Abstract
Barriers to improving the U.S. healthcare system include a lack of interoperability across digital health information and delays in seeking preventative and recommended care. Interoperability can be seen as the lynch pin to reducing fragmentation and improving outcomes related to digital health systems. The prevailing standard for information exchange to enable interoperability is the Health Level Seven International Fast Healthcare Interoperable Resources standard. To better understand Fast Healthcare Interoperable Resources within the context of computerized clinical decision support expert interviews of health informaticists were conducted and used to create a modified force field analysis. Current barriers and future recommendations to scale adoption of Fast Healthcare Interoperable Resources were explored. Identified barriers included variation in Electronic Health Record implementation, limited Electronic Health Record vendor support, ontology variation, limited workforce knowledge, and testing limitations. Experts recommended research funders require Fast Healthcare Interoperable Resource usage, development of an “app store,” incentives for clinical organizations and Electronic Health Record vendors, and Fast Healthcare Interoperable Resource certification development.
Keywords: Decision Support Systems, Clinical; Health Level Seven / standards; Systems Integration; Delivery of Health Care / organization & administration; Electronic Health Records / organization & administration
INTRODUCTION
Rationale
Healthcare began adopting digital systems as far back as the 1960’s. Due to technological limitations systems were siloed by department and lacked interoperability.1 To get a comprehensive view of one patient providers would have to access multiple systems. Healthcare has increasingly digitized, but interoperability continues to be suboptimal due to challenges with standards adoption, implementation, maintenance, and mapping2. Health-related data volume is increasing exponentially, exacerbating fragmentation. Additional data can improve clinical decision making, however it also increases cognitive burden. Within primary care cognitive burden has increased significantly between 2000 and 2019 due to the large number of unique providers with which a primary care provider is expected to coordinate (Inter-Quartile range 40–164, median= 95)3.
As of 2019, the U.S. spends 16.8% of gross domestic product on healthcare, compared to an average of 10.5% in other high-income countries, but performs worst overall4. There is no single cause for this poor performance. However, inefficiency due to a lack of interoperability across Electronic Health Records (EHRs) is one key factor5. Interoperability allows systems to exchange information in a useable format6. If systems were interoperable, providers could have an integrated view of all relevant information, which could improve decision making, health outcomes, provider satisfaction, and patient satisfaction7. Interoperability would also enable dissemination of Computerized Clinical Decision Support (CCDS) across organizations, eliminating duplicative efforts that are estimated to cost the U.S. Healthcare system $25 billion8.
Lack of preventive care also challenges system performance9. When patients do not engage in preventive care they often require more intensive treatments in the future10. Research has shown that Computerized Clinical Decision Support (CCDS) can increase engagement with preventive services by providers and patients11,12. As of 2015, the most recent analyzed medical expenditure panel survey data suggests that approximately 8% of U.S. adults received all recommended preventive services13. Interoperable systems could provide a comprehensive view of clinical data, allowing decisions to be made rather than waiting to repeat services to gain data visibility.
According to the Office of the National Coordinator for Health Information Technology (ONC) CCDS provide timely, patient-specific information to improve quality of care14. CCDS invokes automation to compare patient-specific data against a knowledge base resulting in recommendations at the point-of-care15. By filtering all available patient-specific data through CCDS providers can have actionable evidence-based recommendations specific to individual patients. This could decrease cognitive burden of data gathering, freeing up cognitive capacity for decision making16.
Fast Healthcare Interoperable Resources
A standard data exchange format is needed for CCDS to pull relevant patient specific data for targeted purposes. In 2011, Health Level Seven International (HL7) introduced what is now known as Fast Healthcare Interoperable Resources or FHIR17. FHIR standardizes data exchange in a modular way by breaking down information into resources1. This design allows systems, like CCDS, to request necessary information, rather than all available information1. Since its release FHIR has been embraced by five of the leading EHR vendors18. In addition, the ONC 21st Century Cures Act Final Rules require adoption of FHIR version 4.0.1 for Health IT certification. This certification is required to receive payment from any federal health plan (e.g. Medicare, Medicaid)19.
FHIR has wide-spread support from EHR vendors and federal regulations as the standard for information exchange. However, its use within the context of CCDS has been limited20. A 2019 scientific review of FHIR use found that of 131 publications less than 10% focused on CCDS17. A separate 2021 systematic review of FHIR implementations identified 80 FHIR related publications, but only included one journal article and two conference presentations focused on clinical decision support2. This same 2021 review found significant barriers to FHIR including implementation within applications, standard complexity, adoption challenges, mapping issues, FHIR maintence.2 The purpose of this exploratory report is to describe: (1) barriers to the use of FHIR standards related to the development of shareable, interoperable, EHR integrated CCDS, and (2) recommendations to improve adoption of FHIR standards within this context based on experts’ perspectives.
METHODS
To better understand FHIR as a standard for data exchange within the context of CCDS we conducted expert interviews with five health informaticists who were identified using purposive sampling of a network of informatics experts. Participants were eligible if they had experience in the development and implementation of CCDS that leveraged FHIR. Participants completed a demographic form using Qualtrics prior to interviews. This study was approved through the University of Florida Institutional Review Board and was conducted between October and November 2021.
Expert interviews provide understanding of implicit knowledge, social understanding, and technical knowledge21. The interview guide was developed based on consultation with an expert in CCDS development, see table 1. Individual interviews of informatics experts were conducted over video conferencing. Audio recordings were professionally transcribed and reviewed for accuracy. Content analysis of transcripts was performed by the lead author using NVivo 12 to identify barriers or restraining forces to FHIR use and recommendations for potential driving forces that could encourage more robust use of FHIR in the development of CCDS. Percent coverage of each theme was measured using transcripts that removed introduction, consent process, and farewell exchanges so that only questions, probes, and responses remained. First pass coding of transcripts identified segments discussing either restraints or recommendations. During second pass coding themes were identified within restraints and recommendations separately. Percent coverage of each theme was extracted from NVivo by participant and is reported only for those who discussed a given theme. Transcripts represent information related to themes in addition to information not specifically related to themes. Because percent coverage is based on the total information in a transcript the total percent coverage across themes may not equal 100 percent. Results were reviewed by the research team.
Table 1.
Interview guiding questions
| Questions | |
|---|---|
| 1 | Would you please share your knowledge and/or experience with the use of FHIR standards related to clinical decision support? This could be related to development, interoperability, implementation, sustainability, or scalability. |
| 2 | We have found that end-users value tools that are both evidence-based and provide tailored or individualized recommendations specific to each patient. Do you see FHIR standards being capable of enabling these kinds of tools in an interoperable manner? |
| 3 | In your professional opinion what are some practical real-world solutions given the tools and constraints that exist today? |
| 4 | If you didn’t have to worry about current constraints, how do you think current barriers could be best addressed / solved to increase the adoption and use of FHIR standards in CDS tools? |
A preliminary force field analysis (FFA) was created based on themes identified through content analysis. FFA is a planning methodology rooted in Lewin’s planned approach to change22[22]. It identifies driving and restraining forces for a specific change including relative strength. In a traditional FFA, relative strength of each force is rated on a scale from negative one to positive five with negative ratings for restraining forces and positive ratings for driving forces22. In the FFA conducted for this study we assessed driving and restraining forces related to the use of FHIR for CCDS23. Relative strength was not assessed due to the sequential nature of the interviews and not all themes were discussed by all participants.
RESULTS
On average participants had 22 years of clinical informatics experience and over 200 CCDS projects collectively. Each expert had utilized FHIR in 10 projects on average. Experts also represented a wide variety of organization types including software consulting, non-profit clinical organizations, and academia. Sample size was limited to five due to the limited nature of the expert informatics network.
Restraining forces represented 34% of the average interview based on aggregate coding coverage. Common restraints described by participants included limited workforce experience, limited EHR vendor support, EHR implementation variation, ontology variation and limited resources for testing. Workforce knowledge challenges were described as limited capabilities within the workforce related to integration of CCDS with EHRs being even more limited than development knowledge [coding coverage 4.8%−6.8%, n=4]. According to participants FHIR has developed faster than EHR vendors have been able to support all functionalities. While many EHR vendors offer support for FHIR services our experts reported that EHRs are often not setup to support all services included in the most recent FHIR version [coding coverage 6.5%−10.6%, n=5]. One additional barrier to EHR integration experienced by participants was variation in EHR implementation [coding coverage 2%−18.4%, n=4]. Experts described this as the same EHR software being used across organizations using the same type of data, but it may not be stored in the same location or format. Furthermore, different EHR vendors do not all use standard ontologies to store common data elements [coding coverage 5.1%−6.4%, n=2]. An additional restraint specific to CCDS development was limited availability of clinically complex test patient data [coding coverage 2.4%−5.6%, n=3]. See table 2 for illustrative quotes for restraining force themes.
Table 2.
Quotes supporting restraining force themes.
| Barriers / Restraining Forces | |
|---|---|
| EHR implementation variation | … multiple departments, multiple analysts, multiple clinicians involved … configuring Epic, and as a result you may have the same data element representing multiple data concepts… We observed seeing seven different variations of length of stay, and if you start doing analytics on length of … imagine how poor quality of analytics you’re going to have (Expert 1) …using mammogram as an example, if you pull mammogram against all EHRs you’re going to get anything from a yes to a date to BI-RADS score to a full narrative. It could be anything, and from a CDS standpoint, there’s nothing there that you can work with. (Expert 2) The way people implemented it at a one site, the resources might be modified for that site compared to another for the same resource specification. (Expert 3) Healthcare has the challenges of… one piece of data meaning one thing in your place and meeting another thing in my place, and that semantic interoperability is the promise of FHIR. Given the variability of implementation, it’s the challenge too. (Expert 3) …everybody’s order catalog is different ... it’s hard to find or to determine exactly what to hook up to. Then you have to fit into the logic flow. (Expert 3) |
| Limited EHR vendor support | It is really in the hands of the EHR vendors how well and how reliably consistent they are with FHIR standard, and what we’re observing is that they’re not… they may have multiple versions of the same standard implemented… even if the vendor has good intentions (Expert 1) The biggest challenges we’re seeing is how those standards …on the FHIR side is interpreted very differently from vendor to vendor. (Expert 2) Right now, people oftentimes take a hybrid approach. That is they may have their own data store in their own format and whatnot, but on top of that, they will add a FHIR-edge server… which can help that site do transformations. (Expert 3) At the first site … when we had to go live there weren’t enough [FHIR] services available (Expert 4) |
| Ontology Variation | FHIR is not actually explicit about terminologies … Majority of cases they’re pretty standard terminology, like ICD-10 codes… but as we are observing it doesn’t prevent people from using other terminology codes that are not directly compatible between two systems. (Expert 1) …rather than us allowing multiple observations within the system to have the same LOINC code they started using a custom coding system (Expert 2) How can they get lab vendors to send everything consistently, regardless of what vendor it’s coming from, in a way that it can be loaded and stored consistently… and pulled consistently? (Expert 2) |
| Workforce knowledge | Both FHIR for data as well as FHIR for the computable practice guideline. Right now, there aren’t many people who can do the whole thing, start with paper [guideline] and end up with code (Expert 3) I would say at our organization the IT folks know how to use FHIR … but two years ago when we were integrating services… it wasn’t that way (Expert 4) The expertise needed to develop and interface is still to niche. We have trouble staffing for it. They said use native functionality of our EHR to implement this tool, so that’s what we did. (Expert 5) I think we as an organization have started turning more towards how do we develop people in-house … how do we build a workforce that’s knowledgeable of healthcare data standards, knowledgeable of clinical decision support (Expert 5) |
| Limited data for testing | …realistic sample patients to test … This is not a problem that can be solved by one institution or another. To really avoid the bias problem in either design or implementation … or pathway specs, you want to have robust big data to examine and test with, and that’s still tricky. (Expert 3) …the problem with these test environments is the data is all made [up]. It’s not the same as the live environment, so sometimes things don’t work in the test environment, but they would work in the live environment, but it’s better to flush it out and make sure that it’s not a problem with the code and it’s not just something with that environment. (Expert 4) …have more of those environments, including with realistic patient data. Maybe synthetic data is a way to think about solving this. (Expert 5) |
On average, recommended driving forces represented 13% of interviews based on coding coverage. Participants agreed that incentives are needed for EHR vendors [coding coverage 1%−2.9%, n=4] and clinical organizations [coding coverage 2%−11.8%, n=4] to more fully support FHIR. One expert recommended the development of a professional FHIR certification to support workforce knowledge, skills, and expertise [coding coverage 1.4%, n=1]. The expert elaborated that this would enable organizations to incentivize employees to pursue certification and create recruitment pathways that lead to hiring experts who can better integrate these CCDS systems. Several experts suggested an intriguing strategy that was based on the development of a market or “app store” where developers could offer interoperable systems, built with FHIR, that could be integrated into any EHR system [coding coverage 1.3%−11.3%, n=3]. Lastly, because funding for development of CCDS systems comes in part from research grants, one expert recommended that funding agencies require grant recipients to utilize FHIR as the standard for data exchange with the goal of broader dissemination and utilization [coding coverage 8.3%, n=1]. The FFA (Figure 1) illustrates restraining forces or barriers described and recommendations for potential driving forces offered by participants. See table 3 for illustrative quotes for enabling force themes.
Figure 1.
Preliminary force field analysis of current barriers to the use of FHIR services with CCDS and expert recommendations to improve adoption.
Table 3.
Quotes supporting enabling force themes.
| Recommendation / Potential Driving Force | |
|---|---|
| Research funders requiring FHIR | I think that they’ve gotten better about when their reviewers [research funders] are reviewing proposals looking at how generalizable is this, right? Are they using standards that can be used outside of Epic or Cerner …is this something that’s going to ultimately be shared outside of the organization where it’s being developed, so it’s not a one off? (Expert 4) |
| CCDS “App store” | There are not a lot of tools built on top of FHIR. We are working on number of projects where we’re using FHIR questionnaire resource … I don’t want to invent builder or player for questionnaire… there are a number of open-source half-baked tools to build questionnaires, none of them are great... for FHIR to evolve faster … people should build more tools that you can use with FHIR as a kind of underlying plumbing … for product purposes (Expert 1) A marketplace is the idea where I can go store something or sell something… It can’t be single platform or single EHR specific. Marketplaces have to be EHR agnostic. CDS Connect is a valiant effort to move along these lines, but it’s not a commercial entity. (Expert 3) The analogy is over worn, but I can buy any calendar program for my iPhone and if I don’t like it, I can swap it out for the other one … it’s the idea of this marketplace being very effective. They already installed the EHR, but they should allow us to do more on top. (Expert 3) |
| Clinical organization incentives | … a monetary incentive to the customers of the EMR. Then it’s really the customers that had to go say hey, if you want to continue with my business you need to make this work. I think all the EMR’s responded to that very quickly. (Expert 2) It takes organizational commitment … everyone is in such a rush, and it’s hard to take the time, to do what you need to do whether that’s educating your workforce, sending them to training … then actually looking at your systems and saying OK where can we integrate these standards and what’s the process for swapping out what we’re currently doing currently … coming up with a road map. That all takes time and education and lots of money. I think we need some incentives to support organizations to do that sort of thing. (Expert 4) FHIR apps that are targeting very specific clinical decisions or very specific outcomes for which there’s a significant financial incentive … I don’t know if the calculus changes in that space (Expert 5) It may improve quality in some way, it may improve outcomes, it may improve guideline adherence in some way. That may be great, but the benefit, the direct benefits, do not accrue to the organization. They accrue to the patients, which is fantastic… when we come down to making implementation decisions, it’s harder … if these implementations mean less money or the same money as we might get without them or what we might get with the native functionality of our ER versus some third-party app. (Expert 5) |
| EHR vendor incentives | I’d say that … EHR vendors should more aggressively … adopt the latest versions of the standard. That would develop more flexibility in decision support and make it much more efficient and much more effective. That’s the most important thing …is actually adopting the latest evolution of the standard. (Expert 1) I think the inability to do it [support FHIR], I think was probably was the push …saying this could become a decision point for sites [choosing an EHR] (Expert 2) You must have APIs that at least meet this set of FHIR resources or comply with this set. I think continuing to push on those so that vendors must be open. (Expert 5) |
| FHIR certification | If there was some authoritative body like an HL7 … but where I could send our staff to get up to speed and maintain with some regularity credentials around these standards. Yeah, I think that would be valuable. (Expert 5) |
DISCUSSION
We identified important barriers or restraining forces based on interviews with informatics experts with experience developing CCDS using FHIR. This study found restraining forces of FHIR standards for CCDS are multifactorial and substantial. During interviews restraining forces were discussed far more often than recommended solutions. FHIR standards have developed faster than EHR vendors have supported them. This has resulted in increased development time and cost because developers often need to use a mix of FHIR services in addition to proprietary services specific to the EHR. Furthermore, due to the customization of EHRs across clinical sites not all organizations or even locations within the same organization are collecting data within the EHR in a standardized way. This results in increased workload when mapping CCDS to multiple sites or EHRs. The disconnect between FHIR development and adoption by vendors and resulting implementation barriers to include mapping suggests a rationale for the findings of the Ayaz et al systematic review which found adoption, implementation, and mapping were common problems. Additionally, groups looking to develop or integrate CCDS systems leveraging FHIR often have little experience with FHIR standards resulting in increased cost and time to implement projects. This workforce issue represents a new finding compared to recent publications. Lastly, ongoing maintenance is required to ensure that as EHRs are updated the integrated CCDS remains functional24[24]. One expert so clearly described
…right now…you can have a C grade piece of decision, proprietary decision support, and you can have an A+ grade third-party application from a quality and outcomes perspective or usability perspective, but the barriers to integration tend to be so high and maintenance and sustainability tend to be so high that it’ll never get adopted.
Participants offered a variety of recommendations to address these barriers. All experts agreed that federal policy or incentives are needed to require EHR vendors to support all FHIR resources in the most current version. As of 2021, four of the five largest EHR vendors supported FHIR in some capacity18. However, all experts had experienced limited support for some FHIR services. One common limitation of EHRs is a lack of data storage standards. For example, if medication data is stored using a proprietary ontology, then CCDS developers will need to integrate additional translational code to compute data elements. To address this issue, federal policy could require EHR vendors to use data storage standards like Rxnorm for medications or SNOMED CT for medical concepts. Some efforts have been made to develop an “app store” as recommended by our participants. The Agency for Health Care Quality launched CDS connect in 2016 making development of CCDS easier, but has not reached plug-and-play efficiency our experts described8. CDS connect was designed as a repository for artifacts that outline how CDS for specific problems have been implemented and also included tools for authoring CDS solutions. EHR vendors have created proprietary “app stores”, however only 22% of apps available in them used FHIR25.
LIMITATIONS
Our study represents a thorough discussion by experts of barriers to FHIR use and a preliminary exploration of recommendations. Barriers were discussed for significantly more time than recommendations indicating that experts are tied up dealing with barriers limiting time spent on developing solutions. Due to the small sample size saturation was not reached further limiting generalizability. Future studies should consider a more conventional FFA with a larger sample size based on themes identified here to determine strength of forces and prioritize actions.
CONCLUSION
Barriers to the use of FHIR experienced by the experts interviewed were multifactorial and impacted both the development of CCDS as well as the integration of systems already built. Limited workforce knowledge of FHIR, incomplete support for all FHIR services, and variation in EHR implementation at clinical sites have all made integration of CCDS systems using FHIR more challenging. Development of additional CCDS with FHIR has been hampered by a lack of available clinically complex test data and variation in ontologies used by EHRs. Adding incentives for EHR vendors and clinical organizations to fully support FHIR and common ontologies would decrease barriers to interoperable CCDS development and integration.
Footnotes
Disclaimer
“The views expressed herein are those of the author(s) and do not reflect the official policy or position of Brooke Army Medical Center, the Department of Defense, or any agencies under the U.S. Government.”
Contributor Information
Kristen Shear, Brooke Army Medical Center, 3551 Rodger Brooke Dr. San Antonio, TX 78234.
Ann L. Horgas, Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610-0197.
Robert Lucero, UCLA School of Nursing, 700 Tiverton Ave, Los Angeles, CA 90095.
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