9 3 Data Standards And Enterprise Data Sharing


  • Data standards
  • Enterprise data sharing

Data Standards

Ever since Eli Whitney developed interchange- able parts for rifle assembly, standards have been created and used to make things or pro- cesses work more easily and economically—or, sometimes, to work at all. A standard can be defined in many physical forms, but essentially it comprises a set of rules and definitions that specify how to carry out a process or produce a product. Sometimes, a standard is useful because it provides a way to solve a problem that other people can use without having to start from scratch. Generally, though, a stan- dard is useful because it permits two or more disassociated people to work in some cooper- ative way. Every time you screw in a light bulb or play a media file, you are taking advantage of a standard. Some standards make things work more easily. Some standards evolve over time,1 others are developed deliberately.

The first computers were built without standards, but hardware and software stan- dards quickly became a necessity. Although computers work with values such as 1 or 0, and with “words” such as 10101100, humans need a more readable language (see 7 Chap. 5). Thus, standard character sets, such as ASCII and EBCDIC, were developed. The first standard computer language, COBOL, was written originally to simplify program development but was soon adopted as a way to allow sharing of code and development of software components that could be inte- grated. As a result, COBOL was given official standard status by the American National StandardsInstitute(ANSI).2Inlikemanner, hardware components depend on standards for exchanging information to make them as interchangeable as were Whitney’s gun barrels.
A 1987 technical report from the International Standards Organization (ISO) states that “any meaningful exchange of utterances depends upon the prior exis- tence of an agreed upon set of semantic and syntactic rules” (International Standards Organization 1987). In biomedical infor- matics, where the emphasis is on collection, manipulation, and transmission of informa- tion, standards are essential and their impor- tance is widely recognized by clinicians and policy makers. Requirements for implemen- tation of interoperability standards have been written into laws and regulations. Over the past 2 years, the bipartisan 21st Century Cures Act3 (Hudson and Collins 2017) has codified many standards into everyday use. At present, the standards scene is evolving so rapidly that any description is inevitably outdated within a few months. This chapter emphasizes the need for standards in general, standards development processes, current active areas of standards development, and key participating organizations that are mak- ing progress in the development of usable standards.

A data standard is a type of standard, which is an agreed upon approach to allow for consistent measurement, qualification or exchange of an object, process, or unit of information. For example, the metric system of measurement is a standard. 
Data standards refer to methods of organizing, documenting, and formatting data in order to aid in data aggregation, sharing and reuse. There are many data standards, and data standards can be generated by a research community (e.g., Observational Health Sciences and Informatics (OHDSI), a governmental organization (e.g., International Organization for Standardization (ISO)), or other large organizations. Metadata standards (e.g., DublinCore) are also data standards as they standardize how metadata is formatted in order to ease the sharing of metadata across platforms.

Data standards in biomedicine

The patient care process, which can be varied and complicated, also include numerous processes that can be improved with standardization. A hospital admissions system records that a patient has the diagnosis of diabetes mellitus, a pharmacy system records that the patient has been given gentamicin, a laboratory system records that the patient had cer- tain results on kidney function tests, and a radiology system records that a doctor has ordered an X-ray examination for the patient that requires intravenous iodine dye. Other systems need ways to store these data, to pres- ent the data to clinical users, to send warn- ings about possible drug-drug interactions, to recommend dosage changes, and to follow the patient’s outcome. A standard for coding patient data is nontrivial when one consid- ers the need for agreed-on definitions, use of qualifiers, differing (application-specific) lev- els of granularity in the data, and synonymy, not to mention the breadth and depth that such a standard would need to have.
The inclusion of medical knowledge in clinical systems is becoming increasingly important and commonplace. Sometimes, the knowledge is in the form of simple facts such as the maximum safe dose of a medication or the normal range of results for a laboratory test. Much medical knowledge is more com- plex, however. It is challenging to encode such knowledge in ways that computer systems can use (see 7 Chap. 26), especially if one needs to avoid ambiguity and to express logical rela- tions consistently. Thus the encoding of clinical knowledge using an accepted standard would allow many people and institutions to share the work done by others. One standard designed for this purpose is the Arden Syntax as well as the HL7 standard Clinical Quality Language (Odigie et al. 2019).
Because the tasks we have described require coordination of systems, methods are needed for transferring information from one system to another. Such transfers were traditionally accomplished through custom- tailored point-to-point interfaces, but this technique has become unworkable as the number of systems and the resulting permu- tations of necessary connections have grown. A current approach to solving the multiple- interface problem is through the development of messaging standards. Such messages must depend on the preexistence of standards for patient identification and data encoding.
Over the past decade, non-healthcare domains such as travel, package delivery and e-commerce have adopted, implemented and published standard application programing interfaces (APIs) in order to streamline their business processes and improve efficiency. The adoption of open APIs especially the HL7 Fast Healthcare Interoperability Resources (FHIR®) has increased dramatically and cited proposed regulations as an enabler of improved data sharing (Braunstein 2018).
Data sharing has become an expected functionality for any health IT system. Many of the new initiatives in health require data sharing. Data sharing is essential not only for patient care, but for aggregating data across multiple sites for research. Security must alsobe addressed before such exchanges can be allowed to take place. Before a system can divulge patient information, it must ensure that requesters are who they say they are and that they are permitted access to the requested information (see 7 Chap. 5). Standards exist for this functionality. Although each clinical system can have its own security features, sys- tem builders would rather draw on available standards and avoid reinventing the wheel. Besides, the secure exchange of information requires that interacting systems use standard technologies. Electronic Health Record systems (EHRs) are increasingly adopting standard authorization (OAuth2) and identification (OpenID) by implementing Substitutable Medical Applications Reusable Technology (SMART) on FHIR which allows platform independent applications to be launched within the EHR workflow and utilize EHR data via FHIR (Payne et al. 2015).

It is helpful to separate the discussion of the general process by which standards are created from our discussion of the specific organizations and the standards that they produce. The process is relatively constant, whereas the organizations form, evolve, merge, and are disbanded. This section will discuss how standards are created then identify the many SDOs and an overview of the types of standards they create. This section will also identify other groups and organizations that contribute or relate to standards activities.

The Standards Development Process

The process of creating standards is biased and highly competitive. Most standards are created by volunteers who represent multiple, disparate stakeholders. They are influenced by direct or indirect self-interest rather than judgment about what is best or required. The process is generally slow and inefficient; mul- tiple international groups create competitive
standards; and new groups continue to be formed as they become aware of the need for standards and do not look to see what stan- dards exist. Yet, the process of creating stan- dards largely works, and effective standards are created.
There are four ways in which a standard can be produced:
1. Ad hoc method: A group of interested
people and organizations (e.g., laboratory- system and hospital-system vendors) agree on a standard specification. These specifi- cations are informal and are accepted as standards through mutual agreement of the participating groups. A standard example produced by this method is the DICOM standard for medical imaging.
2. De facto method: A single vendor controls a large enough portion of the market to make its product the market standard. An example is Microsoft’s Windows. A more recent example are the Argonaut Implementation Guides.4 In this case, a collaborative of vendors and academic health systems are creating consensus standards for their requirements.
3. Government-mandate method: A govern- ment agency, such as CMS or the National Institute for Standards and Technology (NIST) creates a standard and legislates its use. An example is the HIPAA standard. Another example is the Consolidated Clinical Data Architecture (CCDA),5 a standard that resulted from the US Government’s creating a set of require- ments and driving a standard to meet those requirements.
4. Consensus method: A group of volunteers representing interested parties works in an open process to create a standard. Most health care standards are produced by this method. An example is the Health Level 7 (HL7) standard for clinical data interchange.

Data Standards' Organizations

Sometimes, standards are developed by organizations that need the standard to carry out their principal functions; in other cases, coalitions are formed for the express purpose of developing a particular standard. The lat- ter organizations are discussed later, when we examine the particular standards devel- oped in this way. There are also standards organizations that exist for the sole purpose of fostering and promulgating standards. In some cases, they include a membership with expertise in the area where the standard is needed. In other cases, the organization pro- vides the rules and framework for standard development but does not offer the expertise needed to make specific decisions for specific standards, relying instead on participation by knowledgeable experts when a new standard is being studied.
This section describes several of the best known and most influential health-related SDOs. Since most standards continue to evolve to accommodate changes in technol- ogy, policy, regulations, and requirements, links are provided to selected standards and SDO information. For a detailed understand- ing of an organization or the standards it has developed, you will need to refer to current primary resources. For instance we recall ANSI ,ASTM ,ASCX12 ,CDISC ,DICOM ,IEEE ,HL7 ,PCHA…

Enterprise data sharing

Data interchange

The recognition of the need to interconnect health care applications led to the develop- ment and enforcement of data interchange standards. The conceptualization stage began in 1980 with discussions among individu- als in an organization called the American Association for Medical Systems and Informatics (AAMSI). In 1983, an AAMSI task force was established to pursue those interests in developing standards.

Specific data interchange standards

As health care increasingly depends on the connectivity within an institution, an enterprise, an integrated delivery system, a geographic system, or even a national inte- grated system, the ability to interchange data in a seamless manner becomes critically important. The economic benefits of data-interchange standards are immediate and obvious. Consequently, it is in this area of healthcare standards that most effort has been expended. All of the SDOs in health care have some development activity in data- interchange standards.

HL7 v2

HL7’s v2 messaging, now superseded by V3, is the organization’s first information exchange standard to become ubiquitous in the healthcare industry. It contains structural and semantic specifications for medical messaging that cover every need of a hospital IT system, from clinical to administrative, including logistical and financial processes. HL7 v2 was designed mainly to support a central patient care infrastructure, with less focus on exchanging data between disparate systems.
According to the organization’s website, 95% of US healthcare institutions use HL7 V2.x in their information systems, with 35 more countries that have adopted the standard to various extents.


for Administration, Commerce, and Transport Standard
The EDI for Administration, Commerce, and Transport (EDIFACT) is a set of international standards, projects, and guidelines for the electronic interchange of structured data related to trade in goods and services between independent computer-based information systems (National Council for Prescription Drug Programs Data Dictionary 1994). The standard includes application-level syntax rules, message design guidelines, syntax imple- mentation guidelines, data element dictionary, code list, composite data-elements dictionary, standard message dictionary, uniform rules of conduct for the interchange of trade data by transmission, and explanatory material.

How does EDI work?
1. Data Preparation
The sender's ERP must generate a data structure with all the information to be integrated into the outbound messages.
2. Data Conversion
Once the data structure arrives at the sender's EDI software, it is converted into the EDI format requested by the recipient.
3. EDI Message Transmission
Once the EDI document has been transformed into the required standard, it is sent to the recipient via an agreed communication system. The EDI software recognizes the recipient and automatically forwards the message, using point-to-point systems such as AS2, OFTP2, FTPS or HTTPS web services, or private services such as VAN or Value Added Networks.
4. Data Reception
Once the message is sent, the process is replicated in reverse. In this way, the recipient validates the message and proceeds with the transformation of the EDI message into a data structure that will integrate into their ERP if there are no errors.

Data exchange standards in healthcare

When it comes to exchanging health data between departments or across institutions, there are so many variables at play that additional rules and descriptions are absolutely necessary. There can’t be any ambiguity when transferring and interpreting information about the patient's allergies or the procedures, materials, and medications required.
Comprehensive data standards in healthcare bring the industry one step closer to achieving seamless interoperability — from individual medical facilities to the national level. Without these standards, we’d still be in the stone age of data communication. Which would mean operating within disparate data silos, not having easy access to information from EHRs (Electronic Health Records), and spending too much time on manual tasks.
Even within a single institution, the IT system can be very complex and have multiple components that need to access clinical data. Here are some of the benefits your health facility can gain by adopting medical data exchange standards:
-Data integration across all systems. Most hospitals use one or more of these: EMR (Electronic Medical Record), RIS (Radiology Information System), LIS (Laboratory Information System), and PACS (Picture Archiving and Communication System). Moving data between them would be a pain without strictly defined standards. Implementing them (along with an effective Hospital Management System) can help ensure error-free data communication and significantly reduce delays.
-Improvements to decision-making. With all essential data available at their fingertips, doctors can make critical decisions faster, often saving patients’ lives. 
-Better compatibility and compliance. With relevant standards in place, you won’t have to worry about inconsistencies or gaps in compliance with laws concerning the privacy and security of patient information. You’ll also be able to easily exchange clinical data with other institutions that adhere to the same industry-proven norms.
-Quicker and more reliable medical billing and claims processing. Standardized codes for medical terms and procedures help streamline the process of billing, which leads to properly substantiated claims and fewer rejections.
All of the perks listed above clearly demonstrate the importance of data standardization and data quality in healthcare. By planning and implementing it as soon as possible, you can save your medical institution’s resources, achieving higher patient satisfaction and a considerable increase in profits.
Now, let’s talk in more detail about the various data interchange standards in healthcare and the ones you should pay special attention to.

How health data standards support healthcare interopeability?

Adopting health data standards in a consistent and comprehensive manner will be key to enabling meaningful healthcare interoperability.
Achieving true healthcare interoperability across the care continuum is a top priority for providers, payers, and other key industry stakeholders.
Seamless, comprehensive data sharing is particularly important for healthcare organizations looking to earn incentive payments through the CMS Promoting Interoperability (PI) Program, formerly known as meaningful use.
The adoption and use of health data standards forms the basis for enabling interoperability across organizations and between EHR systems.
According to ONC, “standards are agreed-upon methods for connecting systems together. Standards may pertain to security, data transport, data format or structure, or the meanings of codes or terms.”
In the healthcare industry, several different standards development organizations (SDOs) create, define, update, and maintain health data standards through collaborative processes that involve health IT users.
While SDOs have created several well-known standards intended to promote interoperability, lack of widespread adoption and use lessen the effectiveness of existing standards. In addition, differences in the way developers implement data standards can slow progress toward achieving healthcare interoperability.

Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License