Quality Data Collection
HCS/588
February 13, 2012
Pam Crocker

Quality Data Collection
Quality is considered a vague concept that can be subjective and unscientific. However, quality can become a definitive concept by collecting and analyzing data. Centers for Medicare and Medicaid Services (CMS) evidence based measures defines quality of care. For example, administering aspirin for acute myocardial infarction patients, making sure that pneumonia patients receive antibiotics in a timely manner, and ensuring smoking cessation programs while hospitalized and at discharge. Senior leadership is responsible for defining quality for the organization. When the leadership team is familiar with using quality measures, the health care team can do the right thing for the customer and increase financial responsibility for the hospital (Dlugacz, 2006). In this paper the subject to explore is potential improvement for the BayCare organization, the data needed to monitor improvement, data collection tools, and two tools that measure and display the quality improvement data.
Potential Areas of Improvement
Three areas of potential improvement for the BayCare organization are falls, hand hygiene, and improving team member satisfaction. Although the team goals for each of these are within the goal target, areas for improvement still exist.
Monitoring Improvement
Monitoring improvement for each potential area requires collecting specific data collection. Hand hygiene requires quality improvement personnel to monitor if a clinician’s hands are washed with soap and water when visibly soiled. Hand hygiene quality improvement also looks at the use of an alcohol-based hand sanitizer that can be used when a clinician’s hands are not visibly soiled but still require cleansing. For example, before entering a patient’s room or coming in contact with the patient, before wearing sterile gloves for an invasive procedure, after contact with a patient e.g. when taking the patient’s blood pressure or pulse, after contact with items in the patient’s room such as medical equipment, and finally after removing gloves. These are recommendations by the Centers for Disease Control and Prevention (CDC) to improve hand hygiene and decrease the transmission of bacteria, fungi, or viruses.
The data needed to monitor improvement for falls includes data on fall prevention education given to patients, a fall risk assessment on every patient at risk of falls, and written material being given to the patient. “Primary outcome [will be] fall incidence rates calculated from self-reported falls” (Shumway-Cook, Siler, LeMier, York, Cummings, and Koepsell, 2007).
Team member satisfaction uses performance measures to become proactive in solving problems. This leads to performance improvement. The data needed to monitor improvement includes ensuring team member’s involvement in measurement development because “measuring the system needs to model how team members think about doing their collective work” (Jones & Schilling, 2011, para. 6). Measure the team’s strategy to be sure that it is aligned with the organization’s business strategy is another way to monitor improvement. Finally, data collection involves measuring team performance to develop information that makes a difference to the team. “Performance measures stimulate effective problem solving” (Jones & Schilling, 2011, para. 8).

Data Collection Tools
Three data collection tools that can be used to collect performance information are statistical process control, control chart analysis, and self-administered surveys. Statistical process control uses numbers and data to review what clinicians do in order to make them behave the way that is wanted (Ransom, Joshi, Nash, & Ransom, 2008). Statistical Process Control is a method used to control organizations’ processes “toward target values, find causes of variation, and successively to eliminate cause” (Brannstrom-Stenberg & Deleryd, 1999, p. S439). The strength of statistical process control helps us to understand any process that generates products, or services (Ransom, Joshi, Nash, & Ransom, 2008). If the process were implemented by the companies own free will, the organization will “experience lower rejection rates, lower quality cost, quality insurance, and/or higher traceability” (Brannstrom-Stenberg & Deleryd, 1999, p. S442). The weakness of statistical process control is that if the organization did not implement the process of their own free will, the organization will experience a lower motivated workforce and higher inspection activities.
Control Chart Analysis goal is to continually improve quality. “Control charts are the primary tool for achieving this goal” (Westbrook, 1991, p. 56). Control Chart Analysis provides data for immediate action for organizations and analysis by quality control teams seeking long-term system improvement. Control charts allow trends to be identified and problems not readily apparent to be determined to improve the work process. A limitation of control chart analysis is the subjective skill analysis required to interpret the variation in the chart. The person analyzing the control chart may not be able to determine specific rules explaining why a chart is out of control (Westbrook, 1991).
Self-administered surveys can describe the characteristics of large populations and make large samples possible. Self-administered surveys have strengths and weaknesses. Some advantages of self-administered surveys are low cost, reduction in biasing error, and greater anonymity. The weaknesses of self-administered surveys are the survey requires simple questions for them to be comprehended based on the printed instructions and definitions, and there is no opportunity to probing. Answers must be accepted as final answers, and those asking the question do not have a chance to clarify ambiguous answers.
The three data collection tools mentioned; statistical process control, control chart analysis, and self-administered surveys are similar in that all three are looking to improve quality. They allow trends to be identified and large populations to be sampled.
Control chart analysis involves subjective skill to interpret the results versus statistical process and self-administered surveys that have lower rejection rates and reduction in biasing errors. A similarity in self-administered surveys and statistical process is that they are low in cost. All three data collection tools allow trends to be identified and better understanding of products and services.
Tools to Measure and Display QI Data Core measures can be obtained using pre-existing automated data sources. Two data collection tools that measure and display the QI data that can be gathered by the data collection tools are a defect check sheet and a patient flow sheet. Defect check sheets are useful in measuring safety and causes of poor quality. The defect check sheet begins with the use of a fish-bone diagram to identify the different causes of a quality problem. From this, a list of the most common defects can be developed. This list will be the basis for the defect checklist. The defect checklist has two columns. The defect or defect cause is listed on the left side of the checklist, and the count is listed on the right side of the checklist (Health Quality Ontario, 2011). This can be used with a statistical process when trying to understand processes that generate products and services. A weakness of this tool can be if the fishbone diagram was not initially conducted, the defect checklist may not have enough information. Strengths of the defect checklist is that it is easy to use, lists the defects on the left hand side of the tool and involves counting results recorded on the right side of the list. A rating scale is another form of a data collection instrument. This is a “recording form used for measuring individual’s attitudes, aspirations, and other psychological and behavioral aspects, and group behavior” (Globusz Publishing, 2012, para. 11). Common examples of rating scales are the Likert scale and 1-10 rating scales. These can be used in self-administered surveys. A strength of the rating scale is that it can be easy to use, offers specific questions to be answered, and can record attitudes. A weakness of the rating scale is that it can be subjective and requires the person completing the tool to rate his or her opinions.
The tools are similar in that they are easy to use and offer information to objective questions. The tools are dissimilar in that the defect check sheet lists objective defects either present or not present, whereas the rating scale answers specific questions but the answers can be variable.
Both of these tools are helpful for health care organizations in that different types of information can be obtained. The defect checklists allow an organization to study a process to determine if there are any defects in the process. The rating scale allows the organization to obtain patient feedback and opinions, such as a patient satisfaction survey.
Conclusion
Quality data collection attempts to “implement statistical process control and/or process capability studies” (Brannstrom-Stenberg & Deleryd, 1999, p. S439). Most organizations implement various data collection methods based on the needs of the customer and the information being obtained. An important aspect of quality improvement is to control allowable processes. The data needed to monitor improvement is obtained by the allowable processes.

References
Brannstrom-Stenberg, A., & Deleryd, M. (1999). Implemetation of statistical process control and process capability studies: requirements or free will? Total Quality Management & Business Excellence, 10(4/5), S439-S446.
Dlugacz, Y. D. (2006). Measuring Healthcare: Using quality data for operational, financial, and clinical improvement. San Francisco, CA: Jossey-Bass.
Globusz Publishing. (2012). Tools for Data Collection. Retrieved from http://www.globusz.com/ebooks/MarketingResearch/00000017.htm
Health Quality Ontario. (2011). Creating a Measurement Plan. Retrieved from http://www.ohqc.ca/en/measurement_tool_4.html
Jones, S. D., & Schilling, D. J. (2011). Measuring Team Performance: A step-by-step, Customizable Approach or Managers, Facilitators, and Team Leaders. Retrieved from http://media.wiley.com/product_data/excerpt/92/07879456/0787945692.pdf
Microsoft Office. (2012). Medical office flowsheet processes assessment. Retrieved from http://office.microsoft.com/en-us/templates/medical-office-flowsheet-processes-assessment_TC001194547.aspx
Ransom, E. R., Joshi, M. S., Nash, D. B., & Ransom, S. B. (2008). The healtcare quality book: Vision, strategy, and tools (2nd ed.). Chicago, IL: Health Administration Press.
Shumway-Cook, A., Silver, I. F., LeMier, M., York, S., Cummings, P., & Koepsell, T. D. (2007). Effectiveness of a Community-Based Multifactorial Intervention on Falls and Fall Risk Factors in Community Living Older Adults: A Randomized Trial. The Journals of Gerontology, 62A(12), 1420 -1427
Westbrook, C. E. (1991). Control Chart Analysis: Art, Science Combine to Monitor Process Variation. Pulp & Paper, 65(5), 56-59.