Mẹo về Which of the following statements accurately describes duplicate patient registration entries in an electronic health record? Chi Tiết
Lê Minh Long đang tìm kiếm từ khóa Which of the following statements accurately describes duplicate patient registration entries in an electronic health record? được Update vào lúc : 2022-08-28 16:16:04 . Với phương châm chia sẻ Bí kíp về trong nội dung bài viết một cách Chi Tiết Mới Nhất. Nếu sau khi đọc Post vẫn ko hiểu thì hoàn toàn có thể lại phản hồi ở cuối bài để Ad lý giải và hướng dẫn lại nha.1. Introduction
With national efforts to invest in electronic health record (EHR) systems and advance the evidence base in areas such as effectiveness, safety, and quality through registries and other studies, interfacing registries with EHRs will become more important over the next few years. While both EHRs and registries use clinical information the patient level, registries are population focused, purpose driven, and designed to derive information on health outcomes defined before the data are collected and analyzed. On the other hand, EHRs are focused on the collection and use of an individual patient's health-related information. While in practice there may be some overlap in functionality between EHRs and registries, their roles are distinct, and both are very important to the health care system. This chapter explores issues of interoperability and a pragmatic “building-block approach” toward a functional, open-standards–based solution. (In this context, “open standards” means nonproprietary standards developed through a transparent process with participation from many stakeholders. “Open” does not mean “không lấy phí of charge” in this context— there may be fees associated with the use of certain standards.)
Nội dung chính- 1. Introduction2. EHRs and Patient Registries3. EHRs and Evidence Development4. Current Challenges in a Preinteroperable Environment5. The Vision of EHR-Registry Interoperability6. Interoperability Challenges6.1. Syntactic Interoperability6.2. Semantic Interoperability7. Partial and Potential Solutions8. Momentum Toward a Functional Interoperability Solution9. The Next Increment9.1. Patient Identification/Privacy Protection9.2. Digital Signatures9.3. Other Related and Emerging Efforts9.4. Data Mapping and Constraints10. What Has Been Done11. Distributed Networks12. SummaryCase Examples for Chapter 15Case Example 32Using system integration software to capture registry data from electronic health recordsCase Example 33Creating a registry interface to incorporate data from multiple electronic health recordsCase Example 34Technical and security issues in creating a health information exchangeCase Example 35Developing a new model for gathering and reporting adverse drug eventsReferences for Chapter 15What are the 5 components of the electronic medical record?What are the five main functions that are performed by an electronic health record?What are the two most common types of electronic medical records?What are the 3 components of the EHR system?
An important value of this approach is that EHR vendors can implement it without major effort or impact on their current systems. While the focus of this guide is on patient registries, the same approach described in this chapter is applicable to clinical research studies, safety reporting, biosurveillance, public health, and quality reporting. This chapter also includes case examples (Case Examples 32, 33, 34, and 35) describing some of the challenges and approaches to interfacing registries with EHRs.
An EHR refers to an individual patient's medical record in digital format. EHRs can be comprehensive systems that manage both clinical and administrative data; for example, an EHR may collect medical histories, laboratory data, and physician notes, and may assist with billing, interpractice referrals, appointment scheduling, and prescription refills. EHRs can also be targeted in their capabilities; many practices choose to implement EHRs that offer a subset of these capabilities, or they may implement multiple systems to fulfill different needs. According to the Institute of Medicine (IOM), an EHR has four core functionalities: health information and data, results management, order entry and support, and decision support.1
The current EHR market in the United States is highly fragmented.2 Until recently, the term “EHR” was broadly applied to systems falling within a range of capabilities. However, since the passage of the American Recovery and Reinvestment Act of 2009 (ARRA), a transformative change has been underway, with a rapid increase in EHR adoption and a strong emphasis on standards and certification. Under ARRA, approximately $27 billion will be spent on incentives and other projects to support the adoption of EHRs over the next several years.3 These incentives have spurred an increase in EHR implementation from 17 percent of U.S. office-based physicians in 2003 to 72 percent in 2012.4
To ensure that the EHRs implemented under the ARRA incentive program contain basic functionalities, new standards and a certification process have been developed. ARRA emphasizes the “meaningful use” of EHRs by office-based physicians and hospitals. Meaningful use refers to the use of certified EHR technology to “improve quality, safety, efficiency, and reduce health disparities; engage patients and families in their health care; improve care coordination; and improve population and public health while maintaining privacy and security.”5 ARRA describes the three main components of meaningful use as (1) the use of a certified EHR in a meaningful manner, such as e-prescribing; (2) the use of certified EHR technology for electronic exchange of health information to improve quality of health care, such as promoting care coordination; and (3) the use of certified EHR technology to submit clinical quality and other measures.6
The Office of the Secretary of Health and Human Services (HHS) has been charged under ARRA with setting standards and certification criteria for EHRs, with interoperability a core goal. Within HHS, the Office of the National Coordinator of Health Information Technology (ONC) is responsible for developing the standards and certification criteria for the meaningful use of EHRs. ONC is using a three-stage approach to developing criteria for meaningful use. Stage 1, released in 2011, sets basic standards for capturing data in an EHR and sharing data between systems. Stage 2, which is under development and scheduled for finalization in 2012, expands the basic standards to include additional functionality and require reporting of more measures (e.g., quality of care measures, base functionality measures). Finally, Stage 3, to be released in 2015, will continue to expand on the standards in Stage 2. ONC is also developing an EHR certification program that will allow EHR vendors to demonstrate that their products contain sufficient functionality to support meaningful use.
Even with increasing standardization of EHRs, there are many issues and obstacles to achieving interoperability (meaningful communication between systems, as described further below) between EHRs and registries or other clinical research activities. Among these obstacles are limitations to the ability to use and exchange information; issues in confidentiality, privacy, security, and data access; and issues in regulatory compliance. For example, in terms of information interoperability and exchange, the Clinical Research Value Case Workgroup has observed that clinical research data standards are developing independently from certain standards being developed for clinical care data; that currently the interface between the EHR and clinical research data is ad hoc and can be prone to errors and redundancy; that there is a wide variety of modes of research and medical specialties involved in clinical studies, thus making standards difficult to identify; and that there are differences among standards developing organizations with respect to health care data standards and how they are designed and implemented (including some proprietary standards for clinical research within certain organizations). With respect to confidentiality, privacy, security, and data access, the Workgroup has pointed out that secondary use of data may violate patient privacy, and that protections need to be put in place before data access can be automated. In the area of regulatory compliance, it notes that for some research purposes there is a need to comply with regulations for electronic systems (e.g., 21 CFR Part 11) and other rules (e.g., the Common Rule for human subjects research).7
The new Federal oversight of EHR standards is clearly guided by the need to ensure that the EHRs that benefit from the market-building impact of the provider incentives will serve the broader public purposes for which the ARRA funds are intended.8 Specifically, the elusive goal that has not been satisfied in the current paradigm is the creation of an interoperable health information technology (HIT) infrastructure. Without interoperability, the HIT investment under ARRA may actually be counterproductive to other ARRA goals, including the generation and dissemination of information on the comparative effectiveness of therapies and the efficient and transparent measurement of quality in the health care system. Ideally, EHR standards will lay the groundwork for what the Institute of Medicine has called the “learning health care system.”9 The goal of a learning health care system is a transformation of the way evidence is generated and used to improve health and health care—a system in which patient registries and similar, real-world study methods are expected to play a very important role. Ultimately, the HIT standards that are adopted, including vocabularies, data elements, data sets, and technical standards, may have a far-reaching impact on how transformative ARRA will be from an HIT perspective.
2. EHRs and Patient Registries
Prior to exploring how EHRs and registries might interface, it is useful to clearly differentiate one from the other. While EHRs may assist in certain functions that a patient registry requires (e.g., data collection, data cleaning, data storage), and a registry may augment the value of the information collected in an EHR (e.g., population views, quality reporting), an EHR is not a registry and a registry is not an EHR. Simply stated, an EHR is an electronic record of health-related information on an individual that conforms to nationally recognized interoperability standards, and that can be created, managed, and consulted by authorized clinicians and staff across more than one health care organization.10 As defined in Chapter 1, a registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. Registries are focused on populations and are designed to fulfill specific purposes defined before the data are collected and analyzed. EHRs are focused on individuals and are designed to collect, share, and use that information for the benefit of that individual.
3. EHRs and Evidence Development
The true promise of EHRs in evidence development is in facilitating the achievement of a practical, scalable, and efficient means of collecting, analyzing, and disseminating evidence. Digitizing information can dramatically reduce many of the scalability constraints of patient registries and other clinical research activities. Paper records are inherently limited because of the difficulty of systematically finding or sampling eligible patients for research activities and the effort required to re-enter information into a database. Digitized information has the capacity to improve both of these requirements for registries, enabling larger, more diverse patient populations, and avoiding duplication of effort for participating clinicians and patients. However, duplication of effort can be reduced only to the extent that EHRs capture data elements and outcomes with specific, consistent, and interoperable definitions—or that data can be found and transformed by other processes and technologies (e.g., natural language processing) into standardized formats that match registry specifications. Besides enabling health care information to be more readily available for registries and other evidence development purposes, bidirectionally interoperable EHRs may also serve an efferent role of delivering relevant information from a registry back to a clinician (e.g., information about natural history of disease, safety, effectiveness, and quality).
4. Current Challenges in a Preinteroperable Environment
Data capture for research purposes, in general, can be challenging for clinicians. Many hospitals, health care facilities, and clinicians' offices that participate in studies use more than one data capture system, and change their workflow to accommodate nonharmonized research demands. In other words, hospitals and practices are changing their workflow to accommodate nonharmonized research demands. As a result, data capture can be awkward and time consuming for clinicians and their staff, especially for a registry in which a large number of patients may fit into a broad set of enrollment criteria. While some of this can be overcome without interoperable systems by means of uploads from these systems to registries of certain standard file formats, such as hospital or clinician office billing files, the need to re-enter data from one system to another; train staff on new systems; and juggle multiple user names, passwords, and devices presents a high barrier to participation, especially for clinicians, whose primary interest is patient care and who are often resistant to change. The widespread implementation of EHRs that are not truly interoperable, coupled with the growth in current and future evidence development activities, such as patient registries, may ironically create significant barriers to achieving the vision of a national, learning health care system. In many respects, clinicians may be part of the problem, if they seek EHRs with highly customized interfaces and database schema rather than those that may be more amenable to interoperability.
Most EHRs are not fully interoperable in the core functions that would enable them to participate in the learning health care system envisioned by the IOM. This deficiency is directly related to a combination of technical and economic barriers to EHRs' adoption and deployment of standards-based interoperability solutions. There are more than 600 EHR vendors,11 many of which provide heavily customized versions of their systems for each client. For some time there was significant interest in adding clinical research capabilities to already implemented EHR systems,12 but this so-called “Swiss army knife” approach did not prove to be technically or commercially effective. Issues ranged from standardization of core data sets to achieving compliance with U.S. Food and Drug Administration (FDA) requirements for electronic systems used in clinical research. And because there is no single national EHR, even if this were achievable it would not meet many registry purposes, since registries seek data across large, generalizable populations. In recent years, the industry has primarily turned back to pursuing an open-standards approach to interacting with, rather than becoming, specialized systems.13 Appendix C describes many of the relevant standards and standards-setting organizations.
Even though many EHR systems are technically uniform, the actual software implementations are different in many ways. As a result, achieving interoperability goals (across the myriad of installed EHRs and current and future registries) through custom interfaces is a mathematical, and therefore economic, impossibility. (See Section 5 below.) An open-standards approach may be the most viable. In addition, as has been tested in many demonstrations and is slowly being incorporated by some vendors into commercial offerings, a user-configurable mechanism to enable the provider to link to any number of registries without requiring customization by the EHR vendor is also an important aspect of a scalable solution.
5. The Vision of EHR-Registry Interoperability
As the EHR becomes the primary desktop interface for physicians and other health care workers, it is clear that registries must work through EHRs in order for interoperability to be feasible. At the same time, there is a rapidly growing need for clinicians to participate in registries to manage safety, evaluate effectiveness, and measure and improve quality of care. As a result, an EHR will need to serve as an interface for more than one registry simultaneously. In considering the need to interface EHRs with patient registries, it is useful to consider the specific purpose for which the patient registry is designed, and how an EHR that is interoperable with one or more registries might lessen the burden, barriers, or costs of managing the registries and other data collection programs. The following potential functions can be thought of with respect to a registry purpose:
Natural history of disease: Identify patients who meet eligibility criteria, alert clinicians, present the relevant forms and instructions, capture uniform data, review the data prior to transmission, transmit data to the registry, and receive and present information from the registry (e.g., population views).
Effectiveness: Identify patients who meet eligibility criteria, execute sampling algorithms, alert clinicians, present the relevant forms and instructions, capture uniform data, review the data prior to transmission, transmit data or analytics, and receive and present information from the registry (e.g., followup schedules, registrywide results).
Safety: Identify events for reporting through triggers, capture uniform data, review the data prior to transmission, transmit data, receive and present requests for additional information, and receive and present safety information from the registry.
Quality: Identify patients who meet eligibility criteria, present the relevant forms and instructions, capture uniform data, review the data prior to transmission, transmit data to the registry for reporting, and receive and present quality measure information and comparators from the registry.
In a truly interoperable system, registry-specific functionality could be presented in a software-as-a-service or middleware model, interacting with the EHR as the presentation layer on one end and the registry database on the other. In this model, the EHR is a gateway to multiple registries and clinical research activities through an open architecture that leverages best-in-class functionality and connectivity. Registries interact across multiple EHRs, and EHRs interact with multiple registries.
6. Interoperability Challenges
Interoperability for health information systems requires communication, accurate and consistent data exchange, and use of the information that has been exchanged. The two core constructs, related to communication and content, are syntactic and semantic interoperability.
6.1. Syntactic Interoperability
Syntactic interoperability is the ability of heterogeneous health information systems to exchange data. There are several layers of syntactic interoperability. First, the physical wiring must be in place, and the TCP/IP (Internet) is the de facto standard. On top of this, an application protocol is needed such as HTTP or SMTP. The third layer is a standard messaging protocol such as SOAP (Simple Object Access Protocol).14 The message must have a standard sequence, structure, and data items in order to be processed correctly by the receiving system.
When proprietary systems and formats are used, the complexity of the task grows dramatically. For n systems, n(n-1)/2 interfaces are needed for each system to communicate with every other one.15 For this reason, message standards are preferred. While this seems straightforward, an example portrays how, even for EHR-to-EHR communication, barriers still exist. Currently, the Health Level Seven (HL7) Version 2 message standard (HL7 v2.5) is the most widely implemented standard among EHRs, but this version has no explicit information model; instead, it rather vaguely defines many data fields and has many optional fields. To address this problem, the Reference Information Model (RIM) was developed as part of HL7 v3, but v3 is not fully adopted and there is no well-defined mapping between v2.x and v3 messages.
Syntactic interoperability assures that the message will be delivered. Of the challenges to interoperability, this is the one most frequently solved. However, solving the delivery problem does not guarantee that the content of the message can be processed and interpreted the receiving end with the meaning for which it was intended.
6.2. Semantic Interoperability
Semantic interoperability implies that the systems understand the data exchanged the level of defined domain concepts. This “understanding” requires shared data models that, in turn, depend on standard vocabularies and common data elements.16
The National Cancer Institute's (NCI) Cancer Bioinformatics Grid (caBIG) breaks down the core components of semantic interoperability into information or data models, which describe the relationships between common data elements in a domain; controlled vocabularies, which are an agreed-upon set of standard terminology; and common data elements, which use shared vocabularies and standard values and formats to define how data are to be collected.
The standardization of what is collected, how it is collected, and what it means is a vast undertaking across health care. Much work has been done and is continuing currently, although efforts are not centralized nor are they equally advanced for different medical conditions. One effort, called the CDASH (Clinical Data Acquisition Standards Harmonization) Initiative, led by the Clinical Data Interchange Standards Consortium (CDISC), aims to describe recommended basic standards for the collection of clinical trial data.17 It provides guidance for the creation of data collection instruments, including recommended case report form (CRF) data points, classified by domain (e.g., adverse events, inclusion/exclusion criteria, vital signs), and a core designation (highly recommended, recommended/conditional, or optional). Version 1.0 was published in October 2008; v1.1 was published in January 2011 and included implementation guidelines, best practice recommendations, and regulatory references. It remains to be seen how widely this standard will be implemented in the planning and operation of registries, clinical trials, and postmarketing studies, but it is nonetheless an excellent step in the definition of a common set of data elements to be used in registries and clinical research.
Other examples of information models used for data exchange are the ASTM Continuity of Care Record (CCR) and HL7's Continuity of Care Document (CCD), which have standardized certain commonly reported components of a medical encounter, including diagnoses, allergies, medications, and procedures. The CCD standard is particularly relevant because it is one that has been adopted as part of CCHIT certification. The Biomedical Research Integrated Domain Group (BRIDG) model is an effort to bridge health care and clinical research standards and organizations with stakeholders from CDISC, HL7, NCI, and FDA. Participating organizations are collaborating to produce a shared view of the dynamic and static semantics that collectively define a shared domain of interest, (i.e., the domain of clinical and preclinical protocol-driven research and its associated regulatory artifacts).18
Even with some standardization in the structure and content of the message, issues exist in the use of common coding systems. For any EHR and any registry system to be able to semantically interoperate, there needs to be uniformity around which coding systems are to be used. At this time, there are some differences between coding systems adopted by EHR vendors and registry vendors. While it is still possible to translate these coding systems and/or “recode” them, the possibility of achieving full semantic interoperability is limited until uniformity is achieved.
The collection of uniform data, including data elements for risk factors and outcomes, is a core characteristic of patient registries. If a functionally complete standard dictionary existed, it would also greatly improve the value of the information contained within the EHR. But, while tremendous progress has been made in some areas such as cancer19 and cardiology,20 the reality is that full semantic interoperability will not be achieved in the near future.
Beyond syntactic and semantic interoperability, other issues require robust, standardized solutions, including how best to authenticate users across multiple applications. Another issue is permission or authorization management. At a high level, how does the system enforce and implement varying levels of authorization? A health care authorization is specific to authorized purposes. A particular patient may have provided different authorizations to disclose information differently to different registries interacting with a single EHR the same time, and the specificity of that permission needs to be retained and in some way linked with the data as they transit between applications. For privacy purposes, an audit trail also needs to be maintained and viewable across all the paths through which the data move. Security must also be ensured across all of the nodes in the interoperable system.
A third key challenge to interoperability is managing patient identities among different health care applications. See Chapter 17 for further discussion.
7. Partial and Potential Solutions
Achieving true, bidirectional interoperability, so that all of the required functions for EHRs and patient registries function seamlessly with one another, is unlikely to be accomplished for many years. However, as noted above, it is critical that a level of interoperability be achieved to prevent the creation of silos of information within proprietary informatics systems that make it difficult or impossible to conduct large registries or other evidence development research across diverse practices and populations. Given the lack of a holistic and definitive interoperability model, an incremental approach to the successive development, testing, and adoption of open, standard building blocks toward an interoperable solution is the likely path forward. In fact, much has been done in the area of interoperability, and if fully leveraged, these advances can already provide least a level of functional interoperability that could significantly ameliorate this potential problem.
From an EHR/registry perspective, functional interoperability could be described as a standards-based solution that achieves the following set of requirements:
The ability of any EHR to exchange valid and useful information with any registry, on behalf of any willing provider, any time, in a manner that improves the efficiency of registry participation for the provider and the patient, and does not require significant customization to the EHR or the registry system.
Useful information exchange constitutes both general activities (e.g., patient identification, accurate/uniform data collection and processing) and specific additional elements, depending on the purpose of the registry (e.g., quality reporting). Such a definition implies an open-standards approach where participation is controlled by the provider/investigator. To be viable, such a model would require that EHRs become certified to meet open standards for basic functional interoperability (the requirements of which would advance over time), but also allow EHRs the opportunity to further differentiate their services by how much they can improve the efficiency of participation.
While the goal of functional interoperability likely requires the creation and adoption of effective open standards, there have been several approaches to partially addressing these same issues in the absence of a unified approach. HIT systems, including some EHRs, have been used to populate registry databases for some time. The Society of Thoracic Surgeons, the American College of Cardiology, and others use models that are based on a central data repository that receives data from multiple conforming systems, on a periodic basis, through batch transfers. Syntactic interoperability is achieved through a clear specification that is custom-programmed by the HIT systems vendor. Semantic interoperability is achieved by the publication of specifications for the data collection elements and definitions on a regular cycle, and incorporation of these by the systems vendors. Each systems vendor pays a fee for the specifications and for testing their implementation following custom programming. In some cases, an additional fee is levied for the ongoing use of the interface by the systems vendor. Periodically, as data elements are modified, new specifications are published and the cycle of custom programming and testing is repeated. While there is incremental benefit to the provider organizations in that they do not have to use multiple systems to participate in these registries, the initial and periodic custom programming efforts and the need to support custom interface requirements make this approach unscalable. Furthermore, participation in one registry actually makes participation in other, similar registries more difficult, since the data elements are customized and not usable in the next program.
The American Heart Association's Get With The Guidelines® program uses a Web services model for a similar purpose. The advantage of the Web services model is that the data are transferred to the patient registry database on a transactional basis (immediately), but the other drawbacks in custom programming and change management still apply. This program also offers an open standards approach through IHE RFD21 or Healthcare Information Technology Standards Panel (HITSP) TP50, both described below. These examples describe two models for using EHRs to populate registry databases; other models exist.
8. Momentum Toward a Functional Interoperability Solution
Significant momentum is already building toward adopting open-standard building blocks that will lead incrementally to functional interoperability solutions. For example, the EHR Clinical Research Value Case Workgroup has focused its use cases on two activities: achieving the ability (1) to communicate study parameters (e.g., eligibility information, CRFs) and (2) to exchange a core data set from the EHR.22 Others in the standards development community have taken a stepwise approach to creating the components for a first-generation, functional interoperability solution. As described below, this solution has already overcome several of the key barriers to creating an open, scalable model that can work simultaneously between multiple EHR systems and registries. Some issues addressed through these efforts include: the need for flexibility in presenting a uniform data collection set that can be modified from time to time without custom programming by the EHR vendor; the need to leverage existing, standardized EHR data to populate portions of the data collection set; and the need to be able to submit the data on a transactional basis to a registry, clinical trial, or other data recipient in a standard format.
A building-block approach to the technical side of this issue is an effective and pragmatic way to build in increments and allow all players in the industry to focus on specific components of interoperability; early successes can then be recognized and used as the basis for the next step in the solution. This is a change from the earlier approaches to this issue, where the problem (and the solution) was defined so broadly that complete semantic interoperability seemed to be the only way to solve the problem; this proved overwhelming and unsupportable. Instead, a working set of industry-accepted standards and specifications that already exist can focus tightly on one aspect of interfacing multiple data capture systems, rather than considering the entire spread of issues that confound the seamless interchange between health care and research systems.
There are many different standards focused on different levels of this interface, and several different key stakeholders that create, work with, and depend on these standards (see Appendix C). A useful way to visualize these technical standards is to consider a stack in which each building block is designed to facilitate one aspect of the technical interface between an EHR and a data collection system (Figure 15–1). The building blocks are modest but incremental changes that move two specific systems toward interoperability and are scalable to different platforms.
Figure 15–1A building-block approach to interoperability. HL7 = Health Level Seven; CDISC = Clinical Data Interchange Standards Consortium; CRD = Clinical Research Data Capture; DSC = Drug Safety Content; RFD = Retrieve Form for Data Capture; RPE = Retrieve Protocol (more...)
This theoretical stack starts with the most basic technical components as the ground layers. Physical network connections, followed by Web services, secure hypertext transfer protocol (), secure socket layer (SSL) communications protocol, and Web browsers create the foundation of the interoperability structure. These standard technologies are compatible across most systems already.
A standard integration profile, Retrieve Form for Data Capture (RFD), is the base of specific interoperability for health care data transfer, and it takes advantage of the Web standards as a way to integrate EHRs and registry systems. RFD is a generic way for systems to interact. In a sense, RFD opens a circuit or provides a “dial tone” to allow an EHR to exchange information with a registry or other clinical research system. RFD was created and is maintained by Integrating the Healthcare Enterprise (IHE). It is also accepted under HITSP as TP50. Specifically, RFD provides a method for gathering data within a user's current application to meet the requirements of an external system (e.g., a registry). In RFD, as Figure 15–2 shows, this is accomplished by retrieving a registry or other data collection form from a source; displaying it within the EHR system to allow completion of the form, with data validation checks, either through direct user entry or automated population from the EHR database; and then returning an instance of the data back to the registry system. Importantly, the EHR initiates the transaction.
Figure 15–2Retrieve form for data capture diagram. CAP = Capability; CCD = Continuity of Care Document; CRD = Clinical Research Data Capture; DSC = Drug Safety Content; EHR = Electronic Health Record; HITSP = Healthcare Information Technology Standards Panel; IHE (more...)
Once an EHR is RFD-enabled, it can be used for multiple use-cases. RFD opens a circuit and allows for information exchanges of different purposes, including registries and clinical trials, quality initiatives, safety, and public health reporting.23
Content profiles such as Clinical Research Data Capture (CRD) build the next level, allowing standard content defined within an EHR to be mapped into the data collection elements for the registry, eliminating duplicate entry for these defined elements. CRD and the Drug Safety Content (DSC) profiles, managed by IHE, build upon the IHE RFD integration profile. Correspondingly, HITSP C76, or Case Report Pre-Populate Component (for Drug Safety), leverages the HITSP TP50 retrieve form for data capture (RFD) transaction package.
CRD allows the functional interoperability solution to leverage standardized content as it becomes defined and available within EHRs. In other words, it is an incremental approach to leveraging whatever content has been rigorously defined and resident within the EHR and is also usable and acceptable to the registry (i.e., content that matches some portion of the registry's defined data elements and definitions). To the extent that these data reside in a common format, they can be used for autopopulation of the registry forms without custom programming. CRD leverages the Continuity of Care Document (CCD), an HL7 standard. In this scenario, the EHR generates the CCD to populate a case report form. The registry uses only the relevant data from the CCD, as determined by the registry system presenting the form. Alternatively, CRD specifies that CDASH, a CDISC standard for data collection elements, may be used as the content message to prepopulate the case report form.
9. The Next Increment
As the basic components of functional interoperability are being tested and implemented, more attention is being focused on the next increments of the building-block approach. The important challenges to be addressed include: patient identification/privacy protection; the potential and appropriate use of digital signatures; other related and emerging profiles, such as querying the EHR for existing data through the Query for Existing Data (QED) profile; and transferring process-related study information as captured in the study protocol (Retrieve Protocol for Execution [RPE]). More extensive work in data mapping and the development of use cases around content are also needed.
9.1. Patient Identification/Privacy Protection
Patients within the context of clinical care are identified by a patient identifier, usually referred to as a medical record number. When these patients participate in a registry, they will also have a patient identifier within the context of the registry's programs. In some cases, where explicit authorization has been obtained, the medical record number may be shared across programs and can be used as a common identifier that links the patient across systems. In other cases, there is a need to anonymize the patient identifier. In the latter situation, infrastructure can be deployed to create unique, anonymized patient identifiers that serve to protect the patients' identity and facilitate secure patient identity management (e.g., Patient Identifier Cross-Referencing [PIX]).21
Beyond anonymizing, it also may be desirable to maintain a cross-referencing of patient identifiers or aliases across multiple systems so that the medical record number within the EHR can be linked back to the identifier within the registry or clinical trial without revealing the patient identity. Pseudonymization is a procedure by which all person-related data are replaced with one artificial identifier that maps one-to-one to the person.22 Pseudonymization allows for additional use cases where it is necessary to link a patient seen in different settings (such as linking back to source records for additional information or monitoring).23 See Chapter 17 for a more detailed discussion of this topic.
9.2. Digital Signatures
Certain registry purposes (such as regulatory reporting) require electronic signatures—for example, when the clinician or investigator attests to the completeness and accuracy of information being submitted for a research purpose. The current paradigm is the investigator's physical or electronic signature on a paper or electronic case report form. The potential and appropriate use of digital signatures may further broaden the set of use cases by which EHRs may be used for secondary purposes. Other approaches to facilitating identity management, signing, and verification, such as Private Key Infrastructure (PKI), provide advantages in terms of nonrepudiation and detection of tampering. In the next wave of the interoperability effort, it will be important to define those scenarios that will require the strength of an enhanced digital signature.
9.3. Other Related and Emerging Efforts
As the building blocks of interoperability develop, additional flexibility will be gained as the registry and EHR can more fully communicate in a common language, both to request more clinical data and to provide the EHR with more information on the workflow requirements of the registry or other study protocol. These requirements point to other work being done to address these issues. Below are three examples from IHE profiles, some of which are under development by the Quality, Research, and Public Health (QRPH) Domain:
Retrieve Protocol for Execution (RPE): This integration profile allows an EHR to retrieve a protocol or a complex set of clinical research instructions necessary to fulfill the specified requirements of a protocol. The availability of these definitions and a set of transactions defined by RPE can provide an EHR with content that may be used to identify patients for a research program based on defined inclusion/exclusion criteria; manage the patient visit schedule and appropriate case report forms or assessments that need to be completed in the appropriate sequence; and/or assist with other clinical activities such as ordering protocol-specified tests or laboratory reports.24 RPE eliminates the need to manually enter data in two places (an EHR and an electronic data capture system collecting data for clinical research), resulting in a lower user burden on sites participating in research, as they are able to contribute EHR data to research protocols without leaving their EHR session.
Redaction Services Profile (RSP): This integration profile addresses the privacy concerns around the exchange of electronic health data. It provides a way to redact certain data (e.g., personal identifiers) before transmitting that data from one system to another (e.g., from an EHR to a QRPH system), and acts as a “safety net” by ensuring that only the necessary and specified data is transmitted. In addition to this function, it also records and maintains an audit trail of the transmissions it facilitates, to support data quality processes.25
Drug Safety Content (DSC): This content profile from the QRPH Domain details which data (and in what format) should be used in the RFD prepopulation transaction between the Form Manager and Form Filler. It is specifically used for reporting adverse events and other data related to drug safety.26
9.4. Data Mapping and Constraints
While the efforts described above continue to expand the use of electronic medical record data for a variety of secondary purposes, it is clear that clinical and research teams, standards, and terminologies need to be further harmonized to maximize the benefits of information sharing across the variety of clinical and research systems. Effective and efficient management requires that harmonization efforts are furthered among vendors and standards organizations. It also requires that use cases continue to be honed and explicitly defined so that new clinical document constraints can be applied as necessary for each specified use case. Use cases will range across study types and across purposes, including drug safety, biosurveillance, and public health. Each clinical document constraint should strive to capture and deliver the information necessary to fully support the level of information sharing required by the scenario that maximizes both the efficiency of the clinical care/research workflow and the value of previously collected relevant data.
10. What Has Been Done
A number of efforts have demonstrated success in implementing several of the aforementioned building-block standards to achieve functional interoperability for registry purposes, including safety, effectiveness, and quality measurement. In one case, a registry that focused on effectiveness in pain management was made interoperable with a commercial EHR using RFD communication.27 In a second case, the Adverse Drug Event Spontaneous Triggered Event Reporting (ASTER) project,28 interoperability was achieved for the purpose of reporting adverse sự kiện information to FDA. (See Case Example 35.) In a third case, a commercial EHR was made interoperable with a quality reporting initiative for the American College of Rheumatology (ACR),29 and to a Physician Quality Reporting Initiative (PQRI) Registry for reporting data to the Centers for Medicare & Medicaid Services (CMS).30 In each case, both the registry and the EHRs were able to exchange useful information and decrease the effort required by the participating physicians.
11. Distributed Networks
It should be noted that the models of interoperability discussed above presume that data are shared between a distributed EHR and a patient registry (or another recipient such as a regulatory authority or a study sponsor). Alternative models may leave all data within the EHR but execute analyses in a distributed fashion and aggregate only results. To effectively accomplish distributed analyses requires either semantic interoperability or the ability to map to a conforming database structure and content, as well as the sophistication of a large number of EHR systems to run these types of queries in a manner that does not require providers to customize or program their systems. Several groups are advancing these concepts, and they may eventually prove to be very suitable for particular registry purposes (e.g., safety or public health surveillance).
PopMedNet™ is one example of a distributed network model.31-33 It is a software application that enables the creation of a distributed health data networks and supports the operation and governance of these networks.34 Through the application, researchers can create and distribute queries to network data partners, who can then execute the queries and return the aggregate results to the researchers. Data partners retain control of their data and can review queries before responding. The PopMedNet application is designed to support a variety of data networks; therefore, the application does not use a specific data model or governance structure, but instead allows each data network to customize its implementation.
Currently, the PopMedNet application is being used for several research projects, including FDA's Mini-Sentinel project; the Agency for Healthcare Research and Quality's (AHRQ) Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings (SPAN); and the Population-based Effectiveness in Asthma and Lung Diseases (PEAL) project. The Mini-Sentinel project is designed to facilitate the development of an active surveillance system for monitoring the safety of medical products. SPAN uses the application to conduct comparative effectiveness research in obesity and attention deficit hyperactivity disorder. The PEAL project aims to understand factors that affect prescribing and adherence to asthma medications.35 The software application was initially developed by the HMO Research Network Center for Education and Research on Therapeutics and the University of Pennsylvania under contract to AHRQ as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program. Additional development was supported by AHRQ under the SPAN project and by FDA under the Mini-Sentinel project.36
12. Summary
Achieving EHR-registry interoperability will be increasingly important as adoption of EHRs and the use of patient registries for many purposes both grow significantly. The linkage of registries with health information exchanges (HIEs) is also important, as HIEs may serve as data collection assistants with which registries may need to interact.37 Achieving interoperability between these data sources is critical to ensuring that the massive HIT investment under ARRA does not create silos of information that cannot be joined for the public good.38 Such interoperability should be based on open standards that enable any willing provider to interface with any applicable registry without requiring customization or permission of the EHR vendor. Interoperability for health information systems requires accurate and consistent data exchange, along with use of the information that has been exchanged. In addition, care must be taken to ensure that integration efforts comply with legal and regulatory requirements for the protection of patient privacy.
While full semantic interoperability remains distant, a great giảm giá of useful work has been and is being done. For example, the adoption of open standards such as HITSP TP50, C76 and IHE RFD, CRD, and DSC greatly enhance the ability of EHRs and registries to function together and reduce duplication of effort. Functional interoperability is a goal that can be achieved in the near term with significant gains in improving workflow and reducing duplication of effort for providers and patients participating in registries.
The successive development, testing, and adoption of open-standard building blocks, which improve functional interoperability and move us incrementally toward a fully interoperable solution, is a bridging strategy that provides benefits to providers, patients, EHR vendors, and registry developers today.
Case Examples for Chapter 15
Case Example 32Using system integration software to capture registry data from electronic health records
DescriptionThe PINNACLE Registry is an office-based, ambulatory cardiology quality improvement registry. The registry collects data to facilitate performance metric evaluation in coronary artery disease, atrial fibrillation, hypertension, and heart failure. SponsorAmerican College of Cardiology Foundation (ACCF) Year Started2007 Year EndedOngoing No. of SitesOver 500 No. of PatientsOver 2,000,000 patient records Challenge
Collection of registry data in an outpatient setting can be challenging. Sites wishing to participate in the PINNACLE Registry can choose to collect and submit their data on paper or electronically. Paper data collection (i.e., having a dedicated clinical abstractor abstract data manually from an existing medical record into a data collection form) can be disruptive to practice workflow. This method also requires such a significant investment in human resources (from both the site and the registry) that the PINNACLE Registry is no longer accepting new sites that submit data on paper.
Electronic data submission involves directly abstracting relevant registry information from electronic health records (EHRs). The registry certified two EHR vendors as fully compatible and able to submit data automatically to the registry, which minimizes the data entry burden on sites. However, many potential sites use other EHRs, and the lack of standardized terminology and data collection formats among the many EHR options available to practices makes it challenging to provide an integration solution that serves the largest possible number of sites.
Proposed SolutionRecognizing these challenges, the American College of Cardiology Foundation (ACCF) partnered with a technology partner to develop the PINNACLE Registry System Integration Solution (SI). The SI is comprised of (1) a Microsoft SQL–based database, which stores registry measures and the data mapping specifications for the relevant EHR, (2) a .NET 4.0-based Windows Service, which interfaces with the EHR and extracts the relevant registry data, and (3) a .NET 4.0-based Windows Client, which configures the data extractions and adjusts mappings to suit the practice's specific use of the EHR. The SI is compatible with any EHR system, including those that have been highly customized the practice level.
The registry team works with potential sites to complete a technical questionnaire, providing details about the practice's technical environment and EHR system. The SI software is then installed on the practice's server, is programmed to collect registry data elements that are already captured in the existing EHR system, and exports the data directly to the registry database. The primary human resource requirement is from the practice's information technology team who work with the technology partner to install the solution on the practice's server.
ResultsCurrently, 80 percent of sites participating in the PINNACLE Registry use the SI to submit their data. The SI software has been successfully installed and implemented 396 sites, which combined use 19 different EHR products. Installation and data mapping is underway sites using 14 other EHR products.
The PINNACLE Registry System Integration Solution allows for the collection of registry data with minimal disruption of practice workflow. By eliminating the need for manual chart abstraction and data entry, some barriers to practice participation are removed. However, this means that if data are missing in the EHR, the same data will be missing in the registry record. Because of the lack of standardization in EHR systems, the SI solution does require time and resources during the startup phase to implement in a particular practice. Until such standards exist, the SI solution is a viable solution for capturing registry data with minimum workflow disruption and minimum human capital commitment.
Key PointExtracting registry data directly from ambulatory EHRs can reduce the data entry burden on participating sites. A software solution that executes this extraction automatically may take time to set up initially, but minimizes workflow disruption during continued registry participation. An integrated solution that is flexible enough to accommodate many different EHR vendors and levels of customization can reduce barriers to registry participation for many sites.
Case Example 33Creating a registry interface to incorporate data from multiple electronic health records
DescriptionThe MaineHealth Clinical Improvement Registry (CIR) is a secure Web-based database system that provides a tool for primary care physicians in the outpatient setting to consolidate and track key clinical information for preventive health measures and patients with common chronic illnesses. SponsorThe project is the result of a collaboration between Maine Medical Center (MMC) Physician-Hospital Organization, MaineHealth, and MMC Information Services. Year Started2003 Year EndedOngoing No. of Sites106 primary care practices (450 providers) No. of PatientsMore than 200,000 Challenge
A physician-hospital organization (PHO) developed a Web-based patient registry to improve quality of care and track patient outcomes across a large network of physicians. Many practices in the network used EHRs and did not have sufficient staff to enter patient data a second time into a registry. In addition, the practices used a wide range of electronic health records (EHRs), and each had unique technical specifications. The registry needed a technical integration solution to reduce the data entry burden on practices that used EHRs, but, due to resource limitations, it could not develop customized interfaces for each of the many different EHRs in use.
Proposed SolutionThe registry elected to allow practices to submit data from their EHRs to the registry in a one-way data transfer. An interface was written against an XML specification. Practices wishing to participate in the registry without doing direct data entry must be able to export their data in a file that conforms to this specification (although HL7 files are accepted when necessary). Data transfers occur on a schedule determined by each site—some send their data in real time while others send on a monthly basis.
Once the registry receives data files, registry staff members review each portion of the data (demographics, vaccinations, office visits, etc.) before signing off on the file and incorporating the data into the registry. Extensive error checking and validation are completed during the initial specification phase to minimize the amount of manual data checking needed during each transfer. The validation phase involves both technical staff and quality improvement staff the practices to ensure that the data are transferred and mapped correctly into the registry database.
ResultsOf the 106 primary care practices participating in the registry, about 60 percent enter data directly into the registry, and about 40 percent contribute data via XML transfer. The organization and management of this initiative have required strong internal support from the registry and from participating practices. Management teams and technical resources were needed during the startup phase and continue to be essential as more practices contribute data via XML transfer.
Key PointTechnical interface solutions between registries and EHRs can be successful, but require a robust organizational commitment from the registry sponsor and participating sites to provide the necessary resources during the setup and launch phases.
Case Example 34Technical and security issues in creating a health information exchange
DescriptionThe Oakland Southfield Physicians Quality Registry is a practice-based registry designed to promote health outcomes and office efficiencies, and to identify early interventions and best practices in primary care practices. The registry integrates and exchanges health information from many sources through the Oakland Southfield Physicians Health Information Exchange (OSPHIE). SponsorOakland Southfield Physicians Year Started2006 Year EndedOngoing No. of Sites150 No. of PatientsNetwork covers more than 250,000 patients Challenge
In 2006, the practice association launched a registry to improve the quality of care in its primary care practices. However, the association quickly realized that it needed to integrate and exchange health information from multiple sources, such as payer claims, pharmacy claims, practice management systems, laboratory databases, and other registry systems, on behalf of more than 150 primary care practices.
Proposed SolutionTo support this requirement, the practice association constructed an HIE. The HIE is a data warehouse made up of multiple data sources that facilitates the collaborative exchange of health information with a network of trading partners and then integrates the patient disease registry data with a wide range of supplemental clinical information. The HIE allows the registry to securely exchange data with trading partners (third-party payers, laboratories, hospitals, registry systems, etc.) via a variety of methods and in a variety of structures. By pushing information both to the registry system and to other systems, the HIE eliminates duplicate data entry. Data transfers occur established intervals, based on record updates or availability of information.
A key aspect of the system is the master patient and physician index, which allows data from various sources to be linked to the proper patient. Prior to import, data received in the registry are validated against a master patient and physician index for accuracy.
ResultsThrough data sharing with the Oakland Southfield Physicians registry, the practice association has been able to facilitate the alignment of multiple data sources, with evidence-based care guidelines available point of care—a value partnership striving to improve health outcomes as well as the efficient access to key health care data points. This solution relies on building trust between trading partners in support of both the secure transfer of information and recommended use.
The HIE has successfully incorporated data from practice management systems, laboratory providers, an e-prescribing system, a registry system, and third-party payers (medical and pharmacy claims detail). Relevant data are currently transmitted on behalf of the participating physicians in a real-time capacity from the HIE to both the registry system and the e-prescribing system. The data warehouse also generates monthly “gaps-in-care reports” for physician clinical quality review and patient outreach.
Key PointAn HIE may be a useful tool for integrating and exchanging data between registries and other systems. When integrating data from many sources, a master patient and physician index can be a critically important tool for ensuring that the incoming data are linked to the appropriate patient.
Case Example 35Developing a new model for gathering and reporting adverse drug events
DescriptionThe Adverse Drug Event Spontaneous Triggered Event Reporting (ASTER) study uses a new approach to the gathering and reporting of spontaneous adverse drug events (ADEs). The study was developed as a proof of concept for the model of using data from electronic health records to generate automated safety reports, replacing the current system of manual ADE reporting. The goals are to reduce the burden sof reporting and provide timely reporting of ADEs to regulators. SponsorBrigham and Women's Hospital, Partners Healthcare, CDISC, CRIX International, Claricode, and Pfizer Inc. Year StartedPilot launched in 2008 Year EndedPilot ended in 2009 No. of SitesN/A No. of PatientsN/A Challenge
Health care data are rapidly being translated into electronic formats; however, to date, safety reporting has not taken full advantage of these electronic data sources. The spontaneous adverse sự kiện reporting system, which relies on reports submitted manually by health care professionals, is still the primary source of data on potential ADEs. However, the availability of large amounts of data in electronic formats presents the opportunity to rethink the spontaneous adverse sự kiện reporting system. A new model could take advantage of the increasing availability of electronic data and improving technology to automate the process of gathering and reporting ADEs. The goals of automated ADE reporting are to reduce the burden of reporting on physicians, improve the frequency with which ADEs are reported, and increase the timeliness and quality of ADE reports.
An automated model, however, must overcome many challenges. The system must be scalable, must incorporate data from many sources, and must be flexible enough to adapt to the needs of many diverse groups. The model must address point-of-care issues (such as burden of reporting), data exchange standards (so that the data are interpretable and valid), and processes for reviewing the ADE reports.
Proposed SolutionThe ASTER study attempted to address these challenges and demonstrate the potential viability of an automated model for facilitating the gathering and reporting of ADEs. ASTER allowed data to be transferred from an electronic health record (EHR) to an adverse sự kiện (AE) case report form and submitted directly to the U.S. Food and Drug Administration (FDA) in the format of an individual case safety report. The process of gathering and reporting ADEs through ASTER involves four steps based on the open-standard “Retrieve Form for Data Capture (RFD)”:
A physician indicates in the EHR that a drug was discontinued due to an ADE.
The system immediately generates an ADE report form that is prepopulated with demographic, medication, vital signs, and laboratory data. The physician sees the form in the EHR.
The physician enters a small amount of additional data, such as outcomes of the adverse sự kiện, to complete the ADE report form.
The form is then processed by a third-party forms manager, who sends it to FDA as a reported spontaneous AE from the physician, in a standard format.
ResultsThe pilot phase of ASTER began in 2008. The goal of this phase was to demonstrate proof of concept for the new model. Specifically, it was hypothesized that (1) if an EHR could help a clinician identify potential AEs, and (2) if the burden of completing an AE form was significantly reduced, then the rate of reporting of spontaneous AEs to FDA could be significantly increased. ASTER recruited 26 physicians, 91 percent of whom had not reported an AE to FDA in the prior year. Following implementation, more than 200 events were reported over a period of 3 months.
Many questions need to be answered before the ASTER model can become more widely used in the United States. For example, initial findings from ASTER suggest that an increased number of events are being reported using this model; this creates a need for the receiver of the reports (i.e., FDA) to have sufficient capacity to respond to the reports. Also, the fields captured in the ASTER model are based on the paper form fields. Moving to a truly digital system may require a change in the data collected to better align with the way data are collected in electronic formats. In 2012, FDA published the results of a quality assessment of the data they received during the ASTER pilot. While the assessment noted the potential value of such an automated reporting system, it also provided suggestions for improving the quality and utility of the data. In the pilot, users selected an ADE description from a predefined list of relatively broad terms; the authors of the FDA report suggested that either this list be amended to include standardized terms for these clinical events, or users enter không lấy phí text to describe the ADE, which could later be coded. Other suggestions included the implementation of real-time edit checks to catch illogical data such as an ADE date that precedes the initiation date of the suspected drug.
This ADE reporting model is now being expanded to include AEs related to medical devices. The “ASTER-D” project, focused on device safety reporting, builds upon the ASTER concepts. A pilot study is currently underway, sponsored by FDA's Center for Devices and Radiological Health (CDRH).
Key PointNew models for gathering and reporting ADEs may be able to leverage electronic health data and emerging technologies to both improve the timeliness of reporting and reduce the burden of reporting on health care professionals.
For More Information://www.asterstudy.com/
Brajovic S, Piazza-Hepp T, Swartz L, et al. Quality assessment of spontaneous triggered adverse sự kiện reports received by the Food and Drug Administration. Pharmacoepidemiol Drug Saf. 2012 Jun;21(6):565–70. [PubMed: 22359404].
Rockoff JD. Pfizer project looks side effects. The Wall Street Journal. 2009 January 2 ://online.wsj.com/news/articles/SB123085142405347511.
Linder JA, Haas JS, Iyer A, et al. Secondary use of electronic health record data: spontaneous triggered adverse drug sự kiện reporting. Pharmacoepidemiol Drug Saf. 2010 Dec;19(12):1211–5. [PubMed: 21155192].
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38.What are the 5 components of the electronic medical record?
Electronic Health Records: The Basics Administrative and billing data. Patient demographics. Progress notes. Vital signs.What are the five main functions that are performed by an electronic health record?
These functions include:. health information and data.. result management.. order management.. decision support.. electronic communication and connectivity.. patient support.. administrative processes and reporting.. reporting and population health..What are the two most common types of electronic medical records?
There are three types of medical records commonly used by patients and doctors: Personal health record (PHR) Electronic medical record (EMR) Electronic health record (EHR)What are the 3 components of the EHR system?
The main components of electronic health record are registration, admissions, discharge, and transfer (RADT) data. Tải thêm tài liệu liên quan đến nội dung bài viết Which of the following statements accurately describes duplicate patient registration entries in an electronic health record?