Question

What are some of the drivers/influences that are changing how data is utilized by Healthcare organizations today?

Explain what is meant by Normalization of data and explain how or why this can be a challenge for organizations.

Answer 1

Answer:

What Are The Data Standards in Healthcare?

Healthcare data standards are the commonly adopted rules regarding definitions and formats of health information, and the methods of recording, storing, and sharing data within medical organizations. At a basic level, data standardization is aimed at defining what information can and should be collected, determining a way for this information to be represented, and deciding how to encode it for further transmission.

The standards involve the following types of data:

• Medications;

• Medical devices;

• Medical records;

• Radiological images;

• Medical monitoring systems;

• Payment information; and

• Reimbursement information.

Healthcare data standards apply to both computer-based and paper-based healthcare data systems. They involve the following components:

• Data elements;

• Interchange formats;

• Terminologies; and

• Representation standards.

Let’s cover each component in more detail.

Data Elements

A data element is a basic unit of information that can be collected, stored, and used in data systems in any clinic, hospital, or other healthcare organization.

Data element examples include:

• Patient name;

• Ethnicity;

• Gender;

• Diagnosis; and

• Cholesterol level.

Healthcare Data Exchange Standards

Data interchange standards cover the rules and recommendations that include common encoding specifications, medical templates for structuring information, document architectures, and information models for determining relationships between different data elements to ensure standardized message formats. In 2003, the Consolidated Health Informatics organization announced its requirement that any federal health care provider has to adopt common messaging formats for data exchanges.

One of the messaging format standardization sets is Health Level 7 International (HL7), created in 1987. This is a healthcare protocol standardized by the American National Standards Institute, commonly known as ANSI. Healthcare data standard HL7 covers the rules of the integration, management, and exchange of electronic healthcare information. It also sets a standard structure and types of messages that may be sent between healthcare institutions.

The functions covered by HL7 include:

• Participant identification;

• Security checks;

• Availability checks;

• Data exchange structuring; and

• Data exchange mechanism.

In the U.S., federal healthcare providers have been using HL7 version 2.5 as a messaging standard for data exchange since 2004. One of the components of HL7 is the Clinical Document Architecture that sets an exchange model for various types of clinical information, including multimedia by utilizing an XML format (the extensible markup language). Adopting the HL7 2.5 is crucial for ensuring comprehensive and standardized sharing of machine-generated, processable health data.

Terminologies

Unified terminologies are crucial for facilitating electronic data collection. That is why ANSI created a detailed framework of information criteria for the development of terminologies in 1998. Later in 2003, the National Committee of Vital and Health Statistics developed a set of basic nonredundant and well-integrated national standards of medical terminologies.

They include basic concepts of terminology development and principles of term representation based on the following technical criteria: the absence of meaningless identifiers, comprehensive multi-hierarchies, nonredundancy, formal definition concepts, and proper links to related terminologies.

Healthcare Data Collection Standards

Knowledge representation and data collection standards cover the methods, formats, and rules of a proper and unified system for computer-based information and how it is collected and represented in medical organizations to help them make evidence-based decisions. These standards are aimed at the implementation of healthcare knowledge into medical automated systems, to improve the quality and efficiency of clinical care.

Knowledge representation standards include the following:

• Alerts and reminders;

• Medical literature referencing;

• Protocols and guidelines development; and

• Implementation of clinical decision support systems.

Clinical practice guidelines have to be modeled and executed in the Guideline Interchange Format commonly known as GLIF. GLIF is a computer-interpretable language created by theInterMed Collaboratory, its focus being to create building-block components which will be easily accommodated by guideline models.

Examples of building-block components are:

• Workflow representation;

• Data references; and

• Logic expressions.

Healthcare representation standards set the rules of using common drug knowledge databases such as Multum, Medi-Span, and FirstDatabank. These databases provide information on toxicology, contraindications, allergies, drug-laboratory inferences, and drug-drug interactions.

Information Security, Confidentiality, and Privacy

Ensuring full data security is crucial for the healthcare industry. Without it, health information could be accessed, stolen, or even modified by unauthorized users. Therefore, implementing various IT security measures is required for any healthcare organization. The main way to ensure information protection is to define the users who can access particular data, which is why organizations that deal with personal health information stored in an electronic manner (ePHI) have to implement data security standards and methods.

Data security methods include:

• Multi-factor authentication;

• e-Certificates;

• Digital Signatures; and

• Cryptography.

There are developed packages of data security standards that are commonly used among healthcare organizations such as ASTM E31.20, ISO TC215 Working Group 4, and CEN / TC251 Working Group 3. They include the following topics: accessing and integrity of health data, data protection policies, health information usage audit, and security of data communication in the Internet environment.

Data Standardization in Healthcare: How to Adopt Data:

The existence of various technologies and tools for data processing and storage helps healthcare providers simplify the methods of how they deal with information. Technological progress, however, can also be a negative factor, in this case: causing difficulties in sharing data among organizations that use different processing and storage methods. So, how do we bring healthcare data into one common format across the board?

Data standardization plays a significant role, especially in the healthcare industry, in creating a common format of data to allow different institutions to collaborate in an efficient way while using various information processing tools. This helps clinics, hospitals, and other medical organizations perform large-scale analytics, mutual research, and share effective methodologies.

The primary large-scale set of healthcare IT data standards is theHealth Insurance Portability and Accountability Act (HIPAA) published in 1996. This act contains the rules of security measures regarding personal health information (PHI) storage and privacy policies aimed at protecting patients from PHI data thefts. The HIPAA was created to solve the problem of uncontrolled and unsecured PHI exchanges, and to avoid data breaches in the healthcare industry; however, the act does not ensure that different healthcare organizations will easily and efficiently process the received information. This is why standardization has been implemented in the medical industry.

The Importance of Standardization in the Healthcare Industry:

The inarguable fact is that caregivers have difficulties implementing new standards and technologies in their organizations. The research made by Amplion has shown that one in every three hospitals still uses older patient communication tools that are over nine years old; therefore, the slow implementation of healthcare data sets and standards is completely expected.

The Advisory Board Company hasreported that hospitals have a slow progress of adopting clinical standardization. In a term of five years, the percentage of patient treatment standards implementation increased from 35% to 50%. The importance of implementing healthcare standards becomes evident after observing some basic issues clinics face.

Manual Routine

Medical information is often recorded by electronic health record systems (EHRS). These systems record data as a simple text, instead of recording it as discrete data, making clinical data difficult to paste into registries. To do so, specialists have to translate patient records (abstraction process) and manually enter particular information into a registry.

Different Elements in EHRSs and Registries

In cases where clinical data is recorded in a structured way as discrete information, it cannot guarantee the full interoperability as some registry elements can be different. To better understand this issue, we refer to the following example: an EHRS records a smoking status of a patient while a registry only displays the data of whether the person has smoked within the last two years. This makes EHRS-based information insufficient for usage in registries.

Different Definitions in Particular Registries

Various registries may utilize different definitions for basic medical concepts such as tachycardia or heavy bleeding. Therefore, the translation of particular patient information becomes much harder since abstraction specialists have the exact definition that the specific registry uses.

These are only some of many problems healthcare organizations face without using data standards in their systems. To make it clear how those standards simplify and improve the work of caregivers, we need to think about how particular components of standardization can help solve the problems mentioned above. For example, data exchange and representation standards unify the format and structure of information and eliminate the need to convert data to make it sufficient for registries. Implementing terminology standards eliminates the issue of using different definitions in various registries. Indeed, data standards have a highly positive impact on the healthcare industry, in general, and the way healthcare institutions exchange information, in particular.