FREE ESSAY ON PRACTICE BRIEF: DESIGNING A DATA COLLECTION PROCESS

PRACTICE BRIEF: DESIGNING A DATA COLLECTION PROCESS

Practice Brief: Designing a Data Collection Process 
Types of Data Collection
In any healthcare organization, data is collected in numerous ways for an ever-increasing
number of reasons. Data may be collected by a monitoring device directly connected to the
patient, or by providers as they make observations or record treatments. Quality
improvement activities often call for data collection where observations of activities,
timeliness, or satisfaction indicators are gathered. Data may be abstracted from primary
sources and collected for unique reporting requirements, such as specialized registries
or claims transactions. With the various types of data collected in many different
methods for varied purposes, it is not surprising that data collection may have escaped
management in the past. 
Why Is It Important?
Data collection should be carefully managed in healthcare organizations. Time spent
collecting data can consume huge portions of a provider's day -- taking him or her away
from more direct patient care activities. Other employees may spend their entire day
collecting data. When you consider the cost of data collection equipment, software,
employee time, benefits, and other overhead, the price of data collection can add up
quickly. And what are you getting for your money? Is the data collected reliable? Is it
comprehensive? Does it provide the necessary detail to answer important clinical and
business decisions? For the price your facility is paying, the answers to these questions
must be yes. 
AHIMA's data quality management model depicts data collection as one of the four primary
data functions. The others are application, warehousing, and analysis. All
characteristics of data quality management should be applied to data collection
processes, including: 
? Accuracy 
? Accessibility 
? Comprehensiveness 
? Consistency 
? Currency 
? Definition 
? Granularity 
? Precision 
? Relevancy 
? Timeliness 
Design Process
When faced with a new application (or use) of data, the following factors should be
considered in constructing the data collection for that application: 
Accountability 
? Who is responsible for coordinating the ongoing data collection process? 
? Who is responsible for monitoring the quality of data collection? 
? Are the appropriate people involved in the design of the data collection methodology? 
? Is the use of the data clear? 
? Who will maintain the data ownership record? How will owners participate in the
collection process? 
? Who will maintain the written data collection process/procedures? 
? Are there other potential applications for this data in related or future areas? 
? How much time will it take to collect the data? 
? What impact will data collection have on staffing requirements? 
Data Definition 
? What data is required for the application? 
? Who owns each data element? 
? Is the data currently collected for another application? Is the data collected at the
appropriate level of detail or granularity? 
? How are definitions for each element determined? What process will be used to modify
definitions? 
? Who will maintain the data dictionary? 
? How will data dictionary changes be communicated? 
? Are the data elements uniquely defined? 
? Is the source of each data element clear? 
? Are there existing standards for the data elements and their definitions? 
? What edits are appropriate for each data element? 
? Are there restrictions on using existing data for this application (i.e., availability,
time, specificity, reliability, definition)? 
? Who has access to the source of this data? 
? How reliable is the data source? 
Process Design/ Standardizing Collection 
? Have the data collected been tested to assure that it will meet the application
requirements? 
? How can collection of this data be incorporated into existing workflows? 
? Is the data collection logically sequenced? 
? How available are the data at the point of collection? 
? Does a secondary process need to be put in place to ensure collection of the data at a
later point? 
? What training is required for those collecting the data? 
? What is the best data-collecting tool? 
? Are those tools available for data collection? 
? Can the data be collected so that it is available for analysis without further
manipulation? 
Quality Monitoring 
? What percentage of data completion is required for the application? 
? What process will be used to monitor quality? 
? Will the data be timely enough for the application? 
? What incentives can be applied to ensure data quality? 
? Will feedback on data quality be provided? How? 
Skills Needed
HIM professionals need the ability to: 
? Select appropriate team members 
? Identify roles and expertise needed 
? Utilize continuous quality improvement techniques 
? Maintain and foster diverse work relationships 
? Manage relationships (i.e., relating to clinicians and the diverse array of employees
and departments within the facility) 
? Communicate verbally, in writing, and on an interpersonal level 
? Conduct group presentations 
? Use influence to achieve positive results 
? Facilitate 
? Initiate improvement 
? Lead by example 
? Promote utilization of data collected 
? Recognize differences in learning and social styles and provide processes to meet those
needs 
Examples of Reengineering Data Collection
Upon installation of a new clinical information system, one organization decided to take
advantage of the increased availability of data to reengineer and improve the quality of
data collection. Initially, discharge information was phoned to the admissions office and
entered into the system by clerks, causing delayed data availability and increased
errors. Nursing staff members were required to enter each patient's discharge date and
time, as well as the type of discharge. In order to have them complete the required
discharge information, they were trained on the definitions of discharge status in the
Uniform Discharge Data Set and instructed on the consequence of data error on payment and
outcome reporting. 
In order to reduce the data collection time for evaluation of record completion, the
director of health information management worked with the operating room staff to capture
the results of their preoperative check of record completeness. By improving the data
collection tool and standardizing the definitions between the two applications, they were
able to concurrently collect information about completeness of history and physicals,
preanesthesia assessments, and consent completion -- eliminating a redundant review of
information post discharge. 
Bibliography
www.rde.com