Here are a few articles on helping providers to properly implement the AIRS software so that they may improve their data collection and reporting. The articles address the most common issues relating to data collection.
Click HERE for the article on choosing and customizing data collection forms.
Click HERE for the article on getting a handle on lagged data.
Click HERE for the article on virus protection.
Click HERE for the article on data backup.
Data Collection and Data Quality Strategies for Success
Jay Fischer, MBA, Ph.D. Office of Data Systems Development and Reporting, AIDS Institute.
Programs face increasing demands for data collection brought about by funding source accountability, accrediting organizations, new automated data collection systems (including AIRS) and internal operations such as program planning and evaluation. While the demands for data collection and its complexity are increasing, many programs have not been able to set aside time to plan an effective data collection and data quality strategy to cope with the changing environment. This article will attempt to suggest ways to strengthen the data collection process and how best to identify deficient data in need of correction. The issues of managing ongoing data entry and on-going quality control of data, once entered are tangentially related, but are much more complex and outside the scope of this article.
There are reports in both AIRS and the External Report Application (ERA) that can help with data quality. Additionally, if your program is funded through the Division of HIV Ambulatory Health Care, there are several data quality reports relating to required Division and AI Section indicators, whose output can be given to you, by your respective sections AI Data Manager. These reports provide both summary totals (how much data is problematic) and also TC_ID’s for specific clients whose data might either be problematic, outdated or should be reviewed and checked by your program. Using these reports can make data correction and review less onerous and more focused. If the quality of data has not been addressed on an ongoing basis, then the number of clients, whose data might need review and/or correction, might be significant. In the ideal, data quality should be continuous. Failing that, if you are faced with a significant amount of deficiencies try and set up an action plan that sets goals and time-frames for correction. Your Contract Manager and Data Manager may be a good resource in helping you to develop realistic and attainable goals for data quality.
While these data quality reports can serve as important building blocks of a total data management program, they involve separate issues and perhaps separate resources. You may find that some are more applicable than others or that they serve as a focal point for further discussion among colleagues at the work place regarding your program’s overall data quality effort.
The most successful data collection strategies remain malleable and can be modified to meet the unique organizational climate and system of service delivery within the organization.
The first phase of any data collection strategy involves deciding what data to collect. For AIRS, there are three data groupings that need consideration. The first group is data elements required by the software. The required fields have field names in a bold black font. The second is additional data elements required by the section(s) of the AIDS Institute or the Division to whom your program reports. You will need to check with the section(s) in question, to learn, what fields, beyond the system requirements, may also be required. The third group is data elements not covered above, that your program wishes to collect. When deciding upon what needs to be collected, items that are required by your section(s) or Division, should be given equal weight to those required by the software. Only when data collection strategies are in place for these two groups of data elements should you move toward collecting the majority of data in AIRS, which is not required. In short, start with reporting requirements and branch out from there. Remember that software required fields along with those required by your section(s)/Division, account for significantly less than 50% of all fields in AIRS. The task is really more manageable than it might first appear.
Deciding on a data collection form is the next step. We have developed a set of data collection forms that correspond to data entry screens in AIRS and are available from for download from the AIRS web site. These are available in both PDF and MS word format. The forms in MS word format can be edited to more closely meet your program’s needs.
Alternatively, you may wish to modify your existing data collection forms already in use in your organization or create new ones, as long as the forms contain the necessary fields. In some of the larger healthcare settings, it may be difficult to modify existing encounter forms so that using our data collection forms is something to seriously consider. When selecting and/or developing data collection forms, try to review existing forms and systems with an eye toward eliminating multiple forms that essentially capture the same information or forms/systems that capture data that is no longer needed. Moreover, make sure that data collection mechanisms exist to capture all the needed data. Where there are gaps, design new data collection mechanisms with appropriate staff input, supervision and program manager/administrator involvement. Be mindful that Electronic Medical Record (EMR) systems if in use at your agency, may have an impact upon potential form design. If your program is utilizing an EMR, try and involve staff in your organization, responsible for EMR implementation, in any discussions pertaining to data collection forms that your program might wish to have.
Some organizations have successfully utilized an interdepartmental/interdisciplinary approach that includes program staff, Medical Records Department staff and facility-wide EMR staff into a single committee, thus making a broader base of information more accessible to a wider range of clinicians who might need to know.
Once forms are selected, who will complete them? MOST programs develop an initial approach that mirrors existing lines of authority and job description. An alternative is to identify those individuals, best suited by virtue of patient flow or contact. This can best be done in a workshop format, where staff come together to "brainstorm." Select a neutral staff member to act as facilitator and recorder. Record on a flip chart those persons, who first come to mind as being asked to routinely complete data collection forms, based on current practice. Include instances where data may need to be accessed externally e.g. warehouse, storage files etc. Have the facilitator go around a second time and ask participants to think how patients/clients move around the organization on an average day in terms of staff contact. Record their perceptions on new flip chart pages. Flow diagrams may help. Compare how patients flow within your program, against who might normally be perceived as completing data entry forms to identify gaps in data collection practices. Where possible and practical, gradually work toward eliminating the gaps.
Successful data collection is the cornerstone of data management that should reflect both the organization and the pattern of service delivery, if it is to be maximally effective. If you have any question or would like to discuss these strategies in more detail, contact Jay Fischer, Office of Data Systems Development and Reporting, AIDS Institute @ (212) 417-4763.