HEALTH DATA QUALITYLecture 3

Data and Information

Health Care Knowledge

Health Care Information

Health Care Data

Data and Information

• Health data is raw health facts stored as characters, words, symbols, measurements, or statistics.

• Data is not very useful for decision making.

• Example:

80%

80% bed occupancy for the month of October of 2011.

Knowledge

• “A combination of rules, relationships, ideas, and experience.” (Johns, 1997)

• Knowledge is used for decision making.

Information

• Information is an extremely valuable asset at every level of a health care organization.

• The same data may provide different information to different users.

• One person’s data may be another person’s information.

• Data creates information. We must have data before we can get information.

Data Quality

• We must acquire high quality data to achieve high quality information.

• Data quality must be established at the granular level.

• Health care data is gathered through patient care documentation by clinical providers and administrative staff.

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.

• Some of the problems presented may actually be reduced with the implementation of effective information technology (IT) solutions.

Medical Records Institute (MRI)

• Medical Records Institute (MRI), a professional organization dedicated to the improvement of patient records through technology, has identified five major functions that are negatively affected by poor-quality documentation (MRI, 2004).

Medical Records Institute (MRI)

1. Patient safety is affected by inadequate information, illegible entries, misinterpretations, and insufficient interoperability.

2. Public safety, a major component of public health, is diminished by the inability to collect information in a coordinated, timely manner at the provider level in response to epidemics and the threat of terrorism.

3. Continuity of patient care is adversely affected by the lack of shareable information among patient care providers.

Medical Records Institute (MRI)

4. Health care economics are adversely affected, with information capture and report generation costs currently estimated to be well over $50 billion annually.

5. Clinical research and outcomes analysis is adversely affected by a lack of uniform information capture that is needed to facilitate the derivation of data from routine patient care documentation [MRI, 2004, p. 2].

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” (MRI, 2004, p. 2).

• 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” (MRI, 2004, p. 2).

Ensuring Data and Information Quality

• Health care decision makers rely on high quality information.

• The issue is not whether quality information is important but rather how it can be achieved.

• 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.

Ensuring Data and Information Quality

• One reason for this is that the quality of the data needed in any situation is driven by the use to which the data or the information that comes from the data will be put.

• Health care organizations must establish data quality standards specific to the intended use of the data or resulting information.

• In the U.S two organizations have published guidance that can assist a health care organization in establishing its own data quality standards: the Medical Records Institute (MRI) American Health Information Management Association (AHIMA)

MRI Principles of Health Care Documentation

1. Unique patient identification must be assured within and across healthcare documentation systems.

2. 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.

3. Confidential and secure authentication and accountability must be provided [MRI, 2004, p. 3].

AHIMA Model

AHIMA Data Quality Model

1. Data accuracy: data that reflect correct, valid values are accurate.

2. Data accessibility: data that are available to the decision makers when they need it.

3. Data comprehensiveness: all of the data required for a particular use must be present and available to the user.

4. 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.

AHIMA Data Quality Model

5. 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. If a health care executive needs a report on the diagnoses treated during a particular time frame, which of these two diagnoses should be included?

6. 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.

AHIMA Data Quality Model

7. Data granularity: data granularity is referred to as data atomicity. Individual data elements are “atomic” in the sense that they cannot be further subdivided. The birth date is at its lowest practical level of granularity when used as a patient identifier. Values for data should be defined at the correct level for their use.

8. Data precision: precision relates to numerical data. For example, drug dosage.

AHIMA Data Quality Model

9. Data relevancy: data must be relevant to the purpose for which they are collected.

10. Data timeliness: timeliness is a critical dimension in the quality of many types of health care data. For example, critical lab values must be available to the health care provider in a timely manner.

Case 1: Documentation Errors

• A nurse administered 5,000 units of Heparin when the order was for 2,500 units. The patient became critically ill as a result. When the documentation was reviewed, it was discovered that the nurse committing the error had misspelled Heparin as ‘‘Hepirin.’’ This spelling error was presented to the jury as an additional demonstration of incompetence. The plaintiff’s attorney argued that Heparin is a commonly used drug and obviously this nurse had no knowledge of it, because she couldn’t spell it correctly. Juries will also doubt the competence of a nurse who writes ‘‘The wound on the left heal is healed.’’

Case 2: Documentation Errors

• The nurse who documented an assessment with a post date was called as a witness. She was asked to explain how she could perform an assessment two days after the patient died. The nurse explained that Friday was the actual due date for the assessment but because she had some extra time on Tuesday, she decided to do it early and put Friday’s date on it to be compliant with the due date. The plaintiff’s attorney then asked, ‘‘Is that the integrity of the entire medical record and the nurse.

Case 3: Documentation Errors

• The continuity of the record also needs special scrutiny on a regular basis. Some institutions allow the record to be ‘‘split,’’ which means placing the progress notes at the bedside while maintaining the rest of the chart documentation at the nurses’ station. To avoid having to go back to the bedside to document, a nurse might take a new progress note sheet, document findings, and then put the page in the chart at the nurses’ station. This documentation will not be in proper sequence with the progress notes from the bedside when they are entered into the chart.

Class Discussion

• 1. How many pediatric members were enrolled as of year-end 2011?

• 2. How many pediatric visits took place in 2011?

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 discrepancy in adherence to standard operating procedures or systems.

Random errors: errors caused as the result of poor handwriting or transcription errors.

Error Type Examples

Error Detection, Prevention, and Correction

• Errors that are not preventable need to be detected so that they can be corrected.

• There are multiple points during data col- lection and processing where system design can reduce data errors.

Data Error Prevention

• Define data and data characteristics in a data dictionary.

• Create user friendly data entry forms or interface.

• Train and motivate users.

• Develop a data collection protocol.

Data Error Correction

• Perform automatic data checks.

• Routinely check completeness of data entry.

• Perform data quality audits.

Improve Data Quality

• Provide data quality reports to users.

• Give feedback of data quality results and recommendations.

• Communicate with users.

IT for Enhancing Data Quality

• Information technology has tremendous potential as a tool for improving health care data quality.

• Clearly, electronic medical records (EMRs) improve legibility and accessibility of health care data and information.

• EMR systems were recorded in an unstructured format.

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, rather than dictating a free-form narrative, 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.

References

• “Health Care Information Systems: A Practical Approach for Health Care Management”By Karen A. Wager, Frances W. Lee, John P. Glaser

• “Information Systems and Healthcare Enterprises”By Roy Rada

• Source: Examples from Schott, 2003, pp. 22-23.

End of Lecture 3