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This article discusses the relationship between healthcare data and information, the characteristics of data quality, and the challenges associated with measuring and ensuring data quality. It also explores the difference between data, information, and knowledge in the context of healthcare.
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TOPIC 2 Health Data Care Quality
Learning Objectives • To be able to discuss the relationship between health care data and health care information. • To be able to identify problems associated with poor quality health care data. • To be able to define the characteristics of data quality. • To be able to discuss the challenges associated with measuring and ensuring health care data quality.
Data, Information, Knowledge What is the difference between data, information and knowledge?
Data, Information, Knowledge The Hierarchy • Processing
Data, Information, Knowledge • Health care data are raw health care facts, generally stored as characters, words, symbols, measurements, or statistics. • Health care data may describe a particular event, but alone and unprocessed they are not particularly helpful for decision making. Data
Data, Information, Knowledge • Informationis processed data. Therefore we can say that health care information is processed health care data. • Example: if we process the figure 97 %, this figure will further indicate or represents some important event such as the average bed occupancy for a hospital for the month of January is 97% occupied, it takes on more meaning. Information
Data, Information, Knowledge Knowledge • Knowledge is seen by some as the highest level in a hierarchy with data at the bottom and information in the middle (Figure 2.1). Knowledge is defined by Johns (1997) as “a combination of rules, relationships, ideas, and experience.” • Another way of thinking about knowledge is that it is information applied to rules, experiences, and relationships, with the result that it can be used for decision making.
Ensuring Data & Information Quality • There are two organizations that have published guidance that can assist a health care organization in establishing its own data quality standards: • The Medical Records Institute (MRI) has published a set of “essential principles of healthcare documentation,” and • The American Health Information Management Association (AHIMA) has published a data quality management tool.
Medical Records Institute (MRI) Medical Records Institute (MRI) is a professional organization dedicated to the improvement of patient records through technology. The MRI argues that there are many steps that must be taken to create systems that ensure quality health care documentation.
Medical Records Institute (MRI) MRI key principles that should be adhered to as these systems are follows: • Unique patient identification must be assured within and across healthcare documentation systems. • Healthcare documentation must be • accurate and consistent, • complete, • timely, • interoperable across types of documentation systems, • accessible at any time and at any place where patient care is needed, • auditable. • Confidential and secure authentication and accountability must be provided.
Medical Records Institute (MRI) Health care documentation has two parts: • Information Capture:“the process of recording representations of human thought, perceptions, or actions in documenting patient care, as well as device-generated information that is gathered and/or computed about a patient as part of health care”. • Handwriting, speaking, typing, touching a screen or pointing and clicking on words or phrases, videotaping, audio recording, and generating images through X-rays and scans. • Report generation:“consists of the formatting and/or structuring of captured information. It is the process of analyzing, organizing, and presenting recorded patient information for authentication and inclusion in the patient’s healthcare record”.
Medical Records Institute (MRI) Medical Records Institute (MRI) has identified five major functions that are negatively affected by poor-quality documentation. Patient safety is affected by inadequate information, illegible entries, misinterpretations, and insufficient interoperability. Public safety, a major component of public health, is reduced by the inability to collect information in a coordinated, timely manner at the provider level in response to epidemics and the threat of terrorism. Continuity of patient care is affected by the lack of shareable information among patient care providers.
Medical Records Institute (MRI) Health care economics are affected, with information capture and report generation costs currently estimated to be well over $50 billion annually. Clinical research and outcomes analysis is affected by a lack of uniform information capture that is needed to facilitate the derivation of data from routine patient care documentation.
Poor Quality Data • Poor-quality data collection and reporting can affect each of the purposes for which we maintain patient records. • At the organizational level a health care organization may find diminished quality in: • Patient care • Poor communication among providers and patients • Problems with documentation • Reduced revenue generation due to problems with reimbursement • Diminished capacity to effectively evaluate outcomes or participate in research activities
Poor Quality Data • These problems are found not only at the organizational level but also across organizations and throughout the overall health care environment. • Solution: Some of the problems presented may actually be reduced with the implementation of effective information technology (IT) solutions.
Ensuring Data and Information Quality Health care decision makers rely on high quality information. Before an organization can measure the quality of the information it produces and uses, it must establish data standards. Unfortunately, there is no universally recognized set of health care data quality standards in existence today. Health care organizations mustestablish data quality standards specific to the intended use of the data or resulting information.
Type of Data Errors • Failures of data to meet established quality standards are called data errors. • A data error will have a negative impact on one or more of the characteristics of quality data. • Systematic errors: are errors that can be attributed to a flaw or inconsistency in adherence to standard operating procedures or systems. • Random errors: errors caused as the result of poor handwriting or transcription errors.
Error Type Examples The following illustration is an example of a hand-written prescription for Metadate ER 10 mg tablets. Metadate is a drug used in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). Due to the similarity in name, poor penmanship and the omission of the modifier "ER", the pharmacy filling the prescription incorrectly dispensed methadone 10 mg tablets. Methadone is a morphine-based product used as a heroin substitution therapy and analgesic. Methadone is not used for the treatment of ADHD.
AHIMA Data Quality Model • AHIMA has published a generic data quality management model and an accompanying set of general data characteristics. • There are similarities between these characteristics and the MRI principles. • AHIMA strives to include all health care data, however, and does limit the characteristics of clinical documentation.
AHIMA Data Quality Model Application – The purpose for which the data are collected. Collection – The processes by which data elements are accumulated. Warehousing – Processes and systems used to archive data and data journals. Analysis – The process of translating data into information utilized for an application. Source: AHIMA, Data Quality Management Task Force, 1998.
Improve Data Quality The AHIMA data quality characteristics below can serve as the basis for establishing data quality standards: Data accuracy. Data that reflect correct, valid values are accurate. Typographical errors in discharge summaries and misspelled names are examples of inaccurate data. Data accessibility. Data that are not available to the decision makers needing them are of no use.
Improve Data Quality Data comprehensiveness. All of the data required for a particular use must be present and available to the user. Even relevant data may not be useful when they are incomplete. Data consistency. Quality data are consistent. Use of an abbreviation that has two different meanings provides a good example of how lack of consistency can lead to problems.
Improve Data Quality Data currency. Many types of health care data become obsolete after a period of time. A patient’s admitting diagnosis is often not the same as the diagnosis recorded upon discharge. Data definition. Clear definitions of data elements must be provided so that both current and future data users will understand what the data mean. One way to supply clear data definitions is to use data dictionaries.
Improve Data Quality Data granularity. Data granularity is sometimes referred to as data atomicity. That is, individual data elements are “atomic” in the sense that they cannot be further subdivided. Data precision. Precision often relates to numerical data. Precision denotes how close to an actual size, weight, or other standard a particular measurement is.
Improve Data Quality Data relevancy. Data must be relevant to the purpose for which they are collected. Data precision. Precision often relates to numerical data. Precision denotes how close to an actual size, weight, or other standard a particular measurement is. Data timeliness. Timeliness is a critical dimension in the quality of many types of health care data.
IT for Enhancing Data Quality Information technology has tremendous potential as a tool for improving health care data quality. Electronic Medical Records (EMRs) improve legibility and accessibility of health care data and information. EMR systems were recorded in an unstructured format (narrative form).
IT for Enhancing Data Quality Physician notes and discharge summaries are often dictated and transcribed. This lack of structure limits the ability of an EMR to be a data quality improvement tool. When health care providers respond to a series of prompts they are reminded to include all necessary elements of a health record entry. Data precision and accuracy are improved when these systems also incorporate error checking.
Summary Health care decisions, both clinical and administrative, are driven by data and information. Data and information are used to provide patient care and to monitor facility performance. It is critical that the data and information be of high quality. After all, the most sophisticated of information systems cannot overcome the inherent problems associated with poor-quality source data and data collection or entry errors.
