based practices.
EHRs also allow for monitoring behavior in relation to practice implementation. Combining multiple clinical decision support strategies tends to yield more meaningful changes in clinician behavior [23–26].
Examples of Implementation Science Research at Geisinger Addressing ASCVD Risk
Described below and summarized in Table 1, three ongoing studies at Geisinger aim to improve the implementation of guidelines-based care for patients with FH who are at increased risk for ASCVD. These studies span implementation phases from pre-implementation to sustainment. They focus on implementation of ASCVD risk calculation, improving the rates of diagnosis of FH in primary care, and improving cascade testing for family members of individuals diagnosed with FH.
Like many implementation science research projects, we assembled multidisciplinary research teams, including engaging health services researchers, economists, behavioral scientists, project management staff, clinicians, and patients to successfully execute research aims. We leveraged organizational buy-in – from leadership to clinic front desk staff - across departments such as primary care, pediatrics, internal medicine, and genetic counseling to support implementation research to improve guidelines-based care for individuals at risk for ASCVD. To ground these studies in an implementation framework, the Conceptual Model of Implementation Research (CMIR) is used to guide assessment of implementation outcomes [8].
Table 2 summarizes outcomes used across studies [8, 27].
Table 1Geisinger studies to improve identification and cascade testing of familial hypercholesterolemia (FH) and develop an implementation protocol for integrating genomic risk information into ASCVD risk managementImplementation science methodologyCARE-FH [28, 29, 31]IMPACT-FH [36–38, 40]RISK-FHPhasePre-Implementation and ImplementationPre-Implementation and ImplementationPre-ImplementationFrameworkConceptual Model of Implementation ResearchConceptual Model of Implementation ResearchConceptual Model of Implementation ResearchCare GapScreening for FHCascade testing for FHUse of genomic information to guide care related to 10-yr ASCVD riskInterventionScreening for FH within primary care settings for patients ages 9 and over, in concordance with guidelinesSharing positive FH genetic test results with family membersASCVD risk tool that integrates genomic and clinical informationStrategies• Constituent engagement to develop strategies• Clinician education and training• Daily patient lists• Patient educational mailings• Streamlined genetic test ordering in EHR with support of genetic counseling assistants• Constituent engagement to develop strategies• Direct contact of close relatives by study staff• Family sharing chatbot sent to relatives by patient• Information packet distributed to family members by patient• Constituent engagement to develop strategies currently in progressKey Methods• Implementation Mapping using:• Qualitative interviews with clinicians• Surveys of clinicians• Quantitative analysis of EHR data• Qualitative focus groups with patients with FH and clinicians at pre-implementation• Quantitative surveys of patients with FH and at-risk relatives at pre-implementation• Qualitative dyadic interviews with patients with FH and their relatives at pre-implementation• Qualitative interviews with patients with FH, relatives, and healthcare professionals post-implementation• Implementation Mapping using:• Qualitative interviews with clinicians• Qualitative interviews with patients• Expert Advisory Panel inputKey Outcomes• Acceptability: clinician and patient satisfaction with screening tools• Penetration: proportion of patients who received FH screening• Adoption: proportion of clinicians who used screening tools• Health: appropriate changes in medications, referral to specialist, or diagnosis in EHR• Timeliness: time to FH screening evaluation• Cost: cost to implement the strategy package• Sustainability: potential for institutionalization of screening for FH in primary care• Qualitative feedback on using testing approach• Reach: relatives who completed cascade testing (either LDL-C screening or genetic testing for FH)• Appropriateness, acceptability, feasibility, implementation, and adoption of each of the cascade testing strategies, pre- and post-implementation• Pre-implementation outcomes: appropriateness, acceptability, feasibility, and implementation of integrating genomic information into ASCVD risk management
Table 2Operationalization of constructs from the conceptual model of implementation research (CMIR) used in example studiesImplementation outcome [8]CARE-FH [28, 29, 31]IMPACT-FH [36–38, 40]RISK-FHAcceptability: Perception of the intervention as agreeable or satisfactoryClinician and patient satisfaction and self-efficacy with the implementation strategy packageThe family communication methods were agreeable and palatable to clinicians, patients, or family membersClinician and patient satisfaction (or dissatisfaction) with using genomic information to inform ASCVD risk management discussionsAppropriateness: Fit, relevance, or compatibility of the intervention within a specified contextClinician perceived fit, relevance, or compatibility of the implementation strategy packageThe perceived fit, relevance, or compatibility of the family communication methods for clinicians, patients, or family membersClinician and patient perception of the fit, relevance, or compatibility of using genomic information to inform ASCVD risk management discussionsFeasibility: Extent to which an intervention can be successfully carried out within a given settingClinician uptake of the FH diagnostic evaluation and clinical utility of the implementation strategy packageThe extent to which the family communication methods can be successfully used within a given healthcare setting, as experienced by patients and cliniciansClinician and patient descriptions of the practicality of using genomic information to guide ASCVD risk management discussions at their health system
Improving Risk Stratification in Familial Hypercholesterolemia (RISK-FH)
The RISK-FH study is currently conducting pre-implementation work exploring the clinician and patient experiences of managing severe hypercholesterolemia due to different underlying causes. Using qualitative methods, 25 clinicians and 28 patients from two health systems were asked to consider how they would like to integrate genomic information into ASCVD risk calculations in primary care settings, including their communication and information preferences. Participants were asked to consider different causes of severe hypercholesterolemia: monogenic FH, polygenic risk, Lipoprotein(a)-related risk, and phenotypic severe hyperlipidemia with no identified genetic cause.
In the ongoing analysis of this data, the RISK-FH team is exploring acceptability, appropriateness, and feasibility of integrating genomic risk causes for severe hypercholesterolemia into ASCVD related care. Using implementation mapping, the study team plans to incorporate constituent and expert feedback about barriers and facilitators to genomic information being incorporated into risk analysis to develop implementation strategies for communication between patients and clinicians about genomically informed ASCVD risk.
Since there is not an existing guideline to incorporate genomic information into ASCVD risk calculations, RISK-FH is working to develop evidence to support such a practice, anticipating implementation efforts if genomic information is incorporated into future guidelines. Ideally, the ongoing constituent engagement for the RISK-FH study will improve primary care clinician discussions about ASCVD risk calculations and associated care management. By developing educational strategies for clinicians communicating with patients about ASCVD risk that incorporates genetic causes, the RISK-FH team aims to improve care provided for individuals living with severe hypercholesterolemia.
Collaborative Approach to Reach Everyone with Familial Hypercholesterolemia: CARE-FH
The CARE-FH study is an ongoing implementation study aiming to increase screening for FH initiated within primary care. The study was developed due to evidence from within Geisinger that highlighted significant underdiagnosis of FH [21, 28]. The CARE-FH team first conducted contextual inquiries with primary care and specialist clinicians to assess the current state of FH screening [28, 29].
Such inquiries identified facilitators (e.g., diagnostic criteria, novel therapies, and interdisciplinary approach) and barriers to screening (e.g., cost, time, clinician knowledge) [28, 29].
The CARE-FH study then leveraged implementation mapping as a systematic approach to identify published strategies that could be applied to improve adherence to FH screening and identification guidelines [28, 30, 31]. Strategies were further refined with clinician partner input and piloted in a single clinic to identify if additional modifications were needed. Ultimately, a multi-strategy approach was created that provided in person education to primary care clinical teams about both screening for FH and utilizing decision support and EHR tools [32].
Education was offered during dedicated time (instead of online instruction or review of written material) and included the option of continuing medical education credits. In addition to clinician education, the CARE-FH study implemented direct-to-patient outreach using a letter to encourage patients to discuss lipid screening at an upcoming scheduled visit [32].
These constituent-engaged methods of developing implementation strategies provided the foundation for broader implementation in a current clinical trial to assess diagnostic evaluation rates among clinicians who received the implementation package compared to those who did not [29, 32]. Patient outcomes, such as new diagnoses, medication changes, referrals, genetic tests ordered, and downstream changes in LDL-cholesterol levels are being assessed to appreciate whether the implementation package improves patient care by increasing the diagnosis of FH [33–35].
By using implementation science methods to engage constituents in developing strategies with the goal of improving guidelines-based identification and management of FH, this study highlights the value of translating constituent feedback into clinical care practice. As results of CARE-FH are obtained, practices which lead to increased FH screening and treatment can be adopted. FH diagnosis rates should improve across the health system.
Hopefully primary care clinicians will be empowered to initiate lipid lowering therapy in affected patients and become less dependent on sub-specialty referrals for preventive treatments. Sustainability planning will allow the continuation of successful practices after study funding has expired. Similar methods may be applied to adapt the strategies used at Geisinger to a broad range of contexts.
Identification Methods, Patient Activation, and Cascade Testing (IMPACT-FH)
The IMPACT-FH study aims to improve testing of at-risk family members following identification of FH in a proband (cascade testing). Cascade testing is a multistep process that typically involves patients diagnosed with FH discussing their diagnosis with family members followed by family members working with their clinicians to have genetic testing and/or lipid screening for FH. The IMPACT-FH team collected historical data from Geisinger regarding the uptake of cascade testing in family members of probands with FH to identify a performance gap between current practice (3.5% uptake of cascade testing) and recommended care (all patients be offered targeted testing).
Through five qualitative focus groups with 22 individuals with FH and 20 clinicians, acceptability, appropriateness, and feasibility of automated screening approaches to flag individuals for FH evaluation was assessed and family communication strategies for FH were considered [36–38]. Constituents felt that methods utilizing genomic and EHR data could promote earlier screening and diagnosis of FH and described the need for both patient and provider notifications and dedicated staff to manage the results [37]. Additionally, the preference for a variety of family communication strategies, including chatbot technology and direct contact by a clinician, emerged as a key finding, underscoring the need to provide probands with options to tailor their approach to each relative.
The importance of clinician education as a key factor for successful implementation of screening and family communication methods for FH cascade testing was also highlighted [36].
In a separate aim of the study, feedback from 98 surveys and 22 dyadic interviews with individuals with FH and their relatives was used to design and optimize three family communication strategies to facilitate cascade testing in relatives of individuals diagnosed with FH: a family packet, chatbot technology, and a direct contact program in which family members were contacted by study genetic counselors [39–41]. These approaches were selected to overcome system-level barriers to cascade testing for FH, including gaps in family communication and limited access to healthcare clinicians and genetic testing services. The prior focus group findings demonstrated that these strategies were acceptable, appropriate, and feasible, and incorporating suggestions from the end users into the program led to the creation of constituent-informed strategies that facilitated actionable communication within families and streamlined cascade testing pathways [41].
Throughout the iterative development process, deliberate attention was given to the content and design of the strategies to ensure that they closely aligned with the needs and preferences of the population while also considering real-world constraints.
The optimized strategies were implemented in a pragmatic trial where 175 probands who received an FH result via genomic screening were given the choice to utilize any combination of the strategies for each of their at-risk relatives. The primary outcome was completion of genetic and/or lipid cascade testing 12 months after result disclosure and strategy distribution. Overall, 26.6% (144/541) of adult relatives with a strategy selected completed testing.
Selection of any strategy for a relative led to 6.23 times higher odds (95% CI: 3.17–12.61) of cascade testing completion when compared to those without a strategy selected; direct contact led to 16.78 times higher odds (95% CI: 8.53, 15.51). Cascade testing was completed in 23.6% of adult relatives with a family packet, 30.3% with chatbot technology, and 80.8% with direct contact selected, a significant increase compared to the historical estimate of 3.5% uptake before program implementation (P < 0.0001) [40].
Additionally, implementation outcomes were further investigated by interviewing 15 probands and 12 relatives who received the optimized strategies, and 6 healthcare professionals involved in their implementation [41]. Participants assessed the program as acceptable, appropriate, and feasible in validated quantitative measures during the interviews. Offering multiple family communication strategies and employing choice was seen as a benefit of the program.
Findings showed that adoption of the strategies was driven by the low-cost cascade testing options offered but barriers to feasibility remained, such as complex workflows and workload concerns, that would need to be considered for future implementation.
Ongoing work in the IMPACT-FH study is focused on adapting family communication strategies from the genomic screening context to the primary care setting. EHR-based informatics tools will be used to alert clinicians about the indication for cascade testing among the at-risk relatives of individuals with FH [42]. The team will employ economic modeling methodology to evaluate cost, efficiency, and performance of FH screening strategies [42].
Further, a mixed methods approach guided by the Integrated Sustainability Framework will be used to identify factors influencing the sustainability of FH screening programs across diverse healthcare settings worldwide [42]. If successful, a primary care-based approach using clinical decision support to identify at-risk relatives could substantially increase cascade testing for families with FH, improving the management of these patients with significant ASCVD-related risk. As with the CARE-FH study, the implementation science methodology could be applied to improve cascade testing for FH implementation strategies at other settings.
Conclusions and Future Directions
Here, we explored specific examples from three research studies at Geisinger that are leveraging implementation science to improve identification of ASCVD risk and management for patients and their family members. These three studies illustrate how the use of implementation science methodology for constituent engagement and strategy design can improve guidelines-based care for patients with FH and other ASCVD-related risk factors. These methods can be used successfully across implementation phases, from pre-implementation planning through to trials designed to assess long term sustainment of a practice.
The RISK-FH study will suggest strategies to improve communication between patients and clinicians related to discussions of 10-year ASCVD risk, augmented by results of genetic testing. The CARE-FH and IMPACT-FH studies have overcome barriers such as clinician knowledge, genetic test ordering complexity, patient identification, and family communication to increase rates of lipid test ordering, FH diagnostic evaluation, and cascade testing. By consolidating risk calculations, laboratory ordering pathways, and educational materials into the standard clinician workflows, these studies aim to improve adoption of guidelines-based practices without adding burdens to clinical practice.
Future directions at Geisinger include continuing to tailor clinical decision support to patients and clinicians, exploring behavioral factors that influence adoption, and partnering with user-experience design teams to iteratively refine workflows. Such strategies may further enhance adoption, efficiency, and the likelihood that our implementation efforts will translate into improved clinical outcomes for individuals with ASCVD-related risk due to severe hypercholesterolemia. Indeed, Suresh et al. summarizes efforts to close the implementation gap for FH by improving identification and treatment for these patients [43].
While some of the findings from these studies can be generalized to other settings, others may be unique to the setting of a learning-based health system environment like Geisinger. Importantly, the implementation science methodology used to achieve successful implementation at Geisinger can be applied in other contexts to facilitate improved ASCVD guidelines-based care and patient outcomes.
Key References
Proctor EK, Bunger AC, Lengnick-Hall R, et al. Ten years of implementation outcomes research: A scoping review. Implement Sci.
2023;18(1):31–z. https://doi.org/10.1186/s13012-023-01286-z.○ Provides an overview of the CMIR framework, which is used by the studies described in detail.Jones LK, Williams MS, Ladd IG, et al. Collaborative approach to reach everyone with familial hypercholesterolemia: CARE-FH protocol. J Pers Med.
2022;12(4):606. https://doi.org/10.3390/jpm12040606.○ Provides an overview of the IMPACT-FH study. Campbell-Salome G, Jones LK, Masnick MF, et al. Developing and optimizing innovative tools to address familial hypercholesterolemia underdiagnosis: Identification methods, patient activation, and cascade testing for familial hypercholesterolemia.
Circ Genom Precis Med. 2021;14(1):e003120. https://doi.org/10.1161/CIRCGEN.120.003120.○ Provides an overview of the CARE-FH study.