1. Configuring Electronic Health Records
Health Summary and Clinical Reminder Reports
Welcome to Configuring Electronic Health Records: Health Summary and Clinical Reminder Reports.
The component, Configuring Electronic Health Records, provides a practical experience with a laboratory component (utilizing the VistA for Education program) that will address approaches to assessing, selecting, and configuring Electronic Health Records (EHRs) to meet the specific needs of customers and end-users.
This material (Comp 11 Unit 6) was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0001.
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Health IT Workforce Curriculum Version 4.0

2. Measuring Information Quality
Data, information, knowledge and wisdom hierarchy
Increase of complexity of understanding higher up the hierarchy
Interrelationship between each level
Each application requires careful analysis and consideration of multiple perspectives
Overlapping influences provide impact on information quality
This lecture discusses the attributes of quality information.
The data/information/knowledge/wisdom hierarchical model of knowledge development has been defined in slightly different ways by numerous authors. Regardless of the finer distinctions that have been articulated, there is agreement that this hierarchical model represents an increasing complexity of understanding and meaning making (or application) as one moves up the hierarchy from data to wisdom. It is also recognized that there are interrelationship between each level in this model and therefore, high quality data and information will have an impact on the development of the higher levels of meaning making that occur. High quality information supports high quality decision making in all areas of health care which results in improved patient safety, increased efficiencies in health care delivery, and more desirable health outcomes.
A number of qualitative dimensions have been defined as ways to measure information quality. Although some of the variables are difficult to apply and quantify as metrics, it is important to be aware of, and consider them, when providing quality information through the EHR reporting systems. It is also important not to consider them in a simplistic manner since application of each dimension of information quality demands careful analysis and consideration of multiple perspectives. For example, it is common for custodians of the data to judge information quality very differently than the users of the information. In addition, a dimension of quality may also be viewed from a purely objective perspective or within the context of a specific use case. For example, when considering the dimension of timeliness, the same information might be considered timely in one situation but not in another.
Finally, all the dimensions of information quality are highly interrelated with a high degree of overlapping influences. These influences may provide a synergistic or antagonistic impact on information quality.

3. 16 Dimensions of Information Quality - 1
Accessibility
Appropriate amount of data
Believability
Completeness
Concise representation
Consistent representation
Pipino, Lee, and Wang provide one of the most comprehensive lists of sixteen variables or dimensions used to evaluate information quality. In this article, the terms data and information are used interchangeably; however, more commonly, data is considered the raw material of information and information is data organized within a specific relational context.
According to Pipino, Lee, and Wang, the sixteen dimensions of information quality and their definitions are:
Accessibility - the extent to which information is available or quickly and easily retrievable. Accessibility would also be dependent upon and relative to the skills and abilities of the person accessing the data.
Appropriate amount of data - the extent to which the volume of data is appropriate to the task at hand. For every task or decision, there is an optimal amount of information after which more information will not create a more desirable outcome, but may impact the outcome negatively by increasing the amount of noise and confusion.
Believability - the extent to which data is regarded as true and credible which may reflect an individual’s assessment of the credibility of the data source, comparison to a commonly accepted standard, or previous experience.
Completeness - the extent to which data is not missing and is of sufficient breadth and depth for the task at hand. The completeness dimension of information can be examined from a number of perspectives.
Concise Representation - the extent to which data is compactly represented. From a logical perspective, this has direct impact on performance issues such as storage and processing time, but simplicity of representation can also influence data accuracy.
Consistent Representation – the extent to which data is represented in the same format. Consistent representation will support consistent interpretation and application of the information.

4. 16 Dimensions of Information Quality - 2
Ease of manipulation
Free-of-error
Interpretability
Objectivity
Relevancy
Reputation
Security
Timeliness
Understandability
Value-added
This slide continues the list of the sixteen dimensions of information quality:
Ease of manipulation - the extent to which data is easy to manipulate and apply to different tasks. One aspect of ease of manipulation would consider the ability to accurately and consistently connect one data set with another related data set for analysis.
Free-of-error - the extent to which data is correct and reliable. Analysis of this dimension would examine accuracy issues at various stages of the information processing life cycle.
Interpretability - the extent to which the data is in appropriate languages, symbols, and units and the definitions are clear.
Objectivity - the extent to which the data is unbiased, unprejudiced, and impartial. In addition to human introduced bias, information bias can also occur from poorly calibrated equipment, poorly crafted information collection tools such as questionnaires, and a multitude of other sources.
Relevancy - the extent to which the data is applicable and helpful for the task at hand. In practice, information is often required to be used that was not designed or collected for that specific purpose. In each of these cases, there is a range of relevancy of information used for the task at hand.
Reputation - the extent to which the data is highly regarded in terms of its source or content. An example of this dimension would be using research information from a peer reviewed journal versus information retrieved from a random web site. Reputations for high quality information also exist within and between organizations.
Security - the extent to which access to data is restricted appropriately to maintain its security. Another aspect of the security dimension is to also ensure that the information is available to those who need it when it is needed. In other words, there is an availability, as well as a restriction, component to the security dimension of quality information. Needless to say, the security dimension of information quality will be an important one to analyze when dealing with privacy and confidentiality tasks.
Finally, the last three measures of information quality are:
Timeliness - the extent to which data is sufficiently up-to-date for the task at hand. As mentioned in the previous example, timeliness is a highly subjective dimension that is determined by the task for which the information is being used.
Understandability – the extent to which data is easily comprehended.
Value-added – the extent to which data is beneficial and provides advantages from its use.

5. 16 Dimensions of Information Quality - 3
There is no single set of metrics can be provide a summative evaluation of information quality
Information quality results from on-going efforts to incorporate the underlying dimensions upon which information quality is assessed in all aspects of information collection, processing and dissemination
Those efforts begin with an understanding of these dimensions of information quality
Experience suggests there is no single set of metrics that can be developed to easily provide a summative evaluation of information quality. Rather, information quality results from on-going efforts to incorporate the underlying dimensions upon which information quality is assessed in all aspects of information collection, processing and dissemination. Those efforts begin with an understanding of these dimensions of information quality.

6. Quality Reporting
Improves quality of care, patient safety and efficiency.
Highlights collection of quality data.
Provides efficiency and consistency through structured routine reports.
Provides flexibility through ad hoc reporting.
The reporting functions of an electronic health record provide critical tools for improving quality of care, patient safety, and efficiency. It is also at this stage of using the electronic health record that the quality of data collection is highlighted as the validity and usefulness of the information generated is directly dependent upon the quality of the data collected. Data or information quality may be measured across the sixteen dimensions, but the relevant importance of each dimension will be determined by the task or decisions being supported.
Electronic health record systems provide reporting efficiency and consistency by using structured, routine reports that are customized to support analysis and decision making related to recurring situations or as a tool to monitor health conditions. The Health Summary report is an example of this type of report. In addition, the EHR also provides flexible reporting through the ability of the user to construct ad hoc reports that will provide information on non-routine issues and problems. It is important that the EHR’s ad hoc reporting tools allow the user to quickly and easily query the EHR for a wide variety of information.

7. Health Summary Reports
Consists of two difference methods
Ad hoc report for a specific patient or group of patients
User-customized, clinical summary report
Examine group of patients using specific set of criteria
Can be viewed online or printed out
The Health Summary reporting function of the EHR consists of two different methods for creating a report. One method is to create an ad hoc report for a specific patient, or group of patients, as needed. This report will not persist in the system and if needed for use again in the future, would need to be recreated. The second method is a user-customized, clinical summary report which can be stored in the EHR system for use in the future (a canned report). It may also be available to other users of the EHR. A number of pre-existing, standard reports are also available for use.
Both of the EHR reports created by the user provide the opportunity to examine a group of patients using a specific set of criteria. If there are criteria and conditions one needs to examine routinely, then those can be built into the EHR's inventory of health summary reports for quick and easy use while providing consistent results. Health summary reports can be viewed online or be printed out.

8. Clinical Reminders
Helps in tracking procedures or activates that may be overlooked
Reports allows users to large and small scale comparisons
EHR makes reminders quick and easy
Users can generate customized reminder reports
The EHR can support clinicians in tracking procedures or activities that may be overlooked, but are important in the care of the patient. Reminders can be helpful in identifying procedures or activities that have “slipped through the cracks.” In addition, the Clinical Reminder reporting function allows users to do large and small-scale comparisons by hospital location, clinic, division, team, or provider. Examples of Clinical Reminders would be reports that alert clinicians to all patients needing pneumococcal immunizations, or to patients needing diabetic foot exams at a specific clinic location. The use of templates by the EHR makes the creation of Clinical Reminder reports relatively quick and easy. Individual users are also able to generate customized Clinical Reminder reports.

9. Health Summary and Clinical Reminder Reports Summary
To design, view and create Health Summary reports
To design, view and create Clinical Reminder reports
How quality reporting supports quality care, patient safety, and efficiency
The 16 dimensions of information quality
This concludes Health Summary and Clinical Reminder Reports.
This unit discussed how to:
Design, view, and create Health Summary reports in the VistA EHR simulation environment,
Design, view, and create Clinical Reminder reports in the VistA EHR simulation environment,
Design, view, and create ad hoc reports,
Describe how quality reporting functions in an EHR support quality care, patient safety and efficiency, and
Identify and explain sixteen dimensions that can be used to define information quality.

10. Health Summary and Clinical Reminder Reports References - 1
Amatayakul MK. Electronic health records: A practical guide for professionals and organizations. 4th ed. Chicago IL: AHIMA; 2009.
Calabrisi RR, Czarnecki T, Blank C. The impact of clinical reminders and alerts on health screenings. The VA Pittsburgh Healthcare System achieves notable results by enhancing an automated clinical reminder system within its CPR--and has the data to prove it. Health Manag Technol Dec; 23(12):32-4.
Carter JH. Electronic health records: A guide for clinicians and administrators. 2nd ed. Philadelphia: ACP Press; 2008.
Eichenwald Maki S, Petterson B. Using the electronic health record. Canada: Delmar Cengage Learning; 2008.
References slide. No audio.

11. Health Summary and Clinical Reminder Reports References - 2
Eichenwald Maki S, Petterson B. Using the electronic health record. Canada: Delmar Cengage Learning; 2008.
Hebda T, Czar P. Handbook of informatics for nurses & healthcare professionals. 4th ed. New Jersey: Pearson; 2009.
Lehman HP, Abbot PA, Roderer NK, Rothschild A, Mandell SF, Ferrer JA, et al, editors. Aspects of electronic health record systems. U.SA: Springer; 2006.
Pipino LL, Lee YW, Wang RY. Data quality assessment. Communications of the ACM. April 2002; 45(4ve): Available from:
References slide. No audio.

12. Configuring Electronic Health Records Health Summary and Clinical Reminder Reports
This material was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0001.
No audio
Health IT Workforce Curriculum Version 4.0